Advances in Agriculture and Fisheries Research [1, 1 ed.] 8194061369, 9788194061366, 938924630X, 9789389246308

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Advances in Agriculture and Fisheries Research Vol. 1

Advances in Agricultu re and Agriculture Fisheries Research Vol. 1

India

. United Kingdom

Editor(s) Dr. Telat Yanik Professor, Department of Aquaculture, Faculty of Fisheries, Atatürk University, Turkey. Email: [email protected], [email protected], [email protected];

FIRST EDITION 2020 ISBN 978-81-940613-6-6 (Print) ISBN 978-93-89246-30-8 (eBook) DOI: 10.9734/bpi/aafr/v1

_________________________________________________________________________________ © Copyright (2020): Authors. The licensee is the publisher (Book Publisher International).

Contents Preface

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Chapter 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.) Ahmed Medhat Mohamed Al-Naggar, M. M. Shafik and M. O. A. Elsheikh

1-28

Chapter 2 Movement Patterns of Chilean Flounder (Paralichthys adspersus) inside Tongoy Bay (Central Northern Chile): Observations Using Passive Acoustic Telemetry Pablo M. Rojas

29-45

Chapter 3 Leaf Amino Acids and Anatomical Traits of Drought Tolerant vs Sensitive Genotypes of Quinoa (Chenopodium quinoa Willd.) under Elevated Levels of Water Stress Ahmed Medhat Mohamed Al-Naggar, Rabie Mohamed Abd El-Salam, Ayman E. Badran, Sedkey T. Boulos and Mai M. El-Moghazi

46-65

Chapter 4 The Abundance, Distribution and Diversity of Benthic Invertebrates of Lake Malombe Orton V. Msiska, Kingsley Kamtambe, James Banda and Barnett Kaphuka

66-79

Chapter 5 Yield Stability and Adaptability of 25 Grain Sorghum B-Lines across Six Environments in Egypt Using AMMI and GGE-Biplot Models Ahmed Medhat Mohamed Al-Naggar, Rabie Mohamed Abd El-Salam and Walaa Yaseen Saad Yaseen

80-96

Chapter 6 Intensive Production of the African Catfish Clarias gariepinus (Burchell, 1822) Fingerlings Using Local Materials in Recycled Water Claudine Tekounegning Tiogué, Delphin Alfred Eva Ambela, Paulin Nana and Minette Eyango Tomedi–Tabi

97-107

Chapter 7 Spatial Distribution of Pterocarpus erinaceus Poir. Natural Stands in the Sudanian and Sudano-Guinean Zones of West Africa: Gradient Distribution and Productivity Variation across the Five Ecological Zones of Togo Kossi Novinyo Segla, Kossi Adjonou, Habou Rabiou, Abdou Raoufou Radji, Adzo Dzifa Kokutse, Babou André Bationo, Mahamane Ali, Christine A. I. Nougbodé Ouinsavi and Kouami Kokou

108-125

Chapter 8 Lessepsian Migration with the First Record of the Red Sea Goatfish, Parupeneus forsskali (Fourmanoir & Guézé, 1976) in the Coastal Waters of Egyptian Mediterranean Sea Sahar F. Mehanna and Eman M. Hassanien

126-136

Chapter 9 Frequencies of Genotypes and Alleles of the K232A Substitution in the DGAT1 Gene in Four Cattle Breeds of Russian Selection Eugene Klimov, Anna Arkhipova and Svetlana Kovalchuk

137-141

Chapter 10 Usage of Probiotics in Aquaculture Cultivation of Tilapia (Oreochromis niloticus) Yuli Andriani, Iskandar and Titin Herawati

142-165

Preface This book covers all areas of agriculture and fisheries research. The contributions by the authors include sorghum bicolor, quinoa, water deficit, Zea mays, drought, root traits, chlorophyll content, substitution K232A, fingerlings, Clarias gariepinus, Pterocarpus erinaceus (Poir.), nile tilapia, Benthic invertebrates, aquatic snails, Parupeneus forsskali, acoustic telemetry, mark-recapture etc. This book contains various materials suitable for students, researchers and academicians in the field of agriculture and fisheries research.

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Chapter 1 Print ISBN: 978-81-940613-6-6, eBook ISBN: 978-93-89246-30-8

Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.) Ahmed Medhat Mohamed Al-Naggar1*, M. M. Shafik1 and M. O. A. Elsheikh2 DOI: 10.9734/bpi/aafr/v1

ABSTRACT The objectives of the present study were: (i) to assess the effects of drought stress at flowering and grain filling and genotype on maize (Zea mays L.) agronomic, physiologic, yield and root traits of maize and (ii) to identify high-yielding drought tolerant genotypes with desirable traits for future use in plant breeding programs. Fifteen commercial hybrids and seven breeding populations were evaluated in the field for two seasons under water stress at flowering (WSF) and grain filling (WSG) compared to well watering (WW). A split plot design with three replications was used. Data analyzed across seasons revealed a significant reduction in grain yield/plant (28.69 and 20.26%), chlorophyll concentration index (30.18 and 44.07%) and 100-kernel weight (6.75 and 12.36%) due to water stress under WSF and WSG, respectively, a significant reduction in ears/plant (11.58%), kernels/row (14.23%), kernels/plant (24.85%), number of whorls occupied with brace roots (9.31%), number of brace roots (18.27%), number of crown roots (11.50%) and root dry weight (28.31%) due to water stress under WSF and in upper stem diameter (18.46%) under WSG, but a significant increase in days to silking (3.50%), anthesis-silking interval (21.17%), barren stalks (26.18%) and crown root length (9.90%) due to water stress under WSF. Moreover, WSG caused significant increases in number of brace roots (10.10%), number of crown roots (14.71%) and root dry weight (11.60%), but caused a significant reduction in branching density of crown roots (10.05%). The best genotypes in grain yield under drought at either flowering or grain filling were characterized by one or more desirable root architecture traits. The cultivars P-3444, Egaseed-77 and SC-128 were considered tolerant to drought at flowering and/or grain filling and would be recommended to future breeding programs to utilize their desirable root traits and grain yield productivity in improving maize drought tolerance. Keywords: Zea mays; drought; ASI; barren stalks; Roottraits; chlorophyll content.

1. INTRODUCTION Maize (Zea mays L.) is one of the most important cereal crops in the world as well as in Egypt. According to FAOSTAT [1], maize acreage in Egypt in 2014 was 750,000 hectares produced 5.8 million tons of grains, with an average yield of 7.73 tons ha-1. However, the local production of maize covers only about 50% of the local consumption and Egypt imports every year more than eight million tons of maize grains mainly for poultry industry. To reach self-sufficiency of maize production in Egypt, efforts are devoted to extend the acreage of maize in the desert. Growing maize in the sandy soils of low water-holding capacity would expose maize plants to drought stress, which could result in negative effects on maize traits. Moreover, the expected future shortage in irrigation water necessitates that Egyptian breeders should pay great attention to develop drought tolerant maize cultivars that could give high grain yield under soil water deficit conditions. _____________________________________________________________________________________________________ 1

Department of Agronomy, Faculty of Agriculture, Cairo University, Egypt. Desert Research Center, Matariya, Cairo, Egypt. *Corresponding author: E-mail: [email protected], [email protected];

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Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

Water is basic requirement for plant growth and development. Without water, the plant goes under drought conditions and severely affects its growth stages and ultimately yield of crops is reduced. Thus, the development of maize cultivars with high and stable yields under drought is an important priority, as access to drought-adapted cultivars may be the only affordable alternative to many smallscale farmers [2]. Maize is considered more susceptible than most other cereals to drought stresses at flowering when yield losses can be severe through barrenness or reductions in kernels per ear [3]. Maize is particularly susceptible to drought at the flowering stage [4,5]. The studies showed that the sensitive period extended from around one week before to two weeks after 50% silking. Yield losses per day of comparable stress, before and after flowering, were around 45 and 60%, respectively, of the peak loss at silking itself [5]. Studies of more recent hybrids suggest that this period of susceptibility may have moved towards early grain filling. Grant et al. [6] reported that although yields were most severely reduced (70%) by stress coinciding with silking, yields were reduced by 40-54% from stresses occurring in the period 10 to 31 days after mid-silk, and kernel number was reduced below control for stresses occurring up to 22 days after silking. NeSmith and Ritchie [7] observed that kernel numbers per plant were reduced 8-20% when the plants were stressed in the period 18 to 31 days after silking, while weight per kernel declined by a significant 21-25%. Recent studies have shown considerable genetic variation in the response of commercial hybrids to drought stress imposed during reproductive growth [8] and in one study, a well-known drought tolerant hybrid, P3223, displayed no additional susceptibility to stress imposed at flowering and it appeared that these responses vary considerably among hybrids [9]. Several investigators emphasized the role of maize genotypes in drought tolerance. Tolerant genotypes of maize were characterized by having shorter anthesis-silking interval (ASI) [10], more ears/plant [10,11] and greater number of kernels/ear [12,11]. The presence of genotypic differences in drought tolerance would help plant breeders in initiating successful breeding programs to improve such a complicated character. There is good evidence suggesting that hybrids maintain their advantage over open pollinated varieties in both stress and non-stress environments [13,14,15]. Drought tolerance might be increased by improving the ability of the crop to extract water from the entire soil profile [16]. Roots are the principal plant organ for nutrient and water uptake. The ability to grow deep roots is currently the most accepted target trait for improving drought tolerance, but genetic variation has been reported for a number of traits that may affect drought response. Therefore, improving our understanding of the interaction between root function and drought in maize could have a significant impact on global food security [17]. Root system architecture is important for plant productivity under edaphic stress [18]. A root system architecture specifically adapted to the prevailing soil conditions might be advantageous. After the onset of drought, water is often found in deeper soil layers. Deeper soil layers are predominantly reached by maize genotypes forming a sparsely branched axile root system [19]. In order to improve plant performance, breeders need to select genotypes with a root architecture adapted to the conditions of the target environment. Root architecture is difficult to evaluate directly in soil. Several high-throughput procedures to measure root systems have been reported. At the flowering stage, roots have been measured in the field [20,21] in soil boxes [22] and in soil columns [23,24]. Growing plants in columns or boxes, filled with soil or artificial substrate, can help to reduce sampling efforts compared to field studies and allows growth under controlled conditions. However, the excavation of roots and measurement of root traits in these systems remains labor-intensive and does not allow for high throughput. In the field, roots and shoots are exposed to very different environmental conditions, especially with regard to temperature, which is an important regulator of root development [25]. Visual scoring using a defined rating system has been employed for high throughput phenotyping of shoot traits. A high throughput method which utilizes visual scoring of the numbers, angles and branching density of brace and crown roots has not yet been used for the investigation of root 2

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

architecture. Trachsel et al. [26] presented a method to visually score 10 root architectural traits of the root crown of an adult maize plant in the field in a few minutes. According to them, visual measurement of the root crown required 2 min per sample irrespective of the environment. The visual evaluation of root architecture will be a valuable tool in tailoring crop root systems to specific environments. Overall, information about root architecture in the field and information about the genetic control of root architecture remains scarce. To start a successful breeding program for improving drought tolerance, available maize populations and commercial single and three-way cross hybrids should be screened under drought stress to identify the best ones for further use in extracting the best parental inbred lines for developing drought tolerant hybrids. The objectives of the present study were: (i) to assess the effect of drought at silking and grain filling stages, genotype and their interaction on maize agronomic, physiological, root architecture and yield traits and (ii) to identify high-yielding drought tolerant genotypes with desirable root traits for use in future breeding programs.

2. MATERIALS AND METHODS This study was carried out in the two successive growing seasons 2016 and 2017 at the Agricultural Experiment and Research Station of the Faculty of Agriculture, Cairo University, Giza, Egypt (30°02'N latitude and 31°13'E longitude with an altitude of 22.50 meters above sea level).

2.1 Plant Materials Seeds of 22 maize (Zea mays L.) genotypes obtained from Agricultural Research Center-Egypt (13 genotypes), Hi-Tec Company (3 genotypes), DuPont Pioneer Company (3 genotypes), Fine Seeds Company (one genotype), Egaseed Company (one genotype), and Watania Company (one genotype) were used in this study (Table 1). These genotypes represent three groups of maize genotypes of narrow- (10 commercial single crosses), medium- (5 commercial 3-way crosses) and broad- (7 populations) genetic base backgrounds and could be used as sources to extract inbred lines for developing drought tolerant hybrid varieties.

2.2 Experimental Procedures Sowing date was April 24th in the 1st season (2016) and April 30ht in the 2nd season (2017). Sowing was done in rows; each row was 4 m long and 0.7 m width. Seeds were over sown in hills 25 cm apart, thereafter (after 21 days from planting and before the 1st irrigation) were thinned to one plant/hill to achieve a plant density of 57,120 plants/ha.

2.3 Experimental Design A split-plot design in randomized complete blocks (RCB) arrangement with three replications was used. Main plots were allotted to three irrigation regimes, i.e. well watering (WW), water stress at flowering (WSF) and water stress at grain filling (WSG). Each main plot was surrounded with an alley (4 m width), to avoid water leaching between plots. Sub plots were devoted to 22 maize genotypes. 2 Each experimental plot included two rows (plot size = 5.6 m ). Total number of experimental plots = 3 irrigation treatments × 22 genotypes × 3 replications = 198.

2.4 Water Regimes 1. Well watering (WW): Irrigation was applied by flooding, the second irrigation was given after three weeks and subsequent irrigations were applied every 12 days. th 2. Water stress flowering (WSF): The irrigation regime was just like well watering, but the 4 and th 5 irrigations were withheld, resulting in 24 days water stress just before and during flowering stage. th 3. Water stress grain filling (WSG): The irrigation regime was just like well watering, but the 6 th and 7 irrigations were withheld, resulting in 24 days water stress during grain filling stage. 3

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

Table 1. Designation, origin and grain color of maize genotypes under investigation Genotype No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Designation Hi-Tec-2031 P-30K09 Fine-1005 Egaseed-77 SC-10 SC-128 Hi Tec- 2066 P-3444 SC-166 P-32D99 Hi Tec 1100 Watania 11 TWC-324 TWC-360 TWC-352 Giza Baladi Population-45 Nubaria Nebraska Midland Midland Cunningham Golden Republic Sweepstakes 5303

Origin Hi-Tec, Egypt DuPont Pioneer, Egypt Fine Seeds, Egypt Egaseed Co., Egypt ARC, Egypt ARC, Egypt Hi-Tec, Egypt DuPont Pioneer, Egypt ARC, Egypt DuPont Pioneer, Egypt Hi-Tec, Egypt Watania Co., Egypt ARC, Egypt ARC, Egypt ARC, Egypt ARC, Egypt ARC, Egypt ARC, Egypt USA Eldorado, Kansas, USA Beltsville, Kansas, USA USA

Genetic nature Single cross Single cross Single cross Single cross Single cross Single cross Single cross Single cross Single cross Single cross 3-way cross 3-way cross 3-way cross 3-way cross 3-way cross Population Population Population Composite Population Population Population

Grain colour White White White White White White White Yellow Yellow Yellow White White White Yellow Yellow White Yellow Yellow Yellow Yellow Yellow Yellow

2.5 Agricultural Practices All other agricultural practices were followed according to the recommendations of ARC, Egypt. Nitrogen fertilization at the rate of 285 kg N/ha was added in two equal doses of Urea 46% before the first and second irrigation. Triple Superphosphate Fertilizer (46% P2O5) at the rate of 70 kg P2O5/ha, was added as soil application before sowing during preparation of the soil for planting. Weed control was performed chemically with Stomp 330-E herbicide (Pendimethalin 33% w/v), just after sowing and before the planting irrigation and manually by hoeing twice, the first before the first irrigation (after 21 days from sowing) and the second before the second irrigation (after 33 days from sowing). Pest control was performed when required by spraying plants with Lannate (Methomyl) 90% (manufactured by DuPont, USA) against corn borers.

2.6 Soil Analysis Physical and chemical soil analyses of the field experiments were performed at laboratories of Soil and Water Research Institute of ARC, Egypt. Across the two seasons, soil type was clay loam: Silt (36.4%), clay (35.3%), fine sand (22.8%) and coarse sand (5.5%), pH (7.92), EC (1.66 dSm-1), SP -3 (62.5), CaCO3(7.7%), Soil bulk density (1.2 g cm ), HCO3 (0.71 mEqu/l), Cl (13.37 mEqu/l), SO4 (0.92 ++ ++ mEqu/l), Ca (4.7 mEqu/l), Mg (2.2 mEqu/l), Na+ (8.0 mEqu/l), K+ (0.1 mEqu/l), N, P, K, Zn, Mn and Fe (371, 0.4, 398, 4.34, 9.08 and 10.14 mg/kg, respectively).

2.7 Data Recorded 1. Days to 50% silking (DTS): The number of days taking from emergence to the day on which 50% of the plants in a treatment showing complete silk emergence. 2. Anthesis-silking interval (ASI): was calculated as the difference between 50% silking and 50% anthesis 3. Plant height (PH): The average height of five randomly selected plants measured in centimeter from the ground level to the tip of the tassel 15 days before harvest.

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Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

4. Ear height (EH): The average height of five randomly selected plants measured in centimeter from base of the plant to the node bearing the upper most ear of the same plants used to measure plant height 15 days before harvest. 5. Barren stalks (BS): measured as percentage (%) of plants bearing no ears relative to the total number of plants in the plot; an ear was considered fertile if it had one or more grains on the rachis. 2 6. Ear leaf area (ELA): It was measured in cm on the ear leaf from five guarded plants/plot, according to Francis et al. [27] as follows: ELA = Leaf length x maximum leaf width x 0.75 7. Chlorophyll concentration index (CCI): It was measured in % on 5 guarded plants/plot by Chlorophyll Concentration Meter, Model CCM-200, USA, as the ratio of transmission at 931 nm to 653 nm through the ear leaf of the plant. (http://www.apogeeinstruments.co.uk/apogeeinstruments-chlorophyll-content-meter-technical-information/) 8. Lower stem diameter (SDL): It was measured in mm with caliper from 5 guarded plants/plot as the stem diameter above second node; two measurements were taken. The first measurement was used as a base line with the second measurement recorded after a 90-degree turn of the caliper. 9. Upper stem diameter (SDU): It was measured in mm with caliper from 5 guarded plants/plot as the stem diameter on third inter node below flag leaf. -1 -1 10. Number of ears plant (EPP): It was estimated by dividing number of ears plot on number of -1 plants plot . 11. Number of rows ear-1 (RPE): Using 10 random ears plot-1 at harvest. -1 -1 12. Number of kernels row (KPR): Using the same 10 random ears plot -1 13. Number of kernels plant (KPP): It was calculated by multiplying number of ears plant-1 by -1 -1 number of rows ear by number of kernels row . 14. 100-kernel weight (100KW) (g): Adjusted at 155g water kg-1 grain. 15. Grain yield plant-1 (GYPP) (g): It was estimated by dividing the grain yield plot-1 (adjusted at -1 15.5% grain moisture) on number of plants plot at harvest. -1 16. Grain yield ha (GYPH) (ton): It was estimated by adjusting grain yield plot-1 at 15.5% grain -1 moisture to grain yield ha . At the end of each water stress treatment (80 and 100 days from emergence for WSF and WSG, respectively) and just after irrigation, three plant roots from each experimental plot were excavated by removing a soil cylinder of 40 cm diameter and a depth of 40 cm with plant base as the horizontal Centre of the soil cylinder. Excavation was carried out using standard shovels. The excavated root crowns were shaken briefly to remove a large fraction of the soil adhering to the root crown. Most of the remaining soil was then removed by soaking the root crown in running water. In a third step remaining soil particles were removed from the root crown by vigorous rinsing at low pressure. The clean roots were measured or visually scored (Fig. 1) for the following traits: 17. 18. 19. 20. 21. 22. 23.

Number of above-ground whorls occupied with brace roots (BW). Number of brace roots (BN). st Angle of 1 arm of the brace roots originating from whorl 1 (BA) (score). Branching density of brace roots (BB) (score). Number of crown roots (CN) (score). Crown roots angle (CA) (score). Branching density of crown roots (CB) (score).

Traits from No. 5 to No. 9 were assigned values from one to nine according to Trachsel et al. [26], where one indicates shallow root angles (10°), low root numbers and a low branching density (0.5 lateral root cm−1). Nine indicates s eep root angles (90°), high numbers and a high bra nching density −1 (7 lateral roots cm ). Representative images depicting contrasts for the various traits are given in Fig. 1 as suggested by Trachsel et al. [26]. 24. Crown root length (CRL) (cm). 25. Root circumference (RC) (cm). 26. Roots (crown and brace) dry weight (RDW) (g).

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Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

Fig. 1. Images of brace roots angle (BA), brace roots branching density (BB), crown roots number (CN), crown root s angle (CA) and crown roots branching (CB) displayed were scored with 1, 3, 5, 7 and 9

2.8 Biometrical Analyses Analysis of variance of the split plot design in RCB arrangement was performed on the basis of individual plot observation using the MIXED procedure of MSTAT ®. Combined analysis of variance across the two growing seasons was also performed if the homogeneity test was non-significant. Moreover, combined analysis for each environment separately across seasons was performed as randomized complete block design. Least significant difference (LSD) values were calculated to test the significance of differences between means according to Steel et al. [28].

3. RESULTS AND DISCUSSION 3.1 Analysis of Variance 3.1.1 Agronomic, physiologic and yield traits Combined analysis of variance across seasons (S) of the split plot design for 16 agronomic, physiological and yield traits of 22 genotypes (G) of maize (10 single crosses + 5 three-way crosses + 7 open-pollinated populations) under three irrigation treatments; namely well watering (WW), water stress at flowering (WSF) and water stress at grain filling (WSG) (for DTS, ASI, PH, EH, BS, EPP, RPE, KPR, KPP, 100-KW, GYPP, GYPH traits) or four irrigation treatments, namely well watering at 6

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

flowering (WWF), well watering at grain filling (WWG), water stress at flowering (WSF) and water stress at grain filling (WSG) for four traits (CCI, SDU, SDL, ELA traits) is presented in Table 2. Mean squares due to seasons were significant (P ≤ 0.05 or 0.01) for 10 out of studied 16 traits, namely days to silking (DTS), anthesis-silking interval (ASI), ears/plant (EPP), 100-kernels weight (100KW), grain yield/plant (GYPP), grain yield/ha (GYPH), chlorophyll concentration index (CCI), lower stem diameter (SDL), upper stem diameter (SDU) and ear leaf area (LEA), indicating significant effect of climatic conditions on such traits. Mean squares due to irrigation regime and genotype were significant (P ≤ 0.05 or 0.01) for all studied traits, except rows/ear (RPE) and ear leaf area (LEA) for irrigation regimes, indicating that irrigation regime has a significant effect on 14 out of 16 traits and that genotype has an obvious and significant effect on all studied agronomic, physiologic and yield traits. st

Mean squares due to the 1 order interaction, i.e. T×S, G×S and G×T were significant (P ≤ 0.05 or 0.01) for all studied traits, except for 8 traits for T × S, namely, DTS, EEP, RPE, KPP, GYPP, CCI, SDU, and SDL, one trait (ELA) for G x S, and two traits (SDL and ELA) for G x T (Table 2). Significance of G×T indicated that means of studied traits of genotypes varied with water supply, confirming previous results [29,30,31,32,33]. Mean squares due to the 2nd order interaction, i.e., G×S×T, were significant (P ≤ 0.01) for all studied traits, except for RPE, SDU and ELA (Table 2), indicating that genotype performance differ from acombination of season x treatment to another combination and that the rank of maize genotypes differ from irrigation regime to another, and from one season to another and the possibility of selection for improved performance under a specific water stress for most studied agronomic and yield traits as proposed by Al-Naggar et al. [34,35,36,37,38]. 3.1.2 Root traits Combined analysis of variance across seasons (S) of the split plot design for 10 root traits of 22 maize genotypes (G) under four irrigation treatments (T), namely well watering at flowering (WWF), well watering at grain filling (WWG), water stress at flowering (WSF) and water stress at grain filling (WSG), is presented in Table 3. Mean squares due to seasons were significant (P ≤ 0.05 or 0.01) for four out of studied 10 root traits, namely brace root whorls (BW), brace root angle (BA), crown root angle (CA) and crown root branching (CB), indicating significant effect of climatic conditions on such traits. Mean squares due to irrigation regime were significant (P ≤ 0.05 or 0.01) for four out of studied 10 root traits, namely crown root number (CN), CB, root circumference (RC) and root dry weight (RDW), indicating that irrigation regime has a significant effect on such root traits. Mean squares due to genotype were significant (P ≤ 0.01) for all studied root traits, indicating that genotype has an obvious and significant effect on all such traits. The role of genotype in root traits is in accordance with findings of Trachsel et al. [26]. Mean squares due to the 1st order interaction were significant (P ≤ 0.05 or 0.01) for three root traits (BN, RC and RDW) due to T×S and four roottraits (BB, CN, CB and RDW) due to G×S. Mean squares due to the 2nd order interaction, i.e. G×S×T, were significant (P ≤ 0.01) for only one trait, namely BB (Table 3), indicating that for such trait, the rank of maize genotypes differ from irrigation regime to another and from one year to another. Combined analysis of variance of a randomized complete blocks design (RCBD) for all studied traits of 22 maize genotypes under each irrigation treatment, (data not presented) indicated that mean squares due to genotypes under all environments were highly significant for all studied traits, indicating the significance of differences among studied genotypes for such traits under each of irrigation regimes. Such genotypic differences in studied traits under well watering as well as water stress at flowering and grain filling were also recorded by previous investigators in maize [13,14,15, 39, 32,33,40,38,41]. 7

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

Table 2. Combined analysis of variance of split plot design for 22 maize genotypes (G) under three/four irrigation regimes (T) across 2016 and 2017 seasons (S) SOV

df

Season (S) R(S) Treatment (T) TxS Error (a) Genotype (G) GxS GxT GxSxT Error (b)

1 4 2 2 8 21 21 42 42 252

Season (S) R(S) Treatment (T) TxS Error (a) Genotype (G) GxS GxT GxSxT Error (b)

1 4 2 2 8 21 21 42 42 252

Season (S) R(S) Treatment (T) TxS Error (a) Genotype (G) GxS GxT GxSxT Error (b)

1 4 2 2 8 21 21 42 42 252

Season (S) R(S) Treatment (T) TxS Error (a) Genotype (G) GxS GxT GxSxT Error (b)

1 4 3 3 12 21 21 63 63 336

Mean squares ASI PH 18.3** 49.9 0.1 53.9 13.8** 22341.4** 4.5** 18508.9** 0.3 785.8 12.1** 5990.6** 1.7** 713** 2.5** 307.9** 2.5** 263.2** 0.7 148 EPP RPE 0.8** 0.1 0.1 1.9 0.7** 2.7 0.01 0.1 0.1 1.5 0.1** 24.2** 0.1** 1.* 0.04** 1.1** 0.1** 0.7 0.02 0.6 100KW GYPP 302.3** 26041.5* 1.6 5318.3 590.5** 47158.4** 182.1** 3864.3 5.6 4686.9 274.3** 12428.3** 24.9** 3439.6** 18.1** 1335.8** 17.7** 1383.5** 4.9 219.5 SDL SDU 300.3** 1585.6** 37.9 8.4 120.9* 156.** 21.4 37.6 31.4 14.2 79** 18.5** 5.7* 4.9** 2.8 3** 4.8* 2.2 3.6 1.7

DTS 644** 3.5 48.1* 7.3 7.7 165.6** 23.6** 6.8** 6.1** 1.6 BS 1882.9** 88.5 1520.2** 1026.6* 149.6 269.5** 136.9** 102.3** 87.7** 50.8 KPP 100284.5 13216.3 735325.9** 32895.6 36955.1 67129.7** 26213.3** 18826.3** 14581.2** 7281.6 CCI 3458.3** 172.5 4432.6** 374.6 152.3 498** 94.8** 148.3** 56.2** 18.36

EH 939.3 54.2 5318.6** 9057** 425.3 2385.6** 265.5** 132.7** 134.6** 51.6 KPR 150.1 2.0 1284.5** 321.2* 54.9 245.6** 48.4** 26.5** 28.6** 7.8 GYPH 124.7** 2.4 2041.1** 225.5** 5.2 707.3** 46.4** 34.8** 19.6** 2.1 ELA 342312.8** 7319.7 63074.7 94133.1* 27629.3 101407.4** 12628.8 8554.9 6414.7 8882.2

DTS = days to 50% silking, ASI = anthesis-silking interval, PH = plant height, EH = ear height, EPP = number of ears per plant, RPE = Rows per ear, KPR = Kernels per row, KPP = Kernels per plant, 100-KW = 100-kernel weight, GYPP = Grain yield/plant, GYPH = grain yield/ha,CCI= Chlorophyll concentration index, SDL= Lower stem diameter, SDU= Upper stem diameter, ELA= Ear leaf area, * and ** indicate significance at 0.05 and 0.01 probability levels, respectively

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Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

3.2 Effect of Deficit Irrigation 3.2.1 Agronomic, physiologic and yield traits Water stress conditions imposed during flowering and grain filling stages caused a significant reduction, of 28.69 and 20.26% in grain yield/plant and 35.53 and 25.51% in grain yield/ha, respectively (Table 4 and Figs. 2 and 3). These results indicate that drought stress at flowering stage had more severe effect on yield than drought at grain filling. This result is in accordance to Denmead and Shaw [29] and Chapman et al. [42], who noted that water stress during the vegetative stage of corn production reduced grain yield by 25%, water stress during silking reduced grain yield by 50%, while water stress during grain fill reduced grain yield by 21%. El-Ganayni et al. [39] also reported 33% yield reduction due to water stress at flowering. On the contrary, Al-Naggar et al. [38] found that drought stress at grain filling stage had more severe effect on yield than drought at flowering. They attributed that to stopping irrigation after flowering stage till the end of the season in their experiment. Differences in results of different investigators might be due to differences in soil properties and climate conditions prevailed during the seasons and locations of different studies. Table 3. Mean squares from combined analysis of variance of split plot design for studied root traits of 22 maize genotypes under four irrigation regimes (T) across 2016 and 2017 years Variance source Season (S) Treatment (T) TxS Genotype (G) GxS GxT GxSxT Season (S) Treatment (T) TxS Genotype (G) GxS GxT GxSxT

Mean squares BW 5.32* 2.78 4.9* 2.91** 0.218 0.449 0.362 CA 103.2** 5.4 10.4 9** 1.7 1.6 1.1

BN 487.8 2139.6** 615.6 1014.5** 85.9 146.8 122.6 CB 28.2** 26** 3.8 13.1** 4.7** 2.5 1.8

BA 33.5** 3.2 3.3 6.1** 2.2 1.5 1.2 CRL 243.5 115.7 201.9 59.4** 13.6 17.2 23.1

BB 5.5 12.9 15.1 16.6** 10.8** 3.7 5.2* RC 107.5 618.1** 232.9* 263.2** 26.9 26.7 32.2

CN 0.4 32.5* 4.3 12.3** 4* 2.5 2.3 RDW 94.5 1336.5** 1278.1** 955.5** 234.1** 132.9 142.4

BW= Number of above-ground whorls occupied with brace roots, BN= Number of brace roots, BA= Brace root angle, BB= Branching density of brace roots, CN= Number of crown roots, CA= Crown roots angle, CB= Branching density of crown roots, CRL= Crown root length, RC= Root circumference, RDW= Roots dry weight, *and ** indicate significance at 0.05 and 0.01 probability levels, respectively

Yield reductions were accompanied by significant (p ≤ 0.05 or 0.01) losses in number of ears/plant by 11.58%, kernels/row by 14.23%, kernels/plant by 24.85% and 100-kernel weight by 6.75% due to drought at flowering and 100-kernel weight by 12.36% due to drought at grain filling stage. It is observed that reduction in kernel weight was more pronounced due to drought at grain filling than that at flowering. Number of kernels per row and per plant was significantly reduced due to drought at flowering, but was not affected with drought at grain filling. This is because number of fertilized eggs (later become grains) is determined at flowering stage, but accumulation of assimilates in grains (which affects grain weight) occurs during grain filling stage. This conclusion is in agreement with that reported by El-Ganayni et al. [39]. Reduction in grain yield and its components due to water deficit at flowering and grain filling stages is in agreement with those of several investigators [4,6,3,39,33,38]. A significant decrease due to water stress at flowering and grain filling was also recorded in plant height (3.18 and 1.03%) and ear height (5.39 and 6.12%), respectively. This result is in agreement with that reported by Al-Naggar et al. [38]. On the contrary, a significant increase due to water stress at flowering stage only was shown for anthesis-silking interval (35.77%), days to silking (3.31%) and barren stalks (BS) (665.3 %) (Table 3). 9

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

Elongation of ASI and increase of BS in maize because of drought stress were reported by several investigators [3,2,39,32-38]. Table 4. Means of studied agronomic, physiological and yield traits under WW (or WWF and WWG), WSF and WSG and change % from WW to WSF and WSG combined across all genotypes and across 2016 and 2017 seasons Trait Days to 50% silking Anthesis-silking interval Plant height (cm) Ear height (cm) Barren stalks% Ears per plant Rows per ear Kernels per row Kernels per plant 100-kernel weight (g) Grain yield/plant (g) Grain yield/ha (ton)

Chlorophyll concentr. index % Lower stem diameter (mm) Upper stem diameter (mm) 2

Ear leaf area (cm )

Parameter Mean Change % Mean Change % Mean Change % Mean Change % Mean Change % Mean Change % Mean Change % Mean Change % Mean Change % Mean Change % Mean Change % Mean Change %

WW 63.96 2.6 252.25 127.03 0.94 0.95 14.02 38.08 499.05 34.18 128.17 9.02 WWF 34.2

Mean Change % Mean Change % Mean Change % Mean Change %

24.73 9.97 708.9

WWG 17.11 49.98** 23.79 3.79 9.46 5.11 666.1 6.04

WSF 66.08 -3.31* 3.53 -35.77** 244.22 3.18* 120.18 5.39** 7.22 -665.3** 0.83 11.85* 13.74 2.0 32.66 14.23** 375.02 24.85** 31.88 6.75** 91.39 28.69** 5.82 35.53** WSF 23.88 30.18** 23.91 3.29 10.05 -0.77 691.1 2.52

WSG 64.33 -0.58 2.53 1.54 249.66 1.03 119.25 6.12** 1.84 -95.02 0.97 -2.65 13.85 1.24 38.05 0.07 508.96 -1.99 29.96 12.36** 102.2 20.26** 6.72 25.51* WSG 9.56 44.07** 22.42 5.76 7.72 18.46** 662.7 0.51

WW= well watering, WWF= well watering at flowering, WWG= well watering at grain filling, WSF = water stress at flowering, WSG= water stress at grain filling, * and ** indicate significance at 0.05 and 0.01 probability levels, respectively. Change %= 100 (WW-WS)/WW

Because other four studied traits (CCI, SDL, SDU, ELA) were measured or scored at different plant ages as compared with controls at the end of each stress, so we had two controls, one for water stress at flowering (WWF) at plant age of 80 days and another one for water stress at grain filling (WWG) at plant age of 104 days, besides two water stress treatments (WSF and WSG). So these traits were analyzed under four irrigation treatments (WWF, WWG, WSF, and WSG). The change in percentage from WWF to WSF and from WWS to WSG was calculated to study the effect of drought at flowering and grain filling, respectively on these traits; however the change from WWF to WWG was calculated to study the effect of plant ageing on such traits. Water stress at flowering and grain filling caused a significant reduction in chlorophyll concentration index (CCI) by 30.18 and 44.07%, respectively (Table 3 and Fig. 2). Water stress at grain filling only caused a significant reduction in upper stem diameter (SDU) by 18.46%. The two traits (SDL and ELA) were not affected significantly by water stress neither at flowering nor at grain filling. It is observed that the effect of WSG on these 10

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

four traits was more severe than the effect of WSF. It is worthy to note that ageing of corn plant from 80 days to 104 days age caused a reduction in all four traits; such reduction reached significance (p≤0.01) for CCI trait (49.98%). 1. Days to 50% anthesis (day) 64

68

63

66

62

64

61

62

60

5 4 3 2 1 0

60 WW WSF WSG

4. Plant height (cm) 260 255 250 245 240 235

3. Anthesis-silking interval (day)

2. Days to 50% silking (day)

WW

WSF WSG

WW

5. Ear height (cm) 10 8 6 4 2 0

125 120 115 110 WW WSF WSG

WW WSF WSG

7. Number of ears/plant

8. Number of rows/ear

WW

14.2

50

14

40

1

30

13.8

20

13.6

10

13.4 WW WSF WSG 10. Number of kernels/plant

0 WW WSF WSG

WW WSF WSG

11. 100-kernel weight (g)

12. Grain yield /plant (g)

40 440

140 120 100 80 60 40

30 20

240

10 40

0 WW WSF WSG

WSF WSG

9. Number of kernels/row

1.5

0

WSG

6. Barren stalks (%)

135 130

0.5

WSF

WW WSF WSG

WW WSF WSG

Fig. 2. Means of studied agronomic and yield traits (1 through 12) under well watering (WW), water stress at flowering (WSF) and at grain filling (WSG) across all maize genotypes and across two seasons

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Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

1. Chlorophyll concentration index (spad)

2. Lower stem diameter (mm) 26

40 24

30 20

22

10 0

20 WWF

WSF

WWG

WSG

WWF

WSF

WWG

WSG

4. Ear leaf area (cm2)

3. Upper stem diameter (mm) 12

750

10

700

8

650 600

6 WWF

WSF

WWG

WWF

WSG

WSF

WWG

WSG

Fig. 3. Means of chlorophyll concentration index (1), lower stem diameter (2), upper stem diameter (3) and ear leaf area (4) across genotypes under well watering at flowering (WWF) and at grain filling (WWG), water stress at flowering (WSF) and at grain filling (WSG) across two seasons 3.2.2 Root traits The changes due to water stress and plant age of the ten measured or scored root traits BW (number of above-ground whorls occupied with brace roots), BN (number of brace roots), BA (angle of 1st arm of the brace roots originating from whorl 1), BB (branching density of brace roots), CN (number of crown roots), CA (crown roots angle), CB (branching density of crown roots), CRL (crown root length), RC (root circumference), RDW (roots dry weight) are presented in Table 5 and Fig. 4. Water stress at flowering stage caused a significant (p ≤ 0.05 or 0.01) reduction in four root traits, namely number of above-ground whorls occupied with brace roots; BW (9.31%), number of brace roots; BN (18.27%), number of crown roots; CN (11.50%) and roots dry weight; RDW (28.31%), but caused a significant increase (elongation) in crown root length; RL (9.90%). On the contrary, water stress at grain filling caused a significant increase in three root traits, namely number of brace roots; BN (10.10%), number of crown roots; CN (14.71%) and roots dry weight; RDW (11.60%), but caused a significant reduction only in branching density of crown roots; CB (10.05%). It is observed that significant changes in root traits caused due to water stress at flowering were, in opposite direction to those caused due to water stress at grain filling, they were in the direction of increase for WSG (in 4 out of 5 traits) and in the direction of decrease for WSF (in 3 out of 4 traits). The trend was that WSF caused a reduction, while WSG caused an increase in most studied trait roots as compared to its corresponding controls. The reason of differences between the effects of water stress at flowering and at grain filling might be due to the age of plant when water stress occurred. Aging of corn plant from WWF (80 days age) to WWG (104 days age) caused a reduction in 8 out of 10 root traits; such reduction reached significance (p≤0.05 or p≤0.01) in five traits, namely branching density of brace roots (12.62%), number of crown roots (30.45%), branching of crown roots (10.81%), root circumference (10.98%) and root dry weight (20.16%). Replacement of older roots with newly 12

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

formed roots is referred to as root turnover [43]. Root turnover is important to the success of individual plants in obtaining water and nutrients. Root turnover is estimated from the difference between cumulative births and deaths, which represents either a net accumulation or disappearance of roots [44,45]. It seems in the present study that the difference is in the negative direction, i.e. roots deaths are faster than accumulation, especially under water stress conditions. Table 5. Means of root and grain yield traits under well watering (WW), at flowering (WWF) and at grain filling (WWG), water stress at flowering (WSF) and at grain filling (WSG), and change % from WW, WWF or WWG to WSF or WSG, respectively across all genotypes and across 2016 and 2017 seasons. Trait BW BN BA (score) BB (score) CN (score) CA (score) CB (score) RL (cm) RC (cm) RDW (g)

Parameter Mean Change % Mean Change % Mean Change % Mean Change % Mean Change % Mean Change % Mean Change % Mean Change % Mean Change % Mean Change %

WWF 2.52

WSF 2.29 9.31* 31.53 18.27** 6.91 -3.17 4.94 7.53 3.38 11.50* 6.89 -1.67 4.59 -0.66 23.90 -9.90* 34.36 0.45 18.76

38.58 6.70 5.34 3.82 6.77 4.56 21.74 34.51 26.17

WWG 2.48 1.82 37.08 3.88 6.71 -0.12 4.67 12.62* 2.65 30.54** 6.49 4.25 4.07 10.81* 22.99 -5.74 30.72 10.98** 20.89 20.16**

WSG 2.64 -6.42 40.83 -10.10* 6.53 2.61 4.69 -0.47 3.04 -14.71* 6.49 -0.06 3.66 10.05* 21.76 5.33 30.87 -0.49 23.32 -11.60*

BW= Number of above-ground whorls occupied with brace roots, BN= Number of brace roots, BA= Brace root angle, BB= Branching density of brace roots, CN= Number of crown roots, CA= Crown roots angle, CB= Branching density of crown roots, CRL= Crown root length, RC= Root circumference, RDW= Roots dry weight, * and ** indicate significance at 0.05 and 0.01 probability levels, respectively

3.3 The Effect of Maize Genotype 3.3.1 Agronomic, physiologic and yield traits Means of studied 16 traits of each of 22 genotypes across all irrigation treatments combined across two seasons are presented in Table 6. Means of each of the 22 maize genotypes showed wide ranges of performance (difference between minimum and maximum) for all studied traits across all irrigation treatments. Genotypes varied for grain yield/ha from 13.0 ton (Genotype No. 8) to 2.69 ton (Genotype No. 22), grain yield/plant from 158.5 g (Genotype No. 6) to 62.5 g (Genotype No. 22), ears/plant from 1.05 (Genotype No. 8) to 0.83 (Genotype No. 4), rows/ear from 15.9 (Genotype No. 14) to 11.9 (Genotype No. 1), kernels/row from 43.6 (Genotype No. 5) to 30.5 (Genotype No. 22), kernels/plant from 592.3 (Genotype No. 8) to 332.4 (Genotype No. 22), 100-kernel weight from 32.0g (Genotype No. 2) to 25.8g (Genotype No. 18), CCI from 31.1% (Genotype No. 7) to 13.5% (Genotype 2 No. 22), SDL from 27.2 mm (Genotype No. 2) to 20.1 mm (Genotype No. 21), ELA from 773.6 cm 2 (Genotype No. 5) to 0.565.4 cm (Genotype No. 9), DTS from 69.7 (Genotype No. 2) to 57.6 (Genotype No. 21), ASI from 4.78 day (Genotype No. 9) to 0.83 (Genotype No. 22), BS from 14.9% (Genotype No. 22) to 0% (Genotypes No. 2, 4, 7 and 11), plant height from 296.9 cm (Genotype No. 5) to 219.7 cm (Genotype No. 21), ear height from 142.2 cm (Genotype No. 5) to 100.2 cm (Genotype No. 9).

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Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

Number of brace root whorls

Brace root number

Brace root angle (score)

50

3

7.2 7 6.8 6.6 6.4 6.2 6

40 30

2

20 1

10

0

0 WWF WSF WWG WSG

WWF WSF WWG WSG

Brace root branching (score)

WWF WSF WWG WSG

Crown root number (score)

6

5

7.2 7

4 4

6.8

3

6.6

2

2

6.4

1 0

6.2

0 WWF WSF WWG WSG

Crown root angle (score)

6 WWF WSF WWG WSG

Crown root branching (sore)

Crown root length (cm)

6

25

WWF WSF WWG WSG

38 36

5

24

4

23

3

22

2

21

30

1

20

28

0

19 WWF

WSF

WWG

WSG

Root circumference (cm)

34 32

26 WWF WSF WWG WSG

WWF WSF WWG WSG

Root dry weight (g) 30 20 10 0 WWF

WSF

WWG

WSG

Fig. 4. Means of root traits across genotypes under well watering at flowering (WWF) and at grain filling (WWG), water stress at flowering (WSF) and at grain filling (WSG) across two seasons

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Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

The genotype No. 8 (Pioneer-3444) exhibited the highest mean values for three traits [GYPH, KPP, EPP] and second highest for GYPP. The genotype No. 6 (SC-128) developed by ARC-Egypt was the highest in GYPP. The genotype No. 4 (Egaseed 77) developed by Fine Seed Co. showed the third highest in grain yield. The genotype No. 5 (SC-10) developed by ARC-Egypt showed the highest means for four traits (KPR, ELA, plant height and ear height); it was the fourth highest in grain yield per plant and per hectare. In general, the commercial varieties P-3444, SC-128, Egaseed-77 and SC-10 were the best genotypes in our experiment, where they showed the highest grain yield across all studied irrigation treatments (Fig. 5); they could be recommended for farmers use under a range of different environments as well as for maize breeding programs. On the contrary, the genotype No. 22 (Pop. Sweepstakes 5303) exhibited the lowest means for six traits, namely GYPP, GYPH, KPP, KPR, CCI and ASI (desirable) and the highest mean (undesirable) for barren stalks%. The genotype No. 21 (Pop. Golden Republic) exhibited the lowest means for three traits, namely DTS, PH and SDL. The genotype No. 18 (Pop. Nubaria) showed the lowest means for 100-KW. It is observed that most of traits with undesirable mean values were exhibited by populations and the vice versa for traits with desirable means, which were mostly shown by the single crosses. Several investigators [13,14,15] suggested that hybrids maintain their advantage over open pollinated varieties across stress and non-stress environments. Table 6. Minimum and maximum values followed by genotype No. (Between brackets) of all studied traits combined across all irrigation regimes and across two seasons Parameter DTS

ASI

Min Max LSD.05

57.6 (21) 69.7 (2) 0.83 RPE

1.78(9) 4.78 (22) 0.54 KPR

Min Max LSD.05

11.9 (1) 15.9 (14) 0.5 CCI (%) 13.5 (22) 31.1(7, 4) 2.43

30.5 (22) 43.6 (5) 1.83 SDL (mm) 20.1(21) 27.2(2, 3, 13) 1.07

Min Max LSD.05

Traits PH EH (cm) (cm) 219.7 (21) 100.2 (9) 296.9 (5) 142.2 (5) 7.98 4.71 KPP 100-KW (g) 332.4 (22) 25.8 (18) 592.3 (8) 38.1(2) 56.02 1.45 SDU ELA 2 (mm) (cm ) 7.5(9) 565.4(9) 11.4(4,6,5) 773.6(5,6) 0.74 53.52

BS%

EPP

0.0(2,4,7,11) 14.9(22) 1.73 GYPP (g) 62.5 (22) 158.5 (6) 9.72

0.83(4) 1.05(8) 0.09 GYPH (ton) 2.69(22) 13.0(8) 0.40

DTS = days to 50% silking, ASI = anthesis-silking interval, PH = plant height, EH = ear height, EPP = number of ears per plant, RPE = Rows per ear, KPR = Kernels per row, KPP = Kernels per plant, 100-KW = 100-kernel weight, GYPP = Grain yield/plant, GYPH = grain yield/ha, CCI= Chlorophyll concentration index, SDL= Lower stem diameter, SDU= Upper stem diameter, ELA= Ear leaf area, *and **indicate significance at 0.05 and 0.01 probability levels, respectively

3.3.2 Root traits Means of studied 12 root traits of each of 22 genotypes across all irrigation treatments combined across two seasons are presented in Table 7. Means of each of the 22 maize genotypes showed wide ranges of performance for all studied root traits across all irrigation treatments. Genotypes varied for number of above-ground whorls occupied with brace roots from 3.0 from (genotype No. 17) to 1.9 (genotype No. 8), number of brace roots from 49.0 (genotype No. 10) to 25.6 (genotype No. 21), angle of 1st arm of the brace roots originating from whorl 1 from 7.7 (genotype No. 19) to 5.5 (genotype No. 1), branching density of brace roots from 6.2 (genotype No. 9) to 3.4 (genotype No. 18), number of crown roots from 4.5 (genotype No. 6) to 1.9 (genotype No. 21), crown roots angle from 8.1 (genotype No. 10) to 5.6 (genotype No. 7), branching density of crown roots from 6.5 15

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

(genotype No. 8) to 3.0 (genotype No. 21), crown root length from 26.1 cm (genotype No. 5) to 20.4 cm (genotype No. 18), root circumference from 38.1 cm (genotype No. 7) to 25.9 cm (genotype No. 21) and roots dry weight from 36.8 g (genotype No. 8) to 11.2 g (genotype No. 20).

Grain yield/plant (g) (combined) 150 125 100 75 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Fig. 5. Means of grain yield/plant 22 maize genotypes across all environments The genotype No. 8 (Pioneer-3444) exhibited the highest mean values for four traits [GYPH, root circumference (RC), crown root branching (CB) and roots dry weight (RDW)] and second highest for GYPP, brace root branching (BB), number of crown roots (CN), crown root length (CRL), i.e. most important yield and root traits. The genotype No. 6 (SC-128) developed by ARC-Egypt was the highest in GYPP and number of crown roots and second highest in crown root branching. The genotype No. 4 (Egaseed 77) developed by Fine Seed Co. showed the third highest in grain yield and the highest in brace root angle (BA). The genotype No. 5 (SC-10) developed by ARC-Egypt showed the highest means for one trait (crown root length; CRL); it gave the fourth highest grain yield per plant and per hectare. The superiority of these three commercial varieties in six root traits (RC, CB, RDW, BB, CN and CRL) for Pioneer-3444, two traits (CN and CB) for SC-128, one trait (BA) for Egaseed 77 and one trait (root length) for SC-10 might be the reason of their superiority in grain yield, because good roots may help the plants to uptake more water and nutrients from the soil for their biological activities. In general, the commercial varieties P-3444, SC-128, Egaseed-77 and SC-10 were the best genotypes in our experiment, where they showed the highest grain yield and the best root architectural traits across all studied irrigation treatments; they could be recommended for farmers use under a range of different environments as well as for maize breeding programs. Table 7. Average, minimum (Min) and maximum (Max) of all studied traits of each genotype combined across all irrigation regimes and across 2016 and 2017 seasons

Average Min Max LSD.05 Average Min Max LSD.05

BW (No.) 2.5 1.9 (8) 3.0 (10,11,17) 0.36 CA (score) 6.7 5.6 (7) 8.1(10) 0.76

BN (No.) 37.1 25.6 (21) 49.0(10) 6.8 CB (score) 4.2 3.0 (21) 6.5 (8) 0.91

BA (score) 6.7 5.5 (1) 7.7(19) 0.74 CRL (cm) 22.8 20.4 (18) 26.1 (5) 2.57

BB (score) 4.9 3.4 (18) 6.2(9) 1.09 RC (cm) 32.7 25.9 (21) 38.1 (8) 2.85

CN (score) 3.2 1.9 (21) 4.5(6) 0.86 RDW (g) 22.3 11.2 (20) 36.8(8) 6.05

Means of minimum and maximum are followed by genotype No. (Between brackets).DTS = days to 50% silking, ASI = anthesis-silking interval, PH = plant height, EH = ear height, EPP = number of ears per plant, RPE = Rows per ear, KPR = Kernels per row, KPP = Kernels per plant, 100-KW = 100-kernel weight, GYPP = Grain yield/plant, GYPH = grain yield/ha, CCI= Chlorophyll concentration index, SDL= Lower stem diameter, SDU= Upper stem diameter, ELA= Ear leaf area

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Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

On the contrary, the genotype No. 22 (Pop. Sweepstakes 5303) exhibited the lowest means for two traits, namely GYPP, GYPH. The genotype No. 21 (Pop. Golden Republic) exhibited the lowest means for two traits, namely BN and CN. The genotype No. 18 (Pop. Nubaria) showed the lowest means for two traits (BB and CRL). It is observed that most of traits with undesirable mean values were exhibited by populations and the vise versa for traits with desirable means, which were mostly shown by the single crosses.

3.4 Genotype × Irrigation Regime Interaction 3.4.1 Agronomic, physiologic and yield traits For agronomic and yield traits measured under WW, WSF and WSG (Table 8 and Fig. 6), it is observed that for DTS, the lowest mean was exhibited by genotypes No. 9, 21 and 21 and the highest mean by genotypes No. 4, 2 and 2, for ASI, the lowest mean by genotypes No. 7, 5 and 20 and the highest mean by genotypes No. 22, 22 and 6, for PH, the lowest mean by genotypes No. 21, 21 and 21 and the highest mean by genotypes No. 5, 5 and 5, for EH, the lowest by genotypes No. 21, 9 and 9 and the highest by genotypes No. 13, 5 and 5, for BS, the lowest mean by genotypes No. 2, 4, 7 and 11 and the highest mean by genotype No. 22, 22 and 17, for EPP, the lowest by genotypes No. 20, 16 and 4 and the highest by genotypes No. 8, 6 and 20, for RPE, the lowest by genotypes No. 1, 11 and 1 and the highest by genotypes No. 9, 9 and 18 , for KPR, the lowest by genotypes No. 22, 16 and 14 and the highest by genotypes No. 5, 5 and 6, for KPP, the lowest by genotypes No. 22, 16 and 22 and the highest by genotypes No. 8, 6 and 8, for 100-KW, the lowest by genotypes No. 18, 18 and 14 and the highest by genotypes No. 2, 2 and 1, for GYPP, the lowest by genotypes No. 19, 22 and 22 and the highest by genotypes No. 1, 6 and 8 and for GYPH, the lowest by genotypes No. 22, 22 and 22 and the highest by genotypes No. 8, 4 and 8, at WW, WSF and WSG, respectively. Table 8. Minimum (Min) and maximum (Max) values of studied agronomic and yield traits of each genotype under each irrigation regime (well watering; WW, water stress at flowering; WSF and water stress at grain filling; WSG) across 2016 and 2017 seasons Parameter

WW

Min Max LSD.05

57.8(21)* 68.3(4)* 1.49

Min Max LSD.05

214.5(21) 291.3(5) 12.7

Min Max LSD.05

0 10.4 (22) 3.99

Min Max LSD.05

11.7(1) 16.5(9) 1.02

Min Max LSD.05

361.8(22) 618(8) 95.62

Min Max LSD.05

82.9(19) 168.191) 23

WSF WSG Days to silking 57.8(21) 57 (21) 70.5(2) 70.3(2) 1.2 1.54 Plant height 212.1(21) 232.4(21) 277.2(5) 322.3(5) 14.38 14.72 Barren stalks (%) 0 0 25.5(22) 8.8(22) 13.0 3.90 Rows/ear 11.6(11) 11.6(1) 15.9(9) 15.9(18) 0.88 0.88 Kernels/plant 237.4(16) 349.8(22) 515.9(6) 655(8) 87.96 109.5 Grain yield/plant 31.8(22) 58.9(22) 156.4(6) 179.7(8) 13.3 12.7

WW

WSF WSG Anthesis silking interval 1.5(8) 1.67(15) 0.4(21) 4.67(22) 6.33(22) 3.7(1,6) 0.8 1.02 .0.88 Ear height 97.2(21) 96.5(9) 104.1(9) 136.5(13) 136.2(5) 160.4(5) 7.6 7.85 9.17 Ears/plant 0.82(20) 0.72(3) 0.75(4) 1.08(8) 1.0(6) 1.17(20) 0.16 0.16 0.16 Kernels/row 30(22) 26.2(16) 32.3(14) 44.9(5) 40.9(5) 45(5) 3.8 3.18 2.48 100-Kernel weight 27.4(18) 23.4(18) 23.5(14) 42.3(2) 38.3(2) 38.2(1) 2.6 2.6 2.4 Grain yield/ha 3.91 (22) 1.39 (22) 2.77 (22) 15.25 (8,5,6) 10.55 (4,8.6) 13.45 (8,6) 0.75 0.63 0.71

*Minimum and Maximum values are followed by genotype number (between brackets)

17

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

Grain yield/plant (g) under WW

180 160 140 120 100 80 60 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22

Grain yield/plant (g) under WSF 170 145 120 95 70 45 20 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22

Grain yield/plant (g) under WSG 170 145 120 95 70 45 20 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22

Fig. 6. Mean grain yield/plant of 22 maize genotypes under WW, WSF and WSG environments For CCI, SDU, SDL and ELA traits (Table 9), data were measured under WWF, WWG, WSF and WSG. For CCI, the lowest mean was exhibited by genotypes No. 1, 19, 22 and 19 and the highest mean was shown by genotypes No. 6, 4, 7 and 7, for SDL, genotypes No. 21, 21, 21 and 21 and the highest mean was shown by genotypes No. 2, 3, 2 and 2, for SDU, genotypes No. 9, 9, 9 and 15 and the highest mean was shown by genotypes No. 4, 4, 4 and 5, for ELA, the lowest means by genotypes No. 22, 9, 22 and 9 and the highest mean by genotypes No. 6, 6, 1 and 5 under WWF, WWG, WSF and WSG, respectively. Results from Tables 8 and 9 concluded that the best genotypes were No. 8 (P-3444) in 3 traits (KPP, GYPP, GYPH) under WSG, and 3 traits (EPP, KPP, GYPH) under WW, the genotype No. 6 (SC 128) in 3 traits (EPP, KPP, GYPP) under WSF and two traits (CCI, ELA) under WWF, the genotype No.5 (SC 10) in 4 traits (PH. EH, SDU, ELA) under WSG, 3 traits (PH, EH, KPR) under WSF, two traits (PH, KPR) under WW , respectively, the genotype No. 7 (Hi-Tec 2066) in one trait (BS) under WW, WSF and WSG, one trait (CCI) under WSG, the genotype No. 4 (Egaseed 77) in three traits (GYPH, BS, SDU) under WSF, two traits (CCI,SDU) under WWG and one trait (SDU) under WWF and the genotype No. 2 (30K09) in two traits (GYPH, SDL) under WSF. 18

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

On the contrary, the worst genotypes were No. 22 (Sweepstakes) in 3 traits (KPR,GYPP,GYPH) under WSG, 4 traits (GYPP, GYPH, CCI, ELA) under WSF and 4 traits (KPR, KPP, GYPH, ELA) under WW, the genotype No. 21 (Golden Republic) in 2 traits (PH, SDL) under WSG, 2 traits (PH,SDL) under WSF, one trait (SDL) under WWF and 2 traits (PH,EH) under WW, the genotype No. 19 (Nebraska) in one trait (CCI) under WSG, and the genotype No. 18 (Nubaria) in one trait (CCI) under WWG and one trait (GYPP) under WW. The four highest and the four lowest performing genotypes under water stress at flowering (WSF) and grain filling (WSG) across seasons are presented in Table 10. Under WSF conditions, the highest mean grain yield/ha was achieved by the single cross Egaseed-77 (developed by Egaseed Co.), followed by P-3444 (developed by Pioneer Co.), SC 128 (developed by ARC, Egypt) and HT-2066 (developed by Hi Tec Co.) in a descending order. The single cross Egaseed-77 was among the four highest genotypes under WSF for GYPH, GYPP, CCI, SDL, SDU and the four lowest genotypes for BS (barren stalks%). The single cross P-3444 was among the four highest genotypes under WSF for GYPH, GYPP, EPP, and KPP. The single cross SC-128 was among the four highest genotypes under WSF for GYPH, GYPP, EPP, KPP, 100-KW, SDU and the four lowest genotypes for DTS and ASI (favorable). The single cross HT-2066 was among the four highest genotypes under WSF for GYPH, GYPP, KPR, KPP, CCI and the four lowest genotypes for BS and EH. Table 9. Minimum (Min) and maximum (Max) values of CCI, SDU, SDL and ELA traits for each genotype under well watering at flowering (WWF) and at grain filling (WWG), water stress at flowering (WSF) and at grain filling (WSG) and change % from WWF and WWG to WSF and WSG, respectively combined across two seasons Parameter Min Max LSD.05 Aver. Min Max LSD.05 Aver. Min Max LSD.05 Aver. Min Max LSD.05

WWF

WWG WSF Chlorophyll concentration index % 26.5(1) 4.4(19) 15.8(22) 42(6) 33.2(4) 36.2(7) 6.4 4.1 4.4 Lower stem diameter (SDL) (mm) 24.7 23.8 23.9 20.5(21)* 20.1(21) 20.6(21) 28(2)* 27(3) 27.2(2) 1.8 2.4 2.1 Upper stem diameter (SDU) (mm) 10 9.5 10.1 8.4(9) 7.6(9) 7.9(9) 11.4(4) 13.5(4) 13(4) 1.3 1 1.4 Ear leaf area (ELA) (cm2) 708.9 666.1 691.1 586.3(22) 528.8(9) 568.4(22) 839.9(6) 809.4(6) 788.5(1) 100.6 104.3 105

WSG 2.6(19) 22.1(7) 4.4 22.4 19.4(21) 27.2(2) 2.4 7.72 6.(15) 11.1(5) 2 662.7 527.2(22) 786.2(1) 96.5

*Minimum and Maximum values are followed by genotype number (between brackets)

Under WSG conditions, the highest mean grain yield/ha was achieved by the single cross P-3444 (developed by Pioneer) followed by SC-128 (developed by ARC), TWC-324 (developed by ARC) and SC-166 (developed by ARC) in a descending order. The single cross P-3444 was among the four highest genotypes in GYPH, GYPP, EPP, KPP, 100-KW, CCI, i.e. most important grain yield and physiological traits. It should be noted that this hybrid was characterized by its ability to stay green even under water stress, which might help it to tolerate water stress at grain filling stage in a way much better than other tested hybrids and populations. The single cross SC-128 was among the four highest genotypes in GYPH, GYPP, KPR, SDU and ELA. The three-way cross TWC-324 was among the four highest genotypes in GYPH, KPR, PH and EH and the four lowest genotypes for BS (barren stalks %). The single cross SC-166 was among the four highest genotypes in GYPH and RPE and the four lowest genotypes for BS (barren stalks %), DTS and EH. In a previous study [35] the single cross 19

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

SC-128 (developed by ARC) proved the highest grain yielder under water stress at flowering among another set of maize genotypes. In contrast, the open-pollinated populations Pop-45, Golden, Nebraska, and Sweep under WSF and Nebraska, TWC-352, Golden and Sweep under WSG were the lowest for GYPH and GYPP. These genotypes were therefore considered as most sensitive to drought at respective growth stages. Several investigators [35-37,39,46] reported the presence of significant interaction between maize genotype and irrigation regime. 3.4.2 Root traits Average, minimum and maximum values under each irrigation treatment for all studied root traits across two seasons are presented in Table 11. Under WWF, WWG, WSF and WSG for BW, the lowest mean was exhibited by genotypes No. 2, 13, 17 and 21 and the highest mean was shown by genotypes No. 17, 19, 4 and 10, for BN the lowest by genotypes No. 21, 12, 4 and 21 and the highest mean was shown by genotypes No. 11, 11, 10 and 10, for BA the lowest by genotypes No. 1, 9, 14 and 1 and the highest mean was shown by genotypes No. 19, 21, 21 and 19, for BB the lowest by genotypes No. 18, 18, 13 and 20 and the highest mean was shown by genotypes No. 5, 15, 6 and 9, for CN the lowest by genotypes No. 18, 19, 13 and 13 and the highest mean was shown by genotypes No. 12, 8, 6 and 3, for CA the lowest by genotypes No. 2, 5, 7 and 1 and the highest mean was shown by genotypes No. 10, 10, 21 and 10, for CB the lowest by genotypes No. 21, 17, 19 and 19 and the highest by genotypes No. 8, 8, 6 and 8, for CRL the lowest by genotypes No. 14, 18, 22 and 22 and the highest mean by genotypes No. 8, 5, 9 and 4, for RC the lowest by genotypes No. 18, 19, 19 and 21 and the highest by genotypes No. 7, 8, 7 and 8 and for RDW the lowest by genotypes No. 20, 18, 19 and 21 and the highest by genotypes No. 8, 8, 5 and 8, respectively. Results concluded that the best genotypes were No. 8 (P-3444) in 3 root traits (CB, RC, RDW) under WSG, 4 traits (CN, CB, RC, RDW) under WWG, 3 traits (CA, CRL, RDW) under WWF, the genotype No. 6 (SC 128) in 3 traits (BB, CA, CB) under WSF, the genotype No.5 (SC 10) in two traits (BB and CRL) under WWF and WWG, respectively, the genotype No. 7 (Hi-Tec 2066) in one trait (RC) under WSF and RC under WWF. On the contrary, the worst genotypes were No. 22 (Sweepstakes) in one trait (CRL) under WSG, one trait (CRL) under WSF, the genotype No. 21 (Golden Republic) in 4 traits (BW, BN, RC, RDW) under WSG, two traits (BN, CB) under WWF, the genotype No. 19 (Nebraska) in one trait (CB) under WSG, and 3 traits (CB, RC, RDW) under WWG and the genotype No. 18 (Nubaria) in two traits (CN, RC) under WWG. The four highest and the four lowest performing genotypes for root traits under water stress at flowering (WSF) and grain filling (WSG) across seasons are presented in Table 12. The single cross Egaseed-77 was amongst the four highest genotypes under WSF for BA and CRL. The single cross P-3444 was amongst the four highest genotypes under WSF for CN, CB and CRL. The single cross SC-128 was amongst the four highest genotypes under WSF for BB, CN, CB, RC, and RDW. The single cross HT-2066 was amongst the four highest genotypes under WSF for CN and RC. Under WSG conditions. The single cross P-3444 was amongst the four highest genotypes in BB, CB, CRL, RC and RDW, i.e. most important grain yield and root architecture traits. It should be observed in the field that this hybrid was characterized by its ability to stay green even under water stress, which might help it to tolerate water stress at grain filling stage in a way much better than other tested hybrids and populations. The single cross SC-128 was amongst the four highest genotypes in BB, CN, CB and RDW. The single cross SC-166 was amongst the four highest genotypes in GYPH and BB. It is interested to note that the best genotypes in grain yield under drought at either flowering or grain filling were characterized by one or more desirable root architecture traits. Accumulating genes of more desirable root characteristics in one genotype might help plants to search water and nutrients in the soil and consequently help plant to accomplish its biological activities and achieve almost its potential grain yield under drought stress at flowering or grain filling stages. The studied single-cross hybrids P-3444, Egaseed-77 and SC-128 were considered high yielding and drought tolerant genotypes under drought stress at flowering and/or grain filling stages and would be offered to future breeding programs to utilize their genes of desirable root system architecture and grain yield productivity traits in improving maize drought tolerance under Egyptian conditions. 20

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

Table 10. The four highest and the four lowest genotypes for studied traits under water stress at flowering (WSF) and grain filling (WSG) across seasons WSF Days to silking Highest 30K09 HT-2031 Eg-77 Fine 1005 Lowest SC-166 Nubaria SC-128 Golden Barren stalks % Highest Sweep Pop-45 Nebraska Midland Lowest Fine 1005 Eg-77 HT-2066 Wat- 11

SC-128 P-3444 SC-166 HT-2066

WSG

WSF

30K09 HT-2031 Eg-77 Fine 1005

Sweep Midland 30K09 Nebraska

HT-2031 SC-128 Fine 1005 Sweep

SC-10 TWC-324 Fine 1005 Watania 11

SC-10 TWC-324 Fine 1005 30K09

SC-10 TWC-324 Giza HT-2031

SC-10 HT-031 TWC-324 Fine 1005

HT-2066 Nubaria SC-166 Golden

SC-128 Golden SC-10 TWC-352 Ears/plant

Pop-45 Nubaria Nebraska Midland

TWC-352 Sweep 32D99 Golden Rows/ear

Sweep Midland Nubaria Golden

Nubaria HT- 2066 Golden SC-166 Kernels/row

Golden HT- 2066 Nubaria SC-166

Sweep Nebraska TWC-352 Midland

SC-128 P-3444 SC-166 32D99

Midland P-3444 Giza SC-10

SC-166 TWC-360 Nubaria Pop-45

Nubaria Pop-45 TWC-360 SC-166

SC-10 TWC-324 Wat- 11 HT-2066

SC-10 HT-2066 TWC-324 SC-128

SC-166 32D99 Hi-Tec 1100 TWC-324 Kernels/plant P-3444 Midland HT-2066 SC-10

WSG Anthesis silking interval

WSF

Lowest Pop-45 HT-1100 Midland HT-2031 Fine 1005 Sweep Giza Eg-77 100-Kernel weight Highest 30K09 HT-2031 SC-128 SC-10 Fine 1005 P-3444 TWC-324 30K09

21

Sweep SC-10 HT-2031 HT-1100

SC-128 Eg-77 P-3444 HT-2066

WSG Plant height

TWC-324 SC-10 HT-1100 HT-2031 Grain yield/plant P-3444 SC-128 Eg-77 SC-10

WSF

WSG Ear height

Sweep 30K09 Pop-45 TWC-352 Midland Sweep Giza TWC-360 Grain yield/ha Eg-77 P-3444 SC-128 HT-2066

P-3444 SC-128 TWC-324 SC-166

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

Midland TWC-352 Sweep Eg-77 Fine 1005 HT-2031 Giza Sweep Chlorophyll concentration index HT-2066 Eg-77 SC-166 Wat- 11

HT-2066 Wat- 11 P-3444 30K09

32D99 HT-1100 HT-2031 Sweep

Sweep SC-166 Nubaria Nebraska

Lowest Sweep Nebraska Nebraska Golden Golden Giza Nubaria TWC-360 Lower stem diameter Highest 30K09 30K09 Fine 1005 TWC-324 TWC-324 Fine 1005 Eg-77 SC-10 Lowest SC-128 Sweep Nubaria P-3444 Sweep SC-166 Golden Golden

22

Golden Pop-45 Nebraska Golden Giza TWC-352 Sweep Sweep Upper stem diameter

Pop-45 Nebraska Golden TWC-352 Nebraska Golden Sweep Sweep Ear leaf area

Eg-77 SC-128 Fine 1005 30K09

SC-10 HT-1100 SC-128 32D99

HT-2031 TWC-324 SC-10 TWC-352

SC-10 SC-128 HT-2066 TWC-324

TWC-360 P-3444 TWC-352 SC-166

HT-2031 P-3444 SC-166 TWC-352

Midland SC-166 Golden Sweep

Sweep Golden Nubaria SC-166

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

Table 11. Average, minimum (Min) and maximum (Max) values under each irrigation treatment for all studied root traits and grain yield across two seasons Parameter Aver. Min Max LSD.05 Aver. Min Max LSD.05 Aver. Min Max LSD.05 Aver. Min Max LSD.05 Aver. Min Max LSD.05

WWF WWG WSF WSG WWF WWG Brace root whorls No. Brace root No. 2.52 2.48 2.29 2.64 39 37.1 2 (2) 1.66 (13) 1.8 (17) 1.5 (21) 27.3 (21) 22.7 (12) 3.1(17) 3.33(19) 2.9 (4) 3.3(10) 47(11) 54.7(11) 0.7 0.81 0.57 0.81 16.58 14.5 Brace root angle (score) Brace root branching (score) 6.7 6.7 6.9 6.5 5.3 4.7 5 (1) 5 (9) 5.8 (14) 4.7 (1) 3.3 (18) 2 (18) 8.3 (19) 7.3 (21) 7.5 (21) 7.5 (19) 7 (5) 7 (15) 1.62 1.88 1.02 1.25 2.38 2.66 Crown root number (score) Crown root angle (score) 3.82 2.66 3.38 3.05 6.8 6.5 1.7 (18) 1(19) 1.8 (13) 1.8 (13) 5.7 (2) 5.3 (5) 6 (12) 4 (8) 5.3 (6) 5 (3) 8 (10) 8 (10) 2.2 1.8 1.3 1.47 1.6 1.92 Crown root branching (score) Crown root length (cm) 4.6 4.1 4.6 3.7 22.4 23.2 3 (2) 2 (17) 3.2 (19) 2.2 (19) 18.6 (14) 18.8 (18) 6 (8) 7.3 (8) 6.3 (6) 6.5 (8) 25.9 (8) 28.1(5) 1.95 2.35 1.49 1.54 6.67 5.1 Root circumference (cm) Root dry weight (g) 34.7 30.7 34.4 30.9 26.2 21 28.1(18) 23.3 (19) 26.5(19) 23.3(21) 8.2 (20) 8.2 (18) 40.4(7) 41(8) 42.5(7) 36.6(8) 40.7 (8) 44.9 (8) 6.48 6.5 4.97 4.95 14.36 12.96 Means of minimum and maximum are followed by genotype No. (Between brackets)

23

WSF

WSG

31.5 23 (4) 43.3(10) 7.3

40.8 25.2 (21) 59(10) 14.76

4.9 3 (13) 6.8 (6) 1.66

4.7 2.3 (20) 6.2 (9) 2.02

6.9 5 (7) 8 (21) 1.2

6.5 5.2 (1) 8.5 (10) 1.25

23.9 21.2 (22) 26.2 (9) 4.1

21.76 16.9 (22) 26 (4) 4.4

18.8 9.8 (19) 33.6 (5) 9.53

23.3 9.9 (21) 40.1(8) 11.53

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

Table 12. The four highest and the four lowest genotypes for root traits under water stress at flowering (WSF) and grain filling (WSG) across seasons WSF WSG Brace root whorls No. Highest Pop-45 32D99 HT-1100 HT-1100 32D99 TWC-360 TWC-360 Pop-45 Lowest Fine 1005 Eg-77 SC-128 P-3444 Eg-77 30K09 P-3444 Golden Brace root branching (score) Highest SC-128 SC-166 TWC-352 SC-128 SC-166 P-3444 32D99 SC-10 Lowest Golden Nubaria Giza Wat- 11 Nebraska Golden TWC-324 Midland Crown root branching (score) Highest SC-128 P-3444 P-3444 HT-1100 TWC-352 HT-2066 SC-166 SC-128 Lowest Fine 1005 Golden Eg-77 32D99 TWC-324 TWC-324 Nebraska Nebraska

WSF WSG Brace root No.

WSF WSG Brace root angle (score)

32D99 TWC-352 Pop-45 HT-1100

Nebraska Golden Fine 1005 Eg-77

32D99 TWC-352 HT-1100 TWC-360

Nebraska SC-10 Golden Sweep

Fine 1005 P-3444 Midland Eg-77 Golden 30K09 Eg-77 Golden Crown root number (score)

SC-128 TWC-352 HT-2066 Giza SC-166 TWC-324 TWC-360 HT-2031 Crown root angle (score)

SC-128 P-3444 HT-2066 TWC-352

Golden 32D99 Midland TWC-324

Fine 1005 HT-2031 SC-128 HT-1100

32D99 Nebraska Midland Golden

Eg-77 SC-166 Sweep Midland TWC-324 TWC-324 Golden Golden Crown root length (cm)

TWC-360 P-3444 P-3444 HT-1100 HT-2031 HT-2031 HT-2066 HT-2066 Root circumference (cm)

P-3444 SC-166 SC-10 Eg-77

HT-2066 TWC-352 TWC-352 SC-128

P-3444 30K09 TWC-352 HT-2031

Nubaria Midland Golden Nebraska

Nebraska Midland Nubaria Golden

Eg-77 P-3444 HT-1100 SC-10

Pop-45 Nubaria HT-2066 Golden Midland Giza Sweep Sweep Root dry weight (g) Highest SC-10 Fine 1005 SC-128 TWC-352

P-3444 HT-1100 SC-128 HT-2031

Lowest Midland TWC-324 Golden Nebraska

Nebraska Midland Nubaria Golden

4. CONCLUSIONS The present results concluded that the reduction in grain yield and its components due to deficit irrigation was more pronounced at flowering (WSF) than under grain filling (WSG). WSF affected number of kernels more than WSG, while WSG affected kernel weight (100-KW) more than WSF. In 24

Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

general, the best performing genotypes in this study under WSF or WSG were the single crosses, while the worst performing genotypes were the open pollinated populations, indicating the importance of heterosis breeding in drought tolerance. The highest yielding genotypes were Eg-77, P-3444, SC128 and HT-2066 under WSF and P-3444, SC-128, TWC-324 and SC-166 under WSG, in a descending order. They were considered tolerant to drought at flowering and/or grain filling and would be recommended to future breeding programs to utilize their desirable agronomic, physiologic, root and yield traits in future breeding programs for improving maize drought tolerance under Egyptian conditions.

COMPETING INTERESTS Authors have declared that no competing interests exist.

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Wright GC, Nageswara Rao RC, Groundnut Water Relations. In: Smartt J (Ed.) The groundnut crop: A scientific bases for improvement. Chapman and Hall, London, UK, 1994;281-325. Henry A, Gowda VRP, Yamauchi A, Shashidhar HE, Serraj R. Root biology and genetic improvement for drought avoidance in rice. Elsevier publishers; Field Crops Research. 2011;122:1-13. Lynch JP. Root architecture and plant productivity. Plant Physiol. 1995;109:7-13. Hund A, Trachsel S, Stamp P, Growth of axile and lateral roots of maize: I Development of a phenotyping platform. Plant Soil in Press; 2009. DOI: 10.1007/s11104-009-9984-2 Laboski CAM, Dowdy RH, Allmaras RR, Lamb JA. Soil strength and water content influences on corn root distribution in a sandy soil. Plant Soil. 1998;203:239–247. Kato Y, Abe J, Kamoshita A, Yamagishi J. Genotypic variation in root growth angle in rice (Oryza sativa L.) and its association with deep root development in upland fields with different water regimes. Plant Soil. 2006;287:117–129. Araki H, Hirayama M, Hirasawa H, Iijima M. Which roots penetrate the deepest in rice and maize root systems? Plant Prod Sci. 2000;3:281–288. Hund A, Ruta N, Liedgens M. Rooting depth and water use efficiency of tropical maize inbred lines, differing in drought tolerance. Plant Soil. 2009;318:311–325. Zhu JM, Brown KM, Lynch JP. Root cortical aerenchyma improves the drought tolerance of maize (Zea mays L.). Plant Cell Environ. 2010;33:740–749. Hund A. Genetic variation in the gravitropic response of maize roots to low temperatures. Plant Root. 2010;4:22–30. Trachsel S, Kaeppler SM, Brown KM, Lynch JP. Shovelomics: High throughput phenol typing of maize (Zea mays L.) root architecture in the field. Plant Soil. 2011;341:75–87. DOI: 10.1007/s11104-010-0623-8 Francis GA, Rutger JN, Palmer AFE. A rapid method for plant leaf area estimation in maize (Zea mays L.). Crop Sci. 1969;9:537-537. Steel RG, Torrie DGH, Dickey DA. Principles and procedures of statistics: A Biometrical rd Approach. 3 ed. McGraw-Hill, New York, USA; 1997. Denmead OT, Shaw RH. The effects of soil moisture stress at different stages of growth on the development and yield of corn. Agron. J. 1960;52:272-274. Moss GI, Downey LA. Influence of drought stress on female gametophyte development in corn (Zea mays L.) and subsequent grain yield. Crop Sci. 1971;11:386-372 Al-Naggar AMM, El-Ganayni AA, El-Ganayni HYx, El-Sayed MY. Direct and indirect selection under some drought stress environments in corn (Zea mays L.). J. Agric. Sci. Mansoura Univ. 2000;25(1):699–712. Al-Naggar AMM, Shabana, R, Sadek SE, Shaboon, SAM. S1 recurrent selection for drought tolerance in maize. Egypt. J. Plant Breed. 2004;8:201-225. Al-Naggar AMM, El-Murshedy WA, Atta MMM. Genotypic variation in drought tolerance among fourteen Egyptian maize cultivars. Egypt. J. of Appl. Sci. 2008;23(2B):527-542. Al-Naggar AMM, Shabana R, Mahmoud AA, Abdel El-Azeem MEM. Shaboon SAM.Recurrent selection for drought tolerance improves maize productivity under Low-N conditions. Egypt. J. Plant Breed. 2009;13:53-70. Al-Naggar AMM, Soliman SM,Hashimi MN. Tolerance to drought at flowering stage of 28 maize hybrids and populations. Egypt. J. Plant Breed. 2011;15(1):69-87. Al-Naggar AMM, Abdalla AMA, Gohar AMA, Hafez EHM. Genotype and drought effects on performance of 254 maize doubled haploid lines × tester crosses. Egypt. J. Plant Breed. 2016a;20(3):671–690. Al-Naggar AMM, Atta MMM, Ahmed MA, Younis ASM. Influence of deficit irrigation at silking stage and genotype on maize (Zea mays L.) agronomic and yield characters. Journal of Agriculture and Ecology Research International. 2016b;7(4):1-16. Al-Naggar AMM, Abd El-Salam RM, Badran AEE, El-Moghazi Mai MA. Effects of genotype and drought stress on some agronomic and yield traits of quinoa (Chenopodium quinoa Willd.). Bioscience Research. 2017;14(4):1080-1090. El-Ganayni AA, Al-Naggar AMM, El-Sherbeiny HY, El-Sayed MY. Genotypic differences among 18 maize populations in drought tolerance at different growth stages. J. Agric. Sci. Mansoura Univ. 2000;25(2):713–727. 26

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Al-Naggar AMM, and Shehab-El-Deen Shehab Deen MT. Predicted and actual gain from selection for early maturing and high yielding wheat genotypes under water stress conditions. Egypt. J. Plant Breed. 2012;16(3):73-92. Dhliwayo T, Pixley K., Menkir A, Warburton M. Combining ability, genetic genetic distances and heterosis among elite CIMMYT and IITA tropical maize inbred lines. Crop Sci. 2009;49:1201– 2009;49:1201 1210. Chapman SC, Crossa J, Edmeades GO. Genotype by environment effects and selection for drought tolerance in tropical maize. 1. Two mode patte pattern rn analysis of yield. Euphytica. 1997;95:1-9. Madhu M, Hatfield JL. Dynamics of plant root growth under increased atmospheric carbon dioxide. Agronomy Journal 2013;105(3):657-669. 2013;105(3):657 Hendrick JJ, Nadelhoffer KJ, Ber JD. Assessing the role of fine roots in carbon carbon and nutrient cycling. Trends Ecol. Evol. 1993;8:174-178. 1993;8:174 DOI: 10.1016/0169-5347(93) 5347(93) 90143.D Fahy TJ, Huges JW. Fine root dynamics in northern hardwood forest ecosystem Hubbard brook experimental forest. Nh. J. Ecol. 1994;82:533-548. 1994;82:533 DOI: 102307/2261262 Parajuli S, Ojha BR, Ferrara GO. Quantification of secondary traits for drought and low nitrogen stress tolerance in inbreds and hybrids of maize ((Zea mays L.). J. Plant Genet. Breed. 2018;2:106.

Biography of author(s)

Dr. Ahmed Medhat Mohamed Al-Naggar Department of Agronomy, Faculty of Agriculture, Cairo University, Egypt. He is Egyptian by nationality and was born on 07/02/1945. He is currently working as a Professor of Crop Breeding in the Agronomy Department, Faculty of Agriculture, Cairo University University,, Giza, Egypt. He has been awarded with several grants and prizes in his career, among which some are as follows: The State Encouragement Prize in Agricultural Science, Academy of Scientific Research and Technology, Ministry of Scientific Research, Egypt, for the Year 2002, Cairo University Prize of Scientific Excellence in the field of Multidiscipline and Future Sciences for the Year 2007, Research Grant from Fulbright Commission, Colorado State University, Fort Collins, Colorado, USA for the Year 1987/1988. 1987/1988. He has supervised 53 Completed (28 M.Sc. and 25 PhD) and 9 On Going thesis of the students. He has authored 192 publications in National and International Scientific Journals on the following disciplines: Crop breeding, Agronomy, Biotechnology, Toleranc Tolerance e to Abiotic stress (Drought, Salinity, High Plant density). The crops of his interest are Maize, Wheat, Grain Sorghum, Quinoa. He is working as Editor, Chief Ch Editor and Reviewer for a number of National and International Scientific Journals. His Data from Google Scholar on 04/08/2019: Citations: 984; h-index: 18; 10-index index 31.

Prof. M. M. Shafik Department of Agronomy, Faculty of Agriculture, Cairo University, Egypt. He is currently working as the Professor of Agronomy, Faculty of Agriculture, Cairo U University, niversity, Giza, Egypt. E mail: [email protected]

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Advances in Agriculture and Fisheries Research Vol. 1 Genotype and Deficit Irrigation Effects on Agronomic, Physiologic, Yield and Root Traits of Maize (Zea mays L.)

M. O. A. Elsheikh Desert Research Center, Matariya, Cairo, Egypt. Currently he is as an Assistant Lecturer, Agronomy Department, Faculty of Agriculture, Al-Azhar University, Assiut, Egypt. Before this he was a Research Assistant, Desert Research Center, Mataryah, Cairo, Egypt.

_________________________________________________________________________________ © Copyright (2020): Authors. The licensee is the publisher (Book Publisher International). DISCLAIMER This chapter is an extended version of the article published by the same authors in the following journal with CC BY license. Annual Research & Review in Biology, 29(5): 1-17, 2018. Reviewers’ Information (1) Raúl Leonel Grijalva-Contreras, Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias, México. (2) Saroj Parajuli, University of Florida, USA. (3) Miguel Aguilar Cortes, Universidad Autonoma Del Estado De Morelos, Mexico.

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Chapter 2 Print ISBN: 978-81-940613-6-6, eBook ISBN: 978-93-89246-30-8

Movement Patterns of Chilean Flounder (Paralichthys adspersus) inside Tongoy Bay (Central Northern Chile): Observations Using Passive Acoustic Telemetry Pablo M. Rojas1* DOI: 10.9734/bpi/aafr/v1 ABSTRACT The movement patterns of juvenile and adult Chilean flounder (P. adpersus) were investigated inside Tongoy Bay using ultrasound signal acoustic receivers from June 2012 to March, 2013. Flounder landings in Tongoy Bay and Puerto Aldea from December 2011 to March 2013 were examined. Multiple regression analysis indicated that the Catch per Unit of Effort of Chilean flounder was significantly and negatively related to temperature and depth. Analyses of site- and time-specific length-frequency distributions indicated movement of Chilean flounder on the time scale of weeks, which was likely due to emigration of fish >30 cm in total length. A mark-recapture study was performed. Visible elastomer paint were used to tag 7,510 Chilean flounder. A total of 12 Chilean flounder individuals of different lengths were tagged with an ultrasound transmission device to monitor their movement inside Tongoy Bay. Adults flounder showed increased activity inside Tongoy Bay during the study period, likely due of the differences in length among the released individuals. Although differences were detected in the area occupied by juvenile and adult flounders in Tongoy Bay, it was also noticed that the smaller sized individuals exhibited changes in behavior after implanting the transmitters that resulted in impaired capacity to move freely. Keywords: Chilean flounder; acoustic telemetry; movement patters; landing; mark-recapture.

1. INTRODUCTION Chilean flounder (Paralichthys adspersus) is a flatfish with bentho-demersal habits of commercial and recreational importance distributed from the locality of Paita (northern Perú) to the Gulf of Arauco (Chile), including the Juan Fernández Archipelago [1,2]. As in the case of other fish, flounders exhibit remarkable bathymetric movements and horizontal migrations. Seasonal variations, the extent and direction of these movements in flounder stocks can differ considerably between species and geographic areas as a result of the large amount of biotic and abiotic factors (i.e. temperature, salinity, dissolved oxygen, depth and type of substrate, currents, availability of food, competition, among others; [3]). In this regard, the habitat use patterns for this species may vary at different spatial, time scales, and ontogeny [4,5,6]. The movement of individuals between habitats can affect instantaneous abundance, growth and mortality estimations, and therefore, the quality of the assessment of a specific habitat. The movement of marine fish inside coastal and estuarine systems may occur in response to resource availability (resource-directed dynamics), or as a result of migration processes (nonresource mediated migration; sensu [7]). It is essential to study the movement and migration under short spatial and time scales in order to understand the role of coastal and estuarine systems as nursery areas for juvenile marine fish, and assess various habitats [8,9,10]. _____________________________________________________________________________________________________ 1

División de Investigación en Acuicultura, Instituto de Fomento Pesquero, P.O. Box 665, Puerto Montt, Chile. *Corresponding author: E-mail: [email protected];

Advances in Agriculture and Fisheries Research Vol. 1 Movement Patterns of Chilean Flounder (Paralichthys adspersus) inside Tongoy Bay (Central Northern Chile): Observations Using Passive Acoustic Telemetry

Seasonal distribution patterns were obtained from catch data analysis and landing records [11]. Nevertheless, these data sources offer a limited perspective of Chilean flounder residence patterns and habitat use. In addition, these traditional sampling sources to obtain distribution data cannot identify individual movement variability or reproductive behavior. The magnitude of variability in individual distribution patterns of Chilean flounder is still little known, even when it comes to the most commercially valuable fish species [12], partially because it is still necessary to recognize the importance of learning about the behavior of a group of fish defined as cohesive fish groups within a stock that display a common movement pattern [13]. To this regard, acoustic telemetry offers new opportunities to investigate the movements and individual behavior of fish at the fine scale. In addition, this technology makes it possible to recognize groups within stocks [14] and offer information related to the link between coastal stocks [3]. The use of acoustic telemetry has made it possible to obtain high resolution data related to the behavior of juvenile flounder in the Gulf of Maine [15]. Nevertheless, in Chile, the scarce experience about the use of acoustic telemetry has focused on movement patterns of fresh water fish [16]. The aim of this study was to research the seasonal distribution and movement of a group of flounder inside Tongoy Bay using passive acoustic telemetry. The movement assessment was done using two complementary strategies: (1) the abundance, distribution and length frequency of the flatfish captured in different fishing zones during an annual period was determined, and simultaneously (2) the dispersion distance of the individuals was estimated by means of a tagging and recapture study.

2. MATERIALS AND METHODS 2.1 Study Zone Tongoy Bay is located in the center-northern zone of Chile, (30°12’S - 71°34’W; Fig. 1) a region with semi-arid characteristics. The bay is open toward the north and stretches across an area of 55.86 km2, with a volume of 2.01 km3 and the mouth section is 0.2 km2. The average depth of the bay is 25 m. Large part of the habitat that is available for early stages and adult Chilean flounder is relatively free from vegetation, except for the presence of seagrass (Heterozostera tasmanica) in the extreme southern part of the bay. The sediment in Tongoy Bay varies from very fine-to-fine sand. In general, the prevailing substrate is very fine sand (~0.11 mm diameter). Nevertheless, very fine sand largely dominates westward and fine sands eastward (Fig. 2).

Fig. 1. Location of Tongoy Bay, central northern Chile 30

Advances in Agriculture and Fisheries Research Vol. 1 Movement Patterns of Chilean Flounder (Paralichthys adspersus) inside Tongoy Bay (Central Northern Chile): Observations Using g Passive Acoustic Telemetry

Fig. 2. Distribution of surface marine sediments in Tongoy Bay

2.2 Flounder Movement Patterns The movement of flounder was assessed using three complementary strategies. First, data obtained from landing records in Tongoy Bay were used to generate length frequency distributions for each capture zone in order to infer movement patters with respect tto o length. The second strategy consisted in an intensive tagging and recapture strategy to reconstruct the individual movement of fish during the same time period. Finally, ultrasound signal transmitters were implanted in a group of flounder to monitor theirr movement routes inside the study area.

2.3 Spatial and Time Abundance of Flatfish To assess the habitat use patterns in short time and space scales, Tongoy Bay was divided into 34 2 zones, each of approximately 4 km (Fig. 3). This way, the landing information mation was recorded on the basis of geographical dimensions. Flounder landings in Tongoy Bay and Puerto Aldea from December 2011 to March 2013 were examined (Tongoy Bay; Fig.. 1). Yield estimation in terms of CPUE (Catch per Unit of Effort) was used as abundance abundance index expressed in grams per hour. A CPUE for each catch was obtained and this value was used as a unit, in such a manner that the different CPUEs estimated by category (locality, month, depth) were determined as the average CPUEs by catch for each category. ategory. Individuals harvested with the use of gill nets (3 m width, 45 m long and 15 15-20 cm mesh size) were measured and weighed in situ,, and later grouped according to length ranges of 2 cm total length (TL), in order to reduce measurement variability and better highlight the catch length frequency distribution.

2.4 Data Analysis A variance analysis was used to assess differences in landings of flounder in terms of time and Anderson Darlingand Bartlett test [18] were used to assess if between fishing zones (ANOVA; [17]). [1 the data complied with variance normality and homogeneity assumptions. A log transformation was used where appropriate (x+1) before applying ANOVA. Length frequency distributions generated for Kruskal-Wallis nonvarious landing periods and fishing zones in the bay were analyzed with a Kruskal parametric test [19] using the following length classes: 5-20, 80 and >80 cm LT. 5 21-40, 41-60, 61-80

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Advances in Agriculture and Fisheries Research Vol. 1 Movement Patterns of Chilean Flounder (Paralichthys adspersus) inside Tongoy Bay (Central Northern Chile): Observations Using g Passive Acoustic Telemetry

Backward stepwise multiple regression analysis (F-to-remove remove = 1) was used to examine the (F relationship between en the CPUE of Chilean flounder (P. ( adpersus)) with temperature, salinity and depth. We checked for collinearity among abiotic variables using correlation analysis, and found none were highly correlated with each other (r ≤ 0.33). Landing data obtained during ng the study were pooled into summer, autumn, winter and spring periods. CPUEs were log(x+1) transformed. Abiotic parameters deviated only slightly from normality and, therefore, were not transformed. All statistics analysis were made using the STATISTICA 7.0 [20].

Fig. 3. Grids with flounder fishing zones in Tongoy Bay

2.5 Mark-recapture A total of 7,510 flounder individuals were tagged with elastomeric paint. A visible implant elastomer tagging system was used (Northwest Marine Technologies, Seattle, Washington, USA). The elastomer was applied through injection with a hypodermic needle in liquid form that solidified within 24 hours. The tags were implanted in the operculum on the fish’s blind side, when the length allowed it, and under the dorsal fin, on the blind side (ventral) in small fish (Fig. 4). The flounders were weighed and measured, and then classified in six length classes, assigning a different color to each one (Table 1). After er tagging the fish, they were placed in observation during 4 days in order to detect negative effects such as abnormal swimming or lack of response to tactile stimulation, and assessed the tag retention rate and if the tagging process caused mortality. The The fish were released in May, 2012 off Puerto Aldea Bay, and the recapture efforts were made between June 2012 and January, 2013, with the use of transects placed parallel to the coastline. Table 1. Length classes with assigned color and number of flatfish marked with elastomeric paint Color mark Red Pink Orange Yellow Green Blue Total

Size range (cm) 5.0 - 6.9 7.0 - 8.9 9.0 - 10.9 11.0 - 12.9 13.0 - 14.9 15.0 - 16.9

Number of marked fishes 1,363 1,304 924 1,319 1,300 1,300 7,510

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Advances in Agriculture and Fisheries Research Vol. 1 Movement Patterns of Chilean Flounder (Paralichthys adspersus) inside Tongoy Bay (Central Northern Chile): Observations Using Passive Acoustic Telemetry

Table 2. Identification of flounders tagged with tracking devices (tag). Codes assigned to each transmitter represented by abbreviations ID and SN (receptor code) Fish size (cm) 10 18 21 23 25 27 32 34 39 40 47 49

Number fishes 1 1 1 1 1 1 1 1 1 1 1 1

Color Pink 1 Pink 2 Red 1 Red 2 Yellow 1 Yellow 2 Green 1 Green 2 Blue 1 Blue 2 Orange 1 Orange 2

ID 4451 3880 4457 4453 4454 4455 3879 4452 3877 4456 4450 3878

SN 1130187 1127425 1130193 1130189 1130190 1130191 1127424 1130188 1127422 1130192 1130186 1127423

The distance between the release zone and transects used in the recapture for each individual was calculated. Maximum and minimum dispersion distances between the central release point and the recapture point were estimated using the initial and final trawl coordinates where each individual was recaptured. Trawling operations (nets 100 m length and 2 m height, with a cod end in the mid sector) were carried out in conditions of relatively low tide (0-75 cm above the mid-level of the lowest tide), therefore, abundance estimations by area unit were made with the low tide areas. In shallow coastal systems, flounder density might vary on the basis of tide level, implying that our stock size estimations probably underestimate the real stock size. To carry out the spatial analysis of the fish recapture information, satellite images of Tongoy Bay were used to create polygons that were representative of the study area. Habitat areas available for flounder and the perimeters of any given area in the bay were calculated with the use of the ArcMap program.

2.6 Acoustic Telemetry A total of 9 acoustic ultrasound signal receivers were anchored (Vemco VR2W-69 kHz) during a period of 9 months in order to determine the movement patterns of juvenile and adult Chilean flounder (Paralichthys adpersus) inside Tongoy Bay, (June 2012 - March 2013). The equipment’s configuration allowed us to cover an area of approximately 42.1 km2 accounting for 74.4% of the total (55.9 km2) area of Tongoy Bay (Fig. 5). The receivers were anchored in zones with depths up to 50 meters, therefore, the anchoring operations area was limited by an isobath of 50 meters depth. The position of receivers was shifted regularly to cover the entire study area, in accordance with the manufacturer’s technical specifications that indicated a range of 300-400 meters of signal reception. In addition, acoustic signal precision tests were made in the study area using a hydrophone (VR100).

Fig. 4. Tagging flounder with elastomeric paint injection

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Advances in Agriculture and Fisheries Research Vol. 1 Movement Patterns of Chilean Flounder (Paralichthys adspersus) inside Tongoy Bay (Central Northern Chile): Observations Using g Passive Acoustic Telemetry

Fig. 5. Tracking acoustic receivers (VR2W) anchored in Tongoy Bay To monitor the movement of flounder in Tongoy Bay, a total of 12 wild different sized individuals (Table 2) were marked with ultrasound transmission devices (19 mm length; 6 mm diameter) (Vemco V7-2x). 2x). Each transmitter has an average pulse frequency of 80 seconds (range = 40 – 120 seconds) and a battery life of 384 days. Each fish was anesthetized with a benzocaine anesthetic tranquilizer for fish (BZ-20®) in a 1 mL solution: 5L BZ-20 BZ 20 before implanting the tags on the dorsal side of the fish (Fig. 6).

Fig. 6. Ultrasonic transmission ssion device (tag) implanted in the dorsal region of a Chilean flounder. The red square indicates the transmitter location After implanting the fish, the affected area was covered with an anti-inflammatory anti inflammatory antibiotic for topical application (Terracortril Spray 125 ml) in order to avoid infections associated to the implant. In parallel, the basic data of each fish was recorded (weight and length) and the tag code number. Before being released, the tagged flounder were kept under recovery during a period of seven days to

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Advances in Agriculture and Fisheries Research Vol. 1 Movement Patterns of Chilean Flounder (Paralichthys adspersus) inside Tongoy Bay (Central Northern Chile): Observations Using Passive Acoustic Telemetry

monitor the healing of the wound and the tag retention. The use of ultrasound transmission devices in fish was subject to the following previous considerations:   

Tag implant tests: without affecting fish behavior while in captivity. Receiver scope test (VR2): 300 - 400 m. Accuracy test with hydrophone (VR100): positioning error of ±4.09 m.

The flounders were released following a visual inspection by divers in order to monitor the response capacity (i.e. search for shelter, camouflage, etc.) displayed by each individual in the presence of potential predators.

2.7 Distance Covered and Distribution Area The data stored during the study period (9 months) in acoustic receivers (Vemco VR2W-69 kHz) were downloaded with a VUE 1.4.2 software (VEMCO Ltd.). The information was organized and coded in data bases, indicating if the presence of tagged fish was detected within a day. The site fidelity was assessed as the probability of detection by one or several receivers with respect to the time elapsed since the release of the fish. Daily activity patterns were based on the probability of detection, according to the time of day. Detections were used to calculate the distance in meters between successive positions (DA→B):

Where, LonA y LatA are the geographic coordinates (UTM) of the first position, and LonB and LatB are the final geographic coordinates. The average speed of tagged fish was obtained on the basis of the time recorded between each -1 -1 detection (m s ). Subsequently, the distance covered by a fish during 24 hours (m día ) was estimated on the basis of the total covered distance and the total monitoring time. The receiving equipment data bases were introduced into a SIG and were assessed with the Animal Movement Analysis ArcView Extension [21]. A layer with bathymetric information (SHOA map) was introduced into the SIG. The distribution area used by tagged fish was obtained by calculating an ad hoc 2 parameter that considered 50% of the contour as the area of main activity (m ) and 95% of the contour as the distribution area [22]. On the positions obtained within the study zone were used, except for the movements of fish that left the study area permanently. The geostatistic analysis and spatial representation of detections were made using ARCGIS 10.0 software. To represent the physical characteristics (i.e. bathymetry, substrate type) of Tongoy Bay geophysically, thematic data coverage was developed by interpolation IDW [23]. In addition, the information provided by the receivers, related to the date and location (inside Tongoy Bay) of tagged fish was assessed using a Tracking Analyst Arc Editor 9.3. This tool allowed us to display, assess and understand spatial patterns and trends in the context of time.

3. RESULTS 3.1 Spatial and Time Patterns in the Use of Habitat in Tongoy Bay A total of 2,039 flounders were landed during the study period, with a total biomass of 2,074 kilograms. Landings recorded of 113 fishing trips used gillnets in the study area. The records are related to 19 vessels that used 5.1 fishing hours obtaining an average catch of 12.1 individuals (~11.4 kg) by vessel. Fishing zones varied depending on the time of year. Landing records obtained from December 2011 to January 2012 (austral summer) showed a higher catch volume in three fishing zones (4, 9 and 11). From April to July 2012 (autumn and austral winter) only landings from zones 2 and 3 were recorded.

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Advances in Agriculture and Fisheries Research Vol. 1 Movement Patterns of Chilean Flounder (Paralichthys adspersus) inside Tongoy Bay (Central Northern Chile): Observations Using g Passive Acoustic Telemetry

Landings were recorded from January to March 2013 (austral summer) from 5 fishing zones (2, 3, 4, 10 and 28). differences in the different catch periods ((F[3;68]=2.958; The CPUE displayed statistically significant differences P=0.0384; Fig. 7a). These differences were explained by differences in abundance recorded in spring and summer months. Length frequency distributions of Chilean flounder harvested throughout the ( [3;2035] = 4.4193; P=0.0042; entire study period displayed statistically significant differences (F =0.0042; Fig. 7b).

Fig. 7a and b. Abundance and length variations of Chilean flounder in Tongoy Bay during the study period, respectively Length ranges in fish harvested in the austral summer period (December 2011 - February 2012; January 2013 - March 2013) varied from 19 - 90 cm TL, with a larger length frequency at 35 cm. During the austral autumn months (April 2012 - May 2012) landed fish displayed lengths that ranged from 28 - 49 cm TL, with a higher frequency at 39 cm. In austral winter months (July 2012 September 2012), the lengths of landed fish ranged from 31 - 62 cm LT, with a higher frequency at 38 cm. Moreover, in austral spring (October 2012 - December 2012) lengths of landed fish ranged from 28 - 64 cm TL, with a higher frequency toward 33 cm. Throughout the study period, length frequency distributions of flounder landed at Tongoy Bay displayed statistically significant differences =0.0003). A unimodal differ (KW test; F[4;15]=10.3064; P=0.0003). distribution was observed during each landing period (summer, autumn, winter and austral spring). In 37 38 cm TL. In autumn, summer, the fish showed a length frequency distribution with a main peak at 37-38 67% of Chilean flounder measured