Animal Agriculture: Sustainability, Challenges and Innovations 0128170530, 9780128170533

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ANIMAL AGRICULTURE

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ANIMAL AGRICULTURE Sustainability, Challenges and Innovations Edited by

Fuller W. Bazer G. Cliff Lamb Guoyao Wu Department of Animal Science, Texas A&M University, College Station, TX, United States

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2020 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-817052-6 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Charlotte Cockle Acquisition Editor: Patricia Osborn Editorial Project Manager: Kelsey Connors Production Project Manager: Omer Mukthar Cover Designer: Greg Harris Typeset by TNQ Technologies

Contents 3. Physiology and pregnancy of beef cattle

Contributors xi Foreword xv

KY G. POHLER, GESSICA A. FRANCO, SYDNEY T. REESE, AND MICHAEL F. SMITH

1. Introduction: significance, challenges and strategies of animal production GUOYAO WU, FULLER W. BAZER, AND G. CLIFF LAMB

Introduction 1 Important roles of animal-source food in human health 2 Global animal agriculture including aquaculture 4 Potential impacts of animal agriculture on human food supply and the ecosystem 5 Major challenges to the sustainability of animal agriculture and potential solutions 9 Companion animals in agriculture 13 Conclusion 14 Acknowledgments 14 References 14

I Beef cattle production 2. Genetics and breeding of beef cattle

Introduction 37 Puberty 38 Regulation of the estrous cycle 39 Establishment and maintenance of pregnancy Physiological changes during gestation 45 Endocrinology of pregnancy 47 Embryonic and fetal loss 48 Parturition and postpartum anestrus 49 References 50

42

4. Reproductive management of beef cattle PEDRO L.P. FONTES, NICOLA OOSTHUIZEN, AND G. CLIFF LAMB

Introduction 57 Challenges 58 Available strategies Conclusion 69 References 70

59

5. Nutrition, feeding and management of beef cattle in intensive and extensive production systems TIM A. MCALLISTER, KIM STANFORD,

RALUCA G. MATEESCU

ALEX V. CHAVES, PRISCILLA R. EVANS,

Historical overview of breeding programs 21 Quantitative nature of economically important traitsdan intrinsic challenge in selection 23 Advances in genomic technologies and genomic selection 26 Future genomic information 29 Where genomic selection can have a great impact for the beef industry? 29 Genomics and sustainability 30 Genetic improvement in climate resilience traits 31 New genomic technologies 32 References 32

EDUARDO EUSTAQUIO DE SOUZA FIGUEIREDO, AND GABRIEL RIBEIRO

Introduction 76 Confined production systems 76 Extensive production systems 84 Nutrient management in beef cattle production systems 90 Greenhouse gas emissions 92 Implications of climate change 92 Conclusion 93 Acknowledgments 94 References 94

vi

Contents

II Lactation and management of dairy cattle 6. Genetics and genomics of dairy cattle ~ FRANCISCO PENAGARICANO

Introduction 101 The basics of genetic selection 102 Selection for traits that increase income 103 Selection for traits that reduce expenses 104 Selection for multiple traits 106 Genomic selection: the latest revolution 110 Effective use of genomics: sire selection 112 Effective use of genomics: replacement heifer selection 113 Novel traits in the genomics era 114 Managing inbreeding and genetic diversity 116 Final remarks 117 References 118

7. Physiology of lactation in dairy cattledchallenges to sustainable production GEOFFREY E. DAHL

Current state of affairs 121 Mammary growth and function 122 Nutrition and metabolism 124 Reproduction 125 Genetic innovations 126 Animal health and well-being 126 Housing and monitoring 127 References 128

8. Reproductive management of dairy cattle WILLIAM W. THATCHER AND JOSÉ E.P. SANTOS

Introduction 131 Control of the reproductive cycle 132 Sequential development and efficacy of reproductive management programs 134 Resynchronization for TAI in cows diagnosed non-pregnant to first TAI 144 Economic and sustainable outcomes of reproductive management 147 Sustainability of reproductive performance 151 References 152

9. Nutrition and feeding of dairy cattle PETER S. ERICKSON AND KENNETH F. KALSCHEUR

Introduction 158 Dairy calf and heifer nutrition and development Nutrition of the dairy cow 164 Conclusion 179 References 179

158

III Sheep and goat production 10. Genetics and breeding of sheep and goats ELISHA GOOTWINE

Introduction 184 Sheep and goat domestication 184 Breed classification 185 Genomic basis for breed variation 186 Genetic evaluation and genomic selection 188 Sheep and goat breeding programs 189 Advanced technologies related to small ruminant breeding 193 Conclusions 195 References 196

11. Reproductive physiology of sheep (Ovis aries) and goats (Capra aegagrus hircus) FULLER W. BAZER

Introduction 199 Reproductive tract anatomy References 208

201

12. Reproductive management of sheep and goats REID REDDEN AND JACOB W. THORNE

Introduction 212 Breeding systems 213 Genetic selection 216 Pre-breeding 218 Breeding season 220 Seasonality 220 Gestation 222 Lambing and kidding 224 Weaning 226

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Contents

Advanced reproductive technology Conclusion 228 References 229

227

13. Sustainable sheep and goat production through strategic nutritional management and advanced technologies SUSAN A. MCCOARD, DAVID R. STEVENS, AND TRAVIS R. WHITNEY

Forages and grazing systems for sustainable farming practices 231 Concluding statements 241 References 242

IV Swine production 14. Modern genetic and genomic improvement of the pig BENNY E. MOTE AND MAX F. ROTHSCHILD

Introduction 249 Domestication of swine and breed development 250 Methods of selection and mating systems 251 Traits of economic importance 253 Initial development of molecular genetic approaches 255 QTL, candidate genes and genetic improvement 255 Sequencing the pig genome 256 Genomic selection 258 Databases 259 Cloning, transgenics, gene editing, and breeding pigs as biological models 259 Future developments and applications to genetic improvement 261 Acknowledgments 261 References 261

16. Reproductive management of swine WILLIAM L. FLOWERS

Introduction 283 Management during the developmental phase 284 Management during the transition from the developmental to the functional phase 287 Management of boars during the functional phase 288 Management of sows during the functional phase 292 Summary 295 References 296

17. Nutrition and feeding of swine HAYFORD MANU AND SAMUEL K. BAIDOO

Introduction 299 Neonatal pig nutrition 300 Nutrition of nursery pigs 302 Nutrition of weaner to finisher pigs 304 Nutrition for gilt development 307 Sow nutrition 307 Conclusion 308 References 309

V Poultry production 18. Poultry genetics and breeding GIRIDHAR ATHREY

A brief history of poultry production 317 Genetic improvement: progress and future directions Global challenges and opportunities 321 Future technologies in poultry breeding 322 Poultry genetics resources 324 Summary 326 References 326

15. Reproductive physiology of swine

19. Reproductive physiology of poultry

RODNEY D. GEISERT, PETER SUTVOSKY, MATTHEW C. LUCY,

CLAIRE S. STEPHENS AND PATRICIA A. JOHNSON

FRANK F. BARTOL, AND ASHLEY E. MEYER

Introduction 263 Boar physiology 264 Sow physiology 267 Pregnancy 270 Parturition 275 Lactation 276 References 279

318

Introduction 331 Ovary structure 332 Regulation of egg production 333 Ovarian hormones 335 Yolk accumulation 338 Laying hen production differs from broiler breeder hen production 339 Ensuring a supply of healthy chicks 341

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Contents

Conclusion 342 Acknowledgments References 342

342

20. Reproductive management of poultry COLIN G. SCANES, LEASEA D. BUTLER, AND MICHAEL T. KIDD

Introduction 350 Physiological control of reproduction 350 Egg development 351 Male reproduction 352 Hormonal control of reproduction 353 Light and reproduction 354 Nutrition and reproductive management 355 Broiler breeder reproduction 356 Induced molting or re-cycling to increase egg production 356 Other aspects of reproductive management in poultry 360 Uses of components of eggs 361 Acknowledgments 362 References 362

21. Precision poultry nutrition and feed formulation CHRISTOPHER A. BAILEY

Introduction 367 Current state of poultry science within the United States 367 A brief history of the National Research Council’s nutrient requirements of poultry 368 Evolution of NRC broiler diets 370 Precision nutrition of poultry in the 21st century 372 Modern industry type broiler diets 374 References 377

VI Biotechnologies and others in animal production 22. Muscle biology and meat quality e challenges, innovations, and sustainability STEPHEN B. SMITH

Sustainability of beef production in the world 381 Growth and development of muscle 383

Beef quality 385 Use of ß-adrenergic agonists in livestock production ß-adrenergic agonists and meat quality 389 References 390

387

23. Genetic improvement of livestock, from conventional breeding to biotechnological approaches ~ AND JASON W. ROSS BLYTHE SCHULTZ, NICK SERAO,

History of animal breeding 393 Genetic improvement of livestock 394 Genomics revolution 394 Identification of quantitative trait loci (QTL) 394 Marker-assisted and genomic selection 395 Biotechnological solutions to advance genetic improvement 396 Assisted reproductive technologies 396 Genetic engineering 399 Early work on transgenesis in livestock 399 Site directed nucleases 400 Genetic improvement of livestock through genetic engineering 401 The whole toolbox 402 References 403

24. Fermentation techniques in feed production ZHAOLAI DAI, LU CUI, JU LI, BINGGEN WANG, LINA GUO, ZHENLONG WU, WEIYUN ZHU, AND GUOYAO WU

Introduction 407 Production of fermented feed 408 Microbial ecology of the fermented feed 424 Safety considerations of fermented feed 425 Perspective and future directions 425 Acknowledgments 426 References 426

25. Mathematical modeling in animal production LUIS ORLINDO TEDESCHI AND HECTOR MANUEL MENENDEZ III

Introduction 431 Classifications of mathematical models 433 A brief history of current mathematical models in ruminant production 437

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Contents

Advanced data analytics for future mathematical models 443 Conclusion 445 References 446

26. Manure treatment and utilization in production systems ZONG LIU AND XIAO WANG

Introduction 455 Manure processing and handling Manure treatment 459 Manure utilization 464 References 466

457

VII Management of animal diseases in livestock and poultry production 27. Management of metabolic disorders (including metabolic diseases) in ruminant and nonruminant animals GUOYAO WU

Introduction 471 Disorders caused by abnormal metabolism, deficiencies or excesses of carbohydrates 473 Disorders caused by abnormal metabolism, deficiencies or excesses of lipids 476 Disorders caused by abnormal metabolism, deficiencies or excesses of amino acids 479 Disorders caused by deficiencies or excesses of vitamins 482 Disorders caused by deficiencies or excesses of minerals 485 Conclusion 489 Acknowledgments 490 References 490

28. Management of pathogens in cattle KEVIN E. WASHBURN

Management of pathogens in cattle Conclusion 499 References 499

493

29. Management of pathogens in swine BRANDON J. DOMINGUEZ

Introduction 501 Swine raising environments 502 Swine housing 503 Water 504 Feed 505 Transportation 505 Biosecurity protocols 506 Health programs 509 Genetic improvements 510 Conclusion 511 References 511

30. Management of pathogens in poultry ZUTAO ZHOU, BANG SHEN, AND DINGREN BI

Introduction 515 Common infectious diseases and pathogens in poultry farms 516 Challenges of pathogen management in poultry production enterprises 516 Key role of biosecurity practices for healthy flocks 520 Disease prevention and management through vaccination 524 Use of antimicrobials 527 Acknowledgments 529 References 529

Index 531

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Contributors Giridhar Athrey Avian Genetics & Functional Genomics, Department of Poultry Science, Texas A&M University, College Station, TX, United States

Zhaolai Dai State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, China

Samuel K. Baidoo Southern Research and Outreach Center, University of Minnesota, Waseca, MN, United States

Brandon J. Dominguez Department of Veterinary Large Animal Clinical Sciences, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, United States

Christopher A. Bailey Department of Poultry Science, Texas A&M University, College Station, TX, United States

Peter S. Erickson Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH, United States

Frank F. Bartol Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, AL, United States

Eduardo Eustaquio de Souza Figueiredo Department of Food and Nutrition, Federal University of Mato Grosso, Brazil, Cuiaba, MT Priscilla R. Evans School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW, Australia

Fuller W. Bazer Department of Animal Science, Texas A&M University, College Station, TX, United States

William L. Flowers Department of Animal Science, North Carolina State University, Raleigh, NC, United States

Dingren Bi State Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, Hubei Province, PR China; Key Laboratory of Preventive Medicine in Hubei Province, Wuhan, Hubei Province, PR China

Pedro L.P. Fontes Department of Animal Science, Texas A&M University, College Station, TX, United States Gessica A. Franco Department of Animal Science, Texas A&M University, College Station, TX, United States

Leasea D. Butler Department of Poultry Science, University of Arkansas, Fayetteville, AR, United States

Rodney D. Geisert Division of Animals, University of Missouri, Columbia, MO, United States

Alex V. Chaves School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW, Australia

Elisha Gootwine Institute of Animal Science, The Volcani Center, Rishon LeZion, Israel

Lu Cui State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, China

Lina Guo State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, China

Geoffrey E. Dahl Department of Animal Sciences, University of Florida, Gainesville, FL, United States

Patricia A. Johnson Department of Animal Science, Cornell University, Ithaca, NY, United States

xi

xii

Contributors

Kenneth F. Kalscheur U.S. Dairy Forage Research Center, USDA-ARS, Madison, WI, United States Michael T. Kidd Department of Poultry Science, University of Arkansas, Fayetteville, AR, United States G. Cliff Lamb Department of Animal Science, Texas A&M University, College Station, TX, United States Ju Li Henan Yinfa Animal Husbandry Co., Xinzheng, Henan, China Zong Liu Department of Biological and Agriculture Engineering, Texas A&M University, College Station, TX, United States Matthew C. Lucy Division of Animals, University of Missouri, Columbia, MO, United States Hayford Manu Department of Animal Science, University of Minnesota, Saint Paul, MN, United States Raluca G. Mateescu Department of Animal Sciences, University of Florida, Gainesville, FL, United States Tim A. McAllister Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre, Lethbridge, AB, Canada Susan A. McCoard AgResearch Grasslands Ltd., Palmerston North, New Zealand Hector Manuel Menendez, III Texas A&M University, Department of Animal Science, College Station, TX, United States Ashley E. Meyer Division of Animals, University of Missouri, Columbia, MO, United States Benny E. Mote Department of Animal Science, University of Nebraska-Lincoln, Lincoln, Nebraska, United States Nicola Oosthuizen Department of Animal Science, Texas A&M University, College Station, TX, United States Francisco Pe~ nagaricano Department of Animal Sciences, University of Florida, Gainesville, FL, United States Ky G. Pohler Department of Animal Science, Texas A&M University, College Station, TX, United States Reid Redden Texas A&M AgriLife Extension, San Angelo, TX, United States

Sydney T. Reese Department of Animal Science, Texas A&M University, College Station, TX, United States Gabriel Ribeiro Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada Jason W. Ross Iowa State University, Ames, Iowa, United States Max F. Rothschild Department of Animal Science, Iowa State University, Ames, Iowa, United States José E.P. Santos Department of Animal Sciences, University of Florida, Gainesville, FL, United States Colin G. Scanes Department of Poultry Science, University of Arkansas, Fayetteville, AR, United States Blythe Schultz Iowa State University, Ames, Iowa, United States Nick Ser~ ao Iowa State University, Ames, Iowa, United States Bang Shen State Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, Hubei Province, PR China; Key Laboratory of Preventive Medicine in Hubei Province, Wuhan, Hubei Province, PR China Stephen B. Smith Department of Animal Science, Texas A&M University, College Station, TX, United States Michael F. Smith Division of Animal Sciences, University of Missouri, Columbia, MO, United States Kim Stanford Alberta Agriculture and Forestry, Agriculture Centre, Lethbridge, AB, Canada Claire S. Stephens Department of Animal Science, Cornell University, Ithaca, NY, United States David R. Stevens New Zealand

AgResearch Invermay, Mosgiel,

Peter Sutvosky Division of Animals, University of Missouri, Columbia, MO, United States; Departments of Obstetrics, Gynecology and Women’s Health, University of Missouri, Columbia, MO, United States

Contributors

Luis Orlindo Tedeschi Texas A&M University, Department of Animal Science, College Station, TX, United States William W. Thatcher Department of Animal Sciences, University of Florida, Gainesville, FL, United States Jacob W. Thorne Texas A&M AgriLife Extension, San Angelo, TX, United States

xiii

Travis R. Whitney Texas A&M AgriLife Research, San Angelo, TX, United States Guoyao Wu Department of Animal Science, Texas A&M University, College Station, TX, United States Zhenlong Wu State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, China

Binggen Wang Henan Yinfa Animal Husbandry Co., Xinzheng, Henan, China

Zutao Zhou State Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, Hubei Province, PR China; Key Laboratory of Preventive Medicine in Hubei Province, Wuhan, Hubei Province, PR China

Kevin E. Washburn Texas A&M University, College of Veterinary Medicine, Department of Large Animal Clinical Sciences, College Station, TX, United States

Weiyun Zhu National Center for International Research on Animal Gut Nutrition, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu, China

Xiao Wang Department of Biological and Agriculture Engineering, Texas A&M University, College Station, TX, United States

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Foreword

The expectations of agriculture and the food supply have never been greater. No longer is the production of food seen as the primary goal, but rather agricultural systems are expected to: (1) improve human health, (2) have very minimal impact on the environment including water use and the generation of environmental contaminants, (3) promote strong local and regional economies as well as (4) maintain the profitability for producers to ensure future generations of farmers. These expectations, and the tradeoffs among these outcomes, were recently described in a report from the National Academies of Sciences, Engineering and Medicine - A Framework for Assessing Effects of the Food System [1]. There is a general consensus that transformative advancements in agriculture systems will be essential to meet the food and nutrient needs of the growing world population through 2050. Enhancements are needed not only to increase production yields, but equally important to increase the human and environmental healthpromoting qualities of the food system. Dietrelated chronic disease costs the United States’ economy billions of dollars in health care costs annually, decreases quality of life and life expectancy, and is a major driver of health care costs globally. Similarly, serious environmental concerns including water scarcity, soil erosion, air quality and agriculture runoff threaten the sustainability of the food supply. Achieving the goal of healthful and sustainable agriculture systems that meet population food and nutrition needs will require substantial advancements in

understanding of the food-diet-nutritiondisease connection. This will include the recognition of heterogeneity among humans in the diet-disease relationship that is rooted in our evolutionary history. The metrics, measures and methodologies needed to establish dietary patterns that delay or prevent chronic disease is still in its infancy. Likewise, rapid development of technological solutions are needed to further limit the environmental footprint of agriculture and conserve the natural resources that sustain and conserve the diversity of life on the planet. Perhaps the great challenge in agriculture will be sustainably meeting the protein, iron and other essential mineral requirements of growing human populations globally. Even today, protein malnutrition and iron-deficiency anemia are commonplace in many parts of the world even among children, who suffer life-long consequences including function deficits as a result of malnutrition during growth and maturation. Increasing the quality and quantity of animal food products, while minimizing the environmental footprint, will require innovations in genetic engineering, reproductive sciences, microbiome science, agricultural and water engineering. This need will intensify with the expected human population expansion and the current increased desirability and demand for animal food products globally. This edited volume brings together leading experts who set the stage by comprehensively addressing the many demands faced by animal agriculture,

xvi

Foreword

review the current state of technologies and provides a much-needed roadmap for achieving the new expectations of animal agriculture. Patrick J. Stover Vice Chancellor and Dean for Agriculture and Life Sciences Director, Texas A&M AgriLife Research

Reference [1] Institute of Medicine and National Research Council. A Framework for Assessing Effects of the Food System. Washington, DC: The National Academies Press; 2015. https://doi.org/10.17226/18846.

C H A P T E R

1 Introduction: significance, challenges and strategies of animal production Guoyao Wu, Fuller W. Bazer, G. Cliff Lamb Department of Animal Science, Texas A&M University, College Station, TX, United States

O U T L I N E Introduction

1

Important roles of animal-source food in human health

2

Global animal agriculture including aquaculture

4

Potential impacts of animal agriculture on human food supply and the ecosystem Potential competition with humans for food and water Potential impacts of animal production on the environment

5 5 5

Introduction

11 12 13

Companion animals in agriculture

13

Conclusion

14

Acknowledgments

14

References

14

9 9

and health of humans. However, the Food and Agriculture Organization (FAO) of the United Nations estimated that in 2016, about 815 million people (10.7% of the world population) were

High-quality protein, along with other nutrients, is essential for optimal growth, development,

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00001-X

Major challenges to the sustainability of animal agriculture and potential solutions Suboptimal efficiency of protein production by animals Banning of the use of antibiotics for growth enhancement Metabolic disorders and infectious diseases Global warming and cold environment

1

Copyright © 2020 Elsevier Inc. All rights reserved.

2

1. Introduction: significance, challenges and strategies of animal production

suffering from chronic deficiencies of nutrients, particularly protein, vitamins and microminerals.1 Globally, 150 million children under five years of age were estimated to be stunted in growth and development in 2017.2 Malnutrition during gestational and neonatal periods affects not only the first generation of offspring, but also at least two subsequent generations through epigenetic-mediated mechanisms.3 The challenge of preventing hunger and malnutrition will become even greater as the global population grows from the current 7.2 billion people to 9.6 billion people by 2050. With increases in income, population, and demand for more nutrient-dense foods, global meat production is projected to increase to 192 million tons per year during the next 35 years.4 These changes in population and dietary practices will result in a substantial increase in the demand for food protein, especially animalsource protein. Livestock, poultry and fish are the biological transformers of low-quality feedstuffs into highquality protein and highly available essential minerals (e.g., calcium, iron, and zinc).5 Thus, animal agriculture plays an important role in improving human nutrition. However, increased production of food proteins is associated with increased emissions of greenhouse gases (CO2, CH4 and N2O) and the over-utilization of water. Consequently, concerns exist regarding impacts of animal agriculture on the environment, ecosystems and sustainability.4 To address these challenging issues, this book comprehensively highlights current reproductive, genetic, nutritional, and management technologies to enhance efficiencies in the production of animal protein, sustain livestock and poultry industries, and reduce the excretion of waste into the environment. Thus, this book provides not only the foundational knowledge of animal science, but also practical solutions to solving current and future problems that face animal agriculture worldwide.

Important roles of animal-source food in human health Nutritionally essential minerals (e.g., iron and zinc) and vitamins (e.g., the B-complex of vitamins and vitamin A) are supplied from animalsource foods.4 In particular, cow’s milk provides an abundance of calcium for bone growth and development, of vitamins and trace minerals for nutrient metabolism, and of conjugated unsaturated fatty acids for improving human health. Of note, animal-source foods are the only dietary source of vitamin B12 for humans. Minerals (e.g., iron, calcium, and zinc) from animalsource foods have greater bioavailabilities for humans than plant-source foods.6 On a dry matter basis, animal-source foods (e.g., eggs, meat, poultry, and seafood) contain greater than 60% protein, whereas most staple foods of plant origin (except for legumes) have protein contents of less than 15% (Table 1.1). Animal proteins contain adequate and balanced amounts of all amino acids for human consumption to promote optimal growth, development, and health.7 In contrast, rice, wheat, corn, potatoes and other non-legume foods are deficient in many amino acids, including those that are not synthesized by the body (lysine, methionine, threonine, and tryptophan) and those that are synthesized by the body (glycine and proline).8 Of note, meat and white rice contain 2.98 and 0.27 g sulfur-containing amino acids (methionine plus cysteine) per 100 g dry matter, respectively (Table 1.1). To meet the Institute of Medicinerecommended dietary allowance for these two amino acids by a 70-kg adult human, daily intakes of meat and white rice would be 45 and 493 g dry matter, respectively. Thus, consumption of animal products can meet adequate requirements of humans for protein, particularly children, while substantially reducing the need for plant-based foods or the ingestion of a large amount of starch. This is important for individuals whose metabolic profiles are compromised

Important roles of animal-source food in human health

TABLE 1.1

Composition of amino acids in meat and plant-source foods a. Protein and amino acid content (% of dry matter)

Food

Protein Lysine SAA Threonine Tryptophan

Meat (30% dry matter)

66.7

4.89

2.98

3.56

0.93

Soybean (87% dry matter)

42.0

2.69

1.05

1.60

0.50

Wheat (87% dry matter)

11.8

0.34

0.47

0.34

0.11

Corn (87% dry matter)

10.8

0.28

0.44

0.35

0.08

0.19

0.27

0.18

0.07

White rice 8.2 (87% dry matter) a

Adopted from Wu et al. (2014).4 To meet the recommended dietary allowance of L-methionine plus L-cysteine for a 70-kg healthy adult (1.33 g/day), the individual would need to consume one of the following foods (g dry matter/day): meat, 45; soybean, 127; wheat, 283; corn, 302; or white rice, 493. Ingestion of meat would substantially reduce that of food grains, while ensuring adequate amino acid nutrition. SAA ¼ sulfur-containing amino acids (L-methionine þ L-cysteine).

by high intake of digestible carbohydrates.9 The excessive amount of starch that would be consumed in wheat flour or white rice can be converted into fat in the body, thereby contributing to the development of obesity, dyslipidemia, and other metabolic disorders.9 Conversely, intake of lean meat plays a key role in promoting protein synthesis and sustaining skeletal-muscle mass and function (including physical strength), while improving insulin sensitivity, ameliorating aging-associated sarcopenia, and reducing white fat accretion.10 Although there is a common belief that there are virtually no nutrients in animal-based foods that are not better provided by plants,4 it is animal-source, but not plant-source, foods that supply taurine (a sulfur-containing amino acid),

3

creatine, anserine (b-alanyl-L-1-methylhistidine), and carnosine (b-alanyl-L-histidine).8 Taurine is essential for the digestion and absorption of dietary fats and lipid-soluble vitamins in humans, and for protecting the eyes, heart, skeletal muscle, and other tissues of humans from oxidative damage and degeneration.11 Taurine-deficient individuals (particularly children who have a low ability to synthesize taurine from methionine and cysteine) suffer from retinal, cardiac and skeletal muscle dysfunction.12 There are reports that adult humans without dietary intake of taurine are at increased risk for taurine deficiency.13,14 Oral ingestion of 0.4e6 g taurine per day for various days improved (a) metabolic profiles in blood; and (b) cardiovascular functions in healthy subjects, as well as in patients suffering from obesity, diabetes, hypertension, or congestive heart failure.15 Creatine is essential for energy metabolism in the brain and skeletal muscle,16 and also plays an important role in antioxidative reactions.17 Adequate provision of dietary creatine may be necessary for maintaining homeostasis and optimal health in humans,18 particularly for vegan athletes who generally have a low intake of creatine and its precursors (arginine, methionine and glycine).19 Finally, carnosine and anserine are antioxidant dipeptides that protect tissues (e.g., skeletal muscle) from oxidative stress.20 Besides maintaining neurological and muscular functions, these small peptides can inhibit the growth of tumor cells through redox signaling, while providing patients with amino acids for protein synthesis.21,22 Note that mammalian milk and eggs from poultry contain large amounts of taurine (0.3e1 mM) and that meat is an abundant source of anserine, carnosine, creatine, and taurine, as shown for beef (Table 1.2).23,24 Interestingly, these nutrients are absent from plants.7 Furthermore, 4hydroxyproline, which protects the intestine from inflammation,25 is abundant in meat and bones, but nearly absent from plants.26 These nutritional facts highlight the importance of animal agriculture in human nutrition and health.

4

1. Introduction: significance, challenges and strategies of animal production

TABLE 1.2

Amounts of small peptides, total amino acids, and creatine in beef cuts a. Type of beef cuts

Amino acids

Chuck Round Loin

b

Anserine , g/kg dry weight

2.79

3.25

3.66

Carnosine , g/kg dry weight

15.2

21.4

24.2

b-Alanine , mg/kg dry weight

b

c

453

615

712

b

2.34

2.78

2.92

d

31.0

33.3

33.7

30.0

31.5

32.9

66.6

70.4

72.0

23.7

24.8

25.3

34.3

35.8

37.0

9.34

9.77

10.0

4-Hydroxyproline , g/kg dry weight 1.73

1.74

1.77

10.2

10.5

Taurine , g/kg dry weight Glycine , g/kg dry weight d

Proline , g/kg dry weight e

Lysine , g/kg dry weight e

Methionine , g/kg dry weight e

Threonine , g/kg dry weight e

Tryptophan , g/kg dry weight f

b

Creatine , g/kg dry weight a

9.6

23

Adopted from Wu et al. (2016) for all variables except creatine. Creatine was determined using our HPLC method (Zhang et al. 2018).24 Chuck ¼ under blade roasts; Loin ¼ top loin steaks; Round ¼ top round steaks. b Absent from plants. c Abundant in meat, but very low in plants. b-Alanine is required for the synthesis of anserine and carnosine in skeletal muscle. d Deficient in all plant proteins, relative to dietary requirements of humans. e Marginally adequate in legumes, but markedly deficient in staple food grains (e.g., corn, wheat, and rice), relative to requirements for humans. f Abundant in meat, but nearly absent from plants.

Global animal agriculture including aquaculture Animal agriculture is a driving force of human civilization, and accounts for 50%e75% of the total amount of agricultural output in industrialized nations or 25%e40% in developing countries.4 According to the Food and Agriculture Organization (FAO) of the United Nations,27 the global numbers of livestock and poultry have increased over the past decades, with 1.5  109

cattle, 2.2  109 sheep plus goats, 1.0  109 swine, and 24  109 poultry in 2018. China and the U.S. have the largest and second largest numbers of domestic animals, respectively. Both extensive and intensive systems are currently used to raise ruminants (e.g., cattle, sheep and goats) and nonruminants (e.g., swine and poultry) worldwide. In ruminant production, grazing (an extensive low-cost system) utilizes 26% of the earth’s ice-free terrestrial surface.4 The plants consumed by ruminants grow in fields that can be fertilized by animal waste (e.g., feces and urine). Such grassland-ruminant operations provide a beneficial recycling of carbon, nitrogen, hydrogen, phosphorus, and sulfur in the ecosystem. In modern farming, ruminants and nonruminants are also raised at a high stocking density (an intensive system) to increase competitiveness of production by generating the highest output at the lowest cost through efficient use of labor, space, economies of scale, and automated machinery.28 Particularly, modern dairy, pork and poultry production involves enclosed buildings to protect animals from inclement weather, wild predators, and widespread of infectious diseases. Intensive animal production requires appropriate nutritional and engineering measures to optimize animal health and welfare and to minimize environmental impacts. Besides the land-based production of animal protein, aquaculture is playing an increasingly important role in agricultural enterprises worldwide. For example, global fish production by aquaculture increased by 31% from 66.4  109 kg in 2012 to 87.0  109 kg in 2018.29 Fish is a popular food to provide high-quality protein and other nutrients for improving human growth and health. Of particular note, between 2001 and 2016, fish consumption per capita in developed countries increased by 25%, from 16 to 20 kg, accounting for 17% of total intake of animal protein.29 A great impediment to the productivity of fish culture is the

Potential impacts of animal agriculture on human food supply and the ecosystem

provision of adequate nutrients, particularly amino acids, which are the most predominant components of diets and the building blocks of tissue protein (the major dry-matter component of animal growth).30 Because fish can convert low-quality plant protein into high-quality animal protein, research on dietary requirements for amino acids is expected to help sustain and grow the global aquaculture industry, while reducing its dependency on threatened ocean fisheries. Feeding fish to fish is not sustainable for aquacultural enterprises.

Potential impacts of animal agriculture on human food supply and the ecosystem Potential competition with humans for food and water In the intensive production systems, ruminants and nonruminants are fed diets containing some staple grains (e.g., corn, wheat, and soybean) that are also foods for humans.5 Thus, there is a perception that animals compete with humans for food. However, ruminants have the ability to extensively convert: (a) cellulose (the major structural component of plant cell walls) into nutritionally significant short-chain fatty acids (acetate, propionate, and butyrate) in the rumen; and (b) ammonia, sulfur and carbohydrates into microbial protein. For this reason, farm animals can consume primarily forages and by-products of plants (e.g., pasture grasses, alfalfa, clovers, hays, straw, and silages). Additionally, diets for both ruminants and nonruminants can include by-products of grains [e.g., wheat middlings, wheat bran, citrus pulp, almond hulls, dried distillers grain with solubles (DDGS) from biofuels industry, and soybean hulls] as sources of energy, protein, and fiber.5 These feed ingredients are inedible for humans and would otherwise be wasted. Results of

5

extensive studies have consistently shown that inclusion of such feedstuffs (e.g., up to 20% DDGS) and synthetic amino acids (e.g., lysine, methionine, threonine and tryptophan) in ruminant and nonruminant diets can substantially reduce the use of staple grains without compromising the production performance of animals. Importantly, livestock, poultry and fish convert low-quality feedstuffs into high-quality foods (e.g., meat, milk, and eggs) for human consumption, as noted previously. This is a distinct advantage of animal agriculture and aquaculture. Increased intake of nutrient-dense animal products by humans can reduce the consumption of staple grains. For example, in China where the animal industry has developed very rapidly over the past two decades, consumption of animal products per capita increased by 30.5 kg in 2010 and consumption of food grains per capita decreased by 85 kg, as compared to 1990.28 Thus, an increase in human consumption of animal products by 1 kg/year resulted in a decrease of 2.8 kg/year in human consumption of food grains. This can be translated into a reduction of annual water use for the production of food-grain crops in the primary cases of swine (Table 1.3), ruminant and poultry (Table 1.4) operations. Thus, well-planned expansion of animal agriculture may be a solution to alleviating the scarcity of water resources, while ensuring an adequate supply of high-quality food protein to humans. Indeed, in regions with a limited availability of groundwater (i.e., Israel), ruminant (e.g., dairy cows and sheep) production is vital to ensure adequate provision of food protein to the population and for export.31

Potential impacts of animal production on the environment Under normal feeding conditions, the rumen and large intestine of ruminants produce large amounts of CH4 and CO2 from the metabolism

6

1. Introduction: significance, challenges and strategies of animal production

Increased consumption of animal products (primarily pork meat) reduces the amount of water needed to produce food grains (primarily rice and wheat) for human consumption a.

TABLE 1.3

Low level of animal production

High level of animal production

Difference between low and high levels of animal production

Animal- or plant-source food

Amount of food (kg)

Water needs (L 3 103)

Amount of food (kg)

Water needs (L 3 103)

Amount of food (kg)

Water needs (L 3 103)

Animal product

24.9

92.3

55.4

169

þ 30.5

þ 76.7

Pork meat

12.2

56.1

17.6

81.0

þ 5.40

þ 24.9

Poultry meat

1.83

7.5

7.17

29.4

þ 5.34

þ 21.9

Beef meat

1.01

11.5

1.57

21.2

þ 0.56

þ 9.80

Sheep meat

0.46

2.82

0.50

3.07

þ 0.04

þ 0.25

Goat meat

0.46

1.86

0.50

2.02

þ 0.04

þ 0.16

Poultry eggs

3.69

9.96

7.55

20.4

þ 3.86

þ 10.4

1.68

1.33

10.8

8.53

þ 9.12

þ 7.20

3.60

1.22

9.70

3.30

þ 6.10

þ 2.08

197

477

112

291

 85

 186

Rice (milled)

87.2

331

55.8

212

 31.4

 119

Wheat flour

74.8

101

30.5

41.3

 44.3

 59.7

Beans

11.2

25.8

11.6

26.7

þ 0.4

þ 0.9

16.6

11.8

7.44

5.28

 9.16

 6.52

b

Milk

c

Aquatic products Food grain d

d

Corn e,f

Potatoes

Sorghum Net change

f

4.99

6.26

5.01

þ 0.02

þ 0.02

h

0.92

2.62

0.40

1.14

 0.52

 1.48

e

e

e

e

e

 109

6.24

g

f

g

The signs “” and “þ” denote a decrease and an increase, respectively. a Taken from Wu et al. (2014).28 Values are expressed per capita. b Milk plus dairy products. c The amount of water needed for the production of aquatic animals (0.34  103 L/kg fresh weight) was adopted from the values reported for the production of tilapia (0.34  103 L/kg fresh weight) and channel catfish (0.33  103 L/kg fresh weight) in fed-pond systems. d To calculate the amount of water needed for crop production, wheat flour and milled rice were converted into whole wheat grain and un-milled rice grain, respectively. Generally, 1 kg whole wheat grain and 1 kg un-milled rice grain are equivalent to 0.85 kg wheat flour and 0.70 kg milled rice, respectively. e Primarily potatoes and sweet potatoes. f Grain-equivalent values reported for potatoes (classified as tubers). Five kilograms of fresh tubers were equivalent to one kilogram of grain. g For calculation of the amount of water needed for the production of tubers (0.16  103/kg food grain), the grain-equivalent values were multiplied first by a factor of 5 (to obtain their fresh weights) and then by 0.16.

of carbohydrates, amino acids, and fatty acids in ingested feedstuffs, as well as ammonia and H2S from the degradation of methionine and cysteine in diets.5 Gas production also occurs in the intestine (primarily the large intestine) of other

animals. In all farm animals, when the dietary supply of amino acids is greater than their utilization for the synthesis of proteins and other bioactive molecules, the excessive amino acids undergo irreversible catabolism to form

7

Potential impacts of animal agriculture on human food supply and the ecosystem

Increased consumption of animal products (primarily milk, chicken meat, and beef meat) reduces the amount of water needed to produce food crops (primarily wheat and potatoes) for human consumption.

TABLE 1.4

Low level of animal production

High level of animal production

Difference between low and high levels of animal production

Food

Amount of food (kg)

Water needs (L 3 103)

Amount of food (kg)

Water needs (L 3 103)

Amount of food (kg)

Water needs (L 3 103)

Animal producta

24.9

55.2

55.4

123

þ30.5

þ67.3

Pork meat

1.33

6.12

2.95

13.6

þ1.62

þ7.48

Poultry meat

2.12

8.69

4.73

19.4

þ2.61

þ10.7

Beef meat

1.70

23.0

3.78

51.0

þ2.08

þ28.0

Sheep meat

0.01

0.06

0.02

0.12

þ0.01

þ0.06

Goat meat

0.01

0.04

0.02

0.08

þ0.01

þ0.04

Poultry eggs

0.95

2.57

2.12

5.72

þ1.17

þ3.15

18.3

14.5

40.7

32.2

þ22.4

þ17.7

0.47

0.16

1.05

0.36

þ0.58

þ0.20

197

205

112

117

e85.0

e88.6

Rice (milled)

13.8

52.4

7.84

29.8

e5.96

e22.6

Wheat flour

85.7

116

48.7

65.9

e37.0

e50.1

Beans

4.53

10.4

2.58

5.93

e1.95

e4.47

21.1

15.0

12.0

8.52

e9.10

e6.48

Potatoes

71.9

11.5

40.9

6.54

e31.0

e4.96

Sorghum

0.0

0.0

0.0

0.0

0.0

0.0

e

e

e

e

e

e21.3

b

Milk

c

Aquatic products d

Food grain

e

e

Corn f

Net change

The signs “” and “þ” denote a decrease and an increase, respectively. a The proportions of animal-source foods (73.5% milk plus dairy products, 8.53% poultry meat, 6.82% beef meat, 5.33% pork meat, 0.042% sheep meat, 0.042% goat meat, 3.82% poultry eggs, and 1.90% aquatic products) in human diets were based on the USDA data for the U.S. population in 2010. The human consumptions of poultry and beef meats per capita in the U.S. are 70.9 and 56.7 lb/year, respectively, in 2010. b Milk plus dairy products. c The amount of water needed for the production of aquatic animals (0.34  103 L/kg fresh weight) was adopted from the values reported for the production of tilapia (0.34  103 L/kg fresh weight) and channel catfish (0.33  103 L/kg fresh weight) in fed-pond systems. d The proportions of grains (43.5% wheat flour, 36.5% potato, 10.7% corn, 7.0% milled rice, 2.3% beans, and 0.0% sorghum) in human diets were based on the USDA data for the U.S. population in 2010. The human consumption of wheat flour per capita in the U.S. is 134.7 lb/year in 2010. e To calculate the amount of water needed for crop production, wheat flour and milled rice were converted into whole-wheat grain and un-milled rice grain, respectively. Generally, 1 kg whole-wheat grain and 1 kg un-milled rice grain are equivalent to 0.85 kg wheat flour and 0.70 kg milled rice, respectively. f Primarily white potatoes. Taken from Wu G, Bazer FW, Cross HR. Land-based production of animal protein: impacts, efficiency, and sustainability. Ann NY Acad Sci. 2014;1328:1828. Values are expressed per capita.

significant quantities of CO2, ammonia plus urea, and H2S plus sulfate via tissue-specific pathways.7 These reactions also occur in response to food deprivation and other catabolic conditions

(including disease and heat stress). The nitrogenous substances (e.g., ammonia, urea, nitrite, and nitrate) excreted from animals can be fermented by soil bacteria to yield nitrous oxide.28

8

1. Introduction: significance, challenges and strategies of animal production

All of the animal-derived metabolic wastes may potentially contribute to soil erosion, environmental pollution (e.g., underground water contamination), greenhouse gas emissions, and global warming, if proper management methods are not implemented. Thus, as noted previously, although modern production systems allow farmers to improve the efficiency of animal production, intensive livestock feeding operations may generate relatively large amounts of manure in small concentrated areas, thereby potentially resulting in substantial challenges to the environment. The FAO of the United Nations cited in 2013 a value of 14.5% for the contribution of global livestock production to greenhouse gas emissions (CO2, CH4, and nitrous oxide) worldwide.32 Such an estimate is substantially lower in developed countries where advanced technologies are adopted for livestock and poultry production. For example, in 2012, U.S. Environmental Protection Agency (EPA) estimated that meatproducing animal agriculture (i.e., beef, swine, sheep, goats, and poultry) systems contributed only 2.1% of total national greenhouse gas emissions.33 An analysis by the U.S. EPA further indicated that, among farm animals, the relative contribution from swine production to the overall national greenhouse gas inventory is extremely small (approximately 0.35%).34 Atmospheric air pollution results primarily from transportation (automobiles), chemical industry, coal use, and burning of biomass, rather than animal agriculture.35 Similarly, low air quality in certain urban regions (e.g., Houston in the 1970s, Los Angeles in the 1960s, and London in the 1950s) with no animal production facilities was caused mainly by the chemical industry and automobiles.36 In both developed and developing countries, livestock and poultry production systems are operated in rural areas, but these regions have the desired blue sky and

high-quality air.36 Thus, it is unlikely that animal agriculture has a significant effect on greenhouse gas emissions, air pollution, and global warming, when compared with the chemical industry and automobiles. Pasture-based and low-input systems for livestock production were perceived to have minimal environmental impacts. However, results of recent studies indicate that, in comparison with the traditional low-input systems, modern beef and dairy operations are more efficient and require fewer resources to produce the same amount of meat and milk, therefore using less land and generating a smaller carbon footprint per unit of meat or milk produced.28 For example, in the U.S., modern beef production (based on data in 2007) requires considerably fewer resources and generates less manure, CH4, nitrous oxide, and carbon foot print than the equivalent system in 1977 (Table 1.5).37 Additionally, available data show that, in the U.S., the number of cows, the amount of feedstuffs, the volume of water, and the surface of land required to produce one billion kg of milk in 2007, as well as the outputs of the manure, CH4, nitrous oxide, and carbon footprint per the same amount of milk yield, were much lower than those in the 1940s (Table 1.5).38 Furthermore, per 1000 pullets or 1000 kg of eggs produced in the U.S., the generation of greenhouse gas emissions and the demand for cumulative energy were substantially lower in 2010, when compared with 1960 (Table 1.5).39,40 Similar results of improved efficiency, reduced carbon footprint, and reduced land use in 2009 versus 1959 have been reported for U.S. pork production operations (Table 1.6).41,42 Thus, today, five pigs produce the same amount of pork that required eight pigs in 1959, with 35% and 78% decreases in carbon footprint and the amount of land required per 1000 pounds of dressed carcass produced, respectively.42

Major challenges to the sustainability of animal agriculture and potential solutions

TABLE 1.5

9

Increases in efficiencies of modern production systems for beef, milk, pullets, and chicken eggs. Beef productiona

Milk productionb

Pullet productionc

Egg production by laying hensd

(Change between 2007 and 1977)

(Change between 2007 and 1940s)

(Change between 2010 and 1960)

(Change between 2010 and 1960)

Number of animals

e70%

e21%

e8.6%

e22%

Amounts of feedstuffs

e81%

e23%

e48%

e42%

Volume of water

e88%

e35%

e48%

e32%

Size of land

e67%

e10%

e72%

e80%

Manure

e18%

e24%

e48%

e42%

Methane (CH4)

e18%

e43%

e

e

Nitrous oxide (N2O)

e12%

e56%

e39%

e47%

Carbon foot print

e16%

e37%

e60%

e71%

Variables Requirements

Generation of wastes

The sign (e) denotes a decrease. a Calculated for the production of 1 billion kg of beef (Capper, 2011) 37. b Calculated for the production of 1 billion kg of milk by lactating cows (Capper et al. 2009) c Calculated for the production of 1000 pullets (Pelletier et al. 2010) 39,40. d Calculated for the production of 1000 kg of eggs (Pelletier et al. 2010) 39,40.

Major challenges to the sustainability of animal agriculture and potential solutions Suboptimal efficiency of protein production by animals A major goal of animal nutrition is to fully realize the genetic potential of livestock, poultry and fish for reproduction, growth (including accretion of protein in skeletal muscle), and resistance to disease, while preventing the excessive accumulation of white adipose tissue.43 Biological efficiency of animal production, which refers to the effectiveness in use of feed to produce tissues (e.g., skeletal muscle), milk, or eggs in agricultural operations, is a major determinant of economic efficiency to minimize costs for livestock, poultry and fish production.28 Natural resources for growing feedstuffs and pasture plants are becoming increasingly limited.

38

.

Because of physiological and biochemical constraints, the digestion of feeds and the conversion of dietary proteins to tissue proteins in animals remain suboptimal (Table 1.7).28,44 Although bacteria in the rumen have a high capacity for converting nonprotein nitrogen into amino acids, this process generates a large amount of ammonia, and its use for the microbial synthesis of amino acids and protein occurs at suboptimal rates.7 Thus, microbial fermentation of dietary nutrients in the rumen of ruminants (e.g., cows, goats, buffalo, and sheep) is an inefficient process. Digestibility of dietary protein in post-weaning non-ruminants (e.g., pigs and chickens) is at best 75e92%, depending on ingredients.5 In addition, the irreversible catabolism of amino acids generates CO2, ammonia, H2S, CH4, urea and uric acid, further resulting in suboptimal efficiency of animal production and potentially adverse effects on the environment.

10

1. Introduction: significance, challenges and strategies of animal production

TABLE 1.6 Increases in efficiency of pork production in the U.S. between 1959 and 2009 a. Variable

1959

2009

Change

Number of hogs marketed, head ( 106)

87.6

112.6

þ29%

Number of breeding pigs, head ( 106)

8.25

5.03

e39%

9

Dressed carcass, lb ( 10 )

12.1

22.8

Species

Weeks of production

Efficiency for protein gain (%)b

Broiler chickens

6

33.3

Laying hens (eggs)

55

31.3

25

23.3

Cows (milk)

44

17.9

þ152%

“Grain fed” beef cattle

54

12.1 6.7

þ88%

Pork productivity lb of dressed carcass/sow/year

1467

3699

lb of dressed carcass/litter

TABLE 1.7 Suboptimal efficiencies of production of animal protein in current agricultural systems a.

Pigs

c

980

1826

þ86%

Grazing beef cattle

76

Consumption of feedstuffs, tons ( 106)

39.8

49.7

þ25%

a

44

Feed:Gain ratio, lb feed/lb of dressed carcass)

6.6

4.4

e33%

gallons ( 109)

32.7

36.2

þ11%

gallons/lb of dressed carcass

2.7

1.6

41%

metric tonnes ( 106)

45.7

56.1

þ23%

kg CO2e/lb of dressed carcass

3.8

2.5

e35%

Cropland use for feed production, acres ( 106)

37.2

15.3

e59%

Efficiency of land use, lb of dressed carcass/acre

326

1494

þ358%

Adapted from Boyd and Cady (2012).41 Dressed carcass is defined as the carcass of the pig after its head, viscera, hair, and tail are removed at slaughter.

Adapted from Wilkinson (2011). and Wu et al. (2014).28 In addition to species differences, the efficiency of protein synthesis from dietary amino acids in skeletal muscle and other tissues decreases with increasing age. For example, approximately 70%, 55%, 50%, and 45% of dietary protein is converted into tissue protein in 14-day-old sow-reared pigs, 30-day-old pigs (weaned at 21 days of age to a corn- and soybean meal-based diet), 110-day-old pigs, and 180-day-old pigs, respectively. Similarly, approximately 52%, 48%, 45%, and 41% of dietary protein is converted into tissue protein in 1-, 2-, 4-, and 6-week-old broiler chickens fed cornand soybean meal-based diets, respectively. b Calculated as edible protein in product (e.g., tissue, eggs, or milk) O protein intake from diet x 100%. Protein is expressed as crude protein. Crude protein content (g/100 g fresh weight) in cow’s milk, beef meat, pig meat, poultry meat, and eggs is 3.3, 19.7, 20.5, 19.1, and 12.7, respectively. The ratio of live body weight to edible meat (kg/kg) is 515:288 for grazing beef cattle, 540:302 for “cereal” beef cattle, 109:78.1 for pigs, and 2.54:2.0 for poultry. A laying hen produces 17.7 kg edible eggs (295 eggs x 60 g/egg) in 55 weeks. In edible meat and eggs, the weight of bone and shell is deducted from the total weight of carcass and egg, respectively. c Note: The value reported by Wilkinson (2011)44 for the efficiency of edible protein gain in growing pigs is much lower than the actual value for protein deposition in the whole body of pigs between weaning (21 days of age) and marketing (180 days of age) under typical feeding (e.g., corn- and soybean meal-based diets) and management conditions.

Overall, the efficiency of utilizing dietary protein to produce whole-body protein during the typical production phase is less than 40% for nonruminants and less than 25% for ruminants.28 Thus, we are facing enormous challenges to sustain the production of high-quality protein by farm animals, as feedstuff resources are becoming increasingly limited. This problem is further exacerbated by the high rates of embryonic and neonatal mortality in livestock species, particularly swine and cattle.45,46

Nutritional means can be developed to enhance the efficiency of animal growth and production. For example, optimal diets should be formulated to provide adequate proportions and amounts of all amino acids, as well as carbohydrates, lipids, vitamins and minerals for animals in a species-dependent manner at the various stages of their life cycle.5 Successful practical examples include the following. First, the long-standing ideal protein concept, which ignored “nutritionally nonessential amino acids”

Water consumption by pigs

Carbon footprint (CO2e)

a

Major challenges to the sustainability of animal agriculture and potential solutions

in the diet, has been modified to include these amino acids in ration formulation.47 Second, dietary supplementation with arginine (e.g., 0.4% or 1%) can increase litter size by one or two in gestating gilts or sows,48 and dietary supplementation with glutamine (e.g., 0.2% or 1%) prevents intestinal dysfunction in weanling piglets.49 Third, dietary supplementation with u6 polyunsaturated fatty acids enhances reproductive performance in cattle.50 Fourth, dietary supplementation with feed enzymes (e.g., phytase, b-glucanase, arabinose, xylanase and b-mannanase) increase the bioavailabilities of dietary minerals, carbohydrates and proteins for swine and poultry.51,52 Fifth, reducing dietary intake of protein, concurrently with supplementation with crystalline amino acids or a-ketoglutarate decreases the excretion of nitrogen without affecting whole-body lean tissue growth in nonruminants.53,54 Improvements in farm animal productivity will not only reduce the contamination of soils, groundwater, and air by excessive excretion of animal wastes, but will also help sustain animal agriculture to produce high-quality proteins for the growing global population in the face of declining resources worldwide. Animal breeding and transgenic animals should also play an important role in increasing the utilization of dietary nutrients for protein deposition in skeletal muscle. For example, disruption of the myostatin gene (a negative regulator of myogenesis) creates pigs that exhibit a double-muscled phenotype, greater body weight, greater longissimus muscle mass, and a 100% increase in the number of muscle fibers than wild-type pigs.55 Second, insertion of a functional uncoupling protein 1 (UCP-1) into pigs (naturally lacking this protein) results in an improved ability to maintain body temperature in response to a cold environment, decreased white fat mass, and increased lean carcass yield.56 Finally, introducing a plant gene for D12 fatty acid desaturase or a C. elegans gene for fatty acid desaturase into the white adipose tissue of pigs allows the animals

11

to synthesize u6 and u3 polyunsaturated fatty acids, so that the use of plant-source oils (e.g., soybean oil, sunflower oil, and peanut oil) and fish oil in diets can be reduced or possibly eliminated to decrease swine production costs.57,58 Thus, recent biotechnologies can aid in sustaining animal agriculture.

Banning of the use of antibiotics for growth enhancement Since the discovery of penicillin in 1928, antibiotics have been used to treat bacterial infections in humans and animals. Since the 1950s, subtherapeutic levels of antibiotics have been included in conventional diets to improve the growth performance of swine and poultry.59 However, due to the development and spread of bacteria that are resistant to antibiotics, feed antibiotics have been banned in many countries (e.g., the European union) and are being phased out in some major swine- and poultryproducing nations (e.g., the U.S. and China). Some bacteria are resistant to one class of antibiotics, and others are resistant to multiple antibiotics, thereby posing a serious global health concern.60 For ensuring the optimal efficacy of antibiotics in treating bacterial infections in animals and humans, there is increasing concern worldwide over antimicrobial resistance (AMR), which can be defined as the ability of bacteria to resist the effects of an antimicrobial substance. The antimicrobial resistance genes in bacteria can be inherited from mother to daughter cells, as well as from one strain to another via plasmid transfer.60 Interestingly, the plasmids (small DNA molecules which are independent from chromosomal DNA) in bacteria often carry information that may benefit their own survival through resistance to antibiotics produced by themselves or by other organisms in their environment.61 When a troublesome antibiotic is not used for a prolonged period of time, resistance levels in bacteria decrease, but can increase again when the antibiotic is used again.62

12

1. Introduction: significance, challenges and strategies of animal production

Antibiotic use in animals can have direct and indirect effects on human health due to: (a) the presence of antibiotic residues in animal products (e.g., meat and milk) consumed by humans; (b) human contact with antibiotic-resistant bacteria from food animals; and (c) the spread of antibiotic-resistant bacteria to various components of the ecosystem (e.g., water and soil). Thus, there is an urgent need to identify new alternatives to feed antibiotics in livestock and poultry production worldwide. This can be greatly facilitated by the use of biotechnology to understand how antimicrobial resistance occurs. Over the past two decades, much effort has been directed toward developing alternatives to feed antibiotics in animal nutrition. Improvements in intestinal health and immunity should serve as guiding principles for the development of alternatives to feed antibiotics. The most widely researched alternatives are probiotics, prebiotics, acidifiers (e.g., formate, propionic acid, butyric acid, fumaric acid, citric acid, and benzoic acid), lipids [e.g., lauric acid, 1-monoglyceride, tributyrate), medium chain fatty acids (e.g., octanoic acid, C8:0 and caproic acid, C6:0), and conjugated fatty acids], essential oils (aromatic oily liquids obtained from plant material), plant extracts, and antimicrobial peptides59,63 Other alternatives to antibiotics include minerals [e.g., 250 ppm copper sulfate (CuSo4) and 2500 ppm zinc oxide (ZnO)], clay minerals, rare earth elements (e.g., lanthanum-yeast mixture), egg yolk antibodies (e.g., IgY), recombinant enzymes (e.g., feed enzymes), spray-dried porcine plasma, yeast culture, bacteriophages, lysozymes, bovine colostrum, lactoferrin, chito-oligosaccarides, seaweed extracts, and functional amino acids.59,63,64 Finally, as for some peptides synthesized by the intestinal mucosa, certain protein hydrolysates from animal sources contain antimicrobial peptides, which exert their actions by damaging the cell membrane of bacteria, interfering with the functions of their intracellular proteins, inducing the aggregation of cytoplasmic proteins, and affecting the metabolism of bacteria.65

Metabolic disorders and infectious diseases Metabolic disorders and infectious diseases can not only cause the death of animals, but also reduce conception rates, fetal growth, postnatal growth, and feed efficiency in surviving animals. Metabolic disorders result from a deficiency or excess of nutrients, environmental pollution, exposure to toxic metals or other toxins, and possibly genetic defects.5 When nonruminants are fed a low-protein diet due to an insufficient supply of soybean meal without amino acid supplementation, they are at high risk for malnutrition and infectious disease. While it is commonly thought that farm animals do not currently suffer from a deficiency of vitamins because of the commercial availabilities of their premixes, this is not so, particularly when feeds are stored in a hot environment for a prolonged period of time. Similarly, dietary intakes of macro- or micro-minerals may be inadequate due to the presence of anti-nutritive factors in feedstuff ingredients. Individual minerals and vitamins can be either deficient or excessive due to human errors in ration formulations. Furthermore, on practical farms, animals may be constantly challenged by bacteria, viruses, parasites and other pathogens to become ill, such that a large amount of energy and amino acids are used to maintain life.66 Thus, effective prevention and treatment of diseases, particularly infectious diseases (e.g., foot and mouth disease, African swine fever, and avian influenza), is essential for successful livestock, poultry and fish production. There are many successful examples of prevention and treatment of nutritional diseases through dietary interventions.5 For example, dietary supplementation with arginine ameliorates ascite-pulmonary hypertension in poultry.67 In addition, dietary supplementation with a mixture of antioxidants (e.g., vitamins E, C, bioflavonoids, and selenium) prevents porcine stress syndrome.68 Furthermore, dietary supplementation

13

Companion animals in agriculture

with phosphorus prevents infertility in cattle grazing on the phosphorus-deficient soil.69 Finally, enteral or intravenous administration of molybdenum can treat the toxicity of excessive dietary copper in sheep.70 Prevention of disease will reduce the metabolic cost of animal production, leading to an increase in the efficiency of feed utilization and a decrease in the excretion of wastes. Besides dietary interventions, animal biotechnologies can help prevent infectious diseases in livestock species. For example, clustered regularly interspaced short palindromic repeatsassociated nuclease-9 (CRISPR/Cas9) gene targeting and somatic cell nuclear transfer (SCNT) technologies have been used to create pigs without the CD163 gene.71 This gene encodes a cellular receptor for the porcine reproductive and respiratory syndrome virus (PRRSV, also referred to as “blue ear disease").71 Thus, pigs with the CD163 knock-out are fully resistant to the PRRSV.72 Also, males and females can be used as breeding stocks to produce generations of PRRSV-resistant offspring. Finally, recombinant DNA technology has been used to produce vaccines against bacterial and viral diseases (e.g., African swine fever virus) by Escherichia coli.73 Thus, biotechnology holds promise for sustaining and enhancing global animal agriculture.

Global warming and cold environment Global surface temperature has increased gradually over the past 50 years. Heat stress reduces feed intake and production of animals, while impairing their immune function and inducing a catabolic state.74 For example, increasing environmental temperatures from 23 to 33  C markedly reduces feed intake, growth performance, and the efficiency of nutrient utilization in swine75 and poultry.76 Consequently, a climate change toward global warming is expected to negatively impact animal production worldwide.77 For example, on a hot day, almost all fish raised in ponds may die, resulting in a huge economic loss. Conversely, in a cold

environment, animals (e.g., cattle and swine) have elevated levels of thyroid hormones that stimulate basal energy metabolism and wholebody protein degradation, thereby leading to the loss of muscle protein or even death.5 Extreme temperatures increase risk for both metabolic disorders and infectious diseases in animals, further compromising their production. Generating new breeds of animals to better adapt to a hot or cold climate is crucial for the future success of animal agriculture. Methods to mitigate heat stress include physical cooling systems (e.g., sprinklers and water baths), and reductions in dietary levels of protein coupled with dietary supplementation with some amino acids (lysine, tryptophan and threonine) or saturated fat.5 These methods are partially effective because they can enhance heat dissipation, decrease whole-body heat production (primarily via reduced whole-body protein metabolism), and attenuate the thermal effect of feeding, but they have the disadvantages of high costs, inadequate provision of most amino acids, and risk for an excess deposition of subcutaneous fat. Thus, novel means for optimal mitigation of production problems brought about by climate change are needed. One effective method is dietary supplementation with Yucca schidigera extract (Yucca; BIOPOWDER), which contains steroidal saponins.5 These phytochemicals are natural structural analogues of glucocorticoids (e.g., corticosterone and cortisol) and antagonize the negative effects of the stress hormones on inducing protein degradation in skeletal muscle and amino acid catabolism in the whole body.

Companion animals in agriculture In many regions of the world, horses, donkeys, mules (male donkeys x female horses), yaks, and camels are used for transportation or as companion animals.78 They perform muscular work or interact with humans in many ways,

14

1. Introduction: significance, challenges and strategies of animal production

including sport competitions and recreational pursuits. Horses are herbivores with a relatively small stomach, but a voluminous cecum and colon. The large intestine of horses has digestive functions similar to those of ruminants, but fiber digestion in horses is not as efficient as in ruminants. This unique digestive physiology allows horses to survive and grow on grazing pastures. Donkeys, mules, yaks, and camels are also herbivores, which consume plants as their diets. All of these animals can adapt to their local environments so well that they are part of the history of human civilization. Because grass and other forages contain low levels of protein, increasing the dietary provision of amino acids is essential to maximize their growth (particularly muscle growth), development, health, and physical strength. Due to space limitations, companion animals are not the focus of this book.

and mitigate undesired effects of agricultural practices. While scientists and producers have made substantial progress to increase feed efficiency in animal production, further improvements are needed for both agricultural competitiveness and good environmental stewardship. New knowledge about animal breeding, nutrition, reproduction, health, and housing environments, as well as meat quality, pathogen management, and biotechnology, which is thoroughly covered in this book, is essential for achieving this goal.

Acknowledgments Work in our laboratories was supported by Agriculture and Food Research Initiative Competitive Grants (2014-6701521770, 2015-67015-23276, 2015-67015-23369, 2016-6701524958, and 2018-505706-95720) from the USDA National Institute of Food and Agriculture, and Texas A&M AgriLife Research (H-8200). We thank our colleagues, students and technicians for their contribution to research programs.

Conclusion Optimal growth and health of humans depend on the adequate consumption of dietary protein with sufficient amounts and proper ratios of all amino acids. High nutritional quality of animal protein, as well as the sole sources of physiologically important amino acids and antioxidant dipeptides from animal products, necessitates the development and sustainability of animal agriculture and aquaculture. Livestock, poultry and fish production also contribute to global economic growth and may reduce demands for water to irrigate food-grain crops. By converting low-quality feedstuffs and forages into high-quality protein, farm animals do not compete with humans for food. When advanced technologies are used, animal agriculture does not contribute significantly to greenhouse gas emissions, atmospheric air pollution, or global warming. Improving feed efficiencies provides a safe and promising strategy to produce animal protein, sustain livestock and poultry industries,

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26. Li P, Wu G. Roles of dietary glycine, proline and hydroxyproline in collagen synthesis and animal growth. Amino Acids. 2018;50:29e38. 27. Food and Agriculture Organization of the United Nations (FAO). Food Outlook; 2018. www.fao.org/3/ CA0239EN/ca0239en.pdf. 28. Wu G, Bazer FW, Cross HR. Land-based production of animal protein: impacts, efficiency, and sustainability. Ann NY Acad Sci. 2014;1328:18e28. 29. Food and Agriculture Organization of the United Nations (FAO). The State of World Fisheries and Aquaculture; 2018. www.fao.org/documents/card/en/ c/19540EN. 30. Li P, Mai KS, Trushenski J, et al. New developments in fish amino acid nutrition: towards functional and environmentally oriented aquafeeds. Amino Acids. 2009;37: 43e53. 31. Moisa S. Israel’s Agriculture. Tel Aviv: The Israel Export and Institute Cooperation Institute; 2014. 32. Food and Agriculture Organization of the United Nations (FAO). Tackling Climate Change through Livestock: A Global Assessment of Emissions and Mitigation Opportunities. Rome, Italy: FAO; 2013. 33. U.S. Environmental Protection Agency. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2010. Washington, D.C: U.S. Environmental Protection Agency; 2012. 34. U.S. Environmental Protection Agency. http://epa. gov/climatechange/emissions/downloads11/USGHG-Inventory-2011-Chapter-6-Agriculture.pdf. 2012; Accessed 08.05.2014. 35. Zhuang X, Wang Y, He H, et al. Haze insights and mitigation in China: an overview. J Environ Sci. 2014;26: 2e12. 36. Jacobson MZ. Atmospheric Pollution: History, Science, and Regulation. Cambridge: Cambridge University Press; 2002. 37. Capper JL. The environmental impact of beef production in the United States: 1977 compared with 2007. J Anim Sci. 2011;89:4249e4261. 38. Capper JL, Cady RA, Bauman DE. The environmental impact of dairy production: 1944 compared with 2007. J Anim Sci. 2009;87:2160e2167. 39. Putmana B, Thomab G, Burekb J, Matlocka M. A retrospective analysis of the United States poultry industry: 1965 compared with 2010. Agric Syst. 2017; 157:107e117. 40. Pelletier N, Ibarburu M, Xin HW. Comparison of the environmental footprint of the egg industry in the United States in 1960 and 2010. Poultry Sci. 2014;93: 241e255. 41. Boyd C, Cady R. A 50-year Comparison of the Carbon Footprint of the U.S. Swine Herd: 1959e2009. London: Camco; 2012.

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1. Introduction: significance, challenges and strategies of animal production

42. Thoma G, Nutter D, Ulrich R, et al. National Life Cycle Carbon Footprint Study for Production of US Swine. Des Moines: National Pork Board; 2011. 43. Wu G, Bazer FW, Dai ZL, et al. Amino acid nutrition in animals: protein synthesis and beyond. Annu Rev Anim Biosci. 2014;2:387e417. 44. Wilkinson JM. Re-defining efficiency of feed use by livestock. Animal. 2011;5:1014e1022. 45. Bazer FW, Johnson GA, Wu G. Amino acids and conceptus development during the peri-implantation period of pregnancy. Adv Exp Med Biol. 2015;843:23e52. 46. Dahlen C, Larson J, Lamb GC. Impacts of reproductive technologies on beef production in the United States. Adv Exp Med Biol. 2014;752:97e114. 47. Wu G. Dietary requirements of synthesizable amino acids by animals: a paradigm shift in protein nutrition. J Anim Sci Biotechnol. 2014;5:34. 48. Wu G, Bazer FW, Johnson GA, et al. Arginine nutrition and metabolism in growing, gestating and lactating swine. J Anim Sci. 2018;96:5035e5051. 49. Wu G, Bazer FW, Johnson GA, et al. Important roles for L-glutamine in swine nutrition and production. J Anim Sci. 2011;89:2017e2030. 50. Cooke RF. Supplementing omega-6 fatty acids to enhance early embryonic development and pregnancy establishment in Bos indicus and B. taurus beef cows. J Anim Sci. 2019;97:485e495. 51. Bedford MR. The evolution and application of enzymes in the animal feed industry: the role of data interpretation. Br Poult Sci. 2018;59:486e493. 52. Cowieson AJ, Roos F. Toward optimal value creation through the application of exogenous monocomponent protease in the diets of non-ruminants. Anim Feed Sci Technol. 2016;221:331e340. 53. Gloaguen M, Le Floc’h N, Corrent E, et al. The use of free amino acids allows formulating very low crude protein diets for piglets. J Anim Sci. 2014;92:637e644. 54. Chen J, Su W, Kang B, et al. Supplementation with a-ketoglutarate to a low-protein diet enhances amino acid synthesis in tissues and improves protein metabolism in the skeletal muscle of growing pigs. Amino Acids. 2018;50:1525e1537. 55. Rao S, Fujimura T, Matsunari H, et al. Efficient modification of the myostatin gene in porcine somatic cells and generation of knockout piglets. Mol Reprod Dev. 2016;83:61e70. 56. Zheng Q, Lin J, Huang J, et al. Reconstitution of UCP1 using CRISPR/Cas9 in the white adipose tissue of pigs decreases fat deposition and improves thermogenic capacity. Proc Natl Acad Sci USA. 2017;114:E9474eE9482.

57. Saeki K, Matsumoto K, Kinoshita M, et al. Functional expression of a Delta-12 fatty acid desaturase gene from spinach in transgenic pigs. Proc Natl Acad Sci USA. 2004;101:6361e6366. 58. Lai L, Kang JX, Li R, et al. Generation of cloned transgenic pigs rich in omega-3 fatty acids. Nat Biotechnol. 2006;24:435e436. 59. Thacker PA. Alternatives to antibiotics as growth promoters for use in swine production: a review. J Anim Sci Biotechnol. 2013;4:35. 60. Koch BJ, Hungate BA, Price LB. Food-animal production and the spread of antibiotic resistance: the role of ecology. Front Ecol Environ. 2017;15:309e318. 61. Kim JS, Cho DH, Park M, et al. CRISPR/Cas9-mediated re-sensitization of antibiotic-resistant Escherichia coli harboring extended-spectrum b-lactamases. J Microbiol Biotechnol. 2016;26:394e401. 62. Dutil L, Irwin R, Finley R, et al. Ceftiofur resistance in Salmonella enterica serovar Heidelberg from chicken meat and humans, Canada. Emerg Infect Dis. 2010;16: 48e54. 63. Yi D, Li BC, Hou YQ, et al. Dietary supplementation with an amino acid blend enhances intestinal function in piglets. Amino Acids. 2018;50:1089e1100. 64. Yi D, Fang QH, Hou YQ, et al. Dietary supplementation with oleum cinnamomi improves intestinal functions in piglets. Int J Mol Sci. 2018;19:1284. 65. Hou YQ, Wu ZL, Dai ZL, et al. Protein hydrolysates in animal nutrition: industrial production, bioactive peptides, and functional significance. J Anim Sci Biotechnol. 2017;8:24. 66. Le Floc’h N, Wessels A, Corrent E, et al. The relevance of functional amino acids to support the health of growing pigs. Anim Feed Sci Technol. 2018;245:104e116. 67. Khajali F, Wideman RF. Nutritional approaches to ameliorate pulmonary hypertension in broiler chickens. J Anim Physiol Anim Nutr. 2016;100:3e14. 68. Apple JK. Nutritional Effects on Pork Quality in Swine Production. U.S. National swine Nutrition Guide; 2015. porkgatewau.org/resource/nutritional-effects-onpork-quality-in-swine-production-2. 69. Greene LW. Designing mineral supplementation of forage programs for beef cattle. J Anim Sci. 2000; 77(E-Suppl):1e9. 70. Wang ZY, Yang YL, Wu WF, et al. Treatment of copper poisoning in goats by the injection of trithiomolybdate. Small Rumin Res. 1992;8:31e40. 71. Burkard C, Opriessnig T, Mileham AJ, et al. Pigs lacking the scavenger receptor cysteine-rich domain 5 of CD163 are resistant to PRRSV-1 infection. J Virol. 2018;92(16): e00415ee00418.

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76. Hu H, Bai X, Shah AA, et al. Dietary supplementation with glutamine and g-aminobutyric acid improves growth performance and serum parameters in 22- to 35-day-old broilers exposed to hot environment. J Anim Physiol Anim Nutr. 2016;100:361e370. 77. Renaudeau D, Collin A, Yahav S, et al. Adaptation to hot climate and strategies to alleviate heat stress in livestock production. Animal. 2012;6:707e728. 78. Pearson RA, Lhoste P, Saastamoinen M, et al. Working animals in agriculture and transport. EAAP Technical Series. 2003;6:1e210.

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P A R T I

Beef cattle production

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C H A P T E R

2 Genetics and breeding of beef cattle Raluca G. Mateescu Department of Animal Sciences, University of Florida, Gainesville, FL, United States

O U T L I N E Historical overview of breeding programs

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Quantitative nature of economically important traitsdan intrinsic challenge in selection

23

Advances in genomic technologies and genomic selection

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Future genomic information

29

Historical overview of breeding programs

29

Genomics and sustainability

30

Genetic improvement in climate resilience traits

31

New genomic technologies

32

References

32

with the goal of protecting the purity of the breed.4 The selection of the beef breeds was done with the goal of the perceived needs of the industrial revolution. The Angus, Hereford, and Shorthorn breeds were developed between 1750 and 1850.5 In the beginning, successful breeding in the breed associations was based on the use of pedigree along with the eye judgment to ensure adherence to formalized breed type.6 In 1936, Lush and Black introduced the idea of objective measures of merit in beef cattle and the first heritability estimates for growth were published by Knapp and Nordskog7 and

Traditional animal breeding programs based on phenotypic measurements and pedigree structure have been used for a long time to create and select the highly specialized breeds we have today.1 Breed formation was accomplished by following the example of Robert Bakewell who selected and mated the best with the best until a certain degree of uniformity of type was achieved.2 The resulting animals were popularized at livestock shows.3 Ancestry was recorded in herd books and breed societies were founded

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00002-1

Where genomic selection can have a great impact for the beef industry?

21

Copyright © 2020 Elsevier Inc. All rights reserved.

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2. Genetics and breeding of beef cattle

by Koger and Knox8 on weight adjustments which fueled research interests. Performance evaluation was first conducted on a “one-toone" basis by a handful of breeders, researchers, and extension agents. From 1940 to 1960, the elite breeds of today were developed. Central bull testing began in 1941 in Texas9,10 and the tests proved to be a successful demonstration of competition based on performance. Beef cattle improvement programs run by extension specialists were started between 1945 and 1950. In 1955, the first beef cattle improvement association was organized in Virginia and was managed by breeders with help from extension agents. The Beef Improvement Federation (BIF) was formed in 1967 to create uniformity, assist in developing programs, encourage education and increase confidence in performance of beef cattle.11 BIF guidelines for uniform beef improvement programs started with the first meeting in 1968 in Kansas City, and continues today where relevant research is presented along with performance updates. The National Sire Evaluation was the focus of one working committees of BIF and in 1971 the committee developed the guidelines for incorporating the use of reference sires as the method for comparing sires.12 The first sire summary was published in 1971 by the American Simmental Association. Breeding values for weight, based on an individual's own weight and relative performance, were introduced to the industry during the 1970 BIF meeting at which members of BIF estimated breeding values by playing the computer cow game.13 One year later, in 1971, these breeding values were incorporated into breeding programs and in 1974 maternal breeding values (milk production reflected in the weaning weight of calves of daughters of the sires in the pedigree) were introduced. The mixed model procedure for sire evaluation was performed during 1980 on field data from the American Angus Association and the American Hereford Association. The sire birthyear group constants showed a linear genetic

trend in yearling weight for the Angus (þ1.2 kg/yr) and Hereford (þ1.5 kg/yr) breeds over two generations (1965e78). Beef breeds recognized AI as a breed improvement tool and at the time, 89% of the sires were genetically directly or indirectly tied through common ancestry. With these ties and the relationships included in a relationship matrix, new analytical procedures were developed to evaluate yearling bulls from across multiple herds.14e16 Performance records were sold initially as a withinherd tool. Henderson presented in a symposium to honor J. L. Lush a statistical method which would become the gold standard in predicting additive genetic merit in livestock.17 The mixed model procedures, which provided Best Linear Unbiased Prediction (BLUP) of breeding values, resulted in a substantial increase in the prediction accuracy by improving the quality of predictions made between contemporary groups such as herds or years, and thus allowing all available data to be used.18 Subsequent improvements in computer power and developments in computational methods allowed the evolution from univariate sire models to multivariate animal models. In 1984, direct additive effects for the dams were included in the model along with the direct effects for sires.19 The inclusion of the dam effects resulted in an increase in pedigree connections accounting for nonrandom mating of dams and would later allow for additive effects of dams, also called the additive maternal effect, to be included in the model.20 Comparing genetic merit among breeds was always of interest, which stimulated the development of statistical methods for multibreed analyses. The grading-up programs common to many breed associations resulted in pedigree datasets with animals from other breeds and performance data accumulating from mixed breed composition cattle. Some commercial producers were interested in crossbreeding which opened up a great opportunity to market composite seedstock. Because crossbreeding was very

I. Beef cattle production

Quantitative nature of economically important traitsdan intrinsic challenge in selection

popular and practiced by many commercial producers, there was a growing need to develop methods to compare genetic merit among breeds. Statistical models using combined data sets or data sets with appropriately structured multibreed data were presented by researchers such as Elzo and Famula21 and Elzo and Bradford22 for sire-maternal grandsire models. Several researchers developed a method for comparing Expected Progeny Difference (EPD) from multiple breeds which provided factors for adjusting EPDs from different breed associations to values that were comparable across breeds.23e26 In the early years, these tables were limited to only birth weights, weaning weights, and yearling growth traits. More recently, the table was updated to include many additional traits and these updates are typically presented during the annual meetings of BIF. The importance of carcass characteristics and meat quality were recognized as a priority for improvement,27 and several breeds followed the lead of the American Angus Association and began collecting carcass data as early as 1974.28 The performance program was developed by the American Angus Association in collaboration with researchers from Iowa State University in the early 1960s and carcass EPDs started to be published in the mid-1980s. The lack of records on the selection candidates combined with a limited number of records on slaughtered progeny resulted in a low accuracy of prediction. Moreover, the prediction accuracy on young animals of breeding age was even lower, because of the long time required to produce progeny for slaughter. In 1998, genetic merit predictions for ultrasound traits measured directly on breeding stock were published by the American Angus Association. By the year 2000, the list of published EPDs for each breed association had increased considerably, with some breed associations having published more than 15 different EPD.

23

Quantitative nature of economically important traitsdan intrinsic challenge in selection The principle of genetic improvement is a very simple one: select above average candidates as parents of the next generation and the next generation will be genetically superior. The challenge lies with the nature of the traits which are the focus of selection programs. Most economically important traits with high priority for the industry to improve are quantitative in nature, being controlled by many genes and environmental factors. These types of traits are difficult and expensive to measure on a large number of animals. Thus, there is a lack of reliable performance measures on the selection candidates, their parents and perhaps their offspring, contributing to our inability to cost-effectively rank selection candidates for all traits of interest.29 The first beef cattle traits included in the National Cattle Evaluations (NCE) were weight traits, and they now include birth, weaning and yearling weights, and even mature weights. US breed association do not report the computed Expected Breeding Value (EBV), but rather the EPDswhich is one-half the EBV. Table 2.1 shows a summary of traits with reported EPDs for the 16 most prominent US beef cattle breeds. Calving ease has been added to most national evaluation systems and, like weaning weight, includes EPD that reflect direct and maternal contributions.30 Carcass traits have been and are still difficult to measure in seedstock herds where the selection candidates are not following the typical feedlot e slaughter schedule. For these animals, most carcass information is derived from ultrasound measures of mainly rib-eye area, intramuscular fat and fat depth.29 A carcass EPD is not reported for all breed associations. Records of eating quality, a complex of individual traits including tenderness, juiciness and flavor, are mostly limited to tenderness, but this is also

I. Beef cattle production

24

2. Genetics and breeding of beef cattle

TABLE 2.1

Traits reported in national cattle evaluation for the 16 most prominent beef cattle breeds in the US. British

a

Breed

Continental

Indicus

AAA

AHA

RAA

ASH

AIC

AGA

AMA

ASA

BAA

NAL

SAL

ABB

ACA

BBU

IBB

SGA

BWT

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

WWT

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

Milk

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

YWT

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

YHT

X

MWT

X

MHT

X

CCW

X

X

X

X

X

X

X

X

X

X

X

X

X

MRB

X

X

X

X

X

X

X

X

X

X

X

X

X

REA

X

X

X

X

X

X

X

X

X

X

X

X

FAT

X

X

X

X

X

X

X

X

X

X

X

X

X

b

Trait

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

RUMP YLD

X

X

X

WBSF

X

X

CED

X

X

X

X

X

X

X

X

X

CEM

X

X

X

X

X

X

X

X

X

SC

X

X

HPG

X

STAY

X X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X X

X

X

X

GL

X

X

X

X

DOC

X

RADG

X

X X

ME

X

X

X

X

X

X

X

DTF TM

X

X

X

X

X

X

X

X

X

X

A1C

X

BCS

X

TI

X

FI

X

X X

BB

X

I. Beef cattle production

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Quantitative nature of economically important traitsdan intrinsic challenge in selection

TABLE 2.1

Traits reported in national cattle evaluation for the 16 most prominent beef cattle breeds in the US.dcont'd British

a

Breed

AAA

AHA

RAA

DMI

X

X

X

$EN

X

$W

X

$F

X

$G

X

$B

X

Continental ASH

AIC

AGA

AMA

ASA

BAA

Indicus NAL

SAL

ABB

ACA

BBU

IBB

SGA

Traitb

X

US

X

TSI

X

$BMI

X

$BII

X

$CHB

X

X

HB

X

GM

X

$CEZ TSI

X

X X

P30D

X

RFI

X

$COW

X

FPI

X

EPI

X

API

X

a

Breed: British: AAA, American Angus Association; ABB, American Brahman Breeders Association; ACA, American Chianina Association (includes Chiangus); AGA, American Gelbvieh Association; AHA, American Hereford Association; AIC, American International Charolais Association; AMA, American Maine Anjou Association; ASA, American Simmental Association; ASH, American Shorthorn Association; Continental; BAA, Braunvieh Association of America; BBU, Beefmaster Breeders United; IBB, International Brangus Breeders Association; NAL, North American Limousin Foundation; RAA, Red Angus Association of America; SAL, American Salers Association; Indicus; SGA, Santa Gertrudis Association. b Trait: $B, dollars beef; $BII, Brahman influence index; $BMI, british maternal index; $CEZ, calving ease index; $CHB, certified Hereford beef index; $COW, maternal productivity; $EN, cow energy value; $F, feedlot index; $G, grid value; $W, weaned calf value; A1C, age at first calf; API, all-purpose index ($/cow exposed, all purpose sire scenario); BB, breed back; BCS, body condition score; BWT, birth weight; CCW, carcass weight; CED, calving ease direct; CEM, calving ease maternal; DMI, dry matter intake; DOC, docility; DTF, days to finish; EPI, efficiency profit index; FAT, fat depth (usually over rib); FI, fertility index; FPI, feeder profit index; GL, gestation length; GM, grid master; HB, heard builder; HPG, heifer pregnancy rate; ME, maintenance energy requirements; MHT, mature height; Milk, weaning weight maternal; MRB, marbling/intramuscular fat; MWT, mature weight; P30D, pregnant at 30 days; RADG, residual average daily gain; REA, rib eye area; RFI, residual feed intake; RUMP, fat depth over rump; SC, scrotal circumference; STAY, stayability; TI, terminal index; TM, maternal total (1/2 bull’s WW EPD þ bull’s MK EPD); TS, teat size; TSI, terminal sire index; US, udder suspention; WBSF, Warner-Bratzler shear force (tenderness); WWT, weaning weight direct; YHT, yearling height; YLD, retail beef yield/percent retail cuts/yield grade; YWT, yearling weight. Adapted and updated from 45

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2. Genetics and breeding of beef cattle

difficult to measure. As a solution, marbling is used as a surrogate for tenderness/eating quality. More recently, a quantitative trait locus (QTL) in the region of the calpain and calpastatin genes was considered for marker-assisted selection, using a Single Nucleotide Polymorphism (SNP) that varies among breeds, most notably between Bos indicus and Bos taurus breeds. Measures of reproductive performance present difficulties as well, since inventory recording systems have not been used by most breed associations until relatively recently, so it is impossible to determine if a female not presented as a dam calved or not.31 Reproductive EPD have, therefore, been limited to scrotal circumference, and more recently, heifer pregnancy. Many other traits of potential economic importance like feed intake are also problematic to measure on a large number of animals, especially under grazing conditions. Other traits like energy requirements for maintenance have been predicted from data on mature weight, body condition score and potential for milk production.32

Advances in genomic technologies and genomic selection Starting in 1990, advances in molecular genetics held the promise that DNA information would lead to greater genetic improvement compared to that based only on phenotypic records. Research efforts were focused on Marker-Assisted Selection (MAS), which consists of two steps: 1) detect and (fine) map genes underlying the traits of interest, i.e., quantitative trait loci (QTL); and 2) include the QTL information into the BLUP-EBV.33 Although the first step of mapping QTL resulted in many QTL being detected, the repeatability of the mapping studies was low where many QTL positions moved or completely disappeared from one study to the next. This is attributed to the majority of QTL having very small effects, which, in combination with the multiple testing situation (testing

thousands of markers), results in a substantial “Beavis effect” of overestimated effects of significant markers.34 In genome-wide association studies (GWAS), the number of tests equals the number of genotyped independent SNPs, which in livestock is typically many thousands. To account for multiple-testing, very stringent P-values are used and, consequently, only QTL with a large effect are found. DGAT1 is one of the very few large QTL detected for fat content in milk,35 while for most traits investigated no reliable QTL were found. Even in dairy cattle, where many QTL mapping studies were conducted on very large populations to ensure sufficient power to detect these effects, less than 10% of the genetic variance of the breeding objective could be explained by these QTL. Hence, by 2005, although the MAS approach was successful for simple traits controlled by one gene, it provided disappointing results for many complex traits which resulted in the limited use of MAS in livestock breeding. In 2001, Hayes and Goddard36 predicted that 50 to 100 genes affected dairy traits, which was considered a high estimate at that time. In the following 15 years, results from GWAS and genomic selection suggested that complex traits are controlled by many genes with very small effects. In 2016, Goddard et al.37 estimated that between 2,000 and 10,000 genes may affect dairy traits. Three breakthroughs have resulted in the current widespread use of DNA information: 1) the development of genomic selection methodology,38 2) the identification of many thousands of SNP markers across the entire genome, and 3) high throughput genotyping technologies (SNP chips) which allow for a cost effective genotyping solution for large number of SNPs. It is important to point out that the seminal paper describing the approach of genomic selection and the statistical methods for accurately estimating the genetic merit from a set of genetic markers spanning the entire genome was published before these markers were available. The subsequent genome sequencing of many of the

I. Beef cattle production

Advances in genomic technologies and genomic selection

livestock species resulted in the discovery of thousands of SNPs across the genome which enabled the SNP-chip genotyping technologies to be developed for most livestock species. In cattle, the 1,000-bulls sequencing project has revealed 30 þ million SNP markers.39 The first high-density and high-throughput genotyping assay was the 10K SNP chip commercialized by Affymetrix.40 Because the number of markers on this panel was insufficient for many genomic studies, a higher density Illumina BovineSNP50 chip (w50,000 SNP) was developed by a consortium of animal scientists using SNP discovery populations in Holstein, Angus and mixed breeds of beef cattle.41 This assay has become the international standard for genomic selection (GS) and GWAS in cattle. Subsequently, the Illumina BovineHD BeadChip containing 777,962 SNPs (777K) became available and is being used in discovery research. Genotyping was embraced by a number of breed associations, and producers of registered cattle are currently driving the implementation of genomic selection within their breeds. However, there is still a great portion of producers not genotyping their cattle because it requires a significant economic investment which is usually difficult to offset unless they retain ownership of weaned calves. This technology can be used in commercial herds to reduce the risk associated with buying herd bulls. A traditional EPD for young bulls is exclusively based on parental average EPDs for all traits, except birth weight and possibly weaning weight. For these young animals, genomicenhanced EPDs are more reliable than traditional EPDs. The decrease in risk is proportional to the reduction in the possible change in value for each EPD as a result of higher accuracy. There is a great potential for the genomic technology to generate considerable value across the entire beef production chain, from the producer to the end product and to the consumer42; but currently, the seedstock sector incurs most of the genotyping expense. Within the seedstock

27

sector, genotyping technologies have been used largely as a marketing tool, and the ability to increase genetic improvement has been a secondary goal. Enhanced marketing results in increased returns on investment which tend to be much higher in the seedstock sector compared to the commercial sector. Many small producers of registered cattle do not have the ability to take advantage of marketing; therefore, the genotyping costs are not economical as they cannot recoup any of their investment. It is expected that this challenge will be overcome in time as the prices of genotyping continue to decline and beef producers continue to become more educated about the value of genomic testing in generating EPDs with greater accuracy. The genomic selection approach proposed in 2001 by Meuwissen et al.38 estimates the breeding value from markers evenly spaced throughout the entire genome. In this approach, the overall breeding value of any animal is predicted by summing up the estimated genetic effect of each genetic marker (Fig. 2.1). The genetic effect of the markers is estimated in large populations of cattle with recorded phenotypes and marker genotype information which are referred to as reference populations. The estimated effects can then be used for selection of candidates with genotypic information, but without any phenotypic records. The effectiveness of the genomic selection approach relies on a large number of genotyped cattle and the number of markers on the genome. The challenge related to the number of genotyped markers in cattle was solved with the advent of large-scale and cheap genotyping methods. Among all livestock industries, the dairy industry was arguably the most successful in implementing this genomic selection approach. Several advantages made it easier for the dairy industry to adopt genomic selection including widespread use of AI, availability of large reference populations of bulls with highly accurate estimates of genetic merit, individual animals

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2. Genetics and breeding of beef cattle

FIG. 2.1 Principle of Genomic Selection. A training population with many individuals genotyped and phenotyped is used to derive a genomic prediction equation. This equation is then used to estimate the genomic breeding value of genotyped individuals without phenotypes.

with high EBV sufficient to offset the costs of genotyping. In contrast with the dairy industry, a multitude of challenges slowed down the adoption of genomic selection in the beef industry. The US beef industry consists of many different breeds selected to fit various beef cattle production systems and environments over a wide geographic area. The beef industry includes more than 80 distinct breeds, although at the seedstock level the top five breeds comprise nearly 80% of all registered animals. A main limitation is the lack of widespread use of AI which constricts the availability of high-accuracy sires for the reference population. In addition, given the substantial differences among the breeds, a separate reference population is required for each breed which further limits the number of animals available for the training population. Compared to the dairy industry which has an industry-wide breeding objective, many

different traits are economically important in different beef industry sectors (e.g., carcass traits are the key profit driver for the processors, but not for the commercial sector). There is also no beneficiary willing to pay for the development of phenotyped and genotyped training populations, such as the AI studs provided for the dairy industry, owing to the limited use of AI in the beef industry. Consequently, the development of training populations in the beef cattle industry has been somewhat disjointed. Some companies, such as GeneSeek (formerly Igenity) and Zoetis (formally Pfizer Animal Genetics), saw this void as a business opportunity43 and paid for the genotyping of semen collections from AI sires put together by individual researchers44 or they developed their own training populations. This involvement of commercial genomics companies introduced a proprietary component into the process of ranking animals based on genetic merit43 and made it difficult to obtain

I. Beef cattle production

Where genomic selection can have a great impact for the beef industry?

validation data for the resulting genomic predictions.45 Other breed associations have developed their own training populations by genotyping AI bulls and obtaining 50K genotypes from the influential bulls that were genotyped at the US Meat Animal Research Center (US-MARC) in Clay Center, Nebraska, as part of the 2,000 Bull Project. Breed associations, in collaboration with the National Beef Cattle Evaluation Consortium, used these data to develop within-breed genomic prediction equations. The advantage of this model is that the breed association has access to the genotypic information and can use this information in conjunction with new performance and pedigree information in the breed database to continuously improve prediction equations. Another limitation lies with the segmented nature of the beef cattle industry which is composed of five main sectors: seedstock, commercial, feedlot, processor, and retail. There are several thousand seedstock breeders and over 750,000 commercial producers. Animals change ownership multiple times in the production chain, and phenotypic performance in downstream segments (e.g., feed efficiency in the feedlot, carcass quality, eating satisfaction) is rarely relayed back to the breeding sector. In contrast to more vertically integrated industries, segmentation of beef cattle industry results in market inefficiency because breeders are rarely rewarded for developing improvement programs that maximize profit for the entire industry. Additionally, in the absence of phenotypes from the commercial, feedlot, processing, and retail sectors, it is difficult to make genetic improvement for traits that are measured in those sectors.

Future genomic information High density SNP chips (800K) are now commercially available and being used in discovery research. Under the assumption that linkage disequilibrium (LD) is preserved between the SNP and QTL across breeds, this high-density SNP chip is expected to allow pooling of training

29

data sets across breeds. When cost is the limiting factor, genotype imputation allows increasing the size of the population with high density genotypes or whole-genome sequences. The imputation from low density SNP panels to 50K or 770K SNP panels has been shown to be highly accurate in beef cattle where there is a high level of LD across the genome. This imputation is highly accurate especially for those breeds that have a large reference population of animals with high density genotypes.46,47 The imputation to whole-genome sequence is more challenging because the low minor allele frequency variants which have a high incidence are much more difficult to impute accurately. For variants with a minor allele frequency above 5%, imputation from the 777K to full sequence is moderately accurate,48 while variants with a frequency lower than 5% are imputed extremely poorly.49 Sequencing genomes of animals in such a way as to maximize the number of haplotypes available in the reference population or animals genetically related to all other animals in the population could increase the accuracy of imputation.50,51 Availability of large populations of beef cattle with full sequence information will positively impact association analyses, and the ability to fine-map genes for economically important traits has the potential to lead to a better understanding of the genetic architecture of complex traits.

Where genomic selection can have a great impact for the beef industry? Genomic selection is most advantageous for traits that are difficult to select for traditionally. The greatest benefit from genomic selection in dairy cattle is the reduction in the generation interval associated with progeny testing. In beef cattle, growth rate, a trait with high economic priority, can be measured on selection candidates at a young age so progeny testing is not needed, making genomic selection less beneficial. However, several other economically important traits in beef cattle, such as feed conversion efficiency,

I. Beef cattle production

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2. Genetics and breeding of beef cattle

beef quality and thermotolerance, are difficult to improve via traditional selection programs. Because phenotypic records for these types of traits are very difficult and expensive, the high cost associated with developing large reference populations of beef cattle is very difficult to justify even for large companies with their own breeding program. For these traits, the best approach may be a multibreed training population and nonlinear analysis based on high-density SNP panels or genome sequence data. Feed efficiency is becoming a popular trait for genomic selection because of the large impact on producer profitability and the possibility to contribute to reducing the environmental footprint for production of beef. Feed efficiency can be measured in many different ways, but accurate measures of dry matter and nutrient intake are required. Residual feed intake (RFI), also known as net feed intake, has become increasingly popular and is considered the standard measure of metabolic efficiency. The RFI is defined as the difference between the actual and predicted dry matter intake for an animal, where the prediction is based on the weight and growth of the animal. The advantage of the RFI is that, conceptually, the RFI measure is independent of growth and mature size. To further eliminate the impact of these traits on the RFI, body condition score or other energy expenditure related traits could be included in the RFI calculation. Feed efficiency in beef cattle is a complex trait and the result of multiple factors and biological processes which are also under the influence of physiological status and environmental/management conditions.

Genomics and sustainability Productivity is at the heart of creating a sustainable food system. The latest scientific advances in genetics, animal nutrition and husbandry techniques, allowed U.S. farmers and ranchers to produce 20% of the world’s beef with only 6% of the global cattle. Producing the

26.2 billion pounds of beef in 2017 was accomplished with a 53% smaller herd than would have been required to produce the same amount in 1975. By 2050, the world’s population is expected to grow from 7.6 billion people to 10 billion, which will pose enormous challenges to securing an abundant and safe food supply. Advances in genomic technologies and research can be part of the solution by allowing further improvement in animal productivity, disease resistance, adaptability to increased variability in the climate (e.g., heat, drought, rainfall), and improved nutritional quality while minimizing environmental impact. The necessary increase in total agricultural output is estimated by the Food and Agriculture Organization of the United Nations at 60% to meet future demands for food. The demand for animal protein is expected to be higher with some estimates that milk production will need to increase by 63%, and meat production by 76%.52 Most of this increase is expected in the developing world, while in the developed world both meat and milk consumption are likely to increase by less than 15%.53 This will have to be accompanied by increases in efficiency of food production, improved animal health and welfare, and a reduction in the environmental footprint for beef and dairy operations. A similar challenge was met in the developed world in the last 75 years through increases in production efficiency mostly achieved through improvements in genetics combined with better nutrition, improved management and, more recently, advances in genomics. Therefore, it is expected that upcoming advances in genetics and genomic technologies will further increase efficiencies in animal production and a sustainable solution to the increased demand for animal protein. Genomic solutions will be required as they hold the promise to meet these demands by increasing livestock production efficiencies in the developing world. Feed efficiency is at the core of any discussion of sustainability given its great potential to impact beef profitability and food security.

I. Beef cattle production

Genetic improvement in climate resilience traits

The main benefit of selecting beef cattle for improved feed efficiency or low residual feed intake (RFI) is a reduction in feed intake without compromising growth and product quality54 which translates to reduced feed costs and increased overall profitability. In addition, increases in feed efficiency are associated with a reduction in greenhouse gas emissions per animal55,56 and decreases in manure production57 which will result in an overall reduction in the environmental footprint. The main challenge for producers is to collect sufficient individual records because feed efficiency is both expensive and time consuming to measure on a large number of animals in order to identify those that have the genotype to increase efficiency of production. The genomic selection approach where high density genotypes and recorded phenotypes in a reference population are used to predict genetic merit of candidates for selection offers a potential alternative for estimating RFI in genotyped animals without phenotypic data. However, the high cost associated with measuring RFI is usually a big impediment to developing a large enough reference population which translates into lower accuracy of the predicted genomic EBV. One approach evaluated as a solution for increasing the reference population size was a multibreed evaluation where information from different breeds can be combined to increase the accuracy. However, this approach resulted only in a small increase in the accuracy of genomic EBV.58e60 This marginal improvement can be attributed to SNP by breed interactions and to the low relationships between animals from different breeds. Many studies using candidate gene approaches65e68 or genome-wide association studies (GWAS)61e64 revealed a large number of genetic markers associated with feed and its components traits. This information can be used in developing cost-effective SNP marker panels to predict a large proportion of the variation in feed efficiency traits. A set of 63 SNPs was associated with 19.4% of the variation in feed

31

efficiency identified by Abo-Ismail69 could allow for a low cost test for estimating molecular breeding values.

Genetic improvement in climate resilience traits Climatic stress is a major factor limiting production efficiencies in beef cattle in tropical and subtropical environments and in dairy cattle throughout most of the world. This stress is expected to increase due to climate change. More than half of the cattle in the world are maintained in hot and humid environments, including about 40% of beef cows in the US. Substantial differences in thermal tolerance exist among breeds and among animals within breeds indicative of opportunities for improvements through selection. For example, B. indicus cattle exhibit increased resistance to many environmental stressors relative to B. taurus, but tend to have slower growth, lower fertility and poor meat quality as they have not been as intensively selected for these traits as specialized B. taurus breeds. Use of genomic tools to produce an animal with superior ability for both thermal adaptation and food production represents an energy-efficient sustainable approach to meet the challenge of global climate change. Although swine, poultry and dairy cattle are more severely affected by heat stress than beef cattle, their confinement and intensive production systems make climate control via housing design and management interventions feasible. Beef cattle, particularly those in the cow-calf segment, are typically reared in extensive systems with limited opportunities for controlling environmental stress. Moreover, fewer solutions are available in developing countries to alleviate the effects of climate change given the limited facilities and management resources. Genetic improvement is one of few feasible strategies for ensuring adequate and sustainable production of beef protein in an increasingly hot world.

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2. Genetics and breeding of beef cattle

Genomic research to find genes associated with mechanisms to combat climate issues are underway. Hamblen et al.70 found that several animal characteristics such as coat score and temperament influence core body temperature responses to heat stress in Brangus heifers. In the same study, core body temperature measured under similar environmental conditions in a population of Brangus heifers of the same age and managed uniformly showed a high level of variation which is indicative of opportunities for improvements in performance through genetic selection. A major dominant-acting genetic effect that improves heat tolerance71,72 is associated with the “slick”coat trait in Senepol cattle. The causal variant comprises a single base deletion producing a frameshift mutation resulting in a truncated isoform of the prolactin receptor.73 Introgression of the slick trait in breeds susceptible to heat stress is possible, but a slow process. Nevertheless, introgression of the slick coat trait into dairy genetics has begun with US Holstein cattle74 and offers a significant potential for genetic improvement of dairy cattle in the tropics.

New genomic technologies Genome editing is a powerful new technology that can efficiently alter the genome of organisms and provides a precise way of introducing desirable alleles into the elite germplasm of a given breed, without the need to bring along the unwanted genetic material that accompanies traditional backcrossing and introgression strategies. The current genome-editing tools are based on the use of nucleases: zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeats/associated nuclease Cas9 (CRISPR/Cas9).75e77 The basic working principle of all these nucleases begins with the creation of site-specific DNA double-strand breaks at the required location in the genome. These double-strand breaks in the

DNA are then repaired through one of the two repair pathways: non-homologous end joining or homology directed repair pathways. The non-homologous end joining pathway is a major DNA repair pathway in cells and ligates the ends at the break directly without a template. Nucleotide deletions or insertions can occur during the non-homologous end joining repair process resulting in indels and repair error related frameshift. As a result, gene knock-out can be efficiently made by nuclease-assisted non-homologous end joining. Compared to non-homologous end joining pathway, the frequency of the homology directed repair is much lower in cells. However, because the repair is accomplished through homologous recombination between a donor DNA template and the target genomic locus, this pathway results in a precise insertion of the donor DNA. Nuclease-assisted HDR is, therefore, used to generate targeted gene knock-in or allele replacement. These nuclease-based genome editing techniques, particularly CRISPR/Cas9, due to its simplicity and high efficiency, have triggered a revolution in a wide variety of biological systems. In animal agriculture, genome editing opens up new possibilities for accelerating genetic selection and creating animals that are hornless, disease-resistant, and heat-tolerant, as well as being producers of less methane and less waste compared to animals in the current production systems.

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un comparisons for growth traits adjusted for within-breed genetic trend using expected progeny differences. J Anim Sci. 1993;71:1419e1428. 25. Barkhouse KL, Van Vleck LD, Cundiff LV. Effect of ignoring random sire and dam effects on estimates and standard errors of breed comparisons. J Anim Sci. 1998;76:2279e2286. 26. Barkhouse KL, Van Vleck LD, Cundiff LV, Buchanan DS, Marshall DM. Comparison of sire breed solutions for growth traits adjusted by mean expected progeny differences to a 1993 base. J Anim Sci. 1998;76:2287e2293.

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27. Lorenzen CL, Hale DS, Griffin DB, et al. National Beef Quality Audit: survey of producer-related defects and carcass quality and quantity attributes. J Anim Sci. 1993;71:1495e1502. 28. Wilson DE. Application of ultrasound for genetic improvement. J Anim Sci. 1992;70:973e983. 29. Golden BL, Garrick DJ, Benyshek LL. Milestones in beef cattle genetic evaluation. J Anim Sci. 2009;87:E3eE10. 30. Willham RL. The covariance between relatives for characters composed of components contributed by related individuals. Biometrics. 1963;19:18e27. 31. Garrick DJ, Golden BL. Producing and using genetic evaluations in the United States beef industry of today. J Anim Sci. 2009;87:E11eE18. 32. Williams JL, Garrick DJ, Speidel SE. Reducing bias in maintenance energy expected progeny difference by accounting for selection on weaning and yearling weights. J Anim Sci. 2009;87:1628e1637. 33. Fernando R, Grossman M. Marker assisted selection using best linear unbiased prediction. Genet Sel Evol. 1989;21:467. 34. Kingsmore SF, Lindquist IE, Mudge J, Gessler DD, Beavis WD. Genome-wide association studies: progress and potential for drug discovery and development. Nat Rev Drug Discov. 2008;7:221e230. 35. Grisart B, Coppieters W, Farnir F, et al. Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Res. 2002;12:222e231. 36. Hayes B, Goddard ME. The distribution of the effects of genes affecting quantitative traits in livestock. Genet Sel Evol. 2001;33:209e229. 37. Meuwissen T, Hayes B, Goddard M. Genomic selection: a paradigm shift in animal breeding. Anim Front. 2016;6:6. 38. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819e1829. 39. Daetwyler HD, Capitan A, Pausch H, et al. Wholegenome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat Genet. 2014;46:858e865. 40. Gibbs RA, Taylor JF, Van Tassell CP, et al. Genomewide survey of SNP variation uncovers the genetic structure of cattle breeds. Science. 2009;324:528e532. 41. Van Tassell CP, Smith TPL, Matukumalli LK, et al. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods. 2008;5:247e252. 42. Differences EP, Evaluations NC. National Beef Cattle Evaluation Consortium ( NBCEC ) White Paper - Delivering Genomics Technology to the Beef Industry Current Usage of Genomics Technology in Beef Cattle Selection. Vol. 12.

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43. Garrick DJ. The nature, scope and impact of genomic prediction in beef cattle in the United States. Genet Sel Evol. 2011;43:17. 44. McClure MC, Morsci NS, Schnabel RD, et al. A genome scan for quantitative trait loci influencing carcass, postnatal growth and reproductive traits in commercial Angus cattle. Anim Genet. 2010;41:597e607. 45. Van Eenennaam AL, Li J, Thallman RM, et al. Validation of commercial DNA tests for quantitative beef quality traits. J Anim Sci. 2007;85:891e900. 46. Calus MPL, Veerkamp RF, Mulder HA. Imputation of missing single nucleotide polymorphism genotypes using a multivariate mixed model framework. J Anim Sci. 2011;89:2042e2049. 47. Sargolzaei M, Chesnais JP, Schenkel FS. A new approach for efficient genotype imputation using information from relatives. BMC Genomics. 2014;15:478. 48. Van Binsbergen R, Bink MCAM, Calus MPL, et al. Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle. Genet Sel Evol. 2014;46:41. 49. Li H, Sargolzaei M, Schenkel F. Accuracy of wholegenome sequence genotype imputation in cattle breeds. In: Proceedings, 10th World Congress of Genetics Applied to Livestock Production. 2014. 50. Druet T, Macleod IM, Hayes BJ. Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions. Heredity. 2014;112:39e47. 51. Stachowicz K, Larmer S, Jamrozik J, Moore SS, Miller SP. Sequencing and genotyping for the whole genome selection in Canadian beef populations. In: Armidale: Association for the Advancement of Animal Breeding and Genetics. 2013:344e347. 52. Alexandratos N, Bruinsma J. World Agriculture: Towards 2030/2050 e The 2012 Revision. 2012. 53. Thornton PK. Livestock production: recent trends, future prospects. Philos Trans R Soc Lond B Biol Sci. 2010;365:2853e2867. 54. Mao F, Chen L, Vinsky M, et al. Phenotypic and genetic relationships of feed efficiency with growth performance, ultrasound, and carcass merit traits in Angus and Charolais steers. J Anim Sci. 2013;91:2067e2076. 55. Basarab JA, Okine EK, Baron VS, et al. Methane emissions from enteric fermentation in Alberta’s beef cattle population. Can J Anim Sci. 2005;85:501e512. 56. Manafiazar G, Basarab JA, Baron VS, et al. Effect of postweaning residual feed intake classification on grazed grass intake and performance in pregnant beef heifers. Can J Anim Sci. 2015;95:369e381. 57. Hegarty RS, Goopy JP, Herd RM, McCorkell B. Cattle selected for lower residual feed intake have reduced daily methane production. J Anim Sci. 2007;85: 1479e1486.

58. Hayes BJ, Bowman PJ, Chamberlain AC, Verbyla K, Goddard ME. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet Sel Evol. 2009;41:51. 59. Erbe M, Hayes BJ, Matukumalli LK, et al. Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. J Dairy Sci. 2012;95: 4114e4129. 60. Bolormaa S, Pryce JE, Kemper K, et al. Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle. J Anim Sci. 2013; 91:3088e3104. 61. Sherman EL, Nkrumah JD, Murdoch BM, Moore SS. Identification of polymorphisms influencing feed intake and efficiency in beef cattle. Anim Genet. 2008;39: 225e231. 62. Sherman EL, Nkrumah JD, Li C, Bartusiak R, Murdoch B, Moore SS. Fine mapping quantitative trait loci for feed intake and feed efficiency in beef cattle. J Anim Sci. 2009;87:37e45. 63. Lu D, Miller S, Sargolzaei M, et al. Genome-wide association analyses for growth and feed efficiency traits in beef cattle. J Anim Sci. 2013;91:3612e3633. 64. Saatchi M, Beever JE, Decker JE, et al. QTLs associated with dry matter intake, metabolic mid-test weight, growth and feed efficiency have little overlap across 4 beef cattle studies. BMC Genomics. 2014;15:1004. 65. Sherman EL, Nkrumah JD, Murdoch BM, et al. Polymorphisms and haplotypes in the bovine neuropeptide Y, growth hormone receptor, ghrelin, insulin-like growth factor 2, and uncoupling proteins 2 and 3 genes and their associations with measures of growth, performance, feed efficiency, and carcass merit. J Anim Sci. 2008;86:1e16. 66. Abo-Ismail MK, Kelly MJ, Squires EJ, Swanson KC, Bauck S, Miller SP. Identification of single nucleotide polymorphisms in genes involved in digestive and metabolic processes associated with feed efficiency and performance traits in beef cattle. J Anim Sci. 2013; 91:2512e2529. 67. Abo-Ismail MK, Vander Voort G, Squires JJ, et al. Single nucleotide polymorphisms for feed efficiency and performance in crossbred beef cattle. BMC Genet. 2014;15:14. 68. Karisa BK, Thomson J, Wang Z, Stothard P, Moore SS, Plastow GS. Candidate genes and single nucleotide polymorphisms associated with variation in residual feed intake in beef cattle. J Anim Sci. 2013;91:3502e3513. 69. Abo-Ismail MK, Lansink N, Akanno E, et al. Development and validation of a small SNP panel for feed efficiency in beef cattle. J Anim Sci. 2018;96:375e397.

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70. Hamblen H, Hansen PJ, Zolini AM, Oltenacu PA, Mateescu RG. Thermoregulatory response of Brangus heifers to naturally occurring heat exposure on pasture. Am Soc Anim Sci. 2017:1e7. 71. Olson TA, Lucena C, Chase CC, Hammond AC. Evidence of a major gene influencing hair length and heat tolerance in Bos taurus cattle. J Anim Sci. 2003;81:80e90. 72. Mariasegaram M, Chase CC, Chaparro JX, Olson TA, Brenneman RA, Niedz RP. The slick hair coat locus maps to chromosome 20 in Senepol-derived cattle. Anim Genet. 2007;38:54e59. 73. Littlejohn MD, Henty KM, Tiplady K, et al. Functionally reciprocal mutations of the prolactin signalling pathway define hairy and slick cattle. Nat Commun. 2014;5:5861.

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74. Dikmen S, Khan FA, Huson HJ, et al. The SLICK hair locus derived from Senepol cattle confers thermotolerance to intensively managed lactating Holstein cows. J Dairy Sci. 2014;97:5508e5520. 75. Doyon Y, McCammon JM, Miller JC, et al. Heritable targeted gene disruption in zebrafish using designed zincfinger nucleases. Nat Biotechnol. 2008;26:702e708. 76. Christian M, Cermak T, Doyle EL, et al. Targeting DNA double-strand breaks with TAL effector nucleases. Genetics. 2010;186:756e761. 77. Ran FA, Hsu PD, Lin CY, et al. Double nicking by RNAguided CRISPR cas9 for enhanced genome editing specificity. Cell. 2013;154:1380e1389.

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C H A P T E R

3 Physiology and pregnancy of beef cattle Ky G. Pohlera, Gessica A. Francoa, Sydney T. Reesea, Michael F. Smithb a

Department of Animal Science, Texas A&M University, College Station, TX, United States; bDivision of Animal Sciences, University of Missouri, Columbia, MO, United States

O U T L I N E Introduction

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Endocrinology of pregnancy

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Puberty

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Embryonic and fetal loss

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Regulation of the estrous cycle

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Parturition and postpartum anestrus

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References

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Establishment and maintenance of pregnancy 42 Physiological changes during gestation

45

Introduction

the heifer/cow must conceive in time to calve early during the subsequent calving season. Any interruption in the preceding cycle constitutes reproductive loss. The development of management strategies to increase reproductive efficiency are dependent upon an understanding of the physiological, cellular, and molecular mechanisms controlling male and female reproduction. The purpose of this chapter is to review the physiological mechanisms controlling the onset of puberty, estrous cycle, establishment and maintenance of pregnancy, parturition, and postpartum anestrus in beef cattle.

Reproductive efficiency has a major impact on the economics of a cow calf operation. Melton1 reported that the impact of reproductive traits on profitability is three- to nine-times more important than other production traits. Optimizing reproductive efficiency depends upon the successful completion of the following events: (1) a heifer must reach puberty before the start of the breeding season, (2) conceive early in the breeding season, (3) calve unassisted, (4) the calf must survive to the time it is marketed, and (5)

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Copyright © 2020 Elsevier Inc. All rights reserved.

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3. Physiology and pregnancy of beef cattle

Puberty The time when pregnancy occurs during the first breeding season has important implications for the subsequent productivity and longevity of a heifer in the herd.2,3 Heifers that conceive early in the breeding season will generally continue to calve early throughout their reproductive life. Early calving cows wean heavier calves and have more time postpartum to return to estrus before the start of the breeding season. The onset of puberty in heifers is influenced by age, body weight, post-weaning nutrition, crossbreeding, bull exposure, and season of birth.4 In general, Bos taurus breeds attain puberty at a younger age than Bos indicus breeds5; however, significant variation in age at puberty exists within breeds. Significant progress has been made regarding our understanding of the physiological and molecular mechanisms controlling the onset of puberty in heifers.6,7 The hypothalamus, pituitary, and ovaries are functional months before the onset of puberty; however, the appropriate hormonal feedback relationships in the hypothalamic-pituitary-ovarian axis are not established until around the time of onset of puberty. As in other mammals, the Gonadostat Theory, which proposes that onset of puberty in females is due to a progressive decrease in the negative feedback of gonadal steroids, also applies to cattle.8 Months before puberty in heifers, the hypothalamus is highly sensitive to the negative feedback of estradiol from the ovaries. As a heifer progresses toward puberty, there is a reduction in estradiol receptors in the hypothalamus, resulting in a decrease in the negative feedback of estradiol and a subsequent increase in the pulse frequency of gonadotropin releasing hormone (GnRH) and luteinizing hormone (LH), which drives follicular maturation. Shortly before the first ovulatory estrus, in a prepubertal heifer, there is a transient rise in luteal progesterone, which is important for

establishing normal length estrous cycles.9 This observation led to the use of progestin treatment of peripubertal heifers and postpartum anestrus cows to initiate normal cycles following synchronization of estrus and ovulation. Heifers do not attain puberty at a specific age or weight, but there is an interaction between age and weight within a breed. Nutrition has a major effect on body weight and timing of puberty, since undernutrition can delay10 and a high plane of nutrition can advance the onset of puberty in heifers.11 Nutrition can affect stimulatory and inhibitory neuronal networks in hypothalamic regions, such as the arcuate nucleus, to regulate GnRH pulse frequency. Leptin is a hormone produced by adipose tissue and concentrations increase as a heifer approaches puberty. Leptin has a permissive effect on the attainment of puberty in cattle. Leptin does not appear to have a direct action on GnRH-secreting neurons, since these neurons do not express the leptin receptor. Alternatively, the action of leptin is likely mediated by neuropeptide Y (NPY) and proopiomelanocortin (POMC) containing neurons that can have an inhibitory or stimulatory effect on hypothalamic GnRH secretion, respectively. Hypothalamic GnRH neurons don’t have estradiol receptors; however, estradiol receptors have been localized to kisspeptin neurons that are in close apposition with GnRH neurons. Kisspeptin is a neuropeptide that stimulates GnRH secretion and can be modulated by several factors, such as nutrition and stress. Important nutritional mediators of kisspeptin release, and thereby GnRH secretion, include NPY, POMC, and leptin. The following model has been proposed to explain the nutritional modulation of puberty in beef heifers6,7: (1) As a heifer progresses toward puberty there is a progressive decrease in estradiol signaling, within the hypothalamus, and an increase in the contact of kisspeptin neurons with GnRH neurons, (2) Elevated circulating concentrations of leptin can inhibit NPY neurons and stimulate

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Regulation of the estrous cycle

POMC neurons, and (3) Increased GnRH secretion may be mediated by decreased inhibition (NPY) and increased stimulation (POMC) of kisspeptin neurons in the arcuate nucleus resulting in increased LH pulse frequency. The combination of plasticity in the hypothalamic neuronal connections and the regulation of release of neuropeptides and neurotransmitters permit the integration of both metabolic and endocrine signals that ultimately regulate pulsatile GnRH secretion.

Regulation of the estrous cycle In cattle, the estrous cycle normally ranges from 17 to 24 days and the duration of estrus is generally 10e18 h; however, considerable variation exists among individual animals (range 30 h).12 The preovulatory gonadotropin surge occurs around the onset of estrus and ovulation occurs approximately 28e30 h later. Unlike horses, pigs, and sheep, ovulation in cattle occurs a number of hours following the end of estrus. The primary sign of estrus in cattle is standing to be mounted and secondary signs of estrus include frequent mounting, watery mucus from the vulva, vocalization, and restlessness. The estrous cycle is divided into three stages (follicular phase, estrus, and luteal phase) and is regulated by hormones secreted by the hypothalamus (GnRH), anterior pituitary gland (follicle stimulating hormone [FSH] and LH), ovary (estradiol and progesterone), and uterus (prostaglandin F2a [PGF2a]). The preceding hormones serve as chemical messengers that circulate in the blood to effect change in specific target tissues that contain receptors that are hormone specific and regulate the preceding phases of the estrous cycle. The combination of hormone secretion and metabolism (e.g., liver, kidneys, and lungs) maintain an appropriate endocrine balance during the follicular phase, estrus, and luteal phase of the estrous cycle. An understanding of the hormonal regulation of preovulatory

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follicular development and luteal lifespan resulted in the development of methods of synchronizing estrus and ovulation in cattle.13 For a list of hormones, their biological functions, their role in estrous synchronization/superovulation, and product names see Table 3.1. A preovulatory follicle and the subsequently formed corpus luteum are the two primary ovarian structures that regulate the estrous cycle through secretion of estradiol and progesterone, respectively. Changes in a preovulatory follicle and corpus luteum, patterns of secretion of LH, estradiol and progesterone, and changes in ovarian blood flow during the ruminant estrous cycle are depicted in Fig. 3.1. Follicular phase. The follicular phase (proestrus) begins with the initiation of corpus luteum regression (luteolysis) and ends with the onset of estrus. Luteolysis is accompanied by a rapid decrease in circulating progesterone resulting in a decrease in the negative feedback on hypothalamic GnRH secretion and pituitary LH secretion. As circulating concentrations of progesterone decrease, LH pulse frequency increases followed by a rapid increase in follicular estradiol secretion. The production of follicular estradiol results from the coordinated actions of LH and FSH on theca and granulosa cells, respectively.14,15 The follicle wall consists of two distinct cell layers (granulosa and theca cells) that are separated by a basement membrane. Granulosa cells are located in the compartment with the oocyte; whereas, theca cells surround the granulosa cells and are in close association with a wreath of capillaries. Theca cells have membrane receptors that bind LH resulting in synthesis of androgens that subsequently diffuse through the basement membrane into granulosa cells. Following FSH binding to its membrane receptors on granulosa cells the enzyme aromatase increases and converts androgens to estradiol. Estrous phase. Increased circulating concentrations of estradiol following luteolysis initiate estrous behavior, increase uterine contractions

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40 TABLE 3.1

3. Physiology and pregnancy of beef cattle

Reproductive hormones, the endocrine gland from which they originate, their functions during the estrous cycle, roles in estrous synchronization, product name, dosages, and route of administration.

Hormone

Endocrine gland

Function of hormone

Biological action in estrous sync. Product name

Progesterone

Corpus luteum

Inhibit estrus

Inhibit estrus

Inhibit ovulation

Inhibit ovulation

Prepares animal Induce cyclicity for pregnancy

Prostaglandin F2a

Uterus

Maintenance of pregnancy

Dominant follicle turnover

Induce luteal regression

Induce premature luteal regression

Melengestrol 0.5 mg/hd/ Acetate (MGAÒ) day

Vaginal insert

LutalyseÒ

5 mL

im inject

Lutalyse Hi-ConÒ

2 mL

im or sq inject

2 mL

im inject

2 mL

im inject

2 mL

im inject

Factryl

2 mL

im inject

FertagylÒ

2 mL

im inject

GONAbreed

1 mL

im inject

estroPLAN Controls secretion of LH

Synchronize follicle wave

Induces gonadotropin surge

Induce ovulation

Feed

1 CIDR per animal (1.38 g)

Ò

Hypothalamus

Route of administration

EAZI-BREED CIDRÒ

EstrumateÒ

Gonadotropin releasing hormone (GnRH)

Dosage

Ò

Cystorelin Ò

Follicle Stimulating Hormone (FSH)

Anterior Pituitary Gland

Initiation of a follicular wave

Superovulation

FollitropinÒ

Depends on application

im inject

Luteinizing Hormone (LH)

Anterior Pituitary Gland

Stimulated by GnRH

Synchronize follicular wave

N/A

N/A

N/A

N/A

N/A

N/A

Induction of ovulation Oocyte maturation

Induction of ovulation

Luteal tissue formation Estradiol

Ovarian follicle

Estrous behavior Induction of gonadotropin surge Sperm transport

Dominant follicle turnover

Estrous behavior

N/A, not applicable.

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Regulation of the estrous cycle

FIG. 3.1 Changes in ovarian structures (preovulatory follicle and corpus luteum), hormones (luteinizing hormone, estradiol, and progesterone) and ovarian blood flow (ovary containing [luteal ovary] or not containing [nonluteal ovary] a corpus luteum) during the three phases of the estrous cycle (follicular, estrus, and luteal phase). Modified from Garverick HA, Smith MF. Female reproductive physiology and endocrinology of cattle. In: Braun WF, Youngquist RS, eds. The Veterinary Clinics of North America, Food Animal Practice. Philadelphia: W.B. Saunders Co; 1995:223e247.

(facilitate sperm transport), and induce the preovulatory gonadotropin surge. The preovulatory gonadotropin surge coordinates the following events that are critical for the establishment of pregnancy: resumption of meiosis within the oocyte, follicular rupture, and luteinization of follicular granulosa and theca cells. LH is considered to be the primary gonadotropin that controls the preceding events. The end of the estrous phase of the cycle is marked by follicular rupture, which is the culmination of a complex cascade of events leading to the activation of proteolytic

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enzymes that digest the follicular wall and allows the egg (oocyte) to be released for fertilization. In mammals, the cellular mechanisms culminating in follicular rupture are similar to inflammation.16 Luteal phase. The luteal phase spans the time of corpus luteum formation and maintenance, which begins with ovulation and ends with luteolysis (Fig. 3.1). Progesterone is the primary secretory product of the corpus luteum and luteal lifespan is regulated by secretions of the anterior pituitary, uterus, ovary, and trophecoderm of the blastocyst.17 The regulation of progesterone secretion is likely controlled by a balance of luteotropic (stimulate progesterone) and luteolytic (inhibit progesterone) stimuli, given that both types of stimuli are secreted concurrently during the estrous cycle. In ruminants, LH is considered to be the primary luteotropic hormone and concentration of luteal LH receptors is positively correlated with changes in progesterone secretion and luteal growth.18 Corpora lutea receive the majority of the ovarian blood flow and blood flow to the ovary containing the corpus luteum and progesterone secretion are highly correlated17 (Fig. 3.1). Progesterone has a central role in female reproductive physiology as it initiates cyclicity in anestrous females, determines estrous cycle length in cycling females, and is required for the maintenance of pregnancy. In cattle, PGF2a is the uterine luteolysin and is commonly used to synchronize estrus in cattle. In the absence of a developing blastocyst/conceptus (embryo and its extra-embryonic membranes), uterine release of PGF2a increases during the late luteal phase and PGF2a is secreted as pulses into the uterine veins on days 17e20 following estrus19 (day 0 ¼ estrus). PGF2a is transported from the utero-ovarian vein into the ovarian artery via a counter-current transfer mechanism20 and is transported to the corpus luteum. PGF2a may have both a direct and an indirect effect on a ruminant corpus luteum to cause luteolysis. The mechanism by which the bovine blastocyst prevents luteolysis is discussed later.

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3. Physiology and pregnancy of beef cattle

Regulation of follicular waves. The bovine estrous cycle consists of two to three follicular waves and each wave begins with the recruitment of a cohort of antral follicles from a pool of growing small follicles.21 One follicle is selected from this cohort for continued growth and becomes dominant. The remaining follicles in the cohort die by a process called atresia. During a nonovulatory follicular wave, the dominant follicle eventually becomes atretic and a new follicular wave is initiated. A viable dominant follicle present at luteolysis will generally become the ovulatory follicle. The estrous cycle length of cows that have three follicular waves is generally longer (20e24 days) compared to cows with two follicular waves (17e20 days). In cattle, follicular waves can be detected during most reproductive states including the prepubertal period, estrous cycle, gestation, and postpartum anestrous period.22 The only exception to the continuous growth and development of follicular waves in cattle is during the last 21 days of gestation. During this time follicles greater than 6 mm in diameter have not been detected.23 Following parturition, follicular waves resumed following a rise in circulating concentrations of FSH,24 and the first dominant follicle appears between days 7 and 15 postpartum in both beef and dairy cows.25,26 Inducing ovulation or atresia of a dominant follicle in a group of females will result in synchronization of the subsequent follicular wave. This approach has been used in the development of fixed-time artificial insemination protocols in cattle.

Establishment and maintenance of pregnancy Gamete transport: In cattle, semen is deposited in the anterior vagina by natural service or in the uterine body by artificial insemination. Lifespan of bovine spermatozoa in the reproductive tract is likely a function of the number of

sperm deposited. With natural service, 0.2e3 billion sperm are normally deposited in the anterior vagina and a relatively large number of sperm will reach the oviduct and may easily live for 24 h bound to oviductal cells. With artificial insemination (e.g., frozen/thawed sperm), only 5e15 million motile sperm are inseminated and fewer will reach the oviduct. Although frozen/thawed sperm may also live for 24 h bound to oviductal cells, many fewer sperm will complete capacitation and be released from the isthmus compared to natural service. Consequently, with artificial insemination, it is necessary to delay insemination following estrous detection so that an adequate number of capacitated sperm are available for fertilization when the oocyte enters the oviduct. Following natural service or artificial insemination, spermatozoa are transported to the oviduct within minutes; however, the first sperm to arrive in the isthmus are not capable of fertilization and frequently are not motile.27 A number of spermatozoa reside in the cervical crypts following natural service, which is an environment more conducive to sperm survival than the vagina. Sperm are transported by both active and passive mechanisms.28 Uterine contractions are one of the mechanisms associated with sperm transport and the ampulla serves as a sperm reservoir prior to ovulation and fertilization. Bovine spermatozoa are not capable of fertilization upon ejaculation and undergo a series of physiological changes to the sperm head and tail (i.e., capacitation) to acquire the capacity to fertilize an oocyte. In ruminants, capacitation generally takes 4e6 h. Following ovulation, the cumulus/oocyte complex is rapidly moved across the surface of the fimbria by ciliary action until it enters the ostium of the oviduct. Movement of the cumulus/oocyte complex through the ampulla of the oviduct occurs by contractions of the muscularis and the action of cilia. Fertilization occurs within the ampulla near the junction of the isthmus.

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Establishment and maintenance of pregnancy

Fertilization: Most of our knowledge of mammalian fertilization is based on species other than cattle29; therefore, what follows are some general mechanisms associated with fertilization in mammalian species. In cattle, as in many other mammals, resumption of meiosis, in vivo, is initiated by the preovulatory gonadotropin surge resulting in ovulation of an oocyte with one polar body in the perivitelline space and chromosomes in metaphase of the second meiotic division from the preovulatory follicle. The isthmus serves as a sperm reservoir and fertilization occurs near the ampullary-isthmus junction. If cumulus cells are present when a capacitated sperm encounters the oocyte, sperm penetrate the sticky matrix of hyaluronic acid with the assistance of hyaluronidase. The plasma membrane surrounding the sperm head binds to one of the zona pellucida proteins that serves as a sperm receptor and the acrosome reaction is initiated. The preceding reaction permits release of acrosin, which aids in penetration of the zona pellucida. Upon penetration of the zona pellucida sperm bind to a receptor on the plasma (vitelline) membrane of the oocyte and initiate a wave of exocytosis of cortical granules (i.e., cortical reaction, zona reaction, or zona block). One of the contents of cortical granules is an enzyme that modifies the zona pellucida to prevent other sperm from fertilizing the oocyte (i.e., block to polyspermy). Entrance of the fertilizing sperm into the oocyte initiates completion of the second meiotic division and extrusion of the second polar body. The sperm head separates from the tail and forms the male pronucleus. Combination of chromosomes in the male and female pronuclei (i.e., syngamy) results in formation of a one-cell embryo or zygote. Zygote to blastocyst stage: Following fertilization, the zygote undergoes a series of cleavage divisions resulting in a progressive decrease in cell (i.e., blastomere) size with each mitotic division. In cattle, the first cell cycle is completed approximately 24 h after fertilization and progression to the 4 and 8 cell stage occurs at

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36e50 h and 56e66 h, respectively.30,31 The maternal to zygotic transition occurs between the 8e16 cell stage, which is the time when transcription of the embryonic genome first occurs. However, some transcriptional activity is evident as early as the 4-cell stage of embryonic development.32e34 Following embryonic genome activation, lineage specification results in formation of the inner cell mass (ICM), which gives rise to the embryo proper, and to the trophectoderm (TE), which eventually gives rise to the chorion of the placenta.35e38 After the 16-cell stage (zday 4e5 postestrus), cells within the embryo adhere to each other (i.e., compaction) via formation of tight junctions resulting in formation of a compact morula. Morula stage embryos become early blastocysts (approximately 100 cells) with the formation of the blastocoele, a fluid filled cavity, and clear separation of the ICM and TE cells. Bovine embryos reach the blastocyst stage around day 7 of development. Thinning of the zona pellucida and rapid cell expansion precedes blastocyst hatching around day 8e9 of development.39 In the bovine blastocyst, enzymatic activity weakens the zona pellucida and the blastocyst will expand and contract causing pressure changes eventually resulting in rupture of the zona pellucida and hatching of the blastocyst.40,41 Blastocyst hatching occurs around day 9e11 in cattle.42 Post hatching embryonic development: Following hatching, the blastocyst relies solely on uterine secretions for further development. The epithelium of uterine glands secretes a complex group of molecules (i.e., histotroph) responsible for blastocyst survival and elongation,43,44 including signaling molecules, regulators of conceptus survival, development, implantation and placentation.45,46 Uterine glands are required for preimplantation development of conceptuses in ruminants since transfer of blastocysts into ewes, in which development of uterine glands was blocked, resulted in embryonic mortality.47,48 There is a bidirectional communication between

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3. Physiology and pregnancy of beef cattle

the developing blastocyst/conceptus and the uterus to modulate endometrial function. Dysregulation of this communication such as inadequate progesterone synthesis or compromised responsiveness of the uterus to progesterone, can lead to early embryonic mortality, which ranges from 25 to 30% in cattle.49 Further discussion of the incidence of early and late embryonic mortality can be found later in this chapter. After blastocyst hatching, there is rapid development of the extraembryonic membranes. For most domestic species, the formation of extraembryonic tissues occurs during the pre-attachment period and involves extensive folding of germ layers to generate the amnion, chorion, allantois, and allantochorion. During this time, the conceptus undergoes extensive elongation from 3 mm at day 13 to about 250 mm by day 17, forming a filamentous threadlike blastocyst that covers the length of the uterine horn. In the pig, this growth is even more impressive with a rate of 4e8 mm increase in length of trophectoderm per hour.42 Around day 15, apposition and adhesion of the trophoblastic cells to the uterine epithelium begins, in which the trophoblast develops finger-like villi that penetrate the opening of uterine glands,42,50 providing a temporary anchor for the conceptus. After day 19, the elongating conceptus adheres to the luminal epithelium and is characterized by the appearance of binucleate cells arising from uninucleate cells of the trophoblast. At this point, placentation starts and uterine lavage to recover the conceptus can cause structural damage, since the trophectoderm and endometrial luminal epithelium are extensively interdigitated in both the caruncular and intercaruncular areas. Maternal Recognition of Pregnancy: By day 16 of gestation, the conceptus must signal its presence to prevent luteolysis and return to estrus. In cattle, the primary conceptus signal is interferon tau (IFNT), which prevents luteolytic pulses of PGF2a.51 Produced by trophoblast cells beginning around day 15 of gestation, IFNT acts

locally on receptors in the uterus to inhibit pulsatile secretion of PGF2a and IFNT also has endocrine effects on the CL.52,53 After 10e12 days of high progesterone, luteolytic PGF2a pulses from the uterus cause CL regression in the nonpregnant animal via lymphatic and venous countercurrent exchange.54e56 However, during maternal recognition of pregnancy in a cow, IFNT inhibits these pulses and expression of oxytocin receptors. Unlike what happens in sheep, oxytocin receptor expression seems to be independent of estrogen; however, the oxytocin receptor in cattle has an IFNT response element.57,58 Without oxytocin binding to the oxytocin receptor, luteolytic pulses of PGF2a are not produced and CL regression will not occur.59 This local action compliments the endocrine effects, which have mainly been shown in sheep, that IFNT has on the CL during this developmental period. Historically it was believed that effects of IFNT were restricted to the uterine; however, more current studies have shown that infusion of IFNT into the uterine vein prevents CL regression after PGF2a administration.60 Interferon tau is released into the uterine vein and induces interferon stimulated genes (ISGs) in the CL and peripheral leukocytes.61 Evidence suggests ISGs provide a luteal protective mechanism for the CL against PGF2a.62,63 Endocrine action of INFT compliments this mechanism by mediating immune cell populations around the CL, especially through induction of MHC I, and regulating luteal and neutrophilic functions.53 Placentation: Proper placentation is required to correctly exchange nutrients, gas and waste products at the fetal maternal interface. The bovine placenta has been well characterized morphologically (cotyledonary) and histologically (syn-epitheliochorial) and is one of the least invasive types of placentas. Cotyledonary placentas are marked by circular areas of attachment dispersed across the entire allantochorion. The term “cotyledon” refers to the fetal side of the attachment; whereas, “caruncle” refers to

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Physiological changes during gestation

the attachment on the endometrial epithelium. For the remainder of this section, the intact unit will be referred to as a placentome. Synepitheliochorial refers to placentas of minimal invasion where giant multinucleated trophoblast cells fuse with maternal epithelium. The earliest stages of gestation are marked by the development of extraembryonic membranes, including the amnion, chorion and allantois, which will support conceptus development throughout gestation. The amnion is the membrane closest to the embryo proper and forms from amnionic folds that encircle the inner cell mass to form the amnion. The allantois is an outgrowth of the embryonic hindgut and is made of the trophectoderm and mesoderm layers. The chorion comprises the outermost protective layers and will help form the cotyledonary tissue during placentation. As gestation progresses, the chorion and allantois will fuse to become the chorioallantois. This membrane becomes highly vascularized and will attach to the endometrium between days 30e33 of gestation.64,65 Early morphological changes associated with bovine placentation begin during the third week of gestation. Major events in early placentation include the following: (1) the allantois becomes visible on day 23; (2) the chorion reaches the tip of the nongravid horn on day 24; (3) the adhesion phase is complete in both uterine horns by day 27; (4) first chorionic indentation into the endometrium is visible on day 32; (5) a few regular projections from the endometrium are evident on the caruncular surface on day 28e33; and (6) approximately 35 cotyledons are formed by day 38.66,67 Initial formation of cotyledons occurs near the embryo between day 30 and 37, with 17e25 cotyledons present on the chorion adjacent to the bovine embryo on day 35.66,67 During placentome formation and interdigitation, fetal chorionic villi migrate toward maternal caruncular tissue and subepithelial capillaries. This process has been described as

45

being similar to “gradually inflating the fingers of a rubber glove (fetal villi) into a swelling mound of jelly.”65 On a cellular level, interdigitation allows for migration of giant binucleate trophoblast cells to the maternal epithelium.68,69 Giant trophoblast cells make up 15e20% of placentome cell population68 and contain fetal products including hormones,70 pregnancy associated glycoproteins (PAGs),71,72 and placental lactogen.73 Extensive remodeling and expansion of these placentomes occurs during early fetal development, which is a time of exponential growth of the fetus. Between days 48 and 150 of gestation, the number and surface area of placentomes can increase three-fold.74 Placentome development allows for greater surface area for fetal-maternal exchange compared to other placenta types due to the concentrated number of villi that form. In cattle, placental tissue growth occurs exponentially throughout gestation, but the increased rate is much less than the increase in fetal weight. Uterine and umbilical blood flow disproportionally increases from mid- to late-gestation to keep placental function on the same pace as fetal growth in order to supply the high metabolic demands of tissue expansion.75,76 Measurement of uterine blood flow during gestation can be a good indicator of adequate fetal nutrient supply. In situations where pregnancy is compromised such as multiple fetuses, environmental heat stress, or inadequate maternal nutrition, a significant decrease in uterine or umbilical blood flow has been observed (see review by Reynolds et al.77). A timeline of the physiological events of embryonic and fetal development can be found in Table 3.2.

Physiological changes during gestation Vaginal and cervical changes: The vagina is the copulatory organ and the passive birth canal during parturition in mammals. The cranial portion is composed by columnar epithelium

I. Beef cattle production

46 TABLE 3.2

3. Physiology and pregnancy of beef cattle

Timing of events during embryonic and fetal development in cattle. Age in days (Day 1 [ ovulation)

References

First cleavage

2

Shea111; Peters112

8 cell stage

3

Shea111

Maternal to zygotic transition

3e4

Telford et al.113

16e32 cell stage (morula)

5

Shea111; Peters112

Blastocyst

7e8

Shea111; Telford et al.113

Expanded blastocyst

8

Shea111

Hatching

9e11

Shea111; Peters112

Elongation

12e13

Shea111; Peters112

Maternal recognition of pregnancy (MRP)

15e17

Peters112

Apposition

19e20

Guillomot114

Adhesion to the endometrium

21e22

Peters112

Binucleate cells appear

19e20

Greenstein et al.115; Wooding and Burton65

Interdigitation of cotyledons and caruncles

25

Peters112

Initiation of placentome development

31e32

Wrobel and S€ uß116

Cotyledon development (17e25)

35e36

Kritzenberger and Wrobel117

Definitive placentation and start of organogenesis

42

Peters112

Testicular or Ovarian development

45e60

Erickson118; Curran119

Embryo to fetus transition

45e50

Kritzenberger and Wrobel117

Bone ossification begins

50e60

Hafez and Rajakoski120

Completion of rumen differentiation and orientation of stomach

70

Vivo et al.121

Marked increase in caruncular vascularization and blood flow

120

Reynolds and Redmer76; Pfarrer et al.122

Completion of caruncular arterial vascularization

150

Reynolds and Redmer76; Pfarrer et al.122

Brown fat detectable

190

Casteilla et al.123

Further cellular differentiation and growth of all tissues

Last third of gestation

Evans and Sack124; Mao125

Stage of embryo/fetal development PRE-CONTACT PHASE

PRE- IMPLANTATION PHASE

IMPLANTATION AND PLACENTATION PHASE

CONTINUOUS GROWTH PHASE

I. Beef cattle production

47

Endocrinology of pregnancy

with high secretory activity while the caudal section lined by stratified squamous epithelium. During estrus (high circulating estradiol), the epithelium of the caudal region thickens dramatically,78 protecting the vagina during copulation and preventing microorganisms from gaining access to the vasculature of the submucosa. The cervix isolates the uterus from the external environment, limiting exposure to harmful bacteria and other microorganisms. During estrus, the primary function of the cervix is to produce mucus to lubricate the vagina for copulation as well as facilitate sperm transport. During pregnancy, when progesterone levels are high, cervical mucus becomes more viscous and forms a seal to protect against foreign materials from entering the uterus and causing infections that could cause embryonic death or a diseased state in the animal.79 Uterine changes: The developing embryo/ conceptus in cattle, spends over 98% of gestation in the uterus. In cattle, the uterus consists of a small uterine body, that is the site of sperm deposition during artificial insemination, and two uterine horns (bicornuate). Throughout pregnancy, the uterus undergoes gradual enlargement to accommodate the growing embryo/fetus. The uterine walls are composed of three different layers, including the serosa (perimetrium), muscularis (myometrium) and mucosa/submucosa (endometrium) that have distinct functions. The myometrium is formed by two layers of smooth muscle (i.e., inner circular and outer longitudinal) that provide motility (i.e., peristaltic contractions) to the uterus. When circulating concentrations of progesterone are low and estradiol high, the uterus will have a certain degree of muscular tone that can be easily palpated per rectum and uterine contractions facilitate sperm transport. Conversely, when circulating concentrations of progesterone are high and estradiol low, the uterus will feel soft and flaccid which is more conducive to receive the conceptus.79 During parturition, myometrial contractions are responsible for expelling the fetus and fetal membranes.

Endocrinology of pregnancy Progesterone: In pregnant cows, the corpus luteum is maintained throughout gestation and undergoes luteolysis approximately 2 days prior to parturition. Concentrations of progesterone in blood remain elevated throughout gestation and are required for pregnancy maintenance and mammary gland development.80 In cattle, the main source of progesterone during pregnancy is the corpus luteum; however, the placenta serves as an additional source of progesterone later in gestation70,81 Luteinizing hormone (LH): Although LH is luteotropic in cattle, circulating concentrations of LH remain relatively low during pregnancy and there is a decrease in LH pulse frequency from 2.6 pulses per 10 h at day 50 to 60 to 1.2 pulses per 10 h at 250 to 260 of gestation.82 Pregnancy associated glycoproteins (PAGs): Pregnancy associated glycoproteins are products of binucleate trophoblast cells which constitute about 15e20% of the fetal trophoblast population. In cattle, these trophoblast cells appear around day 19e21 of gestation, migrate toward the uterine epithelium and secrete PAGs into the maternal circulation as early as day 24 of gestation and peak prior to parturition.83e85 The PAG family is a large gene family (>20 members) with various members expressed at different times during gestation in cattle.86 Although, no clear biological function for PAGs has been identified, their accumulation in maternal blood of ruminant ungulates has become a useful tool for monitoring pregnancy. Indeed, a majority of the published work on PAGs has focused on the development of a reliable tool for diagnosing pregnancy in ruminant species, including cattle, sheep, goats, bison, moose and elk. In addition to serving as an accurate tool for diagnosing pregnancy in ruminants, PAGs may also serve as a marker for monitoring embryonic viability as well as placental function.84,85,87,88

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48

3. Physiology and pregnancy of beef cattle

Placental lactogen (PL): Also known as chorionic somatomammotropin, PL is a polypeptide hormone synthetized by the binucleate cells of the placenta and are secreted into both the maternal and fetal circulations, it is reported to have trophic effects on the mammary gland and growth hormone-like action.89,90 In the maternal circulation, PL concentrations are low during early gestation but steadily increase until the third trimester of gestation when PL concentrations remain constant up to parturition. In the fetal circulation, there is a progressive decline in PL throughout pregnancy; however, it always exceeds the maternal circulation by 8- to 18fold.90

Embryonic and fetal loss Pregnancy failure in cattle is a major factor that impairs profitability of beef and dairy operations. In cattle, reproductive loss has been characterized as early embryonic mortality (prior to day 28 of gestation) and late embryonic/early fetal mortality (occurring after day 28 of gestation91). A summary of pregnancy failure rates in beef cattle can be found in Table 3.3. Early embryonic mortality: Early embryonic mortality denotes death of fertilized ova and embryos up to day 28 of gestation. This period encompasses blastocyst elongation and maternal TABLE 3.3

recognition of pregnancy with losses averaging 25e30% in beef and dairy cows, respectively.92e95 Days 28e30 are commonly used as a set point to determine pregnancy success because it is the earliest time that pregnancy can be accurately diagnosed by transrectal ultrasonography or chemical blood tests in a commercial scenario. The mechanisms leading to early pregnancy loss in cattle involve genetic abnormalities, asynchrony between the uterus and conceptus that leads to failure in maternal recognition of pregnancy or premature luteal regression.96e98 Even though this period encompasses the majority of reproductive failure in cattle, it usually does not affect estrous cycle length, thus not delaying rebreeding. Late embryonic/early fetal mortality: In cattle, late embryonic mortality is usually combined with early fetal mortality to define pregnancy loss, from days 28e60 of gestation. This period includes important physiological changes during placentation, organogenesis, and the transition from an embryo to a fetus. This category of loss is usually diagnosed in commercial farms when a second pregnancy diagnosis occurs. Losses during this period average w8% in beef cows85,99 and w14% in dairy cows, but vary from 3.2% to 42.7%.100e102 Little is known about the mechanisms associated with pregnancy failure during this time, much of which occurs

Incidence of embryonic/fetal mortality in beef cattle.

Cattle type

Early embryonic mortalitya

Late embryonic/Early fetal mortalityb

Bos taurus heifers

42.7 (24e66), % n ¼ 6072

Bos taurus cows

Fetal mortalityc

References

6.9 (4.4e8.5), % n ¼ 1297

2.84 (1.5e4.1), % n ¼ 732

126e134

48.73 (25e58), % n ¼ 5911

5.5 (1.5e13.9), % n ¼ 1487

1.2 % n ¼ 176

83,128,134e142

Bos indicus heifers

42.52 (28e54), % n ¼ 1632

9.81 (9.1e10.6), % n ¼ 647

e

143e147

Bos indicus cows

48.76 (31e66), % n ¼ 11811

5.57 (1.6e16.5), % n ¼ 7611

e

85,99,143,148e153

Presented as average (range), %. a From days 0e28 of gestation. b From days 29e90 of gestation. c From day 90 of gestation to term.

I. Beef cattle production

Parturition and postpartum anestrus

around the time of active placentation and placentome formation (days 35e40 of gestation). Recent reports in beef and dairy cattle suggest potential roles of paternal genetics in this period of loss.99 Even though the incidence of late embryonic mortality is normally less than that of early embryonic mortality, the economic consequences of late embryonic mortality can be significant because it can cause a prolonged delay in conception date and increase the proportion of cows culled at the end of the breeding season.103

Parturition and postpartum anestrus Parturition includes the following stages: Stage I e cervical dilation and increased myometrial contractions due to the removal of the “progesterone block,” Stage II e expulsion of the fetus, and Stage 3 e expulsion of the placenta. Mechanisms associated with parturition have been studied intensely in sheep104 and to a lesser extent in cattle.105,106 The hypothalamicpituitary-adrenal axis is required for the initiation of parturition in ruminants. In cattle, as in sheep, parturition is initiated following increased secretion of cortisol from the fetal adrenal gland, which may be due to fetal stress induced by space limitations in the uterus. A major difference in the hormonal control of parturition between the cow and the ewe is that the corpus luteum is primary source of progesterone in the cow; whereas, the placenta is the primary source in the ewe after day 70 of gestation. In species in which the corpus luteum is the primary source of progesterone (i.e., cattle), induction of luteolysis is the decisive step leading to parturition.106 Increased circulating concentrations of cortisol from the fetal adrenal gland induce steroidogenic enzymes required for the conversion of progesterone to estrogen within the placenta. The elevation in estrogens lead to

49

increased myometrial contractions due to induction of PGF2a and oxytocin receptors. In the cow, cortisol also induces uterine PGF2a secretion, which induces luteolysis and aids in myometrial contractions.105 Relaxin is a hormone that aids in preparation of the pelvis and birth canal for relaxation during parturition. As the calf enters the cervix pressure sensitive neurons are stimulated which synapse with hypothalamic neurons that synthesize oxytocin. Consequently, the myometrial contractions initiated by estrogen and PGF2a are increased by oxytocin secretion from the posterior pituitary gland leading to expulsion of the fetus. Following calving there is a delay until the first ovulatory estrus (i.e., postpartum interval) and resumption of normal estrous cycles. In prepuberal heifers and postpartum beef cows the first ovulation is not accompanied by expression of estrus and results in a short luteal phase (e.g., 7e10 days;9). The short luteal phase is due to an advance in the secretion of luteolytic PGF2a107 and is followed by expression of estrus, ovulation, a normal length luteal phase, and good fertility. In prepuberal heifers and anestrous postpartum cows, progestin treatment (e.g., CIDR; Table 3.1) is commonly used in estrous synchronization protocols to simulate the short luteal phase and induce establishment of normal estrous cycles.13 Mechanisms controlling the postpartum interval have been studied and it is clear that a progressive increase in the frequency of circulating LH pulses is essential for the establishment of normal estrous cycles. Similar to the prepuberal heifer, the anestrous cow is hypersensitive to the negative feedback of estradiol following calving. Pituitary content and circulating concentrations of LH are low immediately after calving and LH pulse frequency increases over time resulting in the first ovulation postpartum. Length of the postpartum interval is dependent upon effects of suckling,108 cow/calf bond,109 dystocia,110 and nutrition.108

I. Beef cattle production

50

3. Physiology and pregnancy of beef cattle

References 1. Melton B. Attaching Economic Figures to Production Traits; 1995. http://animal.ifas.ufl.edu/beef_ extension/bcsc/1995/docs/melton_traits.pdf. 2. Lesmeister J, Burfening P, Blackwell R. Date of first calving in beef cows and subsequent calf production. J Anim Sci. 1973;36(1):1e6. 3. Cushman R, Kill L, Funston RN, Mousel E, Perry G. Heifer calving date positively influences calf weaning weights through six parturitions. J Anim Sci. 2013; 91(9):4486e4491. 4. Day ML, Nogueira GP. Management of age at puberty in beef heifers to optimize efficiency of beef production. Anim Front. 2013;3(4):6e11. 5. Sartori R, Bastos M, Baruselli P, Gimenes L, Ereno R, Barros C. Physiological differences and implications to reproductive management of Bos taurus and Bos indicus cattle in a tropical environment. In: Reproduction in Domestic Ruminants VII. 2010:357e375. 6. Cardoso RC, Alves BR, Williams GL. Neuroendocrine signaling pathways and the nutritional control of puberty in heifers. Anim Reprod. 2018;15(Suppl. 1): 868e878. 7. Amstalden M, Williams G. Kisspeptin and neural networks in pubertal development of domestic female ruminants. In: Reproduction in Domestic Ruminants VIII. 2014:127e140. 8. Day M, Imakawa K, Garcia-Winder M, et al. Endocrine mechanisms of puberty in heifers: estradiol negative feedback regulation of luteinizing hormone secretion. Biol Reprod. 1984;31(2):332e341. 9. Lauderdale J. A review of patterns of change in luteal function. J Anim Sci. 1986;62(Suppl. 2):79e91. 10. Day M, Imakawa K, Zalesky D, Kittok R, Kinder JE. Effects of restriction of dietary energy intake during the prepubertal period on secretion of luteinizing hormone and responsiveness of the pituitary to luteinizing hormone-releasing hormone in heifers 1. J Anim Sci. 1986;62(6):1641e1648. 11. Cardoso R, Alves B, Prezotto L, et al. Use of a stair-step compensatory gain nutritional regimen to program the onset of puberty in beef heifers. J Anim Sci. 2014;92(7): 2942e2949. 12. O’Connor ML, Senger PL. Estrus detection. In: Youngquist RS, Saunders WB, eds. Current Therapy in Large Animal Theriogenology. 1997:276e285. Philadelphia. 13. Lauderdale J. ASAS Centennial Paper: contributions in the Journal of Animal Science to the development of protocols for breeding management of cattle through synchronization of estrus and ovulation. J Anim Sci. 2009;87(2):801e812.

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59. Bazer FW, Burghardt RC, Johnson GA, Spencer TE, Wu G. Interferons and progesterone for establishment and maintenance of pregnancy: interactions among novel cell signaling pathways. Reprod Biol. 2008;8(3): 179e211. 60. Antoniazzi AQ, Webb BT, Romero JJ, et al. Endocrine delivery of interferon tau protects the corpus luteum from prostaglandin F2 alpha-induced luteolysis in ewes. Biol Reprod. 2013;88(6):144, 141e112. 61. Hansen T, Henkes L, Ashley R, Bott R, Antoniazzi A, Han H. Endocrine actions of interferon-tau in ruminants. In: Reproduction in Domestic Ruminants. vol. 7. 2011:325e340. 62. Oliveira J, Henkes L, Ashley R, et al. Expression of ISGs in extrauterine tissues during early pregnancy in sheep is the consequence of endocrine IFN-s release from the uterine vein. Endocrinology. 2008;149:1252e1259. 63. Yang L, Wang X, Wan P, et al. Up-regulation of expression of interferon-stimulated gene 15 in the bovine corpus luteum during early pregnancy. J Dairy Sci. 2010;93(3):1000e1011. 64. Schlafer D, Fisher P, Davies C. The bovine placenta before and after birth: placental development and function in health and disease. Anim Reprod Sci. 2000; 60:145e160. 65. Wooding P, Burton G. Synepitheliochorial placentation: ruminants (ewe and cow). In: Comparative Placentation: Structures, Functions and Evolution. Berlin: Springer; 2008:133e167. 66. Assis Neto AC, Pereira F, Santos TC, Ambrosio C, Leiser R, Miglino M. Morpho-physical recording of bovine conceptus (Bos indicus) and placenta from days 20 to 70 of pregnancy. Reprod Domest Anim. 2010;45(5):760e772. 67. Aires M, Degaki K, Dantzer V, Yamada A. Bovine placentome development during early pregnancy. In: Microscopy: Advances in Scientific Research and Education. Spain: Formatex Research Center; 2014:390e396. 68. Wooding F, Wathes DC. Binucleate cell migration in the bovine placentome. J Reprod Fertil. 1980;59(2): 425e430. 69. Wooding F. Frequency and localization of binucleate cells in the placentomes of ruminants. Placenta. 1983; 4:527e539. 70. Reimers T, Ullmann M, Hansel W. Progesterone and prostanoid production by bovine binucleate trophoblastic cells. Biol Reprod. 1985;33(5):1227e1236. 71. Zoli AP, Guilbault LA, Delahaut P, Ortiz WB, Beckers J-F. Radioimmunoassay of a bovine pregnancy-associated glycoprotein in serum: its application for pregnancy diagnosis. Biol Reprod. 1992;46(1): 83e92.

72. Green JA, Xie S, Quan X, et al. Pregnancy-associated bovine and ovine glycoproteins exhibit spatially and temporally distinct expression patterns during pregnancy. Biol Reprod. 2000;62(6):1624e1631. 73. Wooding F. Localization of ovine placental lactogen in sheep placentomes by electron microscope immunocytochemistry. J Reprod Fertil. 1981;62(1):15e19. 74. Estrella CAS, Kind KL, Derks A, et al. Remodelling of the bovine placenta: comprehensive morphological and histomorphological characterization at the late embryonic and early accelerated fetal growth stages. Placenta. 2017;55:37e46. 75. Hudlika O. Development of microcirculation: capillary growth and adaptation. In: Handbook of Physiology: The Cardiovascular System. 1984:165e216. 76. Reynolds LP, Redmer DA. Utero-placental vascular development and placental function. J Anim Sci. 1995; 73(6):1839e1851. 77. Reynolds LP, Caton JS, Redmer DA, et al. Evidence for altered placental blood flow and vascularity in compromised pregnancies. J Physiol. 2006;572(1): 51e58. 78. Wrobel KH, Laun G, Hees H, Zwack M. Histologische und ultrastrukturelle Untersuchungen am Vaginalepithel des Rindes. Anat Histol Embryol. 1986;15(4): 303e328. 79. Noonan J, Schultze A, Ellington E. Changes in bovine cervical and vaginal mucus during the estrous cycle and early pregnancy. J Anim Sci. 1975;41(4):1084e1089. 80. Randel R, Erb R. Reproductive steroids in the bovine. VI. Changes and interrelationships from 0 to 260 days of pregnancy. J Anim Sci. 1971;33(1):115e123. 81. Shemesh M. Production and regulation of progesterone in bovine corpus luteum and placenta in mid and late gestation: a personal review. Reprod Fertil Dev. 1990;2(2):129e135. 82. Little D, Rahe C, Fleeger J, Harms P. Episodic release of LH during gestation in the cow. J Reprod Fertil. 1982; 66(2):687e690. 83. Pohler K, Geary T, Johnson C, et al. Circulating bovine pregnancy associated glycoproteins are associated with late embryonic/fetal survival but not ovulatory follicle size in suckled beef cows. J Anim Sci. 2013; 91(9):4158e4167. 84. Pohler K, Pereira M, Lopes F, et al. Circulating concentrations of bovine pregnancy-associated glycoproteins and late embryonic mortality in lactating dairy herds. J Dairy Sci. 2016;99(2):1584e1594. 85. Pohler K, Peres R, Green J, et al. Use of bovine pregnancy-associated glycoproteins to predict late embryonic mortality in postpartum Nelore beef cows. Theriogenology. 2016;85(9):1652e1659.

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86. Wallace RM, Pohler KG, Smith MF, Green JA. Placental PAGs: gene origins, expression patterns, and use as markers of pregnancy. Reproduction. 2015;149(3): R115eR126. 87. Perry GA, Smith MF, Lucy MC, et al. Relationship between follicle size at insemination and pregnancy success. Proc Natl Acad Sci USA. 2005;102(14): 5268e5273. 88. Mercadante P, Ribeiro E, Risco C, Ealy A. Associations between pregnancy-associated glycoproteins and pregnancy outcomes, milk yield, parity, and clinical diseases in high-producing dairy cows. J Dairy Sci. 2016;99(4):3031e3040. 89. Blank M, Chan J, Friesen H. Placental lactogens, new developments. J Steroid Biochem. 1977;8(5):403e414. 90. Holland M, Hossner K, Williams S, Wallace C, Niswender G, Odde K. Serum concentrations of insulin-like growth factors and placental lactogen during gestation in cattle I. Fetal profiles. Domest Anim Endocrinol. 1997;14(4):231e239. 91. Pohler KG, Green JA, Geary TW, et al. Predicting embryo presence and viability. Adv Anat Embryol Cell Biol. 2015;216:253e270. 92. Diskin M, Waters S, Parr M, Kenny D. Pregnancy losses in cattle: potential for improvement. Reprod Fertil Dev. 2016;28(2):83e93. 93. Wiltbank MC, Baez GM, Garcia-Guerra A, et al. Pivotal periods for pregnancy loss during the first trimester of gestation in lactating dairy cows. Theriogenology. 2016; 86(1):239e253. 94. Ayalon N. A review of embryonic mortality in cattle. J Reprod Fertil. 1978;54(2):483e493. 95. Sreenan J, Diskin M. The extent and timing of embryonic mortality in the cow. In: Embryonic Mortality in Farm Animals. Springer; 1986:1e11. 96. Forde N, Bazer FW, Spencer TE, Lonergan P. ‘Conceptualizing’the endometrium: identification of conceptus-derived proteins during early pregnancy in cattle. Biol Reprod. 2015;92(6):156, 151e113. 97. Bridges G, Day M, Geary T, Cruppe L. Triennial Reproduction Symposium: deficiencies in the uterine environment and failure to support embryonic development. J Anim Sci. 2013;91(7):3002e3013. 98. Diskin MG, Morris DG. Embryonic and early foetal losses in cattle and other ruminants. Reprod Domest Anim. 2008;43(Suppl. 2):260e267. 99. Franco GA, Peres RFG, Martins CFG, Reese ST, Vasconcelos JLM, Pohler KG. Sire contribution to pregnancy loss and pregnancy-associated glycoprotein production in Nelore cows. J Anim Sci. 2018;96(2):632e640. 100. Vasconcelos J, Silcox R, Lacerda J, Pursley J, Wiltbank M. Pregnancy rate, pregnancy loss, and response to head stress after AI at 2 different times from ovulation in dairy cows. Biol Reprod. 1997:230e231.

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101. Pereira M, Wiltbank M, Vasconcelos J. Expression of estrus improves fertility and decreases pregnancy losses in lactating dairy cows that receive artificial insemination or embryo transfer. J Dairy Sci. 2016;99(3): 2237e2247. 102. Cartmill J, El-Zarkouny S, Hensley B, Lamb G, Stevenson J. Stage of cycle, incidence, and timing of ovulation, and pregnancy rates in dairy cattle after three timed breeding protocols. J Dairy Sci. 2001; 84(5):1051e1059. 103. Silke V, Diskin M, Kenny D, et al. Extent, pattern and factors associated with late embryonic loss in dairy cows. Anim Reprod Sci. 2002;71(1):1e12. 104. Whittle W, Patel F, Alfaidy N, et al. Glucocorticoid regulation of human and ovine parturition: the relationship between fetal hypothalamic-pituitary-adrenal axis activation and intrauterine prostaglandin production. Biol Reprod. 2001;64(4):1019e1032. 105. Shenavai S, Preissing S, Hoffmann B, et al. Investigations into the mechanisms controlling parturition in cattle. Reproduction. 2012;144(2):279e292. 106. Schuler G, Furbass R, Klisch K. Placental contribution to the endocrinology of gestation and parturition. Anim Reprod. 2018;15(Suppl. 1):822e842. 107. Garverick H, Smith M. Mechanisms associated with subnormal luteal function. J Anim Sci. 1986;62(Suppl. 2):92e105. 108. Randel R. Nutrition and postpartum rebreeding in cattle. J Anim Sci. 1990;68(3):853e862. 109. Viker S, McGuire W, Wright J, Beeman K, Kiracofe G. Cow-calf association delays postpartum ovulation in mastectomized cows. Theriogenology. 1989;32(3): 467e474. 110. Laster DB, Glimp HA, Cundiff LV, Gregory KE. Factors affecting dystocia and the effects of dystocia on subsequent reproduction in beef cattle. J Anim Sci. 1973;36(4):695e705. 111. Shea B. Evaluating the bovine embryo. Theriogenology. 1981;15(1):31e42. 112. Peters A. Embryo mortality in the cow. Anim Breed Abstr. 1996;64:587e598. 113. Telford NA, Watson AJ, Schultz GA. Transition from maternal to embryonic control in early mammalian development: a comparison of several species. Mol Reprod Dev. 1990;26(1):90e100. 114. Guillomot M. Cellular interactions during implantation in domestic ruminants. J Reprod Fertil. 1995;49: 39e51. 115. Greenstein J, Murray R, Foley R. Observations on the morphogenesis and histochemistry of the bovine preattachment placenta between 16 and 33 days of gestation. Anat Rec. 1958;132(3):321e341. 116. Wrobel K-H, S€ uß F. Identification and temporospatial distribution of bovine primordial germ cells prior to

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gonadal sexual differentiation. Anat Embryol. 1998; 197(6):451e467. Kritzenberger M, Wrobel K-H. Histochemical in situ identification of bovine embryonic blood cells reveals differences to the adult haematopoietic system and suggests a close relationship between haematopoietic stem cells and primordial germ cells. Histochem Cell Biol. 2004;121(4):273e289. Erickson B. Development and radio-response of the prenatal bovine ovary. J Reprod Fertil. 1966;11(1): 97e105. Curran S, Kastelic J, Ginther O. Determining sex of the bovine fetus by ultrasonic assessment of the relative location of the genital tubercle. Anim Reprod Sci. 1989; 19(3e4):217e227. Hafez E, Rajakoski E. Placental and fetal development during multiple bovine pregnancy. Anatomical and physiological studies. Anat Rec. 1964;150(3):303e316. Vivo J, Robina A, Regod on S, Guillen MT, Franco A, Mayoral A. Histogenetic evolution of bovine gastric compartments during prenatal period. Histol Histopathol. 1990;5(4):461e476. Pfarrer C, Ebert B, Miglino MA, Klisch K, Leiser R. The three-dimensional feto-maternal vascular interrelationship during early bovine placental development: a scanning electron microscopical study. J Anat. 2001; 198(5):591e602. Casteilla L, Forest C, Robelin J, Ricquier D, Lombet A, Ailhaud G. Characterization of mitochondrialuncoupling protein in bovine fetus and newborn calf. Am J Physiol Endocrinol Metab. 1987;252(5):E627eE636. Evans H, Sack WO. Prenatal development of domestic and laboratory mammals: growth curves, external features and selected references. Anat Histol Embryol. 1973; 2(1):11e45. Mao W, Albrecht E, Teuscher F, Yang Q, Zhao R, Wegner J. Growth-and breed-related changes of fetal development in cattle. Asia Australas J Anim Sci. 2008; 21(5):640. Dunne L, Diskin M, Sreenan J. Embryo and foetal loss in beef heifers between day 14 of gestation and full term. Anim Reprod Sci. 2000;58(1):39e44. Mialon M, Camous S, Renand G, Martal J, Menissier F. Peripheral concentrations of a 60-kDa pregnancy serum protein during gestation and after calving and in relationship to embryonic mortality in cattle. Reprod Nutr Dev. 1993;33(3):269e282. Mercadante V, Kozicki L, Ciriaco F, et al. Effects of administration of prostaglandin F at initiation of the seven-day CO-Synchþ controlled internal drug release ovulation synchronization protocol for suckled beef cows and replacement beef heifers. J Anim Sci. 2015; 93(11):5204e5213.

129. Perry G, Smith M, Roberts A, MacNeil M, Geary T. Relationship between size of the ovulatory follicle and pregnancy success in beef heifers. J Anim Sci. 2007;85(3):684e689. 130. Kill LK, Pohler KG, Perry GA, Smith MF. Serumovine Pregnancy Associated Glycoproteins and Progesterone in Beef Heifers that Experienced Late Embryonic/Fetal Mortality. Des Moines, IA: ASAS Midwestern Section; 2013. 131. Lamb G, Larson J, Stevenson J, et al. Synchronization of estrus in suckled beef cows for detected estrus and artificial insemination and timed artificial insemination using gonadotropin-releasing hormone, prostaglandin F, and progesterone. J Anim Sci. 2006;84(2):332e342. 132. da Silva EP, Machado A, Gambin L, et al. Ovarian structures, estrus expression, and pregnancy rate in beef heifers using estradiol cypionate or GnRH as ovulation inductors in timed AI protocol. Reprod Fertil Dev. 2017;29(1):113e114. 133. Colazo M, Kastelic J, Whittaker P, Gavaga Q, Wilde R, Mapletoft R. Fertility in beef cattle given a new or previously used CIDR insert and estradiol, with or without progesterone. Anim Reprod Sci. 2004;81(1): 25e34. 134. Martinez M, Kastelic J, Adams G, Mapletoft R. The use of a progesterone-releasing device (CIDR-B) or melengestrol acetate with GnRH, LH, or estradiol benzoate for fixed-time AI in beef heifers. J Anim Sci. 2002; 80(7):1746e1751. 135. Maurer R, Chenault J. Fertilization failure and embryonic mortality in parous and nonparous beef cattle. J Anim Sci. 1983;56(5):1186e1189. 136. Perry G, Smith M, Lucy M, Roberts A, MacNeil M, Geary T. Effect of Ovulatory Follicle Size at Time of GnRH Injection or Standing Estrus on Pregnancy Rates and Embryonic/Fetal Mortality in Beef Cattle. American Society of Animal Science Western Section; 2003. 137. Dobbins C, Eborn D, Tenhouse D, et al. Insemination timing affects pregnancy rates in beef cows treated with CO-Synch protocol including an intravaginal progesterone insert. Theriogenology. 2009;72(7): 1009e1016. 138. Lamb G, Stevenson J, Kesler D, Garverick H, Brown D, Salfen B. Inclusion of an intravaginal progesterone insert plus GnRH and prostaglandin F2alpha for ovulation control in postpartum suckled beef cows. J Anim Sci. 2001;79(9):2253e2259. 139. Marquezini G, Mercadante V, Olson K, et al. Effects of equine chorionic gonadotropin on follicle development and pregnancy rates in suckled beef cows with or without calf removal. J Anim Sci. 2013;91(3): 1216e1224.

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140. Hill SL, Perry G, Mercadante V, et al. Altered progesterone concentrations by hormonal manipulations before a fixed-time artificial insemination CO-Synchþ CIDR program in suckled beef cows. Theriogenology. 2014;82(1):104e113. 141. Beal WE, Perry RC, Corah LR. The use of ultrasound in monitoring reproductive physiology of beef cattle. J Anim Sci. 1992;70(3):924e929. 142. Stevenson J, Johnson S, Medina-Britos M, RichardsonAdams A, Lamb G. Resynchronization of estrus in cattle of unknown pregnancy status using estrogen, progesterone, or both. J Anim Sci. 2003;81(7): 1681e1692. 143. Pessoa JS, Rolin Filho ST, Ribeiro HFL, Garcia OS, Nunes KB, Amorim BS. Embryonic loss in cows submitted to timed AI in the northeast of para. Cien Anim. 2012;22(Suppl. 1):239e241. 144. Smith M, Nix K, Kraemer D, Amoss M, Herron M, Wiltbank J. Fertilization rate and early embryonic loss in Brahman crossbred heifers. J Anim Sci. 1982; 54(5):1005e1011. 145. Peres R, J unior IC, Sa Filho O, Nogueira GP, Vasconcelos JLM. Strategies to improve fertility in Bos indicus postpubertal heifers and nonlactating cows submitted to fixed-time artificial insemination. Theriogenology. 2009;72(5):681e689. 146. Cordeiro MB, Peres MS, de Souza JM, et al. Supplementation with sunflower seed increases circulating cholesterol concentrations and potentially impacts on the pregnancy rates in Bos indicus beef cattle. Theriogenology. 2015;83(9):1461e1468. 147. Pontes J, Nonato-Junior I, Sanches B, et al. Comparison of embryo yield and pregnancy rate between in vivo and in vitro methods in the same Nelore (Bos indicus) donor cows. Theriogenology. 2009;71(4):690e697.

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148. Sa Filho MF, Marques MO, Girotto R, et al. Resynchronization with unknown pregnancy status using progestin-based timed artificial insemination protocol in beef cattle. Theriogenology. 2014;81(2):284e290. 149. Sa Filho O, Dias C, Lamb G, Vasconcelos J. Progesterone-based estrous synchronization protocols in non-suckled and suckled primiparous Bos indicus beef cows. Anim Reprod Sci. 2010;119(1):9e16. 150. Pfeifer L, Castro N, Neves P, Cestaro J, Schneider A. Comparison between two estradiol-progesterone based protocols for timed artificial insemination in blocks in lactating Nelore cows. Anim Reprod Sci. June 2017;181:125e129. https://doi.org/10.1016/j. anireprosci.2017.03.025. 151. Cooke R, Schubach K, Marques R, et al. Effects of temperament on physiological, productive, and reproductive responses in beef cows. J Anim Sci. 2017;95(1): 1e8. 152. Radigonda VL, Pereira GR, da Cruz Favaro P, et al. Infrared thermography relationship between the temperature of the vulvar skin, ovarian activity, and pregnancy rates in Braford cows. Trop Anim Health Prod. 2017:1e5. 153. Aono F, Cooke RF, Alfieri A, Vasconcelos JLM. Effects of vaccination against reproductive diseases on reproductive performance of beef cows submitted to fixed-timed AI in Brazilian cow-calf operations. Theriogenology. 2013;79(2):242e248. 154. Garverick HA, Smith MF. Female reproductive physiology and endocrinology of cattle. In: Braun WF, Youngquist RS, eds. The Veterinary Clinics of North America, Food Animal Practice. Philadelphia: W.B. Saunders Co; 1995:223e247.

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C H A P T E R

4 Reproductive management of beef cattle Pedro L.P. Fontes, Nicola Oosthuizen, G. Cliff Lamb Department of Animal Science, Texas A&M University, College Station, TX, United States

O U T L I N E Introduction

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Challenges Replacement heifers Postpartum cows Genotype

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Available strategies Breeding season Artificial insemination

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Estrus synchronization and fixed-time artificial insemination Multiple ovulation embryo transfer In vitro fertilization Sex-sorted semen

Introduction

Conclusion

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However, additional advancements in both management and technologies are required to reach a level of animal production that can provide for the global population by 2050.2 Cow-calf operations rely on their females to produce a healthy calf once per year to generate revenue and remain profitable. Females that do not produce a calf annually are utilizing resources that could be used to support more productive cattle. Therefore, reproductive management strategies aimed at improving the overall prolificacy and quality of calves play a large role in increasing overall output and

With the expected increase in demand for beef over the next few decades,1 reproductive performance of beef cattle will not only determine the overall efficiency of cow-calf operations and the United States (US) beef industry, but will significantly impact the world’s food supply. The production of beef cattle has become a more efficient process during the past few decades, which is largely due to the development and adoption of new technologies, as well as an overall improvement in herd genetics.

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profitability of a beef enterprise. There are technologies available to cattle producers that can be utilized to introduce superior genetics into their herds, reduce the transfer of diseases, improve both male and female fertility, and ultimately increase the value of their calves. These technologies include, but are not limited to, the use of a defined breeding season, estrus synchronization, fixed-time artificial insemination (TAI), breeding soundness evaluations of bulls, multiple ovulation embryo transfer (MOET), in vitro fertilization, and the use of sex-sorted semen. However, many beef producers fail to incorporate them into their production system and opt for more traditional approaches.3 Further improvements to fertility, ease of application, and reductions in overall cost will persuade more cattle producers to adopt these reproductive management strategies in the future. This chapter will focus on available reproductive management strategies and their application to address current challenges in beef production systems.

Challenges There are a number of challenges associated with reproductive management in both beef heifers and cows, challenges which need to be overcome to optimize reproductive performance in the beef industry.

Replacement heifers Replacement heifers are the future of a beef cattle operation and producers need to focus on heifer development strategies that maximize their productive potential and keep them performing in the herd for years to come. To maximize lifetime productivity, heifers need to be managed to calve for the first time at approximately 24 months of age. When a lifetime productivity comparison between heifers calving at 2 versus 3 years old was performed, heifers that calved as 2 year olds produced 138 more

kg of weaned calves and had 6%e8% greater economic efficiency.4,5 In addition, heifers that calve early in the calving season not only have a greater probability of becoming pregnant as first calf heifers,6 but also have increased longevity in the herd and produce more kg of weaned calves during their overall productive life.7 Therefore, the timing of conception within the first breeding season is key for long-term productivity of beef females. To become pregnant early in the breeding season, it is paramount that heifers attain sexual maturity prior to the initiation of the breeding season. In recent decades, significant progress has been made in understanding physiological events associated with the attainment of puberty in heifers,8,9 which has allowed for the development of several strategies that increase the percentage of pubertal heifers in the herd prior their first breeding season.

Postpartum cows One of the main factors known to influence reproductive performance of beef herds is the proportion of mature cows in anestrus at the initiation of the breeding season.10 After parturition, beef cows undergo a transitional period of anestrus characterized by a wave-like pattern of follicular growth where dominant follicles undergo atresia prior to ovulation due to a lack of luteinizing hormone (LH) pulses.11 Results of multilocation studies evaluating postpartum cyclicity of Bos taurus beef cows in the US indicate that an average of 50% of cows are anestrus prior to the breeding season. Those studies also demonstrated great variation among different locations, with the proportion of cyclic cows ranging from 17% to 67%.12 Because beef cows in anestrus at the beginning of the breeding season have lower fertility when compared to cyclic cows,13 strategies that increase fertility of non-cyclic cows or increase the proportion of cows cycling prior to the time of breeding have the potential to improve reproductive efficiency of beef herds.

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Available strategies

The effects of days postpartum on pregnancy rate to TAI in suckled beef cows is well documented. Cows that are less than 50 days postpartum at the initiation of the breeding season have significantly poorer pregnancy rates compared to cows that are greater than 50 days postpartum.14e16 A study including more than 8,500 postpartum suckled beef cows, revealed that both multiparous and primiparous cows have decreased pregnancy rates if the interval between calving and TAI is less than 72 days. Furthermore, results of the same study indicated that early calving multiparous cows in adequate body condition have the greatest pregnancy rates, whereas late calving primiparous cows in poor body condition have the poorest pregnancy outcomes,17 indicating that parity, timing of calving, and postpartum nutritional status are crucial to subsequent reproductive performance. Primiparous cows represent an even greater challenge. Although heifers are commonly mated to calve earlier and have greater days postpartum at initiation of the breeding season than primiparous cows, a greater proportion of primiparous cows are in anestrus at the beginning of the breeding season when compared to multiparous cows.13 Since the percentage of cyclic cows at the beginning of the breeding season increases curvilinearly from 9% at
30 days, to a peak of 70% at 81e90 days postpartum, strategies that increase the proportion of cows calving early in the calving season can have major benefits to improve fertility in beef cows, particularly primiparous cows.

indicus-influenced or Zebu breeds fed diets based on warm-season forages and agricultural byproducts. In the US alone, approximately 30% of cattle contain B. indicus genetics, and approximately 40% of beef cows and 50% of the country’s cow-calf producers are located in the southern US, where B. indicus cattle and their crosses are located. Because these cattle have undergone natural selection in hot and humid environments, they have acquired a greater tolerance to heat and parasites when compared to B. taurus cattle, which underwent environmental selection in the arid climates of Europe and Africa. Therefore, B. indicus cattle are better able to regulate body temperature in response to heat stress, and consequently experience a less severe decrease in feed intake, growth, milk yield and reproductive performance when exposed to heat stress conditions.18 Zebu cattle have been subjected to less selection pressure for production traits, such as meat and milk production, compared to B. taurus breeds. Currently, challenges include enhancing beef production efficiency in tropical and subtropical regions, and maximizing the benefits of using B. indicus genetics while minimizing their limitations in intensive beef production systems. It is important to highlight that several physiological differences exist between B. indicus and B. taurus cattle, including differences related to reproductive physiology.19,20

Genotype

One of the most important reproductive management practices for a cow-calf operation is the establishment of a breeding season. By defining a breeding season, a producer is able to develop a defined calving season to match environmental conditions and available resources to gestation and calving periods of beef females, and to ensure that they receive adequate nutrients during those times. In addition, a breeding season allows for the concentration of labor resources,

It is estimated that approximately 70% of the increase in beef production required to meet the growing global demand will come from subtropical/tropical regions of the planet,1 including the southern US, Mexico, Central/South America, Africa, Asia, and Oceania. These regions contain approximately 70% of the world’s cattle population, which are predominantly Bos

Available strategies Breeding season

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and more attention devoted to cows and heifers when calving to minimize the consequences of dystocia events, and maximize calf survival. A survey conducted in the United States showed that only 34% of beef cattle operations had a defined breeding season, 11.5% of operations had two defined breeding seasons, and as many as 54.5% of operations had no set breeding season.21 Many traditional commercial breeding seasons have the intent to place a young, growing calf on forages that are at their peak of quality and availability. Providing growing calves and lactating dams high quality forages allows for maximal calf weight gain through both increased milk and forage intake.22 Without a defined breeding season, producers have difficulty implementing certain reproductive biotechnologies, and must monitor cows for calving throughout the year. By establishing a defined breeding season, calving activity is reduced from being year-round to a specific period of time. More calves will be available for sale at a given time, and these calves will have greater uniformity in terms of size and weight, resulting in an increase in market value.23 Market value of calves increases as a result of an increase in the number of calves in an auction lot, resulting in greater financial income.24

2017 the number of units of dairy semen sold increased 84%, whereas the number of units of beef semen increased 145%.26 This increase in units of semen sold per year indicates that there has been a greater adoption of AI in the beef cattle industry over time. By utilizing AI, superior genetics can be introduced into a herd in a shorter period of time than through the use of a natural service sires alone.27 Semen from proven bulls with the best genetics and most desirable expected progeny differences (EPDs) can be purchased easily and utilized in any beef cattle operation. By selecting for bulls with low birth weights and calving ease EPDs, calving difficulty can be minimized and calf losses associated with dystocia can be reduced. In addition, AI can reduce the number of natural service sires required by a producer, as well as their associated maintenance expenses.23 The use of EPDs support rapid genetic advancements, which subsequently lead to an increase in overall profitability.28 Sires producing semen for AI have EPDs and accuracies of EPDs that are superior to the majority of those from natural service sires. Even when EPDs between AI sires and natural service sires are similar, the accuracies of the EPDs from AI sires are greater; therefore, more confidence can be placed in the performance characteristics of the AI-sired offspring.29

Artificial insemination

Estrus synchronization and fixed-time artificial insemination

Artificial insemination (AI) was one of the first biotechnologies used in farm animal species to improve reproduction. The first reported AI success in a domestic animal was performed by Lazzaro Spallanzani in 1784 with successful AI in a dog. However, it was only in the early 1900s that a Russian scientist named E.I. Ivanhow accomplished the first successful AI in cattle.25 Current techniques for AI have improved drastically with the development of semen evaluation techniques, semen extenders, and proper methods for freezing semen. Between 1990 and

Significant improvements have been made in our understanding of the physiology controlling the bovine estrous cycle. Those advances laid the foundation for the development of estrous synchronization (ES) protocols that utilize exogenous hormones to synchronize estrus and ovulation in cattle. The primary objective of these hormonal treatments is to manipulate the estrous cycle in order to facilitate the adoption of biotechnologies such as AI and embryo transfer by cattle producers. Prior to the establishment

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of effective protocols for synchronization of estrus, the labor associated with visual detection of females in estrus was a major factor limiting adoption these biotechnologies. With the development of ES it is now possible to AI a herd of cows or heifers at a pre-determined fixed time and achieve pregnancy rates greater than 50% without the need for estrus detection.15,30 Consequently, the establishment of effective ES protocols has significantly impacted reproductive management in beef cattle.31 Over the past two decades, there has been a remarkable global increase in the use of AI by beef cattle producers. Although significant progress has been made, and the rate of adoption of biotechnologies has increased, natural service by itself is still by far the main reproductive management strategy utilized by beef producers. It has been estimated that only 7.6% of the beef operations in the US utilize AI, and only 1.6% make use of embryo transfer.21 Conversely, it has been estimated that 89.3% of dairy operations utilize AI, and 8.9% utilize embryo transfer.32 The low rate of adoption for these biotechnologies in the beef industry is likely related to the extensive nature of beef cow-calf operations, indicating that management strategies that minimize the amount of times producers are required to handle their cattle are more likely to be adopted and incorporated into management strategies of producers with large scale operations in the beef industry. As our understanding of the wave-like pattern of follicular growth increased with the use of ultrasonography, research on the exogenous control of the lifespan of the corpus luteum (CL), as well as the use of GnRH agonists to induce ovulation,33 set the stage for development of OvSynch. OvSynch was the first ES protocol that achieved sufficient synchrony of ovulation in the herd to allow the use of TAI.34 The OvSynch protocol was developed and thoroughly validated for use in dairy cows.34e37 Together with its variations, the OvSynch protocol remains the most commonly utilized ES

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protocol in the dairy industry.38 The OvSynch protocol consists of an injection of a GnRH analogue to induce ovulation of potentially responsive follicles, and induce the start of a new follicular wave. Seven days after the first GnRH injection, an injection of prostaglandin F2a (PGF) is administered to induce luteolysis of a potentially present CL, thereby decreasing circulating concentrations of progesterone (P4) and facilitating final growth of the dominant follicle. A second GnRH injection administered 48 h after the PGF induces ovulation of the preovulatory dominant follicle, and TAI is performed 16 h after the second GnRH injection.34 As previously mentioned, the more extensive nature of beef cow-calf operations requires that cattle handling is minimized in order to facilitate the adoption of ES and TAI. Hence, efforts were devoted to developing protocols that reduce the number of times animals are handled. This led to the development of the CO-Synch protocol in which TAI is performed concurrently with the second GnRH injection and yields similar pregnancy rates as those achieved using OvSynch.39 However, a disadvantage of both the OvSynch and the CO-Synch protocols was that 10e20% of the cows exhibited estrus prior to or directly after the PGF injection. Unless those cows were AI following visual detection of estrus, they failed to become pregnant. Hence, a controlled internal drug release (CIDR) insert impregnated with P4 was developed, and incorporated into the CO-Synch protocol from day 0e7 of the regular CO-Synch protocol. The supplemental P4 from the CIDR resulted in significant increases in pregnancy rates to TAI,13e15 and has, therefore, been adopted extensively in the beef industry. Today, the 7-d CO-Synch þ CIDR protocol is the most commonly utilized protocol in the US to synchronize both beef cows and heifers for TAI. By handling cows or heifers three times, beef cattle producers can now synchronize ovulation and AI hundreds of beef females in a single day and attain more than 50% pregnancy rates on the first day of the breeding season.15,30

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The adoption of ES and TAI can have a major impact on the profitability of a cow-calf operation. Modeling exercises indicate a potential increase in net return of $25e40 per calf born from AI.40 Additionally, 72% of respondents to a survey estimated that the additional value of calves from AI breeding compared with natural service breeding was more than $20, whereas 48% of respondents estimated the additional value to be more than $50.41 Data generated from the sales of the Show-Me Replacement Heifer Inc. revealed a premium of $18.69 per pregnant heifer with a calf from AI, and a premium of $24.30 per pregnant heifer that was due to calve during the first 30 days of the calving season.42 However, it is important to mention that some of these economic models may underestimate the magnitude of the economic impact ES and TAI. Some of the benefits associated with the adoption of these technologies go beyond the incorporation of superior genetics through the use of AI. When a multilocation study compared ES and TAI with

natural service, an increase in the proportion of cows calving early in the calving season was observed for cows exposed to TAI. Since calves that are born earlier in the calving season are older at the time of weaning and have more time to gain weight between birth and weaning, shifting the time of calving through the use of ES and TAI increased the weaning weights of calves. Furthermore, a greater proportion of cows exposed to TAI weaned a calf compared to cows only exposed to natural service23 (Fig. 4.1). The impacts of calving date on productivity of offspring and reproductive efficiency of cows are well documented. Data collected over 13 years at the University of Nebraska’s Gudmundsen Sandhills Laboratory indicated that weaning weights are greater for steers born in the first 21 days of the calving season, when compared to steers born in the second and third 21 day intervals.43 In the same study, steers were tracked until slaughter, and final body weight, hot carcass weight, marbling score, and carcass

FIG. 4.1 Percentage of cows calved in 10-day increments of the calving season for cows exposed to the 7-d COSynch þ CIDR protocol followed by fixed-time artificial insemination (TAI) or cows mated by natural service with no estrous synchronization (Control; adapted from Rodgers et al., 2012). *Within 10-day interval treatments differ (P < 0.01). **Within 10-day interval treatments differ (P < 0.05).

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value were greater for steers born earlier in the calving season when compared to steers from the same herd that were born later in the calving season. The influence of early calving on productivity is not restricted to male calves. Heifers born in the first 21 days of the calving season were heavier at weaning and at the time of breeding than those born later in the calving season. Consequently, a greater proportion of those heifers were pubertal prior to the first breeding season, more became pregnant, and a greater percentage calved earlier as first calf-heifers when compared to heifers born in the following 21 days or later.43 Calving distribution is also associated with longevity and lifetime productivity of beef females. By culling females strictly based on reproductive failure, heifers that calve in the first 21 days of their first calving season have increased longevity compared to heifers that calve later,44 indicating that calving date influences the chances of primiparous cows becoming pregnant in the subsequent breeding season. Additionally, heifers that calved in the first 21 days of their first calving season weaned heavier calves during their first six breeding seasons. The main challenge limiting the ability of cows to become pregnant early in the breeding season is postpartum anestrus. The end of gestation in cattle is characterized by reduced ovarian activity. High circulating concentrations of placental-derived steroids suppress the release of gonadotropins and result in the accumulation of follicle stimulating hormone (FSH) and depletion of LH stores in gonadotrophs of the anterior pituitary.45 After calving, circulating concentrations of FSH increase, and the wave-like pattern of follicular growth is re-established.46 However, dominant follicles fail to ovulate due to a lack of LH stored in the anterior pituitary, which limits the pre-ovulatory LH surge required for final follicular maturation and ovulation, and results in atresia of the dominant follicle.47 Luteinizing hormone stores are re-established approximately 2e3 weeks postpartum in cows.

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Ovarian cyclicity and ovulation are rapidly resumed in cows when calves are weaned at birth.47 However, cows that continue to nurse their calves experience a transient suppression of the GnRH surge, which is required for an LH surge; therefore, follicles fail to ovulate and undergo atresia during the early postpartum period in suckled beef cows. As the postpartum period progresses, the negative effects of the presence of the calf decrease, allowing for development and ovulation of the dominant follicle.48 Having a large number of cows cycling prior to the breeding season is key to increasing the number of cows pregnant at the beginning of the breeding season. The proportion of cyclic cows at the beginning of the breeding season increases as the number of days postpartum increase.13 Consequently, cows that calve earlier in the calving season are more likely to have resume estrous cycles prior to the subsequent breeding season which increases their chances of becoming pregnant early in the season. This concept is well illustrated in Figs. 4.2 and 4.3. When multi-herd fertility studies are conducted, it is common to observe a wide variation in pregnancy rates to TAI among herds, and several factors may contribute to these differences, such as genetics, nutrition, herd management, and others. Fig. 4.2 summarizes data collected from 1541 cows in eight different herds. When comparing herds that had pregnancy rates to TAI greater than 50% with herds that had pregnancy rates
50%, the better performing herds had, on average, 88% of the cows calving in the first 30 days of the calving season, whereas the less fertile herds had only 44% of cows calving in the first 30 days of the breeding season (Fig. 4.3A, B). Consequently, the average number of days postpartum at the beginning of the breeding season for cows in the top performing herds was 79 days versus 64 days in the remaining herds. Furthermore, the top performing herds only had 7% of cows that were less than 50 days postpartum at the beginning of the breeding season, whereas the other herds had

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FIG. 4.2 Differences in pregnancy rates among eight different herds. All cows were exposed to a 7-d CO-Synch þ CIDR

estrus synchronization protocol followed by fixed-time artificial insemination 60e66 h after CIDR removal. Marron (black in print version) bars represent the herds with pregnancy rates greater than 50%, whereas gray bars represent the herds with pregnancy rates of less than 50%.

43% of cows that were less than 50 days postpartum. These results highlight the importance of having cows calve early in the breeding season, and indicates that management strategies that increase the proportion of cows calving early in the calving season also influence fertility during subsequent breeding seasons. Since exogenous P4 can induce cyclicity in anestrous cows and prepubertal heifers, ES protocols that include supplemental P4 can be used strategically by cattle producers to increase the number of cows and heifers that are pregnant early in the breeding season. The benefits of P4 supplementation are particularly important for producers with B. indicus cattle. Zebu females generally reach puberty at 22e36 months of age when managed in an extensive production system.49 Accordingly, age at first calving in these heifers can be as high as 44e48 months. Attention has been focused on the development of strategies to reduce the age at puberty in Zebu heifers through genetic selection, nutritional management,50 and pharmacological

treatments.51e53 It is important to mention that the regulation of use of exogenous hormones for ES, such as estradiol and equine chorionic gonadotropin (eCG), differ among countries. Although the use of estradiol benzoate, estradiol cypionate, and eCG for ES and puberty induction is not legal in the US, their use has been legally approved in countries such as Brazil and Australia. Therefore, protocols with acceptable pregnancy rates have been established for TAI in both B. indicus cows54,55 and heifers in those countries.53 Approved protocols can effectively induce puberty in approximately 80% of prepubertal B. indicus heifers,52 which can then be exposed to ES and TAI, yielding conception rates of approximately 50% for those heifers that respond to induction of puberty.53 The effectiveness of these strategies, however, is highly dependent on the nutritional and metabolic status of the heifers. Increased nutrient intake and accelerated rates of body weight gain during specific periods of heifer growth facilitate pubertal development by programming hypothalamic

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65

FIG. 4.3 Calving distribution of cows exposed to a 7-d CO-Synch þ CIDR estrus synchronization protocol followed by

fixed-time artificial insemination 60e66 h after CIDR removal. Panel A: These results are from herds that had pregnancy rates greater than 50%. Eighty-eight percent of the cows calved within the first 30 days of the previous calving season. Panel B: These results are from herds that had pregnancy rates of less than 50%. Only 44% of the cows calved within the first 30 days of the previous calving season.

centers that regulate the onset of puberty.50 Therefore, feeding high energy diets during specific periods of development can be useful to increase the proportion of B. indicus-influenced

heifers that have reached puberty prior to their first breeding season.56 Combining an intensive nutritional management program with a pharmacological protocol for induction of puberty

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can yield acceptable pregnancy rates in Zebu heifers between 12 and 15 months of age. When B. indicus and B. indicus  B. taurus heifers were fed to reach approximately 300 kg prior to breeding between 12 and 15 months of age and were exposed to a P4 and estradiol-based puberty induction protocol, acceptable pregnancy rates to TAI were observed.57 However, there are currently no effective GnRH-based protocols for ES and TAI in B. indicus beef heifers. The use of short-term protocols, such as the PG 5-d COSynch þ CIDR protocol have been evaluated in B. indicus-influenced replacement heifers.58 Exposing heifers to ES and TAI resulted in a greater proportion of heifers becoming pregnant within the first 21 days of the breeding season; however, others have reported inconsistent results when utilizing the same protocol for B. indicus heifers.59 Therefore, there is a need for the development of ES strategies tailored specifically for B. indicus-influenced heifers that do not rely on estradiol-based products. There are several ES and TAI protocols for use in mature B. indicus beef cows; however, these protocols rely on the use of estradiol and often require the use of eCG or temporary calf removal. Because estradiol and eCG products are not commercially available for cattle producers in the US, there are limited alternatives available for ES and TAI in cows with a large B. indicus influence. The PG 5-d COSynch þ CIDR protocol is the only GnRHbased protocol currently recommended for TAI in mature beef cows that does not rely on estradiol-based products. This protocol is similar to the 5-d CO-Synch þ CIDR widely utilized in B. taurus animals; however, a PGF injection is given at the beginning of the protocol in conjunction with the first GnRH, and a second injection of PGF is administered 8 h after the first at CIDR removal. Additionally, TAI is performed 66 h after CIDR removal rather than at 72 h. The rationale behind the PGF injection at CIDR insertion is based on data indicating that B. indicus females appear to be more sensitive to the effects of P4 on

gonadotropin release.60,61 Hence, decreasing concentrations of P4 may facilitate follicular development, and consequently improve pregnancy rates in these females. The value of elimination of the initial GnRH in the PG 5d CO-Synch þ CIDR protocol is under investigation.62 Removing the initial GnRH injection may allow for the elimination of the second PGF injection 8 h after CIDR removal; however, limited data are available for the recommendation of the later strategy. In summary, the combination of pharmacological hormonal supplementation and adequate nutritional management, together with continuous genetic selection for early maturing heifers, have the potential to impact reproductive efficiency of beef females raised in tropical and subtropical regions.

Multiple ovulation embryo transfer The utilization of embryo transfer is an additional opportunity for genetic improvement in a cattle operation. Through embryo transfer, a single, genetically superior female is able to generate a greater number of offspring than through a conventional system, and when coupled with spermatozoa from a genetically outstanding sire, embryos of exceptional genetic quality can be produced. In addition, recipient females of poor or average genetic merit have the opportunity to serve as surrogates and receive an embryo with high genetic value and give birth to calves with greater genetic merit. Through embryo transfer, genetic progress can be hastened, which is particularly useful in cattle due to their relatively long generation interval when compared to other livestock species. Another great advantage of this technology is the ability to transport embryos to areas where biotechnologies for the production of beef need to be advanced, instead of having to transport live animals themselves. Since 1951, when the first calf was produced by embryo transfer, biotechnologies have evolved to allow embryo transfer to take place

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in a commercial setting.63 Initially, embryos were transferred using a surgical procedure in which the uterine horn was exteriorized and the embryo was transferred into the lumen. However, improvements now allow for successful transcervical transfer of bovine embryos.64 In current MOET protocols, donor females are superovulated using FSH, subjected to AI, and a number of embryos are recovered through a uterine flushing technique. Following collection, viable embryos are either transferred fresh to recipient females or frozen for future use. On average 6.9 viable embryos are recovered per uterine flushing in beef females65; however, this number fluctuates depending on breed and age of cow, as well as within breed variation. The transfer of fresh embryos typically yields 10%e15% greater pregnancy rates than those using frozen-thawed embryos.66,67 According to the annual statistical survey of the Data Retrieval Committee for the International Embryo Transfer Society (IETS), the number of fresh and frozen bovine embryos transferred has increased exponentially from 200,000 in 2008, to more than 400,000 in 2017.65 Of those embryos, 52% were transferred in dairy cattle, and 48% in beef cattle. Furthermore, the number of embryo transfers performed globally increased from 361,000 in 1997 to 506,000 in 2012 (Fig. 4.4).22 Overall, the success of embryo transfer depends on a variety of factors associated with the embryo, the recipient, the embryo transfer technician, or an interaction among those factors.68 Suitability of recipients is dependent on several management, nutritional, and estrous cycle control factors to ensure the presence of a functional CL at the time of embryo transfer.68 It is necessary to control the stage of the estrous cycle in order to achieve acceptable synchrony between donors and recipients of the embryos. Development of ES has allowed this synchrony between uterus and embryo to be established, reduced the amount of recipients required, and increased the ease of incorporation of embryo transfer into cattle operations. In addition, ES

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FIG. 4.4 The number of embryos transferred that were produced per year by in vivo and in vitro techniques (IETS Data Retrieval Committee Reports; https://www.iets.org/ accessed December 5, 2018).

has enabled the use of fixed-time embryo transfer (FTET), which eliminates the need for estrus detection in recipient cows. Pregnancy establishment is most successful when embryos are transferred into estrus synchronized cows 6e8 days after being detected estrus or GnRH injection.69 Superovulation protocols increase the number of oocytes ovulated and fertilized to produce multiple embryos per estrous cycle. Currently, the recommended superstimulatory protocol for B. taurus donors involves inserting a CIDR on day 0, followed by injection of 100 mg GnRH 2 days later. Beginning on day 4, donors receive injections of FSH every 12 h. The amount of FSH per injection decreases each day until day 7, resulting in a total of 8 injections and a total of 400 mg of FSH. On day 7, donors receive 2 injections of PGF2a 12 h apart (AM/PM). At the time of the second PGF2a injection, the CIDR insert is removed, and heat detection begins 24 h after CIDR removal and continues until day 11. Donors that are detected in estrus during this period are AI at both 12 and 24 h after onset of estrus. Embryos are flushed 7 days after AI.70 A major limitation to the production of embryos from MOET has been the lack of reliability in successfully inducing superovulation in donor females.71 However, research into the

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development of superovulation protocols and techniques to predict which donor females may respond well to superovulation is ongoing.

In vitro fertilization An alternative to embryos derived from MOET is the production of embryos in a laboratory via in vitro maturation (IVM) and in vitro fertilization (IVF) of oocytes followed by in vitro embryo culture (IVC), which are collectively referred to as IVM/IVF. The first successful generation of live offspring by IVM/IVF was achieved in rabbits in 1959.72 From then onwards, IVM/IVF technology has improved drastically to include development of embryo cryopreservation, which was first successful in 1972 with mouse embryos, and a year later with bovine embryos.73 Following cryopreservation, the need for in vitro sperm capacitation was demonstrated and led to the birth of the first live calf from IVF using fresh semen in 1981.74 Two years later IVM/IVF embryos were successfully generated using frozen semen.75 During the IVF process, oocyte maturation is required such that oocytes complete their first meiotic division.76 Similarly, spermatozoa used for IVF need to undergo capacitation before they are able to fertilize the oocyte.77 Oocytes that mature spontaneously in vitro or in vivo are highly receptive to fertilization. However, oocytes matured in vitro have reduced developmental capacities in comparison to those matured in vivo.76 In addition, the viability of IVF-derived embryos decreases with cryopreservation to a greater extent than in vivo-derived embryos; therefore, these embryos are more likely to be transferred fresh.73 The predominant oocyte collection technique is known as aspiration or ovum pick up (OPU). Through OPU, unfertilized oocytes can be harvested directly from the ovarian follicles of a donor cow or heifer using an ultrasound probe and an aspiration needle. This technique may be performed two to three times during a

cow’s estrous cycle for as long as 6 months,78 which is more frequent than what MOET can be performed. Therefore, a greater number of transferrable embryos per donor can be generated through IVM/IVF than through MOET. As improvements to IVM/IVF techniques are made, costs to generate embryos will likely be reduced, leading to a greater increase in the adoption of IVM/IVF produced embryos. Over the past 15 years the number of in vitroproduced embryos has increased by more than 300% (Fig. 4.4),22 and according to the IETS (2017), 58% of the embryo transfers performed in 2016 were with embryos generated through IVM/IVF. In vitro fertilization may generate pregnancies from a donor female that is already pregnant, and requires fewer units of semen. Furthermore, oocytes can be collected from the antral follicles of ovaries obtained from slaughter facilities, which greatly increases the number of embryos that can be produced, and can eliminate the need for donor females. Finally, the potential disadvantage of a poor response to a superovulation protocol can be avoided by utilizing IVM/IVF.

Sex-sorted semen One of the more recent biotechnologies used in beef cattle operations is that of sex-sorted or sexed semen. Through flow cytometry, sperm cells carrying either an X (X-sperm) or Y chromosome (Y-sperm) are separated based on DNA content, where X-sperm contain approximately 4% more DNA than Y-sperm.79 Flow cytometry was first developed in the early 1980s; however, it produced de-membraned, unviable sperm. By 1989 the procedure had been refined and was able to sort sperm cells without killing or severely damaging them, and in 1991 the sorting procedure was patented by the United States Department of Agriculture.79 The first live birth from sexed semen was in 1989 when rabbits were surgically inseminated,80 and the first calves were produced using sexed semen by

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Conclusion

nonsurgical AI in 1997.81 In 2003, commercial use of sexed semen accelerated when Sexing Technologies Inc. (Navasota, TX) was granted a sorting license.82 Since 2007, the commercialization of sexsorted semen has drastically increased due to enhanced equipment and improvements in processing procedures. During the sorting procedure, sperm cells are stained using a fluorescent dye, Hoechst 33342, which penetrates the sperm membranes and binds to DNA. A laser provides a wavelength of light that causes sperm cells to fluoresce, and a computer will detect and analyze the amount of fluorescence given off. X-chromosome bearing sperm give off approximately 4% more fluorescence than Y-sperm, because they have an additional 4% of DNA. X- and Y-sperm with varying levels of fluorescence are given different electrical charges that allows them to be sorted into different containers when passing between electrical fields.83 There are a number of benefits associated with the utilization of sex-sorted semen, such as selecting calf gender with greater than 90% accuracy, faster genetic progress, and the removal of defective sperm through the sorting process.79 In addition, it is easy to incorporate the use of sexed semen into a management system if AI is already being performed, as it will not change the workflow. However, there are disadvantages that can hamper the adoption of this technology. First, pregnancy rates are generally lower when using sexed semen compared to conventional semen, and are usually in the range of 80%e90% of those from conventional semen.79,82 This reduction in fertility is one of the largest hindrances to its use in beef cattle, and is largely due to a lower post-thaw motility, a reduced number of sperm cells with intact membranes, and acrosomal alterations that can occur during the sorting process.84 Another disadvantage is that sexed semen is more expensive than conventional semen, which limits its economic feasibility. Last, there are currently no official TAI protocols established specifically for the use of

sex-sorted semen, which limits its adoption in the beef industry. Therefore, opportunities exist to optimize pregnancy rates to TAI by developing protocols specifically for the use of sex-sorted semen. The primary utilization of sexed semen is in the dairy industry to generate heifer calves that are able to replace cows for milk production. In the beef industry, sexed semen is used to produce replacement females, and to produce males for the production of beef, since bulls and steers are more efficient at converting feed to muscle. Purebred operations use sexed semen to generate progeny of the desired sex, such as bulls from superior sires or daughters from elite cows. Sex-sorted semen can be used in conjunction with IVF to produce embryos of a desired sex. Combining sexed semen with IVM/IVF has achieved pregnancy rates greater than those of sexed semen used in combination with TAI.85 Although sexed semen is currently utilized in the beef industry, large-scale adoption will require TAI protocols that result in acceptable pregnancy rates. In the past, TAI with sexed semen has resulted in pregnancy rates between 47% and 70% of those from conventional semen.86 However recent improvements in the semen sorting procedure has yielded pregnancy rates of approximately 87% of those from conventional semen.87 Advancements to sperm sexing technologies are continually being made. Together with genetic selection, the use of sexed semen is a good strategy to produce genetically superior animals of the desired sex. As costs decline, and as greater pregnancy rates are achieved, sexed sperm may be increasingly adopted in the beef cattle industry.

Conclusion Through the incorporation of reproductive management strategies and biotechnologies, there is potential to improve reproductive efficiency, genetic quality, and animal performance

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in the beef cattle industry. The level of incorporation of these strategies will depend on future environmental conditions, production systems, ease of use, infrastructure, and cattle markets; however, these technologies will allow producers to maximize the potential of available resources, and aid in the production of sufficient animal protein to provide for the expanding global population.

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un Gregory KE, Koch RM. Lifetime production of beef heifers calving first at two vs three years of age. J Anim Sci. 1991;69(9):3467e3479. https://doi.org/ 10.2527/1991.6993467x. 5. Nunez-Dominquez R, Cundiff LV, Dickerson GE, Gregory KE, Koch RM. Effects of managing heifers to calve first at two vs three years of age on longevity and lifetime production of beef cows. In: Beef Research Program Progress Report 2. Ames: USDA-ARS; 1985:33e35. 6. Burris MJ, Priode BM. Effect of calving date on subsequent calving performance. J Anim Sci. 1958;17(3): 527e533. https://doi.org/10.2527/jas1958.173527x. 7. Lesmeister JL, Burfening PJ, Blackwell RL. Date of first calving in beef cows and subsequent calf production. J Anim Sci. 1973;36(1):1e6. https://doi.org/10.2134/ jas1973.3611. 8. Amstalden M, Cardoso RC, Alves BRC, Williams GL. Reproduction Symposium: hypothalamic neuropeptides and the nutritional programming of puberty in heifers. J Anim Sci. 2014;92(8):3211e3222. https:// doi.org/10.2527/jas2014-7808. 9. Perry GA. Factors affecting puberty in replacement beef heifers. Theriogenology. 2016;86(1):373e378. https:// doi.org/10.1016/j.theriogenology.2016.04.051. 10. Short RE, Bellows RA, Staigmiller RB, Bernardinelli JG, Custer EE. Physiological mechanisms controlling anestrus and infertiliy in postpartum beff cattle. J Anim Sci. 1990;68: 799e816. https://doi.org/10.2527/1990.683799x. 11. Yavas Y, Walton JS. Postpartum acyclicity in suckled beef cows: a review. Theriogenology. 2000;54(1):25e55. https://doi.org/10.1016/S0093-691X(00)00323-X. 12. Day ML. Hormonal induction of estrous cycles in anestrous Bos taurus beef cows. Anim Reprod Sci. 2004;

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24. Leupp JL, Lardy GP, Daly R, Wright CL, Paterson JA. Factors influencing price of North Dakota, South Dakota and Montana feeder calves. NDSU Beef Cattle Range Res Rep. 2008:46e49. 25. Foote RH. The history of artificial insemination: selected notes and notables. J Anim Sci. 2002;80(E-suppl_2):1e10. 26. NAAB. reportAnnual Reports of Semen Sales and Custom Freezing. Semen Sales. 27. Lamb GC, Dahlen CR, Larson JE, Marquezini G, Stevenson JS. Control of the estrous cycle to improve fertility for fixed-time artificial insemination in beef cattle: a review. J Anim Sci. 2010;88(13 suppl l). https:// doi.org/10.2527/jas.2009-2349. 28. Harris DL, Newman S. Breeding for profit: synergism between genetic improvement and livestock production (A review). J Anim Sci. 1994;72:2178e2200. 29. Pruzzo L, Cantet RJC, Fioretti CC. Risk-adjusted expected return for selection decisions. J Anim Sci. 2003; 81(12):2984e2988. 30. Lamb GC, Larson JE, Geary TW, et al. Synchronization of estrus and artificial insemination in replacement beef heifers using gonadotropin-releasing hormone, prostaglandin F2a, and progesterone. J Anim Sci. 2006;84(11):3000e3009. https://doi.org/10.2527/jas. 2006-220. 31. Lamb GC, Mercadante VRG. Synchronization and artificial insemination strategies in beef cattle. Vet Clin North Am - Food Anim Pract. 2016;32(2):335e347. https://doi.org/10.1016/j.cvfa.2016.01.006. 32. National Animal Health Monitoring System - United States Department of Agriculture. Dairy Herd and Management Practices Ob U. S. Dairy Operations. 2014. 33. Macmillan KL, Thatcher WW. Effects of an agonist of gonadotropin-releasing hormone on ovarian follicles in cattle. Biol Reprod. 1991;45(6):883e889. https:// doi.org/10.1095/biolreprod45.6.883. 34. Pursley JR, Mee MO, Wiltbank MC. Synchronization of ovulation in dairy cows using PGF2a and GnRH. Theriogenology. 1995;44(7):915e923. https://doi.org/ 10.1016/0093-691X(95)00279-H. 35. Burke JM, De La Sota RL, Risco CA, Staples CR,
Thatcher WW. Evaluation of timed insemiSchmitt EJP, nation using a gonadotropin-releasing hormone agonist in lactating dairy cows. J Dairy Sci. 1996;79:1385e1393. https://doi.org/10.3168/jds.S0022-0302(96)76496-2. 36. Pursley JR, Kosorok MR, Wiltbank MC. Reproductive management of lactating dairy cows using synchronization of ovulation. J Dairy Sci. 1997;80(2):301e306. https://doi.org/10.3168/jds.S0022-0302(97)75938-1. 37. Pursley JR, Wiltbank MC, Stevenson JS, Ottobre JS, Garverick HA, Anderson LL. Pregnancy rates per artificial insemination for cows and heifers inseminated at a synchronized ovulation or synchronized estrus. J Dairy Sci. 1997;80(2):295e300. https://doi.org/10.3168/jds. S0022-0302(97)75937-X.

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38. Bisinotto RS, Ribeiro ES, Santos JEP. Synchronisation of ovulation for management of reproduction in dairy cows. Animal. 2014;8(SUPPL. 1):151e159. https:// doi.org/10.1017/S1751731114000858. 39. Geary TW, Whittier JC, Hallford DM, MacNeil MD. Calf removal improves conception rates to the Ovsynch and CO-Synch protocols. J Anim Sci. 2001;79(1):1e4. https://doi.org/10.2527/2001.7911. 40. Johnson SK. Possibilities with today’s reproductive technologies. Theriogenology. 2005;64(3):639e656. https:// doi.org/10.1016/j.theriogenology.2005.05.033. 41. Johnson SK, Funston RN, Hall JB, et al. Multi-state beef reproduction Task Force provides science-based recommendations for the application of reproductive technologies. J Anim Sci. 2011;89(9):2950e2954. https://doi.org/10.2527/jas.2010-3719. 42. Parcell JL, Dhuyvetter KC, Patterson DJ, Randle R. The value of heifer and calf characteristics in bred heifer price. Prof Anim Sci. 2006;22(3):217e224. https:// doi.org/10.15232/S1080-7446(15)31097-4. 43. Funston RN, Musgrave JA, Meyer TL, Larson DM. Effect of calving distribution on beef cattle progeny performance. J Anim Sci. 2012;90:5118e5121. https:// doi.org/10.2527/jas2012-5263. 44. Cushman RA, Kill LK, Funston RN, Mousel EM, Perry GA. Heifer calving date positively influences calf weaning weights through six parturitions. J Anim Sci. 2013;91(9):4486e4491. https://doi.org/10.2527/ jas2013-6465. 45. Moss GE, Crowder ME, Nett TM. GnRH-receptor interaction. VI. Effect of progesterone and estradiol on hypophyseal receptors for GnRH, and serum and hypophyseal concentrations of gonadotropins in ovariectomized ewes. Biol Reprod. 1981;25(5):938e944. https://doi.org/10.1095/biolreprod25.5.938. 46. Wiltbank MC, G€ umen A, Sartori R. Physiological classification of anovulatory conditions in cattle. Theriogenology. 2002;57(1):21e52. https://doi.org/ 10.1016/S0093-691X(01)00656-2. 47. Williams GL. Suckling as a regulator of postpartum rebreeding in cattle: a review. J Anim Sci. 1990;68(3): 831e852. 48. Baruselli PS, Reis EL, Marques MO, Nasser LF, B
o GA. The use of hormonal treatments to improve reproductive performance of anestrous beef cattle in tropical climates. Anim Reprod Sci. 2004;82e83:479e486. https://doi.org/10.1016/j.anireprosci.2004.04.025. 49. Nogueira GP. Puberty in south American Bos indicus (zebu) cattle. 2004;83:361e372. https://doi.org/ 10.1016/j.anireprosci.2004.04.007. 50. Cardoso RC, Alves BRC, Williams GL. Neuroendocrine signaling pathways and the nutritional control of puberty in heifers. Anim Reprod. 2018;15(Suppl. 1): 868e878. https://doi.org/10.21451/1984-3143-AR20180013.

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51. J
unior IC, Sa Filho OG, Peres RFG, Aono FHS, Day ML. Reproductive performance of prepubertal Bos indicus heifers after progesterone-based treatments. Theriogenology. 2010;74(6):903e911. https://doi.org/ 10.1016/j.theriogenology.2010.04.015. 52. Rodrigues ADP, Peres RFG, Lemes AP, et al. Progesterone-based strategies to induce ovulation in prepubertal Nellore heifers. Theriogenology. 2013;79(1): 135e141. https://doi.org/10.1016/j.theriogenology. 2012.09.018. 53. Rodrigues ADP, Peres RFG, Lemes AP, et al. Effect of interval from induction of puberty to initiation of a timed AI protocol on pregnancy rate in Nellore heifers. Theriogenology. 2014;82(5):760e766. https://doi.org/ 10.1016/j.theriogenology.2014.06.008. 54. Meneghetti M, Filho OGS, Peres RFG, Lamb GC, Vasconcelos JLM. Fixed-time artificial insemination with estradiol and progesterone for Bos indicus cows I: basis for development of protocols. Theriogenology. 2009;72(2):179e189. https://doi.org/10.1016/j.therio genology.2009.02.010. 55. Peres RFG, J
unior IC, Filho OGS, Nogueira GP, Vasconcelos JLM. Strategies to improve fertility in Bos indicus postpubertal heifers and nonlactating cows submitted to fixed-time artificial insemination. Theriogenology. 2009;72(5):681e689. https://doi.org/ 10.1016/j.theriogenology.2009.04.026. 56. Cardoso RC, Alves BRC, Prezotto LD, et al. Use of a stair-step compensatory gain nutritional regimen to program the onset of puberty in beef heifers. J Anim Sci. 2014;92(7):2942e2949. https://doi.org/10.2527/ jas.2014-7713. 57. Day ML, Nogueira GP. Management of age at puberty in beef heifers to optimize efficiency of beef production. Anim Front. 2013;3:6e11. https://doi.org/ 10.2527/af.2013-0027. 58. Oosthuizen N, Fontes PLP, Sanford CD, et al. Estrus synchronization and fixed-time artificial insemination alter calving distribution in Bos indicus influenced beef heifers. Theriogenology. 2018;106: 210e213. https://doi.org/10.1016/j.theriogenology. 2017.10.028. 59. Yelich JV, Bridges GA. Synchronization response: Bos taurus vs. Bos indicus cattle. Spring. 2012;1:1e12. 60. Randel RD. Seasonal effects on female reproductive functions in the bovine (Indian breeds). Theriogenology. 1984;21(1):170e185. https://doi.org/10.1016/0093691X(84)90315-7. 61. Carvalho JBP, Carvalho NAT, Reis EL, Nichi M, Souza AH, Baruselli PS. Effect of early luteolysis in progesterone-based timed AI protocols in Bos indicus, Bos indicus  Bos taurus, and Bos taurus heifers. Theriogenology. 2008;69(2):167e175. https://doi.org/ 10.1016/j.theriogenology.2007.08.035.

62. Scarpa JO, O’Neil MM, Cardoso RC, Stanko RL, Williams GL. Follicle dynamics and fertility at fixedtime AI of Bos indicus-influenced beef cows synchronized with the 5-Day Bee Synch þ Cidr protocol with or without GnRH on day 0. J Anim Sci. 2017;95(suppl_4): 234. https://doi.org/10.2527/asasann.2017.479, 234. 63. Willett EL, Black WG, Casida LE, Stone WH, Buckner PJ. Successful transplantation of a fertilized bovine ovum. Science. 1951;113:247. 64. Betteridge KJ. A history of farm animal embryo transfer and some associated techniques. Anim Reprod Sci. 2003; (79):203e244. https://doi.org/10.1016/S0378-4320(03) 00166-0. 65. American Embryo Transfer Association. reportAnnual Report of the AETA Statistics Committee for Calendar Year 2017. 66. Leibo SP. Commercial production of pregnancies from One-Step diluted frozen-thawed bovine embryos. Theriogenology. 1986;25(1):166. 67. Sreenan JM, Diskin MG. Factors affecting pregnancy rate following embryo transfer in the cow. Theriogenology. 1987;27(1):99e113. https://doi.org/ 10.1016/0093-691X(87)90073-2. 68. Lamb GC, Mercadante VRG. Selection & management of the embryo recipient herd for embryo transfer. In: Hopper RM, ed. Bovine Reproduction. 2014:723e732. 69. B
o GA, Baruselli PS, Moreno D, et al. The control of follicular wave development for self-appointed embryo transfer programs in cattle. Theriogenology. 2002;57:53e72. 70. Lamb GC, Mercadante VRG, Fontes PLP. Donor and recipient management to optimize embryo technology success. Applied Reprod Strategies Beef Cattle. 2016:197e209. 71. Betteridge KJ. Farm animal embryo technologies: achievements and perspectives. Theriogenology. 2006; 65(5):905e913. https://doi.org/10.1016/j.therio genology.2005.09.005. 72. Hasler JF. Forty years of embryo transfer in cattle: a review focusing on the journal Theriogenology, the growth of the industry in North America, and personal reminisces. Theriogenology. 2014;81(1):152e169. https:// doi.org/10.1016/j.theriogenology.2013.09.010. 73. Palasz AT, Mapletoft RJ. Cryopreservation of mammalian embryos and oocytes: recent advances. Biotechnol Adv. 1996;14(2):127e149. 74. Brackett B, Bousquet D, Boice M, Donawick W, Evans J, Dressel M. Normal development following in vitro fertilization in the cow. Biol Reprod. 1982;1:147e158. 75. Bondioli KR. In vitro fertilization of bovine ooctytes by spermatozoa capacitated in vitro. J Anim Sci. 1983;57(4): 1001e1005. 76. Leibfried-Rutledge ML, Critser ES, Eyestone WH, Northey DL, First NL. Development potential of bovine oocytes matured in vitro or in vivo. Biol Reprod. 1987;36: 376e383.

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77. Parrish JJ, Susko-Parrish JL, Leibfried-Rutledge ML, Critser ES, Eyestone WH, First NL. Bovine in vitro fertilization with frozen-thawed semen. Theriogenology. 1986; 25(4):591e600. 78. Greve T, Madison V. In vitro fertilization in cattle: a review. Reprod Nutr Dev. 1991;31:147e157. 79. Seidel GE. Update on sexed semen technology in cattle. Animal. 2014;8(s1):160e164. https://doi.org/10.1017/ S1751731114000202. 80. Johnson LA, Flook JP, Hawk HW. Sex preselection in rabbits: live births from X and Y sperm separated by DNA and cell sorting. Biol Reprod. 1989;41:199e203. 81. Seidel GE, Allen CH, Johnson LA, et al. Uterine horn insemination of heifers with very low numbers of nonfrozen and sexed spermatozoa. Theriogenology. 1997;48(8):1255e1264. https://doi.org/10.1016/S0093691X(97)00368-3. 82. DeJarnette JM, Nebel RL, Marshall CE. Evaluating the success of sex-sorted semen in US dairy herds from on farm records. Theriogenology. 2009;71(1):49e58. https://doi.org/10.1016/j.theriogenology.2008.09.042. 83. Seidel GE. Overview of sexing sperm. Theriogenology. 2007;68(3):443e446. https://doi.org/10.1016/j.therio genology.2007.04.005.

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84. Carvalho JO, Sartori R, Machado GM, Mour~ao GB, Dode MAN. Quality assessment of bovine cryopreserved sperm after sexing by flow cytometry and their use in in vitro embryo production. Theriogenology. 2010;74(9):1521e1530. https://doi.org/10.1016/ j.theriogenology.2010.06.030. 85. Pellegrino CAG, Morotti F, Untura RM, et al. Use of sexed sorted semen for fixed-time artificial insemination or fixed-time embryo transfer of in vitroe produced embryos in cattle. Theriogenology. 2016;86(3): 888e893. https://doi.org/10.1016/j.theriogenology. 2016.03.010. 86. Thomas JM, Lock SL, Poock SE, Ellersieck MR, Smith MF, Patterson DJ. Delayed insemination of nonestrous cows improves pregnancy rates when using sexsorted semen in timed artificial insemination of suckled beef cows. J Anim Sci. 2014;92(4):1747e1752. https:// doi.org/10.2527/jas2013-7131. 87. Thomas JM, Locke JWC, Vishwanath R, et al. Effective use of SexedULTRATM sex-sorted semen for timed artificial insemination of beef heifers. Theriogenology. 2017; 98:88e93. https://doi.org/10.1016/j.theriogenology. 2017.03.018.

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C H A P T E R

5 Nutrition, feeding and management of beef cattle in intensive and extensive production systems Tim A. McAllistera, Kim Stanfordb, Alex V. Chavesc, Priscilla R. Evansc, Eduardo Eustaquio de Souza Figueiredod, Gabriel Ribeiroe a

Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre, Lethbridge, AB, Canada; bAlberta Agriculture and Forestry, Agriculture Centre, Lethbridge, AB, Canada; cSchool of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW, Australia; d Department of Food and Nutrition, Federal University of Mato Grosso, Brazil, Cuiaba, MT; eDepartment of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada

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Confined production systems Cow/calf production Backgrounding Finishing Grains and by-product feeds Forage sources and processing Feed additives and growth promoters

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Extensive production systems Temperate versus tropical climates Maximizing forage production and quality

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Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00005-7

Extensive, semi-intensive, and intensive pasture systems Grazing management Pasture supplementation Cow/calf, backgrounding, and finishing cattle on pasture

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85 86 88 90

Nutrient management in beef cattle production systems Confined systems Extensive systems

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Implications of climate change

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Conclusion

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Introduction The global population of cattle is z 1.0 billion with the largest populations being in India, Brazil and China.1 Approximately 290 million head of cattle are slaughtered each year, with the United States, Brazil and the European Union producing 48% of the world’s beef.1 The demand for beef is predicted to increase by 16% between 2017 and 2027, with the majority of this increase occurring in developing countries.2 Estimating beef cattle numbers can be challenging, as population estimates often include dairy cattle and veal, which ultimately contribute to the meat supply. This is particularly challenging in areas of the world such as the European Union, where a large portion of the herd is dual purpose, being used equally for meat and milk production. Among livestock production systems, beef cattle production is unique, as production often involves the simultaneous use of both intensive production, in the form of feedlots, and extensive production where cow-calf populations graze tame pasture and rangelands (Fig. 5.1). In the future, the growing demand for beef will likely be met through a process of sustainable intensification, as the availability of grazing lands for extensive production is limited. Climate change-driven processes, like desertification, expansion in croplands and urbanization may further reduce the availability of grazing land in the future. This intensification is already underway in many regions of the world, as evidenced by the increasing capital-intensive investment in beef cattle production in North America, China, Brazil and Australia. The shift

References

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from extensive to intensive beef cattle production systems will require adjustments in nutrition, feeding and management practices. This chapter focuses on aspects of beef cattle production and nutrition under both confined (intensive) and grazing (extensive) production conditions.

Confined production systems Cow/calf production Cows and calves are often managed in confinement in many production systems during periods of drought, in the aftermath of a fire, for protection from predators, or for increased observation and potential intervention during calving. In parts of the world where seasonal supplemental feeding is required, pregnant and/or lactating beef cows are often confined and fed in small pastures at stocking rates that exceed the amount of forage available. More recently, in response to increased land values, urban encroachment or long-term drought, managing beef cattle under continuous confinement in dry lot has been proposed4 and already implemented to a limited extent in North America and in China. Research evaluating management of cows and calves in total confinement is limited.5 Comparing the costs and returns of a cow herd managed continuously in confinement for three years to a traditional pasture-based system, it was noted that while cattle performance does not differ between the systems, production costs were increased by approximately $22 per cowcalf pair raised in confinement.6 Rearing of cattle

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FIG. 5.1 An example of a beef cattle production system that involves the use of both extensive and intensive management production systems. Native and tame pasturelands and conserved forages are the primary feeds provided to the cow herd. Backgrounding cattle are either fed conserved forage-based diets or returned to pastureland when forage is available in the following spring. Finishing cattle are intensively fed high grain diets so as to promote an increase in the fat content and the marbling of beef. The duration that cattle are fed in confined finishing systems depends on their weight when they enter the system, but typically ranges from 60 to 140 days. Adapted from Legesse G, Beauchemin K, Ominski K, et al. Greenhouse gas emissions and resource use of Canadian beef production in 1981 as compared to 2011. Anim Prod Sci. 2015;AN15386.

requires additional investment in infrastructure for shelter, feed delivery, bedding and manure management. On a positive note, energy requirements of the cow may be reduced, as cattle spend less energy to seek out feed and body

condition can be more effectively managed throughout the year, as measured amounts of feed can be provided.6 If forages are scarce, low-cost by-products or crop residues can be fed with restricted amounts of grain and

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supplements. Confinement rearing also enables early weaning of calves, with subsequent benefits to early breeding of heifers, rebreeding of cows and growth of calves.7 Confinement management can also reduce losses from predation and inclement weather, provided that adequate drainage and bedding are supplied. However, rearing calves in dry lots does generate additional management issues. In confinement, the risk of lethal or disabling injury to calves from contact with older cattle is dramatically increased.4 During parturition, cows are not able to isolate themselves from the herd, which can reduce the maternal bond between cows and calves and result in decreased colostrum intake.8 Decreased intake of colostrum by calves increases the risk of infectious disease, as does the accumulation of manure and increased housing density, as these conditions promote the transmission of pathogens within the herd. Smith9 proposed “The Sandhills Calving System” as an approach to reduce transmission of scour-associated bacteria and viruses among newborn calves with their dams in confined pastures. In this system, calves are segregated by age and pregnant cows are moved just prior to calving to areas that have been unoccupied by cattle for several months. Recently, Burson10 compared health outcomes of 250 calves reared in a conventional pasture-based system, typical dry-lot confinement or dry-lot confinement using the Sandhills Calving System. Calf morbidity and mortality were dramatically increased in both confinement treatments, as compared to pasture-reared calves. Total morbidity was 2.5% in pasture-reared as compared to 68.5% and 47.4% in the traditional and Sandhills confinement systems, respectively. Correspondingly, total mortality of calves was 3.6% in the pasture-based systems, as compared to > 20% in confined systems. Although the Sandhills management system did reduce calf morbidity,10 mortality rates were still high compared to pasture-based systems.

Due to concern over antimicrobial resistance, management systems which reduce use of antimicrobials promote the social license of beef production. The greater incidence of disease in confined cow-calf production increases reliance on antimicrobials, as compared to more extensive calving systems. As well, increasing injury, mortality, and morbidity of calves in confinement raises animal welfare concerns. Dairy cattle are commonly managed in confinement, with a 10% death loss in dairy heifer calves reported in the USA11 Dairy calves are often housed and fed individually, so as to reduce the risk of pathogen transmission and enable the intake of individuals to be monitored. However, maintaining beef calves on milk replacer in individual housing systems fails to utilize the milk produced by the cow and would be uneconomical. If confinement cow-calf rearing is the future of beef production, new management practices to reduce injury to calves and the transmission of infectious agents are required. Providing areas where only calves may access highly palatable creep-feed is perhaps a partial solution, as creep-feed would improve calf growth performance and calves could safely rest within the restricted area.

Backgrounding Backgrounding cattle in confinement is especially common in temperate regions of the world where forages are not available year-round. In this system, weaned calves are managed in confined dry lots that usually house from 1,000 to 10,000 head and are fed total mixed rations where 40%e60% of the dry matter is hay or silage. The remainder of the diet is composed of grains or by-products and a pelleted or mash supplement which is composed of a grain-based carrier alongside vitamins, minerals and dietary additives. The supplement usually accounts for 5%e7% of the dietary dry matter. In areas where year-round grazing is possible,

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Confined production systems

confined backgrounding may be used to take advantage of abundant by-product feeds, such as distillers’ grains from ethanol production and oilseed meal, arising from plant oil production or vegetable waste. Backgrounded cattle generally require time to adapt to feed bunks and watering systems. High forage diets promote frame and muscle growth, while avoiding excess fat deposition. Locating watering systems in the middle of the pen can help calves find water upon arrival,12 although the water and feed intake of calves can be reduced for up to two weeks or more as they adapt from pasture to confinement.13 To encourage feed intake of calves upon entry to the feedlot, it is desirable for diets to be similar to those previously consumed, although this can be challenging if calves arrive from multiple herds or originate from pastures. Depending on local market requirements, castration of bulls may be necessary and is a major source of stress. Even with repeated intramuscular injections of an analgesic to control pain, 6 month old bull calves had reduced average daily gain and feed intake for the first week after surgical castration and for up to 4 weeks after band castration.14 Calves that are healthy and not highly stressed may receive TABLE 5.1

vaccinations and be de-horned and/or castrated immediately upon arrival at a backgrounding feedlot. Sewell et al.15 suggested that induction procedures should be delayed for a day or more to allow calves to consume feed and water and to avoid increased rates of stress-induced morbidity early in the feeding period. As calves may be highly stressed by weaning and transport (Table 5.1), morbidity and mortality arising from bovine respiratory disease (BRD) is of major concern during the first 40e60 d of backgrounding.16 Bovine respiratory disease is responsible for 65%e80% of morbidities and 45%e75% of mortalities in feedlot cattle and the heightened use of antimicrobials of greatest relevance to human health.17 Co-mingling calves during purchase from auction markets18 or when filling pens at the feedlot12 can increase the risk of BRD. Pre-conditioning by weaning, vaccination and familiarizing calves with feed bunks and waterers at least 45 days in advance of transport reduces the risk of BRD.19 However, this practice is uncommon due to a lack of clearly-established price premiums for preconditioned calves. Inclement weather during the first week after entry to the feedlot may compound BRD risk in calves already showing signs of morbidity.20 Similarly, contracting BRD may

Impacts of common stressors on the health and growth performance of backgrounding calves.

Stressor

Impacts

Reference

Transport

reduced gain first 2 days

13

Weaning at transport

increased BRD, reduced gain for 42 days

18

Castration

Reduced feed intake and gain for 1e4 weeks

14

Diet change

Reduced feed intake first week

23

New environment

Up to 30% decrease in feed intake, first 4 days

24

Co-mingling calves

Increased BRD, reduced gain for 42 days

18

Bad weather

Reduced gain, increased BRD, mortality

20

Bovine respiratory disease

Reduced calf growth performance for feeding period, reduced carcass value

16

Multiple

1% mortality for pre-conditioned calves compared to 11% mortality for calves exposed to multiple stressors.

25

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exacerbate reductions in feed intake upon entry of calves to the feedlot.18 To reduce incidence of BRD, more than half of high-risk calves receive metaphylactic antimicrobials at induction to North American feedlots.21 High-risk calves include those sourced from auctions, weaned at transport or otherwise highly stressed. However, with greater restrictions on antimicrobials use in livestock,22 alternatives are needed to maintain the health of backgrounding calves (see below).

Finishing Finishing feedlots are larger than backgrounding feedlots and usually house >10,000 head of cattle, with 150e200 animals per pen (Fig. 5.1). Unlike backgrounding diets, finishing feedlot diets contain high amounts of concentrate feeds (>70%) and are designed to increase both subcutaneous and intramuscular (marbling) fat. To avoid digestive disturbances, like ruminal acidosis and bloat, calves must be carefully transitioned from forage-based to concentrate-based diets during finishing. This process usually requires a series of 2e4 diets, where the amount of concentrate feeds is gradually increased over a period of 2e4 weeks. Reducing the duration of adaptation to less than two weeks can impair the growth performance of cattle.26 This transition period is the time when cattle are at greatest risk of developing acidosis or bloat. When cattle first arrive at finishing feedlots they are typically provided with access to a total mixed ration (TMR) receiving diet, consisting primarily of forage and a smaller proportion of concentrate. Initially, the feed intake of newly arrived cattle can be very low and some cattle may not consume feed.27 The introduction of the final high-concentrate diet is typically withheld until all cattle have settled into confinement and exhibit consistent and stable feed intake. Abrupt diet change from forage to grain has been reported by many researchers to result in ruminal acidosis.7 Even when dietary

concentrate is increased using a step-up approach, increases in concentrate may cause acidosis. On the first day of each transition, low ruminal pH values are common and Klopfenstein et al.28 concluded that during adaptation, it is likely that all cattle experience at least some mild level of acidosis. In contrast, Bevans et al.29 accomplished this same objective using a single diet and encountered only a few cases of clinical acidosis. Others have proposed that subclinical acidosis is mainly caused by the high ruminal concentrations of volatile fatty acids arising from the fermentation of starch.30 Low ruminal pH also reduces the diversity of both bacteria and protozoa within the rumen microbiome,31,32 an outcome that is also associated with a reduction in fiber digestibility.33 A shorter adaptation period to grain-based diets tends to be associated with greater variability in pH among individuals as opposed to an absolute pH decline.29 Under these conditions a small proportion of the herd, typically < 2% may develop clinical acidosis. The risk of clinical acidosis and the occurrence of subclinical acidosis can be reduced by increasing the proportion or lowering the quality of the forage in the diet.34 This serves to reduce the rate of ruminal volatile fatty acid production and stimulates rumination and the production of saliva, which contains sodium bicarbonate that buffers ruminal pH. Skillful feeding management can minimize both the occurrence and severity of acidosis, but as long as feedlot cattle are finished on high-grain diets, acidosis will pose a health risk. A detailed understanding of clinical acidosis has been hampered by its low rate of occurrence and the multitude of factors that contribute to the disease (Fig. 5.2).

Grains and by-product feeds A large portion of the feed in confinement feedlots consist of grains that fail to make the quality grade required for human consumption. For example, in Canada, malt barley commands

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FIG. 5.2 Possible factors and interrelationships affecting acidosis in feedlot cattle. Solid arrows indicate relationships known to exist with a high degree of confidence, whereas dotted arrows represent putative relationships. Adapted from Galyean ML, Eng KS. Application of research findings and summary of research needs - bud britton memorial symposium on metabolic disorders of feedlot cattle. J Anim Sci. 1998;76:323e327.

a price that is 51% greater than feed barley,36 but over 75 % of malt barley fails to make grade and is relegated to feed.37 Barley, wheat, corn and sorghum are the most common grain sources fed to confined backgrounding and finishing cattle. Grains which exhibit the fastest rates of ruminal starch digestion generally pose the greatest risk of causing clinical or subclinical acidosis.38 Starch in wheat, barley and triticale are fermented at a faster rate than the starch in corn and sorghum. Absolute rates of digestion vary among grain types, due to environmental and genetic factors. Variation in the rate of fermentation of dry rolled corn and sorghum varieties is greater than among dry rolled wheat and barley varieties. This variation arises primarily from differences in the ratio of vitreous to floury endosperm within these grains, with vitreous endosperm slowing the rate of ruminal starch fermentation. Grains with the greatest rates of ruminal starch digestion are generally also high in total tract starch digestibility. Grains that exhibit a low rate of ruminal starch digestion can increase the amount of starch available for digestion in the small and large intestines. Starch that escapes ruminal digestion often also resists digestion in the lower digestive tract, increasing the amount of starch in feces. Up to 25% of the dry matter

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of feces can consist of starch in feedlot cattle that are fed poorly-processed grains.39 The Food and Agriculture Organization of the United Nations currently estimates that of worldwide production, 30% of cereals, 45% of roots and tubers, 45% of fruits and vegetables and 20% of oilseeds and pulses are wasted each year due to spoilage.40 Although a large portion of these agricultural products are unsuitable for human consumption, they may still have value as feed for livestock. Ruminants are particularly efficient at using fibrous by-products that arise from food processing and bioenergy production of ethanol and biodiesel. For example, distillers’ grains are rich in energy, protein, and minerals and can safely constitute up to 50% of the diet of confined cattle on a dry matter basis.41 Accordingly, if waste streams could be redirected and feed quality preserved, feed resources currently not used by humans would be sufficient to support an expansion in confinement backgrounding and finishing of beef cattle. However, a number of challenges exist in feeding by-products to cattle. Most by-products are high in moisture, making long-distance transport uneconomical and promoting spoilage during handling and storage. In some instances, by-products can be dried prior to transport, as is the case with dried distillers’ grains, although the use of energy in the drying process increases the price of the by-product. Some high moisture by-products may also be preserved through ensiling, but their incorporation into total mixed diets can be challenging. For example, cull vegetables and wet distillers’ grains can contain 70% e80% water and are subject to freezing in cold climates and spoilage in warm climates. Confinement operations located inproximity to sources of these high moisture by-products are best suited to utilize these feed resources.42 Composition of by-product feeds can vary by source and from lot to lot, or even within the same lot,43 making it difficult to balance the nutrient content of the diet to meet the requirements of cattle. The nutrient composition of

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by-products needs to be frequently measured, so diets can be reformulated as necessary. Industrial by-products with potential as feed are numerous and after their safety, utility and management are confirmed, they could be a valuable feed resource for confined cattle.

Forage sources and processing Forages are the foundational feed in all beef cattle production systems. Even in intensive feedlot production (Fig. 5.1), forages account for 80% of the feed consumed by the cattle herd over the production cycle.3 As grass forages mature, protein and soluble carbohydrate concentration in the dry matter (DM) declines and concentration of lignified fiber (DM-basis) increases, resulting in a decline in the overall quality and digestibility.44 This pattern of change in forage quality with advancing maturity occurs regardless of whether the forage is harvested for silage or hay or grazed directly by the animal. In feedlots, forage is most often included in the diet as silage, with corn, barley, wheat and sorghum being the most common sources. Legumes and grasses can also be ensiled, but are more difficult to ensile than cereals because of their lower water-soluble carbohydrate content and higher buffering capacity. Legume silages also tend to be higher than cereal silages in protein and in confined feedlots, it is often more economical to use by-product protein sources like soybean or canola meal. A dry matter content of between 30% and 40% is optimal for the harvest of forages for silage, as yield of digestible dry matter is maximized. Moisture concentrations are also optimal for microbial fermentation and not too high to promote seepage from the silo. This optimal moisture range usually corresponds to the mid-dough stage in cereals or in corn at about 50% milk line within the kernel. Preserving forage as silage as opposed to hay, expands the harvest window during inclement weather and it is generally easier to mix into a

total mixed ration than dry forage. In larger confined operations, silage is often stored in a pit silo which can contain thousands of tonnes, whereas in smaller feedlots it is more often stored in plastic bags. Starch content of cereal forages is a key indicator of their energy value, and harvesting using a silage chopper equipped with a kernel processor can ensure that the starch in the kernels is available for microbial fermentation in the rumen. Preserving forages as hay requires more favorable weather conditions, as the moisture content of the forage must be reduced to less than 15%. Hay is used primarily as feed for the cow herd, when pasture is unavailable during the winter or during periods of drought. Generally, hay is sun-dried in the field, but it can be dried through the use of drum driers, at a significant increase in cost. Desiccants such as sodium or potassium carbonate can also be used to accelerate the drying of legumes, but not grasses. Both legumes (e.g., alfalfa, clover, cicer milk vetch, sainfoin, birdsfoot trefoil) and grasses (e.g., ryegrass, orchard grass, bromegrass, bluegrass, Tifton 85, elephant) can be conserved as hay, with the predominant species being regionally specific to growing conditions. In high rainfall regions, where it is difficult to achieve forage moisture concentrations of < 15%, buffered acids are often applied to the forage at baling to prevent molds from causing spoilage. Forage can be harvested as large round or square bales, or as small square bales. Hay is often processed using a bale processer prior to feeding to the cow herd, which chops the forage to a finer particle size, a practice that can increase the intake of poor to moderate-quality forages.

Feed additives and growth promoters The majority of additives used in confined beef cattle are used during the backgrounding and finishing stages of production to enhance rumen fermentation, improve feed efficiency and prevent rumen acidosis, liver abscesses

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TABLE 5.2

Some feed additives and growth promoters used to alter ruminal fermentation and improve the efficiency of beef cattle production.50

Feed additive/Promoter Primary rationale for use

Probability of economic return for beef cattle

Method of administration

a

Antibiotics e.g., monensin, tetracycline, tylosin

Decreased feed intake, increased feed efficiency, disease treatment/prevention

High

Feed- variable with antibiotic type

Buffers

Stabilization of rumen pH

Low (Feedlot)

Feed -0.75 e 1% of DM

Bacterial direct fed microbials

Maintains low concentrations of lactic acid in the rumen, increases propionate synthesis

Low

Feed -1  108 to 1  1012 colony forming units

Bacteriophage

Pathogen control, disease treatment/prevention

Experimental

ND

Essential oils

Anti-microbial effects

Moderate, highly variable amongst formulations

Feed - < 2 g/d

Saponins

Anti-protozoal effects

Experimental

ND

Tannins

Binds with protein, decreases nitrogen excretion and methane emissions

Experimental

Feed < 3% of DM

Active dry yeast

Improved feed consumption, improved fiber digestion

Moderate

Feed 1e4 g/d

Yeast culture

Improved feed consumption, improved fiber digestion

Moderate

Feed - Variable

Enzymes

Improved fiber digestion

Experimental, depends on formulation

ND

3-Nitrooxypropanol methane inhibitor

Methane reduction, may improve feed efficiency

Experimental

Feed - 1e2 g/d

Nitrate

Methane reduction, may improve feed efficiency in finishing cattle

Depends on price of non-protein nitrogen sources

Feed - Max 2% of diet DM (substitute for urea), adaptation needed

a

Hormonal implants e.g. zeronol, estradiol trenbolone acetate,

Improve feed efficiency and gain, carcass yield

High

Ear implant e 20e200 mg over feed period

a

Increases protein deposition, improves feed efficiency

High

Feed e 8.3e30 mg/kg diet 42 to 20 d prior to slaughter

Beta-agonists eg. ractopamine, zilpaterol a

Antibiotics listed have been banned from inclusion in feed in the European Union and the use of hormonal implants and beta-agonists is also not permitted.

and foot rot (Table 5.2). The ionophores, monensin and lasalocid have been extensively researched and are included in the diet to prevent ruminal acidosis and improve feed

efficiency. Concern over antibiotic resistance has led to the ban of antibiotics for growth promotion in many countries, with ionophores being exempt in some regions as they are not

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used in humans. In North America, tetracycline and tylosin antibiotics are included in diets to prevent liver abscesses and BRD in feedlot cattle. However, the pressure to restrict antimicrobial use in beef cattle production has promoted the exploration of a number of potential alternatives. Direct-fed microbials (DFM) that contain live, beneficial microorganisms may serve as a potential alternative to antimicrobials in beef cattle diets. Yeasts and strains of Lactobacillus would be the most common DFM, although multiple organisms have been investigated.45 These additives can vary in efficacy,46 but have been shown to benefit calves that have just arrived at the feedlot, by improving intake and growth performance. Mechanisms for DFM include competitive exclusion of pathogenic bacteria, immune stimulation and favorably altering ruminal digestion.45 Additional additives, such as bacteriophage, plant bioactives (i.e., essential oils, saponins, tannins), vaccines and immune stimulators (i.e., Cationic liposomes, cytosine-guanine rich DNA motifs), are also being investigated as alternatives to antimicrobials,47 but many of these are still at a developmental stage.48 Efforts to enhance sustainability has also led to the emergence of additives targeted at lowering enteric methane emissions (i.e., nitrate, 3-nitrooxypropoanol), raising the possibility for a claim that they reduce environmental impacts through a reduction in enteric methane emissions. Most additives attempt to gain label claims for improved feed efficiency, rather than disease prevention, as the latter claim triggers the same rigorous assessment by regulatory agencies as is required for the registration of an antibiotic. Hormonal implants and beta agonists generate the most consistent improvement in feed efficiency (i.e., typically 5%e20%) of any of the growth promoters. Steroidal hormones have the added advantage of being administered as an ear implant, enabling cattle to be implanted prior to release on pasture. Most of the other additives listed in Table 5.2 need to be

administered through the diet, making their use in extensive pasture systems challenging and often impractical (see pasture supplementation). Monensin has been incorporated into a slow-release bolus that gradually releases this additive into the rumen, but their use in beef cattle is not widespread. Despite their ability to promote a marked improvement in feed efficiency, the European Union has a total ban on the use of these agents in livestock production, even though research supports that they are safe.49 Prevention of the use of additives that offer benefits in production efficiency without posing a health or environmental threat has negative consequences for the environment, as the production of greenhouse gas emissions and manure arising from beef production are increased. If cattle production systems are to meet the future demand for beef, advanced technologies that improve the efficiency of production, while meeting science-based regulatory requirements, are needed.

Extensive production systems Temperate versus tropical climates The type and scale of beef production must be tailored to regional climates, which are influenced by factors such as longitudinal and latitudinal location, the proximity to warm or cold ocean currents and topography.51 In the southern hemisphere, land north of the Tropic of Capricorn typically experiences tropical, and even monsoonal conditions, with summerdominant rainfall.52 Regions immediately to the south are considered subtropical, with an increasingly temperate climate developing across the Mediterranean and high-rainfall coastal regions that span Australia’s south.51 The interaction between rainfall and temperature dictates the pasture species best suited to a given geographical location. Tropical pastoral zones favor C4 perennial grasses, due to their ability to capture more carbon dioxide, produce

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higher yields, tolerate higher temperatures and lower moisture. In contrast, C3 species occur in temperate regions and are more cold tolerant.53 The C4 species also exhibit faster regrowth after grazing, but are typically higher in neutral detergent fiber and lower in non-structural carbohydrates and protein than C3 species, lowering their nutritional value and digestibility.54 Tropical beef enterprises tend to use pure or composite, large-framed, Bos indicus breeds, as a result of their heat tolerance and resistance to parasites.55 Cold tolerant Bos taurus breeds predominate in temperate regions and have higher meat quality than B. indicus breeds.56 Improved grasslands, especially in areas where rainfall and temperatures are suitable, often contain introduced C3 species. The higher protein content within C3 legumes increases the carrying capacity of pasture and promotes more efficient growth in cattle.54 Winter-dominant rainfall and temperatures between 15 and 25  C provide the optimal conditions for germination of C3 pastures, which provides a rising level of nutrition to support spring-calving herds.57

Maximizing forage production and quality The profitability of a beef enterprise is dependent on the average daily weight gain of each animal unit, a measure representative of pasture productivity. Without sustainably maintaining a diverse, digestible and dynamic pasture sward, the optimal carrying capacity of the pasture cannot be reached.58 Balancing enterprise inputs with pasture management is crucial to maximise the quantity and quality of beef produced.59 Forage production and quality is predetermined by selecting species that are appropriately adapted to the site of pasture production. Aiming for a palatable, compatible pasture composition that is well adapted to the local climate, persists and responds to fertiliser will generate the highest yields of beef per hectare.58 Although native species are persistent, drought tolerant and self-replenishing, they are often

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less digestible and have lower yields than forage varieties that have been specifically selected for greater yield and digestibility.60 Improvements in pasture composition using mostly perennial species can enable higher stocking rates and increased productivity.61 Many mixed farming operations alternate between pasture and cropping phases, to improve fertility via the inclusion of nitrogen-fixing legumes, such as alfalfa, in crop rotations. Although profitable, grazing requires a continuous transfer of nutrients from the lithosphere to biosphere.62 This can drain accessible minerals from soils and if not replaced, results in depleted pastures that are less productive and vulnerable to weed invasion. Soil improvement is vital in creating a nutrient-rich medium that promotes vigorous pasture growth, enabling weeds to be outcompeted.63 Cattle play a key role in this nutrient recycling as the manure that they produce adds organic matter, nitrogen and minerals back into the system.64 Sustainable grazing can also stimulate carbon sequestration in grasslands,65 a process that will be critical in meeting targets to restrict climate change-induced global temperature increases.66 Sustainable management practices, like maintaining a minimum herbage mass of 1000e1500 kg DM/ha, and not exceeding 2500e3000 kg DM/ ha, can also increase annual forage production.67 Preserving ground-cover beyond 70% in the dry season limits top-soil erosion, while in the rainy season, an oversupply of pasture can be conserved.68 This oversupply can often be grazed as stockpiled forage during periods of forage senescence and drought (see below) or harvested as hay (see above).

Extensive, semi-intensive, and intensive pasture systems The productivity of a beef operation is dependent on the intensity of inputs entering the system. If left undisturbed, the natural carrying capacity of extensive pastures is generally lower

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than pastures that are intensively managed.69 By investing in fertilisers, herbicides, irrigation and pasture improvement strategies, it is possible to boost productivity by increasing the stocking density and the yield of beef per hectare. Extensive systems have lower stocking rates and minimal inputs of labor and capital. These operations are usually located on marginal lands, where the availability of water, topography or altitude restricts intensive pasture management. Extensive cattle operations are nearly always holistically managed using traditional stewardship practices that focus on utilising seasonal fluctuations in natural pasture availability and preserving endemic biodiversity.70,71 As stocking density is dependent on climatic variability, extensive operations typically have boom and bust years,51 where cattle are either sold or purchased to match fluctuations in forage availability. Prolonged dry periods can reduce fodder quality and dry matter intake, leading to below average daily weight gains, prolonging the time needed for animals to achieve mature body weight.72 Dry, poor-quality pastures exhibit low digestibility, protein and mineral concentrations, often resulting in energy, protein and mineral deficiencies.73 During the wet season, extensive pastures can become high in protein and digestible fiber, but potentially low in some minerals, such as phosphorus, which can lead to impaired growth and fertility. Provision of a free-choice mineralized salt mixture is a common practice to avoid mineral deficiencies in pastured cattle (see below). Depending on the pasture quality, cattle can be maintained on extensive pastures for 3e5 years before they reach mature weight. Extensive production systems that have the least amount of capital input, while managing the grazing ecosystem in a sustainable manner, are often the most profitable. Intensive beef operations are highly stocked, with the goal of ever-increasing yield to achieve targeted finished weight in a shorter period of time. Such enterprises require increased

land-use and a wide range of inputs, including genetic selection, supplementary feeding and high labor inputs.74 In these systems, supplements are often provided to cattle on pasture to enable them to reach their full genetic potential and to avoid deficiencies. To achieve higher productivity, more complicated grazing management practices, like rotational or zero grazing, are often utilized (see below). The objective of these management practices is to have the cattle consume the forage in the leafy or vegetative stage, when energy and protein content is highest. Forage availability in these systems has to be carefully monitored because if it is impeded, paddocks can quickly become over grazed. Likewise, under wet conditions, the high stocking density can result in cattle damaging pastures through compaction and trampling. Semi-intensive systems contain elements of both intensive and extensive grazing, with the benefit of fewer risks. These systems preserve natural resources during stressful periods, while utilising any excess feed supply during periods of abundance.75 Mixed semi-intensive farming operations often occur in high rainfall zones where paddock management involves rotational grazing, alternating between pasture and cropping phases over multiple years.75 Long rest periods prevent erosion from trampling, while introducing different pasture and cropping species, like legumes, benefits the mineral profile and improves the management of soil organic matter and nitrogen levels.63

Grazing management The selection and implementation of a successful grazing management strategy depends on a deep understanding of the complex interactions among cattle, forage, soil, and the environment. In grazing systems, the nutritional quality and quantity of the pasture available for consumption is the main factor that drives productivity. The selection of forages that are adapted to the soil, environment and the grazing method

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will contribute to a long-lived, productive, and healthy pasture with lower production costs.76 Seasonality results in large fluctuations in the quantity and quality of forage throughout the year, impacting animal performance. Grazing management can be used to alter the sward structure and improve forage quality.77 Sustainable grazing management starts by calculating the average number of animals that a pasture can support during the season or the carrying capacity. High yields and greater longevity of the swards are usually achieved by avoiding overgrazing, where plant persistence and growth are negatively impacted, and under grazing, where plants become overly mature.78 Provision of an appropriate rest period for the plant after grazing is important to restore energy reserves and to support the root development needed to tolerate drought and outcompete weeds.76 Ensuring an appropriate stocking rate [i.e. number of animals on a pasture for a specified time period, usually expressed as Animal Unit (450 kg of live weight) Months (AUM) per hectare] is essential to maximize pasture quality, longevity and beef production per unit of area. The stocking rate of a pasture is determined by a number of factors including vegetative cover, rainfall, fertility and moisture-holding capacity of the soil. The nature of the grazing system used (rotational vs. continuous grazing) and the size and type of animals to be grazed can also influence productivity. Individual weight gains are greater at low stocking rates as cattle can selectively graze the more nutritious parts of plants. At intermediate stocking rates, cattle are less able to preferentially select for diet quality, resulting in a decline in individual weight gain but an increase in gains per unit area. Further increases in stocking density restrict pasture availability and promote pasture degradation, resulting in reduced gains per animal and per area.79 Low stocking rates can also reduce plant diversity in mixed pastures through competitive exclusion, where highly-productive

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plant species shade out less productive species and cattle may only selectively graze the most palatable species.80 In traditional continuous grazing systems, cattle are kept on the same area of pasture throughout the year. In this system, the only way to optimize grazing is to adjust stocking rates to have a perfect balance between forage growth and consumption by cattle. Stocking rates are adjusted to match the daily amount of forage consumed and trampled with the daily forage produced. However, this is rarely achieved, as animals graze selectively and over time some areas in the pasture will be overgrazed and others under grazed. Generally, animals prefer to graze younger swards and will often re-graze these areas after an insufficient rest period, whereas other less-palatable plants will remain ungrazed, mature and lignify. Watering areas, supplement stations, and fencing can be used to improve the grazing distribution of cattle. However, the greater cost of these management tools often impedes their adoption in extensive grazing systems.81 The main advantages of continuous grazing system are the lower management and input costs (fence, watering) and their applicability for slower-growing native forages.76 Rotational grazing systems involve moving cattle to another pasture or paddock before they can re-graze previously grazed plants, thus promoting foliage growth and allowing cattle to continuously graze high quality vegetative stands.76 Post grazing, paddocks are rested until plant energy stores and leaf area are re-established. The simplest system uses two paddocks and as the system intensifies more paddocks are used. For example, a more intensive pasture system may involve 29 paddocks, with cattle grazing each for 1 day and thus allowing a rest period of 28 days for each paddock. When properly managed, this system considers plant physiology, maintains the pasture in a vegetative state and provides a consistent supply of high-quality forage.82

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Optimal stubble height to leave in paddocks after grazing are specific for each pasture type. Grazing intensity can be controlled by varying the stocking density and number of paddocks. Adjusting the number of paddocks will affect the rest period, which changes with seasonal differences in forage growth. During periods of slow growth, more paddocks will be needed to increase the rest period of the pasture. During rapid growth, some paddocks can be harvested for hay, to prevent pastures from becoming overly mature. Usually a 10%e15% increase in carrying capacity can be achieved by establishing a properly managed rotational grazing system.82 In rotational systems, cattle have less opportunity for selective grazing due to greater stocking density, consequently forage is grazed more uniformly, resulting in more homogenous plant growth during the rest period.83 Compared to continuous grazing, rotational grazing systems also improve diet quality84 and forage consumption.85 Beck et al.86 conducted a four year study looking at the effects of stocking rate, forage management, and grazing management on performance and the economics of cowecalf production in the Southeastern United States. Compared to continuous grazing, rotational grazing (0.4 ha/cow) with the use of stockpiled bermuda grass drastically reduced winter feed requirements, while increasing carrying capacity and net returns (107%). Deferred rotational grazing is a management practice that is often used to recover pastures and extend the grazing season. In this system, pasture is periodically rested for a specific time so as to enhance the forage stand and improve plant vigor. The rest period is usually until the forage goes to seed and can be as long as the full growing season. This management is also used to stockpile forages for use in late fall and winter or during the dry season. In this system, pastures mature and consequently, protein content declines and fiber and lignin concentration in the DM increase, reducing animal intake and digestibility.87

Zero grazing involves cattle being confined and the forage is mechanically harvested and delivered to them. This system reduces losses caused by cattle trampling, while preventing selective and overgrazing of pastures. Forages are allowed to grow and can be harvested at the appropriate time that maximises forage yield and quality. This system has the disadvantage of increased costs associated with the daily harvest and transport of forage to the cattle.

Pasture supplementation Due to continuous changes in forage quality, it can be difficult to ensure that grazing cattle are consuming a balanced diet that fully meets their nutrient requirements. Minerals, vitamins, protein, and/or energy may limit cattle productivity, depending on soil conditions, pasture type, forage availability, and sward maturity and structure. Superior grazing management systems maximize forage utilization and supplement only with those nutrients that are impeding production efficiency. Mineral deficiencies or imbalances have been reported for grazing cattle all over the world. Calcium, P, Na, Co, Cu, I, Se and Zn are the most common mineral deficiencies observed in grazing cattle. Low concentrations of minerals in forages can result in deficiencies, but excessive concentrations of minerals, particularly F, Mo and Se can be toxic.88 The vitamin content of pastures is highly variable depending on plant type, climatic conditions, and stage of maturity with vitamin A precursors and vitamin E being the most frequently deficient. As with protein, vitamin A (i.e. b-carotene) and E (i.e. a-tocopherol) concentrations in grasses and legumes decline with increasing plant maturity, often reaching deficient concentrations late in the grazing season.88,89 To avoid deficiencies, it is a standard practice to provide grazing cattle a complete free-choice mineral/vitamin supplement as insurance against production losses. Mineral/vitamin products are usually mixed

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with 50% NaCl to target an intake of 50e70 g per day. In low phosphate, acidic soils in the tropics, mineral/vitamin products should also contain at least 8% phosphate.88 Mineral/vitamin products that are used to supplement cattle on pasture should be formulated on a regional basis with consideration for soil composition, climatic conditions, pasture type and the level of animal productivity. Grazing beef cattle can experience marked seasonal fluctuations in feed supply and pasture quality.90 As a consequence, grazing systems often do not supply sufficient protein and energy to achieve optimal body condition for beef cows or to finish cattle at a young age (24 months) at target carcass weights (300 kg). In addition, increases in land cost promote grazing systems to maximize productivity so as to stay economically competitive against other land uses. Protein and/or energy supplementation can maximize animal productivity, conserve forage, improve forage utilization, increase stocking rates, and influence cattle behavior.91 It is quite challenging, however, to predict the effect of energy and/or protein supplementation on forage intake and utilization. Production responses to supplements have often been either greater or less than expected, due to associative effects of supplements on animal intake and the total energy available to the animal.92 Supplement intake can substitute for pasture intake and associative effects can cause the supplement to decrease the ruminal degradation of fiber. In general, with high quality pastures the variability in impact of supplementation is on intake substitution, whereas on low quality pastures it relates more to alteration in fiber digestion. To formulate a supplement, it is important to first determine the nutrient requirements of the specific class of cattle and to estimate nutrient availability in the forage. The supplement is then formulated to supply only those nutrients that are deficient in the forage to achieve a targeted production outcome. This approach can be complicated, as cattle graze

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forages selectively, making it difficult to estimate the forage nutrient profile that is being consumed. It also can be very difficult to estimate the quantity of forage consumed. Thus, a balance must be achieved between losses in growth performance as a result of a specific nutrient deficiency and the cost of supplying that nutrient in a supplement.91 Supplements are most often formulated to exceed animal requirements so as to account for variation in the nutrient profile of the grazed pasture. Supplemental energy such as cereal grains must be used cautiously as they are more likely to decrease forage intake and digestibility through substitution and associative effects. Economical returns are usually the greatest when low quality forages are supplemented with ruminal degradable protein sources, as forage intake is increased and digestibility improved.92 Examples of nitrogen sources include urea, oilseed meals, cottonseed meal, peanut meal, peas or alfalfa pellets. Forage intake and digestibility are reduced if energy-rich feeds like grains exceed 0.4% of body weight (BW),91 as they negatively impact fiber digestion.88 Highly-digestible fiber by-products, like distiller’s dried grains with solubles (DDGS), soybean hulls, citrus and beet pulp can also serve as energy sources. Supplements are usually fed at 0.05%e1.0% of BW, and intake can be limited through the inclusion of salt (10%e35%) in the supplement to achieve the desired intake.91 Ensuring uniform intake of supplements among all members of the herd can still be challenging, due to variation in palatability and the dominance behavior of individual cattle. Liquid pasture supplements have also been widely used in North America.94 Earlier studies found improvements in both supplement palatability and cattle performance with urea molasses supplements.90 However, results with molasses-based supplements have been inconsistent. Moore et al.92 conducted a meta-analysis using 66 publications and found that gains on native forages supplemented with molasses alone

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or containing non-protein nitrogen (NPN) were lower than those achieved when true protein was included in the molasses supplement.

Cow/calf, backgrounding, and finishing cattle on pasture Cow-calf units have the greatest nutritional demand on pasture, as the cow requires energy for maintenance, lactation, reproduction and for heifers, growth. In grazing systems, the plane of nutrition for the cow should steadily increase throughout gestation and pre-calving, peaking immediately postpartum when nutritional needs are greatest. Post weaning, the nutritional demands of the cow decline and at this point cows can be maintained on low-quality forages.96 Matching the nutritional profile of the pasture with the demands of the animal is a critical step in good pasture management. This is accomplished by having lactating cows graze forage at the vegetative, leafy or boot stages when nutritional quality is the greatest. Even at these stages, some of the energy and mineral needs of the cow will come from body stores. Care must be taken to ensure that mineral deficiencies are not encountered, particularly calcium, which can lead to milk fever, magnesium, which can cause grass tetany, and phosphorus, which can impede reproduction. Provision of mineral or formulated supplements can help avoid these deficiencies. Growing bulls, steers and heifers can also be backgrounded or finished on pasture. Pasturebased backgrounding and finishing is an alternative to the forage-based backgrounding and grain-based finishing employed in confined feedlots. Rates of gain are generally lower than those achieved in confinement, unless intensive grazing practices are employed. Consumer demand for grass-fed beef has been steadily increasing,97 however, under some extensive grazing conditions it can require 3e4 years for growing cattle to reach mature weight. In some cases, finishing of cattle under these conditions is not possible without including a period of

intensive feeding in feedlots. If this is not possible, cattle are often marketed underfinished and at a discount in carcass value.

Nutrient management in beef cattle production systems Confined systems Nutrient management is a continuous environmental concern facing the feedlot industry. It is critical to develop a nutrient management plan (NMP; Table 5.3) that documents when and how much manure is applied to crop lands. Matching crop nutrient requirements with a manure nutrient profile is the most effective way to prevent surface water and groundwater pollution.98 To develop a superior NMP, it is necessary to estimate the total amount of manure produced, analyze its nutrient content, test soil TABLE 5.3 Confined beef cattle system and implications to nutrient management. Nutrient management plan Determine amount of manure produced Determine nutrient content of manure Obtain soil-test recommendations Calibrate application equipment Incorporate manure as quickly as possible Monitor soil nutrient and salt levels Account for residual nutrient carryover when calculating application rates Identify sensitive landscapes with respect to groundwater and surface water vulnerability Keep accurate and detailed records Formulate environmentally responsible diets Consider the existence of pathogens in vegetables products fertilized with manure Adapted from Alberta Agriculture and Forestry (AAF). Manure Nutrient Management. Alberta Agriculture, Food and Rural Development Bulletins. IB004-2000. 2015. Web Available: http://www1.agric.gov.ab.ca/ $Department/deptdocs.nsf/all/irr5716

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Nutrient management in beef cattle production systems

nutrient concentrations and understand the nutrient requirements of the crop that is to be grown.99 For a NMP, computerized records of feed nutrient deliveries to animals can provide key information on nutrient intake, as well as enable nutrient excretion to be estimated. Proper records of cattle or feed sales are needed to estimate the nutrients that are exported off farm, so as to estimate the overall nutrient balance.100 Beef cattle manure (urine and feces) contains organic matter, Nitrogen (N), Phosphorus (P), Potassium (K), Calcium (Ca) and micronutrients,101 as well as potential pathogens.102 Nitrogen volatilization from animal excreta into the atmosphere is a major challenge facing intensive cattle production systems, as well as nitrogen and phosphorus contamination of surface and ground water. However, manure is a valuable fertilizer for crop production and unlike chemical fertilizer, it directly increases soil organic matter. When over-applied, manure can cause water, air, and land pollution.103 Poor manure management creates odors, particulate matter emissions and eutrophication of surface waters.101 It should therefore be applied at rates that do not adversely affect the environment. If feeding distillers grains, concentrations of N and P in manure are increased104 and this is associated with manure application challenges, while also increasing its fertilizer value. Frequent cleaning of pens reduces the amount of N lost through volatilization, making it an efficient management strategy.101 When using manure as fertilizer, the potential presence of microbial pathogens also requires consideration (i.e., Escherichia coli serotype O157:H7, non O157 Shiga-producing E. coli, Salmonella spp., Campylobacter spp., Listeria monocytogenes, Yersinia enterocolitica, Mycobacterium spp. Bacillus spp., Clostridium spp.).105 These pathogens are usually asymptomatic to the host, but are shed into the environment within feces.106 Contamination of fruits and vegetables has been increasingly associated with land application of manure and use of contaminated

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irrigation water for crops.97 This has caused a number of disease outbreaks in humans,107,108 with the likelihood of infection depending on the type of bacteria, its infective dose and the immune status of the infected individual. Owing to the variation in pathogenic potential across the bacterial domain, the number of cells necessary to cause infection differs from one bacterium to another.109 Composting of the manure prior to land-application is one method that kills most pathogens.110 During composting, manure typically reaches > 55  C,111 which is sufficient in decreasing foodborne pathogens to thresholds acceptable to Public Health Agencies.

Extensive systems Grazing management affects the rate and timing of nutrient cycling. Intensive short-duration grazing with a high stocking density results in rapid, uniform forage utilization and manure deposition. In turn, many nutrients become available for pasture regrowth in a short period. Trampling mixes plant residues and manure into the soil, increasing the decomposition rate of organic materials. In contrast, an extensive system using a low stocking rate and density for a complete season may cycle a similar amount of nutrients, but over an extended period of time.112 These factors should be noted to avoid accumulating N, P and K on pasture and increasing the probability of environmental impacts like soil erosion, soil compaction, runoff, eutrophication and groundwater contamination.98,101 Rotational stocking can reduce soil compaction, as compared to intensive continuous stocking, resulting in increased forage yield and vegetative cover.113 Improved forage growth reduces raindrop impact, increases infiltration rates, minimizes soil erosion, and improves water quality.114 These practices can also prevent harmful levels of zoonotic pathogens entering surface and groundwater, decreasing the probability of pathogen spread during heavy rainfall

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and flooding events. This is important because pathogens can survive in the environment for months, depending on pH, concentrations of dry matter, moisture and oxygen, as well as temperature and microbial competition.105 A NMP should be a compulsory part of grazing beef cattle systems, and as with intensive systems, budget and requirement of nutrients for plant production should be balanced against those arising from manure. Care should be taken to minimize point sources of nutrient accumulation, as can occur around water sources or supplement stations. If possible, water should be pumped out of surface water sources to a trough that lies outside of riparian areas, which serve as a buffer against the flow of nutrients and pathogens into streams and lakes. Riparian areas also enhance the level of biodiversity within beef cattle production systems, as they serve as corridors for the movement of wildlife and provide sheltered access to water supplies.

Greenhouse gas emissions Beef cattle produce about between 2.5 and 3.0 billion tonnes of C02-eq of greenhouse gas (GHG) emissions each year, with the intensity of these emissions varying from 14 to over 70 kg C02-eq per kg of beef.115 GHG emissions arising from beef cattle production include CO2, CH4 and N2O, with enteric CH4 emissions accounting for the largest proportion.116 The intensity of GHG emissions from beef is greater in extensive than intensive production systems. A number of factors contribute to this difference, including the longer period of time to reach finished weight in extensive systems and that the emissions per kg of feed consumed are higher for forage-than concentrate-based diets.117 Considerable research has been invested in identifying additives (Table 5.2), developing vaccines, designing animal breeding programs and formulating diets that lower enteric methane emissions from ruminants.118 Of these, dietary

formulation is the most common approach, with increasing grain levels being the most predictable method to lower GHG intensity from beef cattle. Balancing the diet to avoid excessive excretion of N can reduce N2O emissions from manure, while manure handling systems that avoid promoting anaerobic conditions can lower CH4 emissions. If the manure is directed into a biodigester, the CH4 that is produced can be captured and used to generate electricity. Although extensive beef production systems tend to have a larger carbon footprint, many approaches that estimate these emissions do not consider the vast amounts of carbon that are stored in the world’s grazing lands. It is estimated that global grasslands harbor up to 120 billion tonnes of carbon, nearly 50% of that contained in global forests and prudent grazing management practices that promote photosynthesis in grassland ecosystems can add to these stores.119 Furthermore, rangelands serve as habitat for a wide variety of wildlife species, some of which are endangered or threatened with extinction.116 Grasslands also help prevent water and wind erosion and their ability to filter nutrients from both ground and surface water improves water quality. Consequently, a true evaluation of the sustainability of beef cattle production requires a holistic assessment of each production system with an appreciation for their various negative and positive impacts on the delivery of ecosystem services.116

Implications of climate change Unlike poultry or swine, which are mainly housed in climate-controlled barns, the use of extensive pasturelands makes beef production particularly sensitive to climate change. Depending on geographical location, climate change may have either negative or positive impacts on beef production.120 Negative impacts of climate change on beef production will likely be greatest in tropical and subtropical regions.

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Conclusion

An increase in extreme weather events, like droughts and floods, is predicted to occur globally; particularly if climate change induced temperature increases exceed 2 C. These environmental changes will have direct impacts on the physiology, behavior, and welfare of cattle by affecting thermoregulation and the availability and quality of feed. There is also the possibility that climate change could alter the regional distribution of livestock pests and disease, an outcome that could be exacerbated by climate driven changes in land use. Although considerable effort has been expended on lowering GHG from beef cattle production systems, the continued increase in atmospheric GHG has prompted a renewed focus on approaches to adaptation. Adaptation strategies are broad in scope, focusing on the genetic traits and management of crops, pasturelands and the animal. More extreme, future weather events are anticipated to increase the variation in crop and pasture yields. With increasing temperatures, C4 grasses could become more dominant in pastures and, when combined with genetic selection, expansion of these crops into temperate regions is likely to occur, an outcome that is already apparent for corn in Canada.121 A mixture of C3 and C4 species could enhance carbon capture in grasslands that are subject to both warm and cool season rainfall.122 It is also predicted that climate change will alter forage quality, reducing crude protein and lowering the digestibility of fiber. Harvesting and conservation of forage and the use of byproduct feeds may become even more critical to meet shortfalls during times of forage scarcity and to ensure that nutritional requirements of cattle are satisfied throughout the production cycle. Elevated concentrations of atmospheric CO2 may also change the composition of pastureland, promoting bush encroachment,123 which could enhance the amount of carbon captured in these ecosystems, but reduce their productivity for beef production. At this point, the extent that individual plant species are capable of adapting

to climate change remains unknown. Intensive selection programs for traits like temperature and drought tolerance could result in plant varieties that are capable of remaining productive under conditions of high climatic variability. Given current projections, it is uncertain if selection of adaptive traits through traditional breeding programs can keep up with the predicted accelerated pace of climate change. Adaptions of the animal will involve a combination of management practices as well as genetic selection. Integrated crop-livestockforestry systems can provide valuable shade to beef cattle, reducing their susceptibility to heat stress, a system that is already widely used in Brazil.124 In confined feedlots, shade covers, sprinklers and fans can help alleviate heat stress, but at extreme temperatures feed intake is still reduced and productivity is impaired.125 Matching the appropriate breed of cattle to local environmental conditions is already a pivotal step in ensuring the sustainability of beef cattle production systems.126 Selection for health and viability under conditions of extreme climate may not be compatible with traits associated with increased production efficiency or high meat quality. Whole genome sequencing and marker assisted selection may be able to accelerate selection for climate adapted beef cattle. Recent developments in gene editing through CRISPR technologies may also help accelerate breeding programs aimed at enhancing climate adaptation of both plants and beef cattle, but to be widely used, these approaches need to first gain public acceptance.127,128

Conclusion Beef cattle are unique, compared to poultry and swine in that they can convert low-quality forages into high-quality protein for humans. Recently, there has been growing pressure to globally restrict beef production, due to its perceived negative impact on the environment.

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Beef cattle play a significant role in the production of food for humans, from forages and vast tracks of both tame and native pasturelands. In native grasslands, beef cattle largely replace the role of the bison that previously occupied this ecosystem. Care must be taken to ensure that the nutritional needs of beef cattle are aligned with the productivity of the pasture, so as to avoid detrimental impacts on both the animal and the ecosystem. Global appetite for beef is projected to increase and in light of the emerging pressures of climate change and the scarcity of new tracts of pasture and arable land, sustainable intensification will be the only means of satisfying demand. Intensified systems will need to increase the use of by-product feeds and food wastes in beef cattle production. Nutrient management plans will be needed to ensure that nutrient flows are aligned with the principals of a circular bioeconomy. Finally, advanced technologies that improve the efficiency of feed utilization with an emphasis on both the plant and the animal will need to gain societal acceptance if more beef is to be produced on less land.93,95

Acknowledgments CAPES/Brazil, Visiting Professor, Process: PVEX-88881. 169965/2018-01 Citations.

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105. Doyle M, Erickson MC. Reducing the carriage of food borne pathogens in livestock and poultry. Poultry Sci. 2006;85:960e973. 106. Mathusa EC, Chen Y, Enache E, et al. Non-O157 Shiga toxin producing Escherichia coli in foods. J Food Prot. 2010;73:1721e1736. 107. Beutin L, Martin A. Outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 infection in Germany causes a paradigm shift with regard to human pathogenicity of STEC strains. J Food Prot. 2012;75:408e418. 108. Manyi-Loh CE, Mamphweli SN, Meyer EL, et al. An overview of the control of bacterial pathogens in cattle manure. Int J Environ Res Public Health. 2016;13:E843. 109. Bremer VR, Hanford KJ, Erickson GE, et al. Meta Analysis of UNL feedlot trials replacing corn with WDGS. Nebraska Beef Rep. 2010;93:61e62. 110. Gurtler J, Doyle MP, Erickson MC, et al. Composting to inactivate foodborne pathogens for crop soil application: a review. J Food Prot. 2018;81:1821e1837. 111. Erickson MC, Liao J, Ma L, et al. Pathogen inactivation in cow manure compost. Compost Sci Util. 2009;17: 229e236. 112. Baron VS, Mapfumo E, Dick AC, et al. Grazing intensity impacts on pasture carbon and nitrogen flow. J Range Manag. 2002;55:535e541. 113. Franzluebbers AJ, Wilkinson SR, Stuedemann JA. Bermudagrass management in the Southern Piedmont USA x Coastal productivity and persistence in response to fertilization and defoliation refimes. Agron J. 2004;96:1400e1411. 114. Owens LB, Edwards WM, Van Keuren RW. Sediment losses from a pastured watershed before and after stream fencing. J Soil Water Conserv. 1996;51:90e96. 115. Opio C, Gerber P, Mottet A, et al. Greenhouse Gas Emissions from Ruminant Supply Chains e A Global Life Cycle Assessment. Rome: Food and Agriculture Organization of the United Nations (FAO); 2013. 116. Pogue SJ, Kr€ obel R, Janzen HH, et al. Beef production and ecosystem services in Canada’s prairie provinces: a review. Agric Syst. 2018;166:152e172.

117. de Vries M, van Middelaar CE, de Boer IJM. Comparing environmental impacts of beef production systems: a review of life cycle assessments. Livest Sci. 2015;178:279e288. 118. Hristov AN, Oh J, Firkins JL, et al. Special topicsd mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J Anim Sci. 2013;91: 5045e5069. 119. Conant RT, Cerri CE, Osborne BB, et al. Grassland management impacts on soil carbon stocks: a new synthesis. Ecol Appl. 2017;27:662e668. 120. Henry BK, Eckard RJ, et al. Review: adaptation of ruminant livestock production systems to climate changes. Animal. 2018;12(S2):445es456. 121. Guyader J, Baron VS, Beauchemin KA. Corn forage yield and quality for silage in short growing season areas of the Canadian prairies. Agronomy. 2018;8:164. 122. Cullen BR, Eckard RJ, Rawnsley RP. Resistance of pasture production to projected climate changes in south-eastern Australia. Crop Pasture Sci. 2012;63: 77e86. 123. Scheiter S, Higgins SI. Impacts of climate change on the vegetation of Africa: an adaptive dynamic vegetation modelling approach. Glob Chang Biol. 2009;15: 2224e2246. 124. Pinheiro FM, Nair PKR. Silvopasture in the Caatinga biome of Brazil: a review of its ecology, management, and development opportunities. Off Syst. 2018;27: eR01S. 125. Mader TL, Davis MS, Brown-Brandl T. Environmental factors influencing heat stress in feedlot cattle. J Anim Sci. 2006;84:712e719. 126. Eisler MC, Lee MR, Tarlton JF, et al. Agriculture: steps to sustainable livestock. Nature. 2014;507:32e34. 127. Bhat SA, Malik AA, Ahmad SM, et al. Advances in genome editing for improved animal breeding: a review. Vet World. 2017;10:1361e1366. 128. Gao C. The future of CRISPR technologies in agriculture. Nat Rev Mol Cell Biol. 2018;19:275e276.

I. Beef cattle production

P A R T I I

Lactation and management of dairy cattle

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C H A P T E R

6 Genetics and genomics of dairy cattle Francisco Pe~ nagaricano Department of Animal Sciences, University of Florida, Gainesville, FL, United States

O U T L I N E Introduction

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The basics of genetic selection

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Selection for traits that increase income

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Selection for traits that reduce expenses

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Selection for multiple traits

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Genomic selection: the latest revolution

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Effective use of genomics: sire selection

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Introduction Genetic selection programs have transformed the dairy industry worldwide. The current dairy cow produces more than twice as much milk as the dairy cow of 50 years ago, and more than half of that improvement is due to genetic selection. The basic blocks for genetic improvement have been performance records and pedigree information. The development and widespread utilization of national milk recording systems, the introduction of artificial insemination, and

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00006-9

Effective use of genomics: replacement heifer selection

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Novel traits in the genomics era

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Managing inbreeding and genetic diversity

116

Final remarks

117

References

118

the development of accurate genetic evaluation methods have enabled remarkable genetic improvement in dairy cattle populations. The success of these programs has been possible due to the close collaboration between dairy farmers, milk recording organizations, dairy records processing centers, breed associations, breeding companies, government agencies, and agricultural universities. Each organization has a key role in data collection and analysis, product development, education and outreach. It should be noted that the focus of selection

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Copyright © 2020 Elsevier Inc. All rights reserved.

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6. Genetics and genomics of dairy cattle

programs has evolved over time, from an initial emphasis on increasing milk yield and physical appearance, to a current interest in improving production efficiency, milk composition, and animal fitness. Recently, the advent of genomic selection has revolutionized dairy cattle breeding. This technology allows breeders to make accurate selection decisions at a much earlier age, reducing generation intervals, and thereby increasing the rate of genetic progress. In addition, genomic selection provides a mechanism for improving traits that were too difficult or expensive to measure in conventional progeny testing schemes, such as feed efficiency. This chapter provides an overview of different aspects involved in dairy cattle genetic improvement programs, including a concise description of economically relevant traits in dairy cattle breeding, a brief review about selection for multiple traits emphasizing the value of economic selection indices, and a discussion about effective use of genomics for sire and replacement heifer selection. The chapter is largely based on the structure and achievements of the US dairy industry, but the concepts described are applicable to dairy genetic programs worldwide.

The basics of genetic selection Genetic selection is a very powerful tool for achieving lasting gains in dairy cattle performance. Contrary to improvements in nutrition, management or cow comfort, changes achieved through selection are incremental, cumulative and permanent, which makes genetic improvement a very cost-effective strategy. Genetic selection works by identifying and selecting the animals with the highest genetic merit as parents of the next generation, resulting in the genetic (and also phenotypic) improvement of the population in each generation. The rate of genetic gain (R) in a given trait can be calculated as R ¼ h2$S, where S is the selection differential and h2 is the heritability of the trait. The selection differential

measures the superiority of the selected individuals with respect to the entire population, and the heritability captures the proportion of phenotypic variation 
due to additive genetic values h2 ¼ s2A s2P . Note that traits with higher h2 respond more rapidly to genetic selection. In general, type traits, such as stature and body depth, have high heritabilities (h2  35%), whereas production traits, such as milk yield and milk solids, have moderate heritabilities (15%  h2  30%), and fitness traits, such as fertility and health, have typically low heritabilities (h2  10%). Note that the selection differential (S) can be written as the product of selection intensity (i) and the phenotypic standard deviation (sP), and hence, the rate of genetic gain can be written as, R ¼ i$h$sA. More generally, the annual genetic gain is given by the famous breeder’s equation, i$rAC $sA L where DG is the annual genetic gain, i is the selection intensity, a measure of the superiority of the parents of the next generation, rAC is the accuracy of selection, a measure of the precision with which genetic merits are estimated (rAC is equal to the square root of the reliability), sA is the additive genetic standard deviation, and L is the generation interval defined as the average age of parents when their offspring are born. Note that i and L are properties of the population while rAC and sA differ from trait to trait. Animals are selected based on their genetic values, and the prediction of these genetic values occurs through the integration and analysis of multiple types of data, including phenotypic records (e.g., test-day milk yield, days open, health events), pedigree information, and more recently, genotypic data. The most important output of this process, known as genetic evaluation, is the estimate of genetic merit, commonly known in dairy cattle breeding as predicted transmitting ability (PTA). The PTA is an DG ¼

II. Lactation and management of dairy cattle

Selection for traits that increase income

estimate of the relative genetic superiority (or inferiority) that a particular animal will pass to its offspring for a given trait, and hence, represents the most important tool for making selection decisions. PTAs are exceptional tools for comparing and ranking animals, because the difference between the PTAs of two animals is an estimate of the difference expected to be observed in the performance of their progeny. Importantly, each PTA is accompanied by a value of reliability (REL), that measures the accuracy or degree of confidence in the PTA value. It is expressed as a percentage and ranges from 1 to 99. Technically, it is defined as the squared correlation between the true transmitting ability and the predicted ability of a given animal. REL is a function of the heritability of the trait and the amount of information available for the animal; basically, as heritability and the amount of information increases, REL also increases. Therefore, a bull has a more reliable PTA for protein yield than for daughter fertility because protein yield has a higher heritability. Similarly, a bull with many daughters has a more reliable PTA for any given trait than a bull with no or just a few daughters. Finally, another important output of the genetic evaluation is the percentile rank, a measure of the rank or position of the animal within the population evaluated for a given trait of interest. The interpretation of the percentile rank is very straightforward: if a bull ranks for a given trait at the 95th percentile, this means that the bull is genetically superior to 95% of all the evaluated bulls of its breed. In dairy cattle breeding programs, the rate of genetic gain can be described in terms of four paths of selection, namely selection of sires and dams of bulls, and selection of sires and dams of cows.1 The first path, sires of bulls, represents the most elite males in the population that are selected to be the sires of the next generation of bulls. This path is characterized by high accuracy and high selection intensity, top 3e5% of the bulls available in the market. The second path, dams of bulls, represents the group of elite

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females that are mated to bulls from the group sires of bulls in order to produce bull calves. This path is characterized by high selection intensity, these females typically rank in the top 1% of the commercial cow population, but relatively low selection accuracy in the absence of genomic testing. The third path, sires of cows, represent the large group of bulls whose semen is used to generate the new generation of replacement heifers in commercial farms. This path is characterized by high selection accuracy and relatively high selection intensity. Finally, the forth path of selection, dams of cows, involves the large population of cows on commercial farms that are primarily used to produce milk. These cows are typically mated to bulls from the sires of cows group, in order to initiate a new lactation and produce female replacements on the farm. This selection path is traditionally characterized by low selection intensity and low selection accuracy, although recent herd management improvements coupled with the advent of genomic testing are modifying the potential contributions of this path to the overall genetic progress.

Selection for traits that increase income Milk yield. Dairy cattle selection programs have traditionally focused on increasing total lactation milk yield. Fig. 6.1 shows the average milk yield per cow per year between 1957 and 2016 in the US Holstein dairy herd. Notably, average annual milk production has increased from about 13,000 to 28,000 lb in the last 60 years (Fig. 6.1A). Interestingly, much of this improvement in productivity is due to genetic selection. Indeed, genetic improvement for milk yield averaged 146 lb per year, accounting for 57% of the total improvement (Fig. 6.1B). Note that genetic gains should be accompanied by improvements in cow nutrition and management, if not genetic selection would lead to an unrealized potential. Although milk volume remains

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6. Genetics and genomics of dairy cattle

FIG. 6.1 Changes in milk yield for US Holsteins between 1957 and 2016. (A) Phenotypic trend. (B) Changes due to genetics or management: the orange (light gray in print version) area shows changes due to improved management while the blue (black in print version) area shows gains due to increased genetic potential. Source: Council on Dairy Cattle Breeding website (December 2018; https://www.uscdcb.com).

important in some markets, the emphasis placed on milk yield has decreased over time as fat and protein have gained more interest. Genetic merit for milk volume continues to increase because it is highly correlated with milk solids. Milk composition. In many markets, the vast majority of milk is used for making manufactured dairy products, such as cheese, ice cream, butter, and yogurt, among others, rather than for fluid milk consumption. In this situation, increasing fat and protein yield is more important than increasing milk volume. In theory, there are basically two ways to increase milk solids: (i) increasing fat and protein percentages while keeping milk yield constant; or (ii) increasing total milk yield while keeping fat and protein percentages constant. In practice, most breeding programs have increased fat and protein yield by direct selection for these traits, with little concern about whether this increase comes from milk volume or component percentages. In US Holstein cows, in the last 40 years, average annual fat yield increased from 624 to 1079 lb, whereas average annual protein yield increased from 541 to 873 lb. Notably, genetic

selection explains approximately 57% and 66% of the improvements in fat and protein yield, respectively. During recent years there has been growing interest in milk with specific nutritional value, such as specific protein composition (rich in A2 b-casein) or desirable fatty acid profile (high in unsaturated fatty acids), and improved manufacturing properties (coagulation time and curd firmness). If milk processors start to pay premiums for these traits, then farmers will have clear economic incentives to select for altered milk composition and manufacturing attributes.

Selection for traits that reduce expenses Fertility. Reproductive efficiency is a very important economic trait in dairy cattle. Reproductive inefficiency results in increased calving intervals, increased involuntary culling rates, decreased milk production, and delayed genetic progress, among other problems, causing significant economic losses.2 Genetic selection for improved cow fertility is a high priority

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Selection for traits that reduce expenses

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FIG. 6.2 Concomitant changes in milk yield and pregnancy rate in US Holstein dairy cattle. Source: Council on Dairy Cattle Breeding website (December 2018; https://www.uscdcb.com).

worldwide. It is well-documented that production and fertility are negatively correlated, and selection programs that have placed substantial emphasis on yield and have neglected fertility, have inevitably experienced a decline in reproductive performance.3 Fig. 6.2 shows the observed phenotypic trends in total milk yield and pregnancy rate in US Holstein cows in the last six decades. It is clear that concomitant with intensive selection for increased milk yield, pregnancy rate declined steadily until the beginning of the 2000s. Progress on improving reproductive performance began in 2003 with the introduction of daughter pregnancy rate (DPR), considered the primary trait for selection for cow fertility in US dairy cattle.4 An increase of 1% in DPR corresponds to a decrease of approximately 4 days open. Nowadays, besides DPR, two additional female fertility traits are routinely evaluated in US dairy cattle, namely heifer conception rate and cow conception rate. These traits reflect genetic potential in the probability of achieving conception when heifers of lactating cows are inseminated.

Health. Cow health directly impacts dairy farm profitability. Health events cause substantial economic losses, including losses due to onfarm death, premature culling, reduced milk production, and increased veterinary and treatment costs.5 Production and functional traits are negatively correlated, and the intense selection for milk yield in the last decades has compromised health and reduced fitness in dairy cattle.6 Genetic improvement of cow health and welfare is of paramount importance for the dairy industry worldwide. Traditionally, most breeding programs have focused on indirect measures of cow health and fitness, such as length of productive life or somatic cell count as an indicator of udder health. Indeed, genetic evaluations for somatic cell score were developed many years ago to facilitate indirect selection for mastitis resistance.7 However, direct selection for health traits is more effective than indirect selection using indicator traits. In this sense, the Nordic countries have led the development and implementation of genetic selection programs for cow health traits, including clinical

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mastitis and metabolic disorders. Recently, the US dairy industry implemented genetic evaluations in Holstein for six health traits, including milk fever, retained placenta, metritis, displaced abomasum, ketosis, and clinical mastitis. These six health traits are considered the most common and most costly health events impacting US dairy herds. Longevity. The length of productive life, measured from first calving until culling, is commonly used to evaluate the longevity of a lactating dairy cow. This trait is arguably the most direct measure of a cow’s ability to survive on a commercial farm, i.e., cow’s ability to avoid dying on the farm or being culled. In the US, genetic evaluations for productive life have been available since 1994.8 Average length of productive life has increased approximately 0.25 months per year in the last decade in US Holstein cows. Despite this improvement, about 20% of dairy cows still die on the farm instead of being sold, approximately 7% per lactation, representing an economic loss of about $2 billion. In order to alleviate this problem, the US dairy industry recently introduced cow livability, a new longevity trait defined as the probability of a lactation not ending in death or on-farm euthanasia.9 Note that livability measures a cow’s genetic ability to stay alive while on the farm, while productive life measures a cow’s genetic ability to avoid dying on the farm or being culled. Livability is positively correlated with productive life (0.70) and daughter pregnancy rate (0.45), and negatively correlated with somatic cell score (0.25). Calving ability. Calving performance is considered an important functional trait in dairy cattle. Calving difficulty (dystocia) increases labor and veterinary costs, increases mortality in both cows and calves, decreases milk production, and leads to impaired female fertility.10 Genetic evaluations for calving ability include calving ease (direct and maternal), stillbirth rate (direct and maternal) and gestation length. Calving difficulty and stillbirth rate are highly

correlated, and both are related to differences in gestation length.11

Selection for multiple traits There are a large number of traits, including production traits (such as milk yield and milk composition) and functional traits (such as fertility, health, longevity, and calving ability), that directly impact the profitability of any dairy production enterprise. One simple method of multiple-trait selection is the use of independent culling or rejection levels. In this method, first minimum standards or cut-off values are chosen for each of the traits undergoing selection, and then only animals that meet simultaneously all the criteria are selected. For example, one might decide to use only bulls with PTAs that are at least þ35 for protein yield, þ4.5 for productive life, and þ2.8 for daughter pregnancy rate. Although this method is quite popular and allows one to select simultaneously for multiple traits using simple rules, it has some important limitations. First, the threshold values are in general chosen arbitrarily without using any formal approach. In addition, these cut-off values may vary over time due to genetic progress and changes in the definition of the genetic base, and therefore, cut-off values that are appropriate today may be too restrictive or too liberal in the near future. Second, this method ignores the genetic relationships between traits of interest; this adversely impacts the efficiency of selection when we want to select for traits that are genetically correlated, such as production and fertility. Finally, the effectiveness of the independent culling levels decreases rapidly as the number of traits under selection increases; as more traits are considered, fewer bulls meet simultaneously all the criteria, and more importantly, these bulls are probably only marginally superior for each trait. The best approach for selecting animals considering multiple traits is the use of an economic selection index.12 The overall breeding

II. Lactation and management of dairy cattle

Selection for multiple traits

goal (H) is to improve multiple economically relevant traits by selecting animals using a selection index (I). The selection objective is represented as H ¼ a1$G1 þ a2$G2 þ.þ am$Gm where Gi are breeding values and ai represent economic weights. These economic values are based on prices for both inputs (e.g., feed and veterinary costs) and outputs (e.g., milk prices, calf prices) of a dairy production enterprise. The selection index is computed as I ¼ b1$P1 þ b2$P2 þ.þ bk$Pk where Pj are measured phenotypes and bj represent index weights. These weights are calculated as b ¼ P1Ga, where P is the phenotypic (co)variance matrix for the traits included in the selection index, G is the matrix of genetic (co)variances among the traits in the selection index and the breeding objective, and a is the vector of economic values. The correlated response to selection in the breeding objective due to selection based on the selection index is maximized when the traits in the index are accurately measured and highly correlated to the traits in the breeding objective. Contrary to the method based on independent culling levels, selection indices perform well regardless of the number of traits under selection, and even more importantly, these indices allow for selection of animals that are highly superior for one trait and slightly deficient in other traits, which leads to the maximization of the selection response. Economic selection indices are updated periodically in order to include new traits and to reflect price trends.13e15 Table 6.1 shows the evolution of USDA Lifetime Net Merit (NM$) index, probably the most popular index in the US dairy industry. The first USDA index, Predicted Difference Dollars (PD$), included only milk and fat yield. The NM$ was developed in 1994 combining five traits, namely milk yield, fat yield, protein yield, productive life and somatic cell score. Three functional type traits, udder composite, feet and legs composite, and body weight/size composite, were included in NM$ in 2000. Subsequent updates included the

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incorporation of daughter pregnancy rate (2003), calving performance (2006), heifer and cow conception rates (2014), and livability (2017). The last updated of the NM$ index was in August 2018 with the inclusion of six health traits, clinical mastitis, ketosis, retained placenta, metritis, displaced abomasum, and milk fever. These six health traits were added to the index in the form of a health trait sub-index (HTH$). Overall, the emphasis on yield traits has declined over time as health and fertility traits, commonly grouped as fitness traits, were introduced. Nowadays, most economic selection indices include both production and fitness traits.16 Fig. 6.3 shows traits included in selection indices from 13 different countries. Common trait groups include production (e.g., milk volume, protein and fat yield), fertility (e.g., pregnancy rate, calving interval), longevity (e.g., productive life, survival rate), health (e.g., somatic cell counts, clinical mastitis, postpartum disorders), type (e.g., udder conformation, feet and leg score), calving (e.g., calving ease, stillbirth, dystocia), and others (e.g., milking speed, feed efficiency). All these total merit indices include production, fertility, longevity and health traits, although with different emphasis; for instance, for production, the BPI in Australia and the ICO in Spain have a relative weight of 51% while the NVI in the Netherlands has a relative weight of only 26%. Not all the indices include type traits and only few incorporate calving traits. US lifetime merit-based selection indices. The NM$ is the flagship index in the US dairy industry, and it is probably the most appropriate breeding goal for the vast majority of US dairy farms. Fat and protein yield receive the highest relative weights in NM$, representing 27% and 17%, respectively. Longevity traits, female fertility traits, health traits, and functional type traits receive relative weights of 19%, 10%, 6%, and 15%, respectively. Calving ability (CA$), a sub-index that includes both service-sire and daughter calving ease and stillbirth, receives a relative weight of 5%. Overall, current NM$

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108 TABLE 6.1

6. Genetics and genomics of dairy cattle

Evolution of the USDA lifetime net merit (NM$) index.

Traits

PD$ (1971)

MFP$ (1976)

CY$ (1984)

NM$ (1994)

NM$ (2000)

NM$ (2003)

NM$ (2006)

NM$ (2010)

NM$ (2014)

NM$ (2017)

NM$ (2018)

Milk

52

27

2

6

5

0

0

0

1

1

1

Fat

48

46

45

25

21

22

23

19

22

24

27

Protein

e

27

53

43

36

33

23

16

20

18

17

Productive Life

e

e

e

20

14

11

17

22

19

13

12

Somatic Cell Score

e

e

e

6

9

9

9

10

7

7

4

Body Weight Composite

e

e

e

e

4

3

4

6

5

6

5

Udder Composite

e

e

e

e

7

7

6

7

8

7

7

Feet & Legs Composite

e

e

e

e

4

4

3

4

3

3

3

Daughter Pregnancy Rate

e

e

e

e

e

7

9

11

7

7

7

CA$ (calving trait sub-index)

e

e

e

e

e

e

6

5

5

5

5

Heifer Conception Rate

e

e

e

e

e

e

e

e

1

1

1

Cow Conception Rate

e

e

e

e

e

e

e

e

2

2

2

Livability

e

e

e

e

e

e

e

e

e

7

7

HTH$ (health trait sub-index)

e

e

e

e

e

e

e

e

e

e

2

Data obtained from the Council on Dairy Cattle Breeding website (December 2018; https://www.uscdcb.com).

has relative weights of 45% for production traits, 40% for fitness traits, and 15% for functional type traits. The USDA-ARS Animal Improvement Programs Laboratory has also developed three alternative selection indices for producers with special milk markets or production systems. For dairy producers who are paid mainly for milk volume, i.e., markets where the incentives for components are insignificant, the Fluid Merit Index (FM$) is probably the most appropriate breeding goal. FM$ has relative weights of 18% for milk yield, 27% for fat yield, and 0% for protein yield. For dairy farmers who are paid

mainly for milk components, Cheese Merit Index (CM$) is probably the most appropriate economic selection index. Compared to NM$, CM$ places more emphasis on protein yield, and milk volume is more penalized indicating that the selection for more milk solids should be achieved by improving fat and protein percentage rather than improving total milk yield. Pasture-based dairy producers may find the Grazing Merit Index (GM$) index as the most convenient economic selection index; GM$ places roughly the same emphasis on production and health traits as NM$, but more

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Selection for multiple traits

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FIG. 6.3

Economic selection indices for dairy cattle in 13 different countries.

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emphasis on female fertility traits and slightly less emphasis on productive life and livability.

Genomic selection: the latest revolution Genomic selection refers to selection decisions based on genomic-estimated breeding values. These genomic breeding values are calculated using genetic markers across the entire genome.17 This technology has revolutionized dairy cattle breeding worldwide because it allows breeders to make accurate selection decisions at a much earlier age, even when neither the animal nor its offspring have been assessed for the phenotypes of interest. Three major developments allowed the widespread use of DNA information in dairy cattle breeding: the identification of many thousands of single nucleotide polymorphism (SNP) markers spanning the entire bovine genome,18 the development of SNP-chip genotyping technologies that allow the genotyping of thousands of SNP markers in a (very) cost effective manner,19 and the development of suitable statistical methods where genome-wide SNP effects are estimated simultaneously without any significance testing.20 Moreover, several factors make dairy cattle improvement programs ideal for implementing genomic selection, including (i) nearly all relevant traits are sex limited and cannot be measured until the females begin lactating, (ii) individual animals have sufficient value to easily offset the genotypic costs, (iii) massive historical phenotypic data for building large reference populations, and (iv) access to data processing and evaluation infrastructure. Indeed, genomics has undoubtedly caused the most remarkable change in dairy cattle breeding since the introduction of artificial insemination. Hundreds of thousands of animals have been genotyped worldwide, including nearly every potentially elite young animal, and this genomic information is fully integrated into national genetic evaluations. Young bulls and potential elite

females are typically genotyped using mostly medium-density (roughly 50,000) or even highdensity (roughly 700,000) SNP genotyping arrays, while most heifers in commercial farms are genotyped with low-cost, low-density genotyping arrays with roughly 10,000 to 20,000 SNP. Fig. 6.4 shows the number of genotyped animals included in the US Holstein genomic evaluation since January 2011. The first official US genomic evaluation for Holsteins was realized in January 2009, and since then over 2.3 million genotypes have been received. Notably, the vast majority among the genotyped animals are heifers genotyped with low-density SNP chips. The effective use of low-density genotypes for predicting genomic breeding values has been made possible due to the development of efficient imputation algorithms that allow the prediction of the genetic merit of a heifer calf with almost the same accuracy as using mediumdensity SNP data but for a fraction of the cost.21 Genomic selection has the potential to increase genetic gain considerably by reducing generation intervals and increasing selection intensity and selection accuracy. Progeny testing, the basis of dairy cattle breeding programs, is a very expensive and time-consuming process. At least 4.5 years are required for collecting semen of a potentially elite bull, rearing his offspring, and finally predicting his genetic merit based on his offspring’s performance. If the bull is good enough to use in the entire population, then his first sons and daughters will be born when he is about 5.5 years of age. This long generation interval limits the rate of genetic progress. However, genomic testing allows breeders to identify superior bull calves within a few weeks of age, and hence, instead of waiting a minimum of 4.5 years, breeders can used genomic-tested young bulls before 1 year of age. This drastically reduces the generation interval. Similarly, genomic testing of heifer calves allows one to make accurate selection decisions at an early age, and superior females can eventually enter into in vitro fertilization programs,

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Genomic selection: the latest revolution

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FIG. 6.4 Number of genotyped animals included in the US Holstein evaluation since 2011. Low density (LD; less than 40 k) or high density (HD; more than 40 k) SNP chips. Source: Council on Dairy Cattle Breeding website (December 2018; https://www.uscdcb.com).

111

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even before they reach sexual maturity. Moreover, for young bull calves and heifers, genomic testing provides more accurate PTA estimates than traditional parent averages, with average gains in reliability around 30%.22 Finally, greater selection intensity can be achieved using genomics because a large number of selection candidates can be screened in search of elite animals. Overall, by shortening the generation interval and increasing the accuracy and intensity of selection, genomic selection in dairy cattle can at least double annual genetic gains for economically important traits. Fig. 6.5 shows the average net merit (NM$) of marketed US Holstein bulls that entered artificial insemination service in 2005 and later. Interestingly, the rate of genetic improvement in NM$ has increased dramatically since the implementation of genomic evaluation. Note that the benefit of genomics is greatest for lowly heritable traits such as fertility, and traits that can be measured only late in life such as longevity. Indeed, genomic selection in US Holstein cattle has doubled the annual rates of genetic gain for production traits, but has increased from 3-fold to 4-fold for fitness traits, including female fertility, udder health, and productive life.23

Effective use of genomics: sire selection Dairy sire selection has dramatically changed with the implementation of genomic selection. Nowadays, dairy farmers have basically two main options when they make sire selection decisions: use proven, progeny-tested bulls or use young genomic-tested bulls, i.e., young bulls with no progeny that have been evaluated using only their own genomic data. In the US, the National Association of Animal Breeders (NAABs) distinguishes these two groups of bulls as the active (A) bulls, progeny-tested bulls with performance information from at least 10 daughters, and the young genomic-tested (G) bulls, young bulls that do not have offspring yet with milk records. It is important to remark that the number of young genomic-tested bulls currently in the market far exceeds that of progeny-tested bulls. For instance, of the 3,270 Holstein bulls available in the US market in December 2018, 2,735 (84%) were young genomic-tested bulls. Similarly, 403 (79%) of the 510 available Jersey bulls had G status. The key concept regarding young genomictested dairy bulls is that, on average, these young bulls have greater predicted genetic merit values than the proven bulls. For instance,

FIG. 6.5 Average net merit (NM$) of US Holstein bulls by year of entry into the market. Source: National Association of Animal Breeders website (December 2018; https://www.naab-css.org).

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Effective use of genomics: replacement heifer selection

considering the bulls available as semen donors to US dairy farmers in December 2018, the average NM$ of young bulls was $245 and $126 greater than for proven bulls in Holstein and Jersey breeds, respectively. It is worth noting that the changes achieved through genetic selection are cumulative and permanent, and hence, it is expected that the new generation of bulls (G bulls) have (on average) greater genetic merit than the older bulls (A bulls). Now, in the case of the young genomic-tested dairy bulls, higher genetic values are accompanied by lower reliability values. Indeed, considering NM$ of the bulls available in the US market in December 2018, young bulls had 15% and 18% lower reliability values than proven Holstein and Jersey bulls, respectively. This is not surprising considering that the young genomic-tested bulls do not yet have progeny. The question is how dairy farmers should proceed in this scenario, i.e., farmers should use young genomic-tested bulls because they have greater PTA values, or, instead, farmers should use proven bulls because they have more reliable PTA estimates. At this point, it is important to remark that sire selection decisions should be always based on PTA values, and the reliability should be used as a guide to decide how intense to use a bull. Therefore, in this scenario, the best strategy is to use a group or team of young genomic-tested bulls. The advantage of using a group of young bulls is that reliability of the average genetic merit of the team is considerably greater than the reliability of each individual bull.24 The formula for calculating the reliability of a team of young genomic-tested bulls is given by team REL ¼ 1  (1  average RELi)/n, where average RELi is the average REL of individual bulls and n is the number of bulls in the team. For instance, if the reliability of individual young bulls is 70%, the reliability of the genetic merit for a team of three young bulls is about 90%, and if the team increases to six or even twelve young bulls, then the reliability values for the group average between 95% and 98%.

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Effective use of genomics: replacement heifer selection The selection of replacement heifers in commercial dairy farms has been traditionally characterized by very low intensity of selection, because, in general, farmers retain nearly every heifer calf as a future herd replacement. However, recent improvements in herd management and cow comfort have reduced culling rates and improved reproductive efficiency, which has led to the ability to produce an excess of heifers. In addition, sexed semen is now commonly used is dairy farms, generating a considerable surplus of heifer calves. In this context, the selection of replacement heifers is feasible, and genomic testing allows the identification of superior or inferior heifer calves accurately and at an early age. What are the advantages of using genomics for selecting heifer calves? The key point is trying to estimate as precisely as possible the genetic merit of a heifer at a young age. In the absence of genomic information, the selection or culling of a given heifer calf is based on the average genetic merit of her parents, also known as parent average. The reliability of parent average typically ranges from 0 to 0.40 depending on the completeness and accuracy of the pedigree data. Now, if genomic testing is used, then the reliability of the genomic-predicted genetic merit of the heifer calf ranges from 0.60 to 0.75 depending on the trait. Interestingly, this genomic prediction early in life is generally more reliable than the traditional PTA estimated using several lactation records of both the cow and her daughters. Therefore, genomic testing allows farmers to make accurate selection (culling) decisions at an early age; and these decisions are more reliable than those than can be achieved using pedigree information alone. Genomic information on individual heifer calves can be used to reduce feed costs and improve the genetic level of herd replacements.

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The identification of genetically inferior heifer calves allows early culling of these animals, significantly reducing the cost of rearing replacements. Alternatively, these genetically inferior heifers can be inseminated with beef semen to produce high-value crossbred beef calves. Note that cows inseminated with beef semen are in fact removed as parents of the next generation. On the other hand, the identification of superior heifers through genomics can be combined with the use of advanced reproductive technologies to rapidly propagate these animals and generate superior replacements. For instance, highgenetic-merit heifers can be used as donors in either an in vitro fertilization program or an embryo transfer program. Instead, these superior heifers can be inseminated using sexed semen from top sires. It is worth noting that genotyping replacement heifers has extra benefits other than making proper selection and mating decisions, including parentage verification, controlling inbreeding, and avoiding the spread of genetic disorders through genomic-enhanced matings. Arguably, these benefits add value to genomic testing. One of the key points regarding the use of genomics for selecting herd replacements is to demonstrate that the results of the genomic testing are highly correlated with future phenotypic performance. As such, early genomic predictions were compared to subsequent production, udder health, and reproductive performance in the first lactation of Holstein cows.25 Cows were ranked based on their own genomic PTA values (predicted at 12 months of age), and these alternative quartile rankings (from top 25% to bottom 25%) were then compared with the actual phenotypic performance in the first lactation. The 305-day mature equivalent milk yield, average monthly log somatic cell counts, and days open were evaluated as production, udder health, and fertility traits, respectively. If there is an association between genomic testing and future performance, then it is expected that the best heifers in terms of genomic values show

greater phenotypic records. Indeed, for milk production, the observed difference between the top and the bottom quartiles was equal to 4,800 lbs. (Fig. 6.6A). For udder health, the difference in log SCC between the top and the bottom quartile was equal to 0.82 (Fig. 6.6B). For female fertility, the actual difference in days open between those heifers classified as top 25% and those classified as bottom 25% was equal to 21 days (Fig. 6.6C). Therefore, these findings clearly show that early genomic predictions (performed on calves or yearling heifers) can be effectively used as predictors of future performance. In other words, genomic testing can be used to make accurate selection decision at a young age.

Novel traits in the genomics era Genomics has created opportunities to improve traits that are critically important, but too difficult or expensive to measure on the entire population. These relevant phenotypes can be measured only on a relatively small group of genotyped animals, and this reference population can then be used to predict genomic breeding values for the entire population, including young selection candidates.26 Examples of these important traits include feed efficiency,27 methane emission,28 milk progesterone profiles,29 thermoregulation,30 adaptive immune response,31 susceptibility to bovine leukemia virus,32 and resistance to bovine respiratory disease.33 Among these, feed efficiency is probably the most important, as well as the most challenging. Feed represents more than 50% of the total production costs. Hence, improving the efficiency with which dairy cows convert feed into milk has a large economic value. At the same level of production, cows with reduced feed intake requirements are more profitable. It has been suggested that the US dairy industry could save $540 million/year with no loss in milk production by breeding for cows that are more feed efficient. Residual feed intake, the difference

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FIG. 6.6 Phenotypic performance of Holstein cows in their first lactation according to genomic potential. (A) Milk production. (B) Udder health. (C) Reproductive performance. Genomic PTA values were obtained through genomic testing at 12 months of age. DPR, daughter pregnancy rate; SCS, somatic cell score. Adapted from Weigel KA, Mikshowsky AA, Cabrera VE. Effective use of genomics in sire selection and replacement heifer management. In: Paper Presented at: Proc. Western Dairy Management Conference, Reno, NV; 2015.

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between actual intake and intake predicted based on body weight and production level, has been proposed as a selection criterion for improving feed efficiency.34 Interestingly, the selection for lower residual feed intake (improved feed efficiency) has the potential to not only reduce feed costs, but also reduce significant sources of greenhouse gas emissions, such as enteric methane and manure. To date, measures of residual feed intake are limited to research facilities that can precisely determine individual cow feed intake, body weight, body condition score, and milk energy output. Measuring residual feed intake on larger populations, including commercial farms, seems infeasible due to cost and labor constraints. Genomics is an attractive approach for improving feed efficiency because feed intake phenotypes can be collected for a small group of lactating cows and genomicbased breeding values predicted for the entire population.35 In addition, new technologies have been developed that may help predict feed intake. These include sensors for monitoring body temperature, feeding behavior, and physical activity, as well as infrared spectral profiles of milk. These low-cost phenotypes may be combined with direct observations of feed intake to increase the accuracy of genomic evaluations. Dairy bull fertility is another trait that has gained much attention recently. Semen from one service sire bull is used to inseminate hundreds of cows and, thus, one sub-fertile bull could have a major impact on herd reproductive performance. Bull fertility has been evaluated traditionally in the laboratory using different semen attributes, such as sperm morphology, sperm concentration, and sperm motility.36 Unfortunately, these semen traits explain only part of the differences observed in fertility among bulls. Alternatively, bull fertility can be directly evaluated using conception rate records. Since 2008, the US dairy industry has had access to a phenotypic evaluation of bull fertility called Sire Conception Rate (SCR), that is based on a

large, nationwide database of confirmed pregnancy records.37 Interestingly, there is a remarkable variation in SCR among sires, more than 10% conception rate difference between highfertility and low-fertility bulls, and part of this variation is explained by genetic factors.38e40 Recent studies have revealed promising results for predicting SCR values using genomic data.41 Note that SCR records are available only after the bulls are in the market, and hence, early genomic predictions can help the dairy industry make accurate genome-guided selection decisions, such as early culling of predicted sub-fertile bull calves.

Managing inbreeding and genetic diversity Balancing rapid genetic progress and maintenance of adequate genetic diversity has become one of the major challenges of the dairy industry worldwide.42 The loss of genetic diversity can be monitored using the inbreeding coefficient, defined as the probability that the two alleles at any locus in an individual are identical by descent, i.e., the two alleles come from the same ancestor. Inbreeding results from the mating of related individuals e an animal’s inbreeding coefficient is equal to half of the additive genetic relationship between its parents. The mating of related individuals is unavoidable in populations of finite sizes, but this is especially exacerbated in dairy cattle populations due to intense selection and heavy use of reproductive technologies, such as artificial insemination and embryo transfer. As an example, the average inbreeding coefficient for US Holstein cows increased from 0.33% in 1968 to 7.60% in 2018. Inbreeding increases the proportion of loci that are homozygous throughout the genome, some of which causes homozygosity of recessive alleles that negatively impact an animal’s performance. This phenomenon is commonly known as inbreeding depression and tends to be most

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Final remarks

pronounced on fitness traits, but undesirable effects are observed in most traits including production. For instance, in US Holstein cows, lifetime net income decreases about $23 per 1% increase in the inbreeding coefficient.43 In addition, inbreeding increases the chances of the expression of lethal or sub-lethal recessive alleles. Examples of known genetic defects in dairy cattle include bovine leukocyte adhesion deficiency (BLAD), complex vertebral malformation (CVM), deficiency of uridine monophosphate synthase (DUMPS), mulefoot (syndactyly), cholesterol deficiency (HCD), and an increasing list of recessive haplotypes that lead to impaired fertility, early embryonic losses, and abortions.44 Haplotype and SNP tests are now routinely used to identify carriers and track the inheritance of these genetic defects. Inbreeding in the short-term can be controlled in the herd using computerized mate selection programs.45 Given a cow and a list of potential bulls, these programs control the inbreeding of the hypothetical offspring either by (i) minimizing the inbreeding, (ii) maximizing the expected genetic merit subject to a fixed inbreeding threshold, or (iii) maximizing the expected genetic merit after adjustment for anticipated costs of inbreeding

depression. It is possible to control inbreeding without affecting genetic progress. Fig. 6.7 compares the genetic trends in lifetime net merit and the trends in inbreeding of North Florida Holsteins, a large and very progressive US commercial farm, versus the entire US cow population. North Florida Holsteins has an annual genetic trend of $61 while the annual trend for the US cow population is $35 (Fig. 6.7A). This farm has achieved remarkable genetic gains by using genomic testing for selecting heifers combined with embryo transfer and in vitro fertilization for rapid propagation of the best females. Interestingly, these remarkable genetic trends have been achieved while keeping the inbreeding at the same rate as the rest of the US cow population (Fig. 6.7B).

Final remarks Dairy cattle genetic programs have achieved remarkable progress, mainly in production traits. The success of these programs is based on the collection and analysis of massive databases of performance records and pedigree information, widespread use of assisted reproductive

FIG. 6.7 Genetic trend of Lifetime Net Merit (A) versus trend of Inbreeding (B) in Holstein dairy cows. US population of Holstein cows (US; blue [light gray in print version]) and North Florida Holsteins (NFH; orange [black in print version]).

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technologies, and more recently, genomic data. Indeed, genomics has transformed dairy cattle breeding programs because breeders can select young bulls and heifers with sufficient accuracy, reducing generation interval, thereby increasing the rate of genetic gain. Selection objectives have evolved over time, from increasing milk yield to improving milk solids and enhancing health and fertility traits, following the needs and concerns of producers, milk processors, and consumers. The widespread implementation of on-farm sensors and monitoring systems, such as activity and rumination monitors, automated calf feeders, and in-line milk sensors, among others, provide huge amounts of data, generating opportunities to incorporate new traits into genetic selection programs. In addition, new genomic technologies, such as whole-genome sequencing and genome editing, will provide new tools for genetic improvement. In the future, health and fertility traits, as well as environmental sustainability traits, such as feed efficiency and methane emission, will be very important given the increasing concerns of society about dairy cow welfare and the environmental impacts of dairy farming.

References 1. van Tassell CP, van Vleck LD. Estimates of genetic selection differentials and generation intervals for four paths of selection. J Dairy Sci. 1991;74(3):1078e1086. 2. Inchaisri C, Jorritsma R, Vos PLAM, van der Weijden GC, Hogeveen H. Economic consequences of reproductive performance in dairy cattle. Theriogenology. 2010;74(5):835e846. 3. Royal MD, Flint AP, Woolliams JA. Genetic and phenotypic relationships among endocrine and traditional fertility traits and production traits in HolsteinFriesian dairy cows. J Dairy Sci. 2002;85(4):958e967. 4. VanRaden PM, Sanders AH, Tooker ME, et al. Development of a national genetic evaluation for cow fertility. J Dairy Sci. 2004;87(7):2285e2292. 5. Liang D, Arnold LM, Stowe CJ, Harmon RJ, Bewley JM. Estimating US dairy clinical disease costs with a stochastic simulation model. J Dairy Sci. 2017;100(2): 1472e1486.

6. Egger-Danner C, Cole JB, Pryce JE, et al. Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits. Animal. 2015;9(2):191e207. 7. Shook GE, Schutz MM. Selection on somatic cell score to improve resistance to mastitis in the United States. J Dairy Sci. 1994;77(2):648e658. 8. VanRaden PM, Klaaskate EJ. Genetic evaluation of length of productive life including predicted longevity of live cows. J Dairy Sci. 1993;76(9):2758e2764. 9. Wright JR, VanRaden PM. Genetic evaluation of dairy cow livability. J Anim Sci. 2016;94:178. 10. Dematawewa CM, Berger PJ. Effect of dystocia on yield, fertility, and cow losses and an economic evaluation of dystocia scores for Holsteins. J Dairy Sci. 1997;80(4): 754e761. 11. de Maturana EL, Wu XL, Gianola D, Weigel KA, Rosa GJ. Exploring biological relationships between calving traits in primiparous cattle with a bayesian recursive model. Genetics. 2009;181(1):277e287. 12. Hazel LN, Dickerson GE, Freeman AE. The selection index–then, now, and for the future. J Dairy Sci. 1994; 77(10):3236e3251. 13. VanRaden PM. Invited review: selection on net merit to improve lifetime profit. J Dairy Sci. 2004;87(10): 3125e3131. 14. Shook GE. Major advances in determining appropriate selection goals. J Dairy Sci. 2006;89(4):1349e1361. 15. Cole JB, VanRaden PM. Symposium review: possibilities in an age of genomics: the future of selection indices. J Dairy Sci. 2018;101(4):3686e3701. 16. Miglior F, Muir BL, Van Doormaal BJ. Selection indices in Holstein cattle of various countries. J Dairy Sci. 2005; 88(3):1255e1263. 17. Goddard ME, Hayes BJ. Genomic selection. J Anim Breed Genet. 2007;124(6):323e330. 18. Gibbs RA, Taylor JF, Van Tassell CP, et al. Genomewide survey of SNP variation uncovers the genetic structure of cattle breeds. Science. 2009;324(5926): 528e532. 19. Matukumalli LK, Lawley CT, Schnabel RD, et al. Development and characterization of a high density SNP genotyping assay for cattle. PLoS One. 2009;4(4): e5350. 20. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157(4):1819e1829. 21. Weigel KA, Van Tassell CP, O’Connell JR, VanRaden PM, Wiggans GR. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms. J Dairy Sci. 2010;93(5): 2229e2238.

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22. Wiggans GR, Cole JB, Hubbard SM, Sonstegard TS. Genomic selection in dairy cattle: the USDA experience. Annu Rev Anim Biosci. 2017;5:309e327. 23. Garcia-Ruiz A, Cole JB, VanRaden PM, Wiggans GR, Ruiz-Lopez FJ, Van Tassell CP. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc Natl Acad Sci USA. 2016;113(28):E3995eE4004. 24. Schefers JM, Weigel KA. Genomic selection in dairy cattle: integration of DNA testing into breeding programs. Anim Front. 2012;2(1):4e9. 25. Weigel KA, Mikshowsky AA, Cabrera VE. Effective use of genomics in sire selection and replacement heifer management. In: Paper Presented at: Proc. Western Dairy Management Conference, Reno, NV. 2015. 26. Calus MP, de Haas Y, Pszczola M, Veerkamp RF. Predicted accuracy of and response to genomic selection for new traits in dairy cattle. Animal. 2013;7(2):183e191. 27. VandeHaar MJ, Armentano LE, Weigel K, Spurlock DM, Tempelman RJ, Veerkamp R. Harnessing the genetics of the modern dairy cow to continue improvements in feed efficiency. J Dairy Sci. 2016;99(6):4941e4954. 28. Wall E, Simm G, Moran D. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal. 2010;4(3):366e376. 29. Sorg D, Wensch-Dorendorf M, Schopke K, et al. Genetic analysis of new progesterone-based fertility traits in dairy cows measured on-farm. J Dairy Sci. 2017; 100(10):8205e8219. 30. Dikmen S, Cole JB, Null DJ, Hansen PJ. Genome-wide association mapping for identification of quantitative trait loci for rectal temperature during heat stress in Holstein cattle. PLoS One. 2013;8(7):e69202. 31. Thompson-Crispi KA, Sewalem A, Miglior F, Mallard BA. Genetic parameters of adaptive immune response traits in Canadian Holsteins. J Dairy Sci. 2012;95(1):401e409. 32. Abdalla EA, Pe~ nagaricano F, Byrem TM, Weigel KA, Rosa GJ. Genome-wide association mapping and pathway analysis of leukosis incidence in a US Holstein cattle population. Anim Genet. 2016;47(4):395e407. 33. Neibergs HL, Seabury CM, Wojtowicz AJ, et al. Susceptibility loci revealed for bovine respiratory disease complex in pre-weaned holstein calves. BMC Genomics. 2014;15:1164.

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34. Connor EE. Invited review: improving feed efficiency in dairy production: challenges and possibilities. Animal. 2015;9(3):395e408. 35. Yao C, Zhu X, Weigel KA. Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle. Genet Sel Evol. 2016;48(1):84. 36. DeJarnette JM, Marshall CE, Lenz RW, Monke DR, Ayars WH, Sattler CG. Sustaining the fertility of artificially inseminated dairy cattle: the role of the artificial insemination industry. J Dairy Sci. 2004;87(Suppl.): E93eE104. 37. Kuhn MT, Hutchison JL. Prediction of dairy bull fertility from field data: use of multiple services and identification and utilization of factors affecting bull fertility. J Dairy Sci. 2008;91(6):2481e2492. 38. Nicolini P, Amorin R, Han Y, Pe~ nagaricano F. Wholegenome scan reveals significant non-additive effects for sire conception rate in Holstein cattle. BMC Genet. 2018;19(1):14. 39. Han Y, Pe~ nagaricano F. Unravelling the genomic architecture of bull fertility in Holstein cattle. BMC Genet. 2016;17(1):143. 40. Rezende FM, Dietsch GO, Penagaricano F. Genetic dissection of bull fertility in US Jersey dairy cattle. Anim Genet. 2018;49(5):393e402. 41. Abdollahi-Arpanahi R, Morota G, Pe~ nagaricano F. Predicting bull fertility using genomic data and biological information. J Dairy Sci. 2017;100(12):9656e9666. 42. Howard JT, Pryce JE, Baes C, Maltecca C. Invited review: inbreeding in the genomics era: inbreeding, inbreeding depression, and management of genomic variability. J Dairy Sci. 2017;100(8):6009e6024. 43. Smith LA, Cassell BG, Pearson RE. The effects of inbreeding on the lifetime performance of dairy cattle. J Dairy Sci. 1998;81(10):2729e2737. 44. VanRaden PM, Olson KM, Null DJ, Hutchison JL. Harmful recessive effects on fertility detected by absence of homozygous haplotypes. J Dairy Sci. 2011; 94(12):6153e6161. 45. Weigel KA, Lin SW. Use of computerized mate selection programs to control inbreeding of Holstein and Jersey cattle in the next generation. J Dairy Sci. 2000;83(4): 822e828.

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C H A P T E R

7 Physiology of lactation in dairy cattledchallenges to sustainable production Geoffrey E. Dahl Department of Animal Sciences, University of Florida, Gainesville, FL, United States

O U T L I N E Current state of affairs

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Current state of affairs Milk is defined as a “fluid secreted by the mammary gland of females for the nourishment of their young.”1 Milk from ruminants, especially cows, provides a significant source of nutrient dense calories in the form of fluid milk and associated products such as cheese and yogurt. Indeed, a growing body of evidence suggests that including animal source foods in the diet overcomes nutrient deficiencies associated with their lack in the diet, particularly physical

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00007-0

and cognitive stunting.2,3 And, milk protein is the most efficient of all animal proteins when compared on a production per unit of land basis, almost twice the efficiency of poultry meat and rivaling that of maize protein (Fig. 7.1).4 Dairy production ultimately depends on the rate of milk synthesis in the mammary gland, which can be affected by a number of factors. In the US and most developed countries, milk yield per cow has increased tremendously in the past 70 years due to improvements in genetic selection for yield and technical advances in feeding,

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FIG. 7.1 Estimates of square meters of land required to produce 1 gm of human edible protein from various crops or production systems. Copied with permission from Britt et al.4

management and health that support expression of the higher genetic potential. In 2017, the average annual yield per cow was 10,404 kg, and the record yield is 35,451 kg.5 Thus, the modern dairy cow, properly managed, can produce a substantial volume of nutrient rich, human consumable food. And, the potential is more than three-fold greater than currently realized. In developing countries, however, milk yield hovers between 500 and 1,500 kg annually.6 In many countries native breeds serve as the base of the dairy herd, and genetic limitations are apparent in those animals. But, more important to explanation of low yields are management and feeding deficiencies that hamper the ability of even those animals with lower genetic potential to express greater productivity. With the demand for animal source foods increasing in many developing countries as incomes rise, and as awareness of the importance of animal source foods to prevent stunting increases, there is a push to improve the output of milk in total and on a per cow basis. Much of this increase

can be realized with improved feed availability, better management of reproduction and health, and improvements in animal and milk harvest hygiene. Over the next 30 years, continued improvements in milk output will be achieved in developed and developing countries. Rather than a review of mammary biology, the rest of this chapter considers broad areas related to mammary function and cow management that are likely to impact the continued rise in milk yields. Sustainably producing milk in 2050 is likely to be associated with continued increases in output on a per cow basis, but that rise in yield will only occur with improvements in genetic selection, nutrition and management of the herd worldwide. A variety of management interventions are discussed below and some of these factors are summarized in Fig. 7.2.

Mammary growth and function The mammary gland is a modified skin gland developmentally, and secretion of milk components is controlled by the endocrine system.7 Structurally, the secretory epithelial cells are organized into alveoli, which expel milk components into the open area apical to the cell orientation. With regard to structure, there is no difference in the basic secretory unit regardless of productive potential, rather, it is the number of secretory cells that are linked to productivity. Whereas udder size is not highly correlated with output on an animal-to-animal basis, per se, as lactation number advances and udder volume increases there is an increase in milk output. There is little reason to envision substantial increases in udder volume in the future, rather, more efficient output is the route to higher yields. That is not to say, however, that mammary epithelial cell number cannot be manipulated. There is constant loss and regeneration of cells during lactation, but cows typically experience a net loss of about

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FIG. 7.2 Depiction of the relationship of mammary cell number and milk yield throughout the lactation cycle of a cow, with emphasis on factors that can alter cell number and activity across the lactation and dry period. The lactation curve is characterized by a peak in cell number and then activity during the initial 6e8 weeks in milk, as depicted in the open curve. The solid black curve depicts the typical milk yield response observed in cows in many emerging and developing country systems, characterizing the lower peak and overall yield of milk, as well as the shorter duration of lactation. Early in lactation, cell number increases and that can be further stimulated by more frequent milking (IMF) in the first 3 weeks in milk. Following the peak of lactation, cell number and thus, yield, decline as lactation advances. A number of factors such as poor nutrition, sub-clinical mastitis as indicated by high SCC, heat stress and incomplete milk removal can limit the peak and accelerate the decline in yield. Conversely, management interventions such as bovine somatotropin (bST), increased milking frequency, and long day photoperiod can be used to slow the decline in even increase the yield of well-fed cows (illustrated as the shaded curve). The dry period is characterized by involution and some cell loss followed by regeneration of mammary cells as parturition approaches. Dry period interventions that increase mammary development and subsequent yield (i.e., the shaded curve) include short day photoperiod exposure and heat stress abatement. Therefore, many management factors can be manipulated throughout the lactation cycle to further enhance mammary gland output. These factors are discussed throughout this chapter. Adapted from Capuco and Ellis.8

50% of the secretory cells they begin lactation with as that lactation advances.8 Indeed, one area of particular interest is mammary stem cell biology and how that may be harnessed to increase the number and duration of activity of mammary epithelial cells in a given lactation. Mammary growth or mammogenesis begins in utero and development mirrors overall body growth until puberty. Estrogen and progesterone are the dominant hormones related to mammary growth, although adrenal steroids, somatotropin, prolactin and a number of local growth factors also play roles.9 At the onset of estrous cyclicity (or menstruation in primates) the waves of estrogen and progesterone during the cycle drive progressive increases in ductular

expansion into the mammary fat pad, as well as some alveolar development. With pregnancy, mammary growth accelerates under the synergistic effects of greater circulating concentrations of estrogen and progesterone. This is particularly evident with regard to secretory tissue development. But, growth in itself does not mean that milk secretion begins without the process of lactogenesis, which occurs around the time of parturition in response to the periparturient surge of prolactin. That increase in prolactin activates the cellular mechanisms responsible for lactose (and casein) production, and lactose secretion from the mammary epithelial cell results in fluid movement into the alveolar lumen, thus milk secretion. Maintenance of

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milk secretion, i.e., galactopoiesis, depends on continued milk removal from the gland, and endocrine system support via somatotropin, prolactin, thyroid hormones and cortisol; the gonadal steroids have limited effects after lactation is established. With regard to mammogenesis, there are a number of management factors that can be associated with reductions in ultimate productivity, likely because of impaired mammary growth and development. One of the most studied is that of excessive fat accumulation prepubertally and postpubertally, which limits the extent of parenchymal infiltration of the mammary fat pad and, ultimately, the volume of secretory tissue in the gland. Before puberty, when mammary growth proceeds in an allometric manner, nutrient supply appears to be critical to maximize growth. Specifically, when high volumes of whole milk are fed to developing heifers, mammary growth increases and subsequent first lactation yields are improved,10 but these effects appear to be independent of skeletal growth. After puberty, when mammary development returns to an isometric growth rate, neither nutrient supply nor stimulation of the somatotropic axis alters parenchymal accumulation, although overall body growth was improved.11,12 Thus, it appears that prior to puberty, nutrition limits maximal mammary growth whereas that influence recedes after puberty. Manipulation of photoperiod, or the duration of light that a heifer is exposed to each day will also impact mammary growth and eventually, production of milk. Calves that are raised under long days of 16 h of light and 8 h of darkness will have increased mammary parenchymal development when compared to herdmates housed under a short day photoperiod of 8 h of light and 16 h of darkness. Both prolactin and insulin-like growth factor-1 (IGF-1) are increased in heifers housed on long days, and they may be the drivers to increase parenchymal growth and lean body growth in general.13 When those heifers calve, their first lactation yields exceed

those of short-day heifers, further supporting the concept that parenchymal growth is related to milk yield, and that management factors can impact mammary development long before the first lactation begins. Heat stress is the other environmental factor that has significant impacts on mammogenesis and mammary function, both in mature cows and during development. While the effects of heat stress during an established lactation are well described,14 recent studies support the concept that heat stress in late gestation reduces mammary growth in the dry period which leads to lower milk yield in the subsequent lactation and poorer immune status through the transition into lactation.15 Of interest, the developing fetus also suffers negative effects of heat stress, wherein calves born to heat stressed dams subsequently produce less milk in their initial lactation, suggesting that in utero heat stress alters mammary development for life, likely through epigenetic alterations of the genome.16 Because the majority of cows producing milk reside in subtropical or tropical climates worldwide, these findings have significant relevance to future improvements in the efficiency of milk production around the globe.

Nutrition and metabolism The ruminant digestive system offers a significant advantage with regard to feed availability and byproduct utilization. This is particularly relevant when considering the conversion of non-human consumable feeds and byproducts into highly nutritious milk and milk products. For example, crop residues, oilseed meals and other byproducts of food manufacturing comprise at least 40% of dairy diets in developed countries, and are an ever-growing contribution to rations in developing countries.17 In addition, the list of novel alternative feeds is expanding, particularly in developing countries, where scarce land and water resources limit overall

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feed production capacity.18 This is a significant component of the sustainable nature of milk as a food for human consumption. Indeed, by converting non-human consumable byproducts and inedible highly fibrous plant material to milk, dairy cows fill a niche that would otherwise be a drag on waste streams. Further, those byproducts and other roughages would still produce methane as they decayed, so the net production of nutritious food per unit of greenhouse gas (GHG) is likely underestimated because no credit is assigned for the conversion. Despite their ability to convert a variety of forage and byproducts to milk, the volume of feed needed for dairy cows presents an ongoing challenge, one that is exacerbated early in lactation. The metabolic load of a high yielding cow in early lactation represents a significant challenge to maintain normal function while simultaneously secreting 1.5 kg of milk protein, 2.5 kg of lactose and 1.75 kg of milk fat each day, assuming a typical 50 L of milk daily yield. The output of energy dwarfs the maintenance requirements of the rest of the body, and it is perhaps most accurate to say that the cow is an appendage of the gland in early lactation, rather than the reverse. Cows are simply incapable of consuming enough dry matter to meet their energy requirements in early lactation and they enter a phase of negative energy balance, and must mobilize tissue energy reserves. There is a coordination of the somatotropic system such that the catabolic actions predominate to increase mobilization of fat stores,19 and this state continues for 6e8 weeks into lactation when energy intake from dry matter intake exceeds that of milk energy output and energy balance is restored. While substantial literature suggests that the consequences of negative energy balance, including elevated circulating concentrations non-esterified fatty acids and ketone bodies, predisposes the cow in early lactation to metabolic and pathogen-induced disease, it is a normal physiological process that can be monitored

and managed to limit negative effects.20 One of the primary issues is appropriate nutrition during the dry period to avoid excessive body condition at calving. Managing the nutrient intake of cows during the dry period to limit conditioning should be a priority. Significant bodyweight gains, or extended lengths of the dry period, have been associated with greater risk of metabolic disease post-partum. In contrast, dry periods of under 30 days can limit productivity in the next lactation. A number of studies have investigated nutritional management to reduce the nutrient density of prepartum diets to slow the accumulation of body condition in dry cows so that a dry period of 45e60 days can be managed effectively and without overconditioning the cow.21

Reproduction As the final phase of the reproductive process in mammals, normal lactation depends on successful reproduction, and that is particularly important in dairy cattle as milk yield wanes with advancing lactation. Therefore, maintaining annual calving results in the greatest overall milk output. Reproductive performance in heifers is typically quite robust in a well-managed herd. But a steady decline in daughter pregnancy rate (DPR) was associated with increased emphasis on selection for milk yield from 1960 to 2010, and reproductive performance suffered as yield climbed.22 Over the past decade, however, the DPR has increased primarily because of two factors. First, improved genetic selection for DPR has shown significant improvement in reproductive performance.23 Second, the advent of timed breeding protocols, i.e., Ov-synch, have dramatically improved the ability to achieve high levels of reproductive performance in lactating dairy cows.24 The improved ability to attain reproductive success without sacrificing continued milk yield increments, however, may not be limitless. Consumer pressure over the

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use of exogenous hormones for management may affect the ability to take advantage of systems to synchronize ovulation in the future. But, other technologies are being developed and perfected to replace older techniques that become obsolete or unavailable. For example, cow activity monitoring and in-line milk progesterone monitoring25,26 are currently being used to determine estrus in a non-invasive manner. But those approaches require significant financial investments and may not be suitable in many situations in developing countries. Selection for reproductive performance using genomic, and eventually epigenomic methods, may further improve our ability to realize highly fertility along with high milk yield. Sex sorted semen allows for gender selection of over 90%, a substantial boon to the dairy industry with regard to the production of heifers as potentially lactating animals. Obviously the number of females is increased, but with that selection comes a greater ability to improve the overall genetic value of the herd. Alternatively, with the advent of sexed semen, there has been a significant increase in heifer availability throughout the dairy industry in North America.27 As the number of available heifers increases, alternative approaches to breeding the entire herd are realized, specifically the selective mating of the bottom half of the herd to beef sires, in an effort increase the value of those offspring for the beef market. In countries where the dairy industry is less developed, the application of AI is useful to accelerate genetic progress. But, it is important to consider the management and feeding capacity of those countries in concert with genetic improvement, as forage quantity and quality in particular may mask expression of the genetic capacity for production.

Genetic innovations Genomic selection has had a tremendous impact on selection of dairy cattle for superior

traits, but this has been especially true for health and well-being traits relative to production traits.23 As improvements are made in identification of markers for disease resistance and reproductive performance, and even for geographic or management specific traits, it should be possible to select for more resilient animals that are best suited for that production system (e.g., pasture vs. freestall). In addition, better understanding of the epigenomic28 effects on mammary gland function and metabolism will enhance our ability to identify highly productive cows that function well within specific production systems. Indeed, advances in genomic and even epigenomic selection may be of huge impact in developing countries to improve yield in indigenous breeds without sacrificing adaptive traits.29 Selection of superior indigenous bulls may then allow for wider propagation of those improved genetics through coordinated breeding programs using AI and other advanced reproductive technologies. However, it is critical that performance estimates be confirmed under local conditions of adequate management, or improvements will not be realized at the farm level.

Animal health and well-being Udder health is a key component of successful lactation. Mastitis, or inflammation of the gland, is most commonly caused by an invasion of bacterial pathogens, and can cause substantial reductions in yield and quality of milk, and even death of the cow depending on the pathogen and associated complications.30 Despite improvements in milk harvest and sanitation, mastitis continues to be a problem in the dairy industry. Antibiotics are routinely used as a therapy, but a growing body of evidence suggests that many infections will clear-up spontaneously without antimicrobial therapy and the pathogen profile is shifting away from microorganisms that are sensitive to those products available for use in food producing animals.31 Indeed,

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improvements in on-farm identification of particular mastitis pathogens to more effectively target responsive pathogens is likely to realize a reduction in antibiotic use and greater cure rates overall relative to the less discriminate treatment strategies of the past. Moreover, supportive therapies, including fluids and nonsteroidal anti-inflammatory drugs (NSAID’s), to deal with the broader effects of infection with gram negative pathogens such as E. Coli are an important consideration to recovery of the animal and a functional mammary gland.32 Stem cell biology may be useful in overcoming the loss of productivity associated with mastitis, particularly in instances where mammary tissue loss occurs.8 In many cases of severe mastitis, local inflammation of the gland results in loss of secretory tissue and even functionality of individual quarters. Typically, milk secretion from those quarters never recovers, which suggests a loss of regenerative capacity of the mammary epithelial cells. With greater understanding of stem cell biology, it may be possible to develop therapies that overcome the loss of secretory capacity and allow for recovery of milk production from an infected quarter of the mammary gland. Blanket antibiotic therapy for dry cows has yielded great improvements in reducing chronic infections in cows, but is an area of substantial research emphasis currently. There is the potential to dramatically limit the use of antibiotics in the dairy industry. That is due to the fact that many cows may have no infection at the end of lactation, yet all quarters of the mammary gland are treated regardless of infection status. Selecting specific cows or quarters to receive dry cow antibiotic therapy will require improved screening methods to ensure pathogen identification, and a higher level of individual cow management.33 Practices such as gradually reducing milk removal and pharmaceutical interventions34 to rapidly limit milk secretion may sufficiently limit residual milk, even in high producing cows at dry off. This would decrease the potential

for new intramammary infections during the dry period, but field based evidence is lacking to confirm the utility of those approaches. While judicious use of antibiotics will continue to be important for managing mastitis in dairy cattle, consumers and regulators are exerting pressure to minimize the amount and range of antibiotic therapies. Therefore, improvements in host ability to resist pathogen infiltration and establishment of infection offers a potential approach to reduce the volume and necessity of antibiotic use related to dairy management. Transgenic approaches have been used to achieve this goal, but with limited success.35 In addition, these methods would likely face regulatory and consumer acceptance burdens that would limit their rapid translation into industry practice.

Housing and monitoring Evidence continues to emerge that housing affects cow productivity and welfare, and represents an area for further management interventions. Free-stall barns that allow for expression of individual cow behavior for feeding, grooming and mobility are now used widely in developed countries. That will likely continue as the housing method of choice, while tie-stalls and other barn designs that limit animal movement will decline in use. Pasture, as a housing system (vs. nutrition), is an area of interest, as some perceive this as a more “natural” choice for cattle and promote movement toward having all cows having the option for all cows to have outdoor access. However, it is important to consider the effects of heat stress on pastured cattle as there are significant limitations to cooling systems in the pasture setting compared with cows housed in conventional barns.36 As previously mentioned, photoperiod and heat stress can impact mammogenesis and thus alter mammary output of milk. However, there are also direct effects of both environmental factors on milk production. Manipulation of

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photoperiod improves the production efficiency during lactation and the potential for milk yield during the dry period.37 Therefore, appropriate management of the lighting system to which cows are exposed in modern confinement operations can yield significant improvements in efficiency. Heat stress is an example of an environmental insult to productive efficiency across the life cycle of the dairy cow. Lactating cows decrease dry matter intake under conditions above a temperature:humidity index (THI) of 72, and experience a corresponding reduction in milk yield.14 The lower milk yield, however, is more severe than expected from the reduced energy intake, thus the cows are dramatically less efficient. Robotics is another example of an emerging technology that will potentially improve lactation and overall cow management.38 Automated milking systems allow a cow to select the appropriate number of milkings each day, and there is evidence that cows alter the frequency of milking as lactation advances. This is a good example of a technology that will allow animals to express natural behaviors with fewer constraints imposed by the management system itself.39 That, in turn, should improve animal welfare. Individual monitoring of cows is streamlined in automated milking systems and thus “personalized” cow management can be practiced with ease with that technology.40 As monitoring and data reduction become more pervasive in the dairy industry, new opportunities will arise for decision support tools that can be used to optimize individual management of cows regardless of their point in the lactation cycle.

References 1. Webster’s Ninth New Collegiate Dictionary. Springfield, MA: Merriam-Webster Inc.; 1987. 2. Hulett JL, Weiss RE, Bwibo NO, Galal OM, Drorbaugh N, Neumann CG. Animal source foods have a positive impact on the primary school test scores of Kenyan schoolchildren in a cluster-randomised, controlled feeding intervention trial. Br J Nutr. 2014; 111(5):875e886.

3. Iannotti LL. The benefits of animal products for child nutrition in developing countries. Rev Sci Tech. 2018; 37(1):37e46. 4. Britt JH, Cushman RA, Dechow CD, et al. Invited review: learning from the future-A vision for dairy farms and cows in 2067. J Dairy Sci. 2018;101(5):3722e3741. 5. New National Milk Production Record Set. Dairy Herd Management; 2017. https://www.dairyherd.com/ article/new-national-milk-production-record-set. 6. Salmon GR, Marshall K, Tebug SF, et al. The greenhouse gas abatement potential of productivity improving measures applied to cattle systems in a developing region. Animal. 2018;12(4):844e852. 7. Capuco AV, Akers RM. The origin and evolution of lactation. J Biol. 2009;8(4):37.1e37.4. 8. Capuco AV, Ellis SE. Comparative aspects of mammary gland development and homeostasis. Annu Rev Anim Biosci. 2013;1:179e202. 9. Tucker HA. Hormones, mammary growth, and lactation: a 41-year perspective. J Dairy Sci. 2000;83(4):874e884. 10. Moallem U, Werner D, Lehrer H, et al. Long-term effects of ad libitum whole milk prior to weaning and prepubertal protein supplementation on skeletal growth rate and first-lactation milk production. J Dairy Sci. 2010; 93(6):2639e2650. 11. Capuco AV, Dahl GE, Wood DL, Moallem U, Erdman RE. Effect of bovine somatotropin and rumenundegradable protein on mammary growth of prepubertal dairy heifers and subsequent milk production. J Dairy Sci. 2004;87(11):3762e3769. 12. Moallem U, Dahl GE, Duffey EK, Capuco AV, Erdman RA. Bovine somatotropin and rumenundegradable protein effects on skeletal growth in prepubertal dairy heifers. J Dairy Sci. 2004;87(11):3881e3888. 13. Rius AG, Dahl GE. Exposure to long-day photoperiod prepubertally may increase milk yield in first-lactation cows. J Dairy Sci. 2006;89(6):2080e2083. 14. Collier RJ, Dahl GE, VanBaale MJ. Major advances associated with environmental effects on dairy cattle. J Dairy Sci. 2006;89(4):1244e1253. 15. Dahl GE, Tao S, Laporta J. Triennial lactation symposium/bolfa: late gestation heat stress of dairy cattle programs dam and daughter milk production. J Anim Sci. 2017;95(12):5701e5710. 16. Skibiel AL, Pe~ nagaricano F, Amorín R, et al. In utero heat stress alters the offspring epigenome. Sci Rep. 2018;8(1):14609. 17. van Lingen HJ, Fadel JG, Bannink A, et al. Multi-criteria evaluation of dairy cattle feed resources and animal characteristics for nutritive and environmental impacts. Animal. 2018;12(s2):s310es320. 18. Halmemies-Beauchet-Filleau A, Rinne M, Lamminen M, et al. Review: alternative and novel feeds for ruminants: nutritive value, product quality and environmental aspects. Animal. 2018;12(s2):s295es309.

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19. Bauman DE, Peel CJ, Steinhour WD, et al. Effect of bovine somatotropin on metabolism of lactating dairy cows: influence on rates of irreversible loss and oxidation of glucose and nonesterified fatty acids. J Nutr. 1988;118(8):1031e1040. 20. McArt JA, Nydam DV, Oetzel GR, Overton TR, Ospina PA. Elevated non-esterified fatty acids and b-hydroxybutyrate and their association with transition dairy cow performance. Vet J. 2013;198(3):560e570. 21. Dann HM, Litherland NB, Underwood JP, et al. Diets during far-off and close-up dry periods affect periparturient metabolism and lactation in multiparous cows. J Dairy Sci. 2006;89(9):3563e3577. 22. Hansen PJ. Current and future assisted reproductive technologies for mammalian farm animals. Adv Exp Med Biol. 2014:7521e7522. 23. García-Ruiz A, Cole JB, VanRaden PM, et al. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc Natl Acad Sci USA. 2016;113(28):E3995eE4004. 24. Bisinotto RS, Ribeiro ES, Santos JE. Synchronisation of ovulation for management of reproduction in dairy cows. Animal. 2014;8(Suppl. 1):151e159. 25. Bruinje TC, Colazo MG, Ribeiro ES, Gobikrushanth M, Ambrose DJ. Using in-line milk progesterone data to characterize parameters of luteal activity and their association with fertility in Holstein cows. J Dairy Sci. 2019; 102(1):780e798. 26. Mayo LM, Silvia WJ, Ray DL, et al. Automated estrous detection using multiple commercial precision dairy monitoring technologies in synchronized dairy cows. J Dairy Sci. 2019;102(3):2645e2656. 27. De Vries A, Overton M, Fetrow J. Exploring the impact of sexed semen on the structure of the dairy industry. J Dairy Sci. 2008;91(2):847e856. 28. Gonz alez-Recio O, Toro MA, Bach A. Past, present, and future of epigenetics applied to livestock breeding. Front Genet. 2015;6:305. 29. Mrode R, Ojango JMK, Okeyo AM, Mwacharo JM. Genomic selection and use of molecular tools in breeding programs for indigenous and crossbred cattle in developing countries: current status and future prospects. Front Genet. 2019;9:694.

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30. Ruegg PLA. 100-Year Review: mastitis detection, management, and prevention. J Dairy Sci. 2017;100(12): 10381e10397. 31. Lago A, Godden SM. Use of rapid culture systems to guide clinical mastitis treatment decisions. Vet Clin North Am Food Anim Pract. 2018;34(3):389e412. 32. Petersson-Wolfe CS, Leslie KE, Schwarz TH. An update on the effect of clinical mastitis on the welfare of dairy cows and potential therapies. Vet Clin North Am Food Anim Pract. 2018;34(3):525e535. 33. Vanhoudt A, van Hees-Huijps K, van Knegsel ATM, et al. Effects of reduced intramammary antimicrobial use during the dry period on udder health in Dutch dairy herds. J Dairy Sci. 2018;101(4):3248e3260. 34. Boutinaud M, Isaka N, Gandemer E, et al. Inhibiting prolactin by cabergoline accelerates mammary gland remodeling during the early dry period in dairy cows. J Dairy Sci. 2017;100(12):9787e9798. 35. Fan W, Plaut K, Bramley AJ, et al. Persistency of adenoviral-mediated lysostaphin expression in goat mammary glands. J Dairy Sci. 2004;87(3):602e608. 36. Cardoso CS, von Keyserlingk MAG, H€ otzel MJ, Robbins J, Weary DM. Hot and bothered: public attitudes towards heat stress and outdoor access for dairy cows. PLoS One. 2018;13(10):e0205352. 37. Dahl GE, Tao S, Thompson IM. Lactation biology symposium: effects of photoperiod on mammary gland development and lactation. J Anim Sci. 2012;90(3): 755e760. 38. John AJ, Clark CE, Freeman MJ, et al. Review: milking robot utilization, a successful precision livestock farming evolution. Animal. 2016;10(9):1484e1492. 39. John AJ, Freeman MJ, Kerrisk KF, Garcia SC, Clark CEF. Robot utilisation of pasture-based dairy cows with varying levels of milking frequency. Animal. 2018;21:1e7. 40. Halachmi I, Guarino M, Bewley J, Pastell M. Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Annu Rev Anim Biosci. 2018. https://doi.org/10.1146/annurevanimal-020518-114851.

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C H A P T E R

8 Reproductive management of dairy cattle William W. Thatcher, Jos
e E.P. Santos Department of Animal Sciences, University of Florida, Gainesville, FL, United States

O U T L I N E Introduction

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Sequential development and efficacy of reproductive management programs OvSynch PreSynchPGF2a-OvSynch PreSynch using GnRH before-OvSynch Additional refinements to TAI programs Supplemental progesterone (P4) Two injections of PGF Reduction of follicle dominance in timed AI protocol

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Economic and sustainable outcomes of reproductive management Economic TAI/bull breeding TAI/seasonal breeding Dairy heifers Lactating dairy cows

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Introduction Current advancements in reproductive management of dairy cattle reflect a greater comprehension of reproductive processes and the need to integrate the disciplines of physiology, management, nutrition, genetics, economics, veterinary herd health and production medicine in

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00008-2

TAI in dairy heifers

order to sustain both fertility of the lactating dairy cow and reproductive performance of the herd. Partnering with allied industries also is essential for efficient reproductive management and sustainability of dairy cattle enterprises. Cow and herd reproductive performance has evolved dynamically, undergoing periods of subfertility and subsequent restoration. This is

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exemplified with a phenotypic decline in daughter pregnancy rate from the mid-1970s (35%), a nadir in the late 1990s (25%), followed by an increase in daughter pregnancy rate to a current level comparable to what was achieved in the late 1970s (35%). Dynamic changes in reproductive performance occurred in contrast to a steady increase in milk production per cow. Recrudescence of improved reproductive performance reflects the multifactorial challenges met to integrate reproductive processes of the cow. The dairy producer of today has a repertoire of approaches to coordinate the needs of the high producing dairy cow in order to reproduce in an efficient manner. Indeed, holistic science-based approaches are operational that affect the totality of the dairy operation in making decisions to enhance reproduction, health and well-being of both the individual dairy cow and overall herd performance. Dairy herd profitability is strongly and positively associated with reproductive performance. This is because improved reproduction promotes greater income of a herd and cow basis over feed costs, increases production of replacement heifers, decreases costs associated with replacement, and decreases the relative costs associated with reproductive management. The components of reproductive management involve various advancements in assisted reproductive technologies.

Control of the reproductive cycle Foundation principles of a reproductive program Development of reproductive management programs is based on understanding control of reproductive processes involving the hypothalamic, pituitary, ovarian, uterine axis whose interactions control dynamics of the estrous cycle and program both recognition and maintenance of pregnancy. The hypothalamus contains specific peptides (e.g., gonadotropin releasing

hormone, GnRH) that induce the release of pituitary gonadotrophins (follicle stimulating hormone, FSH and luteinizing hormone, LH) and other hormones like activin and inhibin that collectively regulate sequential waves of ovarian follicle development, as well as their secretion of steroids (androstenedione and estradiol), and ovulation. Postovulatory development of the corpus luteum (CL) and its secretion of progesterone (P4) programs the uterus for potential luteolytic secretion of prostaglandin F2a (PGF) to regress the CL and initiate a re-occurring estrous cycle. Alternatively, proper timing of artificial insemination coupled with the process of oocyte maturation leads to fertilization and subsequent development of an embryo/blastocyst which attenuates uterine secretion of luteolytic PGF pulses required for maintenance of the CL for pregnancy. Various reviews describe these processes in detail.1,2 What is distinct is that major regulatory molecules (i.e., GnRH, PGF and P4) are pharmacologically available to integrate these sequential biological processes in a manner that is required for acceptable fertility, which in most cases is similar to or greater than that obtained when cows are inseminated following detection of spontaneous estrus. Furthermore, they are molecules produced naturally by dairy cows and humans, which are not harmful to the environment. These physio-pharmacological agents can be used in an efficient and responsible manner coupled with current technology systems to improve fertility, health, well-being and sustainability of the dairy production system. Basic application and effects of GnRH, P4, and PGF are essential components of reproductive management, as illustrated in Fig. 8.1.3 At onset of estrus, hypothalamic GnRH secretion releases LH and FSH that induces ovulation and recruitment of a cohort of follicles that constitutes the first follicular wave from which a dominant follicle is evident by 5 days. Development of the CL from the ovulatory follicle at w Day 1 is coupled with a rise in P4 that is sustained until

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FIG. 8.1 Diagram of dynamics of follicular and hormonal developments during the estrous cycle of a dairy cow when receiving the Ovsynch timed AI protocol starting on day 6 of the cycle. The target days to start the protocol are 5e9 of the cycle. The first dose of GnRH induces LH and FSH surges that result in ovulation of the first-wave dominant follicle and subsequent recruitment and growth of a new cohort of follicles under high concentrations of progesterone. Prostaglandin F2a is administered 7 d later to regress the original and the newly formed CL resulting in a sharp decline in progesterone concentrations. Growth of the selected pre-ovulatory follicle is accelerated and administration of the second GnRH 56 h later induces LH and FSH surges that result in a synchronized ovulation 24e28 h later. Timed AI should be performed 12e16 h after the second GnRH for optimum fertility.

subsequent CL regression. The first wave dominant follicle is highly estrogenic between days 5 and 8 of the estrous cycle, and an injection of GnRH (Fig. 8.1) will induce ovulation of the dominant follicle in more than 80% of the cows and subsequent recruitment of a new follicular wave because of induced secretions of LH and FSH, respectively. On day 7 after the initial injection of GnRH, an injection of PGF induces regression of both the original CL of the estrous cycle and newly induced CL by GnRH. The resulting decrease in concentrations of P4 with lysis of the CL and continued growth of the recruited dominant follicle of the second follicular wave allow for a timely injection of a second dose of GnRH at approximately 56 h after PGF to induce ovulation of a highly fertile dominant follicle, which typically occurs 24e28 h after GnRH. A timed artificial insemination (TAI) is

performed 12e16 h after GnRH. This is the foundation and premise of the TAI protocol designated as OvSynch.4 The program is a physiological based system that has been further optimized to enhance pregnancy rate in lactating dairy cows. There are several physiological inferences to be appreciated from Fig. 8.1. Evolutionarily, periodic development of follicle waves, with selection of the healthiest dominant follicle, is a means to have selection of a healthy follicle destined to ovulation that produces a meiotically mature oocyte for fertilization. Indeed, follicle development is either suppressed or damaged under certain physiological/environmental periods such as: follicle suppression in late pregnancy that carries over into the postpartum period; follicles present in anovulatory period; heat stress damage to the pool of antral follicles during seasonal

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summer periods; and altered follicular control of dominance in cystic cows. Programs that induces recruitment of healthy/fertile follicles are potentially beneficial in current production systems. Lactating dairy cows have 2 or 3 follicular waves during the estrous cycle, and the ultimate follicular wave produces the preovulatory antral follicle destined to induce estrus and ovulate. This preovulatory follicle has undergone antral follicle growth, selection, and dominance in most cases under high concentrations of P4. This is critical for optimal programming in the proestrus period that follows CL regression because P4 from the preceding estrous cycle primes the uterus to maintain pregnancy during the early stages of gestation. This normal orchestrated sequence of events is programmed essentially within the foundation program depicted in Fig. 8.1.

Sequential development and efficacy of reproductive management programs Excellent pictorials (Figs. 8.2e8.4) describing various developmental approaches for reproductive management programs will be referred to throughout the subsequent sections and they are accessible on-line from the Dairy Cattle Research Council: http://www.dcrcouncil.org/ wp-content/uploads/2018/12/Dairy-CowProtocol-Sheet-Updated-2018.pdf. These reproductive management protocols are useful for producers and managers of reproductive and breeding programs across all aspects of the dairy industry, and they are accessible on-line in both Spanish and English: http://www.dcrcouncil. org/protocols/.

OvSynch The original development of the OvSynch TAI program is described5,6 and represented in Fig. 8.2 that is titled Synchronization methods for TAI, B, OvSynch-48. The program has a

7-day interval between the initial GnRH and the PGF injections followed by a final GnRH injection at 48 h7 or 30e36 h8 after PGF, and TAI 16 h later. Pregnancy at first TAI did not different between OvSynch TAI and cows inseminated following detected estrus: 29.0% versus 30.5%7 and 37.8% versus 38.9%,8 respectively. It was noted that the interval to first insemination was significantly reduced with TAI, but pregnancy per AI did not differ between TAI versus a system based on detection of estrus.8 Assessment of these findings led to the development of the OvSynch-56 h program9 (Fig. 8.2 Synchronization methods for TAI, A. OvSynch-56, C. CoSynch 72). The OvSynch 56 TAI increased pregnancy per TAI (36.1%) compared to either a CoSynch 48 (26.7%) or CoSynch 72 (27.3%). Extending the interval between PGF and the final GnRH of the protocol to 56 h likely lead to a further completion of CL regression prior to the final GnRH injection, extended proestrus, and resulted in a better timing of TAI relative to ovulation. For example, extending the interval between PGF and the final GnRH injection to 60 h eliminated increases in concentrations of P4 in plasma from luteal cells of the regressing CL that had not undergone complete functional luteolysis at 48 h and concurrently maximized the preovulatory peak response of LH to the GnRH injection given at 60 h after PGF.10

PreSynchPGF2a-OvSynch An additional strategy to improve pregnancy per TAI was the understanding that initiation of the program at certain stages of the estrous cycle and hormonal status would enhance the percent of cows ovulating to first GnRH and inducing a follicular wave during a 7-day period of elevated P4 compatible with cows in diestrus. This presynchronization was achieved11 with a traditional synchronization system of injecting PGF twice at a 14-day interval and initiating the OvSynch program 12 days after the

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FIG. 8.2

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Reproductive management strategies for dairy cows. http://www.dcrcouncil.org/protocols/.

presynchronization with the second PGF injection (Fig. 8.2: PreSynchronization methods used before TAI, A. PreSynch). The original experiment evaluating this hypothesis was a factorial arrangement of treatments testing the effects of presynchronization (PreSynch) and

administration of bovine somatotropin (bST) on pregnancy per TAI. In cows that were cycling, based on P4 profiles, presynchronizing the estrous cycle before TAI increased pregnancy (58.9% vs. 41.3%) independent of treatment with bST, although treatment with bST also

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increased pregnancy at first timed AI independent of PreSynch (54.6% vs. 40.5%). Also, cows treated with PreSynch without bST had increased pregnancy per TAI compared with cows not presynchronized and not receiving bST (46.9% vs. 34.4%). Two important points are that PreSynch before OvSynch enhanced pregnancy per TAI because of increased ovulatory response to the initial GnRH of OvSynch, and a greater frequency of PreSynch-OvSynch cows being in diestrus with high P4 concentrations between the first GnRH and the PGF injections compared with cows receiving the OvSynch without pre-synchronization (i.e., follicles recruited by the first GnRH of OvSynch more likely developed under luteal phase P4 concentrations). Across studies11e14 the 11- or 12-day interval between the second injection of PGF of PreSynch and the first GnRH of OvSynch results in increased pregnancy per TAI than the 14-day interval. A major practical question asked by producers is whether they should be inseminating cows during the PreSynch-OvSynch program for first insemination that express estrus after the second PGF injection of the pre-synchronization protocol and the remaining cows not expressing estrus undergo TAI with OvSynch. Comparisons of the two systems concurrently, with or without detection of estrus, under similar management systems are limited. However, an analytical approach with meta-analysis is very useful in integrating a cross-section of experiments under different management conditions, as to whether estrous detection or not during a PreSynch-OvSynch protocol is beneficial to overall pregnancy per AI.15 A meta-analysis considering 20 manuscripts, including 27 herds and 9813 inseminations, compared management strategies for first service using a PreSynch-OvSynch protocol. Cows with 100% TAI after completing a PreSynch-OvSynch had greater pregnancy/TAI compared with cows inseminated after a combination of detected estrus and TAI. There was a

difference of 10.8% units in pregnancy per AI on day 32 after insemination favoring only TAI compared with the combined program of detected estrus and TAI (41.7% vs. 30.9%). The results of this meta-analysis are based on many herds with different managerial conditions and seem to be applicable to high-producing dairy herds under confinement housing. In the array of studies examined, cows that were determined to be cycling or had estrous activity before the first AI were more likely to become pregnant. The underlying mechanism by which PreSynchOvSynch improves pregnancy per TAI in cycling cows is the resulting improved ovulation in response to the first GnRH, which synchronizes the subsequent follicle wave that now develops under elevated P4 concentrations.11,16,17 The PreSynch-OvSynch provides flexibility to producers. If the goal is to maximize pregnancy per AI, then all cows should undergo TAI. If the challenges are to reduce labor, hormonal treatments, sustain a good pregnancy per AI at detected estruses, and achieve the same precise interval to pregnancy then inseminating cows that show estrus within a PreSynch-OvSynch program is a viable option. An additional use of such a TAI program is its use as an experimental platform to test agents that may improve fertility. This was strikingly apparent when detecting fertility effects of bST when given during the OvSynch and early stages of conceptus development in cows that were or were not presynchronized to estrus with PGF.11

PreSynch using GnRH before-OvSynch An alternative strategy for Presynchronization prior to OvSynch is the programmatic use of GnRH and PGF. This strategy is useful in stimulating follicle development and ovulation and would increase the percentage of anovular cows with a follicle responsive to the LH surge induced by the first GnRH of OvSynch (i.e., a follicle on days 6e8 of development). Two such programs are depicted in Fig. 8.2. PreSynchronization

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Sequential development and efficacy of reproductive management programs

methods used before TAI, B and C (Double OvSynch and G-6-G). Basically, Double-OvSynch is a sequential repetition of OvSynch with a 7day interval between the end of the first OvSynch sequence and the beginning of the second OvSynch for TAI (i.e., GnRH - 7 days e PGF 3 days e GnRH - 7 days - OvSynch-56). This is an excellent strategy to maximize the percentage of cows to begin the second OvSynch in the presence of a dominant follicle that is responsive to LH and a CL that results in luteal concentrations of P4 in plasma. The first GnRH injection of the second OvSynch results in 80e85% ovulation that recruits a new dominant follicle in the presence of high concentrations of P4, which is considered essential to development of a potential healthy ovulatory follicle and oocyte for TAI. When comparing Double-OvSynch with PreSynch-OvSynch,18 pregnancy per TAI was greater for DoubleOvSynch than PreSynch-OvSynch (49.7% vs. 41.7%). However, Double-OvSynch increased pregnancy/TAI in primiparous cows (65.2% vs. 45.2%), but not in multiparous cows (37.5% vs. 39.3%). Alterations in ovarian responses attributed to Double-OvSynch adjusted for parity were evident based on an increase in the percentage of cows with moderate (1.0 to 0.2e0.30 mg/kg dry matter48), no beneficial effects of additional iodine supplementation are observed.49,50 It is important to note however, that research has indicated a negative relationship between maternal iodine supplementation in late gestation and immunoglobulin G levels in the newborn lamb51,52 and linked to failure of IgG absorption and thus, passive transfer.49 Thus, while there are potentially some negative effects of supplementing ewes in late gestation with iodine, direct effects on lamb survival and subsequent impact on lamb survival and immune function later in life remain to be established. It is also important to note that

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Forages and grazing systems for sustainable farming practices

consumption of excess amounts of some trace elements/minerals can also be detrimental to animal performance as a result of toxicity, e.g., copper toxicity.53 Supplementation with specific nutrients beyond those required to correct dietary deficiencies can also be beneficial for animal performance. For example, modulation of metabolic signaling pathways that regulate growth, reproduction and immunity can be achieved through supplementation with supra-nutritional levels of specific AA. In sheep, the focus has been on muscle growth, mammary gland development, immune function and fetal development and survival.42 Late gestation intravenous administration of essential AA decreases hypoxia and respiratory and metabolic acidosis in sheep.54 A range of studies have also highlighted critical intervention time windows based on the nutritional demand on the ewes from increased litter size. For example, maternal parenteral supplementation of arginine from 100 to 121 days of gestation increases birth weight of quadruplets,55 while a birthweight response in twins was not observed until the final 7 days of gestation.56 Supplementation with sulphurcontaining amino acids such as methionine and cysteine have also improved wool/fiber production in sheep and goats.57 A practical challenge for AA supplementation to the mature ruminant is rapid degradation of AA in the rumen by microbial proteases and deaminases.58 Prior to development of the ruminant in sheep and goats, unprotected AA can be added directly to the diet to balance nutritional deficiencies or as a nutraceutical to improve performance. However, in the adult, either rumen protected formulations, encapsulation of AA or use of AA analogues, are the most widely used methods. The amount required, duration of supplementation and delivery route will vary depending on the nature of the nutrient (e.g., as a component of a compound feed supplement, lick block technology, boluses, dosing via water supply). The ability to identify critical

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intervention windows where supplementation can be targeted to specific groups of animals with the greatest need (e.g., triplet-bearing ewes) reduces the level of complexity by targeting smaller groups of animals rather than the entire flock. Specific polyunsaturated fatty acids (PUFAs), such as linoleic acid, are a key energy source for brown adipose tissue in lambs.59 However, thermogenesis is unaffected in lambs from twinbearing ewes supplemented with 12 g/ewe per day of algae-derived DHA in the last 30 days of gestation.60 In contrast, supplementation with rumen protected fats in the last 5e9 weeks of gestation has been reported to improve cold tolerance in newborn lambs when included at 2% or 4% but not 8% of the diet in the last 40 days of gestation61 suggesting potential for PUFAs to improve thermogenesis, but in a dose-dependent manner. Polyunsaturated fatty acid supplementation may also improve lamb vigor,62e64 but not birth weight.60 Supplementation with PUFA can also increase gestation length in several species including sheep,62 which leads to improved physiological maturity of the fetus at birth. Variable effects of vitamin and trace element supplementation throughout gestation have been reported for sheep.65 Of the trace elements evaluated (cobalt, copper, iodine, iron, manganese, selenium, zinc, vitamins A and E and n-3 fatty acids), selenium, vitamin E and n-3 fatty acids were identified as important for lamb survival. However, supplementation during the last trimester is relatively uncommon. Vitamin E supplementation (500 mg/kg; 6 weeks prepartum) to twin- and triplet bearing ewes increases lamb birth weight.66 Vitamin E protects biological membranes from oxidative damage by acting as scavengers of reactive oxygen species and is linked to immunoglobulin production.67 Supplementation with Vitamin E in mid-to late-gestation in ewes bearing multiple fetuses can lower the rate of still births.68 It is important to note that many of the studies reported in the

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literature are based on studies undertaken with ewes fed a concentrate or conserved foragebased diets, rather than a pasture-based feeding system. It is well recognized that some diets are deficient in some micronutrients, e.g., dry stored feeds have less vitamin E than spring fresh forage,69 and therefore, many of the studies in the literature may have limited application to pasture-based systems. However, it is well established that trace element supplementation in pasture-fed ewes can improve lamb performance, especially when soils are deficient in some of these key nutrients.70 In contrast to sheep, relatively little research has been undertaken to evaluate the vitamin and mineral requirements of goats, with most available data focused on specific breeds and regions. Further research is required to fully understand potential benefits of trace element and vitamin supplementation in pasture-based systems. The renewed interest of using extracted plant secondary compounds (PSCs) or feedstuffs that contain specific PSC in livestock diets (including some woody plants), is partially due to consumers demanding that the livestock industry reduce its use of synthetic animal health and growth products; some consumer groups in various regions are demanding complete elimination. Thorough reviews of PSC are available,71,72 and reveal that PSC are distributed throughout the plant kingdom. Certain PSC are more toxic than others to herbivores such as hydrolysable tannins, flavonoids, gossypol, hymenoxon, and coumarin. However, from the numerous PSC that have been evaluated in animal diets, terpenoids and condensed tannins (CT; also defined as proanthocyanadins) have shown the greatest promise in regards to enhancing livestock production, rumen microbial efficiency, health, and end-product quality. Both terpenoids and CT are considered safe for ruminant livestock.73 Numerous reviews are available regarding beneficial attributes of terpenoids and CT in

livestock production systems. For example, terpenoids have been reported to: (1) have nematicidal activity74; (2) at times, be more effective against gram-positive versus gram negative bacteria, thus act similar to ionophores75; (3) reduce hyper-ammonia producing bacteria76; (4) increase beneficial bacteria in the rumen77; (5) increase microbial efficiency and microbial protein synthesis78; (6) enhance the shelf life of meat products by reducing aerobic bacteria counts79 or enhancing oxidative stability.80 Feeding plants that contain CT to livestock have been reported to: (1) reduce Haemonchus contortus fecal egg counts,81,82 larvae motility,83,84 abomasal worm burden,85,86 and enhance the efficacy of a synthetic anthelmintic82,84; (2) increase ruminal bypass protein by decreasing microbial degradation87; which can increase animal growth performance88; (3) enhance sensory characteristics of cooked lamb meat89; and (4) enhance the proportion of “healthy” fatty acids in meat and milk.90,91 Undoubtedly, further exploration of the utility of macro- and micro-nutrients in pasturefed sheep and goats on the whole of life performance, identification of critical time frames for intervention and identification of delivery routes that are both cost-effective and practical to implement in pasture-grazing systems would be beneficial and should be the focus of future research. Pasture-based grazing systems present a particularly challenging environment to manipulate nutrition because these systems are often characterized by difficult terrain, vast land masses and remote locations.

Technological advancements and future directives Challenges that face the sheep and goat industry are integrated, extensive, complex, and everchanging. For example, the livestock production industry is tasked with concurrently: (1) producing more product in an increasingly more efficient manner on a smaller footprint with

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Forages and grazing systems for sustainable farming practices

less feed resources; (2) producing high quality human edible products that are not only flavorful but serve as “functional foods”; (3) providing greater public availability of rangeland ecosystem services; (4) significantly reducing the use of human-edible products in livestock feed such as cereal and legume grains; and (5) eliminating animal diseases, zoonotic diseases, growth stimulators, and all forms of antibiotics, whether a human concern or not (e.g., ionophores). These challenges continue to rapidly increase, thus require technological advancements beyond traditional livestock production practices and tools. The following section discusses some of the most promising technological advancements and future directives to optimize and improve production, utilization, management, and pre-treatment of feed sources to meet animal production requirements within a sustainable ecosystem.

Digital technologies Geospatial technologies (e.g., global positioning systems [GPSs] and geographic information systems), along with canopy reflectance and light detection and ranging technologies (LiDARs) and aerial photography, have advanced tremendously over the past 20 years. These technologies have created a foundation upon which numerous other tools have been and will be developed. For example, GPS-guided unmanned aerial vehicle (UAV) platforms are being deployed with payloads such as cameras with picture, video, and thermal imagery capabilities. Examples of how these UAV’s are being used include: (1) assessing rangeland conditions and plant biomass and (2) locating and tracking livestock. Furthermore, various models using LiDAR are being used to accurately predict plant yield and nutritive value.92 Hand-held devices are also being developed that can produce imagery to assess real-time forage quality, which can then immediately be used to adjust grazing management and supplementation strategies.

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Most feed supplementation of livestock on pasture is based on the guesstimated “animal group average” without much consideration of age, stage of production, or body weight. This strategy is not only inefficient, but can dramatically increase supplement intake variation.93 This could potentially lead to reduced animal health (e.g., rumen digestive problems), lifetime productivity, and gain to feed efficiency. Even though new technology related to individually supplementing diets of sheep and goats is in its infancy, it is rapidly being developed. Thus, in the near future, it is highly likely that video cameras will be deployed to monitor individual body weight (e.g., walk-over scale) and body condition. These data can then immediately be used to auto-calculate percent change, to then deliver a specific type of supplement at a targeted amount. Being able to supplement feedstuffs on an individual animal basis will also increase effectiveness of target delivery of animal health products. Supplementation intake could be sent to a smart-phone, which would allow the user to also evaluate illness (e.g., prolonged reduced intake). Video cameras could also be used to evaluate body heat (thermal imagery) and non-normal movement (e.g., gate, lethargy) and send an alert notification to the user. Virtual herding technologies (e.g., collars and ear tags) are also advancing and providing a means to determine animal location. This will provide a significant step forward if technologies can be adapted from the current use in cattle to designs for sheep and goats. Technologies can then be placed into a system that would detect animal behavior and welfare indicators, allowing targeted management of the individual within large herd settings. One practical combination of the previously mentioned technologies could be the development of a mobile feed supplementation delivery system that could be user-controlled or autonomous. This system would be programmed to move to a new location based upon data that it collects regarding current and predicted range conditions and

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animal parameters such as percent change in body weight. While there have been many papers reviewing the potential of these technologies, there have been few real advances in this space. This appears to have been due to the ideas for technology application outstripping either the ability of the technology to deliver specific benefits, as well as the lack of calibration or the inability of current systems to handle the data and provide a useable interface for conversion of the data into readily available information for decision making. In addition, most of the available grazing technology previously mentioned relies on a reliable cellular signal, which is a challenge in many regions, especially rural areas.

Advances in plant genetics and breeding Future opportunities to improve the nutrition of sheep are being developed through plant breeding technologies. Most significant is the progress being made in the feeding value of legumes, forbs and grasses. Techniques have been developed to enable the production of high tannin alfalfa,94 high fructosan ryegrass95 and high lipid ryegrass.96 Further, cultivars of plantain (Plantago lanceolata) have been identified that reduce nitrate leaching during grazing.97 Each of these advances provide nutritional advantages, both in energy and protein utilization. Environmental improvements are also provided through reduced greenhouse gas emissions and nitrogen loss. Finally, potential health benefits through, for example, internal parasite control and delivering greater PUFA concentrations in the diet of pregnant ewes can be gained. Some forages, however, may present future problems if treated as solutions for a single issue, due to unintended consequences on other traits. For example, the high digestibility and sugar levels of fodder beet may reduce greenhouse gas emissions, but feeding during late pregnancy to sheep has been reported to reduce post-natal

growth40 potentially due to a deficiency in protein. High yielding winter brassica crops also have problems with anti-nutritional factors such as S-methylcysteine sulfoxide and glucosinalates,98 though breeding programmes are in place to reduce this risk.

Omics technologies An animal’s overall phenotype is the sum of complex interactions between the animal and microbial genotypes and the environment. Nutrition is one of the most potent environmental forces influencing phenotype and one that producers are able to manipulate. However, knowledge of the factors (including nutrition) regulating and controlling yield, quality and wellbeing in livestock of different genetic backgrounds is incomplete. In the last two decades, molecular biology, coupled with advances in chemistry and bioinformatics, has transformed animal research and our understanding of complex biological systems. Notably, the advent of “omics” technologies, have enabled a holistic view of the molecules that form a cell, tissue or organism. These approaches enable nontargeted and non-biased universal detection of genes (genomics), mRNA (transcriptomics), proteins (proteomics) and metabolites (metabolomics) in biological samples of the host and microbiota.99e101 Systems biology is the term used to describe the integration of these techniques, which facilitates a greater understanding of complex systems by considering the system as a whole. These technological advances offer the potential to better understand normal physiological processes and deliver accurate and rapid animal improvements while simultaneously addressing societal concerns related to natural resource conservation and protection, animal welfare and food safety.102 The complexity of livestock traits (e.g., feed intake and nutritional quality of meat and milk) and biological mechanisms regulating multi-factorial traits (e.g., nutritional

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Concluding statements

programming), are the result of interactions of the structure and function of thousands of genes of the host and microbiota and an almost uncountable number of metabolites. The systems biology approach of combining molecular, genetic, and environmental data with modeling and prediction of functional biochemical networks, relies on continous development of new technologies and methods for analysis of complex data.101 While there are many studies that have applied transcriptomic profiling approaches to understand the effect of nutrition on aspects of physiology in sheep,103,104 there are a dearth of studies with goats. There are relatively few studies that have applied metabolomic technologies in sheep and goats.100 These technologies have also been applied to studies of the microbiome in sheep105,106 and goats.107 Continued development and affordability of high throughput “omics” technologies will facilitate a paradigm-change in our understanding of the interactions among nutrients, microbiota, the animal and the environment (ecophysiology) and complex animal phenotypes. While still in their infancy in terms of application to animal nutrition, these approaches have real potential to unlock new knowledge-based approaches for predictive, preventative and personalized approaches to nutritional management of small ruminants. Key outcomes may include improved health (e.g., prediction of onset of disease, non-invasive diagnostics), wellbeing (e.g., assessment of affective state), productive performance (e.g., survival, growth and feed efficiency), tailoring of product yield/quality, reducing the impact of animals on the environment, and utilization of small ruminants as part of wider ecosystem management.

Concluding statements Pressure is mounting on the ruminant industry to reduce its environmental footprint. Sheep and goats have a unique position of being able

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to capitalize on the nutrition provided from uncultivable grasslands and rangelands. In doing so many of the values of the land, including a way of life for the inhabitants, the utilization of the resource and, the maintenance of the landscape can all be achieved. This then captures the value of the natural resource in milk, meat and fiber products. Progress in the New Zealand sheep industry has demonstrated the efficiency of gains made when implementing available technologies, and maintaining product outputs, while reducing the overall footprint of those products using a forage system. Maintaining grassland and rangeland values through using woody weeds as a feed source for sheep and goats provides promise in preventing further woody weed encroachment on rangelands, while delivering potential benefits through functional nutrition. These advantages will include improved protein utilization and the potential to reduce reliance on chemical interventions such as anthelmintics for internal parasite control. New tools and knowledge are available to further advance those gains. Special nutrient supplementations, both through added ingredients in the diet, and through new forage genetics, have the potential to change both the physiological responses of sheep and goats, and to improve nutrient use efficiency. Tools such as GPS tracking, animal sensors and electronic identification will enable targeted management of individual animals within the flock. Challenges to the collection and use of these data will be significant in some extensive grazing situations, as issues such as power supply, data collection networks and computing resources for processing will need to be addressed. Many tools and techniques are available to the sheep and goat farmer to improve nutrition to increase productivity and efficiency, and more are becoming available. Implementation of these technologies continues to rely on the opportunities of farmers to access information from reliable sources, and to be able to adapt that

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information to their specific circumstances. Continuing efforts need to be made to ensure that both current and future technologies are understood by the trainers of the future.

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54. Rozance PJ, Crispo MM, Barry JS, et al. Prolonged maternal amino acid infusion in late-gestation pregnancy sheep increases fetal amino acid oxidation. Am J Physiol Endocrinol Metab. 2009;297:E638eE646. 55. Lassala A, Bazer FW, Cudd TA, et al. Parenteral administration of L-arginine enhances fetal survival and growth in sheep carrying multiple fetuses. J Nutr. 2011;141:849e855. 56. McCoard S, Sales F, Wards N, et al. Parenteral administration of twin-bearing ewes with L-arginine enhances the birth weight and brown fat stores in sheep. SpringerPlus. 2013;2:684. 57. Reis PJ, Sahlu T. The nutritional control of the growth and properties of mohair and wool fibers: a comparative review. J Anim Sci. 1994;72:1899e1907. 58. Chalupa W. Rumen bypass and protection of proteins and amino acids. J Dairy Sci. 1975;58:1198e1218. 59. Lammoglia MA, Bellows RA, Grings EE, Bergman JW. Effects of prepartum supplementary fat and muscle hypertrophy genotype on cold tolerance in newborn calves. J Anim Sci. 1999;77:2227e2233. 60. Keithly JI, Kott RW, Berardinelli JD, Moreaux S, Hatfield PG. Thermogenesis, blood metabolites and hormones, and growth of lambs born to ewes supplemented with algae-derived docosahexaenoic acid. J Anim Sci. 2011;89:4305e4313. 61. Chen CY, Carstens GE, Gilbert CD, et al. Dietary supplementation of high levels of saturated and monosaturated fatty acids to ewes during late gestation reduces thermogenesis in newborn lambs by depressing fatty acid oxidation in perirenal brown adipose tissue. J Nutr. 2007;137:43e48. 62. Capper JL, Wilkinson RG, Mackenzie AM, Sinclair LA. Polyunsaturated fatty acid supplementation during pregnancy alters neonatal behaviour in sheep. J Nutr. 2006;136:397e403. 63. Pickard RM, Beard AJ, Seal CJ, Edwards SA. Supplementation of Ewe diets with algal biomass rich in docosahexaenoic acid for different time periods before lambing affects measures of lamb viability. Proc Br Soc Anim Sci. 2005:89. 64. Pickard RM, Beard AP, Seal CJ, Edwards SA. Neonatal lamb vigour is improved by feeding docosahexaenoic acid in the form of algal biomass during late gestation. Animal. 2008;2:1186e1192. 65. Rooke JA, Dwyer CM, Ashworth CJ. The potential for improving physiological, behavioural and immunological responses in the neonatal lamb by trace element and vitamin supplementation of the ewe. Animal. 2008; 2:514e524.

66. Capper JL, Wildinson RG, Kasapidou E, et al. The effect of dietary vitamin E and fatty acid supplementation of pregnant and lactating ewes on placental and mammary transfer of vitamin E to the lamb. Br J Nutr. 2005;93:549e557. 67. Huber JT. Vitamins in ruminant nutrition. In: Church DC, ed. The Ruminant Animal: Digestive Physiology and Nutrition. Englewood Cliffs, NJ, USA: Prentice Hall; 1988:313e325. 68. Dønnema I, Randbya AT, Hektoenb L, et al. Effect of vitamin E supplementation to ewes in late pregnancy on the rate of stillborn lambs. Small Rumin Res. 2005; 125:154e162. 69. Kivimae A, Carpena C. The level of vitamin E content in some conventional feeding stuffs and the effects of genetic variety, harvesting, processing and storage. Acta Agric Scand. 1973;19:161e168. 70. Grace ND, Knowles SO. Trace element supplementation of livestock in New Zealand: meeting the challenges of free-range grazing systems. Vet Med Int. 2012;2012:639472. 71. Cheynier V, Comte G, Davies KM, Lattanzio V, Martens S. Plant phenolics: recent advances on their biosynthesis, genetics, and ecophysiology. Plant Physiol Biochem. 2013;72:1e20. 72. Salminen J-P, Karonen M. Chemical ecology of tannins and other phenolics: we need a change in approach. Funct Ecol. 2011;25(2). 73. Rice PJ, Coats JR. Structural requirements for monoterpenoid activity against insects. In: Proc. Symp. Bioregulators for Crop Protection and Pest ControlAmer. Chem. Soc. Series. vol. 557. 1994:92e108. Washington, DC. 74. Lei J, Leser M, Enan E. Nematicidal activity of two monoterpenoids and SER-2 tyramine receptor of Caenorhabditis elegans. Biochem Pharmacol. 2010;79:1062e1072. 75. Smith-Palmer A, Stewart J, Fyfe L. Antimicrobial properties of plant essential oils and essences against five important food-borne pathogens. Lett Appl Microbiol. 1998;26:118e122. 76. McIntosh FM, Williams P, Losa R, et al. Effects of essential oils on ruminal microorganisms and their protein metabolism. Appl Environ Microbiol. 2003;69:5011e5014. 77. Santos FHR, De Paula MR, Lezier D, et al. Essential oils for dairy calves: effects on performance, scours, rumen fermentation and intestinal fauna. Animal. 2015;9: 958e965. 78. Calsamiglia S, Busquet M, Cardoza PW, Castillejos L, Ferret A. Invited review. Essential oils as modifiers of rumen microbial fermentation. J Dairy Sci. 2007;90: 2580e2595.

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and spice essential oils on quality characteristics and shelf-life of mortadella. Meat Sci. 2010;85:568e576. Cunha LCM, Monteiro MLG, Lorenzo JM, et al. Natural antioxidants in processing and storage stability of sheep and goat meat products. Food Res Int. 2018;111: 379e390. Burke JM, Whitley NC, Pollard DA, et al. Dose titration of sericea lespedeza leaf meal on Haemonchus contortus infection in lambs and kids. Vet Parasitol. 2011;181: 345e349. Whitney TR, Wildeus S, Zajac AM. Effect of using redberry juniper (Juniperus pinchotii) to reduce Haemonchus contortus fecal eggs and increase ivermectin efficacy. Vet Parasitol. 2013;197:182e188. Molan AL, Alexander R, Brookes IM, McNabb WC. Effects of sulla condensed tannins on the degradation of ribulose-1, 5-bisphosphate carboxylase/oxygenase (Rubisco) and on the viability of three sheep gastrointestinal nematodes in vitro. J Anim Vet Adv. 2004;3:165e174. Armstrong S, Klein DR, Whitney TR, et al. Effect of using redberry juniper (Juniperus pinchotii) to reduce H. contortus in vitro viability and increase ivermectin efficacy. Vet Parasitol. 2013;197:271e276. Lange K, Olcott DD, Miller JE, et al. Effect of sericea lespedeza (Lespedeza cuneata) fed as hay, on natural and experimental Haemonchus contortus infections in lambs. Vet Parasitol. 2006;141:273e278. Terrill TH, Dykes GS, Shaik SA, et al. Efficacy of sericea lespedeza hay as a natural dewormer in goats: dose titration study. Vet Parasitol. 2009;163:52e56. Patra AK, Saxena J. Exploitation of dietary tannins to improve rumen metabolism and ruminant nutrition. J Sci Food Agric. 2011;91:24e37. Perez-Maldonado RA, Norton BW. The effects of condensed tannins from Desmodium intortum and Calliandra calothyrsus on protein and carbohydrate digestion in sheep and goats. Br J Nutr. 1996;76:515e533. Whitney TR, Smith S. Substituting redberry juniper for oat hay in lamb feedlot diets: carcass characteristics, adipose tissue fatty acid composition, and sensory panel traits. Meat Sci. 2015;104:1e7. Vasta V, Priolo A, Scerra M, et al. D 9 desaturase protein expression and fatty acid composition of longissimus dorsi muscle in lambs fed green herbage or concentrate with or without added tannins. Meat Sci. 2009;82:357e364. Buccioni AM, Pauselli M, Viti C, et al. Milk fatty acid composition in response to diets rich in linoleic acid

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supplemented with chestnut or quebracho tannins in dairy ewes. J Dairy Sci. 2015;98:1145e1156. Noland RL, Wells MS, Coulter JA, et al. Estimating alfalfa yield and nutritive value using remote sensing and air temperature. Field Crop Res. 2018;222: 189e196. Williams GD, Beck MR, Thompson LR, Horn GW, Reuter RR. Variability in supplement intake affects performance of beef steers grazing dormant tallgrass prairie. Prof Anim Sci. 2018;34:364e371. https://doi. org/10.15232/pas.2017-01720. Hancock K, Collette V, Chapman E, et al. Progress towards developing bloat-safe legumes for the farming industry. Crop Pasture Sci. 2014;65:1107e1113. Badenhorst PE, Panter S, Palanisamy R, et al. Molecular breeding of transgenic perennial ryegrass (Lolium perenne L.) with altered fructan biosynthesis through the expression of fructosyltransferases. Mol Breed. 2018;38:21. Beechey-Gradwell ZD, Winichayakul S, Roberts NJ. High lipid perennial ryegrass growth under variable nitrogen, water and carbon dioxide supply. J NZ Grassl. 2018;80:219e224. Box LA, Judson HG. The concentration of bioactive compounds in Plantago lanceolata is genotype specific. J NZ Grassl. 2018;80:113e118. Nichol WW. Nutritional disorders of ruminants caused by consumption of pasture and fodder crops. In: Rattray PV, Brookes IM, Nicol AM, eds. Pasture and Supplements for Grazing Animals. Hamilton: New Zealand Society of Animal Production; 2007:133e149. Van Emon JM. The omics revolution in agricultural research. J Agric Food Chem. 2017;64:36e44. https:// doi.org/10.1021/acs.jafc.5b04515. Goldansaz SA, Guo AC, Sajed T, et al. Livestock metabolomics and the livestock metabolome: a systematic review. PLoS One. 2017;12:e0177675. Suravajhala P, Kogelman LJA, Kadarmideen HN. Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare. Genet Sel Evol. 2016;48:38. Macpherson AJ, de Ag€ uero MG, Ganal-Vonarburg SC. How nutrition and the maternal microbiota shape the neonatal immune system. Nat Rev Immunol. 2017; 17:508. Pe~ nagaricano F, Wang X, Rosa GJ, Radunz AE, Khatib H. Maternal nutrition induces gene expression changes in fetal muscle and adipose tissues in sheep. BMC Genomics. 2014;15:1e34.

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104. Gonzalez-Calvo L, Dervishi E, Joy M, et al. Genomewide expression profiling in muscle and subcutaneous fat of lambs in response to the intake of concentrate supplemented with vitamin E. BMC Genomics. 2017; 18:92. 105. Seshadri R, Leahy SC, Attwood GT, et al. Cultivation and sequencing of rumen microbiome members from the Hungate1000 collection. Nat Biotechnol. 2018;36: 359e367.

106. Huws SA, Creevey CJ, Oyama LB, et al. Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present and future. Front Microbiol. 2018;9:2161. 107. Abecia L, Jimenez E, Martinez-Fernandez G, et al. Natural and artificial feeding management before weaning promote different rumen microbial colonization but not differences in gene expression levels at the rumen epithelium of newborn goats. PLoS One. 2017;12:e0182235.

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P A R T I V

Swine production

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C H A P T E R

14 Modern genetic and genomic improvement of the pig Benny E. Motea, Max F. Rothschildb a

Department of Animal Science, University of Nebraska-Lincoln, Lincoln, Nebraska, United States; b Department of Animal Science, Iowa State University, Ames, Iowa, United States

O U T L I N E Introduction

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Domestication of swine and breed development

250

Methods of selection and mating systems

251

Traits of economic importance

253

Initial development of molecular genetic approaches

255

QTL, candidate genes and genetic improvement

255

Sequencing the pig genome

256

Introduction Pork production is an important source of animal protein worldwide. Pork accounts for nearly 43% of all red meat consumed worldwide.

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00014-8

Genomic selection

258

Databases

259

Cloning, transgenics, gene editing, and breeding pigs as biological models

259

Future developments and applications to genetic improvement

261

Acknowledgments

261

References

261

It is expected that in the developed world, meat consumption will remain steady or increase incrementally in the next 10e20 years. However, enormous demand will push meat consumption much higher in the developing countries of Asia

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Copyright © 2020 Elsevier Inc. All rights reserved.

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14. Modern genetic and genomic improvement of the pig

and Africa. Furthermore, in many of these developing countries, livestock production offers families an opportunity for economic survival. Therefore, pig production is likely to grow and be an integral part of livestock production in both developed and developing countries, except where prohibited for religious reasons. To meet future needs, use of genetic and genomic improvement in pigs must increase. This chapter outlines past and present methods to improve the pig genetically for production of meat and puts these methods in the context of advances in the fields of genomics and swine production.

Domestication of swine and breed development Domestication of the pig was once believed to be initiated about 10,000 years ago in a few geographically isolated regions of the Near East and China. However, this model of domestication is now being challenged as information suggests that the pig was domesticated in several additional regions including Europe and perhaps Japan.1 The process of domestication of the pig led to the first efforts by humans to improve the pig. Methods used by early humans included selection and systems of mating, which are the same approaches used by modern swine breeders. While records of animal improvement date back to about 6,000 years ago, these records focused mostly on ruminants and horses. Aside from the actual domestication process, leaps in genetic improvement of livestock occurred in the 18th century with its epicenter in England. Much of this came about due to the end of the feudal system, which allowed individual owners to learn methods to improve farming and livestock practices. The best known master breeder of this era was Robert Bakewell, an Englishman born in the early 1700s who began his animal breeding work about 1760. The methods Bakewell espoused were widely

imitated, and the beginnings of purebreds and breed societies were constructed. Similar activities began in the Americas as the colonies were being settled. Developing breeds, or defined groups of animals of the same species that possess similar characteristics, was often done by selecting animals for similar coat color or other characteristics. This inherently leads to some level of inbreeding as characteristics become fixed. As breeds developed, breeders began to form associations to protect their stock and investment. These breed societies drove early genetic and breed improvement in the late 1800s and the first half of the 1900s. Today over 300 breeds of swine exist worldwide. Often many of these breeds are similar, but reside in different countries so are considered by some as different breeds. Information on over 68 breeds of swine, including pictures, can be found at an excellent web site: http://www.ansi.okstate.edu/breeds/swine/. In addition, the “Breeds of Pigs” chapter in Genetics of Swine contains an enormous amount of phenotypic information, references about the breeds and source of origin material.2 Additional information about breed diversity and pig resources is fully described in Ollivier and Foulley.3 Despite the rather large number of breeds, most of the commercial pork industry primarily uses a limited number of breeds for pork production as either purebreds or in synthetic (manmade) crosses. In the swine industry, dam lines, (also called maternal lines) which are known for their superior reproductive performance and mothering ability, are crossed with sire lines (also called terminal sire lines), which are known for their rapid growth rate and excellent lean meat production. Common dam line breeds include Landrace and Large White or Yorkshire while common sire line breeds include Pietrain, Hampshire and Duroc. For specialized niche markets, the Berkshire breed has seen a recent increase as a pure breed and, in some instances, a terminal sire due to its superior meat quality.

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Methods of selection and mating systems

Use of such breeds to produce both specialized crosses and synthetic lines is now common and has replaced the older systems of static (F1) or rotational crosses on large commercial pig operations. Each year dire warnings are given about the loss of rare breeds worldwide, especially in Europe and Asia. Many of these include local country breeds that are endangered due to their small population size and limited economic value and are often crossed with more productive breeds. Information on rare pig breeds in the US is available from the American Livestock Breeds Conservancy (http://albc-usa.org/) and worldwide from the United Nations’ Food and Agriculture Organization (FAO) (http://www. fao.org/). FAO has adopted a strategy for breed conservation, but sadly the financial resources needed to maintain them do not exist. Furthermore, unless these local breeds fit some specialized production scheme, they are often overlooked as not economically viable. In the future, genomic sequencing/evaluation may reveal specific underlying genetic contributions that may be worth preserving.

Methods of selection and mating systems In the early 1900s, pig breeders selected animals based on the phenotypic traits of each pig. Breeders might choose animals that were the largest, appeared to be the leanest or had good feet and legs. Today more sophisticated methods are employed, including the use of scales to determine body weight, ultrasound to measure fat and loin eye area, computerized tomography scans for individualized primal yield of carcass components and sophisticated scoring methods for anatomical traits. The primary method of genetic improvement is by determining the best animals of each generation to be parents of the next generation, known as selection. If effective, selection based on either genetic markers or phenotype will increase the

frequency of desirable alleles. This rate of change or genetic improvement in the progeny is expected based on the formula: Progeny mean ¼ heritability  (selected parental mean  population mean) where heritability represents accuracy and (selected parental mean  population mean) is the selection differential. This can be seen as DG ¼ a  i where G is genetic change (improvement), a is accuracy, and i is selection intensity. DG per year is DG ¼ a  i/t where t is generation interval of selected parents. Once parents are chosen, then the use of mating systems is crucial to maximize performance. Such performance depends highly on gene action. While selection acts to increase frequency of the desired alleles, it is most effective for traits with a large percentage of additive gene action and hence a higher heritability. Inbreeding and crossbreeding effects depend greatly on nonadditive gene effects (dominance, epistasis). A review of the relative effects and gene action affecting certain traits is seen in Table 14.1. Historically, there have been three phases of mating systems employed in swine production, pre 1950s, 1950se1980s, and post-1980s. Until about 1950, purebred mating schemes were primarily employed, and these often incur mild levels of inbreeding (mating of relatives). The most popular of these approaches was line breeding, in which breeders used relatives with a common superior ancestor with a desired phenotype. Such approaches are still employed by some purebred breeders. However, such an approach suffers from several problems. First, the “ideal” phenotype may change over time, or a better animal may exist outside of a given herd. Second, the ideal individual may carry an undesirable deleterious recessive allele that

IV. Swine production

252 TABLE 14.1

14. Modern genetic and genomic improvement of the pig

Effects of mating systems on traits of economic importance in swine proportion of genetic variance due to different. Effect of gene action

Trait

Heritability

Inbreeding

Crossbreeding

Nonadditive

Additive

Litter size

low

high/unfavor

high/favorable

high

low

Weaning wt

low

high/unfavor

high/favorable

high

low

Wt gain to mkt

moderate

mod/unfavor

mod/unfavor

moderate

moderate

Backfat

high

low/unfavor

low/favorable

low

high

Carcass quality

high

low/unfavor

low/favorable

low

high

Mkt, market; wt, weight. Table modified from Lasley.34

shows up in some progeny. After the 1950s, purebred breeders continued to use line breeding and selection, but commercial pig producers generally bought purebred males and females of different breeds and then made static F1 crosses to produce commercial females or employed rotational crosses. Such approaches of using a F1 female and a purebred sire (of a different breed) maximized (100%) maternal and piglet heterosis. Rotational crosses will not maximize piglet heterosis unless a sire breed different from the rotationally produced female is used, and rotational crosses will not maximize female heterosis. The advent of commercial swine breeding companies following in the footprint of advances in hybrid corn breeding brought big changes to the pig industry as well. Commercial breeding companies began to use more sophisticated selection methods based on advancing technologies to first measure traits and secondly to estimate breeding values from individual performance and relative’s records. Some independent culling is also performed to eliminate animals/ lines with deformities.4 Selection of superior animals (top 5% males and 20% females) within pure lines occurred primarily at the top or nucleus of the breeding pyramid (see Fig. 14.1), where crosses were made to initially produce

improved synthetic lines. Later, the superior individuals in both pure and synthetic lines were then selected to create the next generation of the elite pure and synthetic lines. For nucleus herds, attempts were made to maximize the selection intensity (called i) while minimizing generation interval (called t) such that the ratio of i/t was maximized for the population in question. The next level of superior animals (approximately males in top 20% and females in top 50%) were also selected. These animals were sent to the multiplication farms and were used to produce crossbred females and males that were then sold to large commercial production units. Development of male (sire) and female (dam) lines and their crosses are based on maximizing total economic output. Therefore, the breed background and traits emphasized are often different to maximize both heterosis and complementarity of strengths from different breeds. It has been estimated that use of purebreds accounted for over 80% of the commercial pigs produced in 1980. Today less than 5% of all commercial pigs are produced by purebred schemes, while the remaining 95% are comprised of more than one breed. The terminal market hogs are most commonly static crosses of terminal sires mated to F1 Yorkshire X Landrace female lines in order to maximize both maternal and terminal

IV. Swine production

Traits of economic importance

FIG. 14.1

253

Selection and breeding pyramid in modern pig breeding. Adapted from Genetics of the Pig.29

heterosis and capture total system production efficiencies of economically important traits at all stages of pork production. Synthetic crossbred males were used as the predominant terminal sire in the 1990s and 2000s primarily due to their increased feed efficiency and the desire to maximize percent lean of the carcass. Today, Duroc or Duroc derived lines are the most widely used terminal sires in the United States with the line 600 Duroc produced by DNA Genetics being utilized on roughly 1/3 of all the commercial sows in the United States and Canada. Traditionally, selection has been made in purebred stock only with little to no measurement of data for genetic selection occurring at the commercial level. Some traits, such as survival to market, have a low genetic correlation between the nucleus and commercial animals. Commercial genetic companies are now venturing into recording survival and production performance on pedigreed or genotyped commercial animals. The commercial genetic companies are utilizing the data collected on crossbred terminal pigs in commercial settings in their genetic evaluations in order to correctly select purebred animals

whose progeny will perform better at the commercial level.

Traits of economic importance Trait emphasis depends on whether selection is within sire or dam lines. Traits of economic importance in sire lines include growth rate, feed efficiency, carcass fat, meat percentage, meat quality, structural hardiness, and survival to market. For dam lines, reproductive traits including age at puberty, litter size, number weaned, milking ability and sow lifetime productivity are usually considered. Traits of economic importance can be defined as those characteristics of the pig that contribute to highest profitability. Such traits have varied over time and hence selection objectives need to be reevaluated as economics and production environments change. Factors affecting pork’s efficient production are vitally important as are traits that affect consumer preferences and pork consumption. Some of these traits are summarized in Table 14.2. The most important traits for commercial pork production in the finishing phase are lean growth, feed intake, and pig survival. Though the cost of feed is roughly 2/3 of the cost of

IV. Swine production

254 TABLE 14.2

Trait

14. Modern genetic and genomic improvement of the pig

Examples of economically important traits, their heritabilities, and relative economic values. Line selected in Heritability

Relative economic value

Explanation of economic value

Growth rate

Terminal/ Maternal

Moderate

þþþþ

Faster growth leads to lower facility costs and increased pounds pork sold

Feed efficiency

Terminal/ Maternal

Moderate

þþþþþ

Greater efficiency (less feed) usually leads to lower cost of gain

Meat/lean %

Terminal

Moderate

þþþþ

Lbs of lean pork product for most production units

Meat quality

Terminal

Moderate to high

þþ

Better quality is desired but most producers (except niche markets) are not paid for quality on an individual animal basis. Packers do select high quality pork for demanding customers.

Litter size/ number weaned

Maternal

Low

þþþþþ

Most important female traits

Sow longevity

Maternal

Low

þþ

Sows who produce three litters pay for their replacement costs

Piglet survival

Terminal/ Maternal

Low

þþþ

Major economic value depending on total income per pig marketed

Disease resistance

Terminal/ Maternal

Low

þþþ

Has major value but limited ways to improve at present

Behavior

Terminal/ Maternal

Low to moderate

þ

Value may increase if sows move to pen production or consumer pressure demands higher welfare standards

raising a pig to market weight, arguably, the two most economically important traits for pork production are reproductive traits and disease resistance. However, due to limitations of properly accessing the full economic value of these traits, they do not receive proper economic weighting. There are several reproductive traits of interest to the pig industry with the two most important being the number of pigs weaned per sow per year and the other more overlooked trait being the reproductive lifetime production of the sow herself. Several research groups have conducted research that has clearly shown genetic variation for these traits. Though consumers are most concerned about the degree of fatness or carcass merit as well as pork quality, pork producers must also pay attention to the ever-growing demand by consumers that the pigs be grown without the use of antibiotics as growth promoters and in facilities that improve animal

welfare. Additionally, pork producers must do all of this while becoming more environmentally conscious by having pigs reduce feed wastage, improve feed efficiency, and produce waste that contains less phosphorous. In modern pig breeding schemes, selection is often on many traits in order to maximize economic value. In principle, an index weighting the traits on their relative economic value and their genetic and phenotypic relationships is considered. In fact, breeders must take care that selection for one or more traits does not have a negative influence on others. Genetic correlations among important traits are provided in Table 14.3. Other traits may become important if production schemes radically change, such as the increasing pressure to move sows from gestation crates to group housing, which may affect behavioral, feed intake, and structural traits

IV. Swine production

QTL, candidate genes and genetic improvement

TABLE 14.3

Genetic correlations among several important pork production traits.

Daily gain with

Genetic correlation

Backfat

.22

Feed per unit gain

.50

Loin eye area

.10

Reproduction

.10

FEED PER UNIT GAIN WITH Backfat

.34

Loin eye area

.35

Reproduction

.20

BACKFAT WITH Loin eye area

.35

Reproduction

.20

Values represent average values assembled from several sources and may differ between lines.

and their relative economic values. Additionally, pleiotropic effects need to be understood before selection schemes can be initiated.

Initial development of molecular genetic approaches Coordinated efforts to better understand the pig genome were initiated in the early 1990s with the development of the international PiGMaP gene mapping project as well as projects by the USDA and US agricultural universities. These projects were structured in such a way that they included cooperation and collaborations by many different institutes. In the United States, the position of a USDA Pig Genome Coordinator was created to facilitate collaborative efforts among scientists from both state and private universities as well as federal laboratories. These operate cooperatively in a Swine Genome Technical Committee, which has been meeting yearly since 1994. The Committee worked to increase collaborative efforts and

255

share information. There were three significant linkage maps with up to 1,000 markers published by the mid 1990s. Since that time, progress in growth of the linkage maps has continued as new gene markers, in particular, single nucleotide polymorphisms (SNPs) have been continuously identified and mapped, but with limited integration of the linkage maps taking place. Initial physical maps were successful and use of two radiation hybrid (RH) panels allowed significant physical maps of many thousand markers to be developed. These panels were then used to map BACs (Bacterial artificial chromosomes) end sequences to form a complete physical map that was used in the pig genome sequencing effort.

QTL, candidate genes and genetic improvement In the mid-1990s, many quantitative trait loci (QTL) experiments were undertaken by using linkage maps to help determine regions underlying traits of importance to the pig industry.5,6 A database called PigQTLdb (http://www. animalgenome.org/QTLdb/) has been constructed combining all published QTL information into one searchable database that allows the user to search by chromosome, trait, or key words from the publications.7 Researchers have identified over 27,963 QTL affecting 670 traits by using both commercial and exotic pig breeds with various population structures from 635 publications to date. An alternative approach to QTL scans undertaken by many researchers was the use of candidate gene analyses using biological or mutational candidate genes from other species to investigate a variety of traits.8 A substantial number of candidate genes have shown significant associations with many traits important to swine production. The first important candidate gene was the Estrogen Receptor (ESR), which was shown to have a significant association

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14. Modern genetic and genomic improvement of the pig

Individual genetic markers and genes considered in marker assisted selection in pigs.

TABLE 14.4

Marker or gene/locus name

Traits affected

11

RYR1 (Halothane)

Lean growth, porcine stress syndrome, meat quality

RN (Rendement Napole) ESR (Estrogen Receptor)

12

Meat quality

9

Litter size 10

MC4R (Melanocortin-4 Receptor)

IGF2 (Insulin like Growth Factor 2)

Lean growth, fatness, feed intake 30

Lean growth, litter size

5

c-KIT Receptor

Coat and skin color 17

MC1R (Melanocortin-1 Receptor)

Red/ black coat color

PRKAG3 (Protein Kinase AMP Activated Gamma3-Regulatory Subunit)14

Meat quality

HMGA1 (High Mobility Group AT-hook1)31

Backfat thickness/growth

CCKAR (Cholecystokinin type A Receptor)32

Feed intake and growth

CAST (Calpain inhibitor)13

Tenderness and meat quality

EPOR (Erythropoietin)33

Litter size

E. Coli receptor F1815

E-coli diarrhea

E. Coli receptor K8816

E-coli diarrhea

GBP5 (Guanylate Binding Protein 5)24

Porcine Reproductive and Respiratory Syndrome

with litter size with effects ranging from 0.25 to over 1 pig per allele per gene copy with variations depending on breed background.9 A mutation in MC4R has shown a significant association with a reduction in feed intake with less back fat or faster growth depending on which allele is inherited.10 Extensively reviewed meat quality genes (HAL, RN) have been reported. Genetic markers identified within these genes enable genetic testing, which allows producers to remove the alleles deleterious to meat quality.11,12 Additional genes including PRKAG3 and CAST have been shown to be associated with improvements in post-mortem pH and tenderness.13,14 Candidate genes or gene regions (K88, FUT1) have been identified to be associated with differences in immune response or disease resistance. FUT1 is associated with reduced post weaning diarrhea in commercial pork production and a polymorphism has been identified to be associated with resistance to K88 E. coli.15,16

Additional genes such as KIT and MC1R have been used by breeding companies to produce pigs that are white in color, a phenotype that is preferred by commercial meat packing companies.5,17 Commercial pig breeding companies initially combined these genetic markers with traditional performance information in markerassisted selection programs to identify and select individuals that have the most genetic potential. Information on commonly used genes and markers is summarized in Table 14.4.

Sequencing the pig genome Sequencing is the unraveling of the DNA to understand the genetic code. It is equivalent to breaking down books into individual sentences and even specific letters in these sentences and words. The letters in the genetic code (A, T, G, and C, representing adenine, thymine, guanine,

IV. Swine production

Sequencing the pig genome

and cytosine, respectively) are combined into “words” and these words are the genes that control traits or contribute to phenotypes of the animal that include rate of growth, level of fat, reproductive performance and disease susceptibility. Knowing the genetic code requires that we apply modern molecular biology or laboratory methods to break up the code into smaller pieces and then “read” the code. In 2003, an international pig genome sequencing committee was formed to lead efforts to fund and to initiate sequencing of the pig genome. During that same time period, a significant Sino-Danish effort to sequence the pig genome was initiated. This effort produced a 0.6 coverage and a large number of SNPs were then released to the public domain. Formation of the international consortium allowed the status of the pig genome to also move toward its goal. Using one of the Radiation Hybrid (RH) panels as a template, a new comparative map was constructed that far exceeded anything to date with an average spacing between comparative anchor loci at 1.15 Mb based on the human genome sequence. A group of international swine genome researchers, called the International Swine Genome Sequencing Consortium, wrote a sequencing proposal that finally secured significant funding from the USDA, National Pork Board, Iowa Pork Producers Association, University of Illinois, Iowa State University, North Carolina Pork Council, North Carolina State University, the Wellcome Trust Sanger Institute, UK and a number of research institutions from around the world including those from China, Denmark, France, Japan, Korea, Scotland and the UK.18 The International Swine Genome Sequencing Consortium continued its activities and the genome assembly (Sscrofa10.2), which is the template for the Consortium’s analysis and annotation efforts and the basis for the pig genome sequence paper, was deposited at the National Center for Biotechnology Information (NCBI) in 2011

257

(ftp://ftp.ncbi.nih.gov/genbank/genomes/ Eukaryotes/vertebrates_mammals/Sus_scrofa/ Sscrofa10.2/). This is the definitive source for the original analyses of the pig genome. The NCBI genome team released an annotated copy of this draft genome sequence (see ftp://ftp.ncbi.nlm. nih.gov/genomes/Sus_scrofa/GFF/). This annotated pig genome sequence is available in the NCBI Genome Browser. Members of the Consortium have also been working with the Ensembl genome project team. The Ensembl team recently completed their GeneBuild for Sscrofa11.1 (a gene build is the organization of the sequence). The Ensembl gene models are based on sequence evidence, including alignments with expressed sequences comprising not only complementary DNA (cDNA) and Expressed Sequence Tag (EST) information in the public databases, but also more than 250 gigabases of RNA-seq data generated by members of the Swine Genome Consortium. The preliminary Ensembl analysis is available on the Pre-Ensembl site (http://pre.ensembl.org/Sus_scrofa/Info/ Index) and the full annotated genome is available on the Ensembl genome browser in Ensembl release 67. The first complete genome sequence manuscript titled “Pig genomes provide insight into porcine demography, domestication and evolution” described not only the generation and analysis of a high quality draft reference genome sequence of a single domesticated pig (Sus scrofa), but also the analysis of 16 other individual genomes including 10 European and Asian wild boar (Sus scrofa) and 6 domesticated pigs.19 Among the major farmed livestock species, the pig is unique because the wild ancestors (wild boar from Europe and Asia) from which it was domesticated are still extant. The analyses provided new insights into the demography of wild boar and their subsequent domestication. It includes evidence of a deep phylogenetic split between Asian and European wild boar which points to their divergence about 1 million years

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14. Modern genetic and genomic improvement of the pig

ago. Further insight in the domestication of the pig in the past 10,000 years as well as the more recent development of specific breeds is also discussed. Researchers have conducted analyses of the pig genome and its gene content in an evolutionary context, and this has revealed accelerated evolution of primarily immune response and olfaction genes. Olfaction, the ability to smell, was clearly important to wild pigs and this has not been lost in the modern pig. Immune response genes may also have helped to make the pig so flexible in a number of environments. The pig is not only an important agricultural species (pork is the most widely consumed meat globally), but also an important biomedical model. The 1000 Genomes research project is designed to sequence genomes from 1000 species and has discovered that all people may carry a burden of potential loss of function mutations. Analysis of individual pig genomes has revealed similar mutations. These recently discovered mutations may potentially offer additional reasons to study the pig as a biomedical model. To date, there are over 350 publicly available individually sequenced pigs with many commercial genetic companies privately sequencing many of their elite sires.20

Furthermore, commercial genetic companies are constructing their own genotyping chips containing markers that are informative in their own populations as well as proprietary markers used in genetic selection. The large expected outputs from the SNP association trials using the 62K pig SNP chip also offered new challenges and opportunities. Pig breeding companies now have literally 100s or 1000s of markers to select on. Genomic selection has evolved rapidly and estimated effects are used to estimate genomic breeding values (GEBVs).22,23 Use of “genomic selection” to select for individual traits or for total economic value is being actively utilized in some genetic improvement programs as is the use of genomic relationships among animals instead of traditional estimated average genetic relationships. The advantage of genomic selection is it can be devoted to traits that are difficult to improve quickly, such as reproduction traits (Table 14.5). Arguably, one of the greatest benefits of utilizing GEBVs for breeding companies is the ability to give individuals in a litter their own GEBV for each trait versus the parent average EBV for traits. One such example is the assignment of independent genomic breeding values for litter size for littermate boars based on their genotypes without requiring progeny testing of their daughters before true individual EBVs can be

Genomic selection Sequencing the pig genome helped to identify millions of SNPs. The SNPs, in turn, were used to create genotyping chips which have the ability to genotype 1,000s of SNPs simultaneously. The first high density pig SNP chip was designed by Illumina and had 62,000 SNPs.21 These were then used to conduct genome wide association studies (GWASs) for trait discovery and have helped to add to the thousands of QTLs discovered. Additionally, commercially available SNP chips consist of a 10K SNP chip as well as the Axiom Porcine Genotyping Array designed by Affymetrix containing 658,692 markers.

TABLE 14.5

Approximate values for EBV and GEBV accuracy for dam line traits.

Trait

EBV accuracy

GEBV accuracy

% Increase

Total number born

0.25

0.42

68

Stillborn

0.26

0.43

65

Survival birth weaning

0.17

0.26

53

Litter weaning weight

0.23

0.35

52

Estimates presented as averages from a recent survey by authors and unpublished public presentations.

IV. Swine production

Cloning, transgenics, gene editing, and breeding pigs as biological models

estimated. While the dairy industry greatly benefits from the use of GEBVs to reduce generation interval of selection, the swine industry mainly benefits from the increased accuracy of selection seen with the use of GEBVs as illustrated in Table 14.5. Finally, for some traits that have been difficult or impossible to measure on the live animal, such as meat quality, once associations have been estimated, breeders will likely have real opportunities to make noticeable genetic improvement through genomics without having to collect data from progeny or siblings. For traits such as disease resistance, structural and environmental soundness, behavior and others that until recently had been ignored, GWAS and genomic selection provide the opportunity for real progress. Researchers at Iowa State University identified the gene guanylate binding protein 5, which was significantly associated with host response to Porcine Reproductive and Respiratory Syndrome (PRRS) virus infection from previous GWAS studies.24 Additionally, researchers at the University of Nebraska recently identified Synaptogyrin-2 as being involved in the replication of Porcine circovirus 2.25 Given that the cost of genotyping on the larger genotyping platforms is typically higher than the smaller marker panels, pig breeding organizations have contemplated whether or not they need all these markers to get improvement at a similar accuracy. One approach that some breeding organizations and other researchers are using is called imputation. Genotype imputation is done by genotyping sires and dams of each line for all 62,000 SNPs or the higher 600K SNP chip. Then, genotyping with a smaller chip (perhaps hundreds to 10,000 SNPs), they can predict the complete genotype (60K SNPs or 600K SNPs) from the genetic and statistical relationships between the fewer markers and the larger marker set. These genomic imputation approaches can save millions of dollars in some breeding schemes and yet obtain reasonable levels of accuracy.

259

Breed purity has always been an issue to small purebred breeders. Researchers at Michigan State University utilized the 10K SNP marker panels to determine the probability of breed composition for animals composed of Duroc, Yorkshire, Landrace or Hampshire breeds.26 Additional uses of genomic selection include more effectively measuring genotype by environmental interactions and developing ways to select specific genotypes or “designer genes” for specialized niche markets and products. This will likely raise incomes, at least for those producing specialized products. Finally, one last area of use for the 1,000s of SNPs is to support the use of DNA to improve food safety and help trace pork products from the “farm to the fork.” Some production and breeding companies have developed and are continuing to evolve a traceability approach to brand products.

Databases Databases play a vital role in providing the tools needed for future genomic discoveries and their uses for pig improvement. Substantial early pig bioinformatics efforts were undertaken by the Roslin Institute, Scotland (www.thearkdb. org) and by the US Bioinformatics Coordinator (www.genome.iastate.edu and http://www. animalgenome.org/). Together these efforts have supported a variety of pig genome efforts as well as displaying the gene maps and other sequence information and tools for the pig genome. Data on SNPs were traditionally found in dbSNP (https://www.ncbi.nlm.nih.gov/projects/SNP/), but this information is transitioning to the European Bioinformatics Institute (EMBL-EBI).

Cloning, transgenics, gene editing, and breeding pigs as biological models While genetically modified (GM) crops are well accepted and utilized for many food sources throughout the world, the use of animal

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14. Modern genetic and genomic improvement of the pig

biotechnologies remain underutilized. Animal biotechnologies include animal cloning, transgenics, and gene editing. Cloning of livestock is possible and has been quite successful in multiplying superior males for artificial insemination. While this technology is helpful in genetic improvement of pigs, there has been limited used in the US and Europe due to regulatory agencies. The use of transgenics falls in a similar place as cloning. With transgenics, genes from different species are inserted into the DNA of, in this case, the pig. Transgenic pig production has been around for a great deal of time, but only recently have commercially novel and perhaps useful transgenic pigs been produced. An example of a commercially relevant transgenic pig is the pig that produces phytase (EnviroPig). Phytase is an enzyme which aides in the digestion and absorption of phosphorus. In the EnviroPig, phytase is expressed in the saliva and hence the pig produces less phosphorus in the manure. This has the greatest benefit in areas where pig production is heavy and manure with large amounts of phosphorus has been polluting the environment. While this pig transgenic has great potential, no approval for the human consumption of such pigs has been allowed in the originating country, Canada, or in the US. Ultimately, the project was halted in 2011. Another novel transgenic pig is one which is rich in omega-3 fatty acids. This might have real health advantages for those that consume pork, but awaits approval for consumption. Gene editing has gained considerable interest with the identification of the CRISPR-Cas9 system,27 which allows for a targeted modification in the DNA sequence of an organism. Researchers can utilize their knowledge of the basic biology of the gene and its protein function to precisely change the DNA sequence, thus altering the protein function of the gene and allowing for edits to stay within the species. Researchers at the University of Missouri used the CRISPR-Cas9 system to modify the CD163

gene such that the PRRS virus is not able to replicate inside the pig.28 This slight modification of the swine genome through gene editing keeps the pigs from succumbing to PRRS which has an annual estimated loss to the United States swine industry of over $660 million per year. Despite this benefit, given the public’s concerns over food safety, it is likely that approval for such technology is years away in the US, Canada and Europe. However, in some cultures, there is a wide range of non-livestock species that are consumed. Therefore, it is conceivable that these countries and cultures may be open to transgenic/gene edited livestock. They may see the importance of useful gene editing which may lead to approval and consumption of reasonable genetically edited animal products such as those with modifications that are already found in nature or those that offer a substantial welfare benefit to society. There has been continued interest in the pig as a biological model for human biology. A recent survey of US government grants, including National Institutes of Health, found hundreds of active grants using pigs as models. Research using pigs as animal models of human conditions has covered a vast array of disciplines such as nutrition, digestive physiology, kidney function, heart function, diabetes, obesity, and skin formation and healing. With the growing evidence of the close relatedness of the pig to the human, as evidenced by new sequence information, the extent of biomedical projects using the pig could be expected to grow in the future. Additionally, shortages of human tissues and organs available for transplantation have created interest in xenotransplantation, and the pig is the preferred donor due to its size and comparative physiology. Recent concerns about retroviruses and difficulties producing transgenic pigs that meet the standards required for safe transplantation have slowed progress in the use of the pig for xenotransplantation. This has caused some companies to scale back their active research in this area.

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References

Future developments and applications to genetic improvement Genetic improvement in pigs during the 20th century was considerable with the development of objective trait measurements, use of breeding values, crossbreeding and the development of commercial lines of swine that grew quickly and efficiently and produced more piglets. The close of the century brought the creation of genetic maps and the discovery of important genetic markers and their use in marker assisted selection for traits of economic importance. Such discoveries and their application to the industry have made the pig a major source of lean red meat. Efforts in the early part of the 21st century have been no less amazing. The initial sequencing efforts are now completed; the development of SNP chips and the advent of genomic selection are beginning to revolutionize pig breeding. It is likely that with these new genomic tools, advances to select disease resistant pigs will be more successful. It is expected that fine tuning of breeding programs to match specific environments as well as specific niche markets or the production of specialized products will also occur. Computer programs, such as MateSel, have been developed that help maximize response to selection and minimize inbreeding in the context of available sires and dams. These programs are the first wave of artificial intelligent approaches that will help improve all decision making in animal agriculture. Unfortunately, the small breeder who led the advancements during the 20th century, but may be a slow adopter of this new technology, and the local breeds with limited commercial relevance will likely be lost in this process. Pig breeding for biomedical models will likely accelerate through the use of animal biotechnology to study human disease or to create animals to supply organs for transplant. Such

261

developments will likely be further advanced using cloning and gene editing to help create a larger number of individuals more suitable for study. Pig breeders, producers and consumers will all benefit from these advances in genetics and genomics and the resulting genetic improvement. This will require adoption of technology, advanced training by many within the livestock industry and education of the public of the safety of these advances.

Acknowledgments Support from the Department of Animal Science and the College of Agriculture University of Nebraska and the Department of Animal Science College of Agriculture and Life Sciences at Iowa State University is gratefully acknowledged. The authors also thank Dawn Koltes, Graham Plastow, Scott Newman, Daniel Ciobanu and other colleagues for providing information and suggestions for this chapter and several breeding companies for providing unpublished data.

References 1. Larson G, Cucchi T, Dobney K. Genetic aspects of pig domestication. In: Rothschild MF, Ruvinsky A, eds. Genetics of the Pig. Wallinford, UK: CAB International; 2011:14e37. 2. Buchanan DS, Stalder K. Breeds of pigs. In: Rothschild MF, Ruvinsky A, eds. Genetics of the Pig. Wallinford, UK: CAB International; 2011:445e472. 3. Ollivier L, Foulley LJ. Pig genetic resources. In: Rothschild MF, Ruvinsky A, eds. Genetics of the Pig. Wallinford, UK: CAB International; 2011:306e325. 4. Nicholas F. Genetics of morphological traits and inherited disorders. In: Rothschild MF, Ruvinsky A, eds. Genetics of the Pig. Wallinford, UK: CAB International; 2011:51e72. 5. Andersson-Eklund L, Marklund L, Lundstrom K, et al. Mapping QTLs for morphological and meat quality traits in a wild boar intercross. Anim Genet. 1996; 27(Suppl. 2):111. 6. Malek M, Dekkers JCM, Lee HK, Baas TJ, Rothschild MF. A molecular genome scan analysis to identify chromosomal regions influencing economic traits in the pig. I. Growth and body composition. Mamm Genome. 2001;12:637e645.

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7. Hu ZL, Dracheva S, Jang W, et al. A QTL resource and comparison tool for pigs: PigQTLdb. Mamm Genome. 2005;16:792e800. 8. Rothschild MF, Soller M. Candidate gene analysis to detect genes controlling traits of economic importance in domestic livestock. Probe. 1997;8:13e20. 9. Rothschild MF, Jacobson C, Vaske D, et al. The estrogen receptor locus is associated with a major gene influencing litter size in pigs. Proc Natl Acad Sci USA. 1996; 93:201e205. 10. Kim KS, Larsen N, Short TH, Plastow GS, Rothschild MF. A missense variant of the porcine melanocortin-4 receptor (MC4R) gene is associated with fatness, growth, and feed intake traits. Mamm Genome. 2000;11:131e135. 11. Fuji J, Otsu K, Zorzato F, et al. Identification of a mutation in porcine ryanodine receptor associated with malignant hyperthermia. Science. 1991;253:448e451. 12. Milan D, Jeon JT, Looft C, et al. A mutation in PRKAG3 associated with excess glycogen content in pig skeletal muscle. Science. 2000;288:1248e1251. 13. Ciobanu DC, Bastiaansen JWM, Lonergan SM, et al. New alleles in calpastatin gene are associated with meat quality traits in pigs. J Anim Sci. 2004;82:2829e2839. 14. Ciobanu D, Bastiaansen J, Malek M, et al. Evidence for new alleles in the protein kinase adenosine monophosphate-activated 3-subunit gene associated with low glycogen content in pig skeletal muscle and improved meat quality. Genetics. 2001;159:1151e1162. 15. Vogeli P, Meijerink E, Fries R, et al. A molecular test for the identification of E. coli F18 receptors - a breakthrough in the battle against porcine oedema disease and post-weaning diarrhea in swine. Schweiz Arch Tierheilkd. 1997;139:479e484. 16. Jørgensen C, Cirera S, Anderson S, et al. Linkage and comparative mapping of the locus controlling susceptibility towards E. coli F4ab/ac diarrhoea in pigs. Cytogenet Genome Res. 2003;102:157e162. 17. Kijas JMH, Wales R, Tornsten A, Chardon P, Moller M, Andersson L. Melanocortin receptor 1 (MC1R) mutations and coat color in pigs. Genetics. 1998;150:1177e1185. 18. Archibald AL, Bolund L, Churcher C, et al. Swine Genome Sequencing Consortium. Pig genome sequence–analysis and publication strategy. BMC Genomics. 2010;11:438. 19. Groenen MAM, Archibald AL, Uenishi H, et al. Pig genomes provide insight into porcine demography and evolution. Nature. 2012;491:393e398. 20. Groenen MAM. A decade of pig genome sequencing: a window on pig domestication and evolution. Genet Sel Evol. 2016;48:23.

21. Ramos AM, Crooijimans RP, Affara NA, et al. Design of high density SNP genotyping assay in the using SNPs identified and characterized by next generation sequencing technology. PLoS One. 2009;4(8):e6524. 22. Meuwissen T, Hayes B, Goddard M. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819e1829. 23. Calus MPL. Genomic breeding value prediction: methods and procedures. Animal. 2010;4:157e164. 24. Koltes JE, Fritz-Waters ER, Eisley CJ, et al. Identification of a putative quantitative trait nucleotide in guanylate binding protein 5 for host response to PRRS virus infection. BMC Genomics. 2015;16. https://doi.org/ 10.1186/s12864-015-1635-9. 25. Walker LR, Engle TB, Vu H, et al. Synaptogyrin-2 influences replication of Porcine circovirus 2. PLoS Genet. 2018;14(10):e1007750. 26. Huang Y, Bates RO, Ernst CW, Fix JS, Steibel JP. Estimation of US Yorkshire breed composition using genomic data. J Anim Sci. 2014;92(4):1395e1404. 27. Doudna JA, Charpentier E. Genome editing. The new frontier of genome engineering with CRISPR-Cas9. Science. 2014;346(6213):1258096. 28. Whitworth K, Rowland RRR, Ewen C, et al. Gene-edited pigs are protected from porcine reproductive and respiratory syndrome virus. Nat Biotechnol. 2015;34. https:// doi.org/10.1038/nbt.3434. 29. Dekkers J, Mather PK, Knoll EF. Genetic improvement of the pig. In: Rothschild MF, Ruvinsky A, eds. Genetics of the Pig. Wallinford, UK: CAB International; 2011: 390e425. 30. Van Laere AS, Nguyen M, Braunschweig M, et al. A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature. 2003;425: 832e836. 31. Kim KS, Thomsen H, Bastiaansen J, et al. Investigation of obesity candidate genes on porcine fat deposition quantitative trait loci regions. Obes Res. 2004;12: 1981e1994. 32. Houston RD, Haley CS, Archibald AL, Cameron ND, Plastow GS, Rance KA. A polymorphism in the 50 -untranslated region of the porcine cholecystokinin type A receptor gene affects feed intake and growth. Genetics. 2006;174:1555e1563. 33. Vallet JL, Freking BA, Leymaster KA, Christenson RK. Allelic variation in the erythropoietin receptor gene is associated with uterine capacity and litter size in swine. Anim Genet. 2005;36:97e103. 34. Lasley JF. Genetics of Livestock Improvement. 4th ed. Englewood Cliffs, NJ: Prentice Hall Inc.; 1987.

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C H A P T E R

15 Reproductive physiology of swine Rodney D. Geiserta, Peter Sutvoskya,b, Matthew C. Lucya, Frank F. Bartolc, Ashley E. Meyera a

Division of Animals, University of Missouri, Columbia, MO, United States; bDepartments of Obstetrics, Gynecology and Women’s Health, University of Missouri, Columbia, MO, United States; cDepartment of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, AL, United States

O U T L I N E Introduction

Fertilization Early embryo and conceptus development Maternal recognition of pregnancy Placentation

263

Boar physiology 264 Testis and spermatogenesis 264 Spermatozoa 265 Epididymal sperm maturation, accessory sex glands and seminal plasma 266 Mating and libido 267

Parturition

275 276 276 277 277 278 279 279

Sow physiology Puberty Estrous cycle Breeding

267 267 268 269

Lactation Mammary gland Mammogenesis Lactogenesis Milk-borne bioactive factors Lactocrine programming

Pregnancy

270

References

Introduction The reproductive anatomy of the sow is characterized by having multi-ovulatory ovaries

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00015-X

270 271 274 274

which ovulate approximately 20e30 follicles that form corpora lutea (CL) giving the ovary a mulberry like appearance (Fig. 15.1A). The fimbria of the oviduct receives the ovulated

263

Copyright © 2020 Elsevier Inc. All rights reserved.

264

15. Reproductive physiology of swine

(A)

Uterus

Cervix

Ampullary-Isthmic Junction

Vagina

Ovary Oviduct Fimbria

Uterotubal Junction Tip of Uterine Horn

Vulva

(B)

Bulbourethral Glands

Prostate Gland

Vesicular Glands

the cervix is continuous with the vagina and does not form a fornix with the vagina as occurs in the cow, ewe and mare. However, the sow’s cervix contains interdigitating prominences (pads) which help lock in the corkscrew shaped glans penis of the boar to stimulate ejaculation during mating. The mature boar has large testes which weigh approximately 375 g each. The major accessory sex glands that contribute to the seminal plasma during ejaculation are the seminal vesicles, prostate and bulbourethral glands (Fig. 15.1B). Seminal vesicles contribute the largest volume to 100e500 mL of seminal plasma during ejaculation. The boar’s bulbourethral glands produce a gel plug to seal the cervix and prevent semen backflow following mating. During erection, the retractor penis muscles relax and the increase in blood pressure within boar’s fibroelastic penis causes the sigmoid flexure to straighten out for mating.

Boar physiology

Urethalis Muscle

Bladder Retractor Penis Muscle

FIG. 15.1

Glans Penis Sigmoid Flexure

Anatomy of the sow (A) and boar (B) reproductive

tracts.

ova from the surface of the follicles during ovulation and transports them to the oviductal ampullary-isthmic junction where fertilization of oocytes and early embryonic development occurs. The isthmus of the oviduct serves as not only a sperm reservoir following mating, but restricts transport of embryos into tips of the uterine horns until secretion of progesterone by CL increases. The two uterine horns of the sow are long (150e200 cm) and communicate via a short uterine body (1 cm) that connects to the cervix. The anterior opening (cervical os) of

Testis and spermatogenesis The testes of boars are ecliptical and oriented upside-down within a non-pendulous scrotum, with thick testicular tunics and adjacent large epididymides the tail of which is dorsally oriented within the scrotum. The scrotum is located in the sub-anal position and divided into two-halves by a scrotal raphe.1 The testis descend into the scrotum shortly before birth. Seminiferous tubules in the boar are very tightly packed, resulting in dense parenchyma from which individual tubules are difficult to extract without enzymatic digestion.2 Following the cross-section of a seminiferous tubule from basement membrane to lumen, spermatogenesis in boars progresses from premeiotic, proliferative and self-renewing phase (spermatogonia), through meiosis (primary and secondary spermatocytes) to the haploid, postmeiotic phase of spermiogenesis, encompassing

IV. Swine production

Boar physiology

spermatid elongation and spermiation (fully differentiated spermatozoa detachment from epithelium). The haploid phase of spermatogenesis results in the formation of accessory sperm structures (acrosome, flagellum, perinuclear theca) as well as in the hypercondensation of sperm DNA when spermatid nuclear histones are supplanted by protamines. Proper sperm nucleus protamination is thus as important for sperm quality and fertility in boars, as it is in other mammals. Based on the arrangement of individual cell types across and along seminiferous tubule sections, spermatogenesis in boars is divided into eight stages (I-VIII) and spermiogenesis into 12 steps (1e12).3 The length of spermatogenesis, i.e. the progression from an undifferentiated A-spermatogonium to a fully elongated, differentiated spermatozoon competent for epididymal maturation and ensuing acquisition of motility takes approximately 34 days. Daily sperm output is estimated to be 15e20 billion with up to 120 billion sperm released per ejaculate.4 Sperm production and quality of sperm in boars is influenced by genetics, nutrition, housing, seasonal effects, and disease.5 Relatively low cholesterol content of plasma membrane makes boar spermatozoa prone to cryodamage and thus refractory to cryopreservation. The size and DNA content differences between X and Y-chrosomome bearing spermatozoa is conducive to semen sexing, though this is not yet practical on a commercial scale due to large ejaculate volume exacerbating physical forces and slow speed of flow cytometric sorting.

Spermatozoa Boars have spatula-shaped spermatozoa (Fig. 15.2) with a head of w9 mm
5 mm and tail length of w30e35 mm. The sperm head has a prominent acrosome, composed of acrosomal matrix sandwiched between the inner and the outer acrosomal membranes, forming a visible

265

(under phase/DIC contrast microscope) acrosomal ridge proximally, and covering a crescent shaped-equatorial segment distally. The postacrosomal region is covered by a thick perinuclear theca which protrudes proximally to overlay the outer leaf of the equatorial segment and underline the inner acrosomal membrane.6 Sperm tail or flagellum is attached to the basal plate of the implantation fossa of the distal head.7 Its proximal segment, called the connecting piece, is made up by nine striated columns caging the proximal centriole, an important cytoskeleton organizing element necessary for pronuclear apposition after fertilization, but until then it is embedded in a dense mass of sperm capitulum. The connecting piece of testicular and upper epididymal spermatozoa is enveloped by a cytoplasmic droplet (CD), the last remanant of spermatid cytoplasmic lobe the rest of which is pinched off as residual body during spermiation. With striated columns continuing distally as nine outer dense fibers (ODFs), the rest of the sperm tail is centered around the axoneme with nine microtubule doublets (one running parallel to each ODF) and a central pair of microtubules, collectively referred to as the 9 þ 2 microtubule arrangement. Distal to the connecting piece, the midpiece of sperm is covered by a helix of mitochondria, collectively termed mitochondrial sheath (MS), which is brought into the oocyte cytoplasm at fertilization, but degraded by the ubiquitin-dependent autophagy to promote clonal, maternal inheritance of the mitochondrial genome. The MS provides energy for sperm motility through respiratory chain generation of ATP, while energy generation by glycolysis resides in the next tail segment. The tail principal piece, and particularly in its fibrous sheath (FS), includes a ribbed quiver of the sperm axoneme which also contains protein kinases (PKA in particular) necessary for protein phosphorylation during motility acquisition and capacitation of sperm. The last part of the tail, the end piece contains axoneme, but it is not covered by FS.

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15. Reproductive physiology of swine

FIG. 15.2 Boar spermatozoon viewed by transmission electron microscopy (A), diagrammatically (B) and by epifluorescence microscopy overlaid with differential interference contrast (C). (A) Top to bottom, cross-sections of apical head with acrosome, postacrosomal sheath with tail connecting piece and capitulum, midpiece with mitochondrial sheath and principal piece with fibrous sheath are shown. (B) Diagram highlighting individual sperm segments on the left and major structural elements/ accessory sperm structures on the right. (C) Left to right, a morphologically normal spermatozoon with green-labeled acrosome showing the crescent-like outline of the equatorial segment, a defective spermatozoon with retained cytoplasmic droplet (arrow) and a spermatozoon undergoing acrosomal exocytosis (green).

Epididymal sperm maturation, accessory sex glands and seminal plasma The boar’s epididymis is composed of nine distinct regions, divided into the initial segment, caput, corpus, and cauda epididymis. Boar spermatozoa acquire the potential for progressive motility during epididymal maturation, through a complex change including, but not limited to disulfide bond cross-linking of sperm proteins, adsorption of the epididymis-secreted proteins and glycans onto the plasma membrane surface and removal of the cytoplasmic droplet, which,

if not shed in the epididymis or immediately after ejaculation (upon mixing with seminal plasma) is detrimental to boar fertility.8,9 Among the boar’s accessory sex glands (Fig. 15.1B), the vesicular gland and bulbourethral glands are very large, and provide the bulk of seminal plasma volume. Inversely, the prostate is very small.1 Voluminous seminal plasma has multiple functions,10 including providing energy, stimulating sperm motility, maintaining spermatozoa in a decapacitated state to prevent premature capacitation and sperm death, buffering and immunosuppressing female reproductive tract, and providing the

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Sow physiology

vehicle for sperm transport up to uterotubal junction (Fig. 15.1B). Also present are activities that influence female reproductive tract mucosa and promote the detachment of sperm CDs which in ejaculated spermatozoa of boars are the most common defect. Boar seminal plasma is rich in fructose, inositol, zinc, bicarbonate, magnesium and calcium. Sperm-binding spermadhesins, porcine seminal plasma protein (PSP) and DQH glycoproteins are among the most abundant components of boar seminal plasma.11 Based on the aforementioned new knowledge, creative ways to manage boar fertility and improve swine AI dose have been developed/contemplated, such as the biomarker based flow cytometric semen evaluation, sperm nanopurification, and semen extender supplementation with antioxidants and additives that are intrinsic to undiluted boar seminal plasma.12

Mating and libido Boars are year-round breeders though their libido and sperm quality may be influenced by seasonal changes of daylight and temperature, the latter being of particular concern during summer heat stress. Boar libido and spermatogenesis are controlled by the hypothalamicpituitary-gonadal axis through gonadotropin releasing hormones, particularly gonadotropinreleasing hormone 2 (GnRH2) that stimulates pulsatile secretion of follicle stimulating hormone (FSH) and luteinizing hormone (LH) from the anterior pituitary, thus stimulating steroidogenesis and testosterone production by testicular interstitial Leydig cells.13 Endocrine stimulation regulates an intricate network of locally produced (by Leydig and Sertoli cells) growth factors and cytokines and transcription factors in the germ cells. In turn, lumicrine factors traveling from rete testis with spermatozoa support the sustenance of epididymis and its ability to mature spermatozoa.

Sow physiology Puberty Gilts are born with an immature ovary that contains primordial, primary and secondary follicles that are incapable of ovulation.14 Attainment of puberty depends on the development of large follicles that can produce adequate estradiol to initiate estrus and a surge of LH that causes ovulation. Growth of ovarian follicles depends on the release of pulses of gonadotropin-releasing hormone (GnRH) from neurons in the hypothalamus. The GnRH travels from the median eminence at the base of the hypothalamus to the anterior pituitary through a portal system of blood vessels. Within the anterior pituitary the GnRH causes the release of gonadotropins [follicle stimulating hormone (FSH) and luteinizing hormone (LH)] from gonadotrophs and into the circulation. Circulating concentrations of FSH are elevated until 10 weeks of age and provide an initial stimulus for the growth and development of ovarian follicles. Initially, there is negative feedback of estradiol on the pulsatility of LH secretion. As the pig ages, the negative feedback effect of estradiol is reduced and this leads to a gradual increase in the number of GnRH and LH pulses per day. Greater LH pulsatility acts at the level of the ovary to stimulate the development of successively larger ovarian follicles that secrete greater amounts of estradiol into the circulation.15 The uterus grows in response to the increasing concentrations of estradiol in the circulation. When ovarian follicles achieve a critical threshold of circulating estradiol then there is estrus and an LH surge that originates from the anterior pituitary gland. The LH surge initiates a cascade of events within the wall of the follicle that leads to follicular rupture and first ovulation (puberty). Puberty generally occurs at 5e7 months of age in pigs (80e120 kg of body weight). The first pubertal estrus in is

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generally less fertile with fewer ovulations compared with subsequent estrous cycles. To achieve larger litters, therefore, gilts are typically inseminated at their second or third estrus after their first (pubertal) estrus. Age at puberty can be influenced by breed, season of the year, nutrition, social environment, and exposure to the boar.16 Puberty is delayed in the summer months and also when young pigs are underfed or confined and crowded in indoor pens. Mixing gilts from different pens or transporting gilts in a truck or trailer causes the short-term release of stress hormones that can advance the age at puberty. Daily exposure of gilts to boars including physical contact, vocalization, and boar odor (musk-smelling compounds [16-androstenes]) can advance the age of puberty in gilts that are between 135 and 160 days of age. Prepubertal gilts that are older than 5.5 months can be treated with P.G. 600; a combination of pregnant mare serum gonadotropin (PMSG; FSH-like hormone) and human chorionic gonadotropin (hCG; LH-like hormone).17 Gilts treated with PG 600 will show estrus 4e10 days after treatment (average ¼ 7 days), but are typically inseminated at the next

estrus because farrowing rate and litter size are greater at the second estrus following PG 600 stimulation.

Estrous cycle The length of estrous cycles for gilts (females that have never been pregnant) and sows (mature females that have given birth to at least one litter) is approximately 21 days (range 18e22 days).18 Endocrine changes during the estrous cycle are presented in Fig. 15.3. There are four stages of the estrous cycle: proestrus, estrus, metestrus, and diestrus. Corpora lutea develop from the cells of the ovulated follicle and produce progesterone; a steroid hormone that maintains pregnancy. During proestrus, circulating concentrations of progesterone decline rapidly. The decrease in progesterone indicates regression of the CL (loss of function). Regression of the CL is caused by the episodic release of prostaglandin F2a (PGF2a) from the uterine endometrium.19 Following regression of the CL and concomitant decline in circulating concentrations of progesterone there is an increase in the frequency of LH pulses that Follicles

Cumulus-Oocyte Complex CA

Ovulation

Follicles

Luteinizing Hormone Surge

Follicles

Ovulation

CL

CL

Progesterone Estrogen

Prostaglandin F2α

0 1

2

Estrus

FIG. 15.3

4

6

8

12

15

Estrous Cycle

17

19 21

Estrus

Ovarian hormonal changes during the estrous cycle of the pig.

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initiates a wave of ovarian follicular development. The development of large ovarian follicles increases the circulating concentrations of the steroid hormone estradiol and the protein hormone inhibin. The increases in both estradiol and inhibin cause a decrease in circulating FSH when the pig approaches estrus and this decrease in FSH ends the process of follicular recruitment. High circulating concentrations of estradiol bring pigs into estrus; the next phase of the estrous cycle. Ovarian follicles are fully mature at the initiation of estrus and are approximately 8 mm in diameter with 10e15 follicles on each ovary.20 Pigs display lordosis behavior or “standing heat” when they are in estrus meaning that they will stand still with erect ears when mounted by the boar or following the application of back pressure by a person. Boar exposure during estrus strengthens the lordosis response to the back pressure test.21 Other signs of estrus include swelling and reddening of the vulva, vocalization (estrual grunts), mounting other female pigs and boar-seeking behavior. In response to estradiol, the cervix becomes rigid and the uterine horns are tightly coiled. Estrus tends to be shorter in gilts (24e48 h) compared with mature sows (24e96 h). Circulating estradiol reaches peak concentrations near the onset of estrus and triggers the LH surge; a massive release of from the anterior pituitary and into the circulation.18 The LH surge lasts for about 10 h and may occur before, during or after the initiation of estrus. Ovulation occurs during a 1e3 h period approximately 30e35 h after the LH surge and when the estrus period is 70% complete. Following ovulation, the granulosa cells and theca cells of the follicle wall become luteinized and form CL. Initially, blood fills the lumen of the ruptured follicle to form the corpus hemmorrhagicum (CH) and the CH is eventually replaced by the CL. The metestrus phase of the estrous cycle follows as a vascular bed is established and luteal cells complete their differentiation and increase their capacity to

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synthesize progesterone. When fully mature, the CL are 8e11 mm in diameter. The presence of multiple CL on each ovary results in a steady increase in circulating concentrations of progesterone. Diestrus, the period of maximal circulating progesterone follows metestrus and continues for approximately 2 weeks during the estrous cycle. Concentrations of progesterone greater than 30e40 ng/mL are achieved during diestrus. The episodic release of PGF2a from the uterine endometrium and the regression of the CL defines the end of diestrus and beginning of the proestrus phase which leads back into estrus. Synchronization of estrus. A group of gilts will cycle at random and come into estrus on any given day. Detection of estrus for breeding purposes, therefore, must be done daily. Synchronization of estrus is done to reduce the time and labor associated with estrous detection and to facilitate the use of AI.17 Estrus in gilts can be synchronized by feeding altrenogest (a progestin) marketed under the trade name MatrixÒ at a dose of 15 mg/day for 14 days. Feeding atrenogest suppresses follicular development in a group of gilts. A synchronous wave of follicular growth occurs within the group after feeding is stopped. Gilts typically express estrus 4e9 days after the last day of altrenogest feeding.

Breeding Detection of estrus is typically done twice daily using a boar.22 Gilts or sows that show an interest in the boar and that stand with erect ears when pressure is applied to the back are considered in estrus.21 Estrus lasts 2e3 days. Gilts and sows ovulate from 20 to 30 ovarian follicles. Oocytes are released from the ovulatory follicles and drawn into the oviduct by ciliated oviductal epithelial cells. Ovulation occurs predictably at 30e35 h after the LH surge. The LH surge, however, does not necessarily occur at the onset of estrus in pigs and this fact makes the timing of AI difficult. Semen has a finite lifespan of approximately 24 h in the reproductive

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tract so gilts and sows are typically inseminated two times during estrus to ensure that viable sperm are in the oviduct at the time of ovulation. Mating or AI is usually done 12e24 h after first detection of estrus and then repeated on the next day. Gilts or sows that are in estrus for a third day may be inseminated three times (once for each day in estrus). Compared with other farm species, the boar produces an extremely large volume of semen. Artificial insemination is used on swine farms because it conserves semen (allowing one boar to breed many females) and enables producers to purchase semen from superior boars for use on the farm.22 When AI is used, semen is diluted to a volume of 50 mL in extender and a total of 2e3 billion sperm are used for each breeding. Pipettes used for artificial insemination (AI) lock into the spiraling cervical rings of the pig to replicate the anatomy of the boar penis. A plastic AI pipette attached to a bag of liquid semen is inserted into the vagina and threaded or inserted into the cervical rings. The semen is then slowly infused into the cervix over 5e10 min. Fertilization of oocytes by sperm occurs in the oviduct. A high percentage of oocytes are fertilized and most gilts or sows become pregnant after breeding (typically >85% of those inseminated). The expectation is that breeding will yield a high percentage of pregnancies. Gestation in the pig lasts approximately 115 days and pigs are expected to have more than 12 piglets per litter. Lactation suppresses follicular development and sows rarely come into estrus while they are lactating.23 Piglets are weaned from sows when they are 21e24 days of age. After weaning sows return to estrus within 1 week and they are AI using procedures identical to those described in the previous section. The production cycle for a sow, therefore, is approximately 142 days (gestation þ lactation þ return to estrus). A highly productive sow, therefore, can farrow nearly 2.5 litters per year and produce over 30 piglets per year.

A small percentage of sows will not return to estrus after weaning (anestrous sows) or will develop large anovulatory follicular cysts on the ovary after weaning (cystic sows).22 Anestrous or cystic sows are routinely culled or may be treated with P. G. 600 in an attempt to bring them into estrus. Reproductive problems (infertility, small litter size, anestrus, etc.) are typically much greater in the summer compared with other seasons and collectively are termed “seasonal infertility” or “summer-time infertility”.24 Pigs will cycle year-round but there is some evidence for an effect of photoperiod on the return to estrus in sows and this could explain some seasonal infertility. Seasonal-infertility may also be caused by heat stress in lactating sows.25 Lactating sows that are heat-stressed consume less feed and the reduced feed consumption increases weight loss during lactation which is associated with poor reproductive outcomes. Farrowing rooms are typically cooled with evaporative coolers or air conditioners during the summer months to reduce the effects of heat stress.

Pregnancy Fertilization At mating, a boar ejaculates up to 500 mL of semen within female cervix, a deposition site facilitated by a corkscrew shaped boar penis. Ejaculation occurs over an extended period of time (around 10 min) in three distinct fractions (initial, sperm-rich and post-sperm rich (gel)), of which the sperm-rich fraction is customarily used when boars are collected for artificial insemination. After transport of sperm the length of the uterine horns to the uterotubal junction, boar spermatozoa reach within 1e2 h the oviductal sperm reservoir near the actual fertilization site, close to ampullary-isthmic junction, where they bind transiently, by their acrosomes, to oviductal lining. Here, their motility and overall metabolism subsides until the signal is issued at ovulation, most likely in the form of progesterone

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released by the ovulatory product, to induce sperm capacitation and hyperactivation, resulting in sperm detachment from the reservoir and fast migration toward the fertilization site.26 Capacitation and hyperactivation is an irreversible (capacitated spermatozoa die unless they reach oocyte), complex structural and molecular remodeling event that encompasses, influx of signaling calcium ions into the sperm, efflux of cholesterols from plasma membrane and zinc ions from sperm interior, as well as increased tyrosine phosphorylation of sperm head and tail phosphoproteins and shedding of spermadhesin proteins from the acrosomal surface that acts as a glue during sperm immobilization on the oviductal mucosa.27 Upon reaching the oocyte, which at that time is mostly devoid of cumulus cells, spermatozoa bind to the zona pellucida surrounding the oocyte. The zona pellucida is composed of three major proteins, of which ZPB and ZPC are thought to be complexed into the sperm receptor. Through binding to what are yet to be confirmed receptors on the acrosome, ZPB-ZPC complex triggers acrosomal exocytosis (formerly acrosome reaction) that enables spermatozoa for zona penetration, assisted by resident acrosomal proteolytic enzymes and sperm tail motility, as well as binding to the plasma membrane of the oocyte. Remnants of the outer acrosome, the acrosomal shroud, remain bound to the surface of the zona pellucida as the sperm head, now solely possessing the inner acrosomal membrane, advances through the zona pellucida, progressively digesting a slanted fertilization slit.28 Subsequently, the sperm head reaches the perivitelline space under the zona pellucida where it adheres to the oocyte plasma membrane, the oolema, an event assisted by IZUMO1 protein on the sperm head equatorial segment and its oocyte binding partner, superglobulin family protein JUNO.29 The sperm plasma membraneoolemma fusion occurs with support from yet to be identified membrane fusion proteins and the incorporation of the head and tail (immotile from the moment of adhesion) is assisted by the

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F-actin rich oocyte cortical microvilli. At the time of fusion, the sperm-borne oocyte activating factor(s) (SOAF) is released from sperm postacrosomal perinuclear theca30 to trigger a signaling cascade that leads to: 1) oscillatory release of second messenger calcium ions from oocyte endoplasmatic reticulum (calcium oscillations), completion of second meiotic division and extrusion of the second polar body by the oocyte, activation of anti-polyspermy defense through plasma membrane depolarization, release of cortical granules that cleave zona pellucida protein ZPA to make zona refractory to additional sperm binding, and also the recently discovered zinc spark, the rapid release of zinc ions from oocyte cortex that could interfere with zona bound-spermatozoa’s own zinc ion signaling.31 Fertilization from zona binding through sperm incorporation is a relatively fast process, taking perhaps less than 1 h in vivo. It is followed by decondensation of the sperm nucleus into a male pronucleus, assisted by removal of sperm protamines and their replacement with oocyte histones, mediated by a variety of oplasmic factors (e.g., glutathione, nucleoplasmin, 26S proteoasome and the aforementioned oocyte histones). Within the next 8 h, the paternal pronucleus (derived from sperm nucleus) and the maternal pronucleus (derived from oocyte chromosomes) become apposed with the help of microtubule-made sperm aster organized by the sperm contributed centriole, and undergo DNA replication required for entry into first mitosis/ embryo cleavage.32

Early embryo and conceptus development After fertilization, the zygotes undergo the first cleavage division within 24 h. The developing four-cell to eight-cell embryos are transported through the oviduct into the uterine horns on days 4e5 post-initiation of estrus (Fig. 15.4). Mitotic cell divisions continue as the zona enclosed embryos develop into a solid ball of cells called a morula. The morulae

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Zygote

2-Cell

4-Cell

8-Cell

Morula

Oviduct 1.5 - 2

Spherical

2

2-3

3-4

Oviod

Day

Compact Morula Uterus

5-6

Tubular

6

Filamentous

Day

11

Hatching Blastocyst

7-8

Early Placenta

Rapid Conceptus Elongation

10

Blastocyst

Placental Attachment

12

13

18

Chorioallantois Necrotic tip

Amnion

24

34

FIG. 15.4

Day

65

116

Time-line of early pig embryo and conceptus development throughout pregnancy.

undergo the process of compaction where the outer cells form tight junctions to polarize and form a cellular seal in the outer surrounding cells. Compaction induces localization of the Naþ/Kþ ion channel pumps to basal cellular membrane which moves Naþ and water into the morula creating a fluid filled cavity called a blastocoel. Expansion of the blastocoel pushes the inner cells of the morula to one pole of the developing blastocyst by days 6e7 of pregnancy (Fig. 15.4). The blastocysts are initially comprised of two distinct cell types: the inner cell mass (epiblast) and the surrounding trophectoderm, which give rise to embryonic and placental tissues, respectively. Blastocysts hatch from the zona pellucida on days 7e8 of pregnancy. Following hatching, the conceptuses (embryo and

extraembryonic membranes) grow in diameter reaching 2e6 mm spherical morphology by day 10e11 of pregnancy (Fig. 15.4). Mesoderm and endoderm cell layers differentiate in the inner cell mass to initiate formation of the embryo and contribute to the development of the extraembryonic membranes (yolk sac, amnion, allantois and chorion) following attachment to the uterine surface. Between days 8e12 of pregnancy, the preimplantation conceptuses use peristaltic movements of the uterine wall and long slender microvilli on their outer surface to migrate throughout the long uterine horns until becoming equidistantly spaced throughout the uterus. Endometrial production of histamine and conceptus estrogen production assist with

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conceptus migration and increased myometrial activity.33 Following migration and equidistant spacing within the long uterine horns, the conceptuses continue to grow and expand through increased cellular proliferation until reaching an approximate spherical diameter of 9e10 mm between days 11e12 of pregnancy. At this stage, the conceptuses rapidly (less than 1 h) transform to an ovoid, tubular and then filamentous morphology, elongating to lengths of 100e200 mm at a rate greater than 30e40 mm/ h (Fig. 15.4). The rapid nature of the remodeling process of conceptuses indicates that elongation occurs through cell migration and deformation, rather than by cell division. The rapid transformation in conceptus morphology during elongation across the uterine luminal surface is stimulated by conceptus expression of interleukin-1b2.34 The conceptuses elongate along the mesometrial side of the uterine horn, attaching and

interlacing to the extensive endometrial folds (Fig. 15.5A) to provide adequate surface area for placental development. Pig conceptuses continue to grow and expand until day 18, reaching lengths as long as 1 m (Fig. 15.4). Despite the competition among the conceptuses to elongate and expand their placentae across the uterine luminal surface, rarely will the tips of the placenta overlap within the uterus. The extensive primary and secondary folding of the endometrium (Fig. 15.5AeC) enhances the surface area so a 20e30 cm length of the uterus can accommodate a placenta that is 2 to 3
its length. Since the conceptuses do not overlap, they must compete for adequate uterine space for survival. Therefore, some early embryonic loss and birth of light weight piglets can be contributed to competition for uterine space during conceptus rapid elongation, especially in females with high ovulation rates.

(B)

(A)

Lumen

Endometrial Folds

Endometrial Glands

Endometrial Folds

(C)

(D)

Cap

Stroma Placental Chorion

Maternal Endometrium

Cap

Glands

Areolae

Chorionic Epithelium

Placental Chorion Endometrium

Surface Epithelium

FIG. 15.5

Placentation of the pig: (A) Endometrium of the uterine displaying the multiple folds on the uterine surface; (B) Histological section of the uterus illustrating the multiple folds and extensive uterine glands; (C) Histological section from the pregnant uterine horn demonstrating the epitheliochorial type placentation in the pig and (D) Placental chorion of the pig placenta showing the multiple areolae that cover the mouths of the uterine glands present in the endometrium.

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Maternal recognition of pregnancy The extensive length of the uterine horns of the pig requires that at least two conceptuses are present in each uterine horn to protect against both the local and systemic sources of endometrial PGF2a delivery to the CL which would result in luteolysis.35 Maternal recognition of pregnancy is a process by which a chemical signal from the developing conceptuses results in the protection of the CL from luteolysis (regression of the CL) and the continued production of progesterone throughout pregnancy. Because the pig placentae does not synthesize sufficient amounts of progesterone during pregnancy, maintenance of pregnancy is totally reliant on functional CL throughout pregnancy. Prior to and during attachment of the conceptus to the uterine surface, the pig conceptuses must signal the maternal endometrium prior to initiation of luteolysis which normally occurs after day 15 of the estrous cycle.36 The signal for maternal recognition in the pig can be through conceptusderived estrogens which are secreted on days 11e12 and 15e30 of pregnancy.34 Conceptus estrogen synthesis is proposed to block luteolysis by moving transport of PGF2a away from the uterine vasculature and sequestering it in the uterine lumen during early pregnancy. The altered transport of PGF2a away from rather than toward the uterine vasculature occurs during luteolysis in the estrous cycle has been termed the endocrine/exocrine theory for maternal recognition in the pig.36,37 New research has indicated that conceptus estrogen production is not essential for CL maintenance to day 24 of pregnancy.34 It is possible that conceptus synthesis and secretion of PGE may also provide the biological stimulus to alter the movement of endometrial PGF2a to maintain CL. In addition, conceptus estrogen stimulates the endometrium to increase expression of prostaglandin E synthase and secrete PGE2 which is consistent with conceptus and/or endometrial PGE2

maintaining CL function and pregnancy to at least day 25e28.

Placentation Swine have an epitheliochorial type of placentation. The diffuse placenta (Fig. 15.4), for which attachment is through loose adhesion between the epithelial microvilli of the trophoblast and uterine endometrial surface (Fig. 15.5C), provides a mechanism for conceptus/endometrial communication through numerous signaling pathways, attachment factors, and nutrient transfer from the mother to the developing fetus. Maternal nutrient flow to the placenta occurs through the numerous folds of the endometrial surface epithelium and the secretion from thousands of endometrial glands (Fig. 15.5B). Development and the formation of the placenta is dependent on the elongation of the conceptus and attachment to the surface of the uterine luminal epithelium. After completing elongation, the conceptus begins to develop its extraembryonic membranes which are responsible for securing nutrients from the maternal blood and acting as a form of protection from maternal immunological recognition as a foreign tissue. The pig extraembryonic membranes consist of the yolk sac, amnion, chorion and allantois. The conceptus first consists of the embryo (epiblast) and the trophectoderm. The primitive endoderm layer forms beneath the embryo, growing downwards and forming the inner surface of the trophoblast. The endoderm continues to grow until it forms a cavity called the yolk sac.38 The yolk sac serves as a temporary absorptive tissue that can act as a reservoir for nutritive materials because the yolk sac’s abundant blood vessels are only separated from the uterine endometrial surface by a thin layer. The mesoderm layer (originates between the endoderm and the developing embryo) begins to grow and surround the yolk sac, while

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pushing against the trophectoderm to form amniotic folds. The combined layers of the mesoderm and the trophectoderm become the chorion, while the amniotic folds grow until they envelop the embryo forming the amnion.38 The amnion sac fills with a water-like fluid in which the embryo is suspended. This provides an environment for the embryo, in which pressure is equalized and the embryo is protected from injury. The allantois develops on days 15e18 while the chorion and amnion form. The allantois forms from the primitive gut of the embryo and takes over the functions of the yolk sac.39 The yolk sac rapidly decreases in size as the allantois grows outward into the extraembryonic coelom or chorionic cavity between days 14e20 of pregnancy.40 The portions of the allantois and chorion that fuse becomes vascularized and are the functional fetal placenta. The portion that does not become vascularized because the chorion and allantois are not fused, is called a necrotic tip of the chorion (Fig. 15.4). The endometrial tissue surrounding the junction between the tips of chorion between two adjoining placentae is not as vascularized as endometrium where the chorioallantois is attached. The endometrial regions where the placentae of adjacent conceptuses meet are clearly demarcated (Fig. 15.6B) and illustrates the 20e20 cm uterine length occupied by the long diffuse placenta. On approximately day 20 of pregnancy, the placenta forms ‘areolae’ (Fig. 15.5D) at the opening of the uterine glands during attachment and are responsible for the uptake of products in uterine gland secretions.39 The chorioallantois expands and presses against the uterine wall as it fills with allantoic fluid. The allantoic fluid accumulation peeks around day 30 of pregnancy (Fig. 15.4). This development of the membranes and accumulation of fluid aids in to the process of placental attachment which expands the uterus (Fig. 15.6A). The volume of allantoic fluid declines to day 45, peaks again at day 60 of

FIG. 15.6

Pregnancy in the pig: (A) Day 35 pregnant uterus and (B) Day 35 uterine horn illustrating the avascular interplacentation sites (arrows) between individual pig placentae.

pregnancy (Fig. 15.4), then recedes to a minimal volume at the end of gestation.40,41

Parturition In the pig, parturition typically occurs at 114e117 days of gestation. Conditions associated with parturition in the pig develop rapidly within approximately two days of parturition.42 While mechanistic details remain to be defined, parturition in the pig is triggered by fetal signals associated with functional maturation of the fetal hypothalamo-pituitary-adrenal axis and

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production of fetal adrenal cortisol.43 In the fetal compartment, hypothalamic secretion of corticotropin releasing factor stimulates production and secretion of adrenocorticotropic hormone (ACTH) by anterior pituitary corticotrophs. In turn, ACTH stimulates cells of the fetal adrenal cortex to secrete cortisol. On the maternal side, parturition is preceded by a gradual increase in estrogens of placental origin in maternal plasma during the final weeks of gestation.42,43 Within two days of parturition, concentrations of cortisol, prolactin and relaxin increase in maternal blood, as concentrations of progesterone decrease, marking luteolysis. The rapid decline in maternal plasma progesterone levels within two days of parturition reflects functional and, ultimately, structural luteolysis driven by uterine release of PGF2a. Uterine prostaglandin production is supported through the actions of both glucocorticoids and estrogen on uterine target tissues. These conditions also support expression of uterine oxytocin receptors and reconstitution of the myometrial contractile syncytium required for coordinated uterine contractions.43 Structural luteolysis is accompanied by a marked increase in relaxin in maternal blood.44 Acting with estrogen, relaxin supports softening and relaxation of the pubic ligaments and dilation of the cervix in preparation for fetal expulsion. Parturition occurs in three stages. Stage I is defined by development of increasingly regular myometrial contractions. This occurs as myometrial sensitivity to oxytocin develops in association with luteolysis, and cervical dilation proceeds, supported by luteal relaxin and estrogen. Stage II is defined by the period of fetal expulsion. This involves coordinated contractions of the uterine myometrium integrated with maternal abdominal muscle contractions. Supported by periparturient endocrine conditions, Stage II is driven by the Ferguson reflex. This is a neuroendocrine mechanism involving neural signals generated by fetal passage through the birth canal (cervix and vagina) that

stimulates posterior pituitary secretion of oxytocin. In turn, oxytocin stimulates myometrial contractions of increasing frequency and strength. Stage III is defined by expulsion of fetal membranes (placental tissues). This is driven primarily by uterine contractions that occur with decreasing frequency as parturition is completed. Estimates of the duration of parturition in the pig vary, but typically occur in the range of 2e12 h for Stage 1, 150e180 min for Stage II, and 1e4 h for Stage III.

Lactation In the pig, as in other mammals, milk serves as an immediate source of energy and nutrients for newborn offspring.45 Additionally, because they are immunologically incompetent at birth, nursing insures the survival of piglets through the passive transfer of maternal immune constituents (both humoral and cellular) in colostrum (first milk) and milk.46 Consumption of colostrum by nursing piglets is also important to insure maturation of a functional gastrointestinal tract and other somatic tissues.47 Moreover, milk-borne bioactive factors, communicated from dam to nursing piglets via a lactocrine mechanism, affect events associated with reproductive tract development with lasting consequences for fecundity in adults.48

Mammary gland Porcine mammary glands are arrayed anatomically in two parallel rows extending from the thoracic to the inguinal areas of the ventral body wall. Domestic pigs typically have 12e14 mammary glands, although they can have more. Structurally, the lactating mammary gland consists of clusters of tubuloalveolar lobules, each lined by epithelial cells (lactocytes) that are surrounded by myoepithelium and vascularized connective tissue containing adipocytes and fibroblasts. The lactocytes function in

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milk synthesis, while myoepithelial cells support milk ejection stimulated by suckling and suckling-induced oxytocin secretion from the posterior pituitary gland. Secreted milk, produced by mammary parenchyma, is delivered to the nipple of each gland via teat canals or galactophores, of which there are usually two.

mammary parenchyma during late gestation is also supported by relaxin and prolactin. Interactions between estrogen and prolactin are particularly important determinants of growth and morphogenesis of the porcine mammary gland.

Mammogenesis

Lactogenesis occurs in two phases. Phase I begins between gestational days 90 and 105, and is marked by onset of the synthesis of milk components by mammary epithelium including lactose, casein and lipids, and their initial accumulation in mammary alveoli. Lactation phase II, which overlaps with phase I, is marked by active milk secretion initiated at farrowing. The colostral period of phase II lactogenesis begins with farrowing and defines the beginning of lactation. A unique secretory product of the mammary gland, production of colostrum (colostrogenesis) is programmed maternally during late gestation.54 The porcine colostral period is relatively short, with transition from colostrum to mature milk occurring within 24e48 h postpartum. Colostrum production per sow on lactation day 1 varies between approximately 2.0 and 5.0 kg over 24 h for a litter of 8e12 piglets.47 Compared to mature milk, colostrum contains more protein, less fat, and less sugar, with the majority of colostral protein represented by immunoglobulins.46 The switch from production of colostrum to production of milk is associated with an increase in volume of milk produced. Maximum production is achieved at about 10 days after this switch. This is followed by a period of sustained milk production. The duration of lactation is determined to a significant extent by time of weaning and can last to 28 days. Lactogenesis and maintenance of lactation require hormonal support.46,55 Complete secretory development of the mammary glands requires a lactogenic complex of hormones that typically includes insulin, estrogens, progesterone, glucocorticoids and a lactogen such as

In the pig, the band of tissue that ultimately gives rise to the mammary glands, referred to as the ‘milk ridge’, appears around embryonic day 14, and nipples are evident by embryonic week four.39 Postnatally, mammary gland development advances with puberty which, in the pig, occurs at 5e7 months of age. During this period the relative abundance of mammary parenchyma increases, while extra-parenchymal tissue decreases.49 Specific regulatory mechanisms responsible for these events in the pig are not clear. However, hormone-sensitive mammogenesis during pregnancy is significant, particularly during the last third of gestation. Progressive ductal growth occurs between pregnancy days 30 and 60, followed by rapid lobuloalveolar growth between days 75 and 90. However, mammary growth and development of lobuloalveolar tissue continue thereafter into late gestation50 even as lactogenesis is beginning.51 These events are sensitive to the effects of hormones including estrogen, relaxin and prolactin. Hormones implicated in support of mammogenesis during the last third of gestation include estrogen of fetoplacental origin, relaxin of CL origin, and prolactin from the anterior pituitary gland. During this period, significant lobuloalveolar growth and parenchymal development occurs when maternal endocrine conditions are defined, in part, by increasing concentrations of estrogens from the placentae beginning around day 60 of gestation.52 Estrogen production and concentrations of estrogens in maternal blood during gestation are related directly to the number of viable conceptuses in utero.53 Growth of

Lactogenesis

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prolactin.52 In the pig, onset of lactogenesis is associated with the preparturient decrease in circulating progesterone related to loss of ovarian luteal function in the dam. Near term, within approximately two days of farrowing, circulating levels of prolactin rise rapidly, remain at maximal levels through farrowing, and decline thereafter through the course of lactation.52 Prolactin, a well-established lactogen in swine, supports mammogenesis, lactogenesis and galactopoesis. A major effector of milk yield, levels of prolactin in maternal circulation within 40 h of parturition,42 as well as the ratio of prolactin to progesterone concentrations at 24 h prepartum,56 are predictors of colostrum production. Conditions that limit prolactin production are associated with depressed milk production (agalactia). Growth hormone is also lactogenic in swine. When administered exogenously, recombinant porcine growth hormone stimulates milk production in sows.57 How endogenous growth hormone contributes to regulation of mammary development and lactogenesis mechanistically in the pig is unclear. However, circulating concentrations of maternal growth hormone increase after farrowing.58 Further, the somatotropic axis - characterized by anterior pituitary secreted growth hormone (somatotropin) acting on cognate receptors in the liver to stimulate hepatic synthesis and secretion of insulin-like growth factors (IGFs) in a negative-feedback system - remains coupled in lactating sows.59 By consequence, levels of IGF-1 in maternal circulation remain elevated in parallel with growth hormone during lactation.59 The integrative action of hormones, growth factors and related functional elements of the somatotropic axis are most important in regulation of metabolism supportive of lactation and the synthesis of milk.

Milk-borne bioactive factors In addition to energy and nutrients, milk contains a complex array of bioactive factors.

Categories of milk-borne bioactive factors (MbFs) found in colostrum and mature milk include proteins, growth factors, native and latent bioactive peptides, oligosaccharides, fatty acid-derived molecules, steroid hormones, microRNAs, and exosomes, as well as maternally-derived somatic cells.46,60 The term ‘lactocrine’ was coined to describe the transmission of MbFs from mother to offspring as a consequence of nursing. Piglets are immunologically incompetent at birth, and depend upon lactocrine transmission of maternal immunoglobulins in order to survive when exposed to infectious disease. Since full immunocompetence is not established until about four weeks of age, immunoglobulins, including IgG, the predominant immunoglobulin in colostrum at birth, as well as secretory IgA and IgM, are delivered to nursing piglets in colostrum and milk through the course of lactation.46 Once consumed, maternally-derived immunoglobulins cross the neonatal intestinal epithelium via various mechanisms46 and enter the peripheral circulation where they confer passive immunity to nursing young. Critically, the window of opportunity for transmission of immunoglobulins (or other macromolecular MbFs) across gastrointestinal epithelium in nursing piglets is open only transiently. Pinocytotic transmission of immunoglobulins can no longer occur by 2 h after birth in the duodenum, 48 h in the jejunum, and 72 h in the ileum, when the gut is said to be ‘closed’.61 However, nursing for as little as 1 h from birth is sufficient to establish passive immunity in neonatal piglets.62 The efficiency of lactocrine transmission of immunoglobulins is also served by the fact that colostral content of immunoglobulins is highest on the first day of lactation.63 Disruption of lactocrine input, resulting in lactocrine insufficiency, can occur naturally through mastitis (infection of the mammary gland), agalactia, or competition among nursing piglets for teat position.64 It can also be imposed by substitution of milk-replacer for colostrum,

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References

resulting in a lactocrine-null condition.48 While severe lactocrine disruption, associated with markedly insufficient transmission of colostral immunoglobulins, can compromise piglet health and lead to significant neonatal loss, less severe lactocrine deficiency can still compromise growth and development of nursing piglets with lasting consequences.64

Lactocrine programming Identification of relaxin as a prototypical MbF in the pig, and evidence implicating relaxin and its receptor as elements of a lactocrine-driven signaling system affecting uterine development during early postnatal life, provided context for proposal of the lactocrine hypothesis. The lactocrine hypothesis posits that MbFs delivered to nursing piglets via a lactocrine mechanism affect the developmental program of uterine and other somatic tissues in the neonate. Studies designed to test the lactocrine hypothesis for maternal programming of postnatal development.48,60 showed that lactocrine signaling not only affects reproductive tract development in nursing piglets,65 but also affects uterine developmental trajectory with lasting consequences for reproductive development and performance in adults.64,66 A maternal lactocrine continuum, connecting processes associated with lactation to uterine development and reproductive performance in adult female pigs, is summarized below and described elsewhere.48,60 Lactocrine-active MbFs are present in colostrum at their highest concentrations at birth, and delivery of MbFs to nursing offspring may be most efficient during the first 24e48 h of postnatal life, prior to gut closure. Events associated with development of the uterine wall (endometrium) during early neonatal life in nursing piglets are supported by lactocrine-active factors. Consumption of colostrum for 12 h from birth, during the lactocrine-programming window, is necessary and may be sufficient to establish an optimal uterine developmental program.

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Because availability of colostrum can vary from piglet to piglet, and colostral quality can vary from anterior to posterior mammary glands, lactocrine inputs required to support optimal reproductive development vary among piglets on a within-litter basis. Lactocrine signaling during the first 24 h of postnatal life affects the uterine developmental program and trajectory that ultimately determines functional uterine capacity in adults. Lactocrine deficiency during this period results in permanent impairment of reproductive performance in adult female pigs, reflected by alterations in patterns of uterine endometrial gene expression during the peri-attachment period of early pregnancy66 and reduced litter size.64 While yet to be demonstrated, reduced functional uterine capacity in neonatally lactocrine deficient adults may affect the ability of these animals to support fetoplacental development optimally, thereby affecting mammogenesis, lactogenesis, and the nature of lactocrine signals in the next generation. By the same token, lactocrine sufficiency from birth supports optimal postnatal programming of lactocrine-sensitive tissues. For the pig, it is clear that lactation, nursing and lactocrine signaling have lifelong implications for development, health and performance.

References 1. Constantinescu GM. Anatomy of the reproductive system. In: Schatten H, Constantinescu GM, eds. Animal Models and Human Reproduction. Hoboken, NJ: John Willey & Sons; 2017:1e58. 2. Sutovsky P. Pig overview. In: Jegou B, Skinner MK, eds. Encyclopedia of Reproduction. vol. 1. Academic Press: Elsevier; 2018:501e507. 3. Parrish JJ, Willenburg KL, Gibbs KM, et al. Scrotal insulation and sperm production in the boar. Mol Reprod Dev. 2017;84:969e978. 4. Swierstra EE. Sperm production of boars as measured from epididymal sperm reserves and quantitative testicular histology. J Reprod Fertil. 1971;27:91e99. 5. Flowers WL. Management of boars for efficient semen production. J Reprod Fertil. 1997;(Suppl. 52):67e78.

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6. Oko R, Sutovsky P. Biogenesis of sperm perinuclear theca and its role in sperm functional competence and fertilization. J Reprod Immunol. 2009;83:2e7. 7. Sutovsky P, Manandhar G. Mammalian spermatogenesis and sperm structure: anatomical and compartmental analysis. In: De Jonge C, Barratt C, eds. The Sperm Cell. Cambridge: Cambridge University Press; 2006:1e30. 8. Lovercamp KW, Safranski TJ, Fischer KA, et al. Arachidonate 15-lipoxygenase and ubiquitin as fertility markers in boars. Theriogenology. 2007;67:704e718. 9. Waberski D, Meding S, Dirksen G, Weitze KF, Leiding C, Hahn R. Fertility of long-term-stored boar semen. Influence of extender (androhep and kiev), storage time and plasma droplets in the semen. Anim Reprod Sci. 1994;36:145e151. 10. Rodriguez-Martinez H, Kvist U, Saravia F, et al. The physiological roles of the boar ejaculate. Soc Reprod Fertil. 2009;(Suppl. 66):1e21. 11. Jonakova V, Manaskova P, Ticha M. Separation, characterization and identification of boar seminal plasma proteins. J Chromatogr B Analyt Technol Biomed Life Sci. 2007;849:307e314. 12. Sutovsky P. New approaches to boar semen evaluation, processing and improvement. Reprod Domest Anim. 2015;50(Suppl. 2):11e19. 13. Lents CA, Thorson JF, Desaulniers AT, White BR. Rfamide-related peptide 3 and gonadotropin-releasing hormone-II are autocrine-paracrine regulators of testicular function in the boar. Mol Reprod Dev. 2017;84:994e1003. 14. Christenson RK, Ford JJ, Redmer DA. Maturation of ovarian follicles in the prepubertal gilt. J Reprod Fertil. 1985;(Suppl. 33):21e36. 15. Esbenshade KL, Ziecik AJ, Britt JH. Regulation and action of gonadotrophins in pigs. J Reprod Fertil. 1990; (Suppl. 40):19e32. 16. Barb CR, Kraeling RR, Rampacek GB. Metabolic regulation of the neuroendocrine axis in pigs. Reproduction. 2002;(Suppl. 59):203e217. 17. Kraeling RR, Webel SK. Current strategies for reproductive management of gilts and sows in North America. J Anim Sci Biotechnol. 2015;6:3. 18. Soede NM, Langendijk P, Kemp B. Reproductive cycles in pigs. Anim Reprod Sci. 2011;124:251e258. 19. De Rensis F, Saleri R, Tummaruk P, Techakumphu M, Kirkwood RN. Prostaglandin F2a and control of reproduction in female swine: a review. Theriogenology. 2012; 77:1e11. 20. Knox RV. Recruitment and selection of ovarian follicles for determination of ovulation rate in the pig. Domest Anim Endocrinol. 2005;29:385e397. 21. Pedersen LJ. Sexual behaviour in female pigs. Horm Behav. 2007;52:64e69. 22. Knox RV. Artificial insemination in pigs today. Theriogenology. 2016;85:83e93. 23. Lucy MC, Liu J, Boyd CK, Bracken CJ. Ovarian follicular growth in sows. Reproduction. 2001;(Suppl. 58):31e45.

24. De Rensis F, Ziecik AJ, Kirkwood RN. Seasonal infertility in gilts and sows: aetiology, clinical implications and treatments. Theriogenology. 2017;96:111e117. 25. Ross JW, Hale BJ, Seibert JT, et al. Physiological mechanisms through which heat stress compromises reproduction in pigs. Mol Reprod Dev. 2017;84:934e945. 26. Suarez SS, Pacey AA. Sperm transport in the female reproductive tract. Hum Reprod Update. 2006;12:23e37. 27. Leahy T, Gadella BM. Sperm surface changes and physiological consequences induced by sperm handling and storage. Reproduction. 2011;142:759e778. 28. Yi YJ, Manandhar G, Oko RJ, Breed WG, Sutovsky P. Mechanism of sperm-zona pellucida penetration during mammalian fertilization: 26s proteasome as a candidate egg coat lysin. Soc Reprod Fertil. 2007;(Suppl. 63):385e408. 29. Wright GJ, Bianchi E. The challenges involved in elucidating the molecular basis of sperm-egg recognition in mammals and approaches to overcome them. Cell Tissue Res. 2015;363:227e235. 30. Oko R, Aarabi M, Mao J, Balakier H, Sutovsky P. Sperm specific ww-domain binding proteins. In: DeJonge C, Barratt C, eds. The Sperm Cell: Production, Maturation, Fertilization, Regeneration. Cambridge, UK: Cambridge University Press; 2017:157e176. 31. Que EL, Bleher R, Duncan FE, et al. Quantitative mapping of zinc fluxes in the mammalian egg reveals the origin of fertilization-induced zinc sparks. Nat Chem. 2015;7:130e139. 32. Sutovsky P. Review: sperm-oocyte interactions and their implications for bull fertility, with emphasis on the ubiquitin-proteasome system. Animal. 2018;12: s121es132. 33. Pope WF, Maurer RR, Stormshak F. Intrauterine migration of the porcine embryo: influence of estradiol-17 beta and histamine. Biol Reprod. 1982;27:575e579. 34. Geisert RD, Whyte JJ, Meyer AE, et al. Rapid conceptus elongation in the pig: an interleukin 1 beta 2 and estrogen-regulated phenomenon. Mol Reprod Dev. 2017;84:760e774. 35. Dziuk PJ. Effect of number of embryos and uterine space on embryo survival in the pig. J Anim Sci. 1968;27: 673e676. 36. Bazer FW, Marengo SR, Geisert RD, Thatcher WW. Exocrine versus endocrine secretion of prostaglandin F2a in the control of pregnancy in swine. Anim Reprod Sci. 1984;7:115e132. 37. Bazer FW, Thatcher WW. Theory of maternal recognition of pregnancy in swine based on estrogen controlled endocrine versus exocrine secretion of prostaglandin F2alpha by the uterine endometrium. Prostaglandins. 1977;14:397e400. 38. Early embryogenesis and maternal recognition of pregnancy. In: Senger PL, ed. Pathways to Pregnancy and Parturition. 2nd ed. Pullman, WA: Current Conceptions; 2009:289e291.

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39. The extraembryonic membranes of mammals and the relations of the embryo to the uterus. In: Patten BM, Carlson BM, eds. Foundations of Embryology. New York: McGraw-Hill Book Company; 1974:317e350. 40. Vallet JL, Miles JR, Freking BA. Development of the pig placenta. Soc Reprod Fertil. 2009;(Suppl. 66): 265e279. 41. Knight JW, Bazer FW, Thatcher WW, Franke DE, Wallace HD. Conceptus development in intact and unilaterally hysterectomized-ovariectomized gilts: interrelations among hormonal status, placental development, fetal fluids and fetal growth. J Anim Sci. 1977;44: 620e637. 42. Foisnet A, Farmer C, David C, Quesnel H. Relationships between colostrum production by primiparous sows and sow physiology around parturition. J Anim Sci. 2010;88:1672e1683. 43. Taverne MA, Van der Weijden GC. Parturition in domestic animals: targets for future research. Reprod Domest Anim. 2008;43(Suppl. 5):36e42. 44. Bagnell CA, Zhang Q, Downey AL. Sources and biological actions of relaxin in pigs. J Reprod Fertil. 1993;(Suppl. 48):127e138. 45. Hovey RC. The marvels of milk and lactation. In: Skinner MK, ed. Encyclopedia of Reproduction. 2nd ed. USA: Academic Press; 2018. 46. Poonsuk K, Zimmerman J. Historical and contemporary aspects of maternal immunity in swine. Anim Health Res Rev. 2018;19:31e45. 47. Farmer C, Quesnel H. Nutritional, hormonal, and environmental effects on colostrum in sows. J Anim Sci. 2009; 87:56e64. 48. Bartol FF, Wiley AA, George AF, Miller DJ, Bagnell. Physiology and endocrinology symposium: postnatal reproductive development and the lactocrine hypothesis. J Anim Sci. 2017;95:2200e2210. 49. Farmer C, Fissette K, Robert S, Quesnel H, LaForest JP. Use of recorded nursing grunts during lactation in two breeds of sows II: effecs on sow performance and mammary development. Can J Anim Sci. 2004;84: 581e587. 50. Ji F, Hurley WL, Kim SW. Characterization of mammary gland development in pregnant gilts. J Anim Sci. 2006;84:579e587. 51. Kensinger RS, Collier RJ, Bazer FW. Ultrastructural changes in porcine mammary tissue during lactogenesis. J Anat. 1986;145:49e59. 52. Dehoff MH, Stoner CS, Bazer FW, Collier RJ, Kraeling RR, Buonomo FC. Temporal changes in steroids, prolactin and growth hormone in pregnant and pseudopregnant giilts during mammogenesis and lactogenesis. Domest Anim Endocrinol. 1986;3:95e105.

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53. Kensinger RS, Collier RJ, Bazer FW, Kraeling RR. Effect of number of conceptuses on maternal hormone concentrations in the pig. J Anim Sci. 1986;62:1666e1674. 54. Baumrucker CR, Bruckmaier RM. Colostrogenesis: IgG1 transcytosis mechanisms. J Mammary Gland Biol Neoplasia. 2014;19:103e117. 55. Rezaei R, Wu ZL, Hou YQ, Bazer FW, Wu G. Amino acids and mammary gland development: nutritional implications for neonatal growth. J Anim Sci Biotechnol. 2016;7:1e22. 56. Loisel F, Farmer C, Van Hees H, Quesnel H. Relative prolactin-to-progesterone concentrations around farrowing influence colostrum yield in primiparous sows. Domest Anim Endocrinol. 2015;53:35e41. 57. Harkins M, Boyd RD, Bauman DE. Effect of recombinant porcine somatotropin on lactational performance and metabolite patterns in sows and growth of nursing pigs. J Anim Sci. 1989;67:1997e2008. 58. Govoni N, Parjeggiani A, Galeati G, et al. Acyl ghrelin and metabolic hormones in pregnant and lactating sows. Reprod Domest Anim. 2007;42:39e43. 59. Lucy MC. Functional differences in the growth hormone and insulin-like growth factor axis in cattle and pigs: implications for post-partum nutrition and reproduction. Reprod Domest Anim. 2008;43(Suppl. 2):31e39. 60. Bagnell CA, Ho TY, George AF, Wiley AA, Miller DJ, Bartol FF. Maternal lactocrine programming of porcine reproductive tract development. Mol Reprod Dev. 2017; 84:957e968. 61. Murata H, Naoika S. The duration of colostral immunoglobulin uptake by the epithelium of the small intestine of neonatal piglets. J Comp Pathol. 1977;87:431e439. 62. Coalson JA, Lecce JG. Influence of nursing intervals on changes in serum proteins (immunoglobulins) in neonatal pigs. J Anim Sci. 1973;36:381e385. 63. Klobasa F, Butuer JE. Absolute and relative concentrations of immunoglobulins G, M, and A, and albumin in the lacteal secretion of sows of different lactation numbers. Am J Vet Res. 1987;48:176e182. 64. Vallet JL, Miles JR, Rempel LA, Nonneman D, Lents CA. Relationships between day one piglet serum immunoglobulin immunocrit and subsequent growth, puberty attainment, litter size, and lactation performance. J Anim Sci. 2015;93:2722e2729. 65. George AF, Rahman KM, Miller DJ, et al. Effects of colostrum, feeding method and oral IGF1 on porcine uterine development. Reproduction. 2018;155:259e271. 66. George AF, Ho T-Y, Prasad N, Keel BN, Miles JR, Vallet JL, Bartol FF, Bagnell CA. Neonatal lactocrine deficiency affects the adult porcine endometrial transcriptome at pregnancy day 13. Biol Reprod. 2018. https://doi.org/10.1093/biolre/ioy180.

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C H A P T E R

16 Reproductive management of swine William L. Flowers Department of Animal Science, North Carolina State University, Raleigh, NC, United States

O U T L I N E Introduction

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Management during the developmental phase 284 Management during the transition from the developmental to the functional phase 287

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Summary

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Management of boars during the functional phase 288

Introduction Reproductive management for swine typically is viewed as beginning around puberty. For boars this involves training them for semen collection and their subsequent management for production of high quality sperm. For sows this involves stimulating estrus and then optimizing production of live pigs. The primary goal during this period is to enhance normal reproductive activity so it is often referred to as the functional phase of reproductive management. There is increasing evidence that the perinatal environment to which piglets are exposed

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00016-1

has permanent effects on their future reproductive success. This should not be surprising since this is a significant period of reproductive organogenesis. The main emphasis during this period needs to be on creating an environment that enhances the maturation of their reproductive organs so it seems reasonable to consider this as the developmental phase of reproductive management. Consequently, a comprehensive program for swine should include both developmental and functional components. Accomplishing this effectively continues to be a challenge for the swine industry because reproductive management is largely reactive.

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The success of decisions made before, during and shortly after breeding cannot be determined for months and when deficiencies are discovered a large numbers of animals are already affected so opportunities for intervention are limited. Establishment of benchmarks during the developmental and functional periods in conjunction with a critical evaluation of production records should provide opportunities for the swine industry to make reproductive management more proactive. Therefore, the main objectives of this chapter are: first, to provide a general overview of reproductive management strategies with emphasis on recent developments that might be useful for establishing production standards predictive of subsequent reproductive success; and second, to discuss analyses of production data from a physiological perspective as a means of identifying deficiencies in management. Hopefully, the end result will be a summary of relevant information that can be used to improve reproduction in a variety of different production systems.

on most physiological systems.3 As a result, birthweight in swine is positively correlated with the development of many organs including the ovaries and testes and this poses the question as to whether it can be used as an early indicator of adult reproductive function.4 Evidence that it can is shown in Fig. 16.1.5,6 The highest reproductive performance is consistently achieved in sows (top panel) and boars (lower panel) with the heaviest birthweights. The most plausible explanation for this is that piglets with heavier birthweights were subjected to less growth restriction and had increased organogenesis.

Management during the developmental phase Formation of the gonads is complete by 70 days of gestation, but the most active period of growth and development occurs during the last 30e40 days of pregnancy.1 Mitotic activity of ovarian follicles stops shortly after birth,1 whereas Sertoli cell mitosis continues at a high rate during the first 3 weeks postnatally and doesn’t begin to decline until boars are about 40e50 days of age.2 This means that gilts possess their lifetime complement of ova shortly after birth, but the sperm production potential of boars isn’t established until they are close to 2 months old. Swine are litter-bearing species and, as a result, their offspring are subject to intrauterine growth restriction: a phenomenon which has been shown to have significant negative effects

FIG. 16.1

Relationship between birthweight of replacement gilts (top panel) and boars (bottom panel) and their adult reproductive performance. Adult reproductive performance was measured as the proportion of females that were rebred after their seventh parity for gilts and total sperm per ejaculate between 6 and 21 months of age for boars.

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This advanced neonatal development, in turn, resulted in reproductive organs with increased efficiency which contributed to their improved adult reproductive performance. What is not known about this relationship is whether it is linear, quadratic, asymptotic, or some combination of these and whether it is consistent across all genetic lines and production systems. At least for females, it appears that there is a minimum birth weight below which it is unlikely that successful reproduction will occur on a regular basis. In the herd from which these data were collected, this minimum appears to be about 1.0 kg because nearly 60% of bred gilts from this birthweight group failed to farrow their first litter. Consequently, use of a minimum birthweight as an early selection criteria for future replacement gilts appears to warrant strong consideration. It is important to recognize that there probably are quantitative differences among herds in terms of what constitutes high or low birth weights. However, qualitatively it would be surprising if there wasn’t a positive correlation between birthweight and adult reproductive performance. It has been known for some time that neonatal nutrition is critical for the growth and survival of piglets. Since the neonatal period also coincides with active periods of ovarian and testicular growth it is reasonable to assume that increased pre-weaning growth also should be associated with enhanced reproductive tract development and, thus, adult reproductive performance. Several experimental approaches have been used to validate this assumption. The most common has been to collect birth weights, weaning weights and other litter characteristics from future replacement animals and retrospectively examine their relationships with lifetime productivity. As is the case with birthweights, there is a strong positive relationship between weaning weight and lifetime productivity in both sows and boars with weaning weight often accounting for 10e30% of the total variation for these traits.7,8 Nevertheless, there are also challenges

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for using weaning weight as a selection tool in that the exact nature of the response curve is not fully understood and there probably are quantitative differences among genetic lines and production environments. In practice, pre-weaning growth and weaning weights are easier to influence via management than birthweight. Supplemental feeding and strategic cross-fostering are two commonly used techniques which could be adapted for this purpose. The latter has been shown to be effective in experimental situations by taking littermate pairs with similar birthweights and assigning them to nurse in litters of either 6 or 12 piglets.7,8 Piglets that were allowed to nurse in small litters had heavier weaning weights and greater pre-weaning gains compared with their siblings that nursed in normal-sized litters. Peak sperm production reached higher levels (110 vs. 80 billion per ejaculate) at younger ages (11 vs. 14 months of age) from boars reared in small litters to weaning compared with their counterparts weaned from normal-sized litters. Similarly, 38% of sows raised in litters of 6 successfully rebred after their sixth parity compared with only 14% of those that were reared and nursed in litters of 12 piglets. Obviously, fostering off 50% of a litter is not feasible in the commercial sector. However, these studies demonstrate the potential that enhancing preweaning growth can have on life-time productivity of boars and gilts/sows. The minimum number of pigs that needs to be removed from a litter in order to realize the benefits in reproductive performance due to enhanced preweaning growth is not known, but recent studies indicate that it may be as low as 25e30% of the litter.9 These studies also provide useful information about interactions between birthweight and preweaning growth. In terms of improving lifetime productivity, reducing competition during lactation essentially yielded no effect for gilts with birthweights less than 1.0 kg; a robust increase for gilts with birthweights between 1.1 and

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1.4 kg; and only a marginal improvement for gilts with birthweights greater than 1.5 kg.7 In contrast, regardless of birthweight, any enhancement of pre-weaning growth had a beneficial effect on adult sperm production in boars.8 These observed differences probably are due to the much longer period of mitotic activity after birth in the testicles compared with the ovaries. Additional research is needed to refine these observations, but it does appear that strategic cross-fostering has potential for enhancing adult reproductive performance. For example, only gilts with intermediate birthweights would need to have access to an environment with reduced competition while nursing, while it appears that all replacement boars could benefit from this strategy. The lack of a response in the lowest birth weight gilts also should be noted. It provides credence for establishing a minimum birthweight for selection of replacement females. Even when their pre-weaning growth was enhanced there was no improvement in their productivity as adults, presumably due to critically underdeveloped reproductive organs. Critical care programs for newborn piglets are increasing in popularity. One of their goals is to ensure that piglets receive adequate colostrum. Both colostrum and mature milk have been shown to contain bioactive compounds which stimulate development of most physiological systems.10 These observations have led to the Lactocrine hypothesis which proposes that milk consumed by piglets early in their life has an important role in programming the development of their reproductive organs. Both male and female piglets that were raised only on milk replacer had significant impairments in testicular or ovarian and uterine development, respectively, compared with their littermates that received colostrum.11 The milk replacer group also had reduced weaning weights which poses the question as to how much of the enhanced development associated with increased pre-weaning growth, discussed previously, is due to colostrum

intake. If the components of colostrum responsible for stimulating reproductive tract development can be identified then it is likely that these can be provided orally to young pigs and provide a way to stimulate the development of their reproductive organs. Clearly, work in this area needs to be enthusiastically pursued. Post-weaning growth is a trait used in the evaluation of nearly all future replacement swine. Studies indicate that for gilts there is a minimum that needs to be achieved.12 However, once this growth rate has been met, there is limited evidence to support the idea that additional increases improve lifetime productivity. In contrast, for boars, positive linear relationships are present among post-weaning growth, testicular size, and adult sperm production.13 This difference is likely due to the fact that, even though it is decreasing, mitosis of Sertoli cells is still occurring until boar are 60 days of age.3 Consequently, the goal of nutritional programs for both sexes should be to ensure that growth rate is above these established minimum levels with additional emphasis placed on optimizing average daily gain for boars. Pen density (space allowance) is another aspect of the production environment during the developmental phase of piglets that has been studied. Current recommendations for piglets weighing between 7 and 60 kg vary from 0.28 to 0.75 m2 per pig.14 Studies have shown that providing less than these minimum requirements has marginal effects on puberty and adult reproductive function.15 Consequently, current management in this area is not viewed as a major factor that limits reproductive performance in adult swine. Interactions between pigs and humans increase significantly after weaning. As a result, this period has been referred to by some as the onset of their socialization window.16 It has been well established that negative experiences with humans causes pigs to be fearful during subsequent encounters; hinders their growth and development; and limits adult reproductive function.17,18 The exact mechanisms involved are

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Management during the transition from the developmental to the functional phase

not know, but the adreno-hypophyseal axis and corticosteroids have been implicated.19 Given the fact sows and boars are in frequent contact with humans in most commercial production systems, minimizing any negative aspects associated with these interactions during this socialization window needs to be a priority. Recent work indicates that incorporation of presumably positive interactions between boars and humans has positive effects on sperm production by adult boars.6 In this study, daily observations and feeding by humans took 7e10 min per day. Boars in the socialization treatment received an additional 30 min, three times per week by having workers stand in front of pens and allowing the boars to approach and interact with them. This began one week after weaning which occurred at 3 week of age and continued for 4 consecutive weeks. After this, all boars were housed together; trained for semen collection; and collected weekly until they were 2 years old. Boars exposed to increased human contact produced an average of 10 billion more sperm per ejaculate compared with their contemporaries and this was independent of their birthweight. From a practical perspective, this resulted in an additional 350 insemination doses over their productive life. These boars also trained for collection at younger ages and had faster reaction times when exposed to the collection dummy compared with those that were managed conventionally. Clearly, additional work is required before definitive recommendations can be made. However, these observations should serve as a reminder of the importance of minimizing negative and enhancing positive encounters between replacement swine and their caretakers.

Management during the transition from the developmental to the functional phase Training boars for semen collection and the timing of when gilts are first exposed to mature

287

boars represent the transition from the developmental to the functional phase of reproductive management. As a result, the age at which both of these occur deserve important considerations. The goal for most boar studs is to have boars trained for semen collection as young as possible over a 3e4 week period. Sperm production increases with age so the decision on when to begin processing ejaculates for insemination doses can be uncoupled from training. However, most production systems try to keep this interval at a minimum since maintaining boars whose semen is discarded is not economically desirable. There is a lack of definitive studies examining relationships between the age at which collection begins and subsequent sperm production so this is an area of reproductive management that definitely requires more attention. Results from research that has been conducted indicate that delaying training for most boars is advantageous.20 Boars that were 1- to 2-months older when training began took less time to train; experienced fewer collection-related problems; and had reduced numbers of ejaculates discarded as adults compared with their younger counterparts. The age at which boars are trained for semen collection typically occurs between 7 and 9 months of age in commercial studs so waiting until the latter portion of this age range could prove to be advantageous. In contrast, there is an inverse relationship between age at puberty and adult reproductive performance in gilts.21,22 Those that attain puberty at younger ages produce more piglets over their lifetime compared with their counterparts that reach sexual maturity at advanced ages. This relationship has been shown via analysis of production records retrospectively and by prospectively selecting for early puberty. Physiological mechanisms that control this response have not been fully elucidated, but probably involve differences in estrogen sensitivity. Females that respond to reduced levels of estrogen would be expected to reach puberty earlier; ovulate more ova; and have superior

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288 TABLE 16.1

16. Reproductive management of swine

Relationship between age at first mating for gilts and number of live pigs produced prior to their removal from the breeding herd (mean
s.e.) in 5 different commercial swine production systems. Age at first mating (days)1,2

Production system

240e280

281e320

320e360

Production system means

A

37
3x (331)

41
2x (319)

26
3y (298)

35
2

B

34
3 (224)

x

31
3 (256)

y

24
4 (243)

30
2

C

27
4

32
5 (421)

x

16
7 (392)

24
4

D

32
3 (245)

31
4

y

24
3 (200)

29
2

E

20
3

24
4 (167)

14
4 (102)

20
3

Age Means

30
2

33
2

21
3

x

x,y

(401)

x

x,y

(150)

y

x,y

(248)

x

y

1

Numbers in parenthesis are number of gilts bred used to calculate each mean. Production system  age interaction, p  0.00001. x,y Means with different superscripts in the same row differ, p  0.05. 2

conception rates compared with their counterparts with higher requirements since estrogens are critically important in all of these reproductive events. In the study discussed previously in which competition during lactation was reduced, all gilts were exposed to boars at 150 days of age, but not bred until they were 210e240 days of age. The proportion of gilts that exhibited their pubertal estrus between 150 and 180 days of age was 22% higher in the gilts that nursed in litters of 6 piglets compared with those raised in litters of 12 piglets. This was similar to the difference that was observed between the two groups in terms of the proportion of sows that farrowed 6 litters. Consequently, it is tempting to speculate that early puberty in gilts may be, in part, due to their neonatal environment. Most commercial swine farms begin to expose gilts to boars as soon as they receive gilts from the multiplication farms which often occurs when the gilts are 6e7 months of age. However, insemination usually does not occur until their second or third observed estrus. As a result, most farms only have data for age at first service and not age at puberty. Those two factors have a high positive correlation so it seems reasonable that farms could use age at

first mating to establish a maximum age limit for breeding gilts. Data from five different commercial production systems examining this relationship are shown in Table 16.1. It is clear that gilts with the oldest ages at first mating had decreased lifetime productivity compared with all or some of their younger counterparts. It also demonstrates that differences in genetics and gilt development programs influence this relationship in terms of what constitutes an “old gilt.” However, the general trend is clear and using herd-specific data for the establishment of a maximum age by which gilts should be bred would be a proactive reproductive management tool that should eliminate females with reduced fertility.

Management of boars during the functional phase Once boars reach puberty, waves of spermatozoa begin to develop every 3e4 days and require 5e6 weeks to acquire fertilizational competence.23 This process should continue uninterrupted for as long as boars are collected which typically is until they are 20e24 months of age. Because Sertoli cell populations are

IV. Swine production

Management of boars during the functional phase

established during the developmental phase the primary emphasis for management programs during their functional phase is to minimize the occurrence of environmental conditions that impair normal spermatogenesis. Identification and elimination of inhibitory factors is critical for the accomplishment of this goal. The proportion of motile sperm or motility is the most commonly used estimate of semen quality by the swine industry so it was used to illustrate how normal sperm production changes in response to different types of stresses (Fig. 16.2). In addition, its relationship with fertility has been well established and resembles an asymptotic function. There is a strong positive correlation between motility and fertility of semen with sperm motilities below 70% after which increases in motility seldom result in significant improvements in farrowing rates or number born alive.24 Consequently, if ejaculates with less than 70% motile sperm are used for breeding, then both farrowing rates and numbers of pigs born alive usually are compromised. When boars are subjected to acute environmental stresses, production of normal sperm responds in a predictable manner based on the time course over which spermatogenesis occurs. Depending on whether the stressor is severe or moderate there is either an immediate (panel A) or attenuated decrease (panel B), respectively. Severe stresses affect both mature sperm stored in the cauda epididymi and developing sperm in the testicles so there is an immediate and sharp decline whereas moderate stresses primarily affect only developing sperm so there are usually 1 or 2 weeks of normal ejaculates following the stress before any decrease in semen quality is observed. In both situations, sperm production doesn’t return to normal for at least 5e7 weeks after the stress has ended because this is normal duration of time required for spermatogenesis.

289

FIG. 16.2 Changes over time in normal sperm production for boars subjected to severe, acute stresses (Panel A); moderate, acute stresses (Panel B); and chronic stresses (Panel C) compared with stresses on semen during or shortly after collection (Panel D).

In contrast, exposure to chronic stresses produce a different pattern. The length of time over which normal sperm production decreases before it reaches its nadir is much longer compared with acute stresses (panel C). This is probably due to the normal progression of homeostasis. Initially, boars adapt to the

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290

16. Reproductive management of swine

suboptimal environment. However, over time, they become less efficient and eventually succumb to the constant presence of the stressor. In many situations, after sperm quality reaches its lowest point, it remains there until the stress is gone, after which time, sperm quality and quantity do not return to pre-stress levels for another 5e7 weeks. Finally, decreases in sperm production that are observed only for 1 or 2 weeks are stresses on the semen during or after collection and not on the boars themselves (panel D). Spermatogenesis requires 5e7 weeks so it is not physiologically possible for boars to recover sooner than this if the stress had affected them. Hence, the most plausible explanation is that something happened to the sperm after ejaculation. Other sperm quality estimates including normal head and tail morphology; acrosome morphology; and several CASA (computer-assisted semen analyses) parameters respond to stresses in the same way as motility so any of these could be used for identification of detrimental management conditions for boars. If boar studs are using motility to determine viable sperm, then the number of insemination doses produced per ejaculate also can be used to monitor the stress load on boars. Analysis of sperm production records described is a retrospective exercise since the stress has to be present first in order for semen quality to be affected. However, it can be used proactively for the early identification and correction of suboptimal conditions. As soon as decreases are observed, a critical review of recent management changes is warranted. During these analyses, it is important to remember that for the acute, moderate and chronic patterns the stressors were present for several weeks prior to the observed decreases in semen quality. A summary of management conditions that have been shown to produce acute and chronic stresses on boars has been reviewed elsewhere.24,25 Many of these have the potential to be both acute and chronic stresses. Exposure

to elevated ambient temperature is a good example. The commonly accepted upper limit of the thermoneutral zone for adult swine is 27  C. Brief periods of temperatures considerably higher than this would fit the definition of an acute stress while exposure to temperatures consistently around 25  C for extended periods has been shown to produce decreases in normal sperm consistent with a chronic stress.24 Variation in their magnitude and duration of a stressor, in most instances, usually determines whether boars respond with an acute or chronic response pattern. Nutrition has been studied extensively in terms of its on influence on spermatogenesis and nutritional deficiencies generally fit the definition of a long-term stress. Severe restriction of either energy or protein for extended periods of time are required to produce significant reductions in sperm quality and quantity.26,27 These are usually observed after libido decreases. Supplementation of boar diets with minerals, vitamins, and different types of fatty acids also is quite popular within the swine industry. Deficiencies in any of these would likely be viewed as a chronic stress. Results from studies investigating their effects on spermatogenesis are equivocal. In general, they appear to produce improvements in sperm production when boars are under duress from other environmental conditions such as increased collection frequencies or heat stress.27 Consequently, their effectiveness probably is due to their ability to compensate for deficiencies in other management areas. Collection frequencies and housing conditions have also been investigated as potential stressors on sperm production. It appears that with both of these the consistency at which they are implemented is an important consideration. Exposure of boars to inconsistent collection patterns and changing housing conditions resulted in significant reductions in both semen quality and quantity.25 This phenomenon may also extend to collection technicians. In the boar socialization study discussed previously,

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291

Management of boars during the functional phase

boars that had not socialized with humans had decreased quantity and quality of sperm numbers when the collection technician was assigned randomly compared with periods when they were collected by the same person. Most of the current information with regards to how management conditions affect spermatogenesis has been obtained from experiments in which only one variable was manipulated. This is critically important from a scientific perspective, but doesn’t reflect commercial production in which boars almost always are subjected to several different types of stresses simultaneously. Recent field observations demonstrated that suboptimal management has an additive effect on sperm production in boars (Table 16.2). In this study, the production environment within four commercial boar studs was evaluated and practices viewed potentially as having negative consequences on sperm production were documented during the winter and summer seasons. The proportion of ejaculates discarded by each boar stud due to quality problems was also recorded.25 Several interesting observations resulted from this exercise. First, it is clear that there is variation in terms of how boars in different studs respond to the same relative stress load. Second, TABLE 16.2

the relative change in the number of potential stressors appear to be more important than the actual number to which boars are exposed. For example, the number of ejaculates rejected due to poor sperm quality was greater in Stud A when the stress level increased from 0 to 2 compared with Stud B where it remained constant with 2 stressors present during both the winter and summer months. Finally, sperm production can be very good even when the production environment is not perfect. Stud B rejected less than 10% of its ejaculates even when there appeared to be several stressors present. The stress level in this stud was the most consistent and it is tempting to speculate that perhaps boars were more efficient at adjusting their homeostatic mechanisms when the stress level was consistent, albeit at a low level, rather than variable. These data should be viewed as preliminary, but they do represent conditions to which most boars in commercial boar studs are exposed. Therefore, assessment of the potential stress load in studs seems to be a practical and proactive approach to prevent reductions in normal sperm production. One of the biggest challenges for reproductive management of boars is the development of

Changes in the ‘stress load’ on boars in commercial studs and their association with production of ejaculates with less than 70% motility or normal morphology. Winter monthsa

Summer monthsb

Studc

Acute stress

Chronic stress

Poor quality ejaculates (%)

Acute stress

Chronic stress

Poor quality ejaculates (%)

A

0

0

6.7
1.0x,y,*

1

1

21.4
3.4x

B

2

0

8.2
1.3x

1

1

9.7
2.8y

C

1

1

2.4
0.9z,*

3

1

18.8
3.7x

D

0

0

4.5
1.1y,z,*

5

1

35.4
6.9z

a

December, January and February. June, July and August. Means are from 2000 ejaculates from each study in each season. x,y,z Means within the same column differ (p  0.05). * Different from Summer Months (p  0.05). Adapted from Flowers.25 b c

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292

16. Reproductive management of swine

proactive semen fertility tests. Use of sperm motility, morphology and several other parameters are effective for identification of some subfertile boars. However, it has been well documented that semen from some boars with excellent semen quality estimates produce poor farrowing rates and numbers of live pigs.20,24 Concentrations of seminal plasma proteins, analysis of sperm capacitation patterns, and the ability of sperm to bind various proteins found in the oviduct are a few approaches that are currently being studied and show promise.28 However, there is still much work to be done with each of these techniques before one or more of them can be used effectively in commercial boar studs.

Management of sows during the functional phase Management of sows during their functional phase of development is the cornerstone for most production systems. A successful breeding after weaning is, in large part, due to how well the sow was managed during her previous lactation. Lactation presents a significant physiological challenge since the metabolic demands of producing milk often exceed nutrient intake and body tissues are catabolized to meet these deficiencies. If this becomes too extreme then the resumption of estrous activity after weaning is compromised because the restoration of normal gonadotropin secretion is delayed. Consequently, maintaining a production environment that maximizes feed consumption during lactation and minimizes loss of body condition is of fundamental importance. Various strategies have been used and their relative effectiveness has been reviewed by others.29,30 These provide excellent guidance for nutritional programs during lactation. It is important to remember that even short-term deficiencies in nutrient intake which last only several days can lead to extended rebreeding intervals and reduced reproductive performance. In these

situations, no significant weight losses were observed yet follicular development and gonadotropin secretion appeared to be reset to its starting point at the beginning of lactation. Therefore, the primary goal for reproductive management for sows during lactation should be to maximize their nutrient intake and prevent even transient periods of decreased consumption. Unfortunately, this often is difficult to accomplish so cross-fostering, split-suckling, and partial weaning strategies have been used to reduce metabolic demand during lactation, especially with high producing sows. All of these can be effective, but if not applied correctly, they can also have negative consequences. The suckling activity of the litter has an inhibitory effect on the release of luteinizing hormone (LH) and follicle stimulating hormone (FSH) which is good since it gives the anterior pituitary gland time to accumulate these hormones early in lactation so there will be adequate levels later to support the growth and ovulation of follicles after weaning.31 Any reduction in the suckling intensity potentially can cause premature releases of gonadotropins (LH and FSH). If this occurs early in lactation, then the weaning-to-estrus interval is delayed significantly since the replenishment of gonadotropin levels in the brain essentially has to start over. If this happens late in lactation, then reduced fertility and erratic estrous cycles often are observed. Consequently, for herds in which sows do not have normal weaning-toestrus patterns, examination of both the nutritional and piglet management programs merit investigation. One management strategy gaining popularity in some production systems is to breed sows while they are still lactating. This requires induction of estrus by temporarily removing the suckling-induced inhibition of gonadotropins and simultaneously stimulating follicular growth and ovulation. Piglet management techniques such as split-suckling and partial weaning can accomplish the former while administration of gonadotropins or strategically

IV. Swine production

Management of sows during the functional phase

planned boar exposure may prove successful for stimulating the latter. Recent studies indicate that breeding sows while they are still nursing their litter is feasible and their subsequent reproductive performance is good.32 Continued research in this area hopefully will result in regimens with increased appeal and applicability for the swine industry. Successful fertilization of oocytes released during ovulation in gilts and sows requires that sufficient numbers of spermatozoa be present in the oviduct several hours prior to ovulation. From a management perspective, semen quality, detection of estrus, and the technical competence of breeding technicians all play important roles in achieving this goal. Breeding regimens tend to be herd specific due to the biological variation associated with the occurrence of ovulation relative to estrus.33,34 However, most involve breeding sows at least once each day of estrus which is sufficient to consistently maintain high fertilization rates. Therefore, insemination strategies currently used in the swine industry seldom limit reproductive performance. A more common problem is management of insemination doses. Even with all the advances in semen extenders, the underlying biological reality is that sperm are programmed for fertilization as soon as they are ejaculated so minimizing the time interval between collection and insemination has a positive effect on fertility. Insemination doses typically are delivered several times per week to most sow farms which creates a situation in which there is semen of different ages, relative to their collection, available for breeding sows. The most common strategy for using semen is “first in, first out.” In other words, the oldest semen samples are used before the newest. A more strategic approach would be to use oldest doses for all first matings and the youngest doses for all second matings. The rationale for this is that the majority of sows ovulate during the latter half of estrus35 so using the youngest semen for those

293

matings increases the probability that the most fertile sperm are inseminated closest to the time of ovulation. When this approach was implemented in a large production system, farrowing rates and litter size increased from 85.1
3.1 to 91.4 þ 2.5% and from 12.8
0.3 to 13.5 þ 0.3 pigs, respectively. Fig. 16.3 illustrates the reproductive physiology associated with the establishment and maintenance of pregnancy and the production consequences that result when these events are disrupted or do not occur. Briefly, problems associated with poor fertilization of oocytes and embryonic deaths prior to Day 12 of pregnancy are classified as conception failures and sows returning to estrus at normal 21-day intervals since they never received the first pregnancy recognition signal. In contrast, complete or partial losses of embryos between Days 12 and 45 of pregnancy result in irregular returns to estrus or reduced total number of pigs born, respectively. These sows received the first pregnancy recognition signal so gestation proceeds normally in terms of progesterone production by corpora lutea on the ovaries. However, conceptuses (embryos and their extra-embryonic membranes) that die during this period are completely reabsorbed by the uterus. If the entire litter is lost then sows return to estrus at intervals greater that 21 days. In contrast, if only a few conceptuses die, then total number of piglets born is reduced. Fetal death losses after Days 45e50 of gestation result in an increase in mummified fetuses or stillborn piglets. When fetuses die between Days 45 and 100 of gestation the uterus tries to reabsorb them. This works reasonably well for soft tissues such as muscles, skin, ligament, and internal organs, but not bone. This results in partially decomposed fetuses or mummies. Their size is positively correlated with the stage of gestation at which death occurred: small and large mummies died closer to Day 45 or Day 100 of gestation, respectively. Finally, stillborn piglets represent fetal deaths

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294

16. Reproductive management of swine

FIG. 16.3 Key reproductive events during pregnancy in swine and their relationship with common reproductive problems. Conception failure and regular returns to estrus are associated with stresses between Days 1 and 14 of pregnancy. Irregular returns to estrus and decreased number of piglets born are associated with stresses between Days 15 and 40 of gestation. Increased number of mummified fetuses are associated with stresses between Days 41 and 100 of pregnancy. Increased number of stillborn piglets are associated with stresses after Day 100 of the 114 days of gestation.

that occurred just before or during parturition since they usually are fully developed without noticeable decomposition. Exposure to elevated ambient temperatures36; physical trauma associated with fighting37; and consumption of feed-borne toxins38 are several stressors that have been shown to negatively affect pregnancy in sows. How sows respond to these depends on when they occur during gestation as outlined in Fig. 16.3. If they occur prior to Day 40 of pregnancy, then farrowing rates are low and sows return to estrus after insemination at regular or irregular intervals. If they occur after Day 40 of gestation, then the number of pigs born alive is low due to an increase in mummified fetuses or stillborn piglets. Although it hasn’t been definitively established, it is reasonable to speculate that stresses have an

additive effect on pregnancy in much the same way that they affect sperm production. Therefore, routine evaluations of the potential stress load to which sows are exposed during gestation also appears to be a practical and proactive way to improve the reproductive performance of the sow herd. Gestation is the longest portion of the sow’s reproductive cycle and provides the best opportunity to enhance the fertility sows via their nutritional management. The general consensus is that their requirements should be based on two criteria: their weight at breeding and targeted weight gain based on their prolificacy.39 Nevertheless, the feeding strategies by which these are accomplished vary considerably within the industry and are the subject of considerable research. Earlier studies indicated

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295

Summary

that overfeeding during the first 30 days of gestation had a negative effect on reproductive performance,40 but subsequent experiments have shown that this phenomenon is unique to gilts and over-conditioned sows.41 Currently, the most prevalent strategy is to feed at a constant level throughout the majority of gestation and make adjustments for individual sows as needed based on evaluation of their body condition. Use of mechanical calipers has become more popular than visual estimation for this purpose due to their increased accuracy and precision.42 The practice of increasing nutrient intake during the last three weeks of gestation or “bump feeding” is common and based on the well-documented observations that nutritional requirements increase during this gestational stage due to the rapid growth and development of the fetuses.39 From a physiological perspective, this is a period where addition of selected vitamins, minerals, and amino acids or “nutritional programming” has been shown to increase various measures of piglet survivability and growth after they are born.41,43,44 This is particularly relevant to high producing genetic lines as a possible way to compensate for the negative consequences of intra-uterine growth restriction. There also is increasing interests in application of nutritional programming to earlier stages of gestation during which the placentae are formed as a means of increasing reproductive performance in sows. Results from these studies appear quite promising and research in this area provides a significant opportunity for enhancing the reproductive management of sows. Manually removing piglets or assisting sows during farrowing is a management practice that has been shown to be beneficial for reducing the birth of stillborn piglets and increasing the survivability of live piglets.45 This is true regardless of whether sows were induced to farrow. In a recent study comparing management practices on sow farms with high and low lifetime

productivity, this management practice was also associated with a reduction in the frequency of retained piglets and the proportion of sows that experienced transient or extended periods of reduced feed intake during lactation.46 While these data are only observational it does seem that sows that deliver their piglets over a shorter period of time with less complications should physiologically and behaviorally transition into lactation with increased efficiency. This should promote a more efficient post-partum recovery which has significant implications, as discussed previously, for her subsequent fertility.

Summary Swine reproductive management is a multifaceted discipline that has both developmental and functional components. The biggest challenge for replacement boars and sows during the developmental phase is the establishment of physiological benchmarks that are correlated with their adult reproductive performance. Birthweight and weaning weights appear to have strong positive relationship lifetime sperm and piglet production in boars and sows, respectively. Their refinement and use in selection programs should be enthusiastically pursued. Delaying training for semen collection toward the end of their pubertal development appears to be beneficial for lifetime productivity in boars while inducing puberty early and breeding at subsequent estrous periods is positively correlated with adult reproductive performance in gilts. The primary emphasis for both during the functional phase is creating management conditions that minimize their exposure to environmental stressors. A working knowledge of the physiology associated with spermatogenesis and the establishment and maintenance of pregnancy is absolutely critical for this process. The importance of colostrum in the development of both male and female reproductive organs;

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16. Reproductive management of swine

identification of sperm characteristics that have a quantitative relationship with boar fertility; and use of nutritional programming to enhance embryonic and fetal development in sows are areas of future research that hold potential for further enhancements in reproductive efficiency for the swine industry. These advancements, in turn, provide opportunities for the swine industry to reduce its environmental footprint. Sows are essentially the basic biological production unit of the swine industry. Therefore, anything that increases their longevity and efficiency should allow significant increases in the amount of pork produced in the face of only marginal increases or perhaps even decreases in the amount of resources invested in sow herds.

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1. McCoard SA, Ford JJ, Fahrenkrug SC, Wise TH. Temporal and spatial localization patterns of Gata4 during porcine gonadogenesis. Biol Reprod. 2001;65(2): 3746e3754. 2. Franca LR, Silva VA, Chiarini-Garcia H, Garcia SK, Debeljuk L. Cell proliferation and hormonal cell changes during the postnatal development of the testis in the pig. Biol Reprod. 2000;63(6):1629e1636. 3. Ashworth CJ, Finch AM, Page KR, Nwagwu MO, McArdle HJ. Causes and consequences of fetal growth retardation in pigs. Reproduction. 2001;(Suppl. 53): 233e246. 4. Foxcroft GR, Dixon WT, Dyck MK, Novak S, Harding JCS, Almeida FCRL. Prenatal programming of postnatal development in the pig. In: RodriguezMartinez H, Vallet JL, Ziecik AJ, eds. Control of Pig Reproduction VIII. Nottingham: Nottingham University Press; 2009:213e231. 5. Flowers B. Neonatal management: realize potential early. In: National Hog Farmer Blueprint Sow Lifetime Productivity. vol. 1. 2015:8e10, 4. Available from: nationalhogfarmer. com/reproduction/blueprint-sow-productivity-potentialcaptured-life. 6. Dysart NE. Effect of Birth Weight and Human Socialization on Reproductive Behaviors, Sperm Production, Semen Quality and Fertility of AI Boars. Raleigh: North Carolina State University; 2012:45e62. Available from: repository.lib. ncsu.edu/bitstream/handle/1840.16/9417/etd.pdf. 7. Flowers WL. Possible physiological benchmarks for sow longevity prior to puberty. In: Morrison B, ed. Proceedings of 2012 Allen D. Leman Swine Conference. St. Paul,

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Minnesota, United States: University of Minnesota Press; 2012:113e117. Available from: conservancy.umn.edu/ bitstream/handle/11299/139383/Flowers.pdf. Griffin JK, Seal MC, Flowers WL. Effect of neonatal environment on adult reproductive performance of boars. J Anim Sci. 2006;84(Suppl. 1):205e206. Meli CA, Flowers WL. Unpublished observations. Hurley WL. Composition of sow milk colostrum and milk. In: Farmer C, ed. The Gestating and Lactating Sow. Wageningen: Wageningen Academic Press; 2015: 193e218. Bagnell CA, Ho T-Y, George AF, Wiley AA, Miller DJ, Bartol FF. Maternal lactocrine programming of porcine reproductive tract development. Mol Reprod Dev. 2017; 84(9):957e968. Bortozzo FP, Bernardi ML, Kummer R, Wentz I. Growth, body state and breeding performance in gilts and primiparous sows. In: Rodriguez-Martinez H, Vallet JL, Ziecik AJ, eds. Control of Pig Reproduction VIII. Nottingham: Nottingham University Press; 2009: 281e292. Johnson RK, Eckardt GR, Rathje TA, Drudik DK. Ten generations of selection for predicted weight of testes in swine: direct response and correlated response in body weight, backfat, and age at puberty. J Anim Sci. 1994;72(8):1978e1988. Guide for the Care and Use of Agricultural Animals in Research and Teaching. 3rd ed. Champaign, IL: Federation of Animal Science Societies; 2010. Estienne M. Reproductive Performance and Longevity in Replacement Gilts Allowed Different Amounts of Floor Space during the Nursery Phase of Rearing. National Pork Board Research Reports; 2014. Available from: pork.org/ research/reproductive-performance-and-longevity-inreplacement-gilts-allowed-different-amounts-of-floorspace-during-the-nursery-phase-of-rearing/. Houpt KA. Swine social behaviors. In: Houpt KA, ed. Domestic Animal Behavior for Veterinarians and Animal Scientists. Ames: Iowa State University Press; 1998: 239e242. Hemsworth PH, Barnett JL. Behavioural responses affecting gilt and sow reproduction. J Reprod Fertil. 1990;(Suppl. 40):343e354. Turner AI, Tilbrook AJ. Stress, cortisol and reproduction in female pigs. In: Ashworth CJ, Kraeling RR, eds. Control of Pig Reproduction VII. Nottingham: Nottingham University Press; 2006:191e204. Hemsworth PH, Barnett JL, Hansen C. The influence of inconsistent handling by humans on the behaviour, growth, and corticosteroids of young pigs. Appl Anim Behav Sci. 1987;17(3e4):245e252. Flowers WL. Genetic and phenotypic variation in reproductive traits of AI boars. Theriogenology. 2008;70(8): 1297e1303.

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References

21. Holder RB, Lamberson WR, Bates RO, Safranski TJ. Lifetime productivity in gilts previously selected for decreased age at puberty. Anim Sci. 1995;61(1):115e121. 22. Bidanel JP, Gruand J, Legault C. Genetic variability in age and weight at puberty, ovulation rate and embryo survival in gilts and relations to production traits. Genet Sel Evol. 1996;28(1):103e115. 23. Garner DL, Hafez ESE. Spermatozoa and seminal plasma. In: Hafez ESE, ed. Reproduction in Farm Animals. 6th ed. Philadelphia: Lea and Febiger; 1993:165e187. 24. Flowers WL. Management of boars for efficient semen production. J Reprod Fertil. 1997;(Suppl. 52):67e78. 25. Flowers WL. Factors affecting the efficient production of boar sperm. Reprod Domest Anim. 2015;50(Suppl. 2): 25e30. 26. Louis GF, Lewis AJ, Weldon WC, Miller PS, Kittock RJ, Stroup WW. The effect of protein and energy intakes on boar libido, semen characteristics and plasma hormone concentrations. J Anim Sci. 1994;72(8):2051e2060. 27. Wilson ME. Boar nutrition for optimum sperm production. In: Foxcroft GR, ed. Proceedings of Banff Pork Seminar. Banff, Alberta, Canada. University of Alberta Press; 2004:295e306. 28. Flowers WL. Selection for boar fertility and semen quality e the way ahead. In: Rodriguez-Martinez H, Vallet JL, Ziecik AJ, eds. Control of Pig Reproduction VIII. Nottingham: Nottingham University Press; 2009:67e78. 29. Aherne FX, Kirkwood RN. Nutrition and sow prolificacy. J Reprod Fertil. 1985;(Suppl. 33):169e183. 30. Kim SW, Weaver AC, Shen YB, Zhao Y. Improving efficiency of sow productivity: nutrition and health. J Anim Sci Biotechnol. 2013;4:26. Available from: jabsci.com/ content/4/1/26/. 31. Britt JH, Armstrong JD, Cox NM, Esbenshade KL. Control of follicular development during and after lactation in sows. J Reprod Fertil. 1985;(Suppl. 33):37e54. 32. vanWettere WHEJ, Weaver AC, Greenwood EC, Terry R, Hughes PE, Kind KL. Controlling lactation estrus: the final frontier for breeding herd management. Mol Reprod Dev. 2017;84(9):883e896. 33. Flowers WL, Esbenshade KL. Optimizing management of natural and artificial matings in swine. J Reprod Fertil. 1993;(Suppl. 48):217e228.

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34. Kemp B, Soede NM. Consequences of variation in interval from insemination to ovulation and fertilization in pigs. J Reprod Fertil. 1997;(Suppl. 52):79e89. 35. Flowers WL. Weaning-to-Estrus Intervals and Breeding with ‘Young Semen’. National Hog Farmer Daily Newsletter; November 28, 2017. Available from: enewspro.penton. com/preview/nationalhogfarmer/NHF-001/ 20171128_NHF-001_957/. 36. Wetteman RP, Bazer FW. Influence of environmental temperature on prolificacy of pigs. J Reprod Fertil. 1985;(Suppl. 33):199e208. 37. Salak-Johnson JL. Social status and housing factors affect reproductive performance of pregnant sows in groups. Mol Reprod Dev. 2017;84(9):905e913. 38. Chaytor AC, Hansen JA, Van Heugten E, See MT, Kim SW. Occurrence and decontamination of mycotoxins in swine feed. Asian-Australas J Anim Sci. 2011; 24(5):723e738. 39. Nutrient Requirements for Swine. 11th Revised ed. Washington, DC: The National Academy Press; 2012. 40. Ashworth CJ. Effect of premating nutritional status and post-mating progesterone supplementation on embryo survival and conceptus growth in gilts. Anim Reprod Sci. 1991;26(4):311e321. 41. Landendijk P. Early gestation feeding and management for optimal reproductive performance. In: Farmer C, ed. The Gestating and Lactating Sow. Wageningen: Wageningen Academic Press; 2015:27e40. 42. Knauer MT, Baitinger DJ. The sow body condition caliper. Appl Agric Eng. 2015;31(2):175e178. 43. Wang J, Feng C, Liu T, Shi M, Wu G, Bazer FW. Physiological alterations associated with intrauterine growth restriction in fetal pigs: causes and insights for nutritional optimization. Mol Reprod Dev. 2017;84(9): 897e904. 44. Wu G, Bazer FW, Johnson GA, et al. Functional amino acids in the development of the pig placenta. Mol Reprod Dev. 2017;84(9):870e882. 45. Vanderhaege C, Maes D, de Kruif A, Dewulf J. Noninfectious factors associated with stillbirths in pigs: a review. Anim Reprod Sci. 2013;139(1):76e88. 46. Flowers WL. Unpublished observations.

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C H A P T E R

17 Nutrition and feeding of swine Hayford Manua, Samuel K. Baidoob a

Department of Animal Science, University of Minnesota, Saint Paul, MN, United States; bSouthern Research and Outreach Center, University of Minnesota, Waseca, MN, United States

O U T L I N E Introduction

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Energy density Minerals and vitamins Ractopamine Matching nutrient requirements to minimize feed wastage Diet and carcass quality Water intake and its effect on the environment

Neonatal pig nutrition 300 Colostrum and milk as nutrient and energy source for neonatal pigs 300 Nutrition of nursery pigs Post-weaning growth check in nursery pigs Dietary protein level during the post weaning period Ingredient selection, processing and usage in nursery diets Energy sources Protein sources Processing feed to increase the nutritive value for piglets

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Nutrition of weaner to finisher pigs Amino acids

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Introduction Neonatal pigs rely on the sow for colostrum and milk for nourishments. The colostrum and

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Sow nutrition Gestation Lactation

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milk provide energy and nutrients to support growth and development of body tissues, immunogenic protection and growth factors. The significance of colostrum and milk for piglets

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Copyright © 2020 Elsevier Inc. All rights reserved.

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has been well documented.1,2 After weaning, pigs are switched from milk to dry feed in the nursery. The nursery nutrition program is complex and depends on many factors that need to be carefully considered to maximize profit. Some of these factors are ingredient selection, weaning age, weaning weight and variation, the quality of the nursery facilities, and farm health status. These need to be evaluated to minimize the post weaning growth check. Approximately, 75% of feed is fed in the grower-finisher phase of production which constitutes roughly 65%e75% of the total cost of producing market hogs.3 Feed wastage could be a challenge at this period. Feeder design and adjustments are therefore critical at this point. Water intake for grower-finisher pigs is a function of feed intake and range from 2.2 to 4.4 L kg 1 water to feed ratio.4 To minimize wastage, reduce cost, and preserve the environments, phase feeding is adopted in grow-finish operations. Additionally, feed enzyme such as Phytase is also included in grower pig diets to decrease the P content of manure enabling farmers to meet requirements for environmental regulation.5 Protein is the most expensive nutrient in swine diets because of its importance in growth, development, and health of pigs.6 Lysine is the first and most limiting amino acid in swine diets. Therefore, amino acids are expressed as a percentage relative to lysine to meet the requirements of pigs.7 However, nutrient concentrations required in swine diet are energy dependent. As such, increases in amino acids concentration in swine diet are accompanied with increased energy levels to optimize productivity. Advances in swine nutrition, genetic selection, housing and disease control have resulted in highly prolific females in the breeding herd with increased efficiency of reproduction compared to production levels in the 1950s. Continuous research in nutrient requirements and management techniques which affect gilt development and sow longevity is required since current maternal line genotypes

have lower appetites and exceptional lean growth potential compared to females two decades ago.8

Neonatal pig nutrition Colostrum and milk as nutrient and energy source for neonatal pigs Neonatal pigs are born without immune protection since immunoglobulin transfer across the porcine placenta is limited.9 Neonates, therefore, require absorption of substantial mass of immunoglobulin (antibody) from colostrum to achieve adequate systemic immunity for protection against diseases postnatal.10 Colostrum consumption bridges the transition from a parenteral nutrient source to an enteral supply of nutrients and contributes approximately 5%e7% of the piglet’s BW during the first h of nursing.11 Colostrum contains high concentrations of protein (casein, immunoglobulins), lactose, micro minerals (copper, iron, iodine, and zinc but less amount of iron), vitamins, hormones, growth factors (insulin-like growth factor, epidermal growth factor, and transforming growth factors), antimicrobial agents (iron-binding antimicrobial protein (lactoferrin); the antibacterial enzyme (lactoperoxidase); the antibacterial and lytic enzyme (lysozyme); antimicrobial heat-stable peptides (defensins), lymphocytes (B and T cells), leukocytes (activated neutrophils, and macrophages), cytokines, soluble CD14, and nucleotides.10,12 The five classes of mammalian immunoglobulin are IgG, IgA, IgM, IgE, and IgD; and intestinal uptake occurs by an endocytic pathway in pigs.13 The neonate gut permeability to these large molecules of antibodies is of limited duration; approximately 24e48 h (depending on time of first suckling) after which gut closure occurs. Conversely, delayed suckling keeps gut ‘open’ for longer time, delaying gut maturation and increasing the risk for pathogen to invade the gut.12 Hence suckling time is of paramount

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Neonatal pig nutrition

importance. Although a substantial amount of immunoglobulin molecules is absorbed intact before gut closure, their digestion by the GI tract is limited.14 Reported absorption of intact Ig in neonatal pigs relative to the amount supplied in colostrum range from 5% to 25%.11,15,16 Immunoglobulin G absorption is greater when newborn piglets are fed porcine colostrum compared to bovine colostrum.15 This suggests that the efficiency of the absorption of Ig depends on the nutrients that are ingested along with the Ig, thus colostrum composition.16 Nutritionist may argue that the yield of colostrum is more important than its composition because of extensive variation in colostrum yield between individual sows whereas colostral protein concentration varies to limited extent.1 Piglets should have enough colostrum from their biological mothers before fostering to another dam to minimize risk of compromised immunity. The need for biological mother’s colostrum is supported by evidence that certain immune cells (lymphocytes) are only absorbed when derived from the biological mother.17 Additionally, nutritional programming effects of maternal colostrum intake on neonatal reproductive development (lactocrine hypothesis) were reported.18 Therefore, for efficient cross-fostering, management should encourage maximum ingestion of colostrum from biological dams and transfer window limited to the first 12 h and 48 h postpartum, respectively.19 Fostering within this time window is advantageous since neonates have not strongly bonded with their biological mother prior to separation.12 The neonatal piglets after birth, experience very large heat loss due to a high surface to volume ratio of the body. They survive low ambient temperature in the extra-uterine environment by depending on oxidation of glycogen from the liver, muscle depots, and ingested colostrum as sources of energy.20 However, neonates are born with limited glycogen stores in the liver and muscles and these depots are fatally depleted of energy after 16 h of fasting.20 As such, neonates are highly susceptible to dying from insufficient

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energy supply1 and more than 50% of piglet pre-weaning mortality occurs during the first three days after farrowing due to inadequate colostrum intake, starvation and hypothermia.21 From quantitative nutritional point of view, glycogen depots in newborn piglets may not be considered since glycogen depots are accumulated during the last 2e4 week prior to parturition and the fact that sow gestation diets are very rich in starch.22 However, from economical and welfare point of view, others have tried to improve the glycogen depots through altered sow nutrition, but results are not unanimously agreed upon.23e26 Glycogen in the liver of neonatal piglets was greater when sows had additional energy from cornstarch 6 d prior to farrow.23 Similarly, hepatic glycogen content of piglets 4 h after birth was greater if their dam consumed either 10% medium chain fatty acids or 10% coconut oil from d 84 of gestation until farrowing, relative to sows fed soybean oil.24 On the contrary, Boyd et al.25 and Newcomb et al.26 did not find any beneficial effect on hepatic glycogen concentration in neonatal piglets when sows were fed additional energy from cornstarch, tallow, soybean oil, or medium-chain triglycerides from the last d 14 of gestation. Genetic selection for increased piglet survival appears to have augmented glycogen retention in liver and muscle tissues during fetal life.27 After the colostrum production, milk secretion starts from second d of lactation.28 Peak milk yield occurs around d 17e19, contingent on the litter weight gain and litter size. At this point, high producing sows may produce 15e17 kg of milk per day.29 Sow milk is composed of protein bound amino acids, free amino acids, fat, lactose, minerals, vitamins, and water.30 Most of the protein bound amino acids concentration is reduced from d 1 to 29 of lactation31 while some free amino acids become more abundant after d 2: threonine, serine, glycine, alanine, cysteine, valine, methionine, leucine, and phenylalanine, glutamic acid, proline, isoleucine, and lysine, aspartic acid, tyrosine, tryptophan, arginine, taurine, citrulline, ornithine, and b-alanine.31

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Nursing piglets may be offered creep feed to supplement their nutrition, but this has limited effect in short lactation since intake is very low.32 Further studies using inert colored markers in creep feed reported that not all piglets in a litter eat creep feed but “eaters” have better initial postweaning feed intake and growth performance than pigs that do not consume creep feed.33

Nutrition of nursery pigs Post-weaning growth check in nursery pigs Weaning is stressful transition in the life of pigs due to abrupt nutritional, immunological and social adaptations.34 Transition from sow milk which is highly digestible and high in lactose, protein, and fat to a dry and lessdigestible starch-based diet results in profound reduction in energy intake for maintenance of epithelial structure,35 lowered transmucosal resistance and leading to enhanced secretory activity in the small intestine.36 Damaged epithelial layers impair nutrient digestibility and provide more substrates for pathogen proliferation,37 and increases pathogen attachment and penetration through the transcellular and paracellular pathways.38 Additionally, during the first few days after weaning, detoxification of ammonia is compromised due to a deficiency of arginine for the hepatic urea cycle leading to build up of epithelial irritant of ammonia.39 All these factors invariably lead to nutritional diarrhea. The inflammatory response to damaged epithelial layers and subsequent production of cytokines [interleukine1 (IL-1), interleukin-6 (IL-6), and tumor necrosis factor-a (TNFa)] and acute phase proteins reduces protein deposition and growth in animals termed as post-weaning growth check. Williams et al.40 reported that pigs with high immune system activation exhibited 11% decreased daily gain, 29% declined in feed intake, 38% reduction in body protein accretion, and 20%

increased feed conversion ratio compared with pigs with low immune system activation, between 6 and 27 kg BW. Additionally, passive immunity from the sows’ secretions is depleted at weaning while the innate and adaptive immune systems of weaner pigs are not fully developed.41 Finally, wean piglets are also exposed to social stressors due to separation from the sows, mixing with unfamiliar littermates and establishment of the social hierarchy within the new group, culminating in elevated cortisol secretion and corticotrophin-releasing factor receptor expression in the intestine of weaned pigs.42 Besides the inflammatory response, newly weaned piglets frequently drink water and have difficulty initiating feeding.43 These factors contribute to post weaning growth check. With the ban of antibiotic growth promotants (AGPs) in the European Union, many management and dietary strategies and numerous additives have been reviewed or studied as replacement of Antibiotic Growth Promoters (AGPs) to address the challenges of post-weaning growth.41,44

Dietary protein level during the post weaning period Providing high-protein diets to piglets post weaning usually increases ADFI, but incidence of post weaning diarrhea (PWD) is greater.45 Crude protein content of weaned pig’s diet may range between 210 and 245 g/kg to support maximum lean growth.46 However, because pancreatic and brush border proteolytic enzymes are not fully established at weaning, piglets’ are limited in their ability to digest and absorb high protein diets.47 The undigested dietary proteins increase intestinal pH, pathogen proliferation, and production of intestinal irritants such as ammonia48 predisposing piglets to etiology of post-weaning colibacillosis (PWCs)49. Also, the catabolism of surplus amino acids generates CO2, ammonia, H2S, CH4, urea and uric acid to pollute the environment.50 But feeding low

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Nutrition of nursery pigs

dietary protein levels compromise performance during the post-weaning period without adequate balance of supplemented crystalline amino acids.51 On the other hand, feeding low crude protein diets supplemented with crystalline amino acids and branched chain amino acids (leucine, isoleucine, and valine) may increase diet cost.52 It is generally recommended to formulate diet to less than 180 g CP/kg with crystalline amino acids fortification during the post-weaning period to minimize expression of PWC.51,53,54 Reduced dietary crude protein reduces the incidence of diarrhea and protects the environment through minimization of about 30%e50% nitrogen excretion depending on original specifications, ingredient selection, and magnitude of reduction in dietary protein concentration. However, crude protein should not be reduced more than 3%e4% units (i.e., not less than 17%) unless valine and (or) isoleucine are supplemented if available.6 Another strategy to reduce PWD is to supply feed to weaner pigs in a mash form instead of pellet feed although pellet feeding reduces feed wastage and improves feed efficiency.55

Ingredient selection, processing and usage in nursery diets Energy sources Feed cost is estimated at 70% of the total cost of swine production and energy is the most expensive component in diets for pigs.3 Maize and wheat are traditionally major energy sources in pig diets in major livestock production centers globally. Other alternatives sources are barley, sorghum, tapioca, broken rice, and oat groats are common energy sources of nursery diets. Rye and triticale are rarely used since they are rich in anti-nutritional factors such as tannin and phytate.56 Cereal by-products, from wheat processing or corn wet-milling are also used in limited quantities since they have high fiber content, poor protein quality, increase bulkiness of feed and thus depress feed intake and therefore

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not incorporated into nursery diets immediately following weaning.57 Oils are more digestible and preferred to animal fats for young pigs. Therefore, oils extracted from maize, soybean and sunflower are preferred during the first two weeks post-weaning58,59 while animal fats, such as white grease, tallow, and lard are ideal in later phases of the nursery period. Protein sources Fish meal, poultry meal, dairy proteins, meat meal, and blood products such as spray dried plasma are good sources of protein for nursery pigs.60 Blood plasma protein improves growth performance because of its high content of immunoglobulins. Egg-derived immunoglobulins are also available to prevent possible transmission of Bovine spongiform encephalopathy (BSE) and other diseases. Plant proteins are soybean meal, soybean protein (in early diets), wheat gluten, potato protein, peas, lupines, sunflower meal, faba beans, and lentils. Soybeans and most other plant protein sources require heat treatment, dehulling, enzymatic hydrolysis, gamma irradiation, and breeding techniques (Biotechnology) to make them suitable for young pigs because they are rich in anti-nutritional factors such as phytic acid, condensed tannins, lectins, protease, and a-amylase inhibitors.60 However, besides soybean meal, other legumes contain lower level of sulfur amino acids and tryptophan and require addition of crystalline amino acids to the diets for growing pigs or mix with cereals improves the protein value.61 Generally, unrefined plant proteins are fed after two weeks post-weaning to avoid inflammatory reactions to antigenic proteins.

Processing feed to increase the nutritive value for piglets Finer particle size increases the surface area available for enzymatic digestion leading to improved nutrient digestibility.62 Reduction of

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wheat DDGS particle size through grinding from 571 to 383 mm increased apparent total tract digestibility of GE and SID of Lys for wheat DDGS-based diets fed to grower pigs.63 However, feeding barley-based diets with particle size (785e434 mm) from 31 kg BW to slaughter; increased stomach ulceration in pigs.64 Therefore, proper particle size to optimize use of feedstuff is recommended. Recommended particle size for maize and soybean meal are 600 and 600e900 mm, respectively for nursery pigs. Generally, for every 100-mm reduction in particle size, feed efficiency is expected to improve by 1.3%.65

Nutrition of weaner to finisher pigs Feed constitutes approximately 65%e75% of the total cost of producing wean to finish pigs and roughly 75% of this amount is fed in the grower-finisher phase of production. Amount of feed consumed depend on several factors included but not limited to genotype, floor space allowance, temperature, and health status of the pigs.66 Pigs selected for improved lean gain will consume more feed while pigs selected for improved lean efficiency (lean gain per unit of feed intake) tend to eat less and have a lower overall daily gain, because selection emphasizes carcass lean and feed efficiency.66

Amino acids Quantitatively, protein is the most expensive nutrient in swine diets because of the important physiological roles of amino acids in growth, development, and health of pigs.6 Amino acids are categorized into two: nutritionally essential amino acids (EAAs) and non-essential amino acids (NEAAs). The carbon skeletons of EAA are not synthesized in vivo and must be supplemented in the diet in adequate amounts67 whereas inter-organ metabolism of amino acids

in the body leads to the de novo synthesis of NEAA.68,69 The term “nutritionally nonessential amino acids” is now considered a misnomer70 and these nutrients are needed in diets for optimal growth and production performance of swine.71 It was reported that pigs do not synthesize sufficient amount of NEAA to maintain their maximum growth and development.72,73 It was reported that under current feeding programs, only 70% and 55% of dietary amino acids are deposited as tissue proteins in 14-day-old sowreared piglets and in 30-day-old pigs weaned at 21 days of age, respectively.6 This suggest that it’s paramount importance to understand the roles and dietary requirements of non-essential amino acids (NEAAs) in swine nutrition. Lysine is the first and most limiting amino acid in swine diets. The requirements of other amino acids are expressed as a percentage relative to lysine since it is the first amino acid that needs to be supplemented to meet the requirements of the pig.7

Energy density Nutrient concentrations required in swine diet are energy dependent. Growing pigs eat to meet their requirement for energy but physiological effect of dietary components may be an important factor in determining feed intake.74 Addition of fat to diets reduces dustiness, increases the energy density and reduces the feed intake. Therefore, nutrient concentrations should be increased to maintain a constant daily intake. Low density energy diet diluted with fibrous feedstuffs, will increase feed intake until gut capacity limits intake.75 Changing nutrient concentrations relative to expected feed intake maintain constant nutrient to energy ratio. For every 1% fat added, feed:gain ratio will improve by approximately 0.04% points.66 However, elevated dietary fat concentration may act as a constraint on feed intake through reduction in the digesta passage rate.76

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Nutrition of weaner to finisher pigs

Minerals and vitamins Besides energy and amino acids, phosphorus (P) is the third most expensive nutrient in diet of pigs. Nutritional intervention to reduce P excretion into the environment include: selection of feed ingredients with greater P digestibility, the use of phytase enzymes to improve P digestibility and closely matching P content of the diet to the actual needs of the pig throughout the growth cycle.77 Approximately 99% and 75% of Ca and P are respectively located in bone, signifying their important role in bone mineralization.78 However, excess Ca in pigs’ diets may increase P excretion due to formation of Ca-P complexes in the intestinal tract but if diets are formulated to meet requirements for standardized total tract digestible (STTD) Ca and STTD P, excesses of Ca and P can be minimized leading to maximum utilization of both minerals. Bone ash and the quantity of Ca and P retained in the body were maximized if the STTD Ca: STTD P ratio was between 1.33:1 and 1.67:1 which are in agreement with the ratio of Ca and P in the body of pigs (from 1.20:1 to 1.60: 1).79 Beside Ca and P, salts in swine diets also provides Na, and Cl. On the other hand, microminerals (iron, zinc, copper, manganese, selenium and iodine) can be provided as mineral premix or a vitamin-mineral premix. High level of zinc oxide or less from potentiated zinc oxide and copper sulfate are mineral salts that can reduce symptoms of diarrhea and promotes growth in post-weaning pigs but inclusion levels may be subjected to local regulations.80 Vitamins are marketed either as premix or in combination with minerals. Vitamin and mineral requirements should be based on current knowledge of pig genotype. The cellular antioxidant system of pigs includes superoxide dismutase (SOD) and glutathione peroxide (GPx) enzymes which can be down or up-regulated base on need. Vitamins, however, must be supplied exogenously. Copeland et al.81 reported that antioxidant

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enzyme activities were reduced, when 120 IU vitamin E and 7,200 IU vitamin D per 3.79 L (1 gallon) drinking water was provided to grower pigs. A comprehensive nutrient level was provided.66

Ractopamine Ractopamine HCl (RAC; Paylean, Elanco Animal Health, Greenfield, IN) is a ß-adrenergic agonist that is included in finisher diets before marketing to increase weight gain, G:F, and carcass yield but may be subjected to trade regulation.82 It repartitions energy to increase fat-free lean by approximately 25%e35%, depending on level of inclusion, and reduces fat deposition. Ractopamine has inclusion rate of 4.5e9.0 g/ ton of finishing diet containing at least 16% crude protein for the last 45e90 lbs of gain.83 Amino acid concentrations need to be increased to support the increased protein deposition (lean gain).83

Matching nutrient requirements to minimize feed wastage As the animals grow heavier, the composition of growth changes (more fat and less lean). Nutrient requirements therefore fall due to changes in the maintenance requirement and the fact feed consumption is increased. Phase feeding is a concept that matches nutrient to the growth stage of the pig.84 It reduces overfeeding and underfeeding and the overall feed cost per pig and eventually reduces nutrient excretion to conserve the environment. Six dietary feeding phases throughout the growerfinisher period are common.66 Separate sex feeding (Split-sex feeding) will more closely match nutrient concentrations in the diet to the requirement of sex of the pig and reduced nutrient excretion to save the environment. From about 37 kg body weight, barrows have greater feed intake capacity without a

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corresponding potential for lean gain, and hence diets with lower amino acid concentrations are fed to reduce wastage. Feeder design can affect feed wastage and efficiency of feed utilization. Researchers in several countries have reported feed wastage and values of 4% in the U.S. (with a range from 2% to 12%), 6% in Great Britain (with a range of 1.5%e20%), and 3% e5% in Denmark.66 Based on N and P retention of 35%, 2% reduction in feed wastage can reduce the N and P in manure by approximately 3% and 1%, respectively and can save $0.71 per pig marketed (assuming a feed efficiency of 2.8 and a feed cost of $0.26/kg).66

Diet and carcass quality Wheat middlings and corn dried distillers’ grains with solubles (DDGS) are by-products commonly included in swine diets. The safe maximum inclusion level of DDGS above which the growth performance is negatively affecting is well characterized. It was reported that growth performance compared with a corn-based diet does not change when DDGS (>9% oil) are added up to 20% or 30% of the diet.85 Inclusion of fat to pig diets increases gain:feed and reduces ADFI but relative increases in ADG and daily ME intakes are variable.86 Diets containing an increased quantity of unsaturated fatty acids are likely to increase soft carcass fat when fed to pigs. Swine breeding and nutrition programs aimed at producing lean and high-quality pork product by eliminating production of Pale Soft and Exudative (PSE) pork and soft carcass fat. Soft fat is problematic during processing bacon, reduced product shelf life, an oily appearance in the retail package, and increased susceptibility to oxidative damage.87 The source of dietary fat has significant effect on softness of fat in the pig carcass.88 Feeding high amounts of unsaturated fatty acids results in soft fat in pig carcass. Therefore, inclusion of high levels of dried distillers’ grains (30% above) with solubles which contains high levels of unsaturated fat (linoleic

acid) results in increased iodine value (IV) and decreased carcass yield89 and production of soft pork fat on carcass.87 However, complete dietary withdrawal of DDGS and wheat midds before marketing has been shown to be successful in lowering IV and improving carcass yield.89,90

Water intake and its effect on the environment Water has plethora of functions in farm animal and it is the single nutrient required in greatest quantity but often referred to as “the forgotten nutrient.”91 This seemingly oversight can be ascribed to the fact that water is widely available in many regions of livestock production. In North America, water is available at little or relatively no cost and therefore not traded commercially like vitamins, minerals, and amino acids. On the contrary, in northern Europe, quantity of water used for livestock production is of great concern; as such water conservation research is a priority.92 Functions of water in growing pigs include: regulation of body temperature, removal of waste products, metabolic functions, movement of nutrients into the body tissues, and for growth.93,94 As the water content of pig changes, the proportion of lean to lipid tissue declines with increasing body weight. Thus, empty body weight of newborn pig and market hog is about 85% and 50% of water, respectively.95 An animal can lose over half of its protein and all of its fat and yet live but could die due to water deprivation. Water intake depends on body weight (BW), age and health of animal, climatic conditions, stage of production, feed intake, and design of drinkers.96 Water meters in pig barns measure water disappearance (animal intake plus wastage) which depends on drinker type, mounting angle, height, location, number of drinkers, and flow rates.97 Excessive water disappearance affects cost, manure production and its quality.97 Therefore, the need to reduce the amount of water wastage from

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Sow nutrition

drinkers to lower water and manure costs.93 Water intake for grower-finisher pigs depend on feed intake and usually expressed as water to feed ratio which range from 2.2 to 4.4 L kg 1.4 However, excessive water intake could be a sign of stress, boredom or hunger according to Patience.95

Nutrition for gilt development Modern maternal line genotypes have lower appetite, exceptional lean growth potential and more sensitive to nutritional management compared to females of two decades ago.98,99 Replacement gilts are expected to have slower growth to limit mature body size, in order to prevent feet and leg problems and excessive fat gain. As such, replacement gilts are provided unlimited access to diet lower in energy to avoid excessive body fat.100 Such diet contains higher concentrations of calcium, phosphorus, chromium, selenium, zinc, vitamin A, and E than diet fed to finishing pig since highly prolific gilts are expected to grow during their first gestation. High level of Ca and P allows for maximum bone mineralization which is mobilized for fetal growth and lactation.99,100 Gilts diet deficient in protein and amino acid delays the onset of puberty. Nutrient requirements of modern highly prolific females are more defined than the genotype two decades ago.101 Therefore, continuous research to update the nutrient requirements and management techniques and technologies are paramount. Overall the goal of gilts nutrition program is to attain body condition score of 3 at first service.102

Sow nutrition Gestation The sow’s role in swine production is either being pregnant or nursing a litter. Wean to estrus interval is therefore expected to be shorter.

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Increasing feed by 50%e100% (flushing) or feeding high energy sources such as dextrose for 10e14 d before first service, increases ovulation rate and litter size.8 Feed fed is usually decreased after mating to an appropriate gestation diet because sows that are overfed throughout gestation have high embryonic mortality, produce small litters, farrowing complications, crush piglets, reduce feed intake during subsequent lactation and are less prolific at the next parity.103 Increased level of dietary energy intake to gilts and sows after mating is reported to reduce systemic progesterone concentrations leading to increased embryo mortality in early pregnancy. However, this observation is not unanimously agreed on in the scientific literature.104 It was reported that sows with back fat depths of 23 mm or more at farrowing have depressed appetite during lactation.103 Pregnant sows are restrictively fed to control body weight and prevent excess weight gain. Hence, energy is the limiting factor for gestating sows and feed allowance necessary to provide energy requirements must be considered first when formulating diets for pregnant sow. Nutrient requirements of sows change significantly with advancement of pregnancy. This factor calls for segregated phase feeding. A 3-phase feeding program is practiced by farmers to meet nutritional needs of sow and to prevent over feeding of nutrients which increase cost and eventually negatively affect the environment through nutrient excretion.105,106 These phases are: 1) early gestation (d 0e30), embryo survival and implantation are impacted, 2) mid-gestation (d 30e75), body growth in young sows and recovery of body reserves lost during lactation in older sows are impacted and 3) late gestation (approximately the last 45 d), during which fetal and mammary growth are impacted.8 Fetal weight, fetal protein content and mammary protein content increase 5, 18, and 27 times, respectively, in the last 45 d of gestation.8 Conceptus protein content has greater priority for nutrient supply than maternal weight gain and increases

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rapidly after d 68 of gestation. Therefore, amino acid and energy requirements are greater in late gestation than in early gestation even though amino acid requirements increase to a higher degree than energy requirements in late gestation. Generally, sow’s feed intake during gestation should be based on objective measure of body weight and back fat depth.107 Feed intake during the last 14e21 days should be regulated to avoid a negative energy balance prior to farrowing, with the goal of higher feed intake in early lactation, easier farrowing and adequate birth weights of newborn pigs.103 However, beneficial effect of feed restriction prior to parturition is reduction of postpartum dysgalactia syndrome (PDS). The PDS in sows is characterized by inadequate and insufficient colostrum and milk production during the first days post-natal which was previously termed mastitis, metritis and agalactiae, or MMA, syndrome.108 Also keeping the feed low in energy and high in fiber through parturition and into the first few days of lactation may improve intestinal function and initiation of lactation.109

weight at weaning.8 Getting sows up 2e3 times per d stimulates sows to urinate and defecate, resulting in drinking and eating, thus optimizing feed intake, lactation performance and return to estrus.8 High temperatures in summer reduced feed intake in sows during heat stress. Fat is often incorporated into lactation diets to compensate for depressed appetite during heat stress because of its lower heat increment and greater efficiency of utilization compare with equivalent metabolizable energy in the form of carbohydrate. However, the practice of feeding fat is controversial. The mammary gland uses fat to produce very rich milk to improve piglet weaning weight112 but addition of fat in lactation sows’ diet could impair subsequent reproductive performance by reducing luteinizing hormone secretion in early lactation113. Generally, fat is included in swine diets from 0.5% to 7%.114 At peak milk production (21 day postpartum), high performing sows have a water intake of up to 40 L per day. Therefore, lack of water negatively affect milk production and availability of good quality water with a flow rate of 1.0 L per min is essential.92,115

Lactation

Conclusion

Over the last 4 decades litter size has increased by three pigs.110 Increased need for nutrients for milk production is derived from mobilization of body reserves since appetite is often deficient after farrowing.103 Mobilization of body reserves lead to excessive weight loss, reduced litter weight gain and extended wean to estrus interval. Feed intake during the first 7e10 d of lactation should be adequate to replenish body reserves and re-establish secretion of hormones which control subsequent reproductive performance.111 Lactation sows are therefore not fed restricted but fed to appetite. Feed restriction during lactation leads to prolong weaning to estrus, reduced pregnancy rates and litter size. Feeder design that has a reservoir to hold a minimum of 9 kg of feed or provide access to full feed 24 h per day results in optimum return to estrus and piglet

The repertoire of immunoglobulins in colostrum and milk represent an important component to provide systemic immunity to neonates. The neonate gut permeability to these large molecules of antibodies is of limited duration due to gut closure. Quality of post-weaning diet is crucial in alleviating the post weaning growth check. Numerous additives have been studied as potential replacement of AGP. Nutritional intervention to reduce P excretion into the environment are selection of feed ingredients with greater P digestibility, the use of phytase enzymes and matching P content of the diet to the actual needs of the pig. Replacement gilts have ad-libitum access to low energy diet fortified with vitamin and minerals to avoid excessive body fat and allows for maximum bone

IV. Swine production

References

mineralization which is mobilized for fetal growth and lactation. Stage of pregnancy and maternal growth rate are factors that determine nutrient requirements of pregnant sows. Phase feeding provides nutritional needs of sow and prevent over feeding of nutrients which negatively affect the environment through nutrient excretion.

References 1. Theil PK, Lauridsen C, Quesnel H, et al. Neonatal piglet survival: impact of sow nutrition around parturition on fetal glycogen deposition, and production and composition of colostrum and transient milk. Animal. 2014;8:1021e1030. 2. Quesnel H, Farmer C, Theil PK, et al. Colostrum and milk production. In: The Gestating and Lactating Sow. Wageningen, The Netherlands: Wageningen Academic Publishers; 2015:173e192 (Chapter 8). 3. Navarro DMDL, Bruininx EMAM, Jong L, et al. Effects of physicochemical characteristics of feed ingredients on the apparent total tract digestibility of energy, DM, and nutrients by growing pigs. J Anim Sci. 2018; 96:2265e2277. 4. Tavares JMR, Filho PB, Coldebella A, et al. The water disappearance and manure production at commercial growing-finishing pig farms. Livest Sci. 2014;169: 146e154. 5. Knowlton KF, Radcliffe JS, Novak CL, et al. Animal Management to Reduce Phosphorus Losses to the Environment; 2004. https://academic.oup.com/jas/articleabstract/82/suppl_13/E173/4807416. 6. Rezaei R, Wang W, Wu Z, et al. Biochemical and physiological bases for utilization of dietary amino acids by young pigs. J Anim Sci Biotechnol. 2013;4:7. 7. NCR. Nutrient Requirements of Swine. 11th ed. Washington, DC, USA: Natl Acad. Press; 2012. 8. Kraeling RR, Webel SK. Current strategies for reproductive management of gilts and sows in North America. J Anim Sci Biotechnol. 2015;6:3. 9. Baxter EM, Edwards SA. Piglet Mortality and Morbidity: Inevitable or Unacceptable? Advances in Pig Welfare. Woodhead Publishing; 2018. 10. Hurley WL, Theil PK. Immunoglobulins in mammary secretions. In: Advanced Dairy Chemistry. 2013:275e294. 11. Lin C, Mahan DC, Wu G, et al. Protein digestibility of porcine colostrum by neonatal pigs. Livest Sci. 2009; 121:182e186. 12. Arnott G, Thorup F. The Welfare Implications of Large Litter Size in the Domestic Pig II. 2013:219e238.

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13. Sangild PT, Trahair JF, Loftager MK, et al. Intestinal macromolecule absorption in the fetal pig after infusion of colostrum in utero. Pediatr Res. 1996;45:595e602. 14. Danielsen M, Pedersen LJ, Bendixen E, et al. An in vivo characterization of colostrum protein uptake in porcine gut during early lactation. J proteom. 2011;74:101e109. 15. Jensen AR, Elnif J, Burrin DG, et al. Development of intestinal immunoglobulin absorption and enzyme activities in neonatal pigs is diet dependent. J Nutr. 2001; 131:3259e3265. 16. Bikker P, Kranendonk G, Gerritsen R, et al. Absorption of orally supplied immunoglobulins in neonatal piglets. Livest Sci. 2010;134:139e142. 17. Bandrick M, Pieters M, Pijoan C, et al. Effect of crossfostering on transfer of maternal immunity to mycoplasma hyopneumoniae to piglets. Vet Rec. 2011;168: 100. 18. Chen JC, Frankshun AL, Wiley AA, et al. Milk-borne lactocrine-acting factors affect gene expression patterns in the developing neonatal porcine uterus. Reproduction. 2011;141:675e683. 19. Thorup F, Eriksen L, Risum D, et al. Predicting piglets at birth with a high risk for mortality. In: Proceedings of the 18th Congress of the International Pig Veterinary Society. 2004:478. 20. Theil PK, Cordero G, Henckel P, et al. Effects of gestation and transition diets, piglet birth weight, and fasting time on depletion of glycogen pools in liver and 3 muscles of newborn piglets. J Anim Sci. 2011;89: 1805e1816. 21. Kirkden RD, Broom DM, Andersen IL, et al. Invited review: piglet mortality: management solutions. J Anim Sci. 2013;91:3361e3389. 22. P
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27. Leenhouwers JI, Knol EF, de Groot PN, et al. Fetal development in the pig in relation to genetic merit for piglet survival. J Anim Sci. 2002;80:1759e1770. 28. Hartmann PE, McCauley I, Gooneratne A, et al. Inadequacies of sow lactation: survival of the fittest. In: Physiological Strategies in Lactation. London, UK: Academic Press; 1984:301e326. 29. Hansen AV, Lauridsen C, Sørensen MT, et al. Effects of nutrient supply, plasma metabolites and nutritional status of sows during transition on performance in the following lactation. J Anim Sci. 2012;90:466e480. 30. Theil PK, Nielsen MO, Sørensen MT, et al. Lactation, milk and suckling. In: Nutritional Physiology of Pigs. 2012:1e47. 31. Wu G, Knabe DA. Free and protein-bound amino acids in sow’s colostrum and milk. Nutrition. 1994;124: 415e424. 32. Muns R, Magowan E. The effect of creep feed intake and starter diet allowance on piglets’ gut structure and growth performance after weaning. J Anim Sci. 2018;96:3815e3823. 33. Sulabo RC, Jacela JY, Tokach MD, et al. Effects of lactation feed intake and creep feeding on sow and piglet performance. J Anim Sci. 2010;88:3145e3153. 34. Kim JC, Hansen CF, Mullan BP, et al. Nutrition and pathology of weaner pigs: nutritional strategies to support barrier function in the gastrointestinal tract. Anim Feed Sci Technol. 2012;173:13e16. 35. Pluske JR, Le Dividich J, Verstegen MWA, et al. Weaning the Pig: Concepts and Consequences. Wageningen: Wageningen Academic Publishers; 2003:17e35. 36. Boudry G, Peron V, Le Huerou-Luron I, et al. Weaning induces both transient and long-lasting modifications of absorptive, secretory, and barrier properties of piglet intestine. J Nutr. 2004;134:2256e2262. 37. Pluske JR, Pethick DW, Hopwood DE, et al. Nutritional influences on some major enteric bacterial diseases of pigs. Nutr Res Rev. 2002;15:333e371. 38. Heo JM, Kim JC, Hansen CF, et al. Feeding a diet with a decreased protein content reduces both nitrogen content in the gastrointestinal tract and post-weaning diarrhea, but does not affect apparent nitrogen digestibility in weaner pigs challenges with an enterotoxigenic strain of Escherichia coli. Anim Feed Sci Technol. 2010; 160:148e159. 39. Brunton JA, Bertolo RF, Pencharz PB, et al. Proline ameliorates arginine deficiency during enteral but not parenteral feeding in neonatal piglets. Am J Physiol. 1999;277:E223eE231. 40. Williams NH, Stahly TS, Zimmerman DR, et al. Effect of chronic immune system activation on the rate, efficiency, and composition of growth and lysine needs of pigs fed from 6 to 27 kg. J Anim Sci. 1997;75:2463e2471.

41. Gallois M, Rothkotter HJ, Bailey M, et al. Natural alternatives to in-feed antibiotics in pig production: can immunomodulators play a role? Animal. 2009;3: 1644e1661. 42. Moeser AJ, Klok CV, Ryan KA, et al. Stress signaling pathways activated by weaning mediate intestinal dysfunction in the pig. Am J Physiol. 2007;292: 173eG181. 43. Torrey S, Toth TELM, Widowski TM, et al. Effect of drinker type on water intake and waste in newly weaned piglets. J Anim Sci. 2008;86:1439e1445. 44. Adeola O, Mahan DC, Azain MJ, et al. Dietary lipid sources and levels for weanling pigs. J Anim Sci. 2013;91:4216e4225. 45. Berrocoso JD, Salda~ na B, Serrano MP, et al. Influence of crude protein content, ingredient complexity, feed form, and duration of feeding of the Phase I diets on productive performance and nutrient digestibility of Iberian pigs. J Anim Sci. 2013;91:1237e1246. 46. Cinq-Mars D, Goulet G, Brisson GJ, et al. Response to piglets to suboptimal protein diets supplemented with lysine, methionine, thereonine and tryptophan. Can J Anim Sci. 1988;68:311e313. 47. Pluske JR, Kerton DJ, Cranwell PD, et al. Age, sex, and weight at weaning influence organ weight and gastrointestinal development of weanling pigs. Aust J Agric Res. 2003;54:515e527. 48. Halas D, Heo JM, Hansen CF, et al. Organic acids, prebiotics and protein level as dietary tools to control the weaning transition and reduce post-weaning diarrhoea in piglets CAB Rev. Perspect Agric Vet Sci Nutr Nat Resour. 2007;2:13. 49. Jeaurond EA, Rademacher M, Pluske JR, et al. Impact of feeding fermentable proteins and carbohydrates on growth performance, gut health and gastrointestinal function of newly weaned pigs. Can J Anim Sci. 2008; 88:271e281. 50. Wu G. Principles of Animal Nutrition. Boca Raton: CRC Press; 2018. 51. Nyachoti CM, Omogbenigun FO, Rademacher M, et al. Performance responses and indicators of gastrointestinal health in early-weaned pigs fed low-protein amino acid-supplemented diets. J Anim Sci. 2006;84: 125e134. 52. Spring S, Shili C, Pezeshki A, et al. Effect of low protein diets with or without supplemented synthetic amino acids on growth performance of nursery pigs. J Anim Sci. 2018;96:75. 53. Heo JM, Kim JC, Hansen CF, et al. Effects of feeding low protein diets to piglets on plasma urea nitrogen, fecal ammonia nitrogen, the incidence of diarrhea and performance after weaning. Arch Anim Nutr. 2008;62:343e358.

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54. Opapeju FO, Rademacher M, Nyachoti CM, et al. Effect of dietary crude protein level on jejunal brush border enzyme activities in weaned pigs. Arch Anim Nutr. 2009;63:455e466. 55. Surek D, Santos SA, da Rocha C, et al. Pelleting of diet for weaning pigs of different initial weights. Pelleting of diet for weaning pigs of different initial weights Peletizaç~ ao da dieta para leit~ oes recem-desmamados com diferentes pesos iniciais. Anim Prod. 2017:1678e4596. 56. Demissew A, Temesgen K, Meresa A, et al. Evaluation of anti-nutritional factor reduction techniques for triticale improved utilization system in Amhara region. J Food Process Technol. 2017;8:681. 57. Woyengo TA, Beltranena E, Zijlstra RT, et al. Nonruminant nutrition symposium: controlling feed cost by including alternative ingredients into pig diets: a review. J Anim Sci. 2014;92:1293e1305. 58. Merriman LA, Walk CL, Parsons CM, et al. Effects of tallow, choice white grease, palm oil, corn oil, or soybean oil on apparent total tract digestibility of minerals in diets fed to growing pigs. J Anim Sci. 2016;94: 4231e4238. 59. Weng RC. Dietary fat preference and effects on performance of piglets at weaning. Asian-Australas J Anim Sci. 2017;30:834e842. 60. Tusnio A, Taciak M, Barszcz M, et al. Effect of replacing soybean meal by raw or extruded pea seeds on growth performance and selected physiological parameters of the ileum and distal colon of pigs. PLoS One. 2017;12:1e6. 61. Stein HH, Benzoni G, Bohlke RA, et al. Assessment of the feeding value of South Dakota-grown peas (Pisum sativum L.) for growing pigs. J Anim Sci. 2004;82: 2568e2578. 62. Liu SY, Selle PH, Cowieson AJ, et al. Strategies to enhance the performance of pigs and poultry on sorghum-based diets. Anim Feed Sci Technol. 2013;181: 1e14. ~ ez JL, Beltranena E, Cervantes M, et al. Effect of 63. Y an phytase and xylanase supplementation or particle size on nutrient digestibility of diets containing distillers dried grains with solubles cofermented from wheat and corn in ileal-cannulated grower pigs1. J Anim Sci. 2011;89:113e123. 64. Morel PCH, Cottam YH. Effects of particle size of barley on intestinal morphology, growth performance and nutrient digestibility in pigs. Asian-Australas J Anim Sci. 2007;20:1738e1745. 65. Steinhart TL, Tokach MD, Derouchey JM, et al. Swine Feed Efficiency: Influence of Particle Size. 2012. 66. Heugten EV, Borg B, Lic MB, et al. Growing-finishing swine nutrient recommendations and feeding management. Ultrasound. 2010;1:1e17.

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67. Baker DH. Comparative nutrition and metabolism: explication of open questions with emphasis on protein and amino acids. Proc Natl Acad Sci USA. 2005;102: 17897e17902. 68. Bergen WG, Wu G. Intestinal nitrogen recycling and utilization in health and disease. J Nutr. 2009;139: 821e825. 69. Wu G. Amino acids: metabolism, functions, and nutrition. Amino Acids. 2009;37:1e17. 70. Hou YQ, Wu G. Nutritionally nonessential amino acids: a misnomer in nutritional sciences. Adv Nutr. 2017;8:137e139. 71. Wu G. Dietary requirements of synthesizable amino acids by animals: a paradigm shift in protein nutrition. J Anim Sci Biotechnol. 2014;5:34. 72. Kim SW, Wu G. Dietary arginine supplementation enhances the growth of milk-fed young pigs. J Nutr. 2004;134:625e630. 73. Mateo RD, Wu G, Moon HK, et al. Effects of dietary arginine supplementation during gestation and lactation on the performance of lactating primiparous sows and nursing piglets. J Anim Sci. 2008;86: 827e835. 74. Oresanya TF, Beaulieu AD, Patience JF, et al. Investigations of energy metabolism in weanling barrows: the interaction of dietary energy concentration and daily feed (energy) intake. J Anim Sci. 2008;86:348e363. 75. Kristensen M, Jensen MG. Dietary fibres in the regulation of appetite and food intake. Importance of viscosity. Appetite. 2011;56:65e70. 76. Azain MJ. Fat in Swine Nutrition. Southern ed. Boca Raton, FL: CRC Press LLC; 2001:95e106. 77. Torrallardona D, Salvad o R, Broz J, et al. The supplementation of low-P diets with microbial 6-phytase expressed in Aspergillus oryzae increases P and Ca digestibility in growing pigs. J Anim Sci. 2012;90:77e79. 78. Blaine J, Chonchol M, Levi M, et al. Renal control of calcium, phosphate, and magnesium homeostasis. Clin J Am Soc Nephrol. 2015;10:1257e1272. 79. Gonzalez-Vega JC, Walk CL, Murphy MR, et al. Requirement for digestible calcium by 25 to 50 kg pigs at different dietary concentrations of phosphorus as indicated by growth performance, bone ash concentration, and calcium and phosphorus balances. J Anim Sci. 2016;94:5272e5285. 80. Aparachita P, Carter SD, Cooper CV, et al. Determination of the efficacy of titrated levels of water soluble zinc Amino acid complex on growth performance of nursery Pigs. J Anim Sci. 2018;96:134. 81. Copeland KR, Scales DB, Hill GM, et al. Effects of vitamins E and D on performance and antioxidant enzymes in nursery pigs. J Anim Sci. 2017;95(Suppl. 2): 192.

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82. Apple JK, Rincker PJ, McKeith FK, et al. Review: metaanalysis of the ractopamine response in finishing swine. Prof Anim Sci. 2007;23:179e196. 83. Graham AB, Goodband RD, Tokach MD, et al. The interactive effects of high-fat, high-fiber diets and ractopamine HCl on finishing pig growth performance, carcass characteristics, and carcass fat quality. J Anim Sci. 2014;92:4585e4597. 84. Che TM, Perez VG, Song M, et al. Effect of rice and other cereal grains on growth performance, pig removal, and antibiotic treatment of weaned pigs under commercial conditions. J Anim Sci. 2012;90:4916e4924. 85. Graham AB, Goodband RD, Tokach MD, et al. The effects of low-, medium-, and high-oil distillers dried grains with solubles on growth performance, nutrient digestibility, and fat quality in finishing pigs. J Anim Sci. 2014;92:3610e3623. 86. Pettigrew JE, Moser RL. Fat in swine nutrition. In: Swine Nutrition. Stoneham, MA: ButterworthHeinemann; 1991:133e146. 87. Xu G, Baidoo SK, Johnston LJ, et al. Effects of feeding diets containing increasing content of corn distillers dried grains with solubles to grower-finisher pigs on growth performance, carcass composition, and pork fat quality. J Anim Sci. 2010;88:1398e1410. 88. Averette GLA, See MT, Hansen JA, et al. The effects of dietary fat sources, levels, and feeding intervals on pork fatty acid composition. J Anim Sci. 2002;80:1606e1615. 89. Hill GM, Link JE, Rincker MJ, et al. Utilization of distillers dried grains with solubles and phytase in sow lactation diets to meet the phosphorus requirement of the sow and reduce fecal phosphorus concentration. J Anim Sci. 2008;86:112e118. 90. Asmus MD, Derouchey JM, Tokach MD, et al. Effects of lowering dietary fiber before marketing on finishing pig growth performance, carcass characteristics, carcass fat quality, and intestinal weights. J Anim Sci. 2014;92:119e128. 91. Brooks PH, Carpenter JL. The water requirement of growing-finishing pigs: theoretical and practical considerations. In: Haresign W, Cole DJA, eds. Recent Advances in Animal Nutrition. Boston: Butterworths; 1990:115e136. 92. Mroz Z, Jongbloed AW, Lenis NP, et al. Water in pig nutrition: physiology, allowances and environmental implications. Nutr Res Rev. 1995;8:137e164. 93. Gonyou HW. Water use and drinker management: a review. In: Proceedings of the Annual Research Report, Prairie Swine Centre, Saskatoon, Saskatchewan, Canada. 1996:74e80. 94. Vermeer HM, Kuijken N, Spoolder HAM, et al. Motivation for additional water use of growing-finishing pigs. Livest Sci. 2009;124:112e118.

95. Patience JF. The importance of water in pork production. Anim Front. 2012;2:28e35. 96. Patience JF, Umboh R, Chaplin K, et al. Nutritional and physiological responses of growing pigs exposed to a diurnal pattern of heat stress. Livest Prod Sci. 2005;96: 205e214. 97. Babot DB, Hermida B, Balcells J, et al. Farm technological innovations on swine manure in Southern Europe. Rev Bras Zootec. 2011;40:334e343. 98. Foxcroft G, Beltranena E, Patterson J, et al. Physiological limits to maximizing sow productivity. In: London Swine Conference Proceedings. London, Ontario: Production at the Leading Edge; 2005:; vols. 29e46. 99. Southern LL, Olayiwola A, DeLange CFM, et al. Nutrient Requirements of Swine. Washington, DC: National Academic Press; 2012. 100. Whitney MH, Masker C. Replacement Gilt and Boar Nutrient Recommendations and Feeding Management. Des Moines, Iowa: U.S. Pork Center of Excellence; 2010. 101. Williams N, Patterson J, Foxcroft GR, et al. Nonnegotiables of gilt development. Adv Pork Prod. 2005; 16:1e9. 102. Gill P. Nutritional management of the gilt for lifetime productivity - feeding for fitness or fatness?. In: London Swine Conference Proceedings. vols. 83e99. 2007. London, Ontario: Today’s challenges.tomorrow’s opportunities. 103. Vignola M. Sow feeding management during lactation. In: London Swine Conference. vols. 107e17. 2009. Tools of the trade. 104. Quesnel H, Boulot S, Serriere S, et al. Post-insemination level of feeding does not influence embryonic survival and growth in highly prolific gilts. Anim Reprod Sci. 2010;120:120e124. 105. Johnston L. Gestating Swine Nutrient Recommendations and Feeding Management. Des Moines, Iowa, U.S: Pork Center of Excellence; 2010. National swine nutrition guide. 106. Moehn S, Franco D, Levesque C, et al. Phase Feeding for Pregnant Sows. Edmonton, Alberta, Canada: Swine Research and Technology Center Agriculture/Forestry Centre University of Alberta; 2012:4e10. 107. Young MG, Tokach MD, Aherne FX, et al. Comparison of three methods of feeding sows in gestation and the subsequent effects on lactation performance. J Anim Sci. 2004;82:3058e3070. 108. Klopfenstein C, Farmer C, Martineau GP, et al. Diseases of the mammary glands. In: Diseases of Swine. 9th ed. Blackwell Publishing; 2006:57e74. 109. Peltoniemi OA, Oliviero C, Halli O, et al. Feeding affects reproductive performance and reproductive endocrinology in the gilt and sow. Acta Vet Scand. 2007;49(Suppl. 1):S6.

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110. National Agricultural Statistic Service NASS. Agricultural Statistics Book by Year. Washington DC, USA: Government Printing Office; 2011. 111. Kauffold J, Gottschalk J, Schneider F, et al. Effects of feeding level during lactation on FSH and LH secretion patterns, and follicular development in primiparous sows. Reprod Domest Anim. 2008;43:234e238. 112. van den Brand H, Kemp B. Dietary fat and reproduction in the post partum sow. Soc Reprod Fertil. 2006; 62:177e189.

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113. Kemp B, Soede NM, Helmond FA, et al. Effects of energy source in the diet on reproductive hormones and insulin during lactation and subsequent estrus in multiparous sows. J Anim Sci. 1995;73:3022e3029. 114. Lewis A, Southern L, eds. Swine Nutrition. Boca Raton: CRC Press; 2001. 115. Leibbrandt VD, Johnston LJ, Shurson GC, et al. Effect of nipple drinker water flow rate and season on performance of lactating swine. J Anim Sci. 2001;79: 2770e2775.

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P A R T V

Poultry production

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C H A P T E R

18 Poultry genetics and breeding Giridhar Athrey Avian Genetics & Functional Genomics, Department of Poultry Science, Texas A&M University, College Station, TX, United States

O U T L I N E A brief history of poultry production

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Genetic improvement: progress and future directions Genetic improvement of performance Improvement of health and welfare traits Preparing for climate change

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Global challenges and opportunities Health and welfare challenges Sex selection in poultry

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A brief history of poultry production Over the last century, production of poultry species has seen astonishing gains around the world and played a major role in reducing global hunger. The emergence of high-performing commercial broiler chickens (meat-type chicken) and layer hens (egg-producers) varieties has expanded the availability of affordable, nutritious animal protein both in the developed world and in emerging economies. While overall

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00018-5

Land and water use, and waste production

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Future technologies in poultry breeding Genomic selection Gene editing & transgenic technologies

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Poultry genetics resources

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Summary

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References

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standardization and optimization of management and nutrition have improved performance, most of the performance gains can be ascribed to genetic and nutritional advances. Poultry meat and egg production have steadily assumed a larger fraction of global animal protein consumption. In the 1960s, the per capita poultry meat consumption in the United States was about 10 kg, but it is now over 50 kg.1 Global poultry meat production over this same period has risen from 8.92 million

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tons in the 1960s to 109.02 million tons in 2013.2 Including turkey, duck, and geese production and consumption adds 7 million tons to those figures. Similarly, the development of commercial egg laying breeds has resulted in the widespread availability and low cost of eggs. As of 2018, more than 90% of the world’s egg production comes from commercial layer varieties. These changes in performance are the result of the highly formulaic and structured nature of modern poultry production. This production model depends for its success on the three pillars of the industrial production models. 1. Genetics: Constant improvement of production breeds based on genetic selection accounts for over 85% of the gains (with nutrition optimized for these highperformance breeds) observed in broiler and layer varieties of poultry. 2. Consistent environment: The widespread adoption of climate-controlled barns, lighting regimens, precision nutritional formulations, and feeding regimens have tremendously improved the consistency of production of poultry, while also improving biosecurity. 3. Animal health: Development of vaccines, and therapeutics for various poultry diseases, and the application genome-enabled biotechnology tools has greatly improved animal health, and reduced the incidence of large scale outbreaks of disease. Over the long history of chickens as a domesticated species, the commercial production of broilers and layers is relatively new, originating within the last century. Only since the 1930s have hybrids (typically Barred Plymouth Rocks and Rhode Island Red Cross breeds) came to dominate commercial broiler production.3 The early years of commercial poultry production also saw major shifts in traits of broiler chickens that were not focused entirely on performance. For example, the white plumage that is now common in all commercial broilers became

common in the 1950s, due to the increased cost of dressing darker feathered birds. However, achieving this entirely cosmetic trait was not easy, as genetic loci for dominant white plumage was not compatible with rapid growth traits. The rate of growth and, driven mostly by the feed conversion ratio has been the most important driver of broiler selection programs. In the period between 1960 and 2010, the growth rate increased at an annual rate or 1.6% and that was accompanied by an improvement in the feed conversion ratio.4 However, these improvements have exacted a toll on various health and welfare traits of chickens. Modern broilers grow so rapidly in their short life (7e8 weeks) that their legs are unable to support their weight and there are also various metabolic disorders. While the origins of these problems in broilers were not clearly understood earlier, an improved understanding of quantitative genetics revealed that intense selection for a single trait (e.g., feed conversion ratio) can alter other traits that are linked through pleiotropy.5,6 In layers, social traits such as aggression and cannibalism may contribute to the heritability of high mortality.7 Due to the social contribution to expression of traits, group selection has been proposed as a method to simultaneously consider multiple traits, including behavioral traits.8e10 Therefore, selection strategies must consider multiple traits, including behavioral and welfare traits that consider differences among rearing environments.

Genetic improvement: progress and future directions As of 2018, poultry meat production has seen consistent increases averaging 1.6% per year over the previous decade. In 2018, poultry meat production increased to 122.5 metric tons over the previous year11 while per capita meat consumption increased by 0.6 kg over the same period. The United States led the world

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Genetic improvement: progress and future directions

in poultry meat production (20.4 million tonnes in 2014), whereas China led the world in poultry livestock count (5.58 billion birds). China also led the world in consumption of poultry meat.2,12 However, per capita consumption was highest in the United States (and North America in general) at 45 kg. Total poultry meat production is expected to increase 17e20% over the next decade to keep up with increasing consumption. Only 10% of poultry meat comes from non-chicken sources (e.g., turkey, ducks, quail). In terms of egg production, global production increased 18% over the decade ending in 2016.11 China was by far the greatest producer of eggs, at 550 billion eggs per year. That is over five times greater than the next highest producer, the United States. Even in the egg production space, chickens are the main source of eggs, with other sources (Ducks, Quail, Turkey etc.) contributing about 14% to total egg production. As the production and consumption of poultry products increases globally, there is an increasing emphasis not just production traits, but also health and welfare traits. Health traits, particularly gut health, are emerging foci due to the ban on antibiotic usage in animal production systems in Europe and in the United States. Genetic changes in modern poultry breeds have been the most important contributor to the improvement of production traits in poultry. It is estimated that, with adequate nutritional support, selective breeding is responsible for at least 85% of the improvements in performance since the 1950s.13,14 The majority of this improvement - both in broilers and layers - has resulted from improvements in feed conversion ratio. This is notable, as the ultimate goals of broiler and layer production systems are quite divergent. Profitability of broiler production depends on fast growth to produce heavy birds, whereas egg production per hen is considered the most important trait in layer operations. In both sectors, selection has improved feed conversion at an annual average of 1.6% over the past three decades.

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Genetic improvement of performance It is commonly stated in the scientific literature that broilers are selected for fast growth rate. However, growth rate itself is not the target of selection. Rather, body weight at the desired age, which is also correlated with feed conversion ratio (FCR) is the trait under selection. This trait has long been a target of selection due to the ease of measurement, high heritability, and observable impact on meat production. However, the emphasis on improving FCR has resulted from our understanding of how dietary energy is partitioned between fat and protein in broilers.15 Unfortunately, the emphasis on weight gain is responsible for several metabolic disorders such as ascites,16 heart disease,10,17 and wooden breast disorder.18e22 The quest for lean muscle mass (and thus requirements for amino acids) adversely affected development of the respiratory and vascular systems and, in turn, has engendered various skeletal and metabolic disorders in broilers.10 In layers, performance at the hen level has been the main focus of selection, and FCR has been the main driver of improvements in laying capacity.14,23 However, the diversification (1980s) of the egg market into processed egg products has focused some selection on traits like percentage of lipids and solids in the egg, in addition to egg size. While increasing egg yield is related to FCR, egg weight and body weight also influence the trait. Hence, poultry geneticists for primary laying hen breeders have also focused on residual feed intake. This method can help to improve FCR independent of selection for body weight.

Improvement of health and welfare traits While selection for resistance to disease sounds like a critical, and mandatory part of any selection scheme, finding the balance between disease resistance and performance has been difficult in the poultry industry. Major

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infectious diseases that cause economic losses on an annual basis are Marek’s disease,24e26 Newcastle Disease,27,28 Avian Flu,29,30 and Infectious Bursal Disease (IBD).31,32 Other infectious agents which are significant from a food safety perspective are Salmonella and Escherichia;33e37 and resistance to these two agents has been the target of selection from time to time. Due to these various causes of mortality and contributors to food safety, disease resistance in poultry has been intensively studied to identify the genes and alleles associated with disease resistance. Selection for increased immune response has always been of importance in poultry breeding; however, once vaccinations and other biotechnological solutions became available, selection for that trait has been de-emphasized. Generally speaking, disease resistance traits have a low heritability (10%), and the variability in resistance among individuals is not considered economically significant or a high priority in breeding schemes. Breeding for improved resistance traits is typically intensified when other means of combating diseases are not economical or effective. Also, while resistance alleles have been identified in experimental lines of poultry, it is not always clear if these alleles can be selected for in commercial populations without affecting performance traits. Finally, the rapid evolution of infectious agents (such as the Marek’s disease virus) renders conventional breeding approaches less effective over the longer term.25,26 Ultimately, changes in governmental regulations (e.g., ban on antibiotic usage) may dictate the basis for taking one strategy over others.38e41 Therefore, resistance mechanisms need to be investigated and genes associated with resistance traits must continue to be discovered. Furthermore, this knowledge needs to be taken into consideration of selection schemes to prevent a reduction in resistance to disease, even if increased resistance is not the primary goal. Due to the longer lifespan of layers, compared to broilers, various health and welfare traits are

important targets of selection. At present, most egg production in the U.S. is slated to move toward completely cage-free rearing systems by 2025. This follows several countries where alternate rearing systems have always been a significant part of the production system.42e44 However, in these rearing environments, behavioral issues such as feather pecking, and associated cannibalism are problematic. Another long-standing welfare problem which has recently become urgent in the minds of U.S. egg producers is the incidence of keel bone damage in cage-free systems. The move from cage to cage-free systems has meant that modern-day layers (much heavier than a few decades ago) experience a high incidence of painful keel bone fractures due to a variety of behavioral issues such as fear and flightiness, leading to collisions.45e47 The damage resulting from collisions are probably exacerbated by a weaker flight musculature, weak bones, and perhaps altered bone metabolism in these laying breeds with high levels of egg production.

Preparing for climate change The 2007 report by the Intergovernmental Panel on Climate Change stated “with high confidence that anthropogenic warming over the last three decades has had a discernible influence on many physical and biological systems.” A related FAO report pointed to livestock production as a major contributor to greenhouse gas emissions.48 Although there is no doubt that agricultural production needs to ramp up to supply the demands for food production in the future, the agricultural sector is mostly unprepared for a warmer climate in the future. Even modest (1e2
C) increases in average temperature are likely to generate tremendous instability across the production chain, starting with feed production. It is difficult to anticipate all challenges that will emerge from climate change, but heat stress is one consequence that is

V. Poultry production

Global challenges and opportunities

assured. Heat stress has long been an issue in poultry production due to the density of poultry in current housing systems. Broilers, which have an exceptionally high metabolic rate, but a poor ability to dissipate heat, which contributes to heat stress.49,50 Furthermore, heat stress can affect fertility and various other performance traits.51,52 Considering the existing challenges with heat stress which are likely to be exacerbated with climate warming, there are increasing calls to ameliorate this condition, including efforts, through breeding, to obtain thermotolerant breeds of poultry,53,54 especially in tropical countries.

Global challenges and opportunities Health and welfare challenges Livestock species pose a threat to human health indirectly as sources of zoonotic diseases. Poultry is among the significant sources of human disease given the intensity and frequency of contact between humans and birds. Recurrent pandemics such as avian influenza (e.g., recent H5N1 outbreaks since 1997) arise through poultry farms. These outbreaks are also devastating to poultry production: in 2015, avian influenza outbreak resulted in the mass euthanasia of approximately 35 million laying hens and approximately six million pullets in the United States (Egg Industry Center, 2015). Dobrowolska and Brown55 showed that the 2015 outbreak could have caused a spike in egg prices due to a 11% decline in supply. Breeding for resistance to avian influenza, or genetic engineering to develop resistant strains is one of the most critical and necessary priorities to sustain and protect poultry production and generate genetic stocks to supply poultry products to meet demands of humanity in the future. In 2019, researchers at the Roslin Institute in Scotland announced the creation of a gene edited chicken strain resistant to contracting avian influenza

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from wild birds. The demonstration of the feasibility and safety of such technologies at large scale is necessary before they become a clearly viable option in poultry breeding. Poultry welfare is another area that has undergone dramatic change in the last two decades. Poultry welfare is a legislative concern in the European Union, but is an emerging challenge on a global scale. Some estimates put egg production from industrial systems at greater than 60%, with 90% of hens today housed in cages.56 However, this percentage is lower in the EU (57%). In cage-free systems, however, fear, aggression and other unfavorable behaviors exact a toll on productivity. In Europe, beak trimming is used to mitigate pecking and mortality, which adds to the costs, whereas in the US and Brazil costs are lower due to the absence of regulations on beak trimming. On a global front, it might be prudent for primary breeders to create breeds selected for cage-free environments, wherein pecking and flightiness are mitigated. Alternatively, future solutions may include gene edited strains of poultry for which requirements for beak trimming are reduced or eliminated without compromising feeding and other natural behaviors. Carlson et al.57 used gene editing to produce hornless cattle to eliminate the need for mechanical de-horning (polling). This method solves an animal welfare issue without affecting either performance or health of the animal, and potentially suggests a way to incorporate welfare traits into poultry of the future.

Sex selection in poultry A major challenge facing the poultry industry in the short term is how to determine the sex of layer chicks pre-hatch. In the layer industry the males are culled as soon as they are sexed, as males do not contribute to egg production. While this practice has been in place for a long time, it became a significant issue after Germany

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banned the culling of males. Subsequently, the U.S. Egg Union decided to stop culling males by 2020. This has created an enormous impetus to discover/identify methods for sexing birds before hatch.58,59 While sexing pre-hatch (using DNA based methods) itself is not a technical challenge, doing so without damaging the developing embryo is the primary issue. Several groups have proposed solutions ranging from gene editing to detection of sex-related molecules, but a scalable, economical solution remains elusive.

Land and water use, and waste production Due to the predominant reliance on cereal grains for production of poultry feed, poultry production has the largest land footprint for feed production, requiring an estimated 93 million ha in 201060 with 74 million ha in nonOECD countries and 19 million ha in OECD countries. Altogether, 44% of the global land area used for livestock production supplies poultry production alone. As land suitable for agriculture is not evenly distributed, there is an over-reliance, and hence over-exploitation of natural resources in some geographical regions than other. On the other hand, the water footprint of broilers and layers is the lowest among all livestock production.12 As poultry production around the world has turned to concentrated industrial complexes, manure and effluvium from production plants draw concerns about air quality or contamination of water bodies. Poultry diets are rich in protein and other nitrogen-containing materials. While poultry species are generally more efficient at converting dietary amino acids to animal protein (compared to ruminants), much of the ingested nitrogen is excreted,61 adding to the environmental impact concerns of industrial poultry production. These concerns, especially in countries where land for agriculture is limited will pose challenges for the sustainability of current production models. Improvements in feed conversion will remain a

priority as feedstuffs become more expensive or new and alternate feed ingredients will become more prominent. Future selection programs may need to consider how performance can be sustained with large changes in diet formulations. Similarly, reductions in manure or ammonia may become priorities dictated by social opinions. In cattle, microbiome engineering has been touted as an approach to reduce greenhouse gas emissions. While microbiome engineering in poultry has thus far remained in the domain of promoting health, this may change as the global human population increases and environmental quality concerns becomes a priority.

Future technologies in poultry breeding Genomic selection Since genetic markers became available as a selection tool, the field has seen several waves of progress applicable to livestock breeding. Soon after the chicken genome was assembled,62 new knowledge of SNP variants across the chicken genome was used immediately in breeding applications. Some of the early SNP panels were proprietary in nature, and it was not until 2013 that a commercially available panel (with 600k loci) become available63 for screening populations and using the data to make selection decisions. On the application side, QTL mapping and Marker Assisted Selection (MAS) never fully realized their potential in poultry breeding due to the high initial costs of generating that information, and the low value of the breeding individuals. Only recently, with the declining cost of the Affymetrix 600k array and low-cost whole genome sequencing, has advanced methods of marker-based selection become attractive in poultry breeding. Recent approaches, like genomic prediction, use genome-wide variation to get the Estimated Breeding Values (EBVs).64e67

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Future technologies in poultry breeding

Genomic prediction of the merit of potential breeders requires a training population whereas the entire population is genotyped and thoroughly phenotyped.68e70 Individuals in the training population are genotyped at a large number of markers, preferably over 10,000 single nucleotide polymorphisms (SNPs) spread across the genome. The training data are used to develop a model to predict breeding values based on SNP genotypes, and this model is used to predict breeding values of the subsequent generation(s). Genomic selection has not been extensively used in poultry because of the large number of potential breeders, and the low value of individual candidates compared to the cost of generating genomewide SNP data. Recently, it has been shown that genomic selection can be useful in shortening the generation interval in breeding layer hens.64,71,72 In broilers, which have short generation intervals, there is hope for improving the accuracy of EBV. However, in both broilers and layer, the improvements in poultry production are not expected to be as great as in cattle breeding, where generation intervals are much longer. The greatest advantage of genomic selection is in improving the accuracy of selection at an early age, before sexual maturity. This was first realized in dairy cattle breeding programs where reduced generation intervals, more accurate selection of young animals, and reduced costs were achieved.73 Genomic selection in poultry, although is in the early stages, is being applied in multiple layer and broiler breeding programs. With the sequencing and annotation of the Turkey genome,74,75 genomic selection in turkeys is likely to be implemented soon. Similarly, ducks, which have historically been a major source of food in Asia will likely benefit from the sequencing and assembly of their genomes.76 Additionally, new statistical models developed for large scale genotype data may provide additional value for poultry breeders.71,77,78

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New sources of genomic information and opportunities for genome-assisted selection come from the constantly evolving landscape of genome sequencing technologies. With the plummeting costs of whole-genome resequencing (e.g., using the Illumina NovaSeq), and emerging technologies to sequence long molecules such as the Oxford Nanopore platform,79e81 it is becoming ever easier to generate whole genome information for multiple individuals at reduced costs. A variation on the theme, potentially well suited for genotyping large populations, is genotype-by-sequencing (GBS).82,83 A major advantage of GBS over SNP arrays is the ability to discover new variation, instead of relying only on known variants. This includes identifying structural variants which are potentially important for animal breeding, but still poorly characterized in poultry species.84

Gene editing & transgenic technologies Gene editing, which allows specific changes to be made directly to the genome of a target organism, is one of the most interesting and promising technologies of the century, and of high relevance to animal breeding.85,86 The small changes made using editing approaches can have large cumulative consequences, potentially to improve performance, enhance disease resistance, increase feed efficiency, and decrease the environmental footprint of livestock operations.87 The most recent toolset for gene editing is the CRISPR-Cas system. This system is based on the CRISPR-Cas adaptive immune system found in a number of bacterial and archaeal species.88e90 The approach uses small non-coding RNAs to guide the Cas9 nuclease to a target site in the eukaryotic genome, where the nuclease cleaves the double-stranded DNA target. The guide RNA contains a 20-nucleotide motif complementary to the target locus, with the target locus

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typically adjacent to a 3-nucleotide sequence termed the protospacer adjacent motif (PAM). While this tool is highly flexible and promising for targeted editing, it shares some limitations with previous generations of genome editing tools, such as TALENS,91,92 especially when targeting the avian germline. Most of the challenges are of a logistical nature - due to the difficulty of accessing the early avian zygote in the hen, and subsequently supporting the developing embryo post-injection/transfection.85 However, recent reports of success in modifying both the somatic and germ cell lineages in chickens indicate that this technology has high potential for application in the poultry industry. Oishi et al.93 targeted the OVA and ovomucoid (OVM) loci, both albumen proteins, using CRISPR/Cas9 vectors. Dimitrov et al.94 were the first to report homology-directed repair in chickens using CRISPR/Cas9. While these and other cases of successful gene editing have clearly established the chicken as a model for studying developmental biology or immunity,95 their potential application in production is less obvious. Chickens represent a massive segment of the global agricultural economy, but the strains of production at this scale are starting to reveal its limits. The phenotypic costs of performance gains (weak musculoskeletal system, metabolic disorders, welfare problems, meat quality issues) are becoming increasingly problematic.23,96 More worrying from a sustainability perspective is that models predict the potential for artificial selection to improve feed efficiency are reaching their limits.97 Furthermore, as a consequence of intensive selection over numerous generations, it is estimated that commercial poultry species (chicken, turkeys) have lost over 50% of their genetic diversity.98e100 Those breeds may no longer respond to selection in the absence of adaptive genetic variation. All these factors provide a strong rationale for utilizing gene editing approaches where feasible in poultry production. Genome editing can provide an

alternative, or at the very least a subsidiary benefit, via alteration of the genome to introduce novel variation or to introduce new traits without selection. Recent work on a gene edited chicken strain resistant to avian flu is a highly promising application that has broad appeal from both a production and a human health perspective. Such applications may be crucial in determining the adoption of genome editing in poultry breeding. Primary breeding companies remain unconvinced of the immediate prospects of gene editing in poultry breeding (Gene Editing Special Symposium, Plant & Animal Genomics conference 2018). Various considerations from the industry perspective remain to be answered before moving forward with gene editing in poultry. These considerations were recently well summarized in a commentary by Janet Fulton.87 They include uncertainty about: a) which traits would be influenced; b) which genes or specific genomic regions should be targeted to influence these traits; c) will the consumer pay extra for chickens with performance enhanced by gene editing; and d) will there be consumer acceptance of the final product? Ultimately, as with most changes in the poultry industry, widespread adoption of gene editing technology may depend on consumer awareness of global constraints on poultry production, and their familiarity and comfort with gene editing. In the meantime, the poultry genetic community needs to test the limits and pitfalls of the technology, and build a foundation based on experimental evidence, and actively prevent misinformation.

Poultry genetics resources Historically, the primary breeders maintained reserve stocks of heritage breeds for their own breeding programs, however the convergence of breeding companies on a few breeds that

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Poultry genetics resources

make up their successful products has resulted in a thinning of heritage-based genetic diversity.101,102 According to a 2005 report by the Food and Agriculture Organization (FAO), over 50% of poultry breeds are at risk of becoming extinct.103 The concept of extinction is not something that may appear immediately applicable to domesticated species, but the global diversity of poultry breeds and the genetic diversity contained within is a finite resource. The preservation of domesticated avian species, breeds, and phenotypes is crucial for meeting current demand, as well as unknown future demands. While the data on breeds from across the world is incomplete, European and North American breeds are most threatened, as they are increasingly replaced by commercial poultry breeds. However, this view may be skewed due to the incomplete record of the world’s native breeds. Some estimates put this number around 7,000 breeds worldwide. Some have argued that as modern poultry breeding is dominated by U.S. and Europeanbased poultry companies, the genetics of commercial chickens is based on Western breeds such as the Cornish and Plymouth Rock, and Rhode Island Red.101,103 That, in turn, has resulted in relatively less knowledge about breeds native to various regions. However, as performance or health problems have emerged in commercial broiler or layer strains, there has been renewed interest in understanding the genetic basis of traits found in native breeds. For example, the Egyptian Fayoumi has shown higher resistance to infections such as Newcastle Disease.104,105 Similarly, introgression of the naked-neck trait (which originated in Malaysian chickens, and improves thermal tolerance) has also taken on particular relevance to modern poultry breeding.106e108 Even as the genetic diversity of most Asian and African breeds remain unexplored, the nature of the poultry breeding economy results in the flow of commercial genetic material from developed nations to emerging economies. In emerging

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economies, the rule of economics dictates that commercial breeds with a performance advantage will be favored over local breeds, which may eventually lead to the loss of local breeds.109 For example, in the three years following the removal of restrictions on international poultry breeding companies from operating in Finland, the local laying hen breeding operations closed completely.110 Concerns about the preservation of indigenous breeds and germplasm have been expressed for over two decades,103,111 but despite various independent efforts to do so,112e119 there are few standardized approaches or consortia that are addressing this issue directly. The cataloging and preservation of indigenous breeds and wild relatives are likely to emerge as a crucial issue in the near future. The strain on global agriculture to produce sufficient feed ingredients, increasing population and demand, and climate change are all expected to challenge current domestic animal production models.120 Loss of indigenous and local breeds is an issue facing all segments of agriculture. In domesticated crops, the discovery of genetic diversity from wild relatives for crop improvement is a key way forward to battle the dual threats of disease and climate change.121 Seed banks and other cryopreservation approaches are being implemented for plants. For poultry, further loss of genetic diversity may be stemmed through the application of various cryopreservation strategies, wherein storage of stem cells or tissue from specialized breeds will allow integration of their genetics into future poultry lines, should their traits become commercially valuable.122,123 Some researchers have also promoted an agroecological model for future poultry production, which argues against standardized breeds for global use, and for the use of local breeds that are adapted to regional feeds, climate, and pathogenic challenges.124 If these approaches gain traction on a global scale, that would slow down the rate of gene diversity lost.

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Summary The poultry industry has witnessed a century of the most astonishing gains in performance. Over the past 50 years, the first gains from directional selection for performance were solidified by the adoption of modern statistical genetics approaches, and the incorporation of molecular and biotechnology tools. As the world enters a period of growing population, increasing ecological disturbances, diminishing natural resources, and perhaps political and economic instability, there are new challenges and opportunities for the poultry industry. On the one hand there are various issues with the existing genetic stocks that threaten long-term sustainability of the current production model. On the other hand, there are new and emerging threats for which poultry may be uniquely suited to adapt to, but as yet unprepared for such threats. The emergence of new genomic approaches and tools may hold the key to future progress, but consumers will likely determine the shape and direction of how the poultry industry responds and develops.

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54. Naga Raja Kumari K, Narendra Nath D. Ameliorative measures to counter heat stress in poultry. World’s Poult Sci J. 2018;74:117e130. 55. Dobrowolska A, Brown S. The Economic Impact of the 2015 Avian Influenza. St. Louis: University of Missouri; 2016. 56. Windhorst HW. Dynamics and Patterns in EU and USA Egg and Poultry Meat Production and Trade. 2017. 57. Carlson DF, Lancto CA, Zang B, et al. Production of hornless dairy cattle from genome-edited cell lines. Nat Biotechnol. 2016;34:479e481. 58. Weissmann A, Reitemeier S, Hahn A, Gottschalk J, Einspanier A. Sexing domestic chicken before hatch: a new method for in ovo gender identification. Theriogenology. 2013;80:199e205. 59. Galli R, Preusse G, Schnabel C, et al. Sexing of chicken eggs by fluorescence and Raman spectroscopy through the shell membrane. PLoS One. 2018;13:e0192554. 60. Mottet A, Tempio G. Global poultry production: current state and future outlook and challenges. World’s Poult Sci J. 2017;73:245e256. 61. Ndegwa PM, Hristov AN, Arogo J, Sheffield RE. A review of ammonia emission mitigation techniques for concentrated animal feeding operations. Biosyst Eng. 2008;100:453e469. 62. International Chicken Genome Sequencing Consortium. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature. 2004;432:695e716. 63. Kranis A, Gheyas AA, Boschiero C, et al. Development of a high density 600K SNP genotyping array for chicken. BMC Genomics. 2013;14:59. 64. Wolc A, Stricker C, Arango J, et al. Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model. Genet Sel Evol. 2011;43:5. 65. Daetwyler HD, Calus MPL, Pong-Wong R, de Los Campos G, Hickey JM. Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking. Genetics. 2013;193:347e365. 66. Calus MPL. Genomic breeding value prediction: methods and procedures. Animal. 2010;4:157e164. 67. Clark SA, van der Werf J. Genomic best linear unbiased prediction (gBLUP) for the estimation of genomic breeding values. Methods Mol Biol. 2013; 1019:321e330. 68. Jonas E, Fikse F, R€ onnegård L, Mouresan EF. Genomic selection. In: Rajora OP, ed. Population Genomics: Concepts, Approaches and Applications. Population Genomics. Cham: Springer International Publishing; 2019:427e480.

69. Stock KF, Reents R. Genomic selection: status in different species and challenges for breeding. Reprod Domest Anim. 2013;48(Suppl. 1):2e10. 70. Tan C, Bian C, Yang D, Li N, Wu Z-F, Hu X-X. Application of genomic selection in farm animal breeding. Yi Chuan. 2017;39:1033e1045. 71. Wolc A, Kranis A, Arango J, et al. Implementation of genomic selection in the poultry industry. Anim Front. 2016;6:23e31. 72. Wolc A, Zhao HH, Arango J, et al. Response and inbreeding from a genomic selection experiment in layer chickens. Genet Sel Evol. 2015;47:59. 73. Schefers JM, Weigel KA. Genomic selection in dairy cattle: integration of DNA testing into breeding programs. Anim Front. 2012;2:4e9. 74. Dalloul RA, Long JA, Zimin AV, et al. Multi-platform next-generation sequencing of the domestic turkey (Meleagris gallopavo): genome assembly and analysis. PLoS Biol. 2010;8. 75. Dalloul RA, Zimin AV, Settlage RE, Kim S, Reed KM. Next-generation sequencing strategies for characterizing the turkey genome. Poult Sci. 2014;93:479e484. 76. Huang Y, Li Y, Burt DW, et al. The duck genome and transcriptome provide insight into an avian influenza virus reservoir species. Nat Genet. 2013;45:776e783. 77. Fernando RL, Cheng H, Golden BL, Garrick DJ. Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals. Genet Sel Evol. 2016;48:96. 78. Zhang X, Misztal I, Heidaritabar M, et al. Prior genetic architecture impacting genomic regions under selection: an example using genomic selection in two poultry breeds. Livest Sci. 2015;171:1e11. 79. Tan MH, Austin CM, Hammer MP, Lee YP, Croft LJ, Gan HM. Finding Nemo: hybrid assembly with Oxford Nanopore and Illumina reads greatly improves the clownfish (Amphiprion ocellaris) genome assembly. GigaScience. 2018;7:1e6. 80. Jain M, Koren S, Miga KH, et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat Biotechnol. 2018;36:338e345. 81. Tyson JR, O’Neil NJ, Jain M, Olsen HE, Hieter P, Snutch TP. MinION-based long-read sequencing and assembly extends the Caenorhabditis elegans reference genome. Genome Res. 2018;28:266e274. 82. Nielsen R, Korneliussen T, Albrechtsen A, Li Y, Wang J. SNP calling, genotype calling, and sample allele frequency estimation from New-Generation Sequencing data. PLoS One. 2012;7:e37558.

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83. Gorjanc G, Cleveland MA, Houston RD, Hickey JM. Potential of genotyping-by-sequencing for genomic selection in livestock populations. Genet Sel Evol. 2015;47:12. 84. Bickhart DM, Liu GE. The challenges and importance of structural variation detection in livestock. Front Genet. 2014;5:37. 85. Woodcock ME, Idoko-Akoh A, McGrew MJ. Gene editing in birds takes flight. Mamm Genome. 2017;28: 315e323. 86. Gonen S, Jenko J, Gorjanc G, Mileham AJ, Whitelaw CBA, Hickey JM. Potential of gene drives with genome editing to increase genetic gain in livestock breeding programs. Genet Sel Evol. 2017;49:3. 87. Fulton JE. Commentary: application of gene editing in the commercial Poultry production industry. Poult Sci. 2018;97:3007e3008. 88. Ran FA, Hsu PD, Wright J, Agarwala V, Scott DA, Zhang F. Genome engineering using the CRISPRCas9 system. Nat Protoc. 2013;8:2281e2308. 89. Jinek M, East A, Cheng A, Lin S, Ma E, Doudna J. RNA-programmed genome editing in human cells. eLife. 2013;2:e00471. 90. Doudna JA, Charpentier E. Genome editing. The new frontier of genome engineering with CRISPR-Cas9. Science. 2014;346:1258096. 91. Park TS, Lee HJ, Kim KH, Kim J-S, Han JY. Targeted gene knockout in chickens mediated by TALENs. Proc Natl Acad Sci USA. 2014;111:12716e12721. 92. Gaj T, Gersbach CA, Barbas CF. ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering. Trends Biotechnol. 2013;31:397e405. 93. Oishi I, Yoshii K, Miyahara D, Kagami H, Tagami T. Targeted mutagenesis in chicken using CRISPR/Cas9 system. Sci Rep. 2016;6:23980. 94. Dimitrov L, Pedersen D, Ching KH, et al. Germline gene editing in chickens by efficient CRISPRmediated homologous recombination in primordial germ cells. PLoS One. 2016;11:e0154303. 95. Sid H, Schusser B. Applications of gene editing in chickens: a new era is on the horizon. Front Genet. 2018;9:456. 96. Hocking PM. Developments in poultry genetic research 1960-2009. Br Poult Sci. 2010;51(Suppl. 1): 44e51. 97. Tallentire CW, Leinonen I, Kyriazakis I. Artificial selection for improved energy efficiency is reaching its limits in broiler chickens. Sci Rep. 2018;8:1168. 98. Aslam ML, Bastiaansen JW, Megens H-J, et al. Genome-wide candidate regions for selective sweeps revealed through massive parallel sequencing of DNA across ten turkey populations. BMC Genet. 2014;15:117.

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99. Whyte J, Glover JD, Woodcock M, et al. FGF, insulin, and SMAD signaling cooperate for avian primordial germ cell self-renewal. Stem Cell Rep. 2015;5: 1171e1182. 100. Rathgeber BM, McCarron P, Budgell KL. Salmonella penetration through eggshells of chickens of different genetic backgrounds. Poult Sci. 2013;92:2457e2462. 101. Hillel J, Groenen MAM, Tixier-Boichard M, et al. Biodiversity of 52 chicken populations assessed by microsatellite typing of DNA pools. Genet Sel Evol. 2003;35:533e557. 102. Granevitze Z, Hillel J, Chen GH, Cuc NTK. Genetic diversity within chicken populations from different continents and management histories. Anim Genet. 2007;38(6):576e583. 103. Hoffmann I. Research and investment in poultry genetic resources e challenges and options for sustainable use. World’s Poult Sci J. 2005;61:57e70. 104. Wang Y, Lupiani B, Reddy SM, Lamont SJ, Zhou H. RNA-seq analysis revealed novel genes and signaling pathway associated with disease resistance to avian influenza virus infection in chickens. Poult Sci. 2014; 93:485e493. 105. Pinard-Van Der Laan MH, Monvoisin JL, Pery P, Hamet N, Thomas M. Comparison of outbred lines of chickens for resistance to experimental infection with coccidiosis (Eimeria tenella). Poult Sci. 1998;77: 185e191. 106. Khan DR, Wecke C, Sharifi AR, Liebert F. Evaluating the age-dependent potential for protein deposition in naked neck meat type chicken. Animals (Basel). 2015;5:56e70. 107. Pitel F, Berge R, Coquerelle G, et al. Mapping the naked neck (NA) and polydactyly (PO) mutants of the chicken with microsatellite molecular markers. Genet Sel Evol. 2000;32:73e86. 108. Chen CF, Huang NZ, Gourichon D, Lee YP, TixierBoichard M, Bordas A. Effect of introducing the naked neck gene in a line selected for low residual feed consumption on performance in temperate or subtropical environments. Poult Sci. 2008;87:1320e1327. 109. Tisdell C. Socioeconomic causes of loss of animal genetic diversity: analysis and assessment. Ecol Econ. 2003;45:365e376. 110. FAO. Domestic Animal Diversity Information System (DAD-IS) j Food and Agriculture Organization of the United Nations; 1987. http://www.fao.org/dad-is. 111. Bessei W. Preservation of Local Poultry Stocks. Colloques de l’ INRA (France) no 50. 1989. 112. Liao Y, Mo G, Sun J, Wei F, Liao DJ. Genetic diversity of Guangxi chicken breeds assessed with microsatellites and the mitochondrial DNA D-loop region. Mol Biol Rep. 2016;43:415e425.

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113. Mahammi FZ, Gaouar SBS, Laloë D, et al. A molecular analysis of the patterns of genetic diversity in local chickens from western Algeria in comparison with commercial lines and wild jungle fowls. J Anim Breed Genet. 2016;133:59e70. 114. Fathi MM, Al-Homidan I, Motawei MI, AbouEmera OK, El-Zarei MF. Evaluation of genetic diversity of Saudi native chicken populations using microsatellite markers. Poult Sci. 2017;96:530e536. 115. Abebe AS, Mikko S, Johansson AM. Genetic diversity of five local Swedish chicken breeds detected by microsatellite markers. PLoS One. 2015;10:e0120580. 116. Nunome M, Kinoshita K, Ishishita S, Ohmori Y, Murai A, Matsuda Y. Genetic diversity of 21 experimental chicken lines with diverse origins and genetic backgrounds. Exp Anim. 2018;68:77e93. 117. Erfan AM, Selim AA, Naguib MM. Characterization of full genome sequences of chicken anemia viruses circulating in Egypt reveals distinct genetic diversity and evidence of recombination. Virus Res. 2018;251:78e85. 118. Lien C-Y, Tixier-Boichard M, Wu S-W, Wang W-F, Ng CS, Chen C-F. Detection of QTL for traits related to adaptation to sub-optimal climatic conditions in chickens. Genet Sel Evol. 2017;49:39.

119. Pham M-H, Tran X-H, Berthouly-Salazar C, TixierBoichard M, Chen C-F, Lee Y-P. Monitoring of genetic diversity in Taiwan conserved chickens assessed by pedigree and molecular data. Livest Sci. 2016;184:85e91. 120. Godfray HCJ, Beddington JR, Crute IR, et al. Food security: the challenge of feeding 9 billion people. Science. 2010;327:812e818. 121. Zhang H, Mittal N, Leamy LJ, Barazani O, Song B-H. Back into the wild-apply untapped genetic diversity of wild relatives for crop improvement. Evol Appl. 2017;10:5e24. 122. Liu J, Cheng KM, Silversides FG. A model for cryobanking female germplasm in Japanese quail (Coturnix japonica). Poult Sci. 2013;92:2772e2775. 123. Delaney ME. Genetic diversity and conservation of poultry. In: Muir WM, Aggrey SE, eds. Poultry Genetics, Breeding and Biotechnology. 1st ed. Oxon, UK: CABI Publishing; 2003:26. 124. Phocas F, Belloc C, Bidanel J, et al. Review: towards the agroecological management of ruminants, pigs and poultry through the development of sustainable breeding programmes. II. Breeding strategies. Animal. 2016;10:1760e1769.

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C H A P T E R

19 Reproductive physiology of poultry Claire S. Stephens, Patricia A. Johnson Department of Animal Science, Cornell University, Ithaca, NY, United States

O U T L I N E Introduction

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Introduction The two types of commercial chickens that contribute meat and eggs to the American diet are broiler chickens and laying strains of chickens. In spite of the fact that most birds have only one ovary, commercial poultry species are quite prolific ovulators, with particularly high levels of productivity observed in the laying hen. Reproductive efficiency is a significant and

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Ensuring a supply of healthy chicks

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important economic factor in the poultry industry. Although remarkable gains have been made, there is still room for improvement, most notably in broiler strains of chickens. Laying hens have a well-defined follicular hierarchy and nearly daily egg production at peak levels. In contrast, broiler chickens or meattype birds have been selected for fast growth and high feed conversion. When fed ad libitum, broiler breeder hens become obese and exhibit

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aberrant follicular development, resulting in ovarian overgrowth. The cause of ovarian overgrowth due to feeding level is unknown and the resulting negative impact on broiler breeder productivity is an important commercial challenge. Increased understanding of the mechanism of ovarian follicle growth and selection has the potential to enhance the sustainability of commercial poultry production. In domestic chickens, sexual maturation occurs around 17e20 weeks of age, depending on environmental cues and genetic strain. Prior to puberty, the ovary is small and dormant. As the hen matures, and at approximately 15 weeks of age, the plasma level of estrogen increases1 which initiates the development of secondary sex characteristics in preparation for the first ovulation. Estrogen stimulates the reddening of the comb and wattle, oviduct development, vitellogenin production by the liver, and formation of the medullary bone.2 Chickens are seasonal breeders and require long day photostimulation for egg production. Generally, when a hen is subjected to more than 12 h of light, LH production rapidly increases.3 The increase in LH leads to the first ovulation and the beginning of the reproductive life of the hen. Most reproductive studies have been done using laying strains of hens, and the work reviewed below reflects that except, where noted, information about the broiler breeder hen is presented.

Ovary structure The single left ovary in the adult hen contains follicles at various stages of development (Fig. 19.1). There is a large pool of small follicles 1e8 mm in diameter, usually described as pre-hierarchal follicles; this group of immature follicles can be further divided into large white follicles (LWF; 3e5 mm in diameter) and small yellow follicles (SYF; 6e8 mm in diameter). Follicles that are less than 5 mm in diameter appear

FIG. 19.1 Ovary of a laying hen with a well-defined follicular hierarchy (F1-F5) and small follicles 6e8 mm and 3e5 mm. The germinal disc (GD) appears as a pale white sphere and a postovulatory follicle (POF) is also visible.

white due to the absence of yellow yolk material. Follicles 6e8 mm in diameter begin to accumulate yellow yolk, although yolk accumulation is slow until a single follicle is selected into the pre-ovulatory hierarchy.4 At the time of follicle selection, the oocyte begins to accumulate yolk and grows from about 8 mm in diameter to about 40 mm before ovulation.4 There are approximately 5e7 pre-ovulatory follicles in the ovary that are arranged in a hierarchy. The largest follicle (F1) will ovulate the next day and the next largest follicle will ovulate on the subsequent day. This arrangement ensures a constant supply of pre-ovulatory follicles for daily egg production (Fig. 19.1). Atretic and postovulatory follicles (POFs) are also visible on the adult ovary (Fig. 19.1). Follicular atresia is typically restricted to small follicles; atresia in large pre-ovulatory follicles rarely occurs under normal physiological conditions and is generally only observed if there is a sudden termination of reproduction.2 Postovulatory follicles consist of the follicular tissue remaining after ovulation, including granulosa and theca cells. In contrast to the corpus luteum in mammals, the postovulatory follicle

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produces low amounts of steroid hormones and regresses gradually after ovulation.5 The absorption of atretic follicles and POF both occur via apoptosis.6

Follicles The functional unit of the ovary is the follicle, which produces a mature oocyte and gonadal hormones that contribute to the regulation of the ovulatory cycle and follicle development. The follicle is comprised of tissue layers that surround and support the developing oocyte. Immediately surrounding the oocyte is the perivitelline layer, which provides a structural fibrous mesh to support the oocyte and yolk material within the pre-ovulatory oocyte.7 Granulosa cells surround the perivitelline layer and oocyte; granulosa cells in small follicles are arranged in a pseudostratified layer, while the granulosa cells in pre-ovulatory follicles are cuboidal and form a single layer around the oocyte. In the large follicles, spaces between the granulosa cells form yolk transport channels.8 The granulosa cell layer has many functions throughout follicle development, including the production of hormones and growth factors that influence follicular development and ovulation. The theca layer, a heterogeneous mixture of collagen, fibroblasts, and steroidogenic thecal cells, surrounds the basal lamina of the granulosa cell layer. The theca and the outer connective tissue layer of the follicle are well vascularized,9 essential for the delivery of liverderived yolk material, growth factors, and hormones.

The oocyte and germinal disc region The germinal disc (GD) is made up of the germinal vesicle of the oocyte and the surrounding cytoplasm.10 The germinal disc region (GDR) refers to the GD and the granulosa cells that immediately cover and surround the GD.10 It has been proposed that yolk does not

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accumulate in this area because yellow yolk is not seen surrounding the GD.10 This may be due to morphological differences in the granulosa cells in the GDR compared to the peripheral granulosa cells. The granulosa cells in GDR are tightly packed whereas non-GDR granulosa cells are loosely packed with spaces between the granulosa cells.10 Granulosa cells in the GDR may also have a different function than non-GDR granulosa cells. GDR granulosa cells are mitotically active and produce lower amounts of steroid hormones than non-GDR granulosa cells.11 GDR cells are not fully differentiated compared to peripheral granulosa cells or non-GDR granulosa cells due to their inability to produce large amounts of steroid hormones.12 Epidermal growth factor (EGF), produced by the GD12e14 may be responsible for the proliferation of granulosa cells in this region. Destruction of the GDR at 24 h prior to ovulation prevents further follicle development and results in follicular atresia.15 The function of the oocyte in follicle development is not fully understood, but it seems that the oocyte, through bi-directional communication with somatic cells in the follicle, participates in its own development or survival. Oocytespecific factors16e18 (to be discussed below) have been identified and their potential role in follicle development examined.

Regulation of egg production Follicle development Folliclar development in the hen is a highly regulated process that results in the daily formation and ovulation of a single yolk filled oocyte. As previously discussed, the development of follicles is assessed based on yolk accumulation and somatic cell differentiation. The early phases are associated with small, slow growing follicles (1e8 mm) and an undifferentiated granulosa cell layer.19 The later stage is a rapid growth phase, associated with heightened yolk up-take, and a

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differentiated granulosa cell layer. A single follicle within the pool of 6e8 mm follicles is selected approximately every 24 h into the pre-ovulatory hierarchy (see Fig. 19.1). The mechanisms underpinning follicle activation and the selection of a single follicle into the follicular hierarchy remain unclear. In mammals, the development of follicles from the primordial to the pre-antral stages can occur independent of gonadotropins.20 This may also be true to some extent in the hen as factors that initiate follicle growth are unknown, although it has been shown that FSH can increase the number of small follicles within the ovary.21,22 The function of FSH is mediated by binding to the FSH receptor (FSHR). Receptor abundance is highest in the 6e8 mm follicle pool, the stage at which selection is believed to occur.23 Woods and Johnson reported that a single follicle within the pool of 6e8 mm follicles has higher expression of FSHR, and proposed that increased FSHR abundance is related to follicle selection.24 Binding of FSH to a functional FSHR initiates the final differentiation and maturation of granulosa cells by transactivating the cAMP second messenger system. Follicle growth in the later growth phases is dependent on the pituitary gonadotropins FSH and LH. As previously mentioned, there have been numerous studies on the regulation of synthesis of steroid hormones in the ovary as well as gonadotropin hormone profiles associated with the ovulatory cycle. Characterization of these hormone profiles has given insight into mechanisms involved in the final growth phase of follicle development. There is, however, a great deal to be learned about how intra-ovarian growth factors and metabolic hormones influence follicle development, particularly the selection of a single follicle into the final growth phase. There is a growing body of evidence in mammals and chickens that the recruitment or selection of a follicle is mediated by paracrine and autocrine factors.25,26

FIG. 19.2 Diagram of the interaction between the oocyte and the surrounding granulosa cells. Granulosa cell factors (including steroid and other protein hormones) affect oocyte function while oocyte factors, including EGF, BMP15, and GDF9 have effects on granulosa cell function.

There are important changes in the follicle when it is selected into the follicular hierarchy. The first is the differentiation of the granulosa cell layer26 and the second is the ability of the oocyte to rapidly accumulate yolk.27 To date, the bulk of research on follicle selection has focused on the differentiation of the granulosa cell layer. The contribution of the oocyte, excluding elucidation of a few of the growth factors that it produces, including EGF,13,14 growth and differentiation factor 9 (GDF9),16 and bone morphogenetic protein 15 (BMP15),17 has received little attention. There is a well-described bi-directional relationship (see Fig. 19.2) between the granulosa cells and the oocyte to produce factors necessary for development, as well as to allow the passage of yolk material to the oocyte surface for subsequent incorporation into the oocyte.4

Ovulatory cycle The ovulatory cycle in the hen is defined as the interval from one ovulation to oviposition and typically occurs every 24e28 h in the commercial laying hen. The hen lays an egg per

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day on sequential days, until reaching a pause day. This span of days is termed the sequence and the number of eggs laid in a sequence is determined by the time between successive ovulations. Mid-sequence, a hen will typically ovulate around 15e45 min after oviposition.30,31 When subjected to a conventional 14 h of light and 10 h of darkness, hens will lay within the first 10 h of light.32 Each egg is laid at a slightly later time of the day with the last egg in the sequence generally laid late in the day.31 The next day is a pause day and the hen will not lay an egg until early the following day. Commercial laying hens in their first year of lay have short ovulatory cycles approximating 24 h, which results in long laying sequences and increased egg production.33

Ovulation The interaction among neuroendocrine hormones in the hypothalamus, gonadotropins in the pituitary, and hormones produced in the ovary, controls ovulation and final maturation of the oocyte. During the ovulatory cycle, estrogen production peaks 4e6 h prior to ovulation, and in contrast to mammals, does not stimulate the LH surge, but may help in priming the hypothalamic-pituitary system for the LH surge.34 The LH surge in the hen is triggered by a positive feedback mechanism initiated by rapid increase in production of progesterone by the pre-ovulatory follicle.34 High circulating concentrations of progesterone stimulate the release of luteinizing hormone-releasing hormone (LHRH) into the hypothalamic/pituitary portal system, resulting in an LH surge 4e6 h before ovulation.35 The follicle ruptures along the avascular region of the follicle called the stigma. Collagenase breaks down the follicular tissue which causes weakening and rupture of the stigma of the follicle, and release of the ovum into the oviduct.36

Ovarian hormones Steroidogenesis The ovary produces various steroid hormones that are essential for proper development and function of the reproductive system. The primary steroid hormones produced within the ovary are estrogens, androgens, and progesterone. Production of these hormones is under the control of the gonadotropins luteinizing hormone (LH) and follicle stimulating hormone (FSH) and their stimulation of second messenger systems.37,38 These gonadotropins bind to their receptors within the follicle and act through the adenylyl cyclase system (AC) that is responsible for cellular responses such as gene transcription and steroidogenesis.37,39 Receptors for gonadotropins are found in both pre-hierarchal and pre-ovulatory follicles. The responsiveness of the different tissue layers within the ovarian follicle and stroma to gonadotropins changes throughout folliclar development. It follows that the production of the different steroid hormones also changes depending on stage of follicle development19,40 or timing during the ovulatory cycle.41 In contrast to mammals, small (prehierarchal) follicles and the ovarian stroma within the ovary produce most of the estrogens42 both in the presence and absence of LH40 and initiate the increase in plasma estrogens during sexual development. Estrogen production by the theca cells of small follicles prior to follicle selection occurs primarily via the D5 pathway.43,44 The granulosa cells of small white follicles and the majority of small yellow follicles do not make steroids,19 due to the lack of cytochrome P450 cholesterol side chain cleavage (P450scc) enzyme activity, which converts cholesterol to pregnenolone.44,45 In contrast to the granulosa cell layer, the theca cells express P450scc, 17a-hydroxylase, 3b-hydroxysteroid dehydrogenase,

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and P450 aromatase, enzymes necessary for cells to synthesize estrogens from cholesterol.46 Pre-ovulatory follicles produce progesterone, testosterone and estrogen. While relatively small amounts of estrogens and testosterone are produced by the theca cells of large follicles, progesterone is the dominant steroid hormone produced by granulosa cells.47e49 The granulosa cells begin to produce increasing amounts of progesterone shortly after follicle selection (w6e8 mm). There is an increase in the number of FSH receptors during follicle selection24 and FSH binding to granulosa cells decreases with increasing folliclar development.50 Cyclic AMP production initiates the expression of LH receptor and steroidogenic acute regulatory protein (STAR) in the granulosa cells shortly after selection, leading to steroidogenesis in this tissue layer.51,52 The largest F1 follicle produces the highest amounts of progesterone via the D4 pathway53; the capacity for high progesterone production is essential for initiation of the LH surge.54

Oocyte factors During early stages of follicle development in mammals, maturation of the growing follicle is gonadotropin independent while paracrine and autocrine factors appear to regulate growth and differentiation.20 Oocytes express GDF9 and BMP15 in numerous mammals including mice, sheep, cattle, humans55e57 as well as other vertebrates.16,17,58 Gene knockout studies show that absence of GDF9 results in inhibition of follicle development, with no progression past the primary follicle stage.59 In mammals, expression of BMP15 mRNA has been found in all stages of follicle development except for the primordial stage.60 Gene deletion studies and naturally occurring loss of function mutations in BMP15, reveal dramatic effects on fertility in a species-, as well as dose-dependent manner. Mice null for BMP15 are sub-fertile and show defects in ovulation whereas mice heterozygous for

inactive copies of BMP15 and GDF-9 have fewer and smaller litters compared to wild type animals.61 The Inverdale ewe, with two inactive copies of the BMP15 gene is sterile, whereas a single inactive copy causes increased ovulation rate including increases in pregnancies with twins and triplet fetuses.62e64 Alterations in BMP15 signaling also affect ovulation rate as a mutation in the type 1 receptor for BMP15, BMPR1B (Alk6), causes an increase in litter size.65 Both GDF9 and BMP15 have been identified in the chicken. Immunohistochemistry, revealed that GDF9 is highly expressed in the oocyte with minimal expression in the surrounding granulosa cells.16 GDF9 appears to be most strongly expressed in smaller follicles wherein it is secreted by the oocyte and promotes granulosa cell proliferation.16 BMP15 mRNA was localized to the oocyte by in situ hybridization and expression was also found in the GDR of pre-ovulatory follicles.17 The biological function of human BMP15 (hBMP15) in the hen was confirmed by its ability to phosphorylate SMAD1.17 hBMP15 treatment of granulosa cells from large preovulatory follicles inhibits gonadotropininduced progesterone production.17 The significance of BMP15 in follicle development and ovulation quota in mammals66 suggested that it could be involved in the rate of follicle development and hierarchy maintenance in chickens. More recent studies with hens18 have characterized BMP15 expression in the early stages of follicle development and found that BMP15 protein is more abundant in 6e8 mm follicles compared to smaller follicles and that BMP15 receptor expression increases with follicle development.18 The function of BMP15 in small follicles was examined using a granulosa cell culture system and results showed that hBMP15 decreases anti-mullerian hormone (AMH) mRNA, increases FSHR, and decreases expression of occludin (OCLN). Occludin is likely important in the initiation of yolk uptake during selection into the follicular hierarchy (discussed below).

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Together, these effects point toward a role of BMP15 in follicle selection as these changes are associated with selection of the next follicle to ovulate from the growing pool of follicles. EGF was identified in the GD of avian follicles12e14 and also in the granulosa and theca cell layers of large and small follicles.67 EGF receptors (EGFRs) are located on the granulosa and theca cell layers within the follicle, indicating that paracrine signaling of EGF may be important in the follicle.68 The granulosa cells proximal to the GD are thought to be “the growth center” and to exist in an undifferentiated state.10,13,69 The localization of EGF and EGFR suggests that EGF could influence granulosa cell differentiation associated with follicle selection. The function of EGF in the follicles of hens has been investigated at various stages of follicle development and is associated with proliferation, differentiation and steroidogenesis in granulosa cells.13,14,68,70e72 Prior to follicle selection, undifferentiated granulosa cells are highly proliferative and unable to produce large amounts of steroid hormones.19 EGFR signaling may keep granulosa cells of pre-hierarchal follicles in an undifferentiated state. After selection, functional FSHR stimulate cAMP production, initiate steroidogenic acute regulatory protein (StAR), LHR, P450scc, all of which are differentiation steps that lead to increased progesterone production.19 Treatment of granulosa cells from prehierarchal follicles with a cAMP agonist (8-br-cAMP) increases progesterone production, (StAR) protein expression and phosphorylation, and progesterone production is enhanced when a MAPK inhibitor (U0126) is added to the cultured cells.71 When the same experiment was performed using differentiated granulosa cells from pre-ovulatory follicles, culture with the MAPK inhibitor decreased progesterone production in granulosa cells stimulated with FSH, LH, and 8-br-cAMP.71 These results indicate that EGFR and MAPK signaling have different effects depending on the differentiation status

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of the granulosa cells. Together, results of these experiments highlight the potential importance of EGF in inhibiting premature differentiation of granulosa cells of small follicles, but do not explain the increase in FSHR associated with follicle selection. Other factors within the ovary may contribute to the increase in FSHR expression essential for follicle selection.

Other intra-ovarian factors The first critical transition in follicle development is the activation of primordial follicles from the resting pool. Growth factors from the microenvironment in the ovary keep follicles from activating and also stimulate primordial follicle development. Stimulatory factors identified in mammals include: KIT ligand, bFGF, GDF-9, insulin, insulin-like growth factor 1 (IGF-1), and insulin-like growth factor II (IGFII).25 It is possible that these factors also influence follicle activation or the number of small follicles growing in the ovary of the hen. One factor that has been examined in the hen is KIT ligand, which is expressed at high levels during early stages of development.73 Although the function of KIT ligand is unknown, this expression pattern suggests a function during early stages of follicle development. It is likely that other paracrine growth factors also influence folliclar activation or the number of small follicles growing within the ovary, although the involvement of such factors in these processes in the hen has not been well studied. Although anti-mullerian hormone (AMH) was first characterized and is best known for its role in regression of the paramesonephric ducts in mammalian and avian species,74,75 it also functions to inhibit follicle development. In mice null for AMH, follicular recruitment is accelerated with the result that primordial follicles are depleted prematurely indicating the function of AMH to regulate primordial follicle activation.76 AMH has a highly regulated pattern of expression during development in

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the hen with highest expression in small follicles and decreased amounts with advancing follicular development.77 The pattern of expression suggests that AMH may be important in the well organized follicular hierarchy of the hen. The AMH specific type II receptor, AMHRII, has been identified in the chick embryo,78 and the expression pattern characterized in the ovary of hens.79 AMHRII is expressed in higher amounts in small follicles and expression is greater in the oocyte as compared to the granulosa cells of small follicles. Expression of both AMH and AMHRII mRNAs is greater in ovaries of non-laying broiler hens, possibly to protect the ovarian reserve.79 There is evidence that the oocyte in the hen regulates AMH expression. Granulosa cells treated with oocyte conditioned medium (OCM) showed a significant decrease in the expression of AMH mRNA.77 When OCM was heat treated at 65
C for 30 min prior to culture with granulosa cells, AMH expression was not affected, indicating the factor(s) responsible for altering AMH is heat labile. Although the factor(s) is unknown, incubation of OCM with GDF-9 antiserum did not block the effect, suggesting that it is not GDF-9.77 Another oocyte factor, bone morphogenetic protein 15 (BMP15) could be the responsible factor as granulosa cells incubated with recombinant human BMP15 expressed less AMH mRNA.18 Other follicular factors, including bone morphogenetic protein 4 (BMP4) and bone morphogenetic protein 6 (BMP6), increase the expression of AMH when cultured with granulosa cells of small follicles.80,81 In addition, vitamin D decreases the expression of AMH in granulosa cells of small follicles.82

Yolk accumulation The ability to rapidly accumulate yolk is an important and visible characteristic of a newly selected follicle (6e8 mm). The final growth

phase requires the transport of lipid rich, liverderived yolk material across the oocyte membrane in preparation for incubation. The chick embryo requires approximately 60 kcal of energy for development, most of which is derived from the yolk.2 Yolk material is primarily composed of four fractions: a low-density fraction (70%), water-soluble fraction (8%), lowdensity fraction of the granule (4%), and phosvitin-lipovitellin fraction (18%).83 The low density fraction is made of very low density lipoprotein that is targeted for yolk deposition (VLDLy) which is about 80% lipid by mass, and the lipovitellins originate from the vitellogenins (VTGs), which are phosphoglycoproteins and only about 15% lipid by mass.83 Throughout follicular development, yolk is deposited into the oocyte, although the rate rapidly increases upon follicle selection. In small follicles, prior to follicle selection, yolk components consist primarily of VTGs and follicles are white in appearance. The majority of the yolk accumulated in the rapid growth phase is yellow, triglyceride-rich VLDLy. VLDLy is a complex made of apolipoprotein, VLDL-II and apoB, and is resistant to lipoprotein lipase.84 Estrogen stimulates the production of yolk material including VLDL, VTGs, apoB, and apoVLDL-II in the liver.85e89 Those findings are based on results of studies that demonstrated estrogen-stimulated production of yolk material by in vivo treatment of roosters with estradiol. Other circulating factors likely contribute to yolk synthesis by working synergistically with estrogen. Estrogen alone could not stimulate VTG production in cultured hepatocytes, although estrogen was effective in the presence of GH or prolactin.90 It is also known that birds that harbor the dwarfing gene (dw), which is a mutation in the growth hormone receptor, have lower yolk weight, clutch size, and number of fast growing follicles in the hierarchy.91e93 GH signaling potentially influences follicular development by modulating yolk synthesis.

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Laying hen production differs from broiler breeder hen production

After yolk material is synthesized and secreted from the liver, it is delivered to the follicle by the vascular system.94 Yolk moves from capillaries in the theca layer, travels from the basal lamina to the oocyte by inter-granulosa cell channels, and is taken-up at the oocyte by receptor-mediated endocytosis.95 A single receptor type on the oocyte surface takes up both VTG and VLDLy.96e99 This VLDL/VTG yolk receptor, a 95 kDa chicken homologue of the mammalian low density lipoprotein receptor (LDLR), has eight ligand binding domain repeats and is called LR8.100 A mutation in the receptor inhibits binding of VLDLy, VTG and rapid yolk accumulation in the final growth phase; oocytes rarely ovulate in these “restricted ovulator” hens.96,101 The mRNA and protein for the yolk receptor, LR8, has been detected throughout follicle development, even in small follicles before selection into the hierarchy.97,102e104 The ability of the oocyte, a single cell, to grow from 0.15 g to 17 g in 5e7 days is an incredible physiological feat.105 This takes the coordination of production of yolk material in the liver and cellular modifications in both the oocyte and somatic cells in the follicle. Knowledge of the processes that support yolk accumulation will likely increase our understanding of follicle selection mechanisms. There are two mechanisms that have been proposed to explain the selective accumulation of yolk by ovarian follicles. Shen et al.102 proposed that yolk receptors are stored in vesicles within the oocyte and, upon follicle selection, these receptors are relocated to the oocyte surface. Absent from this model is what may control the timing of receptor translocation. Others hypothesized structural changes between granulosa cells, allowing for increased passage of yolk material to the oocyte surface.4,10,28,29 Granulosa cells in small follicles are tightly packed together and exhibit epithelial cell characteristics, including the expression of the tight junction protein occludin (OCLN).28 Tight junctions are formed by transmembrane proteins including

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OCLN and allow the selective passage of material through intercellular spaces (Fig. 19.3).106 OCLN is present in hen granulosa cells and abundance differs at different stages of development.28 OCLN is more abundant in small follicles and expression is lower in pre-ovulatory follicles.28 This change in tight junctions may regulate accesss of yolk to the oocyte surface around the time of follicle selection. OCLN is located on the periphery of cells and its two extracellular loops interact with other OCLN protein loops on adjacent cells. This interaction is essential in sealing the paracellular region between cells.107,108 The regulation of OCLN has been studied only in pre-ovulatory follicles of the hen, where activin A and FSH increased its abundance and TGFa decreased the abundance of OCLN.28 Broiler breeder hens on an ad libitum diet have lower amounts of OCLN in granulosa cells from 3 to 5 mm follicles than hens that received restricted feed.29 This suggests that feeding level and its sequelae also alter the abundance of tight junction proteins, which could be one mechanism to increase yolk accumulation in follicles of broiler breeder hens.

Laying hen production differs from broiler breeder hen production Difference in rates of egg production between laying and broiler hens has been documented since early development of the specialized meat-type broiler hen.109 Intense selection pressure for postnatal growth has greatly improved growth and meat production efficiency in the broiler industry. Modern broilers grow quickly and can reach market weight in as little as 4e 6 weeks.110e112 This selection pressure has contributed to increased growth rate and improved feed conversion ratios, which are desirable production traits and have decreased the cost of production.111,113,114 Unfortunately, selection for these traits in broilers has led to unintended consequences that negatively affect

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FIG. 19.3 Schematic representation of the proposed mechanism of yolk accumulation. Occludin (OCLN) is a tight junction protein that has a barrier function in various epithelial cells. It is most abundant in granulosa cells of small follicles and expression decreases as follicle size increases. With the decrease in OCLN expression, yolk particles move between the granulosa cells (GCs) to reach the receptor (LR8) on the oocyte surface. This figure based is on findings from Schuster et al.28 and Stephens and Johnson.29

the physiology of the bird. Several physiological systems have compromised function in the broiler. The systems affected by fast growth include the cardiovascular,115,116 skeletal,117,118 respiratory,115 immune,119 and reproductive systems.120e122 Broiler productivity and further improved efficiencies are compromised if these overall health issues are not addressed. In order to enhance reproductive efficiency of modern broiler breeders, they are maintained on a feed-restriction diet (RF) throughout postnatal life.123 Broiler breeders are the parent flocks to broilers and possess similar growth potential as their broiler progeny. Feed restriction improves egg production, although the practice is not sufficient to restore egg production to levels similar to laying hens. Average annual production for

an RF broiler at 65 weeks of age is 167.8 eggs per hen housed,124 as compared to 282.5 eggs per laying hen housed,125 indicating that RF alone only partially improves egg production in broilers. There are clear physiological and morphological differences between RF broiler hens and broiler hens allowed to eat ad libitum (FF). In all studies, investigators found that broiler breeders on a FF diet consume excessive amounts of feed that results in increased body weight, abnormal numbers of pre-ovulatory follicles, decreased egg production, and increased numbers of abnormal eggs.120,126e131 Paradoxically, the decrease in egg production is associated with increased follicular development. There is conflicting evidence of whether there is a difference

V. Poultry production

Ensuring a supply of healthy chicks

in the number of small follicles in ovaries of hens on the FF diet (3e10 mm in diameter) as one report showed lower numbers of small follicles in FF hens,129 whereas another study120 indicated no difference. Sun et al.130 showed that the weight of the ovarian stroma of FF hens was significantly greater than that for RF hens, indicating that there could be an increased number of small growing follicles 50 lux after photostimulation at 21 or 22 weeks of age.64 Other effects of light intensity: Light intensity has other effects. For instance, increasing light intensity in immature pullets is associated with increased plasma concentrations of FSH.65 Moreover, the ability of a short pulse of light to photostimulate chickens is influenced by light intensity.66 In addition, the ratio of the light intensity during the subjective day to that during the subjective night is important in entraining the rhythm of oviposition.67

Nutrition and reproductive management Overview In poultry, nutrition is integrally linked to the hypothalamo-pituitary-gonadal axis. It has been known for 50 years that egg production in hens stops quickly following fasting.68 The administration of mammalian or avian gonadotropin restores, albeit partially, egg production in starved hens68; this suggesting that underlying cause is the lack of pre-ovulatory LH surges. Fasting is followed rapidly by decreases in plasma concentrations of LH,69 body weight together with precipitous declines in ovarian and oviductal weights.70 Similarly, production of eggs and plasma concentrations of LH decrease quickly after reducing calcium or sodium in the diet of hens.71,72 In young chickens, protein deficiency also has been demonstrated to rapidly cause atrophy of gonads, decrease circulating concentrations of LH and depress responsiveness to GnRH.73

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The NRC Nutrient Requirements of Poultry has been invaluable to researchers and important to the poultry industry recommending minimum levels of nutrients in the feed.74 These requirements are based on the published research prior to the development of the specific edition of NRC Nutrient Requirements of Poultry. Primary breeders publish age specific nutrient recommendations for each of their genetic lines.64,75e77 Examples of such recommendations for energy, protein, lysine and calcium are summarized in Table 20.5. It is clear that the recommendations for calcium content in laying hen diets are very high due to the demands of eggshell formation. Moreover, the levels of calcium in diets are higher as the production cycle proceeds presumably due to the increasing size of the egg. This is the case irrespective of whether the recommendations are for layer or broiler breeder hens (Table 20.5).

Broiler breeder reproduction Nutrition of broiler breeder pullets and reproductive management Feedingprogramsare designed toachievetarget body weights throughout growth with markedly lower weights at 24 weeks old (ad libitum fed 5.65 kg; restricted to achieve target weights 3.06 kg).78 These programs not only decrease the feed needs of the broiler breeder but also reduce mortality and increase egg production.78 Broiler breeder pullets can be fed nutrient restricted diets by programs where birds are fed daily or skip-a-day or feeding four or five or six days per week. There were greater body weights and lower liver weights in 16 week-old pullets fed daily than skip-a-day despite the birds despite their receiving the same amount of feed.79 There were also higher hepatic concentrations of both lipid and glycogen together with the expression of lipogenic enzymes in skip-a-day fed pullets.79,80 Mench considered that feed restrict of broiler breeder females may be associated with

physiological stresses and increased incidence of abnormal behaviors.81 The severity of feed restriction needs to be progressively greater with generation exhibiting increased size/growth rates in broiler chickens.82 The strategy in feed restriction is to reduce caloric intake while maintaining amount of feed consumed. This goal is achieved by increasing the percentage of crude fiber in the diet. Skip-a-day programs have been considered helpful in increasing uniformity within flocks and reducing abnormal behaviors.83

Induced molting or re-cycling to increase egg production Hens can be induced (or forced) to molting at the end of their laying cycle resulting in improved egg production at a lower cost than using replacement pullets (Fig. 20.1). In the USA, 19.7 % of laying hens are molted (re-cycled) each month.5 This process can involve severe nutritional restriction including starvation and/or withholding water and/or reduction in photoperiod.70,84e86 Alternate methods of induced molting include an extremely high zinc diet (20,000 ppm) followed by a conventional layer feed beginning at day 1287 and sodium/chloride-deficient diets.88,89 Broiler breeders are rarely molted, but under certain circumstances, molting may be performed. Most broiler breeder molt programs are achieved by restricting feed consumption and supplementing water containing essential micronutrients allowing utilization of fat stores. This reduced fat stores such that hens achieve a more pullet-like body composition before being photostimulated again. In addition to feed restriction, to induce molting in broiler breeder hens, the daylength is decreased to 8L:16D and light intensity is reduced.85,86 Production levels with molted broiler breeders are about 10% less than their previous laying cycle.85,86 The lower production level appears to be due to there being fewer follicles after a forced molt compared to their initial lay cycle.25

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Induced molting or re-cycling to increase egg production

TABLE 20.5

Dietary levels of nutrients for chickens recommended by primary breeders. Energy kcal kgL1

Crude proteina

Lysinea

Digestible lysinea

Calciumc

Grower

2977e3087

17.5

0.96

0.88

1.0

Developer

2977e3131

16.0

0.83

0.76

1.0

Prelay

2911e2955

16.5

0.85

0.78

2.5

Layer feed 1 (first egg to 2% below peak)

2844e2955

16.0

0.881

0.805

4.15

Layer feed 2 (2% below peak to 90% production)

2844e2944

15.5

0.821

0.750

4.30

Layer feed 3 (89e85% production)

2822e2922

15.25

0.777

0.710

4.40

Layer feed 4 (84e80% production)

2800e2844

15.0

0.761

0.695

4.60

Layer feed 5 (41 weeks)

2749

14.50

0.58

0.62

3.08

Starter (0e4 weeks)

2900

19.00

1.04

0.93

1.00

Grower (5e16 weeks)

2700

15.00

0.72

0.61

1.00

Developer (17 weeks to 1st egg)

2800

15.00

0.74

0.62

1.30

Breeder 1 (1st egg to 35 weeks)

2850

15.0

0.75

0.66

3.05

Breeder 2 (36þ weeks)

2750

14.5

0.72

0.64

3.25

Grower (4 weekse5% egg production)

2800

14e15

0.68

0.61

0.9

Breeder 1 (5% eggs - 35 weeks)

2800

15

0.67

0.60

3.0

Breeder 2 (35e50 weeks)

2800

14

0.62

0.56

3.2

Breeder 3 (>50 weeks)

2800

13

0.58

0.52

3.4

LAYER LINEb

BROILER BREEDERSc Cobb 500F

BROILER BREEDERd Cobb 700

e

Ross 308 and 708

Expressed as % of feed for broiler breeder pullets and hens but g day1 for layers (total feed consumption w97 g after 35 weeks old). Based on Hy-line.75 c Based on Vantress.64 d Based on Cobb-Vantress.128 e Based on Ross.76,77 a

b

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FIG. 20.2 Changes in egg production (%) and feather loss (number of wing cast feathers lost per day for 47 hens). Data from Hoshinon et al.90

FIG. 20.1 Egg production in the first (A) and second cycle (B) of egg production. Time is age in weeks. Arrow indicates induction of lay at 17 weeks old. Data from Yilmaz Dikmen et al. and Gordon et al.114,124.

The terms, forced or induced molt, are open to question as it presumes that molting (loss of feathers) causes rejuvenation of reproduction performance. Molting occurs after resumption of normal feeding and is temporally shifted from ovarian recrudescence (see Fig. 20.2). When feed is withdrawn for 8 days and water withdraw for 2 days, egg production had completely ceased by 6 days (see Fig. 20.2).90 Molting occurred after the resumption of feeding and there were concomitant increases in circulating concentrations of T3 and corticosterone (see Fig. 20.2).90 Circulating concentrations of

LH, estradiol (E2) and progesterone were lower in molting hens than in laying hens or fully recycled hens.90 The effects of an industry molting system on organ weights together with circulating concentrations of ions and corticosterone were evaluated.91 The approach was to combine salt and protein deprivation91: • Layer diet [17.2 % protein, fiber 2.45 %, 0.4 % sodium chloride (NaCl) and metabolizable energy (ME) 1270 Mcal kg1] • Pre-molt diet [17.2% protein, fiber 3.6 %, 0 % added NaCl & ME - 1270 Mcal kg1] for 3 days. • Molt feed 1 [9.7% protein, fiber 3.6 %, 0 % added NaCl & ME - 1218 Mcal kg1] for 21 days • Molt feed 2 [9.7% protein, fiber 3.6 %, 0.3 % added NaCl & ME - 1214 Mcal kg1] for 3 days

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Induced molting or re-cycling to increase egg production

• Molt feed 3 [16.0% protein, fiber 3.2 %, 0.4 % added NaCl & ME - 1274 Mcal kg1] for 7 days • Layer diet [17.2 % protein, fiber 2.45 %, 0.4 % added sodium chloride & ME 1270 Mcal kg1] • Photoperiod was maintained at w17 h light per day.

decreases in the number of gonadotropes expressing LH.93 Oviductal regression occurs due to lack of estrogens and is accompanied by increased expression of peptidases with, for example, expression of the peptidase, cathepsin L.94 It is questioned whether re-cycling/forced molting of hens is consistent with one of the “The Five Freedoms,” namely: “1. Freedom from hunger and thirst” and the need for “ready access to fresh water and a diet to maintain full health and vigor.”95 There is both evidence for and against the process being physiologically stressful. An indicator of stress, heterophil: lymphocyte ratio, was increased after 7 days after feed withdrawal to a forced molt.96 Similarly, induction of molting was accompanied by increased the percentage of heterophils (day 7 and 14) and of eosinophils in one study.97 However, the evidence of molt induction influencing plasma concentration of the stress hormone, corticosterone, is circumspect. No changes in plasma concentrations of corticosterone in force molted hens were reported (also see Table 20.6).91,94 In laying hens subjected to induced molting, plasma concentrations of corticosterone were higher on day 10 in hens subjected to complete

This approach is effective in stopping egg production and decreasing body weight and organ weights.91 It is accompanied by reductions in circulating concentrations of sodium and chloride (see Table 20.6).91 The egg production cycle of about 21 weeks of age to 45e60 weeks old can be extended with a molt between 108 and 120 weeks old.91 The physiological mechanism underlying induced molting included decreased release of GnRH from the median eminence and consequently lack of the pre-ovulatory LH surge. Ovulation completely ceased with 4 days of feed withdrawal.92 Plasma concentrations of LH and progesterone were decreased with 2 days of feed withdrawal.92 The GnRH content of the median eminence was similarly decreased but not until 4 days of feed withdrawal.92 There are also TABLE 20.6

Changes in body and organ weights together with plasma concentrations of sodium, chloride and corticosterone in hens subjected to a molt using an industry approach of a combination of salt and protein deprivation.91 Physiological state

Parameter

Control

Molt week 1

Molt week 2

Molt week 3

Molt week 4

SEM (n [ 5)

Body weight loss (%)

3a

12a,b

19b

21b

13a,b

4.9

18

ab

b

b

a,b

5.0

44

b

b

10

a

Ovary (g)

25

a

Oviduct (g)

64

a

Liver (g)

41.9

a

Small intestine (g) þ

1

Plasma Na (Mequiv L ) 

1

Plasma Cl (Mequiv L ) 1

Plasma CORT (pg mL ) a,b,c

13

13

c

25 a

38.9

a,b

18

c

27 b

29.0

b

46 b

29.7

a,b

a,b

2.3

a,b

37.0

64.1

52.9

46.3

51.4

62.3

3.4

a

147

b

b

b

b

3.7

120

116

b

a,b

296

313

169

a

147

b

146

b

150

117

116

119

3.7

498

282

548

100

Different leters in a row indicate difference p50 K SNPs) SNP chips in the 2000’s,16,17 QTL mapping became a much simpler and cheaper approach. Due to increased genome coverage compared to microsatellites, SNP based QTL mapping provides results with a narrower QTL region under the assumption that at least one of the thousands of SNPs used in the statistical analysis is in high LD with the QTL. In this case, there is no need to create complex and expensive experimental designs to generate LD between SNPs and QTL. Genome-wide association studies (GWAS) quickly became popular among animal breeders, and thousands of QTL for hundreds of traits have been reported for the main livestock species. From traditional (e.g. growth rate18) to novel (e.g. response to disease19) traits, the overall findings have been mostly for QTL with relatively large effects. However, these QTL only explain part of the heritability of the traits that they are linked to, which suggests a lack of statistical power to detect the remaining QTL that contribute to polygenic control of the trait (i.e. dozens to hundreds of loci with small effects on the trait of interest). Nonetheless, these results have advanced our knowledge regarding the genomic architecture of complex traits in livestock. SNP markers have also been used to detect quantitative trait nucleotides (QTN) and causative genes, which can have a direct impact on

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the livestock industry. For example, the WUR10000125 SNP,20 which is in high LD with a putative QTN in the GBP5 (Guanylate binding protein 5) gene,21 is used by breeders to improve growth performance in pigs infected with porcine reproductive and respiratory syndrome virus. Finally, a comprehensive list of reported QTL for cattle, catfish, chicken, horse, pig, rainbow trout, and sheep is available in the Animal QTL Database, a publicly available online tool that includes information on over 150 K QTL from more than 2,000 publications (as of January 2019).22

Marker-assisted and genomic selection The development of genotyping technologies and the identification of QTLs and causative genes made using information on specific genes and/or markers to increase rates of genetic gains in livestock species possible.8 Marker-assisted selection was formulated under a Best Linear Unbiased Prediction (BLUP) approach by Fernando and Grossman.23 Under this approach, breeding values (BV) of animals were estimated based on pedigree while taking into consideration the information on a limited number of genetic markers. This strategy required the discovery of genetic markers that were considered direct (i.e. the causative mutation) or were in high LD with the causative mutation. This was troublesome because, in the late 1980s and 1990s, genetic maps were sparse. Furthermore, QTL mapping demonstrated that most economically important traits are complex (i.e. they are controlled by thousands of genes, most of which have relatively small effects). This indicated that much denser genotypic information was needed to properly capture the genetic variability of animals to accurately estimate BVs. With this in mind, genomic selection (GS) was proposed in 2001 by Meuwissen, Hayes, and Goddard, years before the first commercial, high-density SNP chips were available.24 With

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23. Genetic improvement of livestock, from conventional breeding to biotechnological approaches

GS, BVs are estimated based on genotype, instead of relying on pedigrees and a few genetic markers. Making the same assumptions as in BLUP, the BV of an individual is calculated as the sum of thousands of loci with small effects. The benefits of GS were clear from the beginning. First, the BV of an animal and its relationships with other individuals are more accurately estimated because they are based on observed genotypes instead of genotypes predicted from pedigree. Second, the BV of an individual can be estimated as soon as the animal is genotyped, greatly reducing the generation interval. Given these two improvements, response to selection (i.e. the amount of genetic gain achieved in each generation) can be increased based on the increased accuracy and reduction of the generation interval. This is illustrated in the breeder’s equation:25 Response to Selection ¼ ðIntensity  Accuracy  Genetic VarianceÞ Generation Interval Genomic selection has been widely adopted by the industry across livestock species. The dairy industry was the first to adopt it on a large scale, and currently have more than 1 million genotyped animals in the US26 Gains per year from GS range from 50% to 400% for high and low heritable traits, respectively.27 The overall selection index in swine has achieved gains of more than 50%,28 and the poultry industry has improved accuracies by greater than 50%.29 Finally, with continuous advances in sequencing technologies, the use of whole-genome sequence (WGS) data for GS is increasing. Although WGS data results in limited (1%e5%) increases in accuracy of genomic estimated BVs, the use of WGS data can be used for other purposes. First, QTNs can be identified because direct inferences about associated SNPs are possible with WGS data. Secondly, and perhaps more importantly from a GS perspective, is the use of WGS data to impute genotypic data for animals using a lower SNP density. This strategy

decreases costs without having a major negative impact on the accuracy of genomic estimate of BVs.30 Genomic selection has resulted in a drastic change in how animals are selected in breeding programs, and significant increases in genetic gains have been observed. With continued advances in genotyping, sequencing, computational, and analytical technologies, increasing rates of genetic gains in livestock production are expected to continue.

Biotechnological solutions to advance genetic improvement The increasingly rapid genetic improvements in livestock that we see today are the result of decades of research which enabled the continuous development and refinement of a wide variety of different technologies. This work spans a number of fields, including reproductive physiology, quantitative genetics, and genetic engineering. Although the approaches differ, the overall goal is to increase the number of desired alleles in a given population to produce a specific phenotype either by leveraging genetically superior animals or manipulating particular alleles at the molecular level.

Assisted reproductive technologies Assisted reproductive technologies (ARTs) for livestock are a wide-ranging set of tools that enable the manipulation of many aspects of male and female reproduction. The collective goal of these technologies is to improve animal agriculture by increasing efficiency and productivity, expanding the utilization of superior genetics, reducing disease, and overcoming natural barriers to reproductive success. These technologies play a crucial role in research related to genetic improvement and reproduction and, in many cases, are translated to production settings when the efficiency and cost of

VI. Biotechnologies and others in animal production

Assisted reproductive technologies

the technique make it economically feasible. The dual use of these technologies in research and production allows for their rapid development and evolution, and the large role they play in driving genetic improvement in livestock.

Artificial insemination Artificial insemination is the introduction of semen directly into the female reproductive tract through means other than copulation. Although the first successful use of AI was over 200 years ago,31 the technology was not widely adopted in animal agriculture until the 20th century. Massive technological advances and widespread growth of the use of AI, particularly in dairy cattle, occurred in Europe and the United States beginning in the 1930s and 1940s.32 Although AI is a labor intensive process that requires technical expertise, the overwhelming use of AI in many livestock species (most notably in dairy cattle, swine and turkeys) suggests that the benefits of the technology outweigh the time and cost associated with it. With regard to genetic improvement, AI allows for the propagation and dissemination of superior sire genetics. By eliminating geographical limitations and allowing producers to choose highly vetted sires that align with their breeding systems, AI increases the rate of genetic improvement and production gains. Furthermore, AI allows superior sires to produce significantly more offspring compared to natural service, and even makes it possible to use sires that cannot reproduce naturally or are no longer alive. Artificial insemination is an incredibly powerful tool for genetic improvement at several different levels. Although basic in principle, AI is currently the most widely used reproductive biotechnology for the genetic improvement of livestock in the world. The development and adoption of AI in livestock served as the impetus for several of the ARTs detailed below and paved the way for public acceptance of reproductive interventions in general.32

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Semen sexing and cryopreservation Viable semen is a crucial component of successful AI, and technologies that allowed for the collection, maintenance, storage, and distribution of semen from livestock species mirrored the growth of AI discussed previously. Collection of semen varies by species, situation and setting, but it is usually conducted using an artificial vagina, digital manipulation or electroejaculation. After collection, a variety of semen extenders and cryoprotectants can be added to the ejaculate depending on the downstream application. In some species, such as swine, sheep and turkeys, semen is transported quickly under reduced temperature conditions and administered on a “fresh” basis. In other species, such as cattle, cryopreservation of semen is much more common.32e34 The reason for these species differences is largely related to the relative ease of effectively freezing and thawing semen from cattle, and the existence of an infrastructure that allows for the shipment of fresh sperm. Cryopreservation of sperm was adopted early in the dairy industry alongside AI and allowed for rapid improvements in dairy genetics through progressive breeding programs. In recent years, the emergence and use of sexing sperm in semen in the dairy and beef cattle industries has allowed for even greater genetic gains through the expansion of desired genetic backgrounds while also addressing an important welfare concern. The ability to collect, store and distribute semen has had a profound impact on the genetic improvement of livestock by eliminating geographic limitations and allowing semen from each collection to be extended and used on more than one dam. Further, it provides a way to select the most economically valuable sperm to effect changes in production of the desired sex of offspring based on the production setting.

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Estrous cycle regulation

In vitro fertilization

A key component of a successful AI program is the ability to detect and manage the estrous cycle of recipient females. Accurate estrus, or heat, detection and timing of insemination are critical factors in the efficiency and efficacy of AI in a production setting.35 There are a number of methods and tools for detecting estrus including visual cues, mount detectors, and emerging technologies such as pedometers.36 Although proper estrus detection is pivotal, estrus synchronization protocols, made possible by our understanding of the hypothalamice pituitaryegonadal axis, can make AI programs more effective and efficient.

Over the past 20 years, in vitro fertilization (IVF) techniques have improved significantly allowing for the wide scale adoption of the technique in production systems that already utilized ET as part of their breeding program.38e40 Incorporating IVF with an ET program has a few key benefits. Rather than flushing embryos from the reproductive tract, unovulated oocytes can be directly aspirated from the ovaries of donor females. These unfertilized oocytes are then cultured, subjected to IVF, and the ensuing embryos are transferred into donor females. The benefits to genetic improvement of IVF are twofold. First, there is greater dissemination of desired genetics from both the dam and sire sides. Second, by avoiding the need for flushing embryos from a donor female, in vitro fertilization allows producers to collect more oocytes (all aspirated oocytes present on the ovary compared to oocytes that are fertilized and develop to embryos/blastocyst in utero) from more donor cows at more frequent intervals. Thus, the producer can collect oocytes from open cows, bred cows, heifers, and cows that do not get pregnant. IVF also allows producers to fertilize oocytes from multiple sperm donors using one unit of semen, preserving valuable sire genetic material and saving the producer money. In contrast, IVF also allows oocytes from a single donor to be fertilized by semen from multiple donors and produce offspring representing multiple sires.

Embryo transfer Through the implementation of AI and semen collection, genetic improvement through the rapid dissemination of superior sire genetics is a reality in common livestock species. Although not as scalable, efficient, or widespread, embryo transfer (ET) allows for a similar dissemination of superior dam genetics. In livestock, ET refers to the process of “flushing” embryos of a known developmental stage from the reproductive tract, and transferring the embryos to a recipient female. After recovery from the female reproductive tract, embryos can subsequently be transferred fresh or cryopreserved before being thawed and transferred into uteri of surrogate dams. ET is primarily used in cattle, particularly dairy cattle, and to some degree in horses. Although possible in sheep and goats, the number of ETs per year is paltry in comparison to cattle, and the number is even smaller in swine where the technique is used primarily in research settings.37 Utilizing the combination of AI and ET allows for an expansion of superior genetics from males and females that would not otherwise be possible.

Summary of assisted reproductive technologies Since the advent of widespread AI, ARTs have continued to evolve and develop alongside one another to provide useful tools for a variety of breeding systems/programs designed to ensure that the highest quality genetics are being passed on to the next generation. ARTs continue to have a profound impact on the genetic

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Early work on transgenesis in livestock

improvement of livestock production and research. It is difficult to separate these technologies into their own categories because they are integrally linked and are best used in conjunction with other available biotechnologies. As research continues to develop and improve these technologies, ARTs will remain an important part of efforts to enhance reproductive efficiency and realize genetic improvements.

Genetic engineering From the earliest domestication events and through thousands of generations of purposeful and accidental breeding events, humans have been selecting for desirable traits in livestock and other domesticated animals for over 10,000 years. For most of that time, “breeding decisions” were based on convenience and intuition. With the advent of Bakewell’s goaloriented breeding philosophy, livestock breeding, as we know it today, was born.2 In the two and a half centuries following Bakewell’s work, breeders have made huge improvements in all livestock species.4 This improvement in livestock breeding rapidly increased as our basic understanding of genetics grew throughout the 20th century. Using genetic principles, breeders were/are able to select for complex traits that involve the inheritance of alleles at many different loci. This type of selective breeding for specific traits creates artificial selection pressure that drives evolutionary changes in livestock.41 The application of artificial selection allows breeders to slowly alter traits over many generations (much like natural selection in wild populations), but selective breeding has one very important downside. Because many economically important traits are controlled by several genomic regions that can be correlated, selection of a single trait often leads to unexpected and often detrimental changes in seemingly unrelated traits.42 In contrast to the slow, complex

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process of selection are the more rapid and precise changes introduced by mutations. Technological advances in the last few decades are making it possible, for the first time in human history, for us to harness the power of desired mutations in the precision breeding of livestock. In the wake of the genomics revolution, and the recent and rapid development and adoption of gene editing technologies, the use of genome engineering to accelerate breeding goals, combat disease and improve production in livestock is becoming increasingly relevant. Although a formal definition of genome engineering is difficult to capture, for the sake of this chapter, the following definition will be used: The modification of a targeted location in an organism’s genome using an exogenous agent.

Early work on transgenesis in livestock Although recent news surrounding the powerful and versatile CRISPR technology has captured the minds of the public and researchers alike, genetically modified animals have been a part of animal research since the mid-1970s. Building on cutting edge plasmid/recombinant DNA work done by the biochemists Boyer and Cohen in 1973,43 the first chimeric mouse was created in 1974 by Brinster.44 He combined early embryonic cells of two different mouse strains into one embryo which developed into a chimeric adult mouse. This leap forward in our ability to manipulate genetics at the molecular level sparked an explosion of research in developmental biology, gene function, biotechnology, and a new field developing the techniques used for genetic engineering. The rapid develop of techniques for introducing foreign DNA into animals throughout the 1970s and into the 1980s led to the coining of the term “transgenic” in 1981.45 Following the work done in mice and other model organisms, Hammer et al. reported the production of the first transgenic livestock by integrating a

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transgene into pigs, sheep, and rabbits and showing expression of the transgene in both pigs and rabbits.46 As techniques in the mouse and other model organisms continued to improve, it became apparent that transgenic livestock had the potential to serve as biomedical models and producers of important pharmaceuticals of benefit to human health, increase resiliency in populations at risk for disease and abiotic stress and improve overall productivity and sustainability in animal agriculture. Although these improvements were theoretically feasible, there was a major hurdle in that the technologies used to develop transgenic animals were resource intensive, inefficient, and involved the random incorporation of the transgene into the genome of the animal. These factors contributed to the disappointing decade that followed the initial introduction of transgenic livestock, as most of the benefits mentioned above were not realized until the late 1990s or early 2000s.47 Some of the most notable achievements of the relatively slow process of capitalizing on transgenic livestock include Dolly the Sheep48 (the first mammal cloned using the process of somatic cell nuclear transfer), the AquAdvantageÒ Salmon49 (the only transgenic animal approved for human consumption by the FDA), and more recently disease resistant pigs.50e52

Site directed nucleases Although developments in the production of transgenic animals continues to increase our understanding of basic biology, molecular genetics, and developmental biology, the next major leap forward in genome engineering occurred in the early 2000s with the first use of site directed nucleases (SDN). The use of zinc finger nucleases,53 and eventually transcription activator-like effector nucleases (TALENs),54 revolutionized the way we think about and use genetic engineering in livestock. These SDNs

possessed a number of advantages over previous techniques to develop genetically modified animals including the ability to target specific locations in the genome, and capitalize on the cell’s own DNA repair mechanisms for the introduction of mutations, which may or may not include the introduction of a transgene depending on the desired outcomes. ZFNs and TALENs both use sequence specific DNA binding motifs to direct a nuclease (Fok1) to a specific location in the genome where it dimerizes with another Fok1 nuclease that has been directed to the opposite strand of DNA and creates a double stranded break. Because DNA double stranded breaks cause cell death, there are multiple natural survival mechanisms employed by the cell to “fix” these breaks.55 The most common of these pathways is non-homologous end joining (NHEJ). NHEJ is a quick, error prone method to repair DNA that the cell employs to ensure survival. DNA repair through the NHEJ pathway often results in insertions or deletions at the targeted location resulting in loss of function for the gene of interest without introduction of exogenous DNA into the host genome. Another DNA repair pathway, observed far less frequently compared to NHEJ, is the homology directed repair pathway (HDR). In nature, HDR is used to precisely repair DNA double stranded breaks by using the homologous chromosome sequence as a template. Although much less efficient than NHEJ, this repair pathway can be used in genetic engineering to introduce specific genome modifications by providing a homologous DNA construct that contains the desired modification.56 Although both ZFNs and TALENs were powerful technologies, they were also expensive to synthesize, time consuming to construct, and required the dimerization of Fok1. In recent years, a new system for genome engineering has emerged that utilizes an endogenous system in bacteria that allows them to defend themselves from bacteriophage attacks (similar to the mammalian adaptive immune system).57e61

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Genetic improvement of livestock through genetic engineering

Using Clustered Regularly Spaced Short Palindromic Repeats (CRISPR) and the CRISPR associated proteins (Cas), the CRISPR/Cas system can make targeted double stranded breaks in a wide variety of genomes.62 Through a series of refinements, the technology was eventually distilled into a single guide RNA and Cas9, a single Cas capable of both recognizing and cleaving DNA63,64 In 2013, the Zhang lab at the Broad Institute in Cambridge, Massachusetts demonstrated that the CRISPR/Cas9 system could be used to cleave DNA in eukaryotic cells.65 This revolutionized the way that we think about genome engineering, as this new system is inexpensive, easy to construct, and can target a wide range of loci in different species.66 Since 2013, well over 10,000 papers have been published regarding CRISPR and its uses. The rapid adoption of the technology has allowed research to far outpace application in production settings, but that is beginning to change. As interest in these new technologies continues to increase, organizations have invested in research that explores the potential benefit these technologies could have on food security, disease resistance, and heat tolerance in a variety of livestock species.67 Although it is too early to predict how legislation and consumer acceptance of these products will affect their future use in global agricultural production, the current potential for improvement is incredibly high.

Genetic improvement of livestock through genetic engineering Although still in its infancy, research exploring the use of genetic engineering to improve livestock production, sometimes referred to as precision breeding, is beginning to emerge. Some of the early successes in precision breeding are detailed in this section, and demonstrate the scope and potential opportunity for advancement moving forward. Hickey et al.68 identified three broad categories related to genetic improvement

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in livestock which are reasonable to pursue given the current state of the technologies: introducing favorable alleles for monogenic traits, removing alleles related to disease susceptibility and reductions in production traits, and increasing the frequency of alleles at multiple loci affecting polygenic phenotypes. One of the first uses of precision breeding in livestock utilized TALENs to insert a naturally occurring polled allele, commonly found in beef cattle, into highly productive dairy cattle resulting in offspring with the polled phenotype.69,70 This improved animal welfare by removing the need for dehorning, and avoided the large reduction in genetic merit that would have resulted from introgression of the allele into the genome of dairy cattle using traditional approaches. In contrast to introducing alleles for desired traits is the removal of alleles by genome editing (RAGE) for desired traits which is a strategy breeding programs can utilize for the removal of deleterious variants in the genome that offspring accumulate through inheritance or de novo mutagenesis.71 The use of RAGE to improve disease tolerance in livestock has been demonstrated in creation of Cluster of Differentiation 163 null (CD163/) pigs50,52 and alanyl aminopeptidase, membrane null (ANPEP-/-) pigs51 which are resistant to the porcine reproductive and respiratory syndrome virus and the transmissible gastroenteritis virus, respectively. Although the introduction or reduction of specific alleles both represent powerful strategies for genetic improvement, most traits related to production traits in livestock (e.g. growth performance, litter size, milk yield, etc.) are polygenic. Based on model data, promotion of alleles by genome editing (PAGE), coupled with genetic selection, has a strong potential to advance specific phenotypes more rapidly than genetic selection alone.68,72 In addition to the direct manipulation of alleles affecting traits of interest, recent work is

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beginning to blur the lines between ARTs and genetic engineering. Using the CRISPR/Cas9 system, the Nanos homolog 2 (NANOS2) gene was edited resulting in germline ablated male pigs with otherwise normal testicular development.73 If germ cells or spermatogonial stem cells can be transplanted successfully into these “recipient” sires, simulations estimate that this strategy could improve the genetic gain in commercial sires by 6.5e9.2 years compared to conventional multiplication strategies.74

The whole toolbox Although all of these techniques and technologies are powerful on their own, they are much more powerful when used in conjunction with one another, and collectively have resulted in rapid genetic progress (Fig. 23.1). For example, genomics can be used to identify beneficial modifications, genome editing technologies can be used to modify cells, and those cells can be used to create livestock with various ARTs.

FIG. 23.1

Conventional and biotechnological advancements have accelerated genetic improvements in livestock. This figure represents some of the scientific discoveries, which were followed by industry application in the areas of animal breeding, genomics, assisted reproductive technologies and genetic modifications culminating in rapid genetic improvements in livestock. Genetic improvements will continue to accelerate as new technologies are discovered and applied.

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References

Once these animals are produced, they can be used as models for certain traits, or bred with other populations to introduce the trait of interest using a variety of breeding techniques. Even though this chapter was broken into distinct sections, in practice multiple techniques and technologies can be integrated to accelerate achievement of a desired outcome. As our understanding of genetics and ability to predict and manipulate it continue to improve, the potential for continued improvement through livestock genetics remains high. This genetic improvement will be needed in the future as the industry is expected to produce more products from animal agriculture, while utilizing fewer resources, and decreasing the impact of negative effects of disease and other stressors on animals.

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C H A P T E R

24 Fermentation techniques in feed production Zhaolai Daia, Lu Cuia, Ju Lib, Binggen Wangb, Lina Guoa, Zhenlong Wua, Weiyun Zhuc, Guoyao Wud a

State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, China; bHenan Yinfa Animal Husbandry Co., Xinzheng, Henan, China; c National Center for International Research on Animal Gut Nutrition, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu, China; dDepartment of Animal Science, Texas A&M University, College Station, TX, United States

O U T L I N E Introduction

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Production of fermented feed Production of fermented liquid feed Solid-state fermentation in feed production Strategies to improve silage fermentation Fermented feed for aquaculture The combined use of microorganisms and enzymes in fermentation of feedstuffs Fermented milk for young farm animals

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Introduction With the rapid development of animal agriculture, demands for feed have increased

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00024-0

dramatically over the last decades. The supply of high-quality feedstuff for sustaining the livestock industry and aquaculture is a great challenge for both producers and scientists.

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Copyright © 2020 Elsevier Inc. All rights reserved.

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24. Fermentation techniques in feed production

The supplementation of antibiotics as feed additives (so called “in-feed” antibiotics) was brought into practice to promote growth more than 60 years ago due to its economical advantage through improvements in growth and health of animals.1 However, due to the largescale use of in-feed antibiotics for farm animals, the incidence of antibiotic-resistance in both pathogenic bacteria and normal endogenous bacteria has increased rapidly over the past decade.2,3 Therefore, regulations that restrict or ban the use of in-feed antibiotics were introduced by the European Union in 2006 and are now being considered in many countries (e.g., United States in 2017, China in 2020). With increasing numbers of farm animals, the competition for high-quality food resources such as soybeans and corn for human consumption has also increased in recent years. Thus, the search for new feed resources other than soybeans, fishmeal and corn, together with innovation of feed processing technologies are of utmost importance. Based on current circumstances, extensive research and production practices are being conducted to identify alternatives to in-feed antibiotics for livestock and poultry that also improve the nutritional value and digestibility of non-food resources in feedstuffs. Compared with feed additives such as prebiotics, probiotics, synbiotics, plant extracts or enzymes, production of feedstuffs through fermentation has advantages of improving feed quality and gut ecology. Although fermentation is the key to the production of feed additives such as enzymes, amino acids or direct-fed microorganisms in the feed industry,4,5 the focus is now on progress on technologies for fermentation of feeds (liquid fermentation, solid-state fermentation, and ensiling) and the production of fermented milk. This includes understanding of the microbiology of the feed fermentation processes, as well as the impact of fermented feeds on gut ecology and biosafety.

Production of fermented feed Many techniques are available for manufacturing fermented feeds, including liquid fermentation, solid-state fermentation, and ensiling that have been used widely over the past two decades. Fermented milk can be used in parallel with solid feed for young animals. Although spontaneous fermentation can occur in all types of fermentations, it should be avoided in practice due to the overgrowth of harmful microorganisms and the production of toxic metabolites.6 A schematic diagram of the integrated techniques and procedures used for the production of fermented feeds is provided in Fig. 24.1.

Production of fermented liquid feed Compared with feeding dry feed to animals, liquid feed has an advantage for improving feed intake and animal growth. For example, growing-finishing pigs fed liquid feed (feed: water ¼ 1:3 w/v) have greater weight gains and lean tissue growth rates, compared with pigs fed a solid feed.7 Fermented liquid feed (FLF) provides the animal with not only water and other highly digestible nutrients, but also a large amount of organic acids and beneficial microorganisms such as lactic acid bacteria (LAB) and yeast. Recent progress in techniques used for the production of liquid feed using a variety of feedstuffs and the effects of FLF on feed quality as well as animal growth and health is summarized in Table 24.1. Feedstuffs and various substrates used for the production of FLF varies depending on their availability and the actual conditions of the farm, such as the use of wheat, barley as major carbohydrate sources and soybean meal as a major protein source.8 Usually, lactic acid bacteria (LAB) such as Lactobacillus plantarum are used for the fermentation of carbohydrate-rich feedstuffs. Microorganisms such as Bacillus and

VI. Biotechnologies and others in animal production

Production of fermented feed

409

FIG. 24.1 A schematic diagram of the techniques and procedures used in the production of fermented feeds for farm animals. Fermentation can be achieved by liquid-or solid-state fermentation that involves either a single-step or a two-step process. In the single-step fermentation process, fermentation products from “liquid fermentation/solid-state fermentation I” can be fed directly to animals or further processed (“post-fermentation processing”) by drying or mixing with other feedstuffs (either fermented or unfermented) and feed additives (e.g., minerals and vitamins). In the two-step fermentation process, products from “liquid fermentation/solid-state fermentation I” can serve as a starting material for “Feedstuff II” and further fermented to improve feed quality. For liquid feed fermentation, replacement of 50%e80% of the fermented feed with fresh substrates for a new cycle of fermentation is known as “back slopping”. Before fermentation, “physical/chemical processing” of feedstuffs (including cutting, grinding, acid/alkaline treatment, organic solvent extraction, and autoclaving) and the addition of water can facilitate fermentation. The addition of “non-microbial additives”, such as minerals, nitrogen (e.g., ammonia), carbohydrates (sugars) and enzymes (fibrolytic enzymes, phytase, and enzymes that degrade anti-nutritional factors) can enhance both the fermentation process and the removal of anti-nutritional factors.

Saccharomyces are included in the starter culture when protein-rich feedstuffs (e.g., beans) are substrates in the fermentation system (Table 24.1). Studies on the effect of fermentation temperature on growth and survival of pathogenic bacteria during fermentation of a liquid feed for piglets (59% oats and wheat, 34% fishmeal, skimmed milk and soybean meal, and 7% of minerals and amino acids; one part of feed to 2.5 parts of water) showed that when coincubated with Pediococcus pentosaceus at 20  C, the inoculated Salmonella enterica subsp. enterica serovar typhimurium DT104:30 persisted in the feed for at least 72 h.9 However, no S. typhimurium DT104:30 was detectable when incubated

at 30  C for 48 h. In the case of contamination with Salmonella after fermentation (inoculation of S. typhimurium DT104:30 into fermented feed), S. typhimurium DT104:30 died four- to five-times faster in feed maintained at 30  C. This inhibitory effect of the growth and survival of Salmonella on the growth and survival of S. typhimurium is not solely due to the high concentrations of lactic acid,9 suggesting that other fermentation products, such as antimicrobial compounds of P. pentosaceus, may be present in the culture medium. Compared with spontaneous fermentation (dominant by P. pentosaceus), liquid fermentation of a commercial corn- and soybean meal-based diet with Lactobacillus zeae or Lactobacillus casei can reduce the numbers of

VI. Biotechnologies and others in animal production

TABLE 24.1

Feed stuff

Effects of liquid fermentation of feedstuffs on feed quality as well as animal growth and health. Non-microbial additives

Microorganisms used for fermentation

Techniques

Form Liquid

Barley or wheat

No

Spontaneous fermentation

Mix with water at 1:2.75 (w/w) in a closed tank and incubate at 25  C for 2 days; replace 50% with fresh cereals daily; mix with other feed ingredients when feeding to achieve 50% moisture content.

Wheat flour

Sucrose

Lactobacillus reuteri TMW1.656 (reuteran producer) and Lactobacillus reuteri LTH5794 (levan producer)

Liquid Seed sour dough: mix white wheat and corn flour with 10% (w/w) sucrose, final numbers of 107 CFU/g of two strains of L. reuteri and water to achieve moisture content of 50%; incubate at 37  C for 24 h. Large scale fermentation: inoculate wheat slurry with seed sour dough (10%) then incubate for 24 h, replace 90% with fresh wheat slurry after fermentation; add seed after four cycles of fermentation

Blue lupin (Lupinus angustifolius cv. Neptun) seed

No

Saccharomyces cerevisiae

Soak seeds with 2.5 g/L sodium hypochlorite for 10 min, then wash the seeds with distilled water. Mix 100 g of dried samples of seeds with 400 mL water and add S. cerevisae at a final concentration of 108 cells/g and then incubate for 24 h with continuous mixing. Heat at 70  C for 10 min, then dry at 55  C

Dry

Effects on feed quality and animal growth and health Reduce dry matter content in fermented feeds after fermentation. Fermented wheat: improves ileal digestibility of fat and CH2O. Fermented barley: reduces fecal excretion of dry matter, organic matter and protein.

Application

References

Pig

Sholly et al. (2011)75

Pig L. reuteri TMW1.656 and L. reuteri LTH5794 can survive throughout the intestinal tract after feeding the piglets 20% fermented wheat on day 0 to day 6 and 50% fermented wheat on day 7 to day 21. Reuteran produced in the fermented wheat reduces the numbers of expressed genes in Escherichia coli and the content of heat-stable enterotoxin in the small and large intestines Improve the mass fraction of proteins, in vitro digestibility and biological activity; reduce mass of oligosaccharides and phytate. However, longer fermentation time is required in future studies.

Not defined

Yang et al. (2015)11

KasprowiczPotocka et al. (2016)76

Cereal gain (equal amounts of wheat, barley and triticale) and wet wheat distillers’ grain

No

Enterococcus faecium, Lactobacillus plantarum, Lactococcus lactis and Pediococcus pentosaceus

Resuspend starter culture Liquid powder in tap water and mix with liquid feed (final concentration, 108 CFU/g), incubate for 5 days, “back slopping” by replacing 80% of the content (with/without the addition of starter culture);

Inhibit the growth of enterobacteria and molds. Increase organic acid content in the liquid feed. L. plantarum (from starter culture), Lactobacillus panis (naturally occurring) and Pichia fermentans (naturally occurring) are the dominant microorganisms

Pig

Olstorpe et al. (2010)58

Barley, concentrated liquid whey and soybean meal

Formic acid or potassium sorbate at 1 g, 2 g or 10 g/L

10 mL/L of overnight broth of Lactobacillus plantarum REB1

Initial dry matter content ¼ 27%; fermentation at 21  C for up to 7 days

Liquid

Inhibit the growth of yeasts without affecting lactic acid fermentation

Pig

PlumedFerrer and von Wright (2011)77

Corn and soybean meal-based diet

No

Lactobacillus zeae or Lactobacillus casei

Mix 2 kg diet of powder with 4.4 kg tap water in a 10-L bucket and inoculate with 1  107 CFU/g (final number) L. zeae or L. casei and incubate at 30  C for 48 h

Liquid

Pig Feeds fermented with L. zeae or L. casei reduce the numbers of Salmonella in the spleen and downregulate gene expression of inflammatory cytokines in the intestine of Salmonellachallenged piglets

Yin et al. (2014)10

Basal diet (mainly contain maize, wheat, soybean meal, rapeseed meal)

No

Lactobacillus plantarum NCIMB 40087

Mix feed with water at a ratio of 1:1.3 (w/w) and inoculate the culture of L. plantarum to achieve a final concentration of 109 CFU/kg air-dried feed, then incubate at 26  C for 48 h

Liquid

Reduce feed intake and weight gains in starter and grower broilers; increase lactobacilli and reduce coliform and Streptococcus in the small intestine

Missotten et al. (2013)49

Basal diet (mainly contain soybean meal, wheat bran, fish meal, tallow)

No

Bacillus subtilis var. natto, Saccharomyces cerevisiae

Mix two parts of feed with one part of sterile water in 80-L fermenter and centrifuge at 600 rpm for 30 min at 20  C; inoculate the liquid feed with a final concentration of 109 CFU/g feed B. subtilis var. natto and S. cerevisiae, and then incubate for 24 or 48 h

Liquid

Landes Reduce low-moleculargeese weight sugars and pH after fermentation; improve body weight gain and feed conversion; increase the number of Lactobacillus and reduce the number of E. coli in the small and large intestines; increase glutathione peroxidase activity and reduce malondialdehyde content in the heart and liver

Broiler chicken

Chen et al. (2013)78

Continued

TABLE 24.1

Feed stuff Commercial diet (corn, extruded soybean, wheat bran, fishmeal, lactose)

Effects of liquid fermentation of feedstuffs on feed quality as well as animal growth and health.dcont’d Non-microbial additives CuSO4$5H2O (100e200 mg/kg), ZnSO4$H2O (60e160 mg/kg), FeSO4$H2O (50e150 mg/kg) and KI (0.6 e2.4 mg/kg) in diet

Microorganisms used for fermentation Heat-resistant Bacillus subtilis HEWD113 (1.5  108 CFU/g diet)

Techniques Mix 100 g of feed with 300 g of tap water in polypropylene bag, then heat at 80  C for 30 min and store the product at 22.5  C-33.9  C for 21 days

Form Liquid

Effects on feed quality and animal growth and health 2þ

Application

Addition of Cu inhibits the Pig growth of Aspergillus, Alternaria and Penicillium in the fermented liquid feed; Zn2þ plays an important role in the inhibition of Fusarium

References He et al. (2016)79

Production of fermented feed

S. typhimurium in the spleens of piglets after challenge and can also down-regulate the expression of inflammatory cytokines in the gut.10 Besides nutritional considerations, the production of FLF through using microorganisms with specific functions has been investigated. FLF from wheat flour produced through fermentation by reuteran- and levan-producing Lactobacillus reuteri can be included in diets for weaned piglets at 20%e50% of the diet.11 This method reduces the numbers of Escherichia coli and the heat-stable enterotoxin in the small and large intestines due to the production of reuteran from sucrose in the fermented wheat by L. reuteri.11 Although no effect on the growth of piglets was observed by these authors when a high proportion of fermented wheat as a FLF was included in the piglet diet, results of this study did indicate the feasibility of implementing the concept of probiotics and functional aspects of microbial products to produce FLF for farm animals.

Solid-state fermentation in feed production Compared with liquid fermentation, solidstate fermentation has the advantages of generating less waste water, greater product stability, lower energy use during fermentation, and ease of transportation of the fermentation product.4 Based on these advantages, many fermentation studies and feeding trails have been carried out in recent years. The techniques used for solidstate fermentation of feed and the effect of the fermentation products to improve feed quality, animal growth and animal health are summarized in Table 24.2. Due to the low amount of water used in solidstate fermentation, yeast and fungi are included in the fermentation system to facilitate the fermentation processes.4 Saccharomyces cerevisae is commonly used as the yeast in the starter culture for solid-state fermentation, whereas

413

Aspergillus oryzae and Aspergillus niger are the most used fungi. These two kinds of microorganisms not only consume oxygen in the mixed feeds during the early stage of fermentation, but also produce a large spectrum of enzymes (e.g., cellulase, phytase, and amyloglucosidase), vitamins, and growth factors for bacteria. The bacteria used for solid-state fermentation include lactic acid bacteria (e.g., LAB; L. plantarum, Pediococcus acidilactici, P. pentosaceus) and Bacillus (e.g., Bacillus subtilis) (see Table 24.2). The use of a single feedstuff as the substrate is preferred over mixed feedstuffs for solid-state fermentation because the fermentation process and the quality of the fermented feed can be controlled more easily before the resultant fermentation product can be mixed with other feedstuffs prior to feeding to animals. Solidstate fermentation of soybean meal has been investigated extensively for manufacturing fermentation feeds for non-ruminant species, especially pigs (Table 24.2). Moisture content for the solid-state fermentation of soybean meal ranges from 25% to 50%, depending on fermentation temperature (25  C to 40  C), final conditions of the product (wet or dry) and the starter culture used.12,13 Apart from the fermentation of soybean meal, starter cultures vary with feedstuffs that contain different amounts of protein and carbohydrate (Table 24.2). Usually, Saccharomyces is used as the yeast for fermentation of many feedstuffs, while Bacillus is included in the starter culture when protein-rich feedstuffs are substrates in the fermentation system. In contrast, LAB are included in the starter culture when carbohydrate-rich feedstuffs are used for solidstate fermentation (Table 24.2). Due to high phytate-phosphorus content in rapeseed meal, phytase produced by A. niger14 or from Pichia pastoris15 can be added to the fermentation system. A more economical method is the inclusion of A. niger in the starter culture for the fermentation of rapeseed meal in the presence of wheat bran.16,17 As a means to improving the quality

VI. Biotechnologies and others in animal production

TABLE 24.2

Feed stuff

Summary of effects of solid-state fermentation on feed quality and animal growth and health.

Non-microbial additives

Microorganisms used for fermentation

Techniques

Form

Effects on feed quality and animal growth and health

Application

References

Soybean meal

2.4% sucrose

Saccharomyces cerevisae, Lactobacillus acidophilum, Enterococcus faecalis, Bifidobacterium bifidum, Bacillus licheniformis, Bacillus subtilis

Wet Mix with diluted microbial culture (final concentration, 105 e106 CFU/g) at a ratio of 10:3 and pack in multi-layer polythene bags with a one-way valve, and then incubate at 25  C 5  C for up to 15 days

Inhibit the growth of Salmonella aureus and enterotoxigenic Escherichia coli

Pig

Yu et al. (2009)12

Soybean meal

No

Enterococcus faecium SLB120

Soybean meal (50% moisture, pH 6e7) is mixed with a final concentration of 1  108 CFU/g E. faecium SLB120; incubate at 40  C for 36 h, then dry at 60  C to an estimated moisture of 10%

Dry

Remove trypsin inhibitor by 39% and raffinose and stachyose by 92%; Improve digestibility of amino acids in weaned piglets

Pig

Jeong et al. (2016)13

Soybean meal

Glucose

Bacillus subtilis KC 101, Saccharomyces cerevisae JM 102, Bacillus lactis RG 103

Mix 50 g glucose, 516 g water, 1.426 kg soybean meal, and microorganisms (a final concentration, CFU/gram: 2.0  107 B. subtilis KC 101, 1.0  106 S. cerevisae JM 102, 2.5  106 B. lactis RG 103); incubate at 35  C for 4 days. One-half of the fermented soybean meal is dried at 60  C to a moisture content of 10%

Wet/ Dry

Increase daily gain and feed conversion ratio; wet fermented soybean meal is better than dried fermented soybean meal (supplementation of 5% dry matter basis); increase carbohydrate metabolism and butyrate production; decrease amino acid catabolism and isovalerate production in the large intestine of piglets

Pig

Zhang et al. (2018)19

Soybean meal

No

Lactobacillus plantarum, Bacillus subtilis, Saccharomyces cerevisae

Soak soybean meal with distilled water to achieve 30% moisture content and inoculate with 108 CFU/g of each of the three microorganisms, then incubate anaerobically at 37  C for 48 h; after fermentation, dry at 50  C to a moisture content of 10%

Dry

Increase crude protein, trichloroacetic acid soluble protein and decrease glycinin, ß-conglycinin, trypsin inhibitor, stachyose and raffinose after fermentation; addition of 10% or 15% fermented soybean meal to the diet increases serum levels of IgG, IgM and Ig A and decreases serum urea

Pig

Zhu et al. (2017)21

Soybean meal

Brown sugar (96.2% sucrose), proteases (mixture of neutral protease from B. subtilis 1.398 and acid protease from A. niger 3350 at ratio of 3:1, 50,000 IU/g activity)

Streptococcus thermophiles (CGMCC No. 1.2471), Saccharomyces cerevisiae (CGMCC No. 2.1793), Bacillus subtilis MA139

Mix soybean meal with 0.5% Dry (w/w) brown sugar, 0.3% (w/w) protease, 10% (v/w) liquid starter culture (1  107 CFU/g) and water to achieve a 40% moisture content, then pack in multi-layer polythene bags with a gas-pressure opening valve and incubate at 40  C for 5 days; air-dry products after fermentation

Replacing 6% soybean meal with fermented soybean meal improves average daily gain and average daily feed intake in nursery piglets

Pig

Wang et al. (2014)18

Rapeseed cake

Phytase (6-phytase expressed in Pichia pastoris)

Spontaneous fermentation

Dry Mix rapeseed cake with water at a ratio of 1:2 (w/w) and add phytase (0.1% w/w of dry rapeseed cake); incubate at 30  C for 24 h under anaerobic conditions; after fermentation, deactivate enzyme activity at 70  C within 15 min then dry at 55  C

Reduce the concentrations of phytate-phosphorus and glucosinolates; inclusion of 15% fermented rapeseed cake in the diet of turkeys increases final body weight

Turkey

Dråzbo et al. (2018)15

Cotton seed meal

No

Bacillus subtilis, Aspergillus oryzae and Aspergillus niger

Mix 1 kg of cotton seed meal Dry with 1.2 L distilled water and 105 CFU/mL B. subtilis, A. oryzae and A. niger in a tank fitted with a one-way valve; incubate at 30  C for 7 days; dry at 50  C for 3 days after fermentation.

Reduce crude fiber and gossypol; increase crude protein and lactic acid bacteria after fermentation; replacement of soybean meal with fermented cotton seed meal (20% as-fed basis) does not affect daily weight gain or feed intake, but reduces abdominal fat and ileal coliforms;

Broiler chicken

Jazi et al. (2017)25

Barley (Hordeum vulgare)

No

Yeast (Wickerhamomyces anomalus) or lactic acid bacteria (Pediococcus acidilactici, Pediococcus pentosaceus, and Lactobacillus plantarum)

Crimp and pack into plastic barrels; inoculate with yeast, lactic acids bacteria, or their combination for 6 weeks

Inhibit the growth of mold and bacteria that belong to family of Enterobacteriaceae

Pig

Welin et al. (2015)80

Wet

Continued

TABLE 24.2

Feed stuff Sweet potato (Ipomoea batatas var. Beauregard)

Summary of effects of solid-state fermentation on feed quality and animal growth and health.dcont’d

Non-microbial additives

Microorganisms used for fermentation

Final Saccharomyces concentration boulardii MAY796 per liter: 1 g KH2PO4, 0.5 g MgSO4, 0.5 g MnSO4 and 0.5 g ZnSO4

Techniques

Form

Effects on feed quality and animal growth and health

Application

References

Wet Mix 60 g of sweet potato flour and minerals with 300 mL H2O in a 1-L flask; autoclave at 121  C for 30 min; after cooling, add 10 mL of S. boulardii, and then incubate at 30  C, 300 rpm for 5 days

Improve the digestibility of crude protein, total amino acids, crude fiber, crude fat and ash after fermentation

Not defined

Campbell et al. (2017)20

Wheat bran No

White rote fungi (Pleurotus eryngii)

Mix wheat bran with P. eryngii Dry particles at a ratio of 9:1 (w/w); then add water to achieve moisture content at 60% and incubate at 30  C for 12 days; dry at 70  C for 24 h to reduce moisture content to 10% after fermentation.

Wheat bran fermented by P. eryngii increases lignocellulolytic enzyme activities; replacing 10% (as-fed basis) maize with fermented wheat bran in feed increases the expression of antioxidant molecules in peripheral blood mononuclear cells in broilers

Broiler chicken

Wang et al. (2017)23

No Soybean cotyledon fiber, distiller’s dried grains with solubles (DGGS) and soybean hulls

Aspergillus oryzae, Trichoderma reesei, Phanerochaete chrysosporium

Mix soybean fiber or DGGS with soybean hulls and water to achieve moisture content of 75%; adjust pH to 5 and inoculate with 5% v/w mixed fungus culture or 10% v/w single fungus culture; then incubate at 30  C for 6 days

Wet

Incubation with T. reesei and P. chrysosporium for 36 h and A. oryzae for a further 108 h results in maximum xylanase and cellulase activity

Not defined

Lio and Wang (2012)22

Napiergrass (NH4)2SO4 or pangolagrass

Cellulolytic yeast (Entrophospora sp. NP1) or bacteria (Bacillus subtilis H8)

Mix 100 g dried grass particle (2e3 cm), 5 g (NH4)2SO4 and water in flask (moisture 65%, pH 6.8) and incubate at 30  C for up to 42 days

Dry

Increase protein content and in vitro digestibility after fermentation; the fermented feed can be used to replace corn in the diet for broiler chickens

Broiler chicken

Hsu et al. (2013)81

Rapeseed cake and wheat bran

No

Aspergillus niger CICC41258

Mix rapeseed cake (70%) with wheat bran (30%) and adjust moisture content to 60%, autoclave at 121  C for 20 min; inoculate with 3  107 spore A. niger; incubate at 32e34  C for 72 h; and then dry at 55e65  C for 48 h

Basal diet (40% corn, 40% soybean meal, 20% wheat bran)

No

Bacillus subtilis, Enterococcus faecium

Basal diet (45% corn, 45% soybean meal, 10% wheat bran)

No

Rapeseed No meal, sunflower meal, faba beans, wheat bran, potato pulp Highmoisture maize (HMM)

Increase trichloroacetic acid-soluble protein, crude protein, ether extract content; decrease NDF and phytic acid content; replace 10% (as-fed basis) of dietary rapeseed meal with fermented rapeseed meal improves average daily gain and feed conversion ratio

Pig

Shi et al. (2015, 2016)16,17

Mix feed with sterile water to Wet achieve 40% moisture; inoculate at a cell density of 3  108 CFU/g B. subtilis, 108 CFU/g E. faecium; transfer the feed to plastic bags equipped with a one-way valve; and incubate at room temperature (25  C) for 96 h

Supplementing sow diets with 15% fermented mix feed from parturition to weaning increases sow average daily feed intake, milk production, milk IgA content and nutrient digestibility; promote reproductive performance and growth performance of offspring

Pig

Wang et al. (2018)82

Bacillus subtilis, Enterococcus faecium

Mix basal substrates with water Dry to achieve 40% moisture content; inoculate at a cell density of 108 CFU/g of B. subtilis; incubate at 37  C for 24 h; inoculate 108 CFU/g of E. faecium and incubate anaerobically at 37  C for 48 h; then heat at 105  C for 30 min and dry at 65  C for 24 h

Decrease soybean antigen proteins (beta-conglycinin and glycinin) in the first-stage fermentation; increase the content of crude protein, ash, total phosphorus, small peptides and free amino acids after the second-stage fermentation.

Pig

Shi et al. (2017)83

Lactic acid bacteria (LAB) from a commercial product (Pig Stabiliser 600)

Mix potato pulp (dry matter Wet 14%) with LAB powder at 1 g/t; then mix with other feedstuffed and vacuum-pack in bags; incubate at 35  C for up to 5 days

Increasing wheat bran in the fermentation system increases the solubility of protein and phosphorus

Pig, Poultry

Poulsen and Blaabjerg (2017)84

Kofa Grain alone (5 mL/kg Wet HMM) or enzyme (645 HEC/kg HMM) and sodium benzoate (0.3 g/kg HMM) plus Feedtech F22 (mixed bacteria at a final concentration of 2  105 CFU/g HMM) pack in plastic bags; store at 10  C for 7 weeks

Improve aerobic stability of HMM from 32 h to 104e168 h; no blooming of enterobacteria in fermented liquid feed containing HMM/basal feed mixture at the concentration of 20% (w/w)

Pig

Canibe et al. (2014)44

Feedtech F22 Kofa Grain (Lactococcus lactis, (propionic acid, 370 mg/g; Pediococcus acidilactici, Enterococcus faecium, sodium Lactobacillus plantarum) benzoate, 140 mg/g; sodium propionate, 110 mg/g); cellulase-xylanase and sodium benzoate

Dry

418

24. Fermentation techniques in feed production

of fermentation products, a suitable carbohydrate (e.g., glucose or sucrose) and proteases can also be added to the fermentation system.12,18,19 Investigations of factors affecting the quality of solid-state fermentation have focused on effects of storage temperature and time of survival of yeast (Saccharomyces boulardii) during solid-state fermentation of feedstuffs. Studies with sweet potato flour indicate that storage at 4  C for up to 12 months has no significant effect on the numbers of viable S. boulardii in the fermentation product, but storage at 25  C reduces the numbers of viable S. boulardii by 19% after 8 months and by 40% after 12 months.20 After fermentation, the contents of crude protein and trichloroacetic acid-soluble protein in fermented soybean meal increase, but the contents of crude fiber, phytate-phosphorus, anti-nutritional factors (e.g., glycinin, ß-conglycinin, trypsin inhibitor, stachyose, raffinose, glucosinolates and gossypol) decrease.13,15,21 Interestingly, the total activities of phytase and fibrolytic enzymes [e.g., lignocellulolytic enzymes (xylanase and cellulose) produced by microorganisms] also increase in the feedstuff after fermentation.14,15,22,23 These enzymes help degrade fibers in the feed and release fiberassociated nutrients (e.g., proteins and starch) during fermentation, and also function in the digestive tract of animals if the enzymes are not inactivated by feed processing, pH, and/or endogenous proteases. Up to 20% of the solid-state fermentation product can be added to diets to replace highquality feedstuffs such as fishmeal, soybean meal and corn without adverse effects on growth performance of animals (Table 24.2). Improvements in the digestibility of feeds, feed conversion ratio, anti-oxidant activity, and immune function, along with a reduction in the number of pathogenic bacteria (e.g., E. coli and Salmonella) in the gastrointestinal tract have been reported for both pigs and chickens.12,13,15,19,21,23,24

Strategies to improve silage fermentation The characteristics of the fermentation substrate for ensiling are different from those for liquid- and solid-state fermentation processes. Normally, the fermentation substrate is fresh plant material which contains a large amount of water and the fermentation system harbors abundant active microorganisms. The number of live microorganisms in biomass used as the fermentation substrate for ensiling ranges from 105 to 109 CFU/g, depending on the position of the plant and harvesting method.25 It is noteworthy that, compared with harvesting by hand before chopping, chopping by a machine during harvesting can increase LAB in the grass.25 The microorganisms on the fresh plantsource feedstuffs are dominated by LAB followed by enterobacteria and yeast, and these bacteria and yeast are also the major microorganisms after ensiling.25,26 After ensiling, the most abundant and diverse microorganisms in maize silage are usually LAB and yeast, and the species differ from those present before ensiling.27,28 In order to prevent the overgrowth of harmful bacteria (e.g., enterobacteria, Clostridium), yeast and molds in silage during spontaneous fermentation, starter cultures mainly contain Lactobacillus and yeast in combination with various organic acids (formate, propionate, acetate, and benzoic acid) that are added before ensiling.29 Some examples of techniques used for ensiling and the improvements in feed quality are summarized in Table 24.3. After fermentation, in parallel with the increase in feed quality, the metabolites produced by microorganisms in the silage contribute to improvements in the efficiency of feed utilization by animals. For example, lovastatin, which is produced by Aspergillus terreus when rice straw is used as a fermentation substrate, inhibits the growth of Methanobrevibacter and methane production, while stimulating the growth of Ruminococcus

VI. Biotechnologies and others in animal production

TABLE 24.3

Feed stuff

Summary of effects of fermentation/ensiling on feed quality and growth and health of ruminant. Non-microbial additives

Microorganisms used for fermentation Techniques

Form

Effects on feed quality and animal growth and health

Application

References

Orchardgrass (Dactylis glomerata L.) or wheat (Triticum aestivum L.)

No

Spontaneous fermentation

In the form of balage and stored in large round bales with an in-line wrapper during winter and exposure to air for 32 days

Wet

Tend to increase in situ digestibility of dry matter with exposure time for orchardgrass

Ruminant

Rhein et al. (2005)85

Alfalfa

Formic acid (purity 90%)

Lactobacillus plantarum MTD/1 or spontaneous fermentation

Mix chopped alfalfa in 1 L glass jar at a density of 500 g wilted material per liter; add 106 CFU/g L. plantarum or 4.4 mL/kg formic acid; then store anaerobically at w22  C for 60 days

Wet

The addition of L. plantarum or formic acid reduces pH and ammonia-N of the silage; no effect on in vitro fermentation using rumen fluid as inoculum

Ruminant

Contreras-Govea et al. (2016)86

Rice straw

Urea

Aspergillus terreus ATCC 74135

Mix dried rice straw powder with water (containing 1% urea or not) to achieve 50% moisture and autoclave at 121  C for 15 min; inoculate with 10% 107 spores/mL A. terreus and incubate at 25  C for 8e14 days

Wet

Goat Inhibits growth and methane production by Methanobrevibacter smithii in vitro by lovastatin produced by A. terreus; reduces methane production by 32% and improves dry matter digestibility by 13% in goats fed 40% fermented feeds

Wheat straw (variety, UP 2338)

Mineral solution (0.5 g/L KH2PO4, 0.5 g/L MgSO4$7H2O, 0.5 g/LCa(NO3)2$4H2O, pH 5.5)

Ganoderma sp. rckk02

Mix fungal pellet with autoclaved (121  C for 15 min) wheat straw pellet (1.5e2 cm) at 0.75% w/w, then mix with mineral solution at a ratio of 1:3; and incubate at 30  C for 21 days

Wet

Decrease acid detergent fiber (ADF), neutral detergent fiber (NDF), hemicellulose, lignin and cellulose content till 15th day; increases the intake of dry matter, digestible crude protein, total digestible nutrients and nitrogen in vivo

Goat

Shrivastava et al. (2012)87

Apple pomace

1.5% urea, 0.4% (NH4)2SO4, 0.5% commercial mineral mix

Spontaneous fermentation

Mix nitrogen sources, mineral Dry mix and apple pomace on a concrete surface; remix substrates 3 times per day from d 0 to d 6 of the solid-state fermentation; sundry the products after 6 days of fermentation

Incorporation of 10% of fermented apple pomace in the diet to replace part of the alfalfa improves antioxidant activity in plasma and growth

Lamb

RodríguezMuela et al. (2015)88

Faseleh et al. (2013)30; Mohd et al. (2018)31

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24. Fermentation techniques in feed production

albus in the rumen.30,31 Therefore, the use of starter cultures and additives for ensiling not only improves feed quality and digestibility, but also regulates the community and activity of gut microbiota, as well as the digestion and absorption of nutrients in animals. These novel findings will facilitate the development of new feed additives and technologies for the production of stable silage of high-quality.32

Fermented feed for aquaculture In recent years, high-quality and low-cost fermented feed ingredients of plant origin have been used to replace animal proteins for aquaculture. Some examples of fermented feeds used for aquaculture are listed in Table 24.4. Fermented soybean meal is widely used in aquaculture as a high quality protein source. Replacement of 25% fishmeal in the diet with fermented soybean meal increases weight gain and growth rate of prawns (Macrobrachium nipponense).33 Even 100% replacement of fishmeal with fermented soybean meal does not have adverse effects on the growth of prawns.33 However, when challenged with Aeromonas hydrophila, the mortality of prawns increases when fed a 100% replacement of fishmeal with fermented soybean meal because of impaired non-specific immunity.33 Similarly, replacing 25% of fishmeal with Lactobacillus-fermented soybean meal improves non-specific immunity and reduces oxidative stress in white shrimp (Litopenaeus vannamei).34 Studies with fish (juvenile barramundi) have also shown that replacing 75% of fishmeal in the diet with soybean meal fermented with S. cerevisae enhances the apparent digestibility of dietary protein.35 Mechanisms responsible for the growth-promoting and anti-inflammatory effects of fermented soybean meal are explained in part by the presence of anti-oxidative and antimicrobial peptides produced by Bacillus subtilis in the fermented feeds.36e38 Studies involving other fermented feeds for aquaculture revealed that the replacement of

25%e50% fishmeal in the diet of red sea bream with A. oryzae-fermented rapeseed meal enhances the activities of lysozyme and bactericidal and peroxidase activities, as well as resistance to oxidative stress, without negatively affecting growth performance or nutrient utilization.39 Depending on the characteristics of the feedstuffs used for fermentation, chemical and mechanical treatments (e.g., acetic acid and heating) are commonly used to treat beans before fermentation to reduce fiber content and improve nutrient digestibility.40 Besides, nonmicrobial additives such as ammonia can be added to the fermentation biomass (including distiller’s grain) to promote microbial growth and protein synthesis by microbes.41 Furthermore, through the implementation of a twostep (aerobic and anaerobic) fermentation process, the nutritional values (e.g., an increase in protein content and a decrease in fiber content) of feedstuffs can be improved substantially.41 To date, the amino acids profiles of feed before and after fermentation and the improvement in amino acids digestibility have not been investigated fully. Considering that amino acid nutrition is important in aquatic animals, particularly intestinal health,42,43 studies on quality and amino acid composition of proteins in feed after fermentation and the in vivo digestibility of amino acids in aquatic animals are warranted to screen and optimize feed fermentation techniques for a more sustainable aquaculture.

The combined use of microorganisms and enzymes in fermentation of feedstuffs In some studies, various kinds of enzymes are used alone or in combination with microorganisms to facilitate fermentation of feedstuffs. Enzymes, including proteases from Bacillus or Aspergillus,18 phytase from Aspergillus,14 or Pichia,15 and some commercially available enzymes (cellulase, xylanase, a-galactosidase, b-glucanase and pectinases),44,45 are used to

VI. Biotechnologies and others in animal production

TABLE 24.4 Summary of effects of fermentation on feed quality and growth and health of aquatic animals.

Feed stuff

Non-microbial additives

Microorganisms used for fermentation

Techniques

Form Dry

Solventextracted soybean meal

No

Saccharomyces cerevisiae

Mix 2 kg of soybean meal, Saccharomyces cerevisiae (cell density, 3  106 CFU/g meal) and 1.6 L distilled water; incubate at 30  C for 5 days; and dry at 60  C for 24 h after fermentation

Soybean meal

No

Bacillus subtilis E20

Dry Mix soybean meal with water in a 2-L glass breaker to achieve moisture content of 50%, inoculate B. subtilis (106 CFU/g mixture) after autoclaving at 121  C for 20 min; incubate at 40  C for 72 h; after fermentation, autoclave the mixture; then dry at 45  C until moisture content is below 10%

Rapeseed meal (RM)

No

Aspergillus oryzae

Mix 1 kg of autoclaved RM with 1.3 L sterile water; inoculate with A. oryzae at 4  108 CFU/kg RM; incubate at 30  C for 24 h; further incubate at 37  C and 95% humidity for 24 h on trays; freeze-dry after fermentation

Bean (Phaseolus vulgaris) meal

Soak with 60 mmol/L glacial acetate (pH 3.1) for 16 h

Rhizopus oligosporus NRRL2710

Distillers’ grain

Aqueous ammonia (0.8% v/v), mix with substrate at 1:1 w/v

Trichoderma viride, Rhodopseudomonas palustris

Effects on feed quality and animal growth and health Replacing 75% of the fish meal with fermented soybean meal improves the apparent digestibility coefficient of protein

Application

References

Fish (juvenile barramundi)

Ilham and Fotedar (2017)35

Shrimp Increase protein content and (Litopenaeus total amino acids by 19% after vannamei) fermentation; antimicrobial peptides isolated from B. subtilis fermented soybean meal inhibit growth of Vibrio alginolyticus and V. parahaemolyticus in vitro; replacing 15% of fishmeal with fermented soybean meal can prevent vibriosis in shrimp aquaculture

Shiu et al. (2015)37; Cheng et al. (2017)36

Dry

Replacing 25%e50% of fish meal in the diet with fermented rapeseed meal does not affect growth and nutrient utilization in red sea bream; enhance lysozyme, bactericidal, peroxidase activities and resistance against oxidative stress

Fish (red sea bream)

Dossou et al. (2018)39

Cook at 90  C for 30 min, cool, and incubate at 34.9  C for 51 h; air-dry at 50  C for 24 h

Dry

Decrease fiber, ash and tannin content; increase protein and lipid content and digestibility of dry matter and protein

Fish (tilapia)

Valdez-Gonz
alez et al. (2017)40

Aerobic (T. viride) and anaerobic (R. palustris) two-step fermentation

Dry

Increase crude protein (including true protein), crude fat content; decrease crude fiber and ash content

Fish

Zhang et al. (2013)41

422

24. Fermentation techniques in feed production

remove anti-nutritional factors in feed. In order to compare the efficacy of removing antinutritional factors in peas through fermentation or enzymatic treatment, two methods involving either solid-state fermentation by Bacillus subtilis or mixed exogenous enzymes (a-galactosidase, protease, b-glucanase and pectinases) in acidic conditions (lactic acid and acetic acid) have been investigated.45 Both methods have the capacity to reduce resistant starch and total dietary fiber in peas.45 However, compared with the mixed exogenous enzymes, fermentation by B. subtilis removes more raffinose and trypsin inhibitors, but has a lower ability to degrade phytates in peas. Compared to the maizewheat-soybean meal-based diet, replacement of soybean meal with either fermented or enzyme-treated peas does not affect body weight gain of broilers, but improves feed conversion due to reduced feed intake.45 Although the efficacy of the above methods to improve feed quality for farm animals must be enhanced further, the results indicate positive and important results from combined or multistep fermentation processes for manufacturing feeds. First, the natural characteristics of the fermentation substrate (feedstuff) are the determinant for the selection of enzymes and microorganisms used for fermentation. Second, proper methods (e.g., mechanical disruption, high temperature, acid/alkaline treatment, and organic solvent extraction) for treating raw material before fermentation will facilitate the enzymatic treatment and fermentation processes. Third, whether enzymatic treatment is required before fermentation or whether fermentation precedes enzymatic treatment or the combination of enzymatic treatment and microbial fermentation depends on the properties of the feed and enzymes (either supplemented or produced by microorganisms during fermentation) used. Forth, nutritional evaluations are required to assess both the improvement in feed quality and economical feasibility.

Fermented milk for young farm animals To date, studies on the application of fermented milk to farm animal production are limited. Existing data show that the feeding of Lactobacillus delbrueckii subsp. bulgaricus strain 2038-fermented milk to piglets for two weeks increases the numbers of indigenous lactobacilli and bacterial diversity in the cecum.46 Similar results have been reported for pigs receiving oral administration of the milk fermented with L. casei strain Shirota for two weeks.47 Meanwhile, concentrations of organic acids especially acetate and propionate increase in fecal samples.47 Although these beneficial effects are not likely due to the Lactobacillus strains used for milk fermentation (as their numbers in the gut are very low compared to the indigenous Lactobacillus), the positive results pave the way for further investigation of the methods to effectively ferment milk for farm animals, especially young piglets and calves. Studies involving in situ digestion of fermented milk and fermented soybean meal by beef cattle have shown that milk can be fermented with S. cerevisae and LAB for 72 h to yield commercial yogurt, and further fermentation of milk together with soybean meal for 3 days improves digestion in the rumen.48 Our preliminary in vivo study with low-birth-weight piglets weaned at 21 days of age indicates that fermented milk replacer powder enhances feed conversion ratio during a 28-day period postweaning. Of interest, consumption of 150 mL fermented milk per piglet once daily within the first 28 days post-weaning reduces the intake of solid feed by weanling piglets (Table 24.5). Similar results have been reported for starter and grower broilers, but not finisher broilers when fed a Lactobacillus-fermented moist feed.49 Interestingly, daily feed intake and gains of broilers are reduced when they are fed a fermented feed at an early age, but birds exhibit improvements in feed conversion rate

VI. Biotechnologies and others in animal production

423

Production of fermented feed

TABLE 24.5

Effects of fermented milk replacer powder on the growth of low-birth-weight piglets weaned at 21-days of agea.

Days after weaning

Control D water

Control D fermented milk

Control D non-fermented milk

P-value

AVERAGE DAILY BODY WEIGHT GAIN (G/DAY) d 1-7

45.1  6.1A

-33.3  12.0B

42.1  32.0A

P < 0.05

d 8-14

138.1  13.1

103.6  17.0

144.6  16.0

0.14

d 15-21

254.0  16.0

232.1  19.0

250.0  30.0

0.76

d 22-28

303.2  26.0

300.0  24.0

298.2  28.0

0.99

d 1-14

91.6  2.6A

36.5  2.6B

94.1  19.0A

P < 0.05

d 1-28

185.1  4.4

151.0  1.5

187.1  23.0

0.20

80.8  7.8

139.0  28.0

0.10

248.6  28.0

P < 0.01

AVERAGE DAILY FEED INTAKE (G/DAY) d 1-7

130.2  5.6

d 8-14

307.5  13.0

160.4  5.8

d 15-21

455.5  11.0

336.3  13.0

430.1  31.0

P < 0.05

d 22-28

633.6  25.0

477.1  16.0

589.0  29.0

P < 0.01

d 1-14

218.9  4.1

A

120.6  2.5

193.8  28.0

P < 0.05

d 1-28

381.7  11.0

263.6  6.8

351.7  28.0

P < 0.01

0.15  2.2

0.08

A A A

A

B

A

B B

B B

A A A A

FEED: GAIN RATIO (G/G) d 1-7

2.96  0.28

-3.70  1.9

d 8-14

2.23  0.09

1.57  0.19

1.69  0.14

P < 0.05

d 15-21

1.80  0.05

1.46  0.03

1.70  0.11

P < 0.05

d 22-28

2.10  0.09

1.58  0.02

1.95  0.06

P < 0.01

d 1-14

2.40  0.11

3.34  0.23

2.13  0.19

P < 0.01

d 1-28

2.06  0.06

1.75  0.03

1.90  0.10

P < 0.05

A A A B A

B C B B C

B AB A B AB

a Values are means  SD, n ¼ 9. AC: Within a row, means with different superscript letters are different (P < 0.05). Low-birth-weight piglets were weaned at 21 days of age (body weight at weaning ¼ 3.50  0.33 kg) and fed a corn-soybean meal-whey-fish meal-based diet. Piglets in the fermented milk group or non-fermented milk group were fed 150 mL of fermented milk or non-fermented milk per piglet once daily, respectively, for the first two weeks of the experiment (days 1e14). Milk replacer powder (15% milk powder, 5% glucose, and 80% tap water) were fermented with a mixed starter culture (cell density in milk before fermentation: 4  107 CFU/L Pediococcus pentosaceus, 2  107 CFU/L Lactobacillus plantarum, 4.5  106 CFU/L Bacillus subtilis, and 1.8  107 CFU/L Saccharomyces cerevisae) in a gas-tight plastic bucket fitted with a one-way valve at 30  2  C for 14 h. The amount of fermented or non-fermented milk consumed by piglets was converted to the dry matter basis for calculation of total feed intake. During the entire experiment, all pigs had free access to tap water.

and gut microbiota during grower and finisher stages. The adverse effect associated with early feeding of FLF may be due to the large number of microorganisms and the high concentration of microbe-derived organic acids as fermentation products that either compete with the

host for nutrients or create stress to the under-developed digestive system of young animals.50,51 Among domestic animals, pigs exhibit the most severe (15e25%) naturally occurring intrauterine growth restriction (IUGR) due to uterine

VI. Biotechnologies and others in animal production

424

24. Fermentation techniques in feed production

and placental insufficiencies.52 Most IUGR piglets die before weaning.53 Therefore, development of novel feeding strategies such as fermented milk to rescue IUGR piglets after birth will have a great impact on pig production. Further investigations are needed to optimize feed formulation (including fermented milk powder), the amount and type of microorganisms for fermentation, and feeding strategies to improve the survival and growth of IUGR piglets.

Microbial ecology of the fermented feed After the initiation of feed fermentation under specific temperature, moisture and redox conditions, microorganisms in the feed grow and the biotransformation of varieties of substrates occurs at different rates.54 After fermentation, the active microorganisms plus their metabolic products in the fermented feed will impact the microbial ecology in the gastrointestinal tract of the animal. The characteristics (including the microbiota and the starting material) of liquid fermentation and solid-state fermentation differ greatly from those of ensiling. The microbiology of ensiling has been discussed extensively.25,26,29 Here is a brief summary and discussion of recent progress on the microbiological aspects of liquid fermented feed and solid-state fermented feed, as well as their impact on the microbial ecology of the gastrointestinal tract. Early studies investigating the change in microbial community during fermentation with or without the inoculation of LAB in the liquid fermentation of a basal diet (e.g., barley, concentrated liquid whey, and soybean meal) have shown that a period of nine (9) days is required for the non-fermented liquid feed (NFLF) to reach the LAB level similar to that in FLF inoculated with L. plantarum.55 The numbers of yeast and enterobacteria are variable in NFLF compared to FLF inoculated with L. plantarum, and the numbers of enterobacteria in FLF remain

low. In this study, at 75%e90% replacement rates, the microbial community remained relatively stable between days 20 and 50 of the experiment. It is noteworthy that yeasts and enterobacteria grow faster than LAB for the first week during spontaneous fermentation even when formic acid is added to the feed.55,56 The excessive growth of yeast and the large numbers of enterobacteria present in the FLF will affect the quality and safety of the product.56,57 Therefore, in this operation system, feeding substrates to the biomass can only be carried out after its microbial community is stable and dominated by LAB. Studies on the in FLF samples using a variety of feedstuffs indicate that Candida milleri, Kazachstania exigua, Candida pararugosa and Kazachstania bulderi predominate in the fermented products regardless of the use of LAB in starter cultures.8 However, when fermented with a mix of cereal grains blended with distillers’ grains or whey, Kluyveromyces marxianus, Pichia anomala, Pichia fermentans, Pichia galeiformis and Pichia membranifaciens are the dominant microorganisms.58,59 This may be due partially to different fermentation substrates and different culture conditions used in those studies. After fermentation, the majority of smallmolecular-weight fermentation products in fermented feeds are lactate, followed by ethanol, formic acid, and acetate.8 However, the effects of fermented feed on gut microbial ecology vary, depending on the properties of feedstuff (as fermentation substrates), the starter culture strains used, as well as the physical characteristics and amounts of the fermented feed fed to the animals. Studies over the past decades have shown that pigs fed the feed fermented with Lactobacillus (mostly L. plantarum) have a higher abundance of LAB and a lower abundance of enterobacteria (e.g., E. coli, Salmonella) in the intestine.10,60e62 Similar results have been reported for broiler chickens infected with Salmonella enteritidis.63 Feed fermented with Bacillus or Saccharomyces species have similar effects on

VI. Biotechnologies and others in animal production

Perspective and future directions

the LAB and enterobacteria in the intestines of pigs and broiler chickens; however, their effects on overall microbial diversity and abundance vary greatly due to the techniques used and the gut segments investigated.64e68 The combined use of Lactobacillus, Bacillus and Saccharomyces for feed fermentation yields similar effects on gut microbiota.69,70 In addition, abundances of cellulolytic bacteria Prevotella and butyrate-producing bacteria Roseburia increase after the fermented feeds are fed to pigs.68 The increase in the degradation of dietary fiber by cellulolytic bacteria in the large intestine promotes the production of short-chain fatty acids and this, together with the increased production of lactate (resulting from the increased abundance of Lactobacillus in the intestine), favors butyrate production.71,72 Indeed, studies on the effects of fermented feed on the metabolism of gut microbiota have shown that replacement of 5% (dry matter basis) soybean meal with wet fermented soybean meal increases carbohydrate metabolism and reduces amino acid metabolism in the colon of pigs, leading to an increase in the production of butyrate and a reduction in the formation of isovalerate (a product of the catabolism of branched-chain amino acids by bacteria) in the colon.19 The underlying mechanisms for this regulatory effect on the growth and metabolism of the gut microbiota are not understood. Further studies are warranted to identify the key regulatory factors present in fermented feed and the mode of their actions.

425

contributes to the microbial and chemical hazards of the fermentation products.6 As noted previously, microbial hazards include the presence and overgrowth of toxin-producing E. coli, Salmonella, Clostridium botulinum, Bacillus cereus, Listeria monocytogenes, Mycobacterium bovis, and various molds in fermented feeds and silages, as well as chemical hazards such as phytoestrogens, pyrrolizidine alkaloids, mimosine, tropane alkaloids, tropolone alkaloids, ergot alkaloids, and prussic acid. Fortunately, the occurrence of harmful microorganisms is minimal under usual manufacturing conditions and can be controlled by good quality control practices and optimized fermentation parameters such as composition of the starting materials used for fermentation, formula of starter culture, fermentation conditions, and post-fermentation processes. The survival of S. enterica subsp. enterica serovar typhimurium in liquid feed largely depends on the temperature of fermentation.9 Further, toxins can be present in plants consumed by ruminants and cause adverse effects on the animals.6 Ensiling is a good method to decompose some of the toxins in plants. However, fermentation of plantsource feedstuffs during ensiling does not result in the catabolism of some toxins such as coumestrol.73,74 With the increasing use of new feed resources, identifying microbial and chemical hazards in feedstuffs, as well as developing effective strategies to analyze toxins in the fermentation products and to minimize the adverse effects of toxins on animal production, present great challenges for scientists and producers in the coming years.

Safety considerations of fermented feed Perspective and future directions Feed fermentation is the process of degradation of feed substrates by microorganisms for their growth and production of metabolites, while degrading anti-nutritional factors and toxins in the feeds. However, contaminations and failure of fermentation can result in the overgrowth of harmful microorganisms, as well as the release and production of toxins from plants and microorganisms during fermentation, which

Feed fermentation is a complex process that integrates knowledge from nutrition, physiology, immunology, microbiology, biochemistry, industry design, ecology, economy, and bioinformatics. The use of techniques for feed fermentation depends on the nutritional requirements and digestive physiology of animals, the nutritive value of feedstuffs, fermentation

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24. Fermentation techniques in feed production

characteristics of the microorganisms added to the starter culture, and actual situations on individual farms. However, it is not a small task to accurately determine the nutritional requirements of animals in the post-antibiotic era or the nutritive values of fermented feedstuffs due to differences in (a) the combinations of ingredients, (b) starter cultures, (c) additives to the biomass for fermentation, and (d) fermentation conditions. One should bear in mind that fermentation is required only for selected feedstuffs before they can be fed to animals in order to prevent losses of nutrients. Standards should be made for the evaluation of the nutritive values and for safety of the fermented feed products, and to provide official guidelines to regulate the use of fermented feeds in animal agriculture. The spectrum and transfer of antibiotic-resistant genes in the microorganisms used for feed fermentation should be strictly monitored for the control of animal disease and the protection of human health. The inclusion of functional bacteria of gut origin in starter cultures and in combination with other natural “regulators” of intestinal ecology may help to optimize fermentation processes. Finally, advancing knowledge of metabolomics, the microbiome, bioinformatics and large data analyses will hasten development of fermented feeds for animal production, and this, in turn, will aid in sustaining animal agriculture in the post-antibiotic era.

Acknowledgments Work in our laboratories were supported by the National Key Basic Research Program of China (2013CB127303), National Natural Science Foundation of China (31301979), National Key R&D Program of China (2017YFD0500501), the Zhengzhou 1125 Talent Program, the Open Research Fund of National Center for International Research on Animal Gut Nutrition, and Texas A&M AgriLife Research (H-8200). Conflict of interest The authors declare no conflict of interests.

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15. Dråzbo A, Ognik K, Zaworska A, et al. The effect of raw and fermented rapeseed cake on the metabolic parameters, immune status, and intestinal morphology of turkeys. Poult Sci. 2018;97:3910e3920. 16. Shi C, He J, Yu J, et al. Solid state fermentation of rapeseed cake with Aspergillus niger for degrading glucosinolates and upgrading nutritional value. J Anim Sci Biotechnol. 2015;6:13. 17. Shi C, He J, Wang J, et al. Effects of Aspergillus niger fermented rapeseed meal on nutrient digestibility, growth performance and serum parameters in growing pigs. Anim Sci J. 2016;87:557e563. 18. Wang Y, Lu WQ, Li DF, et al. Energy and ileal digestible amino Acid concentrations for growing pigs and performance of weanling pigs fed fermented or conventional soybean meal. Asian-Australas J Anim Sci. 2014;27: 706e716. 19. Zhang YT, Lu DD, Chen JY, et al. Effects of fermented soybean meal on carbon and nitrogen metabolisms in large intestine of piglets. Animal. 2018;12:2056e2064. 20. Campbell C, Nanjundaswamy AK, Njiti V, et al. Valueadded probiotic development by high-solid fermentation of sweet potato with Saccharomyces boulardii. Food Sci Nutr. 2017;5:633e638. 21. Zhu J, Gao M, Zhang R, et al. Effects of soybean meal fermented by L. plantarum, B. subtilis and S. cerevisieae on growth, immune function and intestinal morphology in weaned piglets. Microb Cell Factories. 2017;16:191. 22. Lio JY, Wang T. Solid-state fermentation of soybean and corn processing coproducts for potential feed improvement. J Agric Food Chem. 2012;60:7702e7709. 23. Wang CC, Lin LJ, Chao YP, et al. Antioxidant molecular targets of wheat bran fermented by white rot fungi and its potential modulation of antioxidative status in broiler chickens. Br Poult Sci. 2017;58:262e271. 24. Jazi V, Boldaji F, Dastar B, et al. Effects of fermented cottonseed meal on the growth performance, gastrointestinal microflora population and small intestinal morphology in broiler chickens. Br Poult Sci. 2017;58:402e408. 25. Pahlow G, Muck RE, Driehuis F, et al. Microbiology of ensiling. In: Buxton DR, Muck RE, Harrison JH, eds. Silage Science and Technology. Madison, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America; 2003:31e93. Agronomy Monograph no. 42. 26. Muck RE. Recent advances in silage microbiology. Agric Food Sci. 2013;22:3e15. 27. Li Y, Nishino N, Li YB. Effects of inoculation of Lactobacillus rhamnosus and Lactobacillus buchneri on fermentation, aerobic stability and microbial communities in whole crop corn silage. Grassl Sci. 2011;57:184e191. 28. Li Y, Nishino N. Monitoring the bacterial community of maize silage stored in a bunker silo inoculated with Enterococcus faecium, Lactobacillus plantarum and Lactobacillus buchneri. J Appl Microbiol. 2011;110:1561e1570.

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29. Muck RE, Nadeau EMG, McAllister TA, et al. Silage review: recent advances and future uses of silage additives. J Dairy Sci. 2018;101:3980e4000. 30. Faseleh JM, Liang JB, Ho YW, et al. Lovastatin in Aspergillus terreus: fermented rice straw extracts interferes with methane production and gene expression in Methanobrevibacter smithii. BioMed Res Int. 2013;2013:604721. 31. Mohd AP, Jahromi MF, Ariff MO, et al. Aspergillus terreus treated rice straw suppresses methane production and enhances feed digestibility in goats. Trop Anim Health Prod. 2018;50:565e571. 32. Coblentz WK, Akins MS. Silage review: recent advances and future technologies for baled silages. J Dairy Sci. 2018;101:4075e4092. 33. Ding Z, Zhang Y, Ye J, et al. An evaluation of replacing fish meal with fermented soybean meal in the diet of Macrobrachium nipponense: growth, nonspecific immunity, and resistance to Aeromonas hydrophila. Fish Shellfish Immunol. 2015;44:295e301. 34. Lin YH, Mui JJ. Comparison of dietary inclusion of commercial and fermented soybean meal on oxidative status and non-specific immune responses in white shrimp, Litopenaeus vannamei. Fish Shellfish Immunol. 2017;63:208e212. 35. Ilham I, Fotedar R. Growth, enzymatic glutathione peroxidase activity and biochemical status of juvenile barramundi (Lates calcarifer) fed dietary fermented soybean meal and organic selenium. Fish Physiol Biochem. 2017;43:775e790. 36. Cheng AC, Lin HL, Shiu YL, et al. Isolation and characterization of antimicrobial peptides derived from Bacillus subtilis E20-fermented soybean meal and its use for preventing Vibrio infection in shrimp aquaculture. Fish Shellfish Immunol. 2017;67:270e279. 37. Shiu YL, Wong SL, Guei WC, et al. Increase in the plant protein ratio in the diet of white shrimp, Litopenaeus vannamei (Boone), using Bacillus subtilis E20-fermented soybean meal as a replacement. Aquacult Res. 2015;46: 382e394. 38. Hou YQ, Wu ZL, Dai ZL, et al. Protein hydrolysates in animal nutrition: industrial production, bioactive peptides, and functional significance. J Anim Sci Biotechnol. 2017;8:24. 39. Dossou S, Koshio S, Ishikawa M, et al. Growth performance, blood health, antioxidant status and immune response in red sea bream (Pagrus major) fed Aspergillus oryzae fermented rapeseed meal (RM-Koji). Fish Shellfish Immunol. 2018;75:253e262. 40. Valdez-Gonz
alez F, Guti
errez-Dorado R, Hern
andezLlamas A, et al. Bioprocessing of common beans in diets for tilapia: in vivo digestibility and antinutritional factors. J Sci Food Agric. 2017;97:4087e4093. 41. Zhang J, Zhang W, Li S, et al. A two-step fermentation of distillers’ grains using Trichoderma viride and Rhodopseudomonas palustris for fish feed. Bioproc Biosyst Eng. 2013;36:1435e1443.

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42. Wu G. Principles of Animal Nutrition. Boca Raton, FL, USA: CRC Press; 2018. 43. Jia SC, Li XY, Zheng SX, et al. Amino acids are major energy substrates for tissues of hybrid striped bass and zebrafish. Amino Acids. 2017;49:2053e2063. 44. Canibe N, Kristensen NB, Jensen BB, et al. Impact of silage additives on aerobic stability and characteristics of high-moisture maize during exposure to air, and on fermented liquid feed. J Appl Microbiol. 2014;116: 747e760. 45. Goodarzi Boroojeni F, Senz M, et al. The effects of fermentation and enzymatic treatment of pea on nutrient digestibility and growth performance of broilers. Animal. 2017;11:1698e1707. 46. Ohashi Y, Tokunaga M, Taketomo N, et al. Stimulation of indigenous lactobacilli by fermented milk prepared with probiotic bacterium, Lactobacillus delbrueckii subsp. bulgaricus strain 2038, in the pigs. J Nutr Sci Vitaminol. 2007;53:82e86. 47. Ohashi Y, Inoue R, Tanaka K, et al. Lactobacillus casei strain Shirota-fermented milk stimulates indigenous lactobacilli in the pig intestine. J Nutr Sci Vitaminol. 2001;47: 172e176. 48. Polyorach S, Poungchompu O, Wanapat M, et al. Optimal cultivation time for yeast and lactic acid bacteria in fermented milk and effects of fermented soybean meal on rumen degradability using nylon bag technique. Asian-Australas J Anim Sci. 2016;29: 1273e1279. 49. Missotten JA, Michiels J, Dierick N, et al. Effect of fermented moist feed on performance, gut bacteria and gut histo-morphology in broilers. Br Poult Sci. 2013;54: 627e634. 50. Dai ZL, Wu G, Zhu WY. Amino acid metabolism in intestinal bacteria: links between gut ecology and host health. Front Biosci. 2011;16:1768e1786. 51. Dai ZL, Wu ZL, Hang SQ, et al. Amino acid metabolism in intestinal bacteria and its potential implications for mammalian reproduction. Mol Hum Reprod. 2015;21: 389e409. 52. Wu GY, Bazer FW, Wallace JM, et al. Board-invited review: intrauterine growth retardation: implications for the animal sciences. J Anim Sci. 2006;84:2316e2337. 53. Wu G, Bazer FW, Burghardt RC, et al. Impacts of amino acid nutrition on pregnancy outcome in pigs: mechanisms and implications for swine production. J Anim Sci. 2010;88:E195eE204. 54. Liu SQ, Tsao M. Enhancement of survival of probiotic and non-probiotic lactic acid bacteria by yeasts in fermented milk under non-refrigerated conditions. Int J Food Microbiol. 2009;135:34e38. 55. Plumed-Ferrer C, Kivel€a I, Hyv€ onen P, et al. Survival, growth and persistence under farm conditions of a

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Lactobacillus plantarum strain inoculated into liquid pig feed. J Appl Microbiol. 2005;99:851e858. Plumed-Ferrer C, von Wright A. Fermented pig liquid feed: nutritional, safety and regulatory aspects. J Appl Microbiol. 2009;106:351e368. Missotten JAM, Michiels J, Ovyn A, et al. Fermented liquid feed for pigs. Arch Anim Nutr. 2010;64:437e466. Olstorpe M, Axelsson L, Schn€ urer J, et al. Effect of starter culture inoculation on feed hygiene and microbial population development in fermented pig feed composed of a cereal grain mix with wet wheat distillers’ grain. J Appl Microbiol. 2010;108:129e138. Olstorpe M, Lyberg K, Lindberg JE, et al. Population diversity of yeasts and lactic acid bacteria in pig feed fermented with whey, wet wheat distillers’ grains, or water at different temperatures. Appl Environ Microbiol. 2008;74:1696e1703. Canibe N, Jensen BB. Fermented and nonfermented liquid feed to growing pigs: effect on aspects of gastrointestinal ecology and growth performance. J Anim Sci. 2003;81:2019e2031. Demeckov
a V, Kelly D, Coutts AGP, et al. The effect of fermented liquid feeding on the faecal microbiology and colostrum quality of farrowing sows. Int J Food Microbiol. 2002;79:85e97. van Winsen RL, Keuzenkamp D, Urlings BAP, et al. Effect of fermented feed on shedding of Enterobacteriaceae by fattening pigs. Vet Microbiol. 2002;87:267e276. Heres L, Engel B, van Knapen F, et al. Fermented liquid feed reduces susceptibility of broilers for Salmonella enteritidis. Poultry Sci. 2003;82:603e611. He Y, Mao C, Wen H, et al. Influence of ad libitum feeding of piglets with Bacillus subtilis fermented liquid feed on gut flora, luminal contents and health. Sci Rep. 2017;7:44553. Kiarie E, Bhandari S, Scott M, et al. Growth performance and gastrointestinal microbial ecology responses of piglets receiving Saccharomyces cerevisiae fermentation products after an oral challenge with Escherichia coli (K88). J Anim Sci. 2011;89:1062e1078. Price KL, Totty HR, Lee HB, et al. Use of Saccharomyces cerevisiae fermentation product on growth performance and microbiota of weaned pigs during Salmonella infection. J Anim Sci. 2010;88:3896e3908. Sun H, Tang JW, Fang CL, et al. Molecular analysis of intestinal bacterial microbiota of broiler chickens fed diets containing fermented cottonseed meal. Poultry Sci. 2013;92:392e401. Wang J, Han Y, Zhao JZ, et al. Consuming fermented distillers’ dried grains with solubles (DDGS) feed reveals a shift in the faecal microbiota of growing and fattening pigs using 454 pyrosequencing. J Integr Agric. 2017;16:900e910.

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69. Yuan L, Chang J, Yin Q, et al. Fermented soybean meal improves the growth performance, nutrient digestibility, and microbial flora in piglets. Anim Nutr. 2017;3:19e24. 70. Xie Z, Hu L, Li Y, et al. Changes of gut microbiota structure and morphology in weaned piglets treated with fresh fermented soybean meal. World J Microbiol Biotechnol. 2017;33:213. 71. Louis P, Flint HJ. Diversity, metabolism and microbial ecology of butyrate-producing bacteria from the human large intestine. FEMS Microbiol Lett. 2009;294:1e8. 72. Flint HJ, Duncan SH, Scott KP, et al. Links between diet, gut microbiota composition and gut metabolism. Proc Nutr Soc. 2015;74:13e22. 73. Moravcov
a J, Kleinov
a T, Loucka R, et al. Effect of additives on coumestrol content in laboratory alfalfa silages. Czech J Anim Sci. 2003;48:425e431. 74. Moravcov
a J, Kleinov
a T, Loucka R, et al. Coumestrol content of alfalfa following ensilage. Anim Feed Sci Technol. 2004;115:159e167. 75. Sholly DM, Jørgensen H, Sutton AL, et al. Effect of fermentation of cereals on the degradation of polysaccharides and other macronutrients in the gastrointestinal tract of growing pigs. J Anim Sci. 2011;89: 2096e2105. 76. Kasprowicz-Potocka M, Borowczyk P, Zaworska A, et al. The effect of dry yeast fermentation on chemical composition and protein characteristics of blue lupin seeds. Food Technol Biotechnol. 2016;54:360e366. 77. Plumed-Ferrer C, von Wright A. Antimicrobial activity of weak acids in liquid feed fermentations, and its effects on yeasts and lactic acid bacteria. J Sci Food Agric. 2011;91:1032e1040. 78. Chen W, Zhu XZ, Wang JP, et al. Effects of Bacillus subtilis var. natto and Saccharomyces cerevisiae fermented liquid feed on growth performance, relative organ weight, intestinal microflora, and organ antioxidant status in Landes geese. J Anim Sci. 2013;91:978e985. 79. He Y, Chen Z, Wen H, et al. Pyrosequencing investigation into the influence of Cu2þ, Zn2þ, Fe2þ and I-

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mixtures on fungal diversity and toxigenic fungal growth in a fermented liquid feed. Anim Nutr. 2016;2: 51e56. Welin JB, Lyberg K, Passoth V, Olstorpe M. Combined moist airtight storage and feed fermentation of barley by the yeast Wickerhamomyces anomalous and a lactic acid bacteria consortium. Front Plant Sci. 2015;6:270. Hsu PK, Liu CP, Liu LY, et al. Protein enrichment and digestion improvement of napiergrass and pangolagrass with solid-state fermentation. J Microbiol Immunol Infect. 2013;46:171e179. Wang C, Lin C, Su W, et al. Effects of supplementing sow diets with fermented corn and soybean meal mixed feed during lactation on the performance of sows and progeny. J Anim Sci. 2018;96:206e214. Shi C, Zhang Y, Lu Z, et al. Solid-state fermentation of corn-soybean meal mixed feed with Bacillus subtilis and Enterococcus faecium for degrading antinutritional factors and enhancing nutritional value. J Anim Sci Biotechnol. 2017;8:50. Poulsen HD, Blaabjerg K. Fermentation of rapeseed meal, sunflower meal and faba beans in combination with wheat bran increases solubility of protein and phosphorus. J Sci Food Agric. 2017;97:244e251. Rhein RT, Coblentz WK, Turner JE, et al. Aerobic stability of wheat and orchardgrass round-bale silages during winter. J Dairy Sci. 2005;88:1815e1826. Contreras-Govea FE, Muck RE, Weimer PJ, et al. In vitro ruminal fermentation of treated alfalfa silage using ruminal inocula from high and low feed-efficient lactating cows. J Appl Microbiol. 2016;121:333e340. Shrivastava B, Nandal P, Sharma A, et al. Solid state bioconversion of wheat straw into digestible and nutritive ruminant feed by Ganoderma sp. rckk02. Bioresour Technol. 2012;107:347e351. Rodríguez-Muela C, Rodríguez HE, Arzola C, et al. Antioxidant activity in plasma and rumen papillae development in lambs fed fermented apple pomace. J Anim Sci. 2015;93:2357e2362.

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C H A P T E R

25 Mathematical modeling in animal production Luis Orlindo Tedeschi, Hector Manuel Menendez, III Texas A&M University, Department of Animal Science, College Station, TX, United States

O U T L I N E Introduction

Modeling animal requirements and the availability of nutrients American models European models Australian models Modeling production efficiency Modeling animal health and diseases

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Classifications of mathematical models 433 Classification based on the optimization context 434 Classification based on the application context 434 Classification based on the time context 435 Classification based on the behavioral context 435 Classification based on the natural context 435 A brief history of current mathematical models in ruminant production

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Introduction As part of the learning process, the human mind is capable of associating, recognizing, and comprehending complicated concepts in creating hypotheses and formulating ideas of abstract topics, but it lacks the ability to numerically process the multitude of variables, which

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00025-2

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Advanced data analytics for future mathematical models

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Conclusion

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are often interconnected and possess many combinatorial layouts, in a timely and accurate fashion. While the cognition mechanisms of the human brain are not fully understood,1,2 a mathematical formulation is unequivocal and absolute. Humans have benefited from mathematical formulations to describe complex concepts, hypotheses, and ideas logically, and then

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Copyright © 2020 Elsevier Inc. All rights reserved.

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solving them as a numerical solution or graphical representations using digital computers. Some scientists believe that the association of digital computing technology with the power of the human mind (obtained through a team of specialists working together to find ways to solve a problem) gave rise to the Second Industrial Revolution.3 Computer models are the interface between the ingenious human mind and mathematical sciences, and mathematical modeling is the process of constructing computer models systematically. Mathematical models are arithmetical representations of the behavior of real devices and objects,4 and ultimately life processes.5 They provide means to go beyond the physical boundaries of our world (i.e., nature) so we can enter the virtual world of possibilities when searching for solutions to problems that cannot be resolved in the real world (Fig. 25.1). The optimum

FIG. 25.1 Schematic representation of the relationship between the real world and the world of models (virtual world). The circles represent the variables, and the arrows between them represent a causal relationship. Reproduced with permission.6

solution from a mathematical model can then be applied to the real world, as illustrated by Tedeschi6 (Fig. 25.1). If the model solution is inadequate or if it fails to meet the desirable outcome, then the mathematical model is re-engineered, a new solution is found, and the process cycles back until a satisfactory outcome is achieved in the real world. Unfortunately, this process happens more often than it is acknowledged (and desired), but it is essential for a successful process.5 For instance, a glitch in a computer model developed by the National Aeronautics and Space Administration (NASA), which was programmed to ignore low ozone readings on the assumption that these readings were instrument measurement errors, prevented an earlier discovery of the disruption of the ozone layer due to its reaction with chlorofluorocarbons (CFCs).7 This incident caused an uproar by the scientific community that had raised a red flag about the negative effect of CFC concentrations on the ozone layer much earlier than NASA’s official acknowledgment. The failure in the NASA program was only confirmed after scientists addressed the political marasmus, and fixed misperceptions and incorrect concepts in the mathematical model.8 No mathematical model is immune to failures, and unintended consequences occur when resistance or ignorance exist regarding the model’s ability and appropriateness to solve problems. For practical purposes, the majority of applications of mathematical models are descriptive, i.e., they describe the behavior of results observed, explanatory, i.e., they explain why the behavior or results occurred, or they are predictive, i.e., they allow us to predict (forecast) future behaviors or results that have not happened yet.4 There are no limits in the boundaries of mathematical models (except for computational purposes), but different modeling methodologies exist for developing “big picture”-type models versus “little picture”-type models, which are built for narrow, defined, specific interdisciplinary perspectives. Thus, mathematical

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Classifications of mathematical models

models can be useful DSS that allow us to overcome the limitations or barriers of the real world regardless of the size of the problem. In many instances, however, the high level of details and complex, nonlinear, dynamic interactions of real-world problems can easily overwhelm the modeling process (and computational aspects), and make us lose sight of the genuine purpose of the mathematical model.9,10 That means, we often lose sight of the forest for the trees by building large, complex models that are redundant and not effective for learning experiences. In agriculture and life sciences, however, the valuable approach of using mathematical models to facilitate research is consistently underutilized because their potential users are not aware of the capabilities and benefits of mathematical models or they believe that model development is complex and difficult.10 Within the sustainability context, despite failed attempts to mitigate climate change by the scientific community,11 the protection of the environment is paramount for providing a standard livelihood for humans in centuries to come, and mathematical modeling is likely the most appropriate tactic to deal with this type of problem.12 Livestock, for instance, are the main source of agricultural nitrogen (N) loss to air and considerable N and phosphorous (P) contaminants to groundwater and surface water.13,14 Additionally, despite the lack of consensus among scientists and different methodologies employed to measure greenhouse gas (GHG) emissions,10 ruminants are thought to be responsible for approximately 18% of the total world anthropogenic GHG emissions.15 Furthermore, given the severe water-related crises around the world and the failures associated with meeting basic human needs and increasing ecological degradation of natural systems and human-caused climate changes, the search for freshwater sustainability has become a quintessential policy for developed nations of the world.16 The freshwater required to raise livestock is part of the

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water footprint crisis, and, in conjunction with the carbon footprint problem, they must be properly accounted for before it is too late just like the ozone layer versus CFC usage story. Besides the direct impact of livestock on the carbon footprint and water footprint, livestock production is also directly impacted by environmental changes (e.g., habitat loss and degradation of terrestrial ecosystems, conversion of forest to agricultural systems, re-zoning of plant distribution)17 and their productivity may be reduced.10 Our modern system to produce food also involves sophisticated structures and organizations with many feedback signals in which small changes within the production chain may undermine the entire system.18 Therefore, DSS play an important role in assisting animal scientists to address unforeseen impacts of livestock on the environment, as well as environmental impacts on livestock production systems such as shortage and quality of feedstuffs, heat stress on animal production, increased occurrence of diseases and parasitism, loss of biodiversity and genetic variation.19 The objectives of this chapter are to (a) provide introductory notes about the classification of mathematical modeling, (b) discuss prominent mathematical models in animal production, more specifically ruminants, and (c) highlight modern techniques for data analyses that can enhance mathematical modeling.

Classifications of mathematical models Meerschaert20 categorizes mathematical models into three major classes: optimization, dynamic, and stochastic (or probabilistic), but more comprehensive classifications exist depending on the developmental context of the model.10,21e23 For example, within an application context, as mentioned above, models can be classified as descriptive versus prescriptive (or predictive or elucidative). Within the time context, models can be classified as static

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(i.e., steady state) versus dynamic, which can be further classified as discrete versus continuous. Within a behavioral context, models can be classified as deterministic versus stochastic (or probabilistic). Within the natural context, models can be empirical or mechanistic (or theoretical or rational). Finally, within the space context, models can be classified as homogeneous versus heterogeneous.

Classification based on the optimization context Linear and nonlinear programming of single or multivariable optimization problems are most commonly used in agriculture,24e31 and animal scientists have been optimizing livestock production since the 1960s.32e37 The goal is to minimize (or maximize) an objective function given one or more constraints (i.e., equations) that set the boundaries of feasible solutions. The decision variables represent the unknown (independent) variables, and their optimized values will yield the solution to the problem. Different algorithms exist for specific types of optimization. The algorithms for linear programming can be broadly organized as Dantzig’s simplex method38,39 or interior point methods (e.g., projective transformationdKarmarkar’s algorithm, ellipsoiddKhachiyan’s algorithm, central path, path following, affine scalingdDikin’s algorithm).40e43 Nonlinear programming adds another level of complexity to the optimization, and there are many algorithms, including the quasi-Newton, conjugate gradient, successive/ sequential linear or quadratic programming, and generalized reduced gradient methods44,45 to name a few. Additionally, genetic algorithms, which are the most well-known evolutionary multi-objective optimizations,46 are biologyinspired meta-heuristic approaches based on natural selection and population genetics, whose main characteristic is to randomly and iteratively

simulate genetic selection, recombination, and mutation to solve large complex problems.46e48 Simulated annealing, on the other hand, is a physics-inspired meta-heuristic optimization approach (analogous to the law of thermodynamics of cooling and crystallization of metals) that is generally used when large numbers of local optima solutions (rather than a global optimum solution) exist.49 The simulated annealing algorithm randomly and occasionally makes changes to the decision variable values that worsen the solution (i.e., objective function) in an attempt to reduce the chances of finding a local optimum solution.50,51 There have been limited uses of these more advanced optimization algorithms because of their large computational intensity, and the relatively easy optimization needs for typical problems in agriculture and life sciences. Nonetheless, with the advancement of fast computing, many mathematical models have linear or nonlinear optimization algorithms to facilitate diet optimization for least-cost or maximum-profit diets.

Classification based on the application context The intention of the use of mathematical models defines the application as descriptive or predictive. Descriptive models summarize or explain how variables and their relationships behave “as-is” within the boundaries used to create the model, and new relationships are not created. Predictive models, on the other hand, use variables’ behavior to create new relationships among variables and frequently output (dependent) variables are forecast outside of the region upon which the mathematical formulation was built. The intention of predictive models is to use past information to forecast future outcomes or even simulate different scenarios and their impact on a given variable of interest.

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Classifications of mathematical models

Classification based on the time context By definition, a static model means that the sum of inflows is equal to the sum of outflows all the time; that means y_ ¼ 0. For static models, variables are time independent. In contrast, dynamic models implicitly employ time and _ time-dependent variables; that means ys 0. Thus, dynamics models are usually expressed as differential equations using one of the many different types of differentiation notation (e.g., Newton). It is assumed that steady-state (i.e., static) models are special cases of dynamic models. However, one can think of steady-state models as the representation of a system like a snapshot (i.e., a still landscape picture) that does not change over time, whereas dynamic models would be analogous to a motion picture (i.e., a film). In this analogy, one could argue that a motion picture (i.e., a dynamic model) is made up of many still images (i.e., many steady-state models), more precisely 24 or more images per second in the case of motion pictures. So in this scenario, a dynamic model is a special case of steady-state models. Thus, the concept of static versus dynamic is relative to the time horizon. For example, a process might be best represented by a dynamic model when daily changes are important, but when years are used as the time horizon, a static model might work as well (or even better than a dynamic model) because daily changes are irrelevant to the variable of interest. Dynamic models are further classified as discrete or continuous. For dynamic discrete models, time is represented by integers and simulation occurs at specific time points when events are raised, i.e., variables of interest change values or state at discrete time points52; thus, these models are commonly referred to as discrete event simulation. In contrast, the time in continuous dynamic models is represented uninterruptedly (0  t  N). Hybrid continuousdiscrete models, also known as discrete dynamical models,53,54 also exist, and their application in agricultural sciences is inexistent to incipient,

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but its use might provide greater benefits than previously anticipated. A classic example of a hybrid continuous-discrete model is the simulation of the dispersion and concentration of ink drops released into a cup of water at specific intervals, or in biological terms, the clearance (metabolism) of drugs injected into an animal’s circulatory system.55

Classification based on the behavioral context In a deterministic model, multiple runs (i.e., iterations, simulations) will yield the same outcome, but for probabilistic (i.e., stochastic) models, it will certainly yield different outcomes, depending on the number of random variables, their probabilistic distribution (i.e., variances), and the correlation among independent variables. Therefore, it is difficult to evaluate the prediction precision and accuracy of probabilistic models5 because they yield variable outcomes. In this case, their distribution shape and frequency needs to be compared with that obtained from observational values. Stochastic models are, however, great tools to inform the probability associated with a specific outcome.

Classification based on the natural context The majority of empirical models are obtained from observational data, and the model represents the best fit of statistical regression to the data, using regression analysis (e.g., leastsquares regression), as depicted in Fig. 25.2A. Mechanistic models rely on the underlying conceptual mechanisms and the combination of elements from different levels of aggregation (i.e., molecule, cells, tissue, organs, body, and herd). The main purpose of these models is to explain how an element at a higher level of aggregation behaves or responds to a collection of elements from one or more levels of lower aggregation.

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FIG. 25.2 The mathematical modeling process of (A) empirical models (linear and nonlinear regressions) and (B) mechanistic or conceptual models. Adapted from Tedeschi.6

These models are rationally and logically derived from observed data, theory (i.e., concepts), and hypotheses,10 as shown in Fig. 25.2B. In that sense, scientific reductionism explains how the parts work together in making up the whole. The concept of the mechanistic model, however, is more complicated as completely mechanistic models may not exist as they must rely on some empiricism when the scientific knowledge is fragile or incipient.10 Another complication emerges regarding the functionality of pure mechanistic models for applied problems. In this situation, simplified equations and calculation logics derived from complex, mechanistic models give rise to functional models,10,56 which might be easier and more appropriate than mechanistic models for practical applications commonly found in agriculture and life sciences. Researchers have greatly benefited from experimental research to understand causal relationships among critical variables of interest using rigorous statistical methods and design,57e62 and analyses of data obtained in a controlled environment. Experimental research is in essence viewed as a reductionist approach

to the understanding of real life, and therefore, it bears all the negative consequences of ignoring unintended consequences that cannot (or could) not be captured. This fact has led some to perceive that scientific reductionism might have contributed to an increased sense of degradation of biophysical environments, distortions of socio-economic conditions, and reallocation of cultural behaviors led by agricultural practices.63 Tedeschi and collaborators11 believe that the 18th-century industrial revolution may not be sufficient to produce enough food to feed a growing population while preventing further environment dilapidation. The question then becomes, if mathematical models are a representation of “reality” based on data collected from experimental research, are mathematical models the fruit of double reductionism of reality? Bawden63 believes that systems thinking can assist with the interface between agriculture and the environment by dealing with their complex relationships in a pragmatic way. The concept of systems thinking is not a novel approach to solve problems; it has been employed in different disciplines: ecology by Bernard Patten, sociology by Niklas Luhmann,

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A brief history of current mathematical models in ruminant production

architecture by Christopher Alexander, and business management by Jay Forrester.11,18,64,65 In this sense, the newest paradigm for developing mathematical models is the incorporation of systems thinking with a big-picture approach in mind.

A brief history of current mathematical models in ruminant production The goal of computer-aided DSS is to represent and manage scientific knowledge for improving the decision-making process of virtually every branch of science: from life sciences (e.g., development of the molecular structure of drugs and the management and planning for sustainable production of foods) to earth and space sciences (e.g., space exploration and global warming). Five categories of DSS have been proposed: communications-driven, data-driven, document-driven, knowledge-driven, and model-driven aspects.66 In the late 1960s, datadriven and model-driven DSS were built based on scientific knowledge, theory development, and operational research concepts. However, it was not until the advancement of microcomputers and software in the mid-1980s that DSS became user-friendly and started being applied practically, mainly by universities and organizations. The development of DSS was tightly connected to the evolution of the architecture and processing power of microcomputers, and the pioneering work of George Dantzig on the invention of the simplex optimization method (as discussed above), Douglas Engelbart on the creation of online systems, and Jay Forrester on the development of systems thinking and system dynamics methodology (as discussed above).66 The predictive characteristic of DSS has contributed enormously to improvements in the productivity and profitability of many agriculture-oriented companies. With the advent of DSS, users could evaluate many production alternatives and choose the best solution for

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each specific condition and desired outcome (e.g., optimization). Ruminant animals are widely utilized to convert human-inedible feedstuffs to nutritious food under widely varying conditions around the world. The goals of enhancing ruminant nutrition are to improve productivity, reduce resource use, and protect the environment. However, scientists often have to extrapolate nutrient requirements and feed values developed under standardized, controlled, laboratory research conditions to all combinations of cattle types, feedstuffs, and environmental and management conditions. For decades, animal scientists (and agriculturalists) have taken advantage of DSS computer models. These tools allow users to appraise feed biological, nutritive, and substitution values; determine quantity and quality of feed required to support different animals’ physiological needs; and estimate animal performance for given values of intake and feed quality. In these cases, DSS can be used as virtual simulators to predict nutritional requirements and feed utilization in a variety of production settings. Comprehensive descriptions of major mathematical models for ruminant nutrition have been discussed in different publications.10,67e70 As the information disseminated around the globe through many venues (e.g., conferences, publications, internet), a significant overlap among mathematical models became more evident.68 Table 25.1 has a simplified evolution timeline of key mathematical models in animal production.

Modeling animal requirements and the availability of nutrients Historically, nutritionists have formulated cattle rations to optimize production responses, as predicted by empirical equations that were developed under controlled research conditions. To account for real-world variations in types of cattle, feeds, and environmental and management conditions, these systems often

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25. Mathematical modeling in animal production

Evolution timeline for development of key livestock related mathematical models.a

Decades

Description

References

1940e1950

Development of energy and nutrient requirements for livestock through the National Research Council publications (poultry and swine in 1944; beef and dairy cattle, and sheep in 1945; and horses in 1949).

84e88,199

1950e1960

Nutrient requirement tables for ruminants in Europe.

200e202

1960e1970

Development of calorimetry and comparative slaughter techniques, the foundation for the development of energy and protein requirements used by many mathematical models.

72,80e82

1970e1980

Development of ruminal dynamics and fiber analysis methodology, the foundation for the development of ruminal fermentation used by many mathematical models. Publication of UK and the Netherlands energy allowances. Preliminary publications of MOLLY by Baldwin.

125,203e205

1980e1990

Advancements in the ruminal fermentation models and metabolism using mechanistic modeling. Publication of the Institut National de la Recherche Agronomique (INRA) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models.

119,130, 206e211

1990e2000

Development of ruminant applied models, including the Cornell Net Carbohydrate and Protein System (CNCPS).

93e96,212,213

2000e2010

Development of livestock and environment integration systems, including the Integrated Model to Assess the Global Environment (IMAGE). Development of AusBeef in Australia, BioParamilk and Feed into Milk in the UK, Karoline in Nordic countries.

122,123,140,214

2010epresent

Latest revisions of many applied nutrition models (NRC, INRA) and modern environment-ruminant integrated models (Ruminant Nutrition SystemdRNS).

10,92,118

a

Adapted from Tedeschi et al.68,69 and Jones et al.215

produced nutritional recommendations that included significant “safety factors.” These extra nutrients were meant to ensure that cattle received the required nutrients, but they often increased nutrient excretion and contributed to adverse effects on water and air quality.71 American models The calculation logic of energy and protein requirements for ruminants are based on the work conducted within the United States Department of Agriculture (USDA) in Beltsville, Maryland, for dairy cattle using respiration calorimetry72e79 and the studies conducted at the University of California-Davis for beef cattle using the comparative slaughter technique.80e83 Methods were then developed to assess the dietary supply of energy and protein to the ruminants, allowing for diet formulation and

balancing of energy and diverse nutrients. The first National Research Council (NRC) publications occurred in 1944 for poultry84 and swine,85 and in 1945 for beef cattle,86 dairy cattle,87 and sheep.88 The latest NRC and National Academies of Sciences, Engineering, and Medicine (NASEM) publications occurred in 2007 for sheep, goats, cervids, and new world camelids89 and horses,90 2012 for swine,91 and 2016 for beef cattle.92 Since the 1940s, the incredible amount of scientific knowledge gathered by the NRC and NASEM committees, and the many innovative and conceptual ideas put forward by their publications have fostered the development of other mathematical models not only by those in academia, but also by commercial entities, including feed mills and consulting companies, and governmental agencies such as the USDA

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A brief history of current mathematical models in ruminant production

Meat Animal Research Center. The Cornell Net Carbohydrate and Protein System (CNCPS) model (and its derivative works) is an example of such scientific synergy. CNCPS-based models are deterministic and static models that were developed from mechanistic principles of rumen function, microbial growth, feed digestion and passage, and animal physiology. The original CNCPS model was first published in 1992 and 1993 in a series of four papers93e96, and it has since been continuously refined and improved.71,97e99 Tedeschi and Fox10 provided comprehensive details about the history of the development and evolution of CNCPS-based models (e.g., CNCPS, CPM-Dairy, LRNS, SRNS, and the Ruminant Nutrition Systemd RNS). The Large Ruminant Nutrition System (LRNS)71 is a computer model that estimates beef and dairy cattle nutrient requirements and supply under specific conditions of animal type, environment (climatic factors), management, and physicochemical composition of available feeds, and it uses the basic computational engine of the CNCPS model, version 5, with additional modifications and implementations. Accounting for farm-specific management, environmental, and dietary characteristics have enabled more accurate prediction of cattle growth, milk production, and nutrient excretion in diverse production situations. Sheep production is an economically important enterprise in many countries. Many feeding studies have been conducted with sheep to determine their nutritional requirements and dietary utilization of nutrients. However, there are fewer dietary evaluation systems for sheep than for cattle, and they are often less developed. They are based on simpler approaches that are more biologically empirical than those developed for cattle. Similarly, production of meat from goats has increased considerably during the last decade, and goats have become an important livestock enterprise in several parts of the world. The Boer breed of goat easily adapts to intense or harsh conditions, which

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has made it a popular choice for the production of animal protein for human consumption. Meat goats can also be used in crossbreeding programs to improve the quality and growth of dairy goat male kids. In collaboration with Cornell University and the University of Sassari in Italy, the Small Ruminant Nutrition System (SRNS) was upgraded100 from the structure of the CNCPS for Sheep, a computer model for predicting the nutrient requirements of sheep and feed biological values on farms.101 Similar to the CNCPS for Cattle (i.e., LRNS), the SRNS predicts energy, protein, calcium and phosphorus requirements, accounting for animal factors (e.g., body weight, age, insulation, movement, milk production and composition, body reserves, mature weight, and pregnancy) and environmental factors (e.g., current and previous temperature, wind, and rainfall) factors. Feed biological values are predicted based on the pool size and fractional degradation and passage rates of carbohydrate and protein fractions, ruminal microbial growth, and physically effective fiber. The system predicts dry matter intake separately for different sheep categories based on equations developed for sheep fed indoors and on pasture. Based on this information, the SRNS predicts the energy balance of the animals. Energy balance is used to predict adult sheep’s body condition score, body weight variations, and, in lactating ewes, the amount of milk produced. For growing sheep, based on the energy balance and the relative size of the lambs, the SRNS predicts average daily gain and the composition of the gain (fat, protein, water, and minerals). For feed biological values, the SRNS predicts ruminal pH based on dietary physically effective fiber, rumen nitrogen, and peptide balances, the digestibility of each nutrient by the rumen and by the whole digestive tract, metabolizable protein from ruminal microbial protein and ruminally-undegraded feed protein, and the energy cost of urea production and excretion. The SRNS model is the most used system for feeding small ruminants around

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the world with many adopters in key countries such as the USA, Australia, Iran, and China, and the SRNS submodel for sheep was adopted by the NRC in 2007.89 The RNS10 is a mechanistic and deterministic model that contains additional submodels and enhancements to the CNCPS model, and a flexible connection with R (a statistical, scripting program) for stochastic modeling purposes. A Brazilian feeding system (BR-Corte) to evaluate nutritional requirements of Zebu cattle (Bos indicus and their crosses) was originally developed in 2006 as the result of an effort of the research group at the Federal University of Viçosa.102 The richness and completeness of body composition data obtained through the comparative slaughter technique are the supporting scientific pillars of the Brazilian feeding system. The BR-Corte equations were developed empirically, and they resemble the NRC’s concepts of energy and protein requirements and dietary supply. The third version was released in 2016103 and updates to the requirements for energy, protein, and minerals, as well as equations to estimate animal intake were proposed. European models There are many nutritional models across different countries in Europe, and they are essentially based on a seminal publication by the Agricultural Research Council (ARC) in the United Kingdom (UK) in 1965,104 and later revised in 1980.104 Both publications use the metabolizable concept for energy and protein. Substantial revisions were made in the late 1980s when technical reports were published by the Agricultural and Feed Research Council (AFRC)105e114 that culminated with the publication of a revised feeding standard for the UK in 1993.115 Limitations in these models included the lack of calorimetric data of dairy cows and reliance on endogenous nitrogen losses measured at a maintenance level of intake.116 Subsequently, and more recently, the Feed into Milk (FiM) model,117 which was

developed to overcome those limitations, was proposed, but it still uses many of the original concepts proposed in the 1960s, and a thorough evaluation is lacking. The Institut National de la Recherche Agronomique (INRA) has recently released an update118 to their nutritional model originally published in 1989.119 The 1989 model provided interesting concepts for evaluating protein value of feeds and the animal requirements regarding true protein that was truly digested and absorbed in the small intestine, the PDI system (in French: proteines digestibles dans l’intestin). The PDI was computed as the sum of the feed protein ruminally undegraded and truly digested in the small intestine (PDI d’origine Alimentaire) and the metabolizable true protein that was truly digested in the small intestine (PDI d’origine Microbienne, PDIM). Each feed had two PDIM values; the metabolizable true protein that could be synthesized from the N it supplied and the amount of microbial protein that could be synthesized from the feed energy available in the rumen. Therefore, when a diet was deficient in ruminally degradable N, PDI was referred to as PDIN, and when a diet was deficient in ruminal fermentable energy, PDI was referred to as PDIE. The 1989 INRA model was essentially a static, deterministic model, but its main contribution was related to the nutritional concepts that sparked many discussions about the dynamics of fermentation in the rumen and the ways it benefits the host animal (ruminant animal). The Nordic countries (Denmark, Finland, Iceland, Norway, and Sweden) have also developed nutritional models that benefited from the principles delineated by the ARC, and many of the differences were related to the utilization of protein by the ruminal microbes (Nordic AAT-PBV system).120 A revised Nordic model, NorFor,121 is a semi-mechanistic model that was developed to overcome limitations in the feed systems available in Western countries for Nordic milk production conditions. NorFor was also influenced by another more

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A brief history of current mathematical models in ruminant production

mechanistic, deterministic, dynamic model, Karoline,122,123 a whole-animal simulation model of lactating cows consisting of two submodels: digestion and metabolism. The Dutch system (DVE/OEB)124 was based on the metabolizable protein concepts proposed by the 1989 INRA model because it was the most accurate system for predicting milk synthesis under Dutch conditions whereas the energy submodels were based on feed units in which one lactation feeding unit (FU) contained 1.65 Mcal of net energy for lactation.70,125 More recently, more mechanistic, deterministic, and dynamic Dutch models that include a detailed rumen fermentation submodel have been developed.126e129 Australian models The nutritional model for the Commonwealth Scientific and Industrial Research Organization (CSIRO) was originally published in 1990130 and updated in 2007.131 It is based on the 1990 ARC recommendations, but many adaptations and improvements were included for subtropical and tropical production of ruminants (cattle and sheep). Similar to NRC and NASEM publications,92,132,133 the CSIRO’s feeding standard130,131 reported that although recycling N can offset intermittent inadequacies of ruminally degradable protein, it will not sustain the animal through a chronic inadequacy of N. The modeling of recycled N is a topic of increased interest and scrutiny to ruminant nutritionists because of the constant need to supplement dietary N for ruminants grazing low-quality forages especially in the tropics and the pressure to reduce environmental contamination of N in temperate regions. Others134,135 have provided more in-depth discussions about protein requirements and recycled N for ruminants. The 1990 CSIRO130 also served as the foundation of GrazPlan, another suite of DSS that includes models for diverse agricultural systems such as cropping, grazing, and animal management.136e139 Additionally, AusBeef,140 a mechanistic and

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dynamic rumen model, was specifically developed to handle ruminal fermentation of different substrate types and their impact on ruminal production of volatile fatty acids and ruminal pH for beef cattle.

Modeling production efficiency During the last decades, the production paradigm has shifted from the traditional goal of maximizing output to optimizing the use of resources and maximizing efficiency. It has long been hypothesized that producers failing to comply with market specifications (carcass yield and quality traits) would be penalized.141e143 With that in mind, several segments in the global beef industry are transitioning to management and marketing of feedlot cattle individually to reduce production of excess fat, increase consistency and quality of products, enhance productivity, and increase economic returns. Management systems for individual cattle have to be developed to help bring individual animals to market at their optimum economic endpoint, avoiding discounts and considering live and carcass incremental cost of gain and carcass prices for various grades. However, determining the production efficiency of beef cattle requires accurately accounting for variables that influence animal performance in each specific stage of production as discussed above, including the type of animals, feedstuffs, environment, and management practices. In reality, this task can be overwhelming or become almost infeasible mainly because beef cattle production in the United States, as well as many other countries, is organized into five major segments (seedstock or cow/calf, backgrounding, feedyard, packing plant, and marketing), and the information feedback among these segments is often incomplete or inadequate. The whole system can only be effective if there is coordination throughout the production and marketing chain.144 The employment of DSS to model selected segments of the beef industry can facilitate the identification of

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25. Mathematical modeling in animal production

production alternatives that enhance production efficiency. The Cattle Value Discovery System for growing cattle (CVDSgc) represents an evolution of a growth model first published by Fox and Black145 to account for differences in breed type and mature size when predicting performance and profitability of feedlot cattle with alternative management systems. Since then, modifications to the system, summarized by Tedeschi and collaborators,146 have improved its accuracy to account for more of the variation in nutrient requirements and performance of growing beef cattle. The CVDSgc was developed for use in individual cattle management for growing beef cattle, and it provides (a) prediction of daily gain, incremental cost of gain and days to finish to optimize profits and marketing decisions while marketing within the window of acceptable carcass weights and composition; (b) predictions of carcass composition during growth to avoid discounts for underor over-weight carcasses and excess backfat; and (c) allocation of feed fed to individual animals for the purpose of sorting of individuals into pens by days to reach target body composition and maximum individual profitability. Following the same logical structure of the CVDSgc, two other models for beef cows and calves (CVDSbc) and dairy cows (CVDSdc) were developed. The CVDSbc147,148 is based on the concept of ranking cows in the herd by their energy efficiency index (EEI), which is calculated as the ratio of the amount of metabolizable energy needed by the cow (or by the cow and calf) during a reproductive cycle (conception to weaning) divided by the weaning weight of the calf. The EEI is calculated iteratively, and it takes into account changes in body weight, fluxes of body reserves, milk production of the cows, forage quality throughout the reproductive cycle, and calf growth. Subsequently, the CVDSbc was modified in the development of the CVDSdc,149 but EEI was based on calculations of the requirements for dairy cows with a novel

dynamic model to account for energy fluxes.150 The EEI of the CVDSdc is also computed iteratively for a reproductive cycle.

Modeling animal health and diseases The outbreak of livestock diseases causes large and unexpected disturbances to agriculture systems that expose the sensitivity and resiliency of affected sectors and the adequacy of surveillance and preventative measures. For instance, economic disturbances caused by foot-andmouth disease (FMD) attributed to an estimated economic impact that ranged from 1 to 15 billion dollars in various countries throughout the world.151 Tedeschi and collaborators152 shed light on additional factors that influence livestock disease, namely the increased risk of disease associated with antimicrobial resistance and climate change. Consequently, increasing livestock production has raised concerns about surveillance and prevention of potential disease outbreak throughout the world, a difficult task.153,154 A well-recognized dilemma with animal diseases is that they may transcend the animal’s ecological boundary and trespasses into the human environment, bringing significant health concerns for humans.155,156 Modeling facilitates an important role in addressing disease related threats by increasing the understanding of livestock disease systems as DST can simultaneously evaluate complex interactions of disease outbreak within livestock production, economic/trade, and government sectors.11,152,157 Many model applications exist to help in the surveillance and prevention of zoonotic diseases within livestock production systems in order to control the disease before a large-scale outbreak occurs, a critical time (i.e., nondetectable to epidemic levels).158,159 Considerable effort has been made to advance the development of mathematical epidemiological models160 to assist in the prediction of outbreaks and controlling mechanisms of FMD,161,162 and bovine spongiform encephalopathy (BSE, i.e.,

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Advanced data analytics for future mathematical models

the mad cow disease)163 and likewise the Creutzfeldt Jakob Disease (CJD) in humans.164 For instance, the USDA North American Animal Disease Spread Model (NAADSM), a result of an international collaboration, couples fundamental principles of disease susceptibility, infected, or removed (SIR) with multiple disease vectors/reservoirs (e.g., animal-to-human), control methods (e.g., vaccination or destruction), and considerations of economic feasibility of outbreak mitigation strategies via simulation; a stochastic, spatially explicit, and state-transition approach.165 Similarly, other models have captured various livestock diseases throughout the world. Niu and collaborators166 developed a spatial and probabilistic model to evaluate the spread of Rift Valley Fever, a livestock disease common to Africa and the Middle East and indicated that surveillance and control play an important role in disease modeling. Modeling enhances the understanding of what surveillance and control measures are likely to be the most effective when coupled with ongoing epidemiological research.167 However, modeling challenges exist such as bridging models to experimentation, genetic research, diverse livestock species, and socio-political-economic factors in addition to enhancing spatial analyses, big data analytics, and model scalability to limited resource areas (i.e., limited data limited).168 Current modeling challenges present opportunities to harness advancing sensor technologies (identification/tracking), big data analytics (bioinformatics), and existing model capabilities (meta-modeling) in order to find high-leverage, systemic solutions, particularly regarding disease surveillance and prevention. For example, Conrad169 modeled agricultural commodity production cycles for grain corn, beef and dairy sectors within the United States and introduced FMD as a hypothetical large-scale outbreak in both livestock sectors. Simulated beef cow-calf operations were more resilient to FMD than sectors further down the supply chain due to

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differences in contact rates and concluded that the targeting of cow-calf versus other cattle production phases (e.g., feedlot) is likely to be an effective and economically feasible prevention strategy. Additionally, scenario results showed that a two-year export ban of beef stabilized prices more quickly than a one-year export ban, a counterintuitive finding regarding price response. Therefore, modeling has the potential to gather more insight into disease surveillance and prevention through the evaluation of immediate and delayed feedback mechanisms.170 Overall, ruminant livestock contributions to global food security are likely to be improved by models that guide surveillance and prevention efforts and increase the resiliency of complex agricultural systems to not only livestock disease outbreak,171,172 but also GHG emissions and climate change.173

Advanced data analytics for future mathematical models Contemporary competition for the use of resources, as well as environmental and economic challenges in animal agriculture, have raised the bar for all major players in the animal industry. DSS are more important than ever because they give users the ability to quickly evaluate multiple scenarios of production and choose options that are more acceptable, sustainable, and resilient. Many challenges exist in animal nutrition science,152 including (a) optimizing the use of feed resources so that producing human food from ruminants results in more human food than would be available without them, (b) the contribution of greenhouse gases by livestock, especially ruminants, and how to diminish it, (c) the need to minimize risks and maximize profits in the feedlot sector, (d) the need to build more responsive and accurate ruminant nutrition DSS models to account for the effects of climate change on animal welfare, nutrient needs, and productivity, while meeting

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consumer demand for high-quality protein food, and (e) the grand challenge of feeding an exponentially growing world population while minimizing livestock’s environmental carbon and water footprint. Therefore, we must incorporate state-of-the-art data analytical techniques to improve the accuracy and precision of DSS. Such techniques include artificial intelligence, machine learning, and deep learning concepts. Technological advances have led to an unprecedented rate of data collection (i.e., big data) within agricultural operations (e.g., precision agriculture and smart farms), but solving contemporary challenges in animal nutrition science with DSS requires more than large data repositories.174,175 Since the late 1950s scientists have sought to bridge the interaction between computers and humans to produce cognitive learning programs capable of problem-solving. Advanced data analytics, machine learning, artificial intelligence, and deep learning, leverage computing power with learning algorithms to process and interpret large heterogeneous data sets that can reliably classify or predict important changes of interest within ruminant livestock systems in near real-time. The first step in the application of advanced analytical techniques is to split processed data (e.g., dimensionally consistent) into training and testing datasets. Training data are used to train the algorithm (i.e., artificial intelligence) to interpret, partition, weight, or categorize explanatory variables to reliably classify or predict variables of interest. The testing dataset is then utilized to evaluate the reliability and accuracy of the algorithm by the identification of true/ false positives and negatives, statistical evaluation (e.g., root mean square error; coefficient of determination) and the optimal number of training runs (i.e., epochs); increased training runs does not always result in improved performance.5,176e178 A wide array of learning algorithms exists (e.g., linear discriminant analysis, back-propagation networks, and regression trees), but analytical performance may vary

depending upon data structure (linear, cluster), type (numeric, categorical) and availability. Limited data may hinder the algorithm from learning how to capture the problem, but this can sometimes be overcome through data generation techniques such as bootstrapping.179,180 Typically analyses that use machine learning, artificial intelligence, or deep learning run various algorithms to evaluate differences in performance and select the most reliable and accurate analytical technique.181 Many advanced analytical techniques have been used in agriculture to optimize the economic and production efficiencies of ruminant livestock systems including the use of Support Vector Machines (SVM) and Artificial Neural Networks (ANN).182 Artificial Neural Networks provide a framework of nodes, as does SVM, within different layers that process input data, interact with one another, and generate an output; similar to how neurons within the human brain communicate, learn, and make cognitive decisions.183,184 However, a significant drawback of ANN is the black box nature of nodes (e.g., SVM) which fail to provide an understanding of how an output was generated. Dong and Zhao176 applied meta-modeling10 using the CNCPS model (Section 3.1.1) and ANN with a three-layer back-propagation neural network to predict in vitro CH4, CO2 and total gas production using carbohydrate fractions of 45 rations for training data and 10 rations for testing data. The ANN accurately and reliably predicted each metric and outperformed a statistical regression prediction of CO2 and total gas production that used the same CNCPS data.185 Similarly, dairy cattle rumen fermentation patterns were predicted from milk fatty acids using ANN (various ANN algorithms) and contrasted with statistical regression using fistulated dairy cows (n ¼ 138).186 The application of ANN did not significantly improve predictions compared to statistical regression results but revealed that ANN was able to perform with fewer input data (experiment related information) and

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Conclusion

confirmed that milk fatty acids have the potential to predict proportions of volatile fatty acids within the rumen.186 Cutting edge data analytics involve the field of deep learning that expands machine learning and artificial intelligence techniques by adding more complexity (“depth”) and enhances data transformation into a hierarchical process through multiple layers of abstraction that quickly solve problems using immense parallelization capabilities.187e190 Advances in deep learning analytics have been limited to livestock biometric applications, specifically cattle image recognition.191e193 For example, a deep learning framework was used to identify cattle from muzzle point image patterns using convolution neural networks (CNN), deep belief networks (DBN), and stacked de-noising auto-encoders (SDAE) with an accuracy of 75.98, 88.46, and 95.99%, respectively.191 Hence evaluation of the accuracy and reliability of different deep learning techniques is important as performance depends on the purpose of the analysis and learning techniques (algorithm, components, and framework) of CNN (e.g., convolution layers, pooling, and node/layer connections), DBN (e.g., extraction and stacked Restricted Boltzmann Machines), and SDAE (e.g., extraction and encoding/decoding). One major disadvantage of deep learning is that the incorporation of expert knowledge into the algorithm through feature engineering increases training time as it is arduous to capture complex processes in deep learning components and framework.187,194 The integration of advanced analytical techniques with DSS is likely to resolve feature engineering constraints, leverage computing capabilities to process and utilize increasing livestock production data, and provide more understanding of black box node algorithms. Therefore, the general overview of advanced analytical techniques provides insight into opportunities to expand state-of-the-art analytics and improve the accuracy and precision of livestock nutrition DSS to most effectively

evaluate multiple scenarios that address contemporary challenges.11,152,195,196

Conclusion It has been a long journey for animal nutritionists. Since the classical experiments on the comparative anatomy of humans and animals by da Vinci in the 1500s to the calorimetry work of Lavoisier in the 1920s, many prominent researchers have contributed their monumental work in the nutritional energetics field (Brody, Kleiber, and Blaxter).10,197 Many methods and techniques have been developed, and knowledge has been gained to lay the foundation for our understanding of animal bioenergetics and metabolism. We must learn from these giants, embrace our current challenges, and build tools that will improve animal production in the future. On the one hand, quality data is the limiting factor in moving forward with science. Experimental animal trials are conducted under laboratory conditions, or experimental stations designed to answer specific questions or test a single hypothesis without considering how the individual pieces work together.10 There are known flaws and limitations on our current scientific knowledge of ruminant nutrition that must be addressed. Such flaws and limitations include energy and protein requirements of beef cattle, restrictions and problems associated with the fixed and long-standing 82% efficiency index of conversion of digestible energy to metabolizable energy, the contribution of microbial protein to metabolizable protein, the quantification of urea-N recycled in the rumen and truly used by the ruminal microbes for anabolism, the efficiency of use of metabolizable protein by the ruminant animal, energy requirement for maintenance for grazing animals, the inconsistencies in predicting protein retained by growing cattle, and energy required for animals under cold-stress conditions among many others.10,198

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On the other hand, quality data are not the problem. Tedeschi and collaborators198 concluded that “perhaps it is time for animal scientists to go back to the drawing board, confront established assumptions and rethink some concepts and relationships before spending more resources on collecting additional data.” Though mechanistic nutrition models are built on the mechanisms underlying their biology, biased data or faulty concepts will ruin any integration of data and concepts, and significantly delay the advancement of nutrition models.10 Despite these drawbacks in our knowledge, many DSS of diverse modeling classifications have been proposed, most of them have been assembled from existing models (a possible modeling problem caused by shortsighted concepts) while few others have been built from the ground up. Other analytical methodologies exist that can rescue pure mechanistic modeling from falling into these pitfalls. Examples include different methodologies for building models (e.g., System Dynamics) or different analytical techniques (e.g., machine learning). Tedeschi and collaborators18 concluded that “System Dynamics is a computer-aided modeling methodology that can be used to perform policy analyses and DSS applied to dynamic problems arising in complex social, managerial, economic, or ecological dynamic systems characterized by interdependence, mutual interaction, information feedback, and circular causality. System Dynamics can be used as a modeling tool to aggregate knowledge to solve different types of problems that have limited scope to a specific location or have broad trends of applications across locations and areas of science. Important issues of broad application include the bearings of animal production in the climate change and the impacts of climate change in animal production, alternative production scenarios of animal and crop integration, associations between animal production and business (economics, marketing).”

Reflecting upon our introductory principle that “computer models are the interface between the ingenious human mind and mathematical sciences, and mathematical modeling is the process of constructing computer models systematically,” we have made tremendous progress, our scientific advancements in ruminant production have been tangible. However, these achievements have been incipient because we are still learning how to connect these concepts through mathematical modeling, a process that is still obscure to many scientists.

References 1. National Research Council. How People Learn: Brain, Mind, Experience, and School. Expanded ed. Washington, DC: National Academy Press; 2000. 2. Perlovsky L, Ilin R. Brain. Conscious and unconscious mechanisms of cognition, emotions, and language. Brain Sci. 2012;2(4):790e834. 3. von Bertalanffy L. General Systems Theory; Foundations, Development, Applications. New York, NY: George Braziller; 1969. 4. Dym CL. Principles of Mathematical Modeling. 2nd ed. Amsterdam: Elsevier/Academic Press; 2004. 5. Tedeschi LO. Assessment of the adequacy of mathematical models. Agric Syst. 2006;89(2e3):225e247. 6. Tedeschi LO. ASN-ASAS Symposium: future of data analytics in nutrition: mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics. J Anim Sci. 2019;97(5):1321e1944. 7. Meadows DH, Meadows DL, Randers J. Beyond the Limits; Confronting Global Collapse, Envisioning a Sustainable Future. Mills, VT: Chelsea Green Publishing; 1992. 8. Meadows DH, Randers J, Meadows DL. The Limits to Growth: The 30-year Update. Chelsea Green Publishing Company; 2004. 9. Ruth M, Hannon B. Modeling Dynamic Economic Systems. New York, NY: Springer; 1997. 10. Tedeschi LO, Fox DG. The Ruminant Nutrition System: An Applied Model for Predicting Nutrient Requirements and Feed Utilization in Ruminants. 2nd ed. Acton, MA: XanEdu; 2018. 11. Tedeschi LO, Muir JP, Riley DG, Fox DG. The role of ruminant animals in sustainable livestock intensification programs. Int J Sustain Dev World Ecol. 2015; 22(5):452e465.

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challenges for the poor. J Semi-Arid Trop Agric Res. 2007;4(1):1e23. https://hdl.handle.net/10568/2205. Rojas-Downing MM, Nejadhashemi AP, Harrigan T, Woznicki SA. Climate change and livestock: impacts, adaptation, and mitigation. Clim Risk Manag. 2017;16: 145e163. Pham X, Stack M. How data analytics is transforming agriculture. Bus Horiz. 2018;61(1):125e133. Wolfert S, Ge L, Verdouw C, Bogaardt M-J. Big data in smart farming e a review. Agric Syst. 2017;153:69e80. Dong R, Zhao G. The use of artificial neural network for modeling in vitro rumen methane production using the CNCPS carbohydrate fractions as dietary variables. Livest Sci. 2014;162(0):159e167. Heald CW, Kim T, Sischo WM, Cooper JB, Wolfgang DR. A computerized mastitis decision aid using farm-based records: an artificial neural network approach. J Dairy Sci. 2000;83(4):711e720. Salehi F, Lacroix R, Wade KM. Improving dairy yield predictions through combined record classifiers and specialized artificial neural networks. Comput Electron Agric. 1998;20(3):199e213. Breiman L. Bagging predictors. Mach Learn. 1996;24(2): 123e140. Villamizar M, Andrade-Cetto J, Sanfeliu A, MorenoNoguer F. Bootstrapping Boosted Random Ferns for discriminative and efficient object classification. Pattern Recogn. 2012;45(9):3141e3153. Chen LJ, Cui LY, Xing L, Han LJ. Prediction of the nutrient content in dairy manure using artificial neural network modeling. J Dairy Sci. 2008;91(12):4822e4829. Liakos GK, Busato P, Moshou D, Pearson S, Bochtis D. Machine learning in agriculture: a review. Sensors. 2018;18(8):1e29. Widrow B, Lehr MA. 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation. Proc IEEE. 1990;78(9):1415e1442. Cohen PR, Feigenbaum EA. Planning and problem solving. In: Cohen PR, Feigenbaum EA, eds. The Handbook of Artificial Intelligence. Butterworth-Heinemann; 1982:513e562. Dong R, Zhao G. Relationship between the methane production and the CNCPS carbohydrate fractions of rations with various concentrate/roughage ratios evaluated using incubation technique. Asian-Australas J Anim Sci. 2013;26(12):1708e1716. Craninx M, Fievez V, Vlaeminck B, De Baets B. Artificial neural network models of the rumen fermentation pattern in dairy cattle. Comput Electron Agric. 2008; 60(2):226e238. Kamilaris A, Prenafeta-Bold u FX. Deep learning in agriculture: a survey. Comput Electron Agric. 2018;147: 70e90.

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188. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85e117. 189. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436e444. 190. Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2010;22(10):1345e1359. 191. Kumar S, Pandey A, Sai Ram Satwik K, et al. Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement. 2018;116:1e17. 192. Ouyang W, Wang X, Zeng X, et al. DeepID-Net: deformable deep convolutional neural networks for object detection. Paper presented at: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); June 2015:7e12. 193. Santoni MM, Sensuse DI, Arymurthy AM, Fanany MI. Cattle race classification using gray level co-occurrence matrix convolutional neural networks. Procedia Comput Sci. 2015;59:493e502. 194. Amara J, Bouaziz B, Algergawy A. A deep learningbased approach for banana leaf diseases classification. In: Paper Presented at: BTW. 2017. Bonn, Germany. 195. Kamilaris A, Kartakoullis A, Prenafeta-Bold u FX. A review on the practice of big data analysis in agriculture. Comput Electron Agric. 2017;143:23e37. 196. Randers J. 2052: A Global Forecast for the Next Forty Years. White River Junction, VT: Chelsea Green Publishing; 2012. 197. Ferrell CL, Oltjen JW. ASAS centennial paper: net energy systems for beef cattle–concepts, application, and future models. J Anim Sci. 2008;86(10): 2779e2794. 198. Tedeschi LO, Galyean ML, Hales KE. Recent advances in estimating protein and energy requirements of ruminants. Anim Prod Sci. 2017;57(11):2237e2249. 199. National Research Council. Recommended Nutrient Allowances for Horses. Washington, DC: National Academy Press; 1949. 200. Agricultural Research Council. The Nutrient Requirements of Farm Livestock. No. 2, Ruminants. London, UK: H.M. Stationery Office; 1965. 201. Leroy AM. Utilization de l’energie des aliments par les animaux. Ann Zootech. 1954;3(4):337e372. 202. Blaxter KL. The Energy Metabolism of Ruminants. London, UK: Hutchinson; 1962.

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203. Waldo DR, Smith LW, Cox EL. Model of cellulose disappearance from the rumen. J Dairy Sci. 1972; 55(1):125e129. 204. Ministry of Agriculture, Fisheries and Food. Energy Allowances and Feeding Systems for Ruminants. vol. 33. London, UK: Her Majesty’s Stationery Office; 1975. 205. Baldwin RL, Koong LJ, Ulyatt MJ. A dynamic model of ruminant digestion for evaluation of factors affecting nutritive value. Agric Syst. 1977;2(4):255e288. 206. France J, Thornley JHM, Beever DE. A mathematical model of the rumen. J Agric Sci. 1982;99(2):343e353. 207. Gill M, Thornley JHM, Black JL, Oldham JD, Beever DE. Simulation of the metabolism of absorbed energy-yielding nutrients in young sheep. Br J Nutr. 1984;52(3):621e649. 208. Baldwin RL, France J, Beever DE, Gill M, Thornley JHM. Metabolism of the lactating cow. III. Properties of mechanistic models suitable for evaluation of energetic relationships and factors involved in the partition of nutrients. J Dairy Res. 1987;54(1):133e145. 209. Baldwin RL, Thornley JHM, Beever DE. Metabolism of the lactating cow. II. Digestive elements of a mechanistic model. J Dairy Res. 1987;54(1):107e131. 210. Baldwin RL, France J, Gill M. Metabolism of the lactating cow. I. Animal elements of a mechanistic model. J Dairy Res. 1987;54(1):77e105. 211. Institut National de la Recherche Agronomique. Alimentation Des Bovins, Ovins & Caprins. Paris, France: INRA-Quae; 1988. 212. Herrero M, Murray I, Fawcett RH, Dent JB. Prediction of the in vitro gas production and chemical composition of kikuyu grass by near-infrared reflectance spectroscopy. Anim Feed Sci Technol. 1996;60(1e2):51e67. 213. Herrero M, Fawcett RH, Dent JB. Bio-economic evaluation of dairy farm management scenarios using integrated simulation and multiple-criteria models. Agric Syst. 1999;62(3):169e188. 214. Bouwman AF, Van der Hoek KW, Eickhout B, Soenario I. Exploring changes in world ruminant production systems. Agric Syst. 2005;84(2):121e153. 215. Jones JW, Antle JM, Basso B, et al. Brief history of agricultural systems modeling. Agric Syst. 2017;155: 240e254.

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C H A P T E R

26 Manure treatment and utilization in production systems Zong Liu, Xiao Wang Department of Biological and Agriculture Engineering, Texas A&M University, College Station, TX, United States

O U T L I N E Introduction

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Manure processing and handling Solid manure Slurry manure Liquid manure

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Manure treatment Manure solid-liquid separation Solid-liquid separation methods Chemical methods to assist solid-liquid separation Pathogen reduction in manure treatment Composting Factors affecting composting process Composting methods

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Introduction Manure is a valuable byproduct of animal agriculture. It fertilizes crop fields by delivering a full spectrum of plant nutrients and organic

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00026-4

Anaerobic digestion system Factors affecting biogas production Designs of anaerobic digestion systems for manure treatment

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Manure utilization Land application Pelletizing Extracting nutrients from liquid and slurry manure Biofuel Emerging value-added products Manure-derived biochar Substrate for microbial culture

464 464 465

References

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463

465 465 465 465 466

matter. However, treating and utilizing manure can be a particularly challenging task for modern animal feeding operations specialized in intensive production. These systems produce a considerable surplus of manure, which has

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Copyright © 2020 Elsevier Inc. All rights reserved.

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26. Manure treatment and utilization in production systems

a high risk of becoming a source of air, water, and soil pollution, as well as a hotspot for pathogens and its vectors. This chapter introduces current technologies and management methods that can help an animal-feeding operation maximize the value of manure and minimize its risks. Manure denotes the mixture of animal excreta, beddings, feed, washing water, and other processes that generate waste from animal operations. The contents of manure vary by the design of animal housing and manure collection system. Manure can be a liquid, slurry, or solid according to its dry matter level. The dry matter is heterogenous in particle size, with more than half being microparticles less than 10 mm. Cattle systems usually simultaneously produce solid and liquid manure, swine systems usually produce slurry manure, and poultry systems usually produce slurry or solid manure. The composition of dry matter in manure is complex; the main constituents among which are carbohydrates, proteins, lipids, lignin, and volatile fatty acids. In most cases, manure contains all 13 of the essential elements required by plants, with high levels of nitrogen (N), phosphorus (P), and potassium (K).1 The composition of manure varies considerably with feed type, age, species of animal, animal housing, and manure management methods. On average, the compositions of manure from different animal species have their distinct characteristics (Table 26.1). Cattle feed is usually roughagedominated with high contents of potassium, sodium, and calcium ions. Their manure consists of a relatively high proportion of cellulose fibers and contains relatively high levels of these salt ions. Swine and poultry feeds are more protein-concentrated, which is reflected in their manure composition. Swine manure is particularly rich in P, as their feed is usually supplemented with additional P. Main risks associated with manure are nutrient pollution, pathogen transmission, as well as gas emission. Excessive manure use on

agricultural fields may lead N and P overload in aquatic systems via leaching and surface runoff. These nutrients have high risks of entering surface water, causing eutrophication and algal bloom. Manure may also contain residues of heavy metals, antibiotics, and hormones. Land-applied manure could become diffusive sources of these substances which give rise to water contamination. Manure is a source of ammonia (NH3), methane (CH4), nitrous oxide (N2O), and malodorous gases including hydrogen sulfide (H2S), phenolic compounds, indoles, and various volatile organic acids. CH4 and N2O released by manure are major contributors to the global greenhouse effect. NH3 and H2S emissions from manure have been associated with numerous health incidents of farmers. In addition, manure odor is one of the leading reasons for complaints from neighbors and the negative public view of animal agriculture. Pathogens can survive for several months in improperly treated manure. During and after an infectious disease outbreak caused by pathogens, the contaminated manure has a high risk of transmitting diseases to the animal herd when it is stored and reused on the farm. In addition, if land-applied the contaminated manure, there would be a high risk of spreading diseases to a larger animal group and transmitting zoonotic diseases to humans if the manure is used to fertilize ready-to-eat crops such as lettuce. The guidelines for manure treatment and utilization is regulated at both the federal and state levels. On the federal level, the Clean Water Act regulates the discharge or proposed discharge of manure into water. The Clean Air Act applies ceilings of annual gas emissions from concentrated animal feeding operations (CAFOs). The National Resource Conservation Service has set a standard for fertilizer and manure application which require proper use of manure nutrients. Most states also set their own respective additional regulations. For example, Texas issues a general permit to CAFOs regarding treatment, utilization, and discharge of manure.

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TABLE 26.1

Animal type

Characteristic of selected animal manure.22e25

Waste production

BOD

Total solids

Volatile solids

lb/day$animal

Moisture content

N

P

%

K

Fiber

Crude protein

pH

% of dry matter

Dairy Calf

27.47

0.31

2.4

2

88

3.34

0.61

2.73

Beef Calf

69

1.54

5.51

4.63

92

5.25

2.36

4.17

Beef Cow

125

2.04

11

9.38

88

2.33

1.20

1.93

51.5

12.1

7

Swine (finisher)

11.2

0.41

1.41

1.13

89

12.18

4.06

6.49

39.2

25.1

7.5

Horse

54.4

1.52

7.61

6.5

85

2.21

0.74

0.74

Layer

0.167

0.008

0.037

0.027

75

6.23

1.92

2.87

The permit requires a pollution prevention plan, nutrient management plan, and detailed analysis of production, storage, treatment, and quality of manure. Processing manure involves a chain of management processes including capturing, handling, storing, treating, and utilization. A system must be designed to encompass manure from when it is voided by the animal to the end-use where it is assimilated into the soil. The manure management system is drastically different among regions. For example, livestock farms in the southern U.S. often treat and store diluted manure in wastewater lagoons and spray the liquid manure on surrounding crop fields. Livestock farms in the Netherlands and Northern Europe often adopt an anaerobic digestion operation to treat manure and recover bioenergy. The main goal of manure utilization is to recover nutrients optimally and reconnect the surplus manure on animal farms to the natural nutrient cycle. Besides nutrients, manure has alternative values. Manure has a considerable heat value that can be recovered in a bioenergy system or used to extract biofuels via anaerobic digestion or thermochemical processes.

7.2 31.7

39.8

Biological and thermochemical processes are two main pathways for generating energy and fuel from manure. For example, during anaerobic digestion, manure is converted into a methane rich biogas by microorganisms (methanogens) in the absence of oxygen (Fig. 26.1). Despite the fact that new technologies and management methods are developed to improve the quality of crops, livestock products, and reduce the cost of production, manure management methods have often remained unchanged. In this chapter, we will review conventional manure treatment and utilization technologies, with a touch of trending ideas.

Manure processing and handling Medium or large animal feeding operations handle a large amount of manure. For example, a dairy cow and a fattening pig produce w20 mt and w0.5 mt of manure per year, respectively. It requires specialized infrastructure and equipment to remove manure from animal houses to the treatment facility, long-term storage site, and land application site for end-use. In most

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26. Manure treatment and utilization in production systems

FIG. 26.1

Manure treatment and utilization.

cases, manure should be properly stored over long-term until the right time for land application. It is essential to select a proper manure processing and handling method according to the physical form of the manure and farm’s utilization demand.

collecting solid manure. Solid manure is often stored in heaps for a period of several months before it is used over the crop growing season. During storage, there can be up to 50% loss of the organic matter in the manure caused by microbial activities.

Solid manure

Slurry manure

Manure is in solid form when it contains more than 15% of dry matter. Typical production systems that generate solid manure includes poultry, beef cattle, and horse operations. Solid manure is the most straightforward to handle as it requires the least amount of land to store. Sloped floor or conveyor belt can be used to regularly remove solid manure from the animal house. In many cases, solid manure is accumulated in the animal house over a short-term of 1 week to several months before it is removed for long-term storage. Scrappers, box scraper, blades, and loaders are commonly used for

Manure is in slurry form when it contains 5e15% of dry matter. Slurry manure is often found in swine, cattle, and dairy production systems. It must be frequently removed from the animal housing floor since slurry has a high risk of odor emission and hygiene issues. The slurry manure can be collected using slotted floors and then accumulates in pits beneath or outside the animal house. For solid floored animal houses, scrappers can be used to collect slurry manure by moving it through an alley. A vacuum truck is an effective way to collect slurry manure especially when the slurry has a

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Manure treatment

relatively high content of solids. A vacuum truck can also directly transfer manure to the storage unit, which simplifies the infrastructure of the manure collecting and handling system. However, using a vacuum truck sometimes requires partially empty the animal house, which may complicate the manure collection process. In addition, manure slurry also can be transferred using pumps and pipelines. However, this process requires special slurry pumps that have devices to cut and chop the solids at the inlet to prevent plugging. Because of high viscosity, it is costly to pump slurry over long distances, causing a relatively high expense for manure slurry treatment. In most cases, slurry undergoes solid-liquid separation to reduce the dry matter content before it is treated, stored, or land-applied. In some cases, a high-pressure slurry pump is used to move slurry by pipelines, and a slurry tanker is also often used to transfer slurry.

Liquid manure Liquid manure contains less than 5% of dry matter. The feces voided by farm animals are either solids or slurry. Liquid manure is produced when wash water is added to slurry manure, or when a significant solid fraction is removed from slurry manure after solid-liquid separation. Liquid manure can be drained over a slightly-sloped animal house floor and transported by gravity in open channels. It can also be conveyed out with a low-pressure pump. This process often uses diluted wastewater from a lagoon or other storage units. A special case is an open-lot system, where the liquid manure is generated by runoff from lot surfaces and contains little dry matter. The runoff is usually drained and stored in receiving basins, while most of the solid manure remains deposited on the lot.

In most cases, lagoons provide simultaneous long-term storage and treatment of liquid manure. The main purpose of liquid manure treatment is to improve the properties of manure for land-application or reuse as wash water by reducing odor and excess solids. The manure treatment process and pathway in a lagoon are determined by its depth.2 For primary treatment of liquid manure, dairy or swine farms often use an anaerobic lagoon with a depth of 2e6 m. The lagoon creates an anaerobic environment allowing bacteria and other organisms to decompose organic matter in the fluid. This process also allows most of the suspended solids in the liquid manure to settle as a P-rich sludge which is removed and treated later.3 Many farms also adopt a facultative lagoon for secondary treatment of liquid manure. A facultative lagoon is relatively shallower than the anaerobic lagoon. It uses both aerobic and anaerobic processes to further treat the liquid manure. After treatment, the lagoon effluent can be used as a liquid fertilizer.4 Regular irrigation equipment such as an irrigation pump is often used for handling lagoon effluent because the solids content has been greatly reduced. For applying liquid manure wastewater, irrigation equipment such as a traveling gun can be used for small and irregular fields, while pivot irrigation systems may be used on large and flat fields.5

Manure treatment Manure solid-liquid separation Treatment of slurry or liquid manure often begins with solid-liquid separation. This process creates a low-moisture solid fraction that can be more easily handled and transported, and a low-strength liquid fraction that can be more easily pumped and treated. Another purpose of solid-liquid separation is to split the nutrients in the solid and liquid fractions. The solid

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manure fraction usually contains relatively high levels of phosphorus and organic nitrogen since the majority of these compounds form coarse particles. The liquid fraction usually contains relatively low levels of phosphorus, high levels of inorganic nitrogen, and most soluble salt ions. Splitting the nutrients gives flexibility for land application, as well as generates better manure composition to meet the needs of plants. Because fine particles typically decompose more rapidly than coarse particles, treatment of these two types of particles in the separated stream can significantly improve the manure treatment efficiency. Solid-liquid separation can be done by sedimentation, filtration, or centrifugations, etc. Chemical agents may be added to the manure to assist in the separation process.

Solid-liquid separation methods Solid-liquid separation may be conducted as a low-cost passive process using a static slope screen, thickener, or sludge bed with weeping wall. A static slope screen is a steeply sloped fine wedge wire with a flat bottom section that allows the solids to be collected. A thickener is a cylindrical tank with a conical bottom. During operation, the influent is added from the top, and the solids in the sediments are removed from the bottom. A sludge bed with weeping wall consists of a shallow concrete basin with a slanted wall that allows liquids to pass through while retaining the solid-rich fraction in the sludge bed. Theoretically, passive systems can achieve high separation efficiency by increasing the sedimentation time to allow fine particles to settle. In practice, these passive systems are often considered more effective than sedimentation for diluted manure. They are also efficient for separating larger particles, typically greater than 100 mm. In many cases, these passive methods are used to prescreen the manure before further separation. The solids settling time can be greatly reduced using centrifugation, where centrifugal force

is orders of magnitude greater than the gravitational force. It is usually carried out in decanter centrifuge which consists of a screw and a bowl rotating at a different angular velocity. This allows the solid fraction to settle on the inside of the bowl and be carried to the end of the bowl where it escapes from a discharge outlet.6 Mechanical screens and filter belts are widely used to filter solids from manure, whereas the liquid moves through a drain by gravity or agitation. A major advantage of these methods is the continuous processing for high flux of manure. Although filter and screen can capture particles finer than mesh size, there is a limit on the mesh size of the filter due to clogging issues. Therefore, the filter method is more efficient for removing coarse particles. Screw press or a press auger are typical pieces of equipment used for filtration with applied pressure. In screw press separators or press auger separators, the slurry enters a cylindrical screen with a screw. Then the liquid fraction passes through the screen and collected in a container surrounding the screen. The slurry is subjected to greater pressure as it moves toward the end of the screw, allowing additional liquid to be removed. Ultimately, a low-moisture solid fraction escapes from the opening at the end of the cylindrical screen.6

Chemical methods to assist solid-liquid separation Chemicals can be added to slurry manure to improve solid-liquid separation efficiency by promoting the precipitation of dissolved ions or coagulation and flocculation of small suspended particles as well as colloidal particles. The target of precipitation is usually orthophosphate ions, which can help further split the P between solid and liquid fractions. Iron or calcium ions can be added to precipitate P by forming insoluble orthophosphate compounds. Multivalent cations such as iron and aluminum ions could also induce coagulation of colloidal

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particles in the slurry by the electrostatic destabilization mechanism between these fine particles. High-charge polyelectrolyte may also induce flocculation in the slurry. The long-chain polymer adheres to small suspended particles and colloidal particles through physical interactions and aggregate into easy-to-settle particles. Among these methods, flocculation using polyelectrolytes including Polyacrylamide (PAM)7,8 and Polydicyandiamide (PDCD)9 increasingly adopted because of their lower environmental impact.

Pathogen reduction in manure treatment Microflora plays a major role in the biochemical transformation of manure during treatment, storage, and after manure land-application. Within which, a small portion of the microbes is pathogens. The most prevalent pathogens are Salmonella spp., Escherichia coli O157:H7, Yersinia spp., Campylobacter spp., cysts, Giardia, and Cryptosporidium.10 Research also indicates that crop fields, after manure application, have higher levels of fecal coliform and fecal streptococci. The main pathogen risk associated with disease outbreaks from manure is that pathogens may survive over an extended period and regrow when conditions become favorable. As a result, manure fertilizer and reused manure wastewater have high risks of becoming the source of diseases. The major route of transmitting zoonotic diseases is the fecal-oral pathway. In this case, the human food supply chain may be sabotaged when pathogen contaminated manure is applied to edible crops. Manure treatment prior to land application is the major barrier against pathogen transmission. Ensuring a storage period is a simple practice for pathogen reduction. It is essential for this strategy that no fresh manure is added to the pile during storage. Composting is a common method for pathogen inactivation in solid manure. The compost temperature needs to be maintained above 50  C for efficient inactivation,

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and the compost piles have to be turned over mixed several times to ensure that this treatment is applied to all of the manure. External heating in the anaerobic digestion process may also be an effective way to inactivate pathogens, thus its digestate is often considered to have a low risk of pathogen contamination. Liming and ammonia treatment are also common active practices for inactivating microorganisms in manure.

Composting Composting is the most widely adopted method to treat solid manure. During this process, microorganisms consume biodegradable organic matter in manure to generate heat, carbon dioxide, and water under an aerobic condition. Composting provides a number of advantages that add value to manure. It removes a considerable amount of moisture, volatile organic matter, and significantly reduces the volume of manure. Composted manure is easier to handle, cheaper to transport, and less likely to emit odors and other gaseous pollutants. The heat released in the composting process not only helps to kill most of the pathogens and weed seeds, but also converts manure to a safe-to-use product. Composted manure provides a slow-release form of nutrients that are less likely to impair water quality. On the other hand, composting has a few limitations. It is a time-consuming process that requires a month up to a year to obtain a properly decomposed product. Composting involves significant loss of nitrogen and carbon compounds resulting in substantially reduced value as a fertilizer. The lost gaseous compounds often contain considerable levels of greenhouse gases including CH4 and N2O.

Factors affecting composting process The composting process consists of an active and a maturation stage. In the active stage, microorganisms consume oxygen (O2) while

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feeding on simple carbohydrates, fats, amino acids, and other labile organic compounds, together with some cellulose, hemicellulose, lignin, and other more resistant organic compounds. In the maturation stage, microbial activities slow down and new compounds are synthesized including the mineralization of slowly degradable matter and the humification of lignocellulosic compounds. Finished compost usually reduces its volume by up to 60%, moisture content by up to 40%, and weight by up to 50%.11 Composting is most efficient when the right temperature, aeration, and moisture conditions are provided for growth of aerobic bacteria. The heat released by the microbial breakdown of organic matter increases the temperature in manure. The temperature can be maintained between 40 and 65  C by properly designing the size and shape of the composting pile. The composting pile can be aerated by turning or using forced ventilation with fans or blowers. The aeration process helps to replenish oxygen and removes excess heat, moisture, and CO2 from the pile. A moisture level of 40e60% is optimal for aeration and bacterial activities in the compost pile. Depending on the climate, the compost pile often needs to be rewetted and/or insulated to maintain the moisture level. In most cases, manure needs to be pre-treated and formulated in order to be effectively composted. Common unsuitable properties of fresh manure are excessive moisture, high nitrogen to carbon ratio, and low porosity.12 Excessive moisture and low porosity impede the aeration process. The ideal C:N ratio for composting is 25:1e30:1, while manure usually contains relatively high nitrogen compared to degradable carbon. This may lead to nitrogen loss by NH3 volatilization or leaching. Solid-liquid separation is usually the first step to overcome these hindering properties. Amendments, such as straw, woodchips, and other agriculture waste with low moisture, high C:N ratio, and coarse

texture are often added to adjust the moisture and nitrogen to carbon ratio and porosity.

Composting methods Windrows, static piles, and in-vessel systems are three common composting methods.11 In the windrow method, manure is in elongated stacks that allow air to pass through. When remained passively aerated, a windrow has a high risk of oxygen depletion, releasing unpleasant odors, and greenhouse gases. To address this problem, a windrow can be remixed mechanically using a bucket loader, manure spreader, or a windrow turner, which reintroduces oxygen and reestablishes the texture of the compost pile. Alternatively, manure can be piled statically, and airflow can be introduced through the pile by installing ventilation pipes inside the system. In this case, a static pile is often insulated to prevent excess heat loss at the surface, ensuring that the temperature of the pile is homogenous. In-vessel composting is usually accomplished in a rotating drum providing constant forced aeration and agitation. This method enables complete control over the temperature, oxygen, and moisture levels to optimize the decomposition process.

Anaerobic digestion system Liquid and slurry manure can produce biogas in an anaerobic digestion system. Anaerobic digestion is a process whereby microorganisms convert organic material in manure into methane-rich biogas in an oxygen-free environment. It is a promising manure treatment technology because the methane can be burned to generate electricity and heat. The biogas may be purified and sent to a gas grid for household and industrial use. Besides energy production, the anaerobic digestion process provides additional benefits including removing excessive organic nutrients in manure, eliminating weed

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seeds, reducing odor, and inactivating pathogens. In spite of these advantages, this technology is not widely adopted in the U.S. especially in southern regions such as Texas, because a digester is often costly to set up and maintain.

Factors affecting biogas production Anaerobic digestion requires a number of syntrophic microorganisms which need to be inoculated into the manure. These microorganisms convert the volatile solids of organic matter in manure through the series of hydrolysis, acidogenesis, acetogenesis, and methanogenesis processes. In the hydrolysis stage, microorganisms break down carbohydrates, large protein and peptides, and lipids into sugars, soluble proteins and amino acids, and long-chain fatty acids, respectively. These smaller organic molecules are converted to volatile fatty acids, alcohols, H2, and CO2 through acidogenisis processes by fermentative bacteria. During these processes, CH4 and CO2 are produced and released from the acetogenesis and methanogenesis stages. Most of the slowly degradable lignified matter remains undigested, which becomes a slurry of digestate that will be further processed.13 Because anaerobic digestion is a complex process that involves interdependent steps, it must be operated as a continuous process subject to many constraints. For example, an anaerobic digester requires constant input of organic load from manure. A sudden increase in the manure load will lead to the accumulation of volatile fatty acids which can result in a toxic environment that inhibits biogas production. This type of digester failure may disrupt the manure treatment for months before the microbial community can be reestablished. Failure can also be induced by a sudden change in the environment that creates difficulty for the microbial communities to adapt, which include changes of pH of the feed, sudden temperature

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fluctuations, a large change in the quality or content of manure, and the presence of antibiotics in the manure. The biogas yield is strongly influenced by the temperature, pH, and C:N ratio of the manure. In most cases, the biogas yield is optimized at a neutral pH and mesophilic temperature of w35  C within a narrow interval. To help maintain the temperature, a digester is often heated and insulated or buried according to the climate. The C:N ratios of the manure feed should be in the range between 20:1 and 30:1. In some cases, manure may have an excess of N which is inhibitory for biogas production due to the accumulation of NH3 in the system. It is helpful to adjust the nutrient composition of the feed by adding amendments consisting of other agriculture waste with a lower C:N ratio. It is also crucial to control the solids content of manure according to the pumping and mixing capabilities of the digester, which is designed to create the resultant hydraulic in relation to the stability of the microbial process. Solid-liquid separation can be used to partially remove solids before digestion, or the liquid can be added or removed to achieve the optimal solids content.

Designs of anaerobic digestion systems for manure treatment The common designs of anaerobic digesters for handling manure are covered anaerobic lagoons and continuous tank reactor. Covered anaerobic lagoons have a simple and low-maintenance design. The manure is stored and treated at the same time in a lagoon that is sealed with an impermeable cover. A covered anaerobic lagoon is appropriate for receiving a large volume of liquid manure flushed from the animal house. As the lagoon is usually unheated, the biogas production efficiency is low and varies with the temperature of the environment. A continuous tank reactor is often employed for treating slurry manure. The tank may be heated for an optimal and stable

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biogas yield. If the slurry is undiluted and has relatively high solids content (11e13%), it is often added via a plug-flow inlet, whereby the reactant content in the tank is pushed from one end to the other. If the slurry is diluted (2e10% solids content), e.g., dairy manure mixed with process water, it is often completely mixed inside the reactor by a motor or pump. A completely mixed digester offers optimal results of co-digestion with other agricultural waste.

Manure utilization Methods of manure utilization include land-application, pelletizing, biofuel production, nutrient extraction, and feed-stock for various value-added products, etc. One of the main goals of utilizing manure is to recycle its nitrogen and phosphorus nutrients. It is essential for manure utilization to take into account all of the waste, all of the time, and all the way. For example, utilization of slurry manure should take into account both solid and liquid fractions produced by solid-liquid separation processes. A major barrier for manure utilization is high transportation cost of fresh manure and its products. Conventionally, a local solution is preferred. However, as surplus manure is becoming a pressing problem for many animal production regions, there is growing interest in developing manure utilization methods, such as pelletizing and nutrient extraction, that can cost-effectively export excessive manure to the manure-deficient regions.

Land application Manure from different chains of management has different fertilizer characteristics. For N, liquid manure usually has a relatively high proportion of highly-available N for plants. Solid manure from cattle and swine usually has a relatively high proportion of organic N, which is unavailable to plants in the short term. A large fraction of the organic N in

poultry manure is in the form of uric acid, which will be mineralized shortly after land application. The P in manure is predominantly in a slow-release inorganic form. Chelated or complexed organics of micronutrients such as Fe, Mn, B, Zn, and Cu are present in manure. Manure can also indirectly affect the availability of soil elements by altering the pH of the soil. Manure has a nearly fixed nutrient ratio between N and P. Applying manure to meet the nitrogen needs can lead to the over application of P. To overcome this problem, manure is often applied to fulfill the plant P requirement, and mineral N fertilizer is supplemented to fill the gap for the proper ratio of P and N. The nutrient imbalance problem can be improved by solid-liquid separation, which provides flexibility in establishing the nutrient ratio by separating N and P in the manure into the solid and liquid fractions, respectively. Unlike most mineral fertilizers designed to deliver immediately available nutrients to plants, the N and P released from manure are less immediate and less predictable. Manure application often requires careful assessment of fertilizer value to account for the slow-release of the nutrients. The timing of application needs to be considered for the nutrient release characteristics, as well as the storage capacity of the manure management system. This often limits the application of manure to plants with a short growing season. The slow-release nature of manure fertilizer can help reduce nutrient loss for some farming systems. However, manure is also associated with a great risk of nutrient-loss because of the slow-availability and unpredictability of nutrients content, results in over-application of nutrients. The nutrient availability in manure is soil and climate dependent. There is a significant state-to-state difference in the formula for predicting nutrient availability in manure; hence, the amount of manure applied to lands drastically different among different regions.

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Manure utilization

Fresh manure has a higher fertilizer value than treated manure such as compost, pellets, and digestate, because there are significant potential losses of nutrients associated with treatment and storage. Land application of fresh manure is more restrictive because of the potential to spread pathogens, especially when manure is applied to edibles plants. The USDA’s National Organic Program guideline suggests that fresh manure may be applied to soil for a certain period of time before harvesting even if the crop may not come into contact with the soil.

Pelletizing Transportation and land-application costs limit the utilization of manure as a substitute for chemical fertilizers. At the current costs of loading, hauling, and spreading, it is not economically feasible to transport manure and compost over dozens of miles in most cases. Manure handling and transportation costs can be reduced by pelletizing. During this process, compression and heat are applied to solid manure to granulate or mold it into pellets of uniform size. Manure is often concurrently dried during pelletizing or pre-dried using a solar field or thermal methods. The combination of pressure and heat can chemically stabilize manure, while preventing loss of N. As a result, manure pellets preserve and concentrate most of the nutrients, making them easier and cheaper to be transported to the enduser. For handling, manure pellets are usually dry, stable, and odorless products that can be packaged and land-applied like mineral fertilizer pellets.

Extracting nutrients from liquid and slurry manure There are increasing commercial interests in extracting nutrients from liquid manure. One method is to harvest struvite (MgNH4PO4$6H2O) by precipitation, which is

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capable of separating more than 50% of phosphorus in the manure liquid.14 Manure-derived struvite is a high-value fertilizer that can deliver slow release nitrogen and phosphorus to the soil. It is also possible to obtain concentrated liquid fertilizer by removing water from liquid or slurry manure using methods such as ultrafiltration, evaporation, and reverse osmosis.

Biofuel Manure has a considerable energy value. The higher heating value of the dry matter in manure is w20 MJ/kg, which is comparable to brown coal and firewood.15 Historically, dried manure is widely used as a fuel source for cooking and home heating in many countries. Direct combustion of manure in a generator is impractical and inefficient because manure contains high levels of incombustible residuals. It can be burned in a power plant using the co-firing method by mixing it with other solid fuels. The most efficient way to produce energy from manure is to convert it into a fuel that can drive a conventional internal combustion engine. For liquid and slurry manure, anaerobic digestion can be implemented to produce a biogas that usually comprises 55e65% CH4, which can be used in a commercial generator after dewatering and removal of H2S. For solid manure, pyrolysis can be used to produce syngas, bio-oil, and biochar. Depending on the temperature, heating rate, and resident time, pyrolysis can yield different amounts of those products. The syngas, rich in H2, CO, and CH4, can be burned in a gas turbine for energy; the bio-oil, after refining, can be blended with conventional liquid fuels; and biochar can be used for direct combustion.

Emerging value-added products Manure-derived biochar Manure can be converted to biochar by pyrolysis when it is heated between 300 and

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700  C in a low oxygen environment. Manure biochar consists of highly porous polycyclic aromatic hydrocarbons. Most of the plant nutrients in the manure are unvolatilised and retained in the biochar. Manure biochar has a low-density porous structure with a large active surface area which make it a unique soil-conditioning agent capable of reducing soil bulk density and enhancing aeration and water/nutrient-holding capacity of the soil.16 With a large active surface, manure biochar can be used as an absorbing agent to sequester pollutants in soil and agricultural waste.17 It is also used as a bulking agent to enhance the efficiency of manure composting.18 Substrate for microbial culture Besides being a soil amendment and fertilizer, animal manure also has great potential as a nutrient source for microbial growth because of its rich nitrogen and phosphorus content which is a key nutrient in microbial culture media, as well as most other essential nutrients.19 In recent decades, the demand for microbial growth media from biotechnological fermentation and biofuel production has significantly increased, which leads an increasing demand for costeffective nutrient sources.20 Therefore, abundant and cheap microbial culture media such as animal manure for large scale biomass production is desired. In addition, animal manure is a rich complex medium likely to contain more easily convertible nitrogen, such as proteins or peptides, with smaller molecular weight, compared with other available organic nutrients. Previous results suggest that animal manure can be a great source of protein-rich material for many purposes.21 Microalgae are photosynthetic organisms with relatively simple nutrient requirements for growth. They can be used as human feed, animal feed, biofuels, and a source of valuable components used in the cosmetic and pharmaceutics industries. Microalgae such as Spirulina platensis, also known as Arthrospira platensis, has a high protein content and it is also a great source of

vitamins, minerals, and polyunsaturated fatty acids. If treated properly and managed under optimal conditions, a tremendous amount of manure generated from large animal farms could be turned into high-value biomass. The desired growth conditions for microalgae include appropriate dilutions of manure that allow sufficient light for their photosynthesis, proper manure feeding amount and frequency, ideal agitation, temperature, and aeration, etc.

References 1. Dou ZK, Galligan DT, Ramberg CF, et al. A survey of dairy farming in Pennsylvania: nutrient management practices and implications. J Dairy Sci. 2001;84(4): 966e973. 2. Tyson T, Mukhtar S. eXtension Foundation. Liquid Manure Storage Treatment Options, Including Lagoons; 2013. https://articles.extension.org/pages/19941/liquidmanure-storage-treatment-options-including-lagoons. 3. Worley JW. Manure Storage and Treatment Systems; 2007. https://coastalgadnr.org/sites/default/files/crd/ CZM/NPSProgram/SFNMPch3.pdf. 4. Mukhtar S. Proper Lagoon Management to Reduce Odor and Excessive Sludge Accumulation. Agricultural Communications, The Texas A&M University System; 2018. https://nutrientmanagement.tamu.edu/content/ tools/lagoonmanagement.pdf. 5. Fulhage C, Harner J. eXtension Foundation. Liquid Manure Collection and Handling Systems; 2015. https://articles. extension.org/pages/8905/liquid-manure-collectionand-handling-systems. 6. Hjorth M, Christensen KV, Christensen ML, et al. Solide liquid separation of animal slurry in theory and practice. A review. Agron Sustain Dev. 2010;30(1):153e180. 7. Garcia MC, Vanotti MB, Szogi AA. Simultaneous separation of phosphorus sludge and manure solids with polymers. Trans ASABE. 2007;50(6):2205e2215. 8. Liu Z, Carroll ZS, Long SC, et al. Use of cationic polymers to reduce pathogen levels during dairy manure separation. J Environ Manag. 2016;166:260e266. 9. Meng XL, Nie Y, Sun J, et al. Functionalized dicyandiamide-formaldehyde polymer as efficient heterogeneous catalysts for conversion of CO2 into organic carbonates. Green Chem. 2014;16(5): 2771e2778. 10. Bicudo JR, Goyal S. Pathogens and manure management systems: a review. Environ Technol. 2003;24(1): 115e130.

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References

11. Alberta Agricultural, Food, and Rural Development. Manure Composting Manual; 2004. https://www1. agric.gov.ab.ca/$department/deptdocs.nsf/all/agdex 8875/$file/400_27-1.pdf?OpenElement. 12. Governo J. eXtension Foundation. Composting Livestock or Poultry Manure; 2016. https://articles.extension. org/pages/8844/composting-livestock-or-poultrymanure. 13. Hamilton D. eXtension Foundation. Processing Biomass into Biogas; October 23, 2015. https://articles. extension.org/pages/30313/processing-biomass-intobiogas. 14. Liu Y, Kwag JH, Kim JH, et al. Recovery of nitrogen and phosphorus by struvite crystallization from swine wastewater. Desalination. 2011;277(1e3):364e369. 15. Annamalai K, Sweeten JM, Ramalingam SC. Estimation of gross heating values of biomass fuels. Trans ASAE. 1987;30(4):1205e1208. 16. United States Environmental Protection Agency (US EPA). National Cattlemen’s Beef Association. Beneficial Uses of Manure and Environmental Protection; 2015. https://www.beefusa.org/CMDocs/BeefUSA/Media/ Beneficial%20Uses%20of%20Manure%20FINAL%20Aug 2015.pdf. 17. Cao X, Ma L, Gao B, et al. Diary-manure derived biochar effectively sorbs lead and atrazine. Environ Sci Technol. 2009;43(9):3285e3291. 18. Prost K, Borchard N, Siemens J, et al. Biochar affected by composting with farmyard manure. J Environ Qual. 2013;42(1):164e172.

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19. Burkovski A, Kr€amer R. Bacterial amino acid transport proteins: occurrence, functions, and significance for biotechnological applications. Appl Microbiol Biotechnol. 2002;58(3):265e274. 20. Manginot C, Roustan JL, Sablayrolles JM. Nitrogen demand of different yeast strains during alcoholic fermentation. Importance of the stationary phase. Enzym Microb Technol. 1998;23(7e8):511e517. 21. Yao W, Wu X, Zhu J, et al. Utilization of protein extract from dairy manure as a nitrogen source by Rhizopus oryzae NRRL-395 for l-lactic acid production. Bioresour Technol. 2010;101(11):4132e4138. 22. United States Department of Agriculture (USDA), Natural Resources Conservation Service (NRCA). Agricultural Waste Management Handbook. Part 651, Agricultural Waste Characteristics; 1992. https://www. wcc.nrcs.usda.gov/ftpref/wntsc/AWM/handbook/ ch4.pdf. 23. American Society of Agricultural Engineers (ASAE) Standard. Manure Production and Characteristics; 2005. http://www.agronext.iastate.edu/immag/pubs/ manure-prod-char-d384-2.pdf. 24. Chen S, Wen Z, Liao W, et al. Studies into using manure in a biorefinery concept. In: Twenty-Sixth Symposium on Biotechnology for Fuels and Chemicals. vol. 121e124. 2005: 999e1015. 25. Lorimor J, Powers W, Sutton A. Midwest Plan Service. Manure Characteristics; 2004. https://www.canr.msu. edu/outreach/uploads/files/ManureCharacteristics MWPS-18_1.pdf.

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27 Management of metabolic disorders (including metabolic diseases) in ruminant and nonruminant animals Guoyao Wu Department of Animal Science, Texas A&M University, College Station, TX, United States

O U T L I N E Introduction

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Disorders caused by deficiencies or excesses of vitamins 482

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Disorders caused by deficiencies or excesses of minerals 485

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Conclusion

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Acknowledgments

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Introduction Metabolism is the foundation of life. Dietary macronutrients undergo biological oxidation via complex pathways to form CO2 and H2O with the concomitant release of energy to support physiological processes.1 The Krebs cycle plays a central role in bridging glucose, fructose, fatty acid, and amino acid metabolism in a

Animal Agriculture https://doi.org/10.1016/B978-0-12-817052-6.00027-6

cell-, tissue-, age-, and species-specific manner. In animals, enzymes (almost exclusively proteins) require vitamins and nutritionally essential minerals as cofactors to catalyze nearly all intracellular reactions. Metabolic pathways are regulated by hormones, redox signaling, covalent modifications of proteins, dietary and other environmental factors, and intracellular concentrations of substrates and

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Copyright © 2020 Elsevier Inc. All rights reserved.

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metabolites. All these factors are vital to animal growth, development, immunity, production, health and survival. Because metabolic control via both short-term and long-term mechanisms helps animals adapt to nutritional, physiological and environmental changes, individual reactions, biochemical pathways, and acid-base balances are well integrated to maintain physiological homeostasis in healthy organisms.2 Disturbances of homeostasis for a prolonged period of time may result in disease, which is defined as a disorder of structure or function in the body that is typically manifested by specific

signs and symptoms (Fig. 27.1). In animals, metabolic disorders (including metabolic diseases) occur due to one or more of the following reasons: low-quality diet; inadequate or excessive intake of nutrients; impairments in digestion, absorption, utilization, or storage of nutrients; imbalances and antagonisms among nutrients; excessive excretion of nutrients; increased nutrient requirements by cells, tissues or the whole body due to physiological or environmental changes; abnormal metabolic control; dehydration; and toxins in the environment and diet. Thus, metabolic disorders include, but are not limited to, nutritional or acquired diseases,

FIG. 27.1 Metabolic disorders (including metabolic diseases) in ruminant and nonruminant animals. Defective biochemical pathways due to deficiencies in their enzymes, coenzymes or cofactors can cause abnormal nutrient metabolism (either inherited or acquired), resulting in multi-organ dysfunctions, diseases and even death in livestock and poultry. As, ascites syndrome; CLA, conjugated linoleic acid; FLHS, fatty liver hemorrhagic syndrome; FLKS, fatty liver and kidney syndrome. a Disorders (including diseases) caused by the deficiency of a vitamin include polioencephalomalacia in ruminants and beriberi (thiamin), pellagra (niacin), burning-foot syndrome (pantothenic acid), neural tube defects (folate), scurvy (vitamin C), photophobia (riboflavin), xerophthalmia and keratomalacia (vitamin A), rickets and osteomalacia (vitamin D), liver steatosis (choline), myopathy and liver necrosis (vitamin E), hemorrhage (vitamin K), and infertility (vitamins A and E). bDisorders (including diseases) caused by the deficiency of a mineral include milk fever in cows, rickets, and osteomalacia (calcium), grass tetany (magnesium), anemia and hemorrhage (iron), ammonia toxicity (manganese), Keshan’s disease (selenium), goiter (iodine), dental caries (fluorine), Menke’s and Wilson’s diseases (copper), and infertility (phosphorus). cDisorders (including diseases) caused by an excessive production of amino acid metabolites include hyperhomocysteinemia, hyperammonemia, gout, melanosis, and porphyria. dDisorders (including diseases) caused by an excessive lipid-soluble vitamin include hypervitaminosis A, D, E and K, whereas diseases caused by an excessive mineral include polioencephalomalacia in ruminants (sulfate), hypertension (sodium), hyperkalemic periodic paralysis in horses (potassium), copper toxicity, and selenosis (selenium).e This reaction is catalyzed by urease in the rumen fluid of ruminants and the intestine of all animals. VII. Management of animal diseases in livestock and poultry production

Disorders caused by abnormal metabolism, deficiencies or excesses of carbohydrates

although the latter are usually the focus of the practitioners in animal production. Characteristic syndromes (e.g., goiter from the deficiency of iodine; and metastatic calcification from excessive calcium) can be produced experimentally by inducing deficiencies or excesses of individual nutrients. On farms, metabolic disorders often result from simultaneous deficiencies or excesses of multiple nutrients and are commonly associated with bacterial, fungal, viral or parasitic infections. The objective of this chapter is to highlight major metabolic disorders (including metabolic diseases) as well as their prevention and treatment in animals.

Disorders caused by abnormal metabolism, deficiencies or excesses of carbohydrates Carbohydrates are the most abundant nutrients in the feedstuffs of ruminants, swine and poultry, but the foods of strict carnivores contain only a limited amount of carbohydrates. The digestion of polysaccharides differs markedly among species.1 In nonruminants, starch and glycogen are digested in the small intestine to yield free glucose, whereas dietary fibers are fermented by microbes in the large intestine to short-chain fatty acids (SCFAs). In contrast, in ruminants, starch, glycogen, and dietary fibers are extensively fermented in the rumen to produce SCFAs and methane, whereas dietary fiber that escapes the forestomach is further fermented in the large intestine. Consequently, in ruminants, there is little or no absorption of glucose from the small intestine into blood, but very active conversion of propionate into glucose in the liver, while acetate and butyrate are used for ATP production, fatty acid synthesis or ketogenesis. Glucose metabolism occurs through glycolysis, the Krebs cycle, the pentose cycle, uronic acid pathway, gluconeogenesis, glycogenesis, glycogenolysis, and other related pathways.1 Excessive glucose is generally stored as glycogen, primarily in the liver and skeletal

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muscle. In the fed or post-absorptive state, glucose is almost the exclusive metabolic fuel for the brain. Under all physiological conditions, glucose is the exclusive source of energy for red blood cells, while serving as a major metabolic fuel in cells of the immune system, retina, and renal medulla. In all cell types, glucose is the main source of NADPH that is a cofactor by many enzymes, including nitric oxide (NO) synthase, glutathione peroxidase, and fatty acid synthase. Thus, the maintenance of glucose homeostasis in blood, which is achieved through the digestion of dietary carbohydrates as well as the endogenous synthesis and catabolism of glucose, is essential to the survival, growth and development of mammals, birds and fish. Abnormal metabolism of carbohydrates can result in low or high concentrations of glucose and glycogen in tissues (e.g., blood, liver and skeletal muscle). On the other hand, butyrate is the major metabolic fuel for epithelial cells of the large intestine and, therefore, dietary fiber plays an important role in intestinal health. Disorders resulting from abnormal carbohydrate metabolism are highlighted in the following sections. Diabetes. Type-I diabetes mellitus results from the destruction of pancreatic b-cells that leads to an insulin deficiency, and type-II diabetes mellitus from the reduced sensitivity of tissues to insulin. Hyperglycemia causes oxidative stress, resulting in retinal damage, blindness, impaired blood flow (which may lead to amputation), and muscle weakness. Diabetes is rare in food-production animals, but common in companion animals (e.g., cats and dogs) due to overfeeding and physical inactivity. Interfering with the autoimmune activation of macrophages or inhibiting the release of inflammatory molecules by these cells through dietary or therapeutic means (e.g., oral administration of glutaminase inhibitors or NO synthase inhibitors; consumption of caseinbased diets) may prevent or delay type-I diabetes mellitus.3,4 Insulin must be used to treat type-I diabetes mellitus. Reducing excess

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accumulation of fats in tissues through controlling dietary intake of fat and starch and improving the sensitivity of tissues to insulin through exercise and dietary supplementation with 1% arginine or oral interferon-tau (8 mg/ kg BW/day, rat dose) is effective in reducing the concentration of glucose in the blood of animals with type-II diabetes mellitus.5 Hypoglycemia. This disorder occurs when the rate of glucose supplied in the diet and endogenous synthesis is lower than the rate of glucose utilization in animals. In ruminants, a deficiency of vitamin B12 (a vitamin required for converting propionate into glucose) also results in hypoglycemia. In species (e.g., pigs) that have only 1% fat in the body at birth and lack the ability to synthesize ketone bodies (major metabolic fuels for the brain when the concentration of glucose in blood is low), hypoglycemia is primarily responsible for the death of neonates exposed to cold temperatures. Glucose synthesis requires not only substrates (e.g., amino acids, lactate, and glycerol), but also energy and NADH.1 Thus, inadequate intake of nutrients contributes to hypoglycemia in all animals. Furthermore, when animals consume dietary substances that inhibit the b-oxidation of fatty acids (e.g., enzymes or carnitine palmitoyl transferase-I of this pathway) in the liver and kidney (the organs that are capable of gluconeogenesis), hypoglycemia also occurs. Sufficient provision of starch, fatty acids, amino acids, vitamins and minerals in diets, along with the housing of animals in a thermo-comfortable environment and the avoidance of toxic substances, is essential for the prevention and treatment of hypoglycemia. Ruminal acidosis in ruminants. The high content of starch and monosaccharides in grain-based diets of ruminants results in a sudden decrease in ruminal fluid pH due to the rapid production of lactic acid from the highly digestible carbohydrates by microbes via glycolysis, leading to metabolic acidosis in the rumen (ruminal pH < 5.5).1 A low ruminal pH inhibits the growth of cellulolytic bacteria and acetate-

producing bacteria, but promotes the growth of propionate-producing bacteria in the rumen, thereby reducing roughage digestion and acetate production, but stimulating propionate formation. Thus, ruminant diets should contain < 45% non-fiber carbohydrates (g/100 g of diet, dry matter basis) and must consist of sufficient forages and roughages to maintain the rumen in a healthy state. Ruminal bloat (ruminal tympany) in ruminants. The rumen of ruminants produces a large amount of gasses (primarily CO2 and CH4) through the microbial fermentation of nutrients (mainly carbohydrates).1 When the rate of production of the gasses exceeds the rate of their elimination by eructation (in the case of frothy bloat) or when the gasses cannot escape due to the blockage of the esophagus or the inhibition of the nerves controlling contractions of the rumen wall, the gasses (about 95%) form foams with water in the presence of surface active components.6 The latter, which have both hydrophilic and hydrophobic properties, include plant- or bacteria-derived phospholipids, glycolipids, lipopolysaccharides, as well as hydroxylated and cross-linked fatty acids (mycolic acids) in the rumen fluid. The foams are stabilized by water-soluble proteins, extracellular polymeric substances (mainly mucopolysaccharides), divalent ions (Ca2þ and Mg2þ), and suspended small-size plant particles. These factors increase the viscosity of the rumen fluid. The rapid fermentation of highly digestible substances (e.g., mono- and oligo-saccharides, beet pulp, and molasses) also acutely decreases the pH of the ruminal fluid, further impairing rumen motility and gas removal. The large volume of foam generates internal pressure on the vital organs (e.g., the heart and lungs), leading to multi-organ (e.g., circulatory and respiratory) dysfunction and eventually death of the animals. Pasture or dietary management is highly effective for preventing ruminal bloat in grazing or feed-lot ruminants, respectively. Examples include mixed types of pasture legumes and grass (containing a high content

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Disorders caused by abnormal metabolism, deficiencies or excesses of carbohydrates

of fiber) to maintain no more than 50% alfalfa (or clover) on the pasture or a gradual adaptation to a diet consisting of cereal grains and dried forages. When frothy bloat occurs, treatments include: (1) removal of ruminal gases through a trocar or cannula; (2) removal of the ruminal digesta through a stomach tube; (3) oral administration of an antifoaming agent, such as vegetable oil or mineral oil (400e500 mL/large animal), which also reduces microbial activity; (4) an emergency rumenotomy; and (5) placement of a rumen fistula for short-term relief. Equine exertional myopathy. This condition (also known as equine exertional rhabdomyolysis, azoturia, or Monday morning disease) may result from excessive lactate production or low pH in skeletal muscles of an exercising horse, and inadequate flow of blood to the muscles, leading to hypoxia-induced muscle cramps and damage.7 Equine exertional myopathy is likely a hereditary disorder and triggered by multiple factors, including the overfeeding of nonstructural carbohydrates (e.g., starch-rich grain); poor conditioning or fitness; a sudden increase in workload; imbalances of electrolytes; deficiencies of antioxidant minerals and vitamins; and undesirable weather conditions. As a preventative means, reducing the dietary intake of starch (e.g., limiting grain consumption) is essential to manage horses susceptible to equine exertional myopathy. Treatments may include the restoration of electrolyte balances and removal of grain rations; dietary supplementation with arginine (the precursor of NO as the major vasodilator) and vegetable oil (a major metabolic fuel for skeletal muscle); and limited exercise plus a low-carbohydrate, high-fat diet to reduce lactate production and ensure adequate ATP production in skeletal muscle. Glycogenosis. This disorder, either inherited or acquired, results from an excessive accumulation of glycogen in the liver and skeletal muscle, affecting the physiological functions of these organs. The pathology of glycogenosis involves deficiencies of enzymes in glycogen synthesis (e.g., glycogen synthase), glycogen breakdown

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(e.g., glycogen phosphorylase), or glycolysis (e.g., phosphofructokinase-1). In livestock, this disease may result from intoxication with the alkaloid called castanospermine (an inhibitor of a-glucosidase).8 Treatment of glycogen storage disease is dependent on its type. Animals with the inherited disorder are generally fed frequently with small meals of carbohydrates (e.g., cornstarch) to prevent low blood glucose levels. Animals with acquired disease may be fed diets containing low starch, but high levels of amino acids/protein and vegetable oil as energy sources for the liver and skeletal muscle. Diarrhea in sucrose-fed neonatal pigs. Sucrase hydrolyzes sucrose (a plant disaccharide) to glucose and fructose. However, this enzyme is completely absent from the small-intestinal mucosa of piglets at birth and is detectable only after 7 days of age. The specific and total activities of sucrase increase markedly thereafter until 8 weeks of age. Thus, when fed a large amount of sucrose, neonatal pigs cannot digest it. Its osmotic effect attracts water from the smallintestinal mucosa and blood into the lumen of the small intestine, leading to the formation of the watery digesta. Therefore, the diets of preweaning piglets should contain little sucrose. Diarrhea in lactose-fed chicks. Lactase hydrolyzes lactose (only present in milk) to glucose and galactose. However, this enzyme is barely detectable in the proventriculus, the pancreas, and the mucosa (including enterocytes) of 1- to 7-day-old or older chickens. Thus, post-hatching chickens cannot digest lactose in their stomach or small intestine and, therefore, cannot tolerate a large amount of this disaccharide. When fed a diet containing a high level of lactose (e.g., > 20%, as-fed basis) or milk, chicks exhibit diarrhea as explained previously, leading to impaired growth and even death. Turkey poults cannot tolerate 3.25% lactose (as-fed basis) in diets. Of note, due to the presence of microflora in the large intestine, chicks can tolerate some lactose in diets (e.g.,  4%, as-fed basis) without affecting their growth performance.

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Galactosemia. Galactose is a component of lactose. An inherited deficiency of key enzymes (e.g., galactokinase or galactose-1-P uridylyltransferase) for galactose degradation results in galactosemia, which is characterized by an enlarged liver, cirrhosis of liver, renal failure, cataracts, vomiting, seizure, and brain damage.9 Chickens have low hepatic galactokinase activity and are very susceptible to the galactose toxicity syndrome. Thus, 10% or 15% of galactose in a diet causes high mortality in broilers.1 Poultry diets should not include moderate or high levels of galactose (e.g., > 4%, as-fed basis). Note that chicks can tolerate some galactose in diets (e.g.,  4%, as-fed basis) without affecting their growth performance because the microflora in their large intestine contains enzymes for degrading this sugar.

Disorders caused by abnormal metabolism, deficiencies or excesses of lipids Lipids include fats (triacylglycerols), fatty acids, phospholipids, cholesterol, and related metabolites. Fats and free long-chain fatty acids (also known as nonesterified fatty acids) circulate in blood as an albumin complex. In the small intestine, dietary lipids are emulsified by glycine- and taurine-conjugated bile salts, undergo enzymatic hydrolysis to free fatty acids and monoacylglycerols, and are then assembled into mixed micelles.1 Products of lipid digestion are transported from the mixed micelles into the enterocyte where they are re-assembled with apolipoproteins into chylomicrons and other lipoproteins for export into lymphatic vessels and then returned into the blood circulation. When the dietary intake of fats is greater than the amount of fats broken down between meals, de novo synthesis of fatty acids is reduced and excessive amounts of dietary energy are stored as fats in the body. In contrast, when an animal consumes a low-fat but high-starch diet, excessive carbohydrates are used for the synthesis of fatty acids and fats in the body. When deficiencies of nutritionally essential u3 and u6

unsaturated fatty acids occur, animals exhibit numerous syndromes, such as skin lesions, growth restriction, and reproductive failure.10 Long-chain fatty acids are quantitatively the major energy substrates for the liver, skeletal muscle, heart, and kidneys of mammals and birds in both fed and fasting states and, therefore, play an important role in the function of these tissues. Fatty acids areoxidizedtoCO2 and H2O primarily in the mitochondria of cells via the pathway of ß-oxidation. Chain shortening of very long-chain fatty acids occurs in peroxisomes, and the resulting shorter acyl-CoA enters the mitochondria for ß-oxidation. While fats fulfill essential physiological functions, their excessive intake contributes to a variety of metabolic disorders and chronic diseases, including liver and kidney steatosis (excessive accumulation of fats to cause tissue degeneration), obesity, diabetes, and cardiovascular disease in animals. Under the conditions of no or inadequate energy intake, fats are mobilized from adipose tissue through the action of hormone-sensitive lipases to generate free fatty acids and glycerol. In most animals except for pigs, fatty acids are oxidized to produce ketone bodies, which are major metabolic fuels for the brain when the concentration of glucose in blood is reduced. Disorders caused by abnormal lipid metabolism are highlighted in the following sections. Essential fatty acid deficiency syndrome. Linoleic acid (u6, C18:2) and a-linolenic acid (u3, C18:3) are nutritionally essential fatty acids for all animals.1 In cats, which lack D6-desaturase to convert linoleic acid into arachidonic acid (u6, C20:4), arachidonic acid is also a nutritionally essential fatty acid. When deficiencies of nutritionally essential u3 and u6 fatty acids occur, animals exhibit numerous syndromes, including skin lesions, growth restriction, reproductive failure, increased susceptibility to infection, thrombocytopenia, impairment of neurological development, poor wound healing, and alopecia (hair loss).10 Dietary supplementation with the deficient essential fatty acid (e.g., a rumenprotected form in the case of ruminants) can effectively treat those syndromes.

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Obesity. A chronic imbalance between energy intake and expenditure in an animal causes excessive fat deposition in the body, leading to obesity, dyslipidemia, insulin resistance, and type-II diabetes mellitus. Companion animals (e.g., dogs, cats, and untrained horses) have a higher risk for obesity than livestock and poultry that are processed for meat production at a relatively young age. Pigs have no brown adipose tissue and naturally start to accumulate large amounts of white adipose tissue beginning at 45 kg of body weight (BW). Interestingly, even when pigs are fed a low-fat diet, their carcass fat increases disproportionately by 10-fold between 45 and 115 kg BW (a market weight). Significant body fat accumulation in beef cattle occurs only when they are fed grain-based (finishing) diets (typically in feedlots). Hormonal and nutritional strategies have been explored to regulate fat content in farm animals. For example, intramuscular administration of recombinant porcine somatotropin to barrows (50 mg/kg BW/day) or increasing the dietary intake of protein and amino acids (e.g., 1% supplemental arginine) can reduce fat deposition in animals.11 In addition, exercise can prevent obesity by stimulating 50 -AMP-activated protein kinase (AMPK) signaling and enhancing energy expenditure. Bovine fatty liver syndrome. In dairy cows, excessive mobilization of adipose tissue during early lactation releases long-chain fatty acids during a period of inadequate energy intake.12 When the rate of hepatic uptake of lipids from blood exceeds the rate of oxidation of fatty acids plus secretion of lipids by the liver, a large amount of fats is accumulated in the liver to cause hepatic lipidosis (fatty liver syndrome). Affected cows are more likely to suffer from peri-parturient metabolic, health and production problems, such as milk fever, ketosis, impaired hepatic function, low feed intake, mastitis, metritis, impaired lactation, and reduced reproductive performance. Fatty liver syndrome commonly occurs in obese high-producing cows, with mortality being as high as 25%. Despite the name of this metabolic

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disorder, affected cows may not always be visibly overweight at calving time. Prevention of obesity or an overweight condition is key to reducing the incidence of bovine fatty liver syndrome. As for nonruminant animals,1 dietary supplementation with citrulline or rumen-protected arginine may stimulate the hepatic oxidation of fatty acids to minimize risks for dyslipidemia in lactating cows. Treatments may include: (a) intravenous administration of glucose (60 g/h) and KCl; (b) intramuscular administration of Protamine zinc insulin (150e200 units); and (c) oral administration of propylene glycol (as a glucose precursor; 0.5e1 L per day) along with intramuscular administration of adrenocorticotropic hormone (600 units on day 1, 400 units on days 2 and 3, none on day 4, and 200 units on day 5) to promote the release of amino acids from skeletal muscle that can be used for the synthesis of glucose.13 Ketosis in lactating dairy cows. Ketosis refers to elevated concentrations of ketone bodies (acetoacetate, b-hydroxybutyrate, and acetone) in blood due to the excessive oxidation of fatty acids to acetyl-CoA relative to the oxidation of acetyl-CoA to CO2 and water in mitochondria.1 This metabolic disorder usually occurs in cows during early lactation when their appetite is depressed after calving and dietary energy intake cannot meet the increasing metabolic demands for milk production.14 This period of negative energy balance affects all cows because they mobilize their fat reserves to meet energy needs of the brain, lactating mammary glands, muscles, and other tissues. Factors that reduce feed intake and impair gluconeogenesis will increase risks for ketosis. Treatment should be based on the primary cause of the negative energy balance and ketosis. Increasing concentrations of glucose in plasma through oral administration of propylene glycol (e.g., 300e900 mL once daily) or intravenous administration of 250 g glucose (i.e., 500 mL of 50% Dextrose) can help to restore physiological concentrations of glucose in blood and to inhibit hepatic ketogenesis.14 Metabolism of glucose generates oxaloacetate, which facilitates the

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entry of acetyl-CoA into the Krebs cycle for oxidation, thereby reducing the conversion of acetyl-CoA into ketone bodies. Low-fat milk syndrome (milk fat depression) in dairy cows. This disorder refers to a reduction in the concentrations of milk fats (up to 50% or more) with little or no change in concentrations of lactose or protein in milk.15 Affected lactating cows often consume diets with low fiber content, but high contents of concentrates and unsaturated fatty acids. In grazing cows, milk fat depression may occur at certain critical times of the year (e.g., early spring and autumn) and in regions where pasture grasses contain a low content of fiber. These cows have a severely low ruminal pH and their rumen produce a relatively high amount of trans-10, cis-12 conjugated linoleic acid (CLA) that inhibits fatty acid synthesis by the mammary epithelial cells. The concentration of trans-10, cis-12 CLA in the abomasum at  0.2 g/100 g of total fatty acids can induce milk fat depression.15 This nutritional problem can be prevented by either providing adequate intake of dietary fiber or dietary supplementation with rumen-protected palmitic acid (412 g/day).16 Pregnancy toxemia. When the rate of hepatic oxidation of fatty acids to acetyl-CoA exceeds that of acetyl-CoA to CO2 and water in gestating dams, large amounts of acetyl-CoA are converted into ketone bodies in the liver, resulting in ketosis. Pregnancy toxemia is most prevalent in ewes and does that carry multiple fetuses during late gestation, because the supply of dietary energy and carbohydrates due to underfeeding cannot meet the needs of the mother plus her fetuses. This metabolic disorder also occurs in other mammals, such as beef cows, dairy cows, dogs, and mares during pregnancy. In ewes and does, risks for pregnancy toxemia increase when concentrations of glucose in maternal plasma are less than 30 mg/100 mL, which is lower than the normal values of 40e60 mg glucose/100 mL.17 Due to excessive fat mobilization to generate free fatty acids, overly fat ewes and does are also susceptible to pregnancy

toxemia under stressful conditions such as cold weather, heavy rain, and transportation. The incidence of this disease can be minimized by careful management and proper nutrition. For the prevention of pregnancy toxemia, molasses or grain concentrates may be supplemented to diets to increase the hepatic production of glucose. Treatment may include oral propylene glycol or corn syrup (as rapid sources of energy and glucose) at the rate of 200 mL four times daily along with 3e4 L of an electrolyte solution.17 Fatty liver hemorrhagic syndrome in laying hens. This disease occurs almost universally in prolific laying hens in cages that are fed highenergy diets, particularly during warm, summer months when the birds are often challenged with heat stress. Due to the chronic excessive energy balance, affected birds exhibit fatty liver syndrome and have a high mass of abdominal fats, and possibly pale combs.18 The enlarged liver is prone to damage and bleeding. Hemorrhage often occurs when a hen is straining to lay her egg. In some cases, blood loss from hepatic hemorrhage results in a high rate of mortality. Improvements in diets (e.g., dietary supplementation either with arginine to reduce fat deposition via enhancing mitochondrial fatty acid boxidation or with Yucca extracts to ameliorate heat stress via antagonizing the actions of high levels of glucocorticoids), housing, and management can alleviate fatty liver hemorrhagic syndrome in laying hens. Fatty liver and kidney syndrome in chickens. This disorder occurs in chickens (particularly 2to 5-week-old chickens) due to a biotin deficiency.19 Biotin is a coenzyme of ATP- and HCO 3 -dependent carboxylases: pyruvate carboxylase, acetyl-CoA carboxylase, propionyl-CoA carboxylase, and ß-methylcrotonyl-CoA carboxylase.1 In affected birds, the liver and kidneys are pale and swollen, and contain abnormally high lipid deposits, whereas concentrations of pyruvate and lactate are elevated while concentrations of HCO3  and glucose are reduced.19 In addition, lactic acidosis, which occurs as a

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result of the accumulation of pyruvate due to reduced pyruvate carboxylase activity, is a major factor contributing to the morbidity and mortality in birds with fatty liver and kidney syndrome. Dietary supplementation with biotin prevents this disease. Yellow fat disease. This disease (also called steatitis or pansteatitis) is characterized by a marked inflammation of white adipose tissue, lipid peroxidation, and the deposition of the “ceroid” pigment in adipocytes, resulting in a rubbery texture and yellowish appearance.20 The possible pathological factor is an excessive intake of oxidized unsaturated fatty acids in spoiled diets, together with a deficiency of vitamin E or other antioxidants. The disease, which may be accompanied with myopathy, occurs most commonly in nonruminants (e.g., cats, dogs, ferrets, fish, foals, horses, mink, pigs, poultry, rabbits, rats, and reptiles) and rarely in ruminants. The disorder can be treated with oral administration of vitamin E (e.g., 15 and 30 mg/day for mink and cats, respectively) along with removal of the offending unsaturated fatty acids in feedstuffs.20 Gangliosidoses. This is a group of inherited lipid storage disorders caused by the excessive lysosomal accumulation of lipids known as gangliosides in the brain, spinal cord, liver, spleen, kidney, heart, and skeletal muscle.21 The diseases result from the lack of or low activity of enzymes to hydrolyze ganglioside monosialic acids (GM), which then accumulate in the lysosome to destroy the nerve. GM1 and GM2 gangliosidoses result from the deficiency of b-galactosidase and b-hexosaminidase to break down GM1 gangliosides (monosialotetrahexosylganglioside) and GM2 gangliosides (b-D-GalNAc-(1 / 4)-[a-Neu5Ac(2 / 3)]-b-D-Gal-(1 / 4)-b-D-Glc-(141)eNoctadecanoylsphingosine), respectively. Reducing dietary intake of lipids may help ameliorate this metabolic disorder. Gaucher’s disease (glucocerebrosidosis or glucosylceramide lipidosis). This is another inherited lysosomal storage disease. It occurs because glucocerebroside (mainly derived from

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gangliosides in the brain) cannot be adequately degraded due to the deficiency of b-glucocerebrosidase.22 Thus, the lipid is accumulated in the lysosomes of certain organs (particularly the spleen and liver), resulting in their enlargement and dysfunction. Affected animals include dogs, pigs and sheep. Reducing dietary intake of lipids may help ameliorate this metabolic disorder.

Disorders caused by abnormal metabolism, deficiencies or excesses of amino acids The growth of organisms depends on the deposition of protein in their tissues, such as the placenta, skeletal muscle, and small intestine. Dietary protein is hydrolyzed by proteases and peptidases (oligo-, tri-, and di-peptidases) to generate tripeptides, dipeptides, and free amino acids in the lumen of the gastrointestinal tract. These digestion products are absorbed into enterocytes. The absorbed amino acids that are not degraded by mucosal cells enter the portal vein for utilization by extra-intestinal tissues. Except for immunoglobulins, dietary protein has no nutritive value to animals unless it is digested.1 Thus, animals have dietary requirements for amino acids, but not protein. Optimizing amino acid nutrition can improve the growth performance and feed efficiency of animals, while improving their health and resistance to infectious disease. Based on nitrogen balance or growth, amino acids have been traditionally classified as nutritionally essential or nonessential for animals. However, growing evidence shows that endogenous synthesis of amino acids cannot maximally support the survival, growth, reproduction, or lactation of animals.1 For example, inadequate provision of dietary glycine impairs the growth of young pigs, particularly low-birth-weight piglets. The functions of amino acids beyond protein synthesis must be considered when formulating diets for animals to improve the efficiency of their nutrient utilization and

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well-being. For example, NO (a metabolite of arginine) kills pathogens, such as bacteria, parasites, and viruses. Thus, adequate amino acid nutrition plays an important role in protecting animals from infectious diseases, such as African swine fever. In addition, as with arginine, dietary supplementation with glycine may reduce embryo/fetal death and enhance conceptus growth in animals (including swine, sheep, goats and cattle) through improving protein synthesis as well as anti-oxidative and anti-inflammatory responses. Excesses, deficiencies, or imbalances of amino acids, defective regulation of intracellular protein turnover, or abnormal amino acid metabolism can result in metabolic disorders or diseases. These aspects are highlighted in the following sections. Amyloidosis. This disorder is characterized by the deposition of amyloid fibrils (firm and solid extracellular substances) into tissues, such as the spleen, liver, kidney, brain, lymph nodes, adrenal glands, and joints. Amyloid is a protein produced by reticuloendothelial cells, histiocytes, and plasma cells and contains less than 5% carbohydrates.23 Thus, although the name of the disease means “starch-like”, it results mainly from the abnormal synthesis or degradation of extracellular proteins or misfolding of proteins due to multiple factors, including chronic infections, neoplasms, and aging. Amyloidosis can affect different organs in different animals, and severe amyloidosis can lead to life-threatening organ failure. Therefore, amyloidosis is a fatal progressive disease in mammals, birds and other species. Because no curative treatment is currently available, prevention should focus on the feeding of balanced diets, good hygiene, and avoidance of stress. Kwashiorkor. This disease results primarily from a severe deficiency of dietary protein or amino acids, which not only limits intracellular protein synthesis, but also exacerbates the deficiency of other nutrients (including vitamin A and iron) whose absorption and transport require protein carriers. Syndromes of

kwashiorkor include poor health, stunted growth of the young, impaired development (including cognitive development) of the young, skeletal muscle wasting, anemia, physical fatigue and weakness, edema, losses of calcium and bones, reduced lactation performance, and impaired fertility.24 Protein malnutrition also increases the risk for metabolic syndrome and dysfunction of multiple organs (including the heart and kidneys). Marasmus is a more severe form of undernutrition that results from the dietary deficiencies of both protein and energy. Adequate dietary intakes of amino acids and energy can effectively prevent and treat both kwashiorkor and marasmus. Hyperammonemia. The accumulation of ammonia in plasma is highly toxic to the brain and other organs (such as the liver and kidneys) and also results in oxidative stress by depleting cellular glutathione.25 When the supply of dietary amino acids greatly exceeds their utilization by the animal, these nutrients are oxidized to form large amounts of ammonia in the body. Alternatively, in mammals, when the hepatic urea cycle (the main pathway for ammonia detoxification in mammals) is defective due to inborn errors or nutritional factors (e.g., the low availability of arginine, N-acetylglutamate, manganese, or vitamin B6), hyperammonemia also occurs. Mammals that do not synthesize arginine (such as cats, minks and ferrets) or mammalian neonates that have a limited ability to synthesize arginine (e.g., pigs, sheep and cattle) depend on a sufficient dietary intake of arginine for maintaining the hepatic urea cycle in an active state; hyperammonemia is a common cause of morbidity and mortality of these species. Additionally, in ruminants, rapid hydrolysis of dietary urea by bacterial urease in rumen fluid to form ammonia relative to the utilization of ammonia for microbial protein synthesis also results in hyperammonemia, a condition known as urea toxicity.1 Of note, bovine, ovine, and porcine embryos are highly sensitive to elevated levels of ammonia.25 For

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Disorders caused by abnormal metabolism, deficiencies or excesses of amino acids

example, compared with 12% crude protein (CP) in the diet, feeding 14% and 16% CP-diets to gestating gilts increases the concentration of ammonia in maternal plasma and reduces the number of live-born piglets in a dosedependent manner.26 In some cases, an amino acid imbalance or antagonism can lead to impaired protein synthesis, increased oxidation of amino acids to ammonia, and eventually hyperammonemia.27 Thus, dietary provision of all proteinogenic amino acids in sufficient amounts and proportions with respect to other nutrients (particularly manganese, a cofactor of arginase) is essential for the prevention and treatment of hyperammonemia. This goal can be achieved by the inclusion of a relatively small amount of crystalline amino acids or animalsource protein (e.g., bovine blood meal, poultry blood meal, meat & bone meal, poultry byproducts, fishmeal, intestine-mucosa product, whey powder, fish paste, cheese powder, and insect meal) in plant-based diets for livestock and poultry. Gout. Gout is caused by elevated levels of uric acid (a metabolite of ammonia, glycine, aspartate and glutamine) in plasma due to increased protein intake, a deficiency of arginine, and/or inactive arginase. At high levels, uric acid crystallizes in joints, tendons, and surrounding tissues, resulting in red, tender, hot, and swollen tissues. Depending on species, uric acid is degraded via enzymatic reactions to allantoin or excreted from the body. Gout occurs commonly in humans, other great apes, and birds (i.e., species that do not have uricase for uric acid degradation), but is rare in most other animals with active uricase.28 Decreasing dietary intake of protein and increasing ammonia removal can reduce the production of uric acid and the incidence of gout in sensitive species. Hyperhomocysteinemia. This disease is defined as the elevation of homocysteine in plasma due to either nutritional factors or inherited defects in homocysteine metabolism (e.g., a deficiency of cystathionine b-synthase or

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reductase).29 N5,10-methylenetetrahydrofolate Homocysteine is generated from methionine via the transsulfuration pathway in the liver, and can be either degraded to cystathionine by cystathionine b-synthase (a vitamin B6-dependent enzyme) or remethylated into methionine by methionine synthase (a vitamin B12-dependent enzyme). Thus, tetrahydrofolate (H4folate), vitamin B6, and vitamin B12 play an important role in removing homocysteine. As an oxidant to inactivate NO, the accumulation of homocysteine in plasma reduces blood flow and results in cardiovascular dysfunction. Avoiding an excessive intake of methionine and consuming adequate water-soluble vitamins can prevent nutritional homocysteinemia. Syndromes can also be alleviated by dietary supplementation with either methyl group donors for converting homocysteine into methionine or with serine for converting homocysteine into cystathionine. Melanosis. This disorder is characterized by the deposition of a brownish-black pigment [e.g., melanin or melanin-like substances in tissues, such as the skin, mammary gland, adipose tissue, and lymph nodes.30 Melanosis is either inherited or acquired. It is more common in pigs than in other livestock species, and is a serious problem in farm-raised crustaceans. Of interest, melanosis also occurs in young ruminants (e.g., calves and lambs). Granules of melanin or related substances are produced and released by melanocytes, and enter the blood circulation to be carried to distant sites in the body. Abnormal storage of melanin or related substances can result from abnormal processes of its production, transport or both. Interestingly, genetically predisposed pig breeds (e.g., the Nero Siciliano pig in Italy) develop melanosis after consuming acorns likely due to the oxidation of its abundant phenolic substrates by tyrosinase that acts on both tyrosine and polyphenols.31 Note that the pigmentations could negatively affect sensory characteristics of meat and its marketability, but do not pose a health risk for consumers.

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Porphyria. Porphyria is a group of diseases in which porphyrins build up in tissues. These substances are formed from glycine and succinylCoA in the liver, and are precursors of heme. The latter is the major component of certain proteins, including hemoglobin, myoglobin, and numerous heme-containing enzymes.1 Porphyrins are accumulated due to mutations in one or more genes that encode for the enzymes to affect the skin, nervous system, liver and other tissues. Symptoms of porphyria include abdominal pain, vomiting, seizures, constipation, high blood pressure, tachycardia, and paralysis. For animals with porphyria, avoiding sunlight is key to recovery. Pulmonary arterial hypertension (ascites syndrome) in chickens. This disease involves genetic, environmental and immune factors, and is associated with a relative deficiency of arginine.32 Vascular and liver damage occurs in affected birds, resulting in the accumulation of fluid (containing yellow protein clots) in ventral hepatic, peritoneal, or pericardial spaces. The incidence of ascites syndrome is greater in fastthan slow-growing broilers likely due to the competition of multiple metabolic pathways (e.g., protein synthesis vs. NO generation) for arginine. The disorder occurs because of an imbalance between cardiac output and the capacity of the pulmonary vasculature to accommodate ever-increasing rates of blood flow, and increased vasoconstriction of the pulmonary arterioles.32 Cold stress increases predisposition to ascites syndrome. Dietary supplementation with 1% arginine to chickens can prevent the disease. This is economically important, because the disease is a major cause of mortality and morbidity in broiler production.

Disorders caused by deficiencies or excesses of vitamins Vitamins are organic compounds required in small amounts for normal metabolism and growth of animals. Based on their solubility in

water, vitamins are classified as water- or lipidsoluble. Chemically, lipid-soluble vitamins are isoprene derivatives, but water-soluble vitamins have little in common. Nearly all vitamins are destroyed by oxidation, heat, and light to various degrees. Therefore, conditions for storage and processing of feeds can influence their nutritive values. Except for niacin and vitamin D, most vitamins are not synthesized by cells in animals. Furthermore, vitamin C is not synthesized by primates or guinea pigs, but is formed by most of other animals.1 Thus, almost all vitamins are essential nutrients for nonruminants. With the exceptions of vitamin B12, vitamin K and biotin, intestinal bacteria do not contribute a nutritionally significant quantity of vitamins to the animal hosts.1 Most feedstuffs contain vitamins, but their content varies widely. For example, concentrate feeds contain B vitamins, while forages are good sources of carotenes. In contrast, dried forages provide vitamin D2 (equivalent to vitamin D3), but this vitamin is virtually absent from typical corn- and soybean meal-based diets for poultry and swine. Furthermore, animal products are rich in most vitamins (including lipid-soluble vitamins), but are deficient in vitamin C and pantothenic acid. Forages are good sources of many water- and lipid-soluble vitamins. Bacteria in the rumen can synthesize almost all vitamins if sufficient precursors (including cobalt) are available. For this reason, meat from ruminants is an important dietary source of vitamin B12. The absorption and transport of vitamins, as well as the storage of lipid-soluble vitamins, depend on proteins as carriers. Bioactive forms of vitamins participate in biochemical reactions as cofactors or coenzymes, and, therefore, are vital to animal metabolism. Deficiencies in these nutrients result in diseases that can be cured by dietary supplementation of the deficient vitamins, their precursors, or their sources (e.g., meat, egg, milk, vegetables, legumes, and grains).33 However, excessive intakes of vitamins, particularly lipid-soluble vitamins

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Disorders caused by deficiencies or excesses of vitamins

(e.g., 4- to 20-times the normal nutritional requirements, depending on nutrients, species and age), are also harmful to animals; therefore, excessive intakes of vitamins must be avoided. Diseases caused by deficiencies or excesses of vitamins in animals are summarized in the following sections. Beriberi. This disease is caused by a thiamin deficiency. Thiamin diphosphate participates in the oxidative decarboxylation of a-ketoacids for ATP production and in transketolase reactions for generation of NADPH. Beriberi is characterized by muscular and nerve degeneration, brain dysfunction, edema, leg disorders, and paralysis.33 In addition, certain characteristics of thiamin deficiency are observed in farm animals.1 For example, pigs have inflammatory lesions in the gastrointestinal tract, diarrhea, dermatitis, hair loss, and respiratory dysfunction. In ruminants, the thiamin deficiency results in polioencephalomalacia that is characterized by circling movements, head pressing, stargazing, blindness, and muscular tremors. Photophobia. This disorder can result from a deficiency of riboflavin that is a major component of flavin mononucleotide and flavin adenine dinucleotide.1 They are cofactors of oxidoreductase enzymes (flavoproteins) that are involved in many biochemical reactions, including the Krebs cycle, fatty acid b-oxidation, NO synthesis, folate metabolism, D-amino acid oxidation, and purine degradation. In addition to photophobia, the major signs of riboflavin deficiency in animals include itching or teary eyes, loss of visual acuity, low appetite, reduced growth, depression, dizziness, lesions in the corners of the mouth, dermatitis, nerve degeneration, infertility, burning mouth or tongue, and embryonic abnormalities.33 Pellagra. This disease is caused by a niacin deficiency. Niacin is a component of nicotinamide adenine dinucleotide (NAD) and nicotinamide adenine dinucleotide phosphate (NADP), which participate in intracellular redox reactions.1 NAD also serves as a substrate for poly (ADP-ribose) polymerase, which catalyzes the

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attachment of ADP-ribose to various chromosomal proteins. Niacin-deficient animals grow poorly. Pellagra is characterized by the fourD’s: dermatitis, diarrhea, dementia, and death.1 When pigs and poultry are fed diets with a high corn or sorghum content, niacin deficiency may occur due to reduced synthesis of niacin from tryptophan. In these farm animals, deficiency symptoms also include anorexia, enteritis and vomiting (pigs), as well as bone disorders, feathering abnormalities, and inflammation of the mouth and upper part of the esophagus (poultry). Burning-foot syndrome. This condition results from a deficiency of pantothenic acid. Pantothenic acid is converted into 40 -phosphopantetheine, which is the prosthetic group of coenzyme A, acyl carrier protein of fatty acid synthase, and N10-formyltetrahydrofolate dehydrogenase. Deficiency of pantothenic acid is rare because the substance is widely distributed in foods. A deficiency in pantothenic acid can be induced experimentally or when animals (e.g., pigs and poultry) are fed diets lacking this vitamin, causing a loss of appetite, slow growth, skin lesions, loss of hair, depression, fatigue, infertility, ulceration of the intestine, weakness, and eventually death.1 Neural tube defects. These disorders are caused by a folate deficiency. Active folate in cell metabolism is H4folate in the form of H4folate polyglutamates. They are the carriers of activated one-carbon units (CH3e, eCH2e, eCH ¼ , O]CHe, and HN]CHe), and cofactors in reactions that comprise one-carbon metabolism.34 Folate deficiency causes poor growth, anemia, homocysteinemia, birth defects, poor bone development, low fertility, and poor hatchability of eggs. Folinic acid (5-formyl tetrahydrofolic acid) is used for treating folate deficiency in animals. Diets, particularly those for gestating dams, should contain adequate folate (in a synthetic form or provided from fresh green plants). Scurvy (bleeding gum). Some species (e.g., pigs, poultry and ruminants) can synthesize vitamin C (ascorbate) from glucose.1 Vitamin C

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is a donor of reducing equivalents, and can reduce such compounds as O2, nitrate, and cytochromes a and c. Vitamin C is required for the syntheses of hydroxyproline and hydroxylysine in collagen, catecholamines from tyrosine, bile acid from cholesterol, carnitine, and steroid hormones. Scurvy results from a vitamin C deficiency. In addition to bleeding gums, syndromes include connective tissue abnormalities (e.g., subcutaneous and other hemorrhages, soft swollen gums, loose teeth, and capillary fragility), impaired wound healing, muscle weakness, fatigue, depression, oxidative stress, and poor growth.35 Liver steatosis. This disease results from a choline deficiency. As a component of phosphatidylcholine, choline is essential for the structure and function of biological membranes and for the inter-organ transport of lipids.1 As a precursor of ceramide, choline plays an important role in transmembrane signaling. As a component of platelet-activating factor, choline participates in blood clotting, implantation of the conceptus in the uterus, and uterine contractions. Finally, as a substrate for the synthesis of acetylcholine, choline is required for neurological function. Syndromes of choline deficiency in animals include hepatic steatosis, as well as fatty liver, impaired growth, and neurological dysfunction.36 Thus, choline is an essential nutrient for animals that cannot synthesize a sufficient amount to meet their requirements. Xerophthalmia and keratomalacia. These diseases result from a vitamin A deficiency (hypovitaminosis A). Vitamin A is essential for vision and embryonic survival. ß-carotene (the precursor of vitamin A) can stabilize peroxide free radicals and is an antioxidant at low oxygen concentrations.1 The initial symptom of hypovitaminosis A is defective night vision. Further depletion of vitamin A results in keratinization of epithelial tissues of the eyes, lungs, gastrointestinal and genitourinary tracts, and decreased mucous secretion. The final deterioration of the eye’s tissues, which is known as xerophthalmia

(the extreme dryness of the conjunctiva) and keratomalacia (dryness with ulceration and perforation of the cornea), leads to blindness.33 Rickets and osteomalacia. Animals with a vitamin D deficiency (hypovitaminosis D) exhibit abnormal bone structure (rickets) in young animals and osteomalacia in adults.33 Vitamin D (vitamin D2 from dried plants and vitamin D3 from subcutaneous synthesis through exposure to sunlight) is converted into 1,25-dihydroxyvitamin D (calcitriol) through inter-organ metabolism.1 Calcitriol stimulates intestinal absorption of calcium and phosphate, and also regulates bone calcification. Thus, vitamin D is essential for the growth and development of healthy and strong bones. A dietary source of vitamin D is not needed if animals (except for cats and dogs) are exposed to sunlight during the day. However, vitamin D must be supplemented to plant-based diets of the livestock and poultry housed entirely indoors, because the content of vitamin D2 in dried hay varies considerably and there is little synthesis of vitamin D3 in the body. Vitamin D supplementation is desirable, especially for young ruminants and pregnant animals on winter diets, or when outdoor animals are exposed to air pollution that blocks much of the sunlight needed for endogenous synthesis of vitamin D3. Nutritional myopathy and liver necrosis. These disorders result from a deficiency of vitamin E (an antioxidant) that acts directly by breaking free-radical chain reactions.1 Muscular degeneration (myopathy) and hepatic necrosis in pigs, exudative diathesis in chicks, white muscle disease in lambs and calves, are the most frequent manifestations of vitamin E deficiency in farm animals. In addition, hypovitaminosis E causes anemia in animals due to a decrease in the synthesis of hemoglobin and a shortened life-span of erythrocytes; impaired reproduction (e.g., a decrease in sperm production and dead fetuses, spontaneous abortion, and fetal resorption); muscle weakness and muscular dystrophy; skin and ocular lesions; and edema.33

VII. Management of animal diseases in livestock and poultry production

Disorders caused by deficiencies or excesses of minerals

Hemorrhage. This condition occurs when an animal is deficient in vitamin K (hypovitaminosis K). Plants and green algae can synthesize vitamin K1, whereas bacteria can synthesize vitamin K2. Vitamin K3 (menadione) is a synthetic vitamin K commonly used in feedstuffs.1 As a coenzyme of procoagulation factors (II, VII, IX, and X), vitamin K is required for blood clotting in response to cuts and other forms of trauma. Thus, hemorrhage occurs in animals with a vitamin K deficiency, leading to the loss of blood, anemia, and death.33 Hypovitaminosis K also results in poor bone growth and health, as well as cardiovascular and immunological dysfunctions. Hypervitaminosis A. Vitamin A toxicity occurs when the capacity of retinol-binding protein for binding vitamin A has been exceeded such that the cells are exposed to high concentrations of unbound retinol or retinoic acids. The accumulation of excessive vitamin A damages tissues. Syndromes of hypervitaminosis A include severe liver fibrosis, bone and eye damage, hair loss, irritability, nausea, vomiting, headache, and birth defects.33 Hypervitaminosis D. Excessive vitamin D promotes pathological increases in the intestinal absorption of calcium and the mobilization of calcium from the skeleton (bone resorption), leading to defective mineralization or metastatic calcification in tissues (e.g., large arteries, myocardium, gastric mucosa, lung, and kidneys). At the cellular level, excessive 25-hydroxyvitamin D (a metabolite of vitamin D) can bind vitamin D receptor to stimulate gene transcription, and also compete and displace 1,25-dihydroxyvitamin D from vitamin D-binding protein to enhance the concentration of “free” 1,25-dihydroxyvitamin D that leads to it exerting abnormal biological activity. Thus, like hypovitaminosis D, hypervitaminosis D can also result in rickets. Other symptoms of vitamin D toxicity are thirst, itchiness, diarrhea, malaise, weight loss, polyuria, poor appetite, neurological deterioration, hypertension, nausea, vomiting, headache, and bone pain.33

485

Hypervitaminosis E. When tocopherol is oxidized, it becomes a free radical species. Thus, high levels of dietary vitamin E can be toxic to mammals and birds. The syndromes of hypervitaminosis E in animals include hepatic injuries and excessive accumulation of white-fat; testicular atrophy; slow development of secondary sex characteristics; teratogenic effects; poor growth, abnormal bone calcification, increased reticulocytosis, and mortality of embryos.33 Hypervitaminosis K. Vitamin K3 is an oxidant that can also undergo monovalent reduction to the semiquinone radical and then be further oxidized by O2 to a quinone with the formation of superoxide anion. At high concentrations, vitamin K3 in blood oxidizes hemoglobin into methemoglobin, and also causes instability of erythrocytes, hemolysis and fatal anemia, as well as jaundice, hyperbilirubinemia and kernicterus in neonates. Irritability, nausea, vomiting, headache, and renal toxicosis also occur in response to hypervitaminosis K.33

Disorders caused by deficiencies or excesses of minerals Minerals (inorganic elements) are present in both feedstuffs and animals. Some minerals are present in the body at concentrations  400 mg/kg BW and they are called macrominerals: sodium, potassium, chlorine, calcium, phosphorus, sulfur, and magnesium. Animals contain small amounts of about 40 minerals at concentrations < 100 mg/kg BW, and they are termed micro (trace)- minerals. The following 16 micro-minerals are known to have physiological functions in animals: iron, copper, cobalt, manganese, zinc, iodine, selenium, molybdenum, chromium, fluorine, tin, vanadium, silicon, nickel, boron, and bromine.1 Deficiencies of minerals cause specific symptoms, in addition to the common problems of reduced feed intake, growth restriction, impaired development, and even death.33 Some of the minerals may become

VII. Management of animal diseases in livestock and poultry production

486

27. Management of metabolic disorders (including metabolic diseases)

toxic to animals when fed at high levels (e.g., 1.5to 5-times the normal nutritional requirements, depending on nutrients, species and age), and other minerals (e.g., cadmium, mercury, lead, beryllium, arsenic, and aluminum) are toxic to animals at much lower levels and must be avoided at all times in diets. Toxic levels of most metals inhibit many enzymes in animal cells and bacteria. Diseases resulting primarily from deficiencies or excesses of minerals are highlighted in the following sections. Electrolyte imbalance and osmotic disorders. Sodium chloride (NaCl) is the table salt. Naþ and Cl are the most abundant cation and anion in blood, respectively, and, therefore, play an important role in regulating hydration and extracellular osmotic pressure of the body. In addition, Naþ is required for the transport of glucose, amino acids, and various ions (e.g., I, Cl, and phosphate) across cell membranes; sodium pump (NaeK-ATPase) activity; Naþ/Hþ exchange; regulation of blood pressure; action potentials in neurons and skeletal muscle; and the regulation of food intake.1 In addition, Cl plays an important role in: acid-base balance, the production of gastric HCl, intestinal secretion, and the maintenance of a hydrated state of the mucus. NaCl deficiency reduces extracellular osmolarity and body hydration, impairs acid-base balance, increases the alkali reserve of the blood (alkalosis) abnormally due to an excess of bicarbonate, increases the urinary excretions of calcium and magnesium, and causes growth restriction and muscle weakness.33 Note that excessive intake of NaCl reduces food consumption and can cause death in animals. Potassium (K) is the most abundant cation in cells. It is required for the regulation of intracellular osmolarity, activity of the sodium pump, functions of the nerves and muscle cells to establish the resting membrane potential, the conversion of cholesterol into pregnenolone, the conversion of corticosterone into aldosterone, and Hþ-Kþ-ATPase activity.1 Syndromes of K deficiency include muscle weakness, spasms, tetany, paralysis, numbness (particularly in legs

and hands), low blood pressure, frequent urination, and thirst. Hypokalemia can also cause cardiac rhythm abnormalities and cardiac arrest in animals.33 Milk fever (post-parturient hypocalcemia or parturient paresis). This is a disorder mainly of lactating cows post-calving. It is a metabolic disease caused by a low concentration of calcium in blood (7 d)

Encephalomyelitis

Live viral vaccine, chick-embryo propagated

Wing web (>8 wk, and 4 wk before start of lay)

Fowl pox

Modified live vaccine

Wing web (>8 wk, and 4 wk before start of lay)

Laryngotracheitis

Modified live vaccine

Intraocular (>4 wk)

Mycoplasma gallisepticum

Live strain(s) of Mycoplasma gallisepticum

Intraocular or spray (>9 wk)

Marek’s disease

Live, Turkey herpesvirus and/or SB-1 or Rispens CVI 988 strain of chicken herpesvirus

Subcutaneous injection

Newcastle disease

Live, B1 type (B1 or LaSota strains) of NDV

Drinking Water (14e21 d) or coarse spray (revaccination, > 4 wk)

Infectious bronchitis

Live, Massachusetts type of IBV

Drinking Water (14e21 d) or coarse spray (revaccination, > 4 wk)

Infectious bursal disease

Live vaccine

Drinking Water (>7 d)

Marek’s disease

Live, Turkey herpesvirus

Subcutaneous injection

Newcastle/infectious bronchitis

Live, B1 type (B1 or LaSota strains) of NDV plus Massachusetts type of IBV

Drinking Water (14e21 d) or coarse spray (revaccination, > 4 wk)

Infectious bursal disease

Live vaccines

Drinking water (8 wk)

Encephalomyelitis

Live viral vaccine, chick-embryo propagated

Wing web (>8 wk)

Tenosynovitis

Modified live virus, or inactivated vaccine

Subcutaneous injection (6e7 d)

Fowl pox

Modified live vaccine

Wing web (>8 wk)

Laryngotracheitis

Modified live vaccine

Intraocular (>4 wk)

Fowl cholera

Live attenuated vaccines or inactivated vaccines

Wing web (>10 wk, live vaccine); Subcutaneous injection (inactivated vaccines).

Broiler

Broiler Breeders

(Continued)

VII. Management of animal diseases in livestock and poultry production

526

30. Management of pathogens in poultry

TABLE 30.3

Selected vaccines used on poultry farmsa.dcont’d

Bird

Vaccine

Type

Immunization routes

Turkeys

Newcastle disease

Live, B1 type (B1 or LaSota strains) of NDV

Drinking water (2e3 wk) or coarse spray (revaccination, > 4 wk)

Fowl cholera

Live or inactivated vaccines

Drinking water (live vaccine, > 6 wk) or subcutaneous injection (inactivated vaccine)

Erysipelas

Live or inactivated vaccines

Drinking water (live vaccine) or subcutaneous injection (inactivated vaccine)

Encephalomyelitis

Live vaccines

Drinking water

Hemorrhagic enteritis

Live vaccines

Drinking water (30 d)

a This is not a complete list of vaccines for listed birds, poultry producers should contact their veterinarians for professional advices, as vaccination programs can vary significantly from farm to farm.

their biological nature, vaccines can be classified as live or killed organism vaccines. Live vaccines are against live organisms that can infect the birds to some extent. Live vaccines can be obtained by isolation of naturally occurring low virulence strains, or selection of attenuated strains through in vivo/in vitro passage or mutagenesis, or genetic engineering of virulent strains.44 Generally speaking, live vaccines offer longer protective immunity than vaccines to killed organisms, likely because the microbes in live vaccines can survive in birds for some time to stimulate longer and more effective immune responses. However, live vaccines are not as safe, since some vaccines are still quite high in virulence and the use of such vaccines not only causes illness in birds, but also infects other birds. In addition, recombination between live vaccine strains with wild endemic strains may produce new strains that can escape the host’s immune surveillance. To obtain the best protection from immunization with live vaccines, extra care should be taken toward their storage, shipping, dosing and administration. In general, live vaccines are stored in cold and shipped

within a container to maintain a cold temperature. The vaccines typically have a limited shelf-life, so it is essential that they are used before expiration. Killed vaccines are against microorganisms that are inactivated by chemical or physical means, or components derived from microorganisms. They will not cause the corresponding disease, but have the capacity to stimulate protective immune responses. It is generally believed that killed vaccines are safer than live vaccines, but the immunity they induce may not be as robust. In addition to live and killed vaccines, new types of vaccines in the form of recombinant proteins, DNA vaccines, viral or bacterial vector vaccines have emerged in recent years.45e47 Yet, they require more investigations to overcome technological and economical challenges before commercial use.46,48 Irrespective of the type of vaccine, following proper vaccination protocols is essential to achieve the most benefits and maximal protection. Table 30.3 summarizes some of the commercially available vaccines for broiler breeders, broilers, commercial layers and turkeys. There are many

VII. Management of animal diseases in livestock and poultry production

Use of antimicrobials

different ways to administer a vaccine to poultry (In-ovo injection, subcutaneous/intramuscular injection, spray, drinking water vaccination, wing stab, intraocular/nasal drop); therefore, it is critical to have the correct method of vaccine delivery. In addition, immunization programs may vary from farm to farm and from region to region. Consultation with a poultry veterinarian to design an appropriate immunization plan is highly recommended.

Use of antimicrobials While vaccination is not available to prevent all diseases, and biosecurity measures only reduce the risks of infection but cannot guarantee full success at all times. Therefore, diseases may occur even when these procedures are properly implemented. As a consequence, having effective treatment options becomes a critical element for poultry health management. Antimicrobials are commonly used for this purpose and they often include antibiotics, anthelmintics, antiprotozoal drugs and pesticides. Antimicrobials that are commonly used in poultry production systems are summarized in Table 30.4. Although antimicrobial treatments are available, their use needs to be judicious. Arbitrary application is not acceptable. Consultation with a professional veterinarian for physical examination and laboratory diagnosis of diseases is necessary, because the application of antimicrobial treatments is not straightforward. Co-infections, particularly with microorganisms that cause immune suppression, can significantly complicate the effects of antimicrobials. In addition, the symptoms associated with the diseases may need to be taken into account in deciding the route of delivery of the medication. For example, if sick birds lose appetite significantly, administration of antimicrobials by feeding will not be the best choice. Since poultry produce meat and eggs for

527

human consumption, extra care should be taken to minimize drug residue in meat and egg products, and their accumulation in humans.49,50 For this reason, most antimicrobials have a withdrawal time, which is the minimal time after drug administration before harvesting poultry products for food production, and assure that drug residues in food products are below the maximally allowed level.51 Last but not least, there are always concerns on the development of antimicrobial resistance. It is certainly true that human and animal health is increasingly challenged with antimicrobial resistance and our way of using antimicrobials in daily life do have a role in the development of such resistance.52,53 As such, the use of antimicrobials should be tightly regulated. The old way of using sub-therapeutic doses of antibiotics in feed as growth promotants is strictly prohibited in many countries, as it can potentially induce emergence of antibiotic resistance.54 In fact, there is a strong interest in antibiotic free farming worldwide and the use of antimicrobials is not allowed on organic farms. As a consequence, people start to look for alternatives that may substitute for antimicrobials in the future.21 The field is moving rapidly and numerous alternatives have some levels of anti-infection and growth promotion activities. These include, but are not limited to bacteriophages, bacteriocins, antimicrobial peptides, pro-, pre-, and synbiotics, plant extracts, essential oils, immunomodulatory agents (cytokines, bacterial extracts), and feed enzymes (phytase, xylanase, lysozyme etc).55,56 Nonetheless, at present, none of these alternatives have the consistent efficacy compared to antibiotics and there is a long way to go to before we can replace antimicrobials with such alternatives. As a result, both proper use of antibiotics and continuous development of antimicrobial alternatives are required to ensure long-term sustainability of the poultry industry.

VII. Management of animal diseases in livestock and poultry production

528 TABLE 30.4

30. Management of pathogens in poultry

Frequently used antimicrobials on poultry farms.

Type

Antimicrobial

Dosage

Application

Aminoglycosides

Gentamycin

Inj: 7.5e10 mg/kg body weight; Oral: 20e30 mg/kg body weight

For the treatment of enteric infections, pullorum disease, colibacillosis etc.

Streptomycin

Inj: 20e30 mg/kg body weight

Against fowl cholera, avian tuberculosis and avian chlamydiosis

Bambermycins

Bambermycin

In feed: 62.5 ppm

In feed additive to prevent gram-positive bacterial infections.

Beta-Lactams

Amoxicillin

Oral: 20e40 mg/kg body weight

Ampicillin

In water: 0.02e0.05%

Against fowl cholera and avian spirochetosis etc.

Chlortetracycline

In feed: 200e600 ppm

Oxytetracycline

In feed: 300e500 ppm

Doxycycline

In feed: 100e200 ppm

Fluoroquinolones

In water: 100e200 ppm

Against salmonellosis, colibacillosis, fowl cholera and so on.

Sulfadiazine

In water: 0.1e0.2%

Trimethoprim

In water: 150e300 ppm

For the treatment of fowl cholera, infectious serositis, as well as coccidiosis and toxoplasmosis.

Erythromycin

In water: 125 ppm

Tylosin

In water: 500 ppm

Tilmicosin

In water: 200e400 ppm

Avermectin

In feed: 0.1 mg/kg body weight

Against gastrointestinal nematodes and external parasite such as mites and lice.

Albendazole

In feed: 10e20 mg/kg body weight

To treat a variety of parasitic diseases including nematodes, tapeworms and external parasites infections.

Niclosamide

In feed: 20e50 mg/kg body weight

To deworm and treat tapeworms and trematodes infections.

Levamisole

In feed: 25 mg/kg body weight

For deworming of nematodes and treatment of ascariasis.

Diclazuril

In feed: 1 ppm

To treat or prevent poultry coccidiosis

Toltrazuril

In water: 25 ppm

Monensin

In feed: 90e110 ppm

Salinomycin

In feed: 60 ppm

Maduromycin

In feed: 5e6 ppm

Organophosphates

According to product labeling

Tetracyclines

Quinolones

Norfloxacin Sulfonamides

Macrolides

Anthelmintics

Ivermectin

Praziquantel

Anti-coccidiosis

Pesticides

To treat chlamydiosis, fowl typhoid, paratyphoid, pullorum disease, colibacillosis, infectious coryza, avian spirochetosis etc.

To treat necrotic enteritis and Mycoplasma infection.

Against mites, ticks, lice and so on.

VII. Management of animal diseases in livestock and poultry production

References

Acknowledgements Work in the authors’ labs are supported by the National Key Research and Development Program of China (2017YFD0501304 to BS and 2106YFD0501605 to ZZ) and the Natural Science Foundation of Hubei Province (grant No. 2017CFA020, to BS).

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45. Ferreira TB, Alves PM, Aunins JG, Carrondo MJT. Use of adenoviral vectors as veterinary vaccines. Gene Ther. 2005;12:S73eS83. 46. Meunier M, Chemaly M, Dory D. DNA vaccination of poultry: the current status in 2015. Vaccine. 2016;34(2): 202e211. 47. Triyatni M, Jilbert AR, Qiao M, Miller DS, Burrell CJ. Protective efficacy of DNA vaccines against duck hepatitis B virus infection. J Virol. 1998;72(1):84e94. 48. Haygreen L, Davison F, Kaiser P. DNA vaccines for poultry: the jump from theory to practice. Expert Rev Vaccines. 2005;4(1):51e62. 49. Chen T, Cheng GY, Ahmed S, et al. New methodologies in screening of antibiotic residues in animal-derived foods: Biosensors. Talanta. 2017;175:435e442. 50. Diaz-Sanchez S, D’Souza D, Biswas D, Hanning I. Botanical alternatives to antibiotics for use in organic poultry production. Poultry Sci. 2015;94(6):1419e1430. 51. Cornejo J, Pokrant E, Carvallo C, Maddaleno A, San Martin B. Depletion of tylosin residues in feathers, muscle and liver from broiler chickens after completion of antimicrobial therapy. Food Addit Contam A. 2018;35(3): 448e457. 52. Hoelzer K, Wong N, Thomas J, Talkington K, Jungman E, Coukell A. Antimicrobial drug use in food-producing animals and associated human health risks: what, and how strong, is the evidence? BMC Vet Res. 2017;13. 53. McDermott PF, Zhao S, Wagner DD, Simjee S, Walker RD, White DG. The food safety perspective of antibiotic resistance. Anim Biotechnol. 2002;13(1):71e84. 54. Walsh TR, Wu YN. China bans colistin as a feed additive for animals. Lancet Infect Dis. 2016;16(10): 1102e1103. 55. Patterson JA, Burkholder KM. Application of prebiotics and probiotics in poultry production. Poultry Sci. 2003; 82(4):627e631. 56. Cheng GY, Hao HH, Xie SY, et al. Antibiotic alternatives: the substitution of antibiotics in animal husbandry? Front Microbiol. 2014;5.

VII. Management of animal diseases in livestock and poultry production

Index Note: ‘Page numbers followed by “f” indicate figures and “t” indicate tables’.

A Accessory sex glands, 266e267 Acid detergent fiber (ADF), 167e168 Active (A) bulls, 112 Aerial photography, 239 Allantoic sac, 207 Allantois, 45 American Angus Association, 22e23 American Hereford Association, 22 American Simmental Association, 22 Amino acids, 2e3 in meat and plant-source foods, 3t Amnion, 45 Ampullary-isthmus junction, 43 Anaerobic digestion system, 462e463 Anemia, 487 Angora goats, 185 Animal biotechnologies, 13 Animal breeding, 11, 184, 393e394 Animal-derived metabolic wastes, 5e8 Animal group average, 239 Animal nutrition, 9e10 Animal production animal-source food, in human health, 2e3 consumption of, 6te7t global animal agriculture, 4e5 potential competition with humans, food and water, 5 potential impacts, on environment, 5e8 Animal proteins, 2e3 in current agricultural systems, 10t genetic potential, of livestock, 9e10 pork production, 10t uncoupling protein (UCP)-1, 11 Animal-source food, in human health, 2e3 Animal Unit (450 kg of live weight) Months (AUM), 87

Anserine (b-alanyl-L-1-methylhistidine), 3 Anterior pituitary, 208 Antiluteolytic signal, 205 Antimicrobial resistance (AMR), 11 Antimicrobials, 527 Anti-mullerian hormone (AMH), 337e338 Aquaculture, 4e5 Artificial insemination (AI), 42, 60, 227e228, 397 Aseasonality, 221 Assisted reproductive technologies (ART), 396e399 Atmospheric air pollution, 8 Atresia, 42 Automated milking systems, 128

B Bacteria digest, 168 Bacterial artificial chromosomes (BACs), 255 Bacteriophage, 84 Banning, of antibiotics, 11e12 Basal energy metabolism, 13 Beavis effect, 26 Beef cattle climate change, impacts, 92e93 confined production systems backgrounding, 78e80 cow/calf production, 76e78 feed additives and growth promoters, 82e84 finishing, 80 forage sources and processing, 82 grains and by-product feeds, 80e82 embryonic and fetal loss early embryonic mortality, 48 late embryonic/early fetal mortality, 48e49 pregnancy failure, 48

531

endocrinology, of pregnancy Luteinizing hormone (LH), 47 Pregnancy-associated glycoprotein (PAG), 47 Placental lactogen (PL), 48 progesterone, 47 establishment and maintenance, of pregnancy fertilization, 43 gamete transport, 42 maternal recognition, of pregnancy, 44 placentation, 44e45 post hatching embryonic development, 43e44 zygote, to blastocyst stage, 43 estrous cycle, regulation of estrous phase, 39e41 follicular phase, 39 follicular waves, regulation of, 42 luteal phase, 41 ovarian structures, 39, 41f preovulatory gonadotropin surge, 39 reproductive hormones, 40t extensive production systems extensive cattle operations, 86 forage production and quality, 85 grazing management, 86e88 intensive beef operations, 86 intensive pasture management, 86 pasture supplementation, 88e90 semi-intensive systems, 86 temperate vs. tropical climates, 84e85 greenhouse gas emissions, 92 manure, 90e91 nutrient management confined systems, 90e91 extensive systems, 91e92 parturition, 49

532 Beef cattle (Continued) physiological changes, gestation embryonic and fetal development, in cattle, 46t uterine changes, 47 vaginal and cervical changes, 45e47 postpartum anestrus, 49 production systems, 76 puberty, 38e39 Beef cattle improvement programs, 21e22 Beef cattle, reproductive management of challenges genotype, 59 postpartum cows, 58e59 replacement heifers, 58 strategies AI, 60 breeding season, 59e60 ES, 60e66 fixed-time artificial insemination, 60e66 IVF, 68 multiple ovulation embryo transfer, 66e68 sex-sorted semen, 68e69 Beef Improvement Federation (BIF), 21e22 Beef production ß-adrenergic agonists, 387e389 high-quality protein, 387e388 muscle fiber types, 388e389, 388f beef quality, 385e387 consumers, 387 marbling, 385e387, 386f developed countries, 382e383 Australia, 382e383 China, 382 Europe, 382 Japan, 382 Korea, 382 United States of America, 382e383 developing countries, 381e382 muscle fibers early embryonic growth, 383 late embryonic/fetal growth, 383e385 types, 385 muscle growth and development, 383e385

Index

muscle hypertrophy, 385 satellite cells, 385 Beef quality, 29e30 Beriberi, 483 Best Linear Unbiased Prediction (BLUP), 22 Beta agonists, 84 Bimodal pattern, 144 Biofuel, 465 Biotechnologies, 389e390 Blackhead breed, 185 Blanket antibiotic therapy, 127 Blastocyst hatching, 43 Boar physiology, 264e267 Body weight (BW), 89 Booroola mutation, 188 Bos indicus, 23e26, 38 Bos taurus, 23e26, 38, 58 Bovine blastocyst, 41 Bovine leukocyte adhesion deficiency (BLAD), 116e117 Bovine respiratory disease (BRD), 79e80 Bovine somatotropin (bST), 134e136 Bovine spermatozoa, 42 Brangus heifers, 31e32 Breed associations, 28e29 Breeder’s equation, 102 Breed formation, 21e22 Breeding, 269e270 Breeding goal (H), 106e107 Breeding regimens, 293 Breeding Soundness Exam (BSE), 219 Breeding systems, sheep and goats accelerated programs, 214e216 annual production, 213 opportunistic production, 213 Broiler breeder hen production, 339e341 Brucella Ovis, 219 Buck Effect, 221 Bulbourethral glands, 202 Bump feeding, 295 Burning-foot syndrome, 483

C Ca absorption, 171 Ca concentrations, 171e172 Calf morbidity, 78 Calving ability (CA), 107e110 Calving distribution, 62e63, 65f Carbohydrates energetics, 169

fat, 169e171 fat digestion, 170 glycerol-type fats, 169 higher quality forages, 169 incomplete biohydrogenation, 170 microbial fermentation, 168 non-glycerol- type fats, 169 processing, 168e169 and protein utilization, in rumen, 168 rumen-active fats, 170 rumen-inert fats, 170 sources of, 167 types, 167e168 unsaturated fats, 169 Carbon skeletons, 168 Carnosine (b-alanyl-L-histidine), 3 Caruncle, 44e45 Casein, 161 Cashmere goats, 185 Cation-anion difference, 171 Cationic liposomes, 84 Central bull testing, 21e22 Cervix, 47 Cheese Merit Index, 107e110 Chemical methods, 460e461 Chlorofluorocarbons (CFCs), 432 Cholesterol deficiency, 116e117 Chorionic somatomammotropin, 48. See also Placental lactogen (PL) Chorionic somatomammotropin hormone 1 (CSH1), 208 Climate adaptability, 185 Climatic stress, 31e32 Clinical acidosis, 80 Clustered regulatory interspaced short palindromic repeats (CRISPRs)/ Cas9, 13, 32, 194, 400e401 Colostrometer, 159 Colostrum, 158e159 breed effects, 160 environmental effects, 160 pasteurization, 160 quality and feeding, 159 replacers and supplements, 160 storage of, 161 Co-mingling, 498 Commercial swine farms, 288 Companion animals, in agriculture, 13e14 Complementary DNA (cDNA), 257 Complex vertebral malformation (CVM), 116e117

533

Index

Computer-assisted semen analyses (CASA), 290 Concentrated animal feeding operations (CAFOs), 456e457 Confined production systems, beef cattle backgrounding, 78e80 cow/calf production, 76e78 feed additives and growth promoters, 82e84 finishing, 80 forage sources and processing, 82 grains and by-product feeds, 80e82 Confinement management, 76e78 Confinement rearing, 76e78 Consumer demand, 90 Controlled internal drug release (CIDR), 61, 138e139 removal, 64f, 66e68 Copper toxicity, 489 Corpora lutea (CL), 41, 263e264 Corpus hemorrhagicum (CH), 204 Corpus luteum (CL), 61, 132, 147t, 203 CoSynch 48, 134 CoSynch 72, 134 CO-Synch protocol, 61 Cotyledon, 44e45 Counter-current transfer mechanism, 41 Cow-calf operations, 57e58, 494e495 Cow-calf units, 90 Cowper’s glands, 202 Creatine, 3 Creep feeding, 226 Critical care programs, 286 Crossbreeding, 192 Crude protein (CP), 165e166, 235 Cryopreservation, 68, 397 Crystalline amino acids, 10e11 Cyclic cows, 58 Cytosine-guanine rich DNA, 84

D Dairy calf calf feeding, development, 162e163 calf starter grain, 161e162 colostrum, 158e159 breed effects, 160 environmental effects, 160 pasteurization, 160 quality and feeding, 159 replacers and supplements, 160 storage of, 161 dairy calves, 158

milk, in digestive tract, 161 post-weaned heifer, feeding, 163e164 precision feeding, 164 water, 162 Dairy cattle, 497 genetic diversity, 116e117 genetic selection basics of, 102e103 revolution, 110e112 genomics use novel traits, in genomics, 114e116 replacement heifer selection, 113e114 sire selection, 112e113 inbreeding, 116e117 lactation animal health and well-being, 126e127 current state of affairs, 121e122 genetic innovations, 126 housing and monitoring, 127e128 mammary growth and function, 122e124 nutrition and metabolism, 124e125 reproduction, 125e126 multiple traits selection, 106e110 reproductive management dairy heifers, 148e149 economic outcome, 147e148 lactating dairy cows, 149e151 principles of, 132e134 sequential development, 134e143 sustainability, 151e152 TAI/bull breeding, 148 TAI resynchronization, in cows, 144e147 TAI/seasonal breeding, 148 traits selection, increasing income milk composition, 104 milk yield, 103e104 traits selection, reducing expenses calving ability, 106 fertility, 104e105 health, 105e106 longevity, 106 Dairy cow, nutrition of amino acids, 166 carbohydrates energetics, 169 fat, 169e171 fat digestion, 170

glycerol-type fats, 169 higher quality forages, 169 incomplete biohydrogenation, 170 microbial fermentation, 168 non-glycerol- type fats, 169 processing, 168e169 and protein utilization, in rumen, 168 rumen-active fats, 170 rumen-inert fats, 170 sources of, 167 types, 167e168 unsaturated fats, 169 minerals macrominerals, 171 microminerals, 171 nutrition, 171 MP, 166 protein synchronization, 166 vitamins biotin, 176 niacin, 176 vitamin A, 175 vitamin D, 175 vitamin E, 175 water, 164e165 Dairy herds, 131e132, 160 Daughter pregnancy rate (DPR), 104e105, 125e126 Deferred rotational grazing, 88 Deficiency of uridine monophosphate synthase (DUMPS), 116e117 Dental caries, 488 Detection and ranging technologies (Li- DARs), 239 Dietary supplementation, 10e11 Diet formulation, 164 Digestible carbohydrates, 2e3 Digestible energy (DE), 169 Direct-fed microbials (DFM), 84 Disease prevention, 524e527 Distiller’s dried grains with solubles (DDGS), 89 Distillers grains, 81 Double-OvSynch, 136e137 Dried distillers grain with solubles (DDGS), 5 Dry cow feeding, 178e179 Dry matter (DM), 82 Dry or non-lactating phase, 178e179 Dual-purpose breeds, 185

E East Friesian sheep, 185

534 Economic selection indices, 107, 109f Egg production regulation, 333e335 Electrolyte imbalance, 486 Embryonic genome activation, 43 Embryonic mortality, 43e44 Embryo transfer (ET), 151, 227e228, 398 Endocrine system, 122e123 Endogenous retroviruses, 184e185 Endometrial epithelium, 44e45 Endometrial function, 43e44 Epididymal sperm maturation, 266e267 Epididymis, 201e202 Epigenetic-mediated mechanisms, 1e2 Epigenomic effects, on mammary gland function, 126 Equine chorionic gonadotropin (eCG), 64e66 Escherichia coli, 13 Estimated breeding values (EBVs), 217 Estradiol, 39e41, 64e66 Estradiol-17b (E2), 204 Estrogen receptor (ESR), 255e256 Estrous cycle, 268e269, 268f, 398 estrous phase, 39e41 follicular phase, 39 follicular waves, regulation of, 42 luteal phase, 41 ovarian structures, 39, 41f preovulatory gonadotropin surge, 39 reproductive hormones, 40t Estrous synchronization (ES), 60e66 Exogenous hormones, 60e61 Expected breeding value (EBV), 23e26 Expected progeny difference (EPD), 23, 27, 60 Expressed sequence tag (EST), 257 Extensive production systems, beef cattle extensive cattle operations, 86 forage production and quality, 85 grazing management, 86e88 intensive beef operations, 86 intensive pasture management, 86 pasture supplementation, 88e90 semi-intensive systems, 86 temperate vs. tropical climates, 84e85 Extraembryonic membranes, 44

Index

F Fat rump sheep, 186 Fat-tailed sheep, 186 Fatty acids, 170 FecB mutation, 218 Fecundity, 217e218 Feed additives essential oils, 177 ionophores, 176 probiotics, 176 rumen buffers, 176e177 sodium butyrate, 177 Feed conversion efficiency, 29e30 Feed conversion ratio (FCR), 319 Feed enzymes, 10e11 Feeding prilled fats, 170 Feed production, fermentation techniques enzymes, 420e422 fermented feed for aquaculture, 420 fermented liquid feed (FLF), 408e413, 410te412t future directions, 425e426 lactic acid bacteria (LAB), 408e409 liquid fermentation of feedstuffs, 410te412t microbial ecology, 424e425 microorganisms, 420e422 milk for young farm animals, 422e424 perspective, 425e426 safety considerations, 425 silage fermentation, 418e420 solid-state fermentation, 413e418, 414te417t Feedstuffs, 5 Ferment carbohydrates, 168 Fermented liquid feed (FLF), 408e413, 410te412t Fertility, 217e218 Fetal-placental circulation, 207 Fiber digestibility, 80 Fibroblast growth factor 5 (FGF5), 194 Fibrous sheath (FS), 265 Finishing feedlots, 80 Fixed-time artificial insemination (TAI), 42, 57e58, 60e66 Fixed-time embryo transfer (FTET), 67 Fluid Merit Index (FM$), 107e110 Flushing, 219 Follicle development, 333e334, 334f

Follicle dominance, reduction, 140e141 Follicles, 333 Follicle stimulating hormone (FSH), 63e64, 201e202, 292 Follicle suppression, 133e134 Follicle wall, 39 Follicular estradiol secretion, 39 Follicular maturation, 38 Food and Agriculture Organization (FAO), 1e2, 4, 30, 81 Free-stall barns, 127 Frost-free freezers, 161 Functional traits, 106

G Gamete transport, 42 Gas production, 5e8 Gene introgression, 192 GeneSeek, 28e29 Genetically modified (GM), 194, 259e260 Genetic engineering, 399 Genetic evaluation, 102e103 Genetic improvement animal breeding, 393e394 artificial insemination, 397 assisted reproductive technologies (ART), 396e399 biotechnological solutions, 396 Clustered Regularly Spaced Short Palindromic Repeats (CRISPR), 400e401 cryopreservation, 397 embryo transfer, 398 estrous cycle regulation, 398 genetic engineering, 399 genomics revolution, 394 homology directed repair pathway (HDR), 400 livestock, 394 marker-assisted and genomic selection, 395e396 non-homologous end joining (NHEJ), 400 quantitative trait loci (QTL), 394e395 semen sexing, 397 site directed nucleases (SDN), 400e401 transgenesis in livestock, 399e400 in vitro fertilization, 398 whole toolbox, 402e403, 402f Genetics and breeding, of beef cattle breeding programs, history, 21e23

535

Index

future genomic information, 29 genetic improvement, in climate resilience traits, 31e32 genomics and sustainability, 30e31 genomic technologies and genomic selection, 26e29 GS impact, beef industry, 29e30 new genomic technologies, 32 quantitative nature, of traits, 23e26 Genetic selection programs, 101e102 Genome editing, 32 Genome-wide association studies (GWAS), 26, 30e31, 186, 258 Genomic breeding values (GEBVs), 258e259 Genomic information, 113e114 Genomic selection (GS), 26e27, 28f, 30e31 Genomics revolution, 394 Genomic-tested (G) bulls, 112 Genomic testing, 113 Genotype, 59 Geospatial technologies, 239 Germinal disc region, 333 Gestation, 294e295 birthing, 225e226 crutching, 224 embryonic and fetal development, in cattle, 46t pregnancy determination, 223e224 proper nutrition, 223 range lambing/kidding, 224e225 shearing, 224 shed lambing/kidding, 225 uterine changes, 47 weight management, 223 Gilt development, 307 Global animal agriculture, 4e5 Global surface temperature, 13 Global warming and cold environment, 13 Glucocorticoids, 13 Gluconeogenesis, 165e166 Glucose, 168 Glutamine, 10e11 Goiter, 174, 488 Goitrogens, 174 Gonadostat Theory, 38 Gonadotrophin releasing hormone (GnRH), 38, 203 Gram-negative bacteria, 176 Granulosa cells, 39, 333 Grass tetany, 487

Grazing intensity, 87e88 Grazing management deferred rotational grazing, 88 rotational grazing systems, 87e88 sustainable, 87 traditional continuous grazing systems, 87 zero grazing, 88 Grazing Merit Index, 107e110 Greenhouse gas emissions (GHG), 2, 8, 92, 124e125, 433 Gross energy (GE), 169 Growth hormone (GH1), 208 Growth hormone receptors (GHRs), 208

H Haemonchus contortus, 223 Half-breds, 191 Haplotype, 116e117 Health trait sub-index, 107 Healthy flocks biosecurity practices, 520e524 Heat stress, 124 Hemorrhage, 485 Heritability (h2), 188 High-fibrous feedstuffs, 235e236 Homology directed repair pathway (HDR), 400 Hormonal feedback relationships, 38 Hormonal implants, 84 4-hydroxyproline, 3 Hyperammonemia, 480e481 Hyperkalemic periodic paralysis (HYPP), 489 Hypervitaminosis A, 485 Hypervitaminosis D, 485 Hypervitaminosis E, 485 Hypervitaminosis K, 485 Hypocalcemic cow, 171e172 Hypophosphatemia, 172 Hypothalamic GnRH neurons, 38e39 Hypothalamic-pituitary-ovarian axis, 38 Hypothalamus, 38e39, 132, 203

I Immunoglobulin G levels, 236e237 Inbreeding, 116e117 Inflammation, of gland, 126e127 Information nucleus flocks, 189 Inner cell mass (ICM), 43 Insemination, 293

Insulin-like growth factor-1 (IGF-1), 124 Intensive production systems, 5 Interferon regulatory factor 2 (IRF2), 205 Interferon stimulated genes (ISGs), 44 Interferon tau (IFNT), 44, 204 International Embryo Transfer Society (IETS), 67 International Swine Genome Sequencing Consortium, 257 Intestinal dysfunction, 10e11 Intramuscular adipocytes, 386 in vitro embryo culture (IVC), 68 in vitro fertilization (IVF), 57e58, 68, 398 in vitro maturation (IVM), 68 in vitro produced (IVP) embryos, 151 in vitro sperm capacitation, 68 Irreversible catabolism, 5e8

J Janus activated kinases (JAKs), 205 Jersey breed, 160 Juniperus spp., 235

K Keshan’s disease, 487e488 Kisspeptin, 38e39

L Lacaune dairy sheep breeding program, 189 Lactating anovular dairy cows, 148 Lactating cows, 167 Lactation, 276e279 lactocrine programming, 279 lactogenesis, 277e278 mammary gland, 276e277 mammogenesis, 277 milk-borne bioactive factors, 278e279 yields, 124 Lacteal-based colostrum replacers, 160 Lactobacillus, 84 Lactobacillus plantarum, 408e409 Lamb crop, 217 Laparoscopic technique (LAI), 227 Laying hen production, 339e341 Leptin, 38 Libido, 267 Linkage disequilibrium(LD), 29 Lipid-soluble vitamins, 170e171 Lipogenesis, 165e166

536 Liquid manure, 459 Liquid pasture supplements, 89e90 Liver steatosis, 484 Livestock production, 8, 249e250 Livestock production systems, 76 Local Awassi sheep, 185 Luteal progesterone, 38 Luteinizing hormone (LH), 38, 47, 58, 201e202, 292 Luteolysis, 39e41

M Macrominerals, 171 calcium, 171e172 chloride, 173 magnesium, 172e173 phosphorus, 172 potassium, 172 sodium, 173 sulfur, 173 Mammary gland, 122e123 Manure deposition, 91 Manure solid-liquid separation, 459e460 Manure treatment anaerobic digestion system, 462e463 biofuel, 465 chemical methods, 460e461 composting methods, 461e462 concentrated animal feeding operations (CAFOs), 456e457 dry matter, 456 extracting nutrients, 465 factors affecting composting process, 461e462 handling, 457e459 land application, 464e465 liquid manure, 459 manure-derived biochar, 465e466 manure solid-liquid separation, 459e460 manure utilization, 457 pathogen reduction, 461 pelletizing, 465 slurry manure, 458e459 solid-liquid separation methods, 460 solid manure, 458 substrate for microbial culture, 466 utilization, 464e466 value-added products, 465e466 Manure utilization, 457 Marker-assisted and genomic selection, 395e396

Index

Markov-Chain Simulation Model, 150 Mastitis, 126e127, 171e172 Maternal lines, 250e251 Mathematical modeling chlorofluorocarbons (CFCs), 432 classifications, 433e437 descriptive models, 434 greenhouse gas (GHG) emissions, 433 little picture-type models, 432e433 National Aeronautics and Space Administration (NASA), 432 optimization context, 434 Mating, 267 Medium chain fatty acids, 12 Merino sheep, 185 Metabolic and infectious diseases, 12e13 Metabolic disorders abnormal metabolism, 473e476 amyloidosis, 480 bovine fatty liver syndrome, 477 deficiencies, 473e476 diabetes, 473e474 diarrhea galactosemia, 476 lactose-fed chicks, 475 sucrose-fed neonatal pigs, 475 equine exertional myopathy, 475 excesses of amino acids, 479e482 excesses of carbohydrates, 473e476 excesses of lipids, 476e479 excesses of minerals, 485e489 excesses of vitamins, 482e485 fatty liver and kidney syndrome, 478e479 fatty liver hemorrhagic syndrome, 478 gangliosidoses, 479 glycogenosis, 475 gout, 481 hyperammonemia, 480e481 hypoglycemia, 474 ketosis, 477e478 Krebs cycle, 471e472 kwashiorkor, 480 long-chain fatty acids, 476 low-fat milk syndrome, 478 melanosis, 481 metabolic pathways, 471e472 obesity, 477 porphyria, 482 pregnancy toxemia, 478

ruminal acidosis, 474 ruminal bloat, 474e475 yellow fat disease, 479 Metabolizable energy (ME), 169 Metabolizable protein (MP), 166 Mg deficiency, 172 Microbial fermentation, 9e10 Microbial pathogens, 91 Microbial protein, 5 Microminerals, 171 copper, 173 iodine, 174 iron, 174 manganese, 174 molybdenum, 174 selenium, 175 zinc, 174 Milk and milk replacer feeding, 161 Milk fever, 171, 486 Milk proteins, 161 Milk replacer feeding, 162 Milk replacer types, 161 Mineral nutrition, 171 Minerals macrominerals, 171 microminerals, 171 nutrition, 171 Mitochondrial sheath (MS), 265 Mitogen activated protein kinase (MAPK), 205 Modern production systems, 9t Monensin, 84 Monte-Carlo simulation, 149 Morula, 43 Motile sperm, 289 Motility, 289 Mulefoot, 116e117 Mules, 191 Multiple ovulation embryo transfer (MOET), 57e58, 66e68 Myosin heavy chain-IIX (MHC-IIX), 388e389 Myostatin (MSTN), 188 Myostatin gene disruption, 11

N National Association of Animal Breeders (NAABs), 112 National Beef Cattle Evaluation Consortium, 28e29 National Cattle Evaluations (NCE), 23e26 National Center for Biotechnology Information (NCBI), 257

Index

National Sheep Improvement Program (NSIP), 191 National Sire Evaluation, 22 Natural resources, 9e10 Neonatal nutrition, 285 Neonatal pig nutrition, colostrum and milk, 300e302 Net energy (NE), 169 Net feed intake, 30. See also Residual feed intake (RFI) Neural tube defects, 483 Neuropeptide, 38e39 Neuropeptide Y (NPY), 38 Neutral detergent fiber, 164, 167e168 Newborn piglets, 286 New Zealand Romney sheep, 185 Nitrogenous substances, 5e8 Nitrous oxide, 5e8 Non-cyclic cows, 58 Non-fiber carbohydrates (NFC), 168 Non-homologous end joining (NHEJ), 400 Non-human consumable byproducts, 124e125 Non-protein nitrogen (NPN), 89e90, 165e166 Nonruminants, 4e5 Nonsteroidal anti-inflammatory drugs (NSAID’s), 126e127 Non-structural carbohydrates (NSC), 168 Nursery pigs nutrition dietary protein level, 302e303 energy sources, 303 ingredient selection, 303 piglets, nutritive value for, 303e304 post-weaning growth check, 302 processing and usage, 303 protein sources, 303 Nutrient-dense animal products, 5 Nutrient management plan (NMP), 90 Nutrition, 290 Nutritionally nonessential amino acids, 10e11

O Oilseeds, 170e171 Omics technologies, 240e241 Online Mendelian Inheritance in Animals (OMIAs) database, 188 Oocytes, 68, 293, 333, 336e337 Open herds, 498 Oral/injectable antibiotics, 218e219

Osmotic disorders, 486 Outer dense fibers (ODFs), 265 Outside stressors, 497 Ovarian hormones, 335e338 Ovary structure, 332e333, 332f Overgrazing, 233 Oviducts, 201 Ovine uterine luminal, 205 Ovis aries, 188, 199 OvSynch, 134 OvSynch-48, 134 OvSynch-56 h program, 134 OvSynch protocol, 61 Ovulation, 335 Ovulatory cycle, 334e335 Ovum pick up (OPU), 68

P Parturient paresis, 171. See also Milk fever Parturition, 275e276 Pathogen reduction, 461 Pathogens management antimicrobials, 527 biosecurity protocols, 506e509 animal movements, 506e507 deliveries, 507e508 disease surveillance, 508e509 genetic improvements, 510e511 health programs, 509e510 personnel, 508 cattle co-mingling, 498 cow/calf, 494e495 dairy cattle, 497 definition, 493e499 environment, 497e498 nutrition, 498e499 open herds, 498 outside stressors, 497 stocker/feedlot, 495e496 disease prevention, 524e527 healthy flocks biosecurity practices, 520e524 poultry, 516e520, 517te519t infectious diseases, 516 poultry production enterprises, 516e520 swine feed, 505 feral swine, 502 pasture swine, 502 specialized operations, 502e503 swine housing, 503e504

537 swine raising environments, 502e503 transportation, 505e506 water, 504 vaccination, 524e527 Pediococcus pentosaceus, 409e413 Pellagra, 483 Pelletizing, 465 Pen density, 286 “Personalized” cow management, 128 PG 5-d CO-Synch + CIDR protocol, 64e66 Phenotypic records, 102e103 Phosphoinositide 3-kinase (PI3K), 205 Photoperiod, 124 Photophobia, 483 Physical cooling systems, 13 Physio-pharmacological agents, 132 Pig, genetic and genomic improvement breed development, 250e251 cloning, 259e260 databases, 259 domestication, of swine, 250e251 future developments, 261 gene editing, 260 genomic selection, 258e259 molecular genetic approaches, 255 selection methods and mating systems, 251e253 sequencing, pig genome, 256e258 traits, of economic importance, 253e255 transgenics, 260 Piglet management, 292e293 Piglets, 285 PigQTLdb, 255 Placental lactogen (PL), 48 Placental tissue growth, 45 Placentome, 44e45 Plantago lanceolata, 240 Plant bioactives, 84 Plant secondary compounds (PSCs), 238 Plant-source foods, 2e3 Plasma membrane, 43 Polar overdominance, 192 Polioencephalomalacia, 489 Poll Dorset sheep, 185 “Polypay” synthetic breed, 192 Polyunsaturated fatty acids (PUFAs), 237

538 Porcine reproductive and respiratory syndrome (PRRS), 258e259 Pork production, 249e250 Postpartum cows, 58e59 Post-peak milk production, 170 Post-weaning growth, 286 Poultry, 516e520, 517te519t anti-mullerian hormone (AMH), 337e338 artificial insemination, 360 bioreactors, 361 broiler breeder hen production, 339e341, 356 broodiness, 360 cage free (colony), 360e361 conventional, 360e361 egg development, 351e352 egg production, 356e360 egg production regulation, 333e335 eggshell, 352 egg white, 352 embryonic development, 350e351 enriched and free-range systems, 360e361 follicle development, 333e334, 334f healthy chicks, 341e342 hormonal control of reproduction, 353e354 induced molting, 356e360 laying hen production, 339e341 light and reproduction, 354e355 light intensity, 355 male reproduction, 352e353 nutrition and reproductive management, 355e356 oocyte factors, 336e337 other intra-ovarian factors, 337e338 ovarian hormones, 335e338 ovulation, 335, 361 ovulatory cycle, 334e335 photoperiodic induction, 354e355 photorefractoriness and reproduction, 355 physiological control, 350e351 poultry reproduction, 355 reproductive phases, 350 reproductive physiology follicles, 333 germinal disc region, 333 granulosa cells, 333 oocyte, 333 ovary structure, 332e333, 332f semen characteristics, 352t

Index

steroidogenesis, 335e336 transgenic chickens, 361 yolk, 351e352, 361 yolk accumulation, 338e339 Poultry genetics/breeding climate change, 320e321 future technologies, 322e324 gene editing, 323e324 genetic improvement health and welfare traits, 319e320 performance, 319 progress and future directions, 318e321 genomic selection, 322e323 global challenges, 321e322 health and welfare challenges, 321 history, 317e318 industrial production models, 317e318 land and water use, 322 opportunities, 321e322 resources, 324e325 sex selection, 321e322 transgenic technologies, 323e324 waste production, 322 Poultry production enterprises, 516e520 Practical dairy cattle feeding bunk space, 177e178 cow comfort, 178 diet balancing, 177 dry cow feeding, 178e179 feeding, 178 water, 178 Pre-breeding, sheep and goats evaluation, 218 management, of female, 218e219 management, of male, 219 Precision poultry nutrition apparent metabolizable energy requirements (AMEn), 372e374 21st century, 372e374 dried grains with solubles (DDGS), 368 exogenous enzyme, 377 fish solubles and meal (FSM), 368 ground limestone, 377 ionophore coccidiostats, 377 meat and bone meal (MBM), 368 modern industry type broiler diets, 374e377 National Research Council’s nutrient requirements, 368e370

NRC broiler diets, 370e372 nutritionally nonessential amino acids, 372 poultry science, United States, 367e368 ROSS 708 management guide, 375e377, 376t unidentified growth factors (UGF), 368 Veterinary Feed Directive (VFD), 374e375 Veterinary Feed Directive (VFD), 2017, 368 Predicted Difference Dollars, 107 Predicted transmitting ability (PTA), 102e103 Pregnancy conceptus development, 271e273 early embryo, 271e273 fertilization, 270e271 maternal recognition, 274 placentation, 273f, 274e275, 275f Pregnancy associated glycoproteins (PAGs), 45, 47, 144 Pregnancy diagnosis (PD), 145 P4 releasing intravaginal device (PRID), 141e142 Preovulatory follicle, 133e134 PreSynch-OvSynch protocol, 136 PreSynchPGF2a-OvSynch, 134e136 PreSynch, using GnRH beforeOvSynch, 136e138 Pre-weaning growth, 285 Production traits, 106 Progesterone, 41, 47 Progesterone receptors (PGRs), 204 Prolactin receptors (PRLRs), 208 Proopiomelanocortin (POMC), 38 Prostaglandin F2a (PGF), 61, 132 injections of, 139e140 Prostaglandin synthase 2 (PTGS2), 204e206 Protein degradation, 13 Protein kinases (PKA), 265 Proteolytic enzymes, 39e41 Puberty, 267e268

Q Quantitative trait loci (QTL), 23e26, 186, 255e256, 394e395

R Ractopamine hydrochloride (RH), 388

Index

Radiation hybrid, 255, 257 Range lambing/kidding, 224e225 Receiver operating characteristic (ROC), 140 Receptors for estrogen (ESR1), 204 Relaxin, 49 Replacement heifers, 58 Reproductive efficiency, 37, 104e105 Reproductive management challenges genotype, 59 postpartum cows, 58e59 replacement heifers, 58 dairy cattle dairy heifers, 148e149 economic outcome, 147e148 lactating dairy cows, 149e151 principles of, reproductive program, 132e134 sequential development, 134e143 sustainability, 151e152 TAI/bull breeding, 148 TAI resynchronization, in cows, 144e147 TAI/seasonal breeding, 148 sheep and goats advanced reproductive technology, 227e228 breeding season, 220 breeding systems, 213e216 fecundity, 217e218 fertility, 217e218 genetic selection, 216e218 gestation, 222e224 global production, 213 intensive vs. extensive production, 212e213 pre-breeding, 218e219 seasonality, 220e222 weaning, 226e227 strategies AI, 60 breeding season, 59e60 ES, 60e66 fixed-time artificial insemination, 60e66 IVF, 68 multiple ovulation embryo transfer, 66e68 sex-sorted semen, 68e69 Reproductive tract anatomy, sheep and goats conceptus development, 206e207

estrous cycles, goats and ewes, 203 ewes, pregnancy, 207e208 female reproductive tract, 201 luteolysis and estrous cycles, 203e205 parturition, in ewes and goats, 208 pregnancy, does and ewes, 205e206 of ram and buck, 201e203 Residual feed intake (RFI), 30e31 Robotics, 128 Rotational grazing systems, 87e88 Rotational stocking, 91e92 Roughages, 167 Rumen degradable protein (RDP), 166 Rumen epithelial development, 161e162 Rumen fermentation, 170 Rumen microbes, 170 Rumen-protected amino acids (RPAA), 167 Rumen undegradable protein (RUP), 166 Ruminal bacteria, 168 Ruminant digestive system, 124e125 Ruminants, 4e5

S Saccharomyces cerevisae, 413 Salmonella enterica, 409e413 Salmonella typhimurium, 409e413 Salt deficiency, 173 Scrapie, 188 Seasonality aseasonal production, 221e222 seasonal vs. aseasonal, 220e221 Seasonally polyestrous, 200 Secreted phosphoprotein 1(SPP1), 207 Seedstock sector, 27 Selenosis, 489 Semen sexing, 397 Seminal plasma, 266e267 Senepol cattle, 31e32 Sertoli cells, 201e202 Service to society, 231e232 Sex-sorted semen, 57e58 conjunction, with IVF, 69 utilization of, 69 Shed lambing/kidding, 225 Sheep and goats, 199e200 advanced technologies, small ruminant breeding cloning, 193 gene editing, 194e195 transgenesis, 193e194

539 breed classification, 185e186 breeding programs breeding aims, 189e190 Israel, 192 milk production, France, 190e191 UK sheep industry, 191 US sheep industry, 191e192 wool-producing industries, NZ, 190 domestication, 184e185 functional nutrition and management strategies feed quantity and quality, 236 nutrient supplementation strategies, 236e237 PUFA, 237 genetic breeding, 184 genetic evaluation, 188e189 genomic basis, breed variation health, 188 horn phenotype, 188 lamb production, 188 meat production, 188 genomic selection, 188e189 history, sheep, 199 puberty, 200 reproductive management advanced reproductive technology, 227e228 breeding season, 220 breeding systems, 213e216 fecundity, 217e218 fertility, 217e218 genetic selection, 216e218 gestation, 222e224 global production, 213 intensive vs. extensive production, 212e213 pre-breeding, 218e219 seasonality, 220e222 weaning, 226e227 reproductive tract anatomy conceptus development, 206e207 estrous cycles, goats and ewes, 203 ewes, pregnancy, 207e208 female reproductive tract, 201 luteolysis and estrous cycles, 203e205 parturition, in ewes and goats, 208 pregnancy, does and ewes, 205e206 of ram and buck, 201e203 rumen microbiome genetics, 195

540 Sheep and goats (Continued) seasonal breeding, 200 small ruminants, role of, 234e236 sustainable intensification, grazing lands, 232e234 technological advancements, production digital technologies, 239e240 omics technologies, 240e241 plant genetics and breeding, 240 Sheep blastocysts, 206 Sheep Genetics, 217 Sheep Improvement Limited, 217 Signal transducer and activator of transcription 1 (STAT1), 205 Signet, 217 Silage fermentation, 418e420 Single nucleotide polymorphism (SNP), 23e26, 110, 184, 255 Single sire vs. multi sire mating, 220 Site directed nucleases (SDN), 400e401 “Slick”coat trait, 31e32 Slurry manure, 458e459 Solid-liquid separation methods, 460 Solid manure, 458 Solid-state fermentation, 413e418 Somatic cell nuclear transfer (SCNT), 13, 193 Sow nutrition gestation, 307e308 lactation, 308 Sow physiology, 267e270 Soybean feedstuffs, 166 Spermatogenesis, 264e265 Spermatogonia, 201e202 Spermatozoa, 265, 266f Stem cell biology, 127 Steroidal hormones, 84 Steroidogenesis, 335e336 Stocker/feedlot, 495e496 Streptomyces cinnamonensis, 176 Structural carbohydrates, 167e168 Subcutaneous fat, 13 Superficial glandular (sGE), 205 Superovulation protocols, 67e68 Supplemental feeding, 285 Supplemental progesterone (P4), 138e139 Sustainable grazing management, 87 Swine nutrition and feeding of neonatal pig nutrition, 300e302

Index

nursery pigs, 302e304 weaner to finisher pigs, 304e307 reproductive management of developmental phase, 284e287, 284f developmental to functional phase, 287e288, 288t functional phase, 288e292, 289f gestation, 294e295 sows management, 292e295, 294f reproductive physiology of accessory sex glands, 266e267 boar physiology, 264e267 breeding, 269e270 corpora lutea (CL), 263e264 epididymal sperm maturation, 266e267 estrous cycle, 268e269, 268f fibrous sheath (FS), 265 lactation, 276e279 libido, 267 mating, 267 mitochondrial sheath (MS), 265 outer dense fibers (ODFs), 265 parturition, 275e276 pregnancy, 270e275 protein kinases (PKA), 265 puberty, 267e268 seminal plasma, 266e267 sow physiology, 267e270 spermatogenesis, 264e265 spermatozoa, 265, 266f testis, 264e265 Swine Genome Technical Committee, 255 Synthetic amino acids, 5

T TAI programs, 138 Taurine, 3 Temperature:humidity index (THI), 127e128 Testis, 264e265 Thawing frozen colostrum, 161 Thermotolerance, 29e30 The Sandhills Calving System, 78 Timed artificial insemination (TAI), 132e133 in dairy heifers, 141e143 resynchronization, in cows, 144e147 Total lambs born, 186 Total mixed ration (TMR), 80 Toxicity, 174

Traditional continuous grazing systems, 87 Transcription activator likeeeffector nuclease (TALEN), 32, 194 Transforming growth factor b (TGF-b), 188 Transgenesis in livestock, 399e400 Transgenic animals, 11 Triple-purpose breeds, 185 Trophectoderm, 43, 206

U Udder health, 126e127 Uncoupling protein 1 (UCP-1), 11 Uniform forage utilization, 91 United Nations’ Food and Agriculture Organization (FAO), 251 United States Department of Agriculture, 68e69, 386e387 Unmanned aerial vehicle (UAV), 239 USDA lifetimeNet Merit index, 107, 108t U.S. Environmental Protection Agency (EPA), 8 U.S. Food and Drug Administration (FDA), 388 US Holstein cattle, 31e32 US lifetime merit-based selection indices, 107e110 US Meat Animal Research Center (US-MARC), 28e29 Uterine glands, 43e44 Uterine luteolysin, 41 Uterine walls, 47

V Vaccination, 524e527 Value-added products, 465e466 Value of reliability (REL), 102e103 Virtual herding technologies, 239e240 Volatile fatty acids (VFA), 161e162, 168

W Water-soluble vitamins, 175 Weaner to finisher pigs, 304e307 amino acids, 304 diet and carcass quality, 306 energy density, 304 feed wastage, nutrient requirements to, 305e306 minerals and vitamins, 305

541

Index

ractopamine, 305 water intake, 306e307 Weaning, 226e227 Weanling piglets, 10e11 Whey, 161 White adipose tissue, 9e10 White head breed, 185 White muscle disease (WMD), 175 Wholebody protein degradation, 13 Whole toolbox, 402e403, 402f

Wildland-urban interface (WUI), 234e235 Wilson’s disease, 488 Woody weed management programs, 234e235 w6 polyunsaturated fatty acids, 10e11

Y Y chromosome (Y-sperm), 68e69 Yolk accumulation, 338e339

Yucca schidigera, 13

Z Z-disk proteins, 387 Zebu breeds, 59 Zero grazing, 88 Zilpaterol hydrochloride (ZH), 388 Zinc-finger nucleases (ZFNs), 32, 194 Zoetis, 28e29 Zona pellucida, 43

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