Crop Physiology - Case Histories for Major Crops [First Edition] 9780128191941

Crop Physiology: Case Histories of Major Crops updates the physiology of broad-acre crops with a focus on the genetic, e

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Crop Physiology - Case Histories for Major Crops [First Edition]

Table of contents :
Front Cover
Crop Physiology Case Histories for Major Crops
Chapter 1 Maize
1 Introduction
1.1 Global trends
1.2 Main production areas
1.3 Maize in rotations: Suitability of previous and consequences for following crops
1.4 Multiple cropping
2 Crop structure, morphology, and development
2.1 Main phenological events
2.2 Genotypic and environmental drivers of maize development
2.2.1 Temperature
2.2.2 Photoperiod
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Canopy size and light interception
3.1.2 Radiation-use efficiency and its response to environmental factors
3.1.3 Crop growth rate and growth duration in response to management practices
3.2 Capture and efficiency in the use of water
3.2.1 Environmental patterns of water supply and demand
3.2.2 Root expansion and senescence, root size, architecture, and functionality
3.2.3 Crop water use and canopy conductance as related to canopy architecture, stomatal conductance, and canopy-atmos ...
3.2.4 Water use efficiency
3.2.5 Management practices under water deficits
3.3 Capture and efficiency in the use of nutrients
3.3.1 Nutrient absorption, assimilation, accumulation, and remobilisation
3.3.2 Effects of nutrients on crop development, growth, and grain yield
3.3.3 Nutrients diagnosis and fertilisation requirements Nitrogen
Supply–demand balance
Soil determinations
Plant determinations
Simulation models
Remote sensing Other nutrients
3.3.4 Interaction with agronomic practices
4 Grain yield and quality
4.1 Kernel number
4.2 Kernel weight
4.3 Biomass partitioning
4.4 Grain quality
4.4.1 Kernel hardness
4.4.2 High-oil maize and acidic specialties
5 Concluding remarks: Challenges and opportunities
Chapter 2 Rice
1 Introduction
1.1 Global significance of rice
1.2 Rice ecosystem classification with emphasis on water availability
1.3 Crop management
1.3.1 Crop establishment
1.3.2 Water-saving methods
1.3.3 Mechanisation
2 Crop structure, morphology, and development
2.1 Germination and seedling emergence
2.1.1 Importance of seedbed in direct seeded rice
2.1.2 Lodging in broadcasted rice
2.1.3 Deep planting
2.2 Phenological development
2.2.1 Drivers of phenological development
2.2.2 Global warming effect
2.2.3 Crop establishment methods
2.2.4 Crop ripening and maturity
2.3 Shoot development and growth
2.4 Rood development and growth
2.4.1 Shallow root system
2.4.2 Deep roots
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Crop growth analysis with radiation interception
3.1.2 Radiation use efficiency as reflection of leaf photosynthesis rate
3.1.3 Radiation use efficiency as related to canopy structure
3.2 Capture and efficiency in the use of water
3.2.1 Water balance in lowlands
3.2.2 Water requirement and water use efficiency Effect of crop establishment methods Effect of water-saving methods Other factors
3.3 Capture and efficiency in the use of nutrients
3.3.1 Nitrogen Plant N uptake and the fate of N in the field
Nitrogen uptake and plant N concentration
Nitrogen losses from the field Nitrogen use efficiency under favourable conditions
Timing of fertiliser application affecting NUE
Site-specific N management
Controlled-release N fertiliser
Genotypic variation
Interaction between nitrogen and water
3.3.2 Phosphorus Localised P application Interaction between P and water Genotypic variation
3.3.3 Potassium
3.3.4 Micronutrients
4 Yield and quality
4.1 Sink–source relations
4.1.1 Determination of sink size Panicle number Spikelet number Grain set Potential grain size Application of yield component expression Transport system and sucrose conversion
4.1.2 Assimilate supply to fill grains
4.1.3 Genotypic variation in sink–source limitation to yield Varieties with increased sink size had higher yields Advantages of hybrids, particularly japonica-indica hybrids Other factors affecting genotypic variation in grain yield
4.2 Response to abiotic factors
4.2.1 Water deficit Types of drought and genotype × management options Adaptive traits
4.2.2 Effect of increased CO 2 concentration Crop growth Grain yield and quality
4.2.3 Submergence
4.2.4 High temperature Reproductive growth Grain yield
Importance of night-time temperature
Genotypic variation
Future global warming effect
4.2.5 Low temperature
4.2.6 Salinity
4.3 Crop management for yield and quality
4.3.1 Crop establishment Comparison of direct seeding and transplanting
Weeds Ratooning Perennial rice
4.3.2 Water-saving technologies Alternate wetting and drying irrigation Aerobic rice
4.4 Mechanisation
5 Concluding remarks: Challenges and opportunities
5.1 Adaptation mechanisms to reduced water input in irrigated system
5.1.1 Dry direct seeding
5.1.2 AWD
5.1.3 Aerobic rice
5.2 Adaptation mechanisms for drought avoidance in rainfed lowland rice
5.3 Adaptation mechanism for mechanised rice farming
5.3.1 Direct seeding, particularly drill planting
5.3.2 Combine harvesting
5.4 Factors determining grain set
5.5 Enhancing yield potential
5.6 Head rice yield
Chapter 3 Wheat
1 Introduction
1.1 Wheat origin, production, and yield
1.2 Trends in production, area, and yield
2 Crop structure, morphology, and development
2.1 Yield determination
2.1.1 Yield components
2.1.2 Grain number determination
2.1.3 Determination of potential grain weight
2.2 Crop phenology
2.2.1 Generation, appearance, and growth of organs Initiation of leaves, spikelets, and florets Appearance of leaves and tillering and growth of stems, spikes, and grains
2.2.2 Phenological phases and scales
2.2.3 Environmental factors affecting wheat development Temperature per se Vernalisation Photoperiod
2.2.4 Genotypic differences and main genetic factors
3 Capture and efficiency in the use of resources
3.1 Capture and use efficiency of radiation
3.1.1 Dynamics of radiation interception
3.1.2 Radiation use efficiency
3.2 Capture and efficiency in the use of water
3.2.1 Crop evapotranspiration
3.2.2 Water use efficiency
3.2.3 Harvest index
3.3 Capture and efficiency in the use of nutrients
3.3.1 Nutrient absorption, assimilation, accumulation, and mobilisation Nutrient uptake efficiency
3.3.2 Effects of nutrients on wheat growth Nutrient uptake and partitioning Crop nutrient demand
4 Yield responsiveness to management and breeding
4.1 Yield responsiveness to management and breeding
4.1.1 How management practices affect yield Sowing date, density, and arrangement Fertilisation and irrigation Management of other constrains
4.1.2 Impact of wheat breeding on grain yield and next steps
4.1.3 Perspectives of wheat under climate change
5 Quality
5.1 Grain quality traits
5.2 Grain proteins, nutrients, fibre, and healthy traits Grain nutrients, fibre, and healthy traits
5.3 Sensitivity of grain quality traits to environmental stresses
5.4 Grain quality and crop management Nitrogen and other nutrient fertilisers
6 Concluding remarks: Challenges and opportunities
Chapter 4 Barley
1 Introduction
1.1 Global trends in harvested area and yield
2 Crop structure, morphology, and development
2.1 Differentiation of vegetative and reproductive organs
2.2 Dynamics of initiation and appearance of vegetative and reproductive organs
2.2.1 Leaf and spikelet initiation into the apex
2.2.2 Leaf emergence
2.2.3 Tillering
2.3 Genotypic and environmental drivers of barley development
2.3.1 Temperature
2.3.2 Vernalisation
2.3.3 Photoperiod
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Canopy size and radiation interception
3.1.2 Radiation-use efficiency (RUE)
3.2 Capture and efficiency in the use of water
3.2.1 Environmental characterisation of water stress
3.2.2 Root architecture and functionality
3.2.3 Scaling from leaf to canopy: From stomatal conductance to water use efficiency
3.3 Capture and efficiency in the use of nutrients
3.3.1 Soil nitrogen acquisition
3.3.2 Efficiency in the use of nitrogen and its partitioning to the grains
3.3.3 Critical nitrogen dilution curve
3.3.4 Relationship between grain yield and grain protein concentration
3.4 Requirement of other nutrients
4 Grain yield and quality
4.1 Grain number and the critical period
4.2 Grain filling
4.3 Barley uses and grain quality
4.4 Environmental factors altering quality
4.5 Genetic factors determining malting and brewery quality
5 Concluding remarks: Challenges and opportunities
Chapter 5 Sorghum
1 Introduction
1.1 Agronomic context
2 Crop structure, morphology, and development
2.1 Crop phenology
2.2 Adaptation to environmental conditions
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.2 Capture and efficiency in the use of water
3.2.1 Increasing access to water
3.2.2 Restricting preanthesis water use through canopy architecture
3.2.3 Restricting preanthesis water use through transpiration rates
3.3 Capture and efficiency in the use of nutrients
3.3.1 Nitrogen uptake and dynamics
3.3.2 N dynamics preanthesis
3.3.3 N dynamics postanthesis
3.3.4 Molecular analysis of soil microbes involved in the N cycle
3.3.5 Phosphorus
4 Yield and quality
4.1 Grain number and size
4.2 Grain quality
4.2.1 Sorghum grain compositional quality
4.2.2 End-use defines the value of different elements of compositional quality
4.3 Crop stresses and effects on grain yield determination
4.3.1 Water stress
4.3.2 Temperature stress
5 Concluding remarks: Challenges and opportunities
5.1 Challenges and opportunities
5.2 Research priorities
Chapter 6 Oat
1 Introduction
1.1 Production and nutrition
1.2 Agronomic roles of oat in farming systems
1.2.1 Soil and environment for oat production
1.2.2 Annual or multiple cropping system
1.2.3 Oat in crop rotation
1.2.4 Oat as a cover crop
2 Crop structure, morphology, and development
2.1 Phenology and critical growth stages
2.2 Genotypic differences
2.3 Environmental effect
2.4 Manipulation of plant development
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Canopy architecture and interception of radiation Early growth of leaf area Crop ideotype and canopy architecture
3.1.2 Radiation use efficiency Duration of leaf photosynthesis Variations in radiation use efficiency
3.2 Capture and efficiency in the use of water
3.2.1 Oat water use and adaptation to water stress
3.2.2 Agronomic options to improve crop water use
3.2.3 Water use efficiency
3.2.4 Water use efficiency in water-limited environment
3.2.5 Agronomic options to improve transpiration efficiency
3.3 Capture and efficiency in the use of nutrients
3.3.1 Capture of nitrogen
3.3.2 Management to improve nitrogen use efficiency
3.3.3 Phosphorus
4 Grain yield and quality
4.1 Grain yield and yield components
4.1.1 Interactions between genotype, environment, and management on grain yield
4.1.2 Agronomic options to improve harvest index
4.2 Grain quality
4.3 Forage quality
5 Concluding remarks: Challenges and opportunities
Chapter 7 Quinoa
1 Introduction
2 Crop structure, morphology, and development
2.1 Seed germination and conservation
2.2 Phasic development
2.2.1 Developmental scales
2.2.2 Temperature responses
2.2.3 Photoperiod responses
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Radiation capture
3.1.2 Radiation use efficiency
3.1.3 Source activity during grain filling under high-yield conditions
3.2 Capture and efficiency in the use of water
3.2.1 Climate patterns
3.2.2 Managing water use (ETc)
3.2.3 Managing the proportion of water used by transpiration (T/ET)
3.2.4 Transpiration efficiency (TE)
3.2.5 Response of harvest index to water
3.3 Capture and efficiency in the use of nutrients
3.3.1 N uptake and partitioning
3.3.2 Nitrogen dilution curve and other allometric relationships
3.3.3 N uptake efficiency
3.3.4 Nitrogen utilisation efficiency
3.3.5 Yield vs protein concentration and interactions with other grain composition traits
4 Yield and quality
4.1 Critical periods of yield determination
4.2 Dry matter and numeric yield components
4.3 Grain weight
4.4 Reproductive partitioning and limitations to grain yield
4.5 Other stresses and interactions between stresses
5 Grain quality
6 Concluding remarks: Challenges and opportunities
Chapter 8 Soybean
1 Introduction
2 Crop structure, morphology, and development
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.2 Capture and efficiency in the use of water
3.2.1 Capture of water
3.2.2 Water use efficiency
3.3 Capture and efficiency in the use of nitrogen
3.4 Dry matter and nitrogen partitioning
3.5 Other nutrients
4 Yield and quality
4.1 Yield potential and yield gaps
4.2 Seed quality
5 Concluding remarks: Challenges and opportunities
Chapter 9 Field pea
1 Introduction
1.1 Origin and agronomy
1.2 Pests and diseases
1.2.1 Insect pests
1.2.2 Fungal and bacterial disease
2 Crop structure, morphology, and development
2.1 Seed and plant characteristics
2.2 Phenology
2.2.1 Phenological progression
2.2.2 Photoperiod and temperature
2.2.3 Effect of extreme temperature and water stress
2.3 Critical period
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Radiation interception
3.1.2 Radiation use efficiency
3.2 Capture and efficiency of use of water
3.2.1 Environmental and temporal patterns of water supply and demand
3.2.2 Capture of water
3.2.3 Water use efficiency
3.3 Capture and efficiency in the use of nutrients
3.3.1 Nitrogen Critical nitrogen concentration and residual soil nitrogen
3.3.2 Other nutrients
4 Yield and quality
4.1 Grain number and weight
4.1.1 Plant population density
4.1.2 Grain number and grain weight
4.2 Biomass and harvest index
4.3 Yield and quality trade-offs
5 Concluding remarks: Challenges and opportunities
Chapter 10 Chickpea
1 Introduction and agronomic context
1.1 Origin and ecology
1.2 The role of chickpea in farming systems
2 Crop structure, morphology, and development
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.2 Capture and efficiency in the use of water
3.2.1 Environmental patterns of water supply and demand
3.2.2 Canopy traits
3.2.3 Root traits
3.2.4 Water use efficiency
3.3 Capture and efficiency in the use of nutrients
3.3.1 Nitrogen
3.3.2 Other nutrients
3.3.3 Salinity
4 Yield and quality
4.1 Yield and its components
4.2 Seed quality
5 Concluding remarks: Challenges and opportunities
Chapter 11 Peanut
1 Introduction
1.1 Area, production, and yield
2 Crop structure, morphology, and development
2.1 Sowing to emergence
2.2 Emergence to flowering
2.3 Flowering to maturity
2.3.1 Temperature
2.3.2 Water
2.3.3 Interactions between temperature and water, and between temperature and photoperiod
2.4 Combining sowing date and genotype to match growing environment
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.2 Capture and efficiency in the use of water
3.2.1 Transpiration
3.2.2 Transpiration efficiency
3.2.3 Harvest index
4 Capture and efficiency in the use of nutrients
4.1 Nitrogen
4.1.1 N fixation
4.1.2 Response to soil mineral N
4.2 Calcium
4.3 Phosphorus
4.4 Zinc
5 Grain yield and quality
5.1 Grain yield
5.1.1 Ideotype breeding
5.2 Seed quality
5.2.1 Utilisation
5.2.2 Health benefits and concerns
5.2.3 Seed maturity
5.2.4 Blanchability
5.2.5 Oleic to linoleic acid ratio (hi-oleic)
5.3 Trade-offs between yield and quality traits
6 Concluding remarks: Challenges and opportunities
Chapter 12 Common bean
1 Introduction
1.1 Climatic zones
1.2 Major growing regions
1.3 Role in farming systems
1.4 Implications of climate change
2 Crop structure, morphology and development
2.1 Morphological variation
2.2 Taxonomy and gene pools
2.3 Phenological development
2.3.1 Vegetative development
2.3.2 Reproductive development
2.4 Determinancy and growth habit
2.5 Critical stages of crop development
2.6 Strategies for adaptation to climate change
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Canopy architecture
3.1.2 Photosynthesis at the leaf and canopy scale
3.2 Capture and efficiency in the use of water
3.2.1 Above-ground mechanisms
3.2.2 Below-ground mechanisms
3.3 Capture and efficiency in the use of nutrients
3.3.1 Nitrogen
3.3.2 Phosphorus
4 Yield and quality
4.1 Yield and related traits
4.2 Nutritional quality
5 Concluding remarks: Challenges and opportunities
Chapter 13 Lentil
1 Introduction
2 Crop structure, morphology, and development
2.1 Crop structure: height and branching
2.2 Phenological development
2.2.1 Sowing to emergence
2.2.2 Emergence to flowering
2.2.3 Flowering to maturity
2.3 Development and adaptation to stress
2.3.1 Elevated temperature
2.3.2 Water stress
2.3.3 Salinity
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Leaf area index and extinction coefficient
3.1.2 Radiation use efficiency
3.1.3 Lodging
3.2 Capture and efficiency in the use of water
3.2.1 Patterns of water supply and demand
3.2.2 Root system
3.3 Capture and efficiency in the use of nutrients
3.3.1 Nitrogen
3.3.2 Phosphorus
3.3.3 Micronutrients
4 Yield and quality
4.1 Reproductive development
4.1.1 Yield components
4.1.2 Seed quality and composition
5 Concluding remarks: Challenges and opportunities
Chapter 14 Lupin
1 Introduction
2 Crop structure, morphology, and development
2.1 Crop development
2.2 Branching patterns
2.3 Use of restricted branching in lupin breeding
2.4 Dwarfism
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.2 Capture and efficiency of use of water
3.3 Capture and efficiency of use of nutrients
3.3.1 Nitrogen
3.3.2 Phosphorus
4 Yield and quality
4.1 Yield
4.2 Yield components
4.3 Pod wall
4.4 Grain protein
5 Concluding remarks: Challenges and opportunities
Chapter 15 Faba bean
1 Introduction
1.1 Origin of the crop
1.2 Cropping environment and production
2 Crop structure, morphology, and development
2.1 Crop structure
2.1.1 Canopy
2.1.2 Roots
2.1.3 Flowers and fruits
2.1.4 Flowering types
2.2 Vegetative and reproductive responses
2.2.1 Temperature
2.2.2 Photoperiod
2.2.3 Vernalisation
2.2.4 Hardening
2.3 Quantifying phenological development
2.3.1 A phenological scale
2.3.2 Phenological indices for simulation of faba bean development Sowing–emergence–first flower–last flower–physiological maturity
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Canopy development
3.1.2 Radiation capture
3.1.3 Photosynthesis: Leaf to canopy
3.1.4 Growth rates and RUE
3.2 Capture and efficiency in the use of water
3.2.1 Crop water balance
3.2.2 Adaptation to water shortage Phenology Stomatal responses Canopy responses Root systems Options for future progress
3.3 Capture and efficiency in the use of nutrients
3.3.1 Mineral nutrients
3.3.2 N 2 fixation mechanism and rates
3.3.3 Soil acidification and root–root interactions in intercropping
3.3.4 N uptake, storage, and mobilisation
4 Yield and quality
4.1 Crop yield
4.1.1 Yield progress
4.1.2 Benchmarking yield and yield gaps
4.2 Yield components
4.2.1 Grain size
4.3 Indeterminate, determinate, and semideterminate cultivars
4.4 Nutritional issues and grain quality
4.5 Role of faba bean in cropping system productivity
4.6 Biotic stresses
5 Concluding remarks: Challenges and opportunities
5.1 Maintaining yield gain
5.2 Optimal cultivar design
5.3 Intercropping
5.4 Coordination of faba bean research
Chapter 16 Sunflower
1 Introduction
1.1 The role of sunflower crop in farming systems
1.2 Sunflower-based cropping systems
1.3 Implications of climate change for sunflower cropping
1.4 Sunflower crop physiology research since the 1980s
2 Crop structure, morphology and development
2.1 Growth stages and phenophases
2.2 Drivers of crop phenology and development
2.3 Manipulation of crop development to match critical periods and environments
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Potential and stress-limited canopy growth
3.1.2 Natural and stress-limited canopy senescence
3.1.3 Potential and stress-limited canopy architecture and radiation interception
3.1.4 Potential and stress-limited crop radiation-use efficiency
3.1.5 Other stresses Cold Salinity
3.2 Capture and efficiency in the use of water
3.2.1 Water management in sunflower
3.2.2 Root expansion and senescence, root size, architecture and functionality
3.2.3 Canopy conductance as related with stomatal conductance, canopy architecture and canopy–atmosphere coupling
3.2.4 Root–shoot ratio and root–shoot integration
3.2.5 Water use and water-use efficiency at crop level
3.3 Capture and efficiency in the use of nutrients
3.3.1 N requirement and uptake
3.3.2 Efficiencies: Uptake per unit N in soil, biomass per unit N uptake and N harvest index
3.3.3 Diagnostic tools: critical N dilution curves, N nutrition index and remote sensing
3.3.4 Other nutrients: K, P and B
4 Yield
4.1 Frameworks of yield elaboration
4.2 Allocation of dry matter
4.2.1 Biomass partitioning
4.2.2 Harvest index
4.3 Components of grain yield
4.3.1 Grain number
4.3.2 Grain weight
4.3.3 Interactions between grain number and grain weight
5 Grain and oil quality
5.1 Physiology of oil accumulation
5.1.1 Fatty acids biosynthesis
5.1.2 Oil accumulation dynamics
5.1.3 Relationship between oil and protein concentrations
5.2 Factors affecting oil concentration effects on oil concentration
5.2.1 Genotypic variation of oil concentration
5.2.2 Effect of intercepted solar radiation on oil concentration
5.2.3 Effect of temperature on oil concentration
5.2.4 Effect of water availability, nitrogen and plant density on oil concentration
5.3 Sunflower oil quality
5.3.1 Fatty acid composition
5.3.2 Tocopherols and phytosterols
6 Concluding remarks: Challenges and opportunities
Chapter 17 Canola
1 Introduction
1.1 Origin, development and uses
1.2 Global production systems
1.2.1 Winter canola sown in autumn
1.2.2 Spring canola sown in spring
1.2.3 Spring canola sown in autumn
1.3 Canola cropping systems
1.3.1 Rotated monocrops
1.3.2 Intercropping
1.4 Agronomic implications of predicted climate change
2 Crop structure, morphology and development
2.1 Phenological stages
2.1.1 Temperature
2.1.2 Vernalisation
2.1.3 Photoperiod
2.2 Impact of development on yield potential and adaptive management
2.2.1 Green bud visible stage
2.2.2 Critical period
2.2.3 Seed filling period
2.3 Matching sowing date with varietal phenology in diverse environments
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Radiation capture
3.1.2 Canopy management
3.1.3 Lodging
3.2 Capture and efficiency in the use of water
3.3 Capture and efficiency in the use of nutrients
3.3.1 Nitrogen
3.3.2 Sulphur and phosphorus
4 Yield and quality
4.1 Allocation of dry matter
4.2 Yield components
4.2.1 Seed number
4.2.2 Seed size
4.3 Seed quality—Oil and protein
5 Concluding remarks: Challenges and opportunities
Chapter 18 Potato
1 Introduction
1.1 Potato production systems
1.2 Climate change
2 Crop structure, morphology, and development
2.1 Drivers of potato development
2.1.1 Temperature Tuber yield response to temperature
2.1.2 Photoperiod
2.1.3 Light quality
2.1.4 Hormones
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Radiation capture
3.1.2 Radiation-use efficiency
3.1.3 Radiation interception and RUE in intercrops
3.2 Capture and efficiency in the use of water
3.3 Capture and efficiency in the use of nutrients
3.3.1 Critical nutrient dilution curves Nitrogen and phosphorus nutrition indexes
3.3.2 Nitrogen-use efficiency Nitrogen uptake efficiency Nitrogen utilisation efficiency
3.3.3 Phosphorus-use efficiency Phosphorus uptake efficiency Phosphorus utilisation efficiency Harvest index
3.3.4 Roots traits for nutrient uptake
4 Yield and quality
4.1 Yield
4.2 Quality
5 Conclusion: Challenges and opportunities
Chapter 19 Cassava
1 Introduction
1.1 Origin of the crop
1.2 Production environment
1.3 Cassava production
1.4 Role in the rural economy
1.5 Cassava in cropping systems
2 Crop structure, morphology and development
2.1 Crop structure
2.2 Stem cuttings
2.3 Flower induction and branching
2.4 Production of nodal units
2.5 Leaves
2.6 Stomates
2.7 Tuber formation and growth
3 Growth and resources
3.1 Capture and efficiency in use of radiation
3.1.1 Canopy expansion and senescence
3.1.2 Interception of solar radiation
3.1.3 Leaf photosynthesis
3.1.4 Canopy photosynthesis
3.1.5 Growth and respiration
3.1.6 Crop growth rate
3.1.7 Radiation-use efficiency
3.2 Capture and efficiency in use of water
3.2.1 Soil and crop water balance
3.2.2 Root systems and water uptake
3.2.3 Canopy responses to water shortage
3.2.4 Stomatal control of crop water status
3.2.5 Crop conductance and atmospheric coupling
3.2.6 Capacitance
3.2.7 Transpiration- and water-use efficiencies
3.2.8 Response of cassava to timing and duration of water shortage
3.3 Capture and efficiency in use of nutrients
3.3.1 Cassava growth in response to soil fertility
3.3.2 Accumulation and cycling of nutrients
3.3.3 Extraction of nutrients in harvest
3.3.4 Detection and remedy of nutrient deficiencies
3.3.5 Further issues with key macro-nutrients Nitrogen Phosphorus Potassium
3.3.6 Nutrient use efficiency in biomass production
4 Yield and quality
4.1 Yield formation in cassava
4.2 Optimal design for high yield and stability
4.3 Yield progress
4.4 Potential yield and yield-gap analysis
4.4.1 Rainfed water-limited potential yield according to edapho-climatic zone
4.4.2 A regional yield-gap analysis from Brazil
4.4.3 Closing the yield gap in Africa
4.5 Nutrient management for sustainable yield
4.5.1 Macro-nutrients for maintenance of yield
4.5.2 Comparative nutrient extraction by cassava and alternative crops
4.5.3 Intercrops, alley crops, and green manures
4.6 Yield and production prospects under climate change
4.6.1 Measured responses of cassava to climate change
4.6.2 Some predicted responses of cassava to climate change
5 Concluding remarks: Challenges and opportunities
5.1 A two-part future
5.2 Reduced production costs and greater labour productivity
5.3 Super high-yielding cultivars for favourable areas
5.4 High-yielding cultivars for drought-prone areas
5.5 General considerations for field research
5.6 Conceptualising knowledge
5.7 Closing comment
Chapter 20 Sugar beet
1 Introduction
1.1 Commodity sugar
1.2 History
1.3 The crop
1.4 Breeding—G × E effect
1.5 Seed production
1.6 Cultivation and management
1.7 Winter beet cultivation
1.8 Growers’ management—G × E × M effect
2 Crop structure, morphology, and development
2.1 Emergence
2.2 Bolting
2.3 Leaf and canopy formation
2.4 Storage root development
2.5 Cambium ring formation
2.6 Sugar storage
2.7 Assimilate partitioning
2.8 Limitations: Regulation of partitioning
2.9 Implications of a sink limitation for breeding and management
2.10 Genotype by environment interactions
2.11 Temperature stress
2.12 Manipulation of crop development as an adaptation to climate change
2.12.1 Early sowing
2.12.2 Winter beet cultivation in temperate climates
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.2 Capture and efficiency in the use of water
3.2.1 Effect of drought stress
3.2.2 Genetic variation for drought tolerance
3.2.3 Causes of yield reduction under drought
3.3 Capture and efficiency in the use of nutrients
3.3.1 Nitrogen Physiological processes N management
3.3.2 Potassium and sodium Physiological processes K and Na management
3.3.3 Boron Physiological processes B management
4 Yield and quality
4.1 Yield and quality traits
4.2 Impact on quality
4.3 Sugar beet yield types
4.4 Improvements through breeding
4.5 Sugar beet storage
5 Concluding remarks: Challenges and opportunities
Author contributions
Chapter 21 Sugarcane
1 Introduction
2 Crop structure, morphology, and development
2.1 Germination
2.2 Tillering
2.3 Stalk elongation
2.4 Flowering and seed formation
2.4.1 Agronomy
2.4.2 Genotype effects
2.5 Maturation
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Components of GLAI
3.1.2 Extinction coefficient
3.1.3 Dynamics of FIPAR Agronomic management
3.1.4 Radiation use efficiency Atmospheric CO2 concentration Solar radiation and temperature Water status Nitrogen status Reduced growth phenomenon
3.1.5 Prospects for yield improvement through breeding for improved radiation capture and efficiency of use Improved radiation capture Improved RUE Phenotyping for FI and RUE
3.2 Capture and efficiency in the use of water
3.2.1 Water uptake Potential water uptake Water limited water uptake
3.2.2 Transpiration efficiency
3.2.3 Increasing water uptake and WUE agronomically
3.2.4 Crop improvement for increased WUE
3.3 Capture and efficiency in the use of nutrients
3.3.1 External N use efficiency (NUEe) Agronomic aspects of NUEe
3.3.2 Internal N use efficiency (NUEi) Prospects for increasing NUEi Photosynthetic NUE Leaf [N]
4 Yield and quality
4.1 Whole plant biomass partitioning
4.2 Internode sucrose accumulation
4.3 Agronomic management to maximise sucrose yields
4.4 Breeding for high sucrose yields
4.5 Climate change impacts on yield
5 Concluding remarks: Challenges and opportunities
Chapter 22 Cotton
1 Introduction
2 Crop structure, morphology, and development
2.1 Developmental phases
2.1.1 Stand establishment
2.1.2 Canopy development
2.1.3 Flowering and boll development
2.1.4 Crop maturity
2.2 General considerations
3 Growth and resources
3.1 Capture and efficiency in the use of radiation
3.1.1 Canopy radiation interception
3.1.2 Photosynthesis
3.1.3 Radiation use efficiency
3.1.4 Challenges and opportunities with climate change
3.2 Water use efficiency
3.3 Capture and efficiency in the use of nutrients
3.3.1 N uptake
3.3.2 Intrinsic nitrogen use efficiency
4 Yield and quality
4.1 Genotype
4.2 Production environment
4.2.1 Water
4.2.2 Nutrients
4.2.3 Temperature
5 Concluding remarks: Challenges and opportunities
Back Cover

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Crop Physiology

Case Histories for Major Crops

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Crop Physiology

Case Histories for Major Crops

Edited by

Victor O. Sadras

South Australian R&D Institute, and The University of Adelaide, Adelaide, SA, Australia

Daniel F. Calderini

Institute of Plant Production and Protection, Universidad Austral de Chile, Valdivia, Chile

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-819194-1

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Contents Contributors xiii Preface xvii Acknowledgements xxi

1. Maize María E. Otegui, Alfredo G. Cirilo, Sergio A. Uhart, and Fernando H. Andrade 1 Introduction 3 1.1 Global trends 3 1.2 Main production areas 3 1.3 Maize in rotations: Suitability of previous and consequences for following crops 5 1.4 Multiple cropping 6 2 Crop structure, morphology, and development 6 2.1 Main phenological events 6 2.2 Genotypic and environmental drivers of maize development 8 3 Growth and resources 10 3.1 Capture and efficiency in the use of radiation 10 3.2 Capture and efficiency in the use of water 12 3.3 Capture and efficiency in the use of nutrients 17 4 Grain yield and quality 24 4.1 Kernel number 25 4.2 Kernel weight 26 4.3 Biomass partitioning 27 4.4 Grain quality 28 5 Concluding remarks: Challenges and opportunities 29 References 30

2. Rice Shu Fukai and Len J. Wade 1 Introduction 45 1.1 Global significance of rice 45 1.2 Rice ecosystem classification with emphasis on water availability 46 1.3 Crop management 47

2 Crop structure, morphology, and development 48 2.1 Germination and seedling emergence 48 2.2 Phenological development 50 2.3 Shoot development and growth 52 2.4 Rood development and growth 53 3 Growth and resources 54 3.1 Capture and efficiency in the use of radiation 54 3.2 Capture and efficiency in the use of water 56 3.3 Capture and efficiency in the use of nutrients 59 4 Yield and quality 65 4.1 Sink–source relations 65 4.2 Response to abiotic factors 71 4.3 Crop management for yield and quality 79 4.4 Mechanisation 83 5 Concluding remarks: Challenges and opportunities 84 5.1 Adaptation mechanisms to reduced water input in irrigated system 84 5.2 Adaptation mechanisms for drought avoidance in rainfed lowland rice 85 5.3 Adaptation mechanism for mechanised rice farming 85 5.4 Factors determining grain set 85 5.5 Enhancing yield potential 85 5.6 Head rice yield 86 Acknowledgement 86 References 86

3. Wheat Gustavo A. Slafer, Roxana Savin, Dante Pinochet, and Daniel F. Calderini 1 Introduction 99 1.1 Wheat origin, production, and yield 99 1.2 Trends in production, area, and yield 99 2 Crop structure, morphology, and development 101 2.1 Yield determination 101 2.2 Crop phenology 107 v

vi  Contents

3 Capture and efficiency in the use of resources 118 3.1 Capture and use efficiency of radiation 118 3.2 Capture and efficiency in the use of water 122 3.3 Capture and efficiency in the use of nutrients 126 4 Yield responsiveness to management and breeding 131 4.1 Yield responsiveness to management and breeding 131 5 Quality 139 5.1 Grain quality traits 139 5.2 Grain proteins, nutrients, fibre, and healthy traits 141 5.3 Sensitivity of grain quality traits to environmental stresses 141 5.4 Grain quality and crop management 142 6 Concluding remarks: Challenges and opportunities 144 References 145

4. Barley Daniel J. Miralles, L. Gabriela Abeledo, Santiago Alvarez Prado, Karine Chenu, Román A. Serrago, and Roxana Savin 1 Introduction 165 1.1 Global trends in harvested area and yield 165 2 Crop structure, morphology, and development 165 2.1 Differentiation of vegetative and reproductive organs 166 2.2 Dynamics of initiation and appearance of vegetative and reproductive organs 167 2.3 Genotypic and environmental drivers of barley development 170 3 Growth and resources 172 3.1 Capture and efficiency in the use of radiation 172 3.2 Capture and efficiency in the use of water 174 3.3 Capture and efficiency in the use of nutrients 179 3.4 Requirement of other nutrients 182 4 Grain yield and quality 183 4.1 Grain number and the critical period 183 4.2 Grain filling 184 4.3 Barley uses and grain quality 184 4.4 Environmental factors altering quality 186 4.5 Genetic factors determining malting and brewery quality 187

5 Concluding remarks: Challenges and opportunities 188 References 188

5. Sorghum Andrew Borrell, Erik van Oosterom, Barbara George-Jaeggli, Daniel Rodriguez, Joe Eyre, David J. Jordan, Emma Mace, Vijaya Singh, Vincent Vadez, Mike Bell, Ian Godwin, Alan Cruickshank, Yongfu Tao, and Graeme Hammer 1 Introduction 197 1.1 Agronomic context 197 2 Crop structure, morphology, and development 199 2.1 Crop phenology 199 2.2 Adaptation to environmental conditions 200 3 Growth and resources 200 3.1 Capture and efficiency in the use of radiation 200 3.2 Capture and efficiency in the use of water 204 3.3 Capture and efficiency in the use of nutrients 206 4 Yield and quality 210 4.1 Grain number and size 210 4.2 Grain quality 211 4.3 Crop stresses and effects on grain yield determination 212 5 Concluding remarks: Challenges and opportunities 213 5.1 Challenges and opportunities 213 5.2 Research priorities 214 References 214

6. Oat Bao-Luo Ma, Zhiming Zheng, and Changzhong Ren 1 Introduction 223 1.1 Production and nutrition 223 1.2 Agronomic roles of oat in farming systems 225 2 Crop structure, morphology, and development 226 2.1 Phenology and critical growth stages 226 2.2 Genotypic differences 227 2.3 Environmental effect 228 2.4 Manipulation of plant development 228 3 Growth and resources 228 3.1 Capture and efficiency in the use of radiation 228

Contents  vii

3.2 Capture and efficiency in the use of water 233 3.3 Capture and efficiency in the use of nutrients 235 4 Grain yield and quality 238 4.1 Grain yield and yield components 238 4.2 Grain quality 240 4.3 Forage quality 241 5 Concluding remarks: Challenges and opportunities 241 References 242

7. Quinoa H. Daniel Bertero 1 Introduction 251 2 Crop structure, morphology, and development 252 2.1 Seed germination and conservation 252 2.2 Phasic development 252 3 Growth and resources 254 3.1 Capture and efficiency in the use of radiation 254 3.2 Capture and efficiency in the use of water 256 3.3 Capture and efficiency in the use of nutrients 260 4 Yield and quality 268 4.1 Critical periods of yield determination 268 4.2 Dry matter and numeric yield components 269 4.3 Grain weight 269 4.4 Reproductive partitioning and limitations to grain yield 270 4.5 Other stresses and interactions between stresses 271 5 Grain quality 271 6 Concluding remarks: Challenges and opportunities 273 References 273

8. Soybean Patricio Grassini, Nicolas Cafaro La Menza, Juan I. Rattalino Edreira, Juan Pablo Monzón, Fatima A. Tenorio, and James E. Specht 1 Introduction 283 2 Crop structure, morphology, and development 288 3 Growth and resources 293 3.1 Capture and efficiency in the use of radiation 294 3.2 Capture and efficiency in the use of water 296

3.3 Capture and efficiency in the use of nitrogen 299 3.4 Dry matter and nitrogen partitioning 301 3.5 Other nutrients 302 4 Yield and quality 303 4.1 Yield potential and yield gaps 303 4.2 Seed quality 304 5 Concluding remarks: Challenges and opportunities 307 Acknowledgements 307 References 308

9. Field pea Lachlan Lake, Lydie Guilioni, Bob French, and Victor O. Sadras 1 Introduction 321 1.1 Origin and agronomy 321 1.2 Pests and diseases 322 2 Crop structure, morphology, and development 323 2.1 Seed and plant characteristics 323 2.2 Phenology 324 2.3 Critical period 326 3 Growth and resources 326 3.1 Capture and efficiency in the use of radiation 326 3.2 Capture and efficiency of use of water 328 3.3 Capture and efficiency in the use of nutrients 329 4 Yield and quality 331 4.1 Grain number and weight 331 4.2 Biomass and harvest index 333 4.3 Yield and quality trade-offs 334 5 Concluding remarks: Challenges and opportunities 334 Acknowledgements 334 References 334

10. Chickpea Vincent Vadez, Amir Hajjarpoor, Lijalem Balcha Korbu, Majid Alimagham, Raju Pushpavalli, Maria Laura Ramirez, Junichi Kashiwagi, Jana Kholova, Neil C. Turner, and Victor O. Sadras 1 Introduction and agronomic context 343 1.1 Origin and ecology 343 1.2 The role of chickpea in farming systems 343 2 Crop structure, morphology, and development 345 3 Growth and resources 345 3.1 Capture and efficiency in the use of radiation 345

viii  Contents

3.2 Capture and efficiency in the use of water 348 3.3 Capture and efficiency in the use of nutrients 350 4 Yield and quality 351 4.1 Yield and its components 351 4.2 Seed quality 352 5 Concluding remarks: Challenges and opportunities 353 References 353

11. Peanut Rao Rachaputi, Yashvir S. Chauhan, and Graeme C. Wright 1 Introduction 361 1.1 Area, production, and yield 361 2 Crop structure, morphology, and development 361 2.1 Sowing to emergence 362 2.2 Emergence to flowering 363 2.3 Flowering to maturity 363 2.4 Combining sowing date and genotype to match growing environment 365 3 Growth and resources 366 3.1 Capture and efficiency in the use of radiation 366 3.2 Capture and efficiency in the use of water 367 4 Capture and efficiency in the use of nutrients 368 4.1 Nitrogen 368 4.2 Calcium 369 4.3 Phosphorus 370 4.4 Zinc 370 5 Grain yield and quality 370 5.1 Grain yield 370 5.2 Seed quality 372 5.3 Trade-offs between yield and quality traits 375 6 Concluding remarks: Challenges and opportunities 375 Acknowledgements 376 References 376

12. Common bean Millicent R. Smith and Idupulapati M. Rao 1 Introduction 385 1.1 Climatic zones 385 1.2 Major growing regions 385 1.3 Role in farming systems 386 1.4 Implications of climate change 387 2 Crop structure, morphology and development 389 2.1 Morphological variation 389

2.2 Taxonomy and gene pools 389 2.3 Phenological development 390 2.4 Determinancy and growth habit 390 2.5 Critical stages of crop development 391 2.6 Strategies for adaptation to climate change 392 3 Growth and resources 392 3.1 Capture and efficiency in the use of radiation 392 3.2 Capture and efficiency in the use of water 394 3.3 Capture and efficiency in the use of nutrients 396 4 Yield and quality 398 4.1 Yield and related traits 398 4.2 Nutritional quality 400 5 Concluding remarks: Challenges and opportunities 400 Acknowledgements 401 References 401

13. Lentil Akanksha Sehgal, Kumari Sita, Abdul Rehman, Muhammad Farooq, Shiv Kumar, Rashmi Yadav, Harsh Nayyar, Sarvjeet Singh, and Kadambot H.M. Siddique 1 Introduction 409 2 Crop structure, morphology, and development 410 2.1 Crop structure: height and branching 410 2.2 Phenological development 411 2.3 Development and adaptation to stress 412 3 Growth and resources 415 3.1 Capture and efficiency in the use of radiation 415 3.2 Capture and efficiency in the use of water 415 3.3 Capture and efficiency in the use of nutrients 417 4 Yield and quality 418 4.1 Reproductive development 418 5 Concluding remarks: Challenges and opportunities 421 References 421

14. Lupin Alejandro del Pozo and Mario Mera 1 Introduction 431 2 Crop structure, morphology, and development 432 2.1 Crop development 433 2.2 Branching patterns 434

Contents  ix

2.3 Use of restricted branching in lupin breeding 436 2.4 Dwarfism 437 3 Growth and resources 438 3.1 Capture and efficiency in the use of radiation 438 3.2 Capture and efficiency of use of water 440 3.3 Capture and efficiency of use of nutrients 441 4 Yield and quality 443 4.1 Yield 443 4.2 Yield components 443 4.3 Pod wall 444 4.4 Grain protein 444 5 Concluding remarks: Challenges and opportunities 445 Acknowledgement 445 References 445

15. Faba bean M. Inés Mínguez and Diego Rubiales 1 Introduction 453 1.1 Origin of the crop 453 1.2 Cropping environment and production 454 2 Crop structure, morphology, and development 455 2.1 Crop structure 455 2.2 Vegetative and reproductive responses 458 2.3 Quantifying phenological development 459 3 Growth and resources 461 3.1 Capture and efficiency in the use of radiation 461 3.2 Capture and efficiency in the use of water 464 3.3 Capture and efficiency in the use of nutrients 466 4 Yield and quality 469 4.1 Crop yield 469 4.2 Yield components 470 4.3 Indeterminate, determinate, and semideterminate cultivars 471 4.4 Nutritional issues and grain quality 472 4.5 Role of faba bean in cropping system productivity 472 4.6 Biotic stresses 473 5 Concluding remarks: Challenges and opportunities 474 5.1 Maintaining yield gain 475 5.2 Optimal cultivar design 475 5.3 Intercropping 475 5.4 Coordination of faba bean research 476 Acknowledgements 477 References 477

16. Sunflower Philippe Debaeke and Natalia G. Izquierdo 1 Introduction 483 1.1 The role of sunflower crop in farming systems 483 1.2 Sunflower-based cropping systems 484 1.3 Implications of climate change for sunflower cropping 484 1.4 Sunflower crop physiology research since the 1980s 485 2 Crop structure, morphology and development 485 2.1 Growth stages and phenophases 485 2.2 Drivers of crop phenology and development 487 2.3 Manipulation of crop development to match critical periods and environments 487 3 Growth and resources 488 3.1 Capture and efficiency in the use of radiation 488 3.2 Capture and efficiency in the use of water 492 3.3 Capture and efficiency in the use of nutrients 495 4 Yield 497 4.1 Frameworks of yield elaboration 497 4.2 Allocation of dry matter 497 4.3 Components of grain yield 498 5 Grain and oil quality 500 5.1 Physiology of oil accumulation 500 5.2 Factors affecting oil concentration effects on oil concentration 501 5.3 Sunflower oil quality 502 6 Concluding remarks: Challenges and opportunities 504 References 505

17. Canola John A. Kirkegaard, Julianne M. Lilley, Peter M. Berry, and Deborah P. Rondanini 1 Introduction 519 1.1 Origin, development and uses 519 1.2 Global production systems 519 1.3 Canola cropping systems 523 1.4 Agronomic implications of predicted climate change 524 2 Crop structure, morphology and development 526 2.1 Phenological stages 526 2.2 Impact of development on yield potential and adaptive management 527

x  Contents

2.3 Matching sowing date with varietal phenology in diverse environments 529 3 Growth and resources 530 3.1 Capture and efficiency in the use of radiation 530 3.2 Capture and efficiency in the use of water 533 3.3 Capture and efficiency in the use of nutrients 535 4 Yield and quality 536 4.1 Allocation of dry matter 536 4.2 Yield components 538 4.3 Seed quality—Oil and protein 540 5 Concluding remarks: Challenges and opportunities 542 References 543

18. Potato X. Carolina Lizana, Patricio Sandaña, Anita Behn, Andrea Ávila-Valdés, David A. Ramírez, Rogério P. Soratto, and Hugo Campos 1 Introduction 551 1.1 Potato production systems 551 1.2 Climate change 553 2 Crop structure, morphology, and development 554 2.1 Drivers of potato development 556 3 Growth and resources 559 3.1 Capture and efficiency in the use of radiation 559 3.2 Capture and efficiency in the use of water 562 3.3 Capture and efficiency in the use of nutrients 565 4 Yield and quality 574 4.1 Yield 574 4.2 Quality 575 5 Conclusion: Challenges and opportunities 577 References 578

19. Cassava James H. Cock and David J. Connor 1 Introduction 589 1.1 Origin of the crop 589 1.2 Production environment 589 1.3 Cassava production 590 1.4 Role in the rural economy 591 1.5 Cassava in cropping systems 591 2 Crop structure, morphology and development 592 2.1 Crop structure 592 2.2 Stem cuttings 592

2.3 Flower induction and branching 592 2.4 Production of nodal units 593 2.5 Leaves 594 2.6 Stomates 595 2.7 Tuber formation and growth 596 3 Growth and resources 597 3.1 Capture and efficiency in use of radiation 597 3.2 Capture and efficiency in use of water 600 3.3 Capture and efficiency in use of nutrients 606 4 Yield and quality 612 4.1 Yield formation in cassava 613 4.2 Optimal design for high yield and stability 614 4.3 Yield progress 615 4.4 Potential yield and yield-gap analysis 616 4.5 Nutrient management for sustainable yield 619 4.6 Yield and production prospects under climate change 621 5 Concluding remarks: Challenges and opportunities 625 5.1 A two-part future 625 5.2 Reduced production costs and greater labour productivity 625 5.3 Super high-yielding cultivars for favourable areas 626 5.4 High-yielding cultivars for droughtprone areas 626 5.5 General considerations for field research 627 5.6 Conceptualising knowledge 628 5.7 Closing comment 628 References 628

20. Sugar beet Christa M. Hoffmann, Heinz-Josef Koch, and Bernward Märländer 1 Introduction 635 1.1 Commodity sugar 635 1.2 History 635 1.3 The crop 636 1.4 Breeding—G × E effect 637 1.5 Seed production 637 1.6 Cultivation and management 638 1.7 Winter beet cultivation 639 1.8 Growers’ management—G × E × M effect 639 2 Crop structure, morphology, and development 640 2.1 Emergence 640 2.2 Bolting 641 2.3 Leaf and canopy formation 641 2.4 Storage root development 642

Contents  xi

2.5 2.6 2.7 2.8 2.9

Cambium ring formation 644 Sugar storage 645 Assimilate partitioning 645 Limitations: Regulation of partitioning 648 Implications of a sink limitation for breeding and management 648 2.10 Genotype by environment interactions 649 2.11 Temperature stress 650 2.12 Manipulation of crop development as an adaptation to climate change 650 3 Growth and resources 651 3.1 Capture and efficiency in the use of radiation 651 3.2 Capture and efficiency in the use of water 652 3.3 Capture and efficiency in the use of nutrients 655 4 Yield and quality 660 4.1 Yield and quality traits 660 4.2 Impact on quality 661 4.3 Sugar beet yield types 661 4.4 Improvements through breeding 661 4.5 Sugar beet storage 663 5 Concluding remarks: Challenges and opportunities 663 Author contributions 664 References 664

21. Sugarcane Abraham Singels, Phillip Jackson, and Geoff Inman-Bamber 1 Introduction 675 2 Crop structure, morphology, and development 677 2.1 Germination 677 2.2 Tillering 678 2.3 Stalk elongation 679 2.4 Flowering and seed formation 679 2.5 Maturation 680

3 Growth and resources 680 3.1 Capture and efficiency in the use of radiation 680 3.2 Capture and efficiency in the use of water 688 3.3 Capture and efficiency in the use of nutrients 693 4 Yield and quality 697 4.1 Whole plant biomass partitioning 699 4.2 Internode sucrose accumulation 700 4.3 Agronomic management to maximise sucrose yields 701 4.4 Breeding for high sucrose yields 701 4.5 Climate change impacts on yield 703 5 Concluding remarks: Challenges and opportunities 703 References 705

22. Cotton John Snider, Mike Bange, and Jim Heitholt 1 Introduction 715 2 Crop structure, morphology, and development 716 2.1 Developmental phases 716 2.2 General considerations 721 3 Growth and resources 721 3.1 Capture and efficiency in the use of radiation 721 3.2 Water use efficiency 728 3.3 Capture and efficiency in the use of nutrients 730 4 Yield and quality 733 4.1 Genotype 733 4.2 Production environment 734 5 Concluding remarks: Challenges and opportunities 739 References 740 Index 747

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Contributors Numbers in parentheses indicate the pages on which the authors’ ­contributions begin.

L. Gabriela Abeledo  (165), Department of Plant Production, School of Agriculture, University of Buenos Aires, Buenos Aires; CONICET, Buenos Aires, Argentina Majid Alimagham  (343), Institute for Research and Development (IRD), UMR DIADE, University of Montpellier, Montpellier, France Fernando H. Andrade  (3), INTA-National University of Mar del Plata and CONICET, Balcarce, Buenos Aires, Argentina Andrea Ávila-Valdés  (551), Graduate School, Faculty of Agricultural Sciences, Austral University of Chile, Campus Isla Teja; Research Center in Volcanic Soils, Austral University of Chile, Valdivia, Chile Mike Bange  (715), Grains Research and Development Corporation, Toowoomba, QLD, Australia Anita Behn  (551), Institute of Plant Production and Protection, Austral University of Chile, Valdivia, Chile Mike Bell  (197), University of Queensland, School of Agriculture and Food Sciences, Gatton, QLD, Australia Peter M. Berry  (519), ADAS, High Mowthorpe, North Yorkshire, United Kingdom H. Daniel Bertero (251), Department of Plant Production, School of Agriculture, University of Buenos Aires; IFEVA-CONICET, Buenos Aires, Argentina Andrew Borrell  (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Warwick, QLD, Australia

X. Carolina Lizana (551), Institute of Plant Production and Protection; Research Center in Volcanic Soils, Austral University of Chile, Valdivia, Chile Yashvir S. Chauhan  (361), Department of Agriculture and Fisheries, Kingaroy, QLD, Australia, Queensland Alliance for Agriculture and Food Innovation (QAAFI), University of Queensland, Australia Karine Chenu (165), University of Queensland, Brisbane, QLD, Australia Alfredo G. Cirilo  (3), Department of Crop Production and Environmental Management, INTA Experimental Station, Pergamino, Argentina James H. Cock (589), Emeritus, The International Center for Tropical Agriculture (CIAT), Palmira, Colombia David J. Connor  (589), Department of Agriculture and Food, The University of Melbourne, Melbourne, VIC, Australia Alan Cruickshank  (197), Agri-Science Queensland, Department of Agriculture and Fisheries, Warwick, QLD, Australia Philippe Debaeke (483), INRAE (National Research Institute for Agriculture, Food and Environment), Université de Toulouse, UMR AGIR, Castanet-Tolosan, France Alejandro del Pozo (431), Plant Breeding and Phenomics Center, Faculty of Agricultural Science, University of Talca, Talca, Chile Joe Eyre  (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia

Daniel F. Calderini (99), Institute of Plant Production and Protection, Universidad Austral de Chile, Valdivia, Chile

Muhammad Farooq (409), Department of Crop Sciences, College of Agricultural and Marine Sciences, Sultan Qaboos University, Muscat, Oman; Department of Agronomy, University of Agriculture, Faisalabad, Pakistan; The UWA Institute of Agriculture and School of Agriculture & Environment, The University of Western Australia, Perth, WA, Australia

Hugo Campos  (551), International Potato Center, Lima, Peru

Bob French (321), Department of Primary Industries and Regional Development, Merredin, WA, Australia

Nicolas Cafaro La Menza (283), Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States


xiv   Contributors

Shu Fukai  (45), University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD, Australia

Lijalem Balcha Korbu  (343), Ethiopian Institute of Agricultural Research (EIAR), Debre Zeit Research Center, Debre Zeit, Ethiopia

Barbara George-Jaeggli (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Agri-Science Queensland, Department of Agriculture and Fisheries, Warwick, QLD, Australia

Shiv Kumar  (409), International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco

Ian Godwin (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD, Australia Patricio Grassini  (283), Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States Lydie Guilioni (321), L’Institut Agro, Montpellier SupAgro, Department of Biology and Ecology, Montpellier, France Amir Hajjarpoor  (343), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Crop Physiology Laboratory, Patancheru, Telangana, India Graeme Hammer  (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD, Australia Jim Heitholt  (715), Department of Plant Sciences, University of Wyoming, Powell, WY, United States Christa M. Hoffmann  (635), Institute of Sugar Beet Research, Göttingen, Germany Geoff Inman-Bamber  (675), College of Science, Technology and Engineering, James Cook University, Townsville, QLD, Australia Natalia G. Izquierdo  (483), CONICET - IIDEAGROS, Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Balcarce, Argentina Phillip Jackson  (675), CSIRO Agriculture and Food, Australian Tropical Science Innovation Precinct, James Cook University, Townsville, QLD, Australia David J. Jordan  (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Warwick, QLD, Australia Junichi Kashiwagi  (343), Crop Science Lab., Research Faculty of Agriculture, Hokkaido University, Sapporo, Japan Jana Kholova (343), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Crop Physiology Laboratory, Patancheru, Telangana, India John A. Kirkegaard (519), CSIRO Agriculture and Food, Canberra, ACT, Australia Heinz-Josef Koch (635), Institute of Sugar Beet Research, Göttingen, Germany

Lachlan Lake (321), South Australian R&D Institute, and The University of Adelaide, Adelaide, SA, Australia Julianne M. Lilley  (519), CSIRO Agriculture and Food, Canberra, ACT, Australia Bao-Luo Ma  (223), Agriculture and Agri-Food Canada, Ottawa Research and Development Centre (ORDC), Ottawa, ON, Canada Emma Mace (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Agri-Science Queensland, Department of Agriculture and Fisheries, Warwick, QLD, Australia Bernward Märländer  (635), Institute of Sugar Beet Research, Göttingen, Germany Mario Mera (431), Department of Agricultural Production, Faculty of Agricultural and Forestry Sciences, University La Frontera, Temuco, Chile M. Inés Mínguez  (453), Department of Agricultural Production, School of Agricultural Engineering, Food Technology and Biosystems, and Research Centre for the Management of Agricultural and Environmental Risks; Technical University of Madrid (UPM), Madrid, Spain Daniel J. Miralles (165), Department of Plant Production, School of Agriculture; University of Buenos Aires and IFEVA-CONICET, Buenos Aires, Argentina Juan Pablo Monzón (283), Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States; CONICET, Balcarce, Buenos Aires, Argentina Harsh Nayyar  (409), Department of Botany, Panjab University, Chandigarh, India Erik van Oosterom  (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD, Australia María E. Otegui  (3), Department of Plant Production, School of Agriculture, CONICET at INTA Pergamino Experimental Station and University of Buenos Aires, Buenos Aires, Argentina Dante Pinochet (99), Institute of Agricultural Engineering and Soil Science, Universidad Austral de Chile, Valdivia, Chile Santiago Alvarez Prado  (165), Department of Plant Production, School of Agriculture, University of Buenos Aires, Buenos Aires; CONICET, Buenos Aires; IFEVA, Buenos Aires, Argentina

Contributors    xv

Raju Pushpavalli  (343), CSIRO Agriculture and Food, Floreat, WA, Australia Rao Rachaputi (361), Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Gatton, Australia David A. Ramirez  (551), International Potato Center, Lima, Peru Maria Laura Ramirez  (343), Mycology and Mycotoxicology Research Institute (UNRC-CONICET), Río Cuarto, Córdoba, Argentina Idupulapati M. Rao  (385), International Center for Tropical Agriculture (CIAT), Cali, Colombia Juan I. Rattalino Edreira (283), Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States Abdul Rehman  (409), Department of Crop Sciences and Biotechnology, Dankook University, Cheonan-si, Korea Changzhong Ren (223), Baicheng Academy of Agricultural Sciences, Baicheng, China Daniel Rodriguez  (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia Deborah P. Rondanini  (519), Universidad de Buenos Aires, Buenos Aires, Argentina Diego Rubiales (453), Institute for Sustainable Agriculture, CSIC, Córdoba, Spain Victor O. Sadras (321, 343), South Australian R&D Institute, and The University of Adelaide, Adelaide, SA, Australia Patricio Sandaña (551), Institute of Plant Production and Protection; Research Center in Volcanic Soils, Austral University of Chile, Valdivia, Chile Roxana Savin  (99, 165), Department of Crop and Forest Sciences, University of Lleida—AGROTECNIO Center, Lleida, Spain Akanksha Sehgal  (409), Department of Plant and Soil Science, Mississippi State University, Starkville, MS, United States Román A. Serrago (165), Department of Plant Production, School of Agriculture, University of Buenos Aires, Buenos Aires; CONICET, Buenos Aires, Argentina Kadambot H.M. Siddique  (409), The UWA Institute of Agriculture and School of Agriculture & Environment, The University of Western Australia, Perth, WA, Australia Abraham Singels (675), South African Sugarcane Research Institute, Mount Edgecombe; Department of Plant and Soil Sciences, University of Pretoria, Pretoria, South Africa Sarvjeet Singh  (409), Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India

Vijaya Singh (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD, Australia Kumari Sita  (409), Institute of Himalayan Bioresource Technology (IHBT), Palampur, India Gustavo A. Slafer  (99), ICREA, Catalonian Institution for Research and Advanced Studies, and Department of Crop and Forest Sciences, University of Lleida— AGROTECNIO Center, Lleida, Spain Millicent R. Smith  (385), School of Agriculture and Food Sciences, University of Queensland; Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia John Snider (715), Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, United States Rogério P. Soratto (551), College of Agricultural Sciences and Center of Tropical Roots and Starches, São Paulo State University (UNESP), Botucatu, SP, Brazil James E. Specht  (283), Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States Yongfu Tao (197), University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Warwick, QLD, Australia Fatima A. Tenorio  (283), Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States Neil C. Turner  (343), School of Agriculture and Environment, Faculty of Science, The University of Western Australia, Perth, WA, Australia Sergio A. Uhart  (3), Faculty of Agronomy, National University of the Northeast, Santiago del Estero, Argentina Vincent Vadez  (197, 343), Institute for Research and Development (IRD), UMR DIADE, University of Montpellier, Montpellier, France; International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Crop Physiology Laboratory, Patancheru, Telangana, India Len J. Wade  (45), University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD, Australia Graeme C. Wright  (361), Peanut Company of Australia, Kingaroy, Queensland, Australia Rashmi Yadav  (409), ICAR-National Bureau of Plant Genetic Resources, New Delhi, India Zhiming Zheng (223), Agriculture and Agri-Food Canada, Ottawa Research and Development Centre (ORDC), Ottawa, ON, Canada

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Preface In 2009 we published the first edition of Crop Physiology: Applications for Genetic Improvement and Agronomy, with a ­second edition in 2015. The premise of these books was that farmers rely primarily on two technological outputs: varieties and practices. Plant breeding and agronomy are, therefore, the two technologies with immediate application. Hence crop physiology is most valuable when it engages meaningfully with breeding and agronomy. This remains the premise of this book. Our previous books were structured around processes—development, the carbon, water, and nitrogen economies of crops, and the formation of yield in the context of cropping systems. In Hall (2018) updated his crop physiology book with a focus on processes in a timely context of climate change. With at least three process-based books between 2009 and 2018, we felt it was time for an alternative, albeit not new, perspective. In this book, we follow Evans (1975) scheme of revisiting physiological processes for individual crops. Chapters open with a crop-specific agronomic context and deal with development, the carbon, water, and nitrogen economies of individual crops, the formation of yield, and aspects of quality. The first group of chapters deals with cereals—maize, rice, wheat, barley, sorghum, and oat. Maize, rice, and wheat provide most of the energy and a good part of protein to human diets worldwide, and improvements in the yield of these crops are the core of food security for the foreseeable future. Barley and sorghum are mostly feed grains supporting the increasing demand for animal products in increasingly wealthy segments of some societies. Wheat, barley, and oat have a similar critical period when grain number, and in turn yield, is more vulnerable to stress (Fig. 1). Oat has become a niche crop after a peak in the early 20th century when mechanical engines replaced horses in transport and industries, including agriculture (Fig. 2); this highlights the disruptive nature of any meaningful technology. In common with quinoa, there is a potential for larger acreages of oat motivated by health and nutrition drivers, but the future of both crops is uncertain beyond their present niche. Physiologically, maize and sorghum share a C4 metabolism and are particularly adapted to warmer environments. However, maize and sorghum differ in the reproductive biology; owing to its lower apical dominance, tillering makes sorghum more comparable to rice and wheat. The second group of chapters deals with grain legumes. Soybean stands out as the world’s largest source of vegetal protein (Fig. 3a) and is critical to food security together with the three big cereals and potato. Common aspects of crop physiology in grain legumes include a typical critical period for yield response to stress (Fig. 1). In comparison to cereals, the critical window is delayed towards pod set and grain fill in legumes. Two main reasons underlie the difference in the critical period between small-grain cereals and grain legumes. Firstly, flowering is a convergent processes of development across stems from different categories in cereals. In comparison, flowering in pulses refers to the first flower on the main branch. The second reason, linked with the previous, is the contrasting flowering biology between determinate cereals and the extended flowering period of semi-determinate or indeterminate crops like grain legumes and canola. The resulting approximate 40 days difference in the timing of maximum grain yield sensitivity to stress supports a genuine physiological contrast between grain legumes and cereals with agronomic implications. Other common aspects are nitrogen fixation and implications for the role of legumes in cropping systems and qualitative (determinate vs. indeterminate nodules) and quantitative idiosyncratic features (e.g. degree of inhibition of nitrogen fixation by soil mineral nitrogen). The pulses are increasingly important but remain minor crops (Fig. 3b). As expected from the magnitude of the crop production, research efforts lag in other grain legumes relative to soybean but with no apparent proportionality, whereas current production of soybean is 8 times larger than the production of peanut and 20 times larger than the production of chickpea; a coarse measure of research output is only 5 times larger (Fig. 3c). Quantifying the gap between actual production and research aimed at improved production warrants investigation. Peanut sets pods underground. Because pods do not transpire, they do not receive xylem-transported Ca from the roots but absorb Ca directly from the surrounding soil through mass flow. These physiological traits have unique implications for calcium management in peanut. Sunflower, canola, and cotton feature oil-reach seeds with physiological implications (e.g. lower radiation use efficiency than seed crops with starchy grain), some similarities (e.g. influence of temperature on fatty acid composition in seed oil


xviii  Preface

FIG. 1  Annual grain crops have species-specific critical windows for yield determination when grain set is more sensitive to stress. Circles represent small-grain cereals (wheat, barley, and oat), and squares are for pulses and canola. As an example of a non-grain crop for comparison, triangles are for potato. Summary of small-grain cereals and pulses is from Sadras, V.O., Dreccer, M.F., 2015. Adaptation of wheat, barley, canola, field pea and chickpea to the thermal environments of Australia. Crop Past. Sci. 66, 1137–1150; and potato from Sandaña and Lizana, data not published.

Oat acreage in US (thousands of acres)


Ford Motor Company funded Ford T released First moving assembly line

600 500 400 300 200 100 0

1860 1880 1900 1920 1940 1960 1980 2000 2020

Year FIG.  2  An example of technological disruption: time-trend of oat acreage in the USA in relation to milestones in the automobile industry. Sources: FAOSTAT and Wikipedia.

reserves), and contrasting seasonality and roles in cropping systems. Sunflower acreage and research effort is decreasing in contrast to the worldwide expansion of canola. Potato, cassava, sugar beet, and sugar cane are grown for carbohydrate-rich vegetative organs. They share a lack of (or tenuous) critical period in comparison to a marked sensitive stage in seed crops (Fig. 1). The potential decoupling of growth and reproduction is therefore not an issue in these species, but some physiologically interesting and agronomically important trade-offs require attention, e.g. between expansive growth and storage of sucrose in sugar cane. In contrast to many other plant species, which exclude sodium, sugar beet is a halophyte that absorbs and utilises it and can respond positively to sodium fertilisation. Victor O. Sadras Daniel F. Calderini

Preface  xix

FIG. 3  Time trend of (a) global production of soybean, (b) global production of common bean, peanut, and chickpea, and (c) scientific papers on chickpea and peanut. (b) and (c) are soybean-to-other-crop ratio. Ratios in (c) are calculated from number of papers per decade with ‘crop’ and ‘yield’ in title from Web of Science, where ‘crop’ is ‘soybean’, ‘chickpea’, and ‘peanut or groundnut’. Sources: (a, b) FAOSTAT.

References Evans, L.T., 1975. Crop Physiology: Some Case Histories. Cambridge University Press, Cambridge. 374 pp. Hall, A.E., 2018. Crop Responses to Environment. Adapting to Global Climate Change. CRC Press, Boca Raton.

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Acknowledgements Our most sincere appreciation to the authors for their expertise, time, and understanding. We thank our host organisations, the South Australian Research and Development Institute, and Universidad Austral de Chile, for their support with this project. We thank Elsevier and Academic Press teams Nancy Maragioglio, Rafael Trombaco, Kavitha Balasundaram, and Paul Prasad for their professional support. We thank Victoria Abarzúa and Gabriela Carrasco-Puga for logistics and revision of references. We thank the publishers who permitted reproduction of previously published material. To Ana and Magda for their love, support, and patience. The editors


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Image source: Authors

Chapter 1

Maize María E. Oteguia, Alfredo G. Cirilob, Sergio A. Uhartc, and Fernando H. Andraded a

Department of Plant Production, School of Agriculture, CONICET at INTA Pergamino Experimental Station and University of Buenos Aires, Buenos Aires, Argentina, bDepartment of Crop Production and Environmental Management, INTA Experimental Station, Pergamino, Argentina, cFaculty of Agronomy, National University of the Northeast, Santiago del Estero, Argentina, dINTA-National University of Mar del Plata and CONICET, Balcarce, Buenos Aires, Argentina

1 Introduction 1.1  Global trends Native to the New World, maize is the main staple for a large part of humankind, being especially important for several Latin American and African countries. In these countries, maize can be consumed as porridge (such as grits, polenta, or ugali), popcorn, roasted kernels, vegetable (fresh, frozen, or canned sweet maize), flour, or meal (bread, tortillas, chips, extruded snacks, etc.). Worldwide, its grains are used to produce ethanol (for beverages or as a fuel source), cooking oil, and starch. Grains and by-products are also used as animal feed, whereas its biomass is an energy source and is used as silage. Despite its broad distribution across all continents (Fig. 1.1a left), four of the top ten maize producing countries (Fig. 1.1b left) are in the Americas, where it covers the largest area worldwide. Maize is well-adapted around the world and returns high yields (Fig. 1.1c left). Low technological development sets a limit to crop yield in otherwise suitable environments (e.g. Africa). Land cropped to maize increased at a rate of close to 1 Mha y− 1 between 1961 and 2005 (Fig. 1.1a right) and increased to over 4 Mha y− 1 afterwards. A good part of the change has been driven by crop substitution, such as rice by maize in China. Variations in area produced similar variations in overall production (Fig.  1.1b right), with rates that increased from 10.5 to 34 M t y− 1 in almost the same period (breakpoint in 2003). Contrarily, global grain yield (GY) increased at a constant rate of 66 kg ha− 1 y− 1 for the whole period. Not surprisingly (Hall and Richards, 2013), outstanding breeding events such as the delivery of Bt corn to market in 1996 did not produce equivalent landmarks in maize production during the evaluated period. Climate extremes, such as drought or heat stress, can lead to harvest failures and threaten the livelihood of agricultural producers and the food security of communities. Improving the understanding of their impacts on maize GY is crucial to enhance the resilience of the global food system. Climate factors, including mean climate and climate extremes, explain 16%– 39% of the variance of yield anomalies (YA), with 10%–31% of the explained variance attributable to climate conditions (Fig. 1.2). YA related more closely with temperature extremes than with precipitation-related factors (Vogel et al., 2019). The forecast for future scenarios is a loss of climatic suitability for maize in Sub-Saharan Africa and Latin America regions but accompanied by an expansion in the northern hemisphere, particularly in Europe. The relative change in climatically suitable areas for future maize production was estimated for the top five producers. Production in 2050 is expected to increase 8% for the USA and 4% for China, and to decrease 5% for Brazil, 2% for Argentina, and 11% for México. The incidence of low temperature and waterlogging, presently common in Europe and Asia, is projected to diminish, whereas heat stress in Africa and drought stress in South America are projected to increase (Ramirez-Cabral et al., 2017).

1.2  Main production areas The USA is the world leader in annual maize production, with 38.9 Mha (Fig. 1.1a) that produce 392 Mt (Fig. 1.1b). Twenty percent of the production is exported. After the introduction of teosinte in the USA territory thousands of years ago, many native communities adopted it as a staple crop as far north as Canada, reaching a production of ca. 13.9 M t y− 1. First European settlers to arrive to Western USA learned maize cultivation from native Americans and spread the crop eastwards. Presently, maize is produced in a variable proportion in most USA states, Iowa being the leader in total production and GY (Fischer and Edmeades, 2010), closely followed by Illinois and Nebraska. Crop Physiology: Case Histories for Major Crops. Copyright © 2021 Elsevier Inc. All rights reserved.


4  Crop Physiology: Case Histories for Major Crops


Production (M t)



1000 750

10.5 M t y–1




4000 66 kg ha–1 y–1 2000

³12 t ha–1


34 M t y–1

6000 Yield (kg ha–1)




³335 M t


0.88 M ha y–1


100 1250

³37 M ha


4.36 M ha y–1


Area (M ha)


0 1960

1980 Year 2000


60 50




40 30 20


45 11.3





North America






South America




Regional Production Shares (%)

Regional Production Shares (%)

FIG. 1.1  Left panels: world distribution (averaged for the 2008–17 period) of (a) area cropped to maize, (b) production, and (c) GY. Right panels: evolution of annual records of each variable for the 1961–2017 period. Lines represent (a and b) bilinear and (c) linear fitted models. The corresponding breakpoints (vertical arrows in a and b) and slopes (all variables) are indicated. Based on FAO, 2019. Records accessed on 1st Aug 2019, http://www.fao. org/faostat/en/#data/QC.


140 120
















60 40








South America


20 0

North America

FIG. 1.2  (a) Mean regional production shares per continent during 1990–2008 and standard deviation (SD) of production anomalies (after detrending) relative to mean production and (b) Explained variance of production shares accounted for yield anomalies (YA), mean climate conditions plus extreme events (MCC + EE), and mean climate conditions (MCC). Adapted from Vogel, E., Donat, M.G., Alexander, L.V, Meinshausen, M., Ray, D.K., Karoly, D., Meinshausen, N., Frieler, K., 2019. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14, 054010,

China is the second global maize producer (Fig. 1.1b). Over the past few years, maize has become the main crop of the country, both in production (257 M t y− 1) and cultivated land (42.4 Mha− 1). This change is attributed to the increasing local demand for livestock feed (i.e. animal protein). A greater proportion of urban, wealthier population has driven consumption of meat products per capita to increase at a rate of 1.38 kg y− 1 since 2011. Over the past 25 years, maize production has increased 125%, while rice production has experienced a modest 7% increase. While in the 1940s, two-thirds of the Chinese maize crop production were used for human consumption, presently, 60% is used as animal feed, 10% for direct human consumption, and the remaining for industry. Brazil is the third world maize producer, with nearly 94.5 M t annually and 17.3 Mha. This country is consolidating as the second main exporter. The production comes from two contrasting systems. One is called safra (full season), characterised by sowing starting early in August up to October–November. The other is called safrinha that presently occupies

Maize Chapter | 1  5

two-thirds of total area cropped to maize. The safrinha corresponds to late-sown maize (starting in January up to February), usually rotated with soybean to avoid fast soil nitrogen (N) decay. It predominates in the Mato Grosso states at the CentreWest of the country, which have displaced the traditional maize producing area located in the south–south east. The fourth maize producer is Argentina, accounting for approximately 6.4 Mha and reaching 51 M t y− 1 in 2018–19. Argentina is presently the third exporter, close to Brazil. Like in the neighbour country, there are two main sowing dates: the early one that ranges between late August and early November and the late one from December to end of January. Delayed sowing became economically feasible with the introduction of Bt maize in 1997 (Otegui et al., 2002) and gained fast adoption after the severe drought of 2008–09. It presently occupies 45%–55% of the total area. Córdoba, in the geographical centre of the country, is presently the main maize producer province of Argentina. The EU produces 61 M t of maize per year. France, chiefly in the south, leads maize production within the EU. The Aquitania region delivers 21% of overall France production, which is presently 14 M t y− 1 in 1.7 Mha. The crop is generally sown between April and May and is harvested between September and November. Owing to low local consumption, most of the French production is exported. Other important maize producers in the EU are Romania, Hungary, Spain, Germany, and Poland. Ukraine, along with Russia, is the principal maize producer in the Black Sea region. Ukraine has vast sectors of black soils known as chernozems, which are among the most fertile in the world. Ukrainian growers produce 32 M t of maize annually in 4.3 Mha. In 2018–19, Ukraine accounted for 16% of global maize exports, becoming the fourth exporter. India produces 28 M t of maize annually in 9.2 Mha. The crop is primarily grown in the northern states, including Uttar Pradesh, Bihar, Himachal Pradesh, and Rajasthan. Uttar Pradesh and Bihar account for nearly 16% and 14% of the country’s maize production, respectively. The crop is usually sown at the beginning of the rainy season, between mid-May and July, and is harvested between November and January. Maize is the most important crop grown in Mexico, with almost 60% of the country’s cropland from sea level up to 2600 masl. Occident, Bajio, and Sinaloa regions contribute with nearly 60% of total production. Climate allows for two harvests per year, which in 2018–19 produced 27 M t in an area of 7.4 Mha. The first sowing takes place in spring–summer (April–July) and accounts for 75% of the annual production. The remaining 25% corresponds to the second one, which takes place during autumn–winter and is conducted under irrigation. Mexico has no export surplus but presently meets the internal demand of white maize for human consumption plus a small part for animal feed. However, it still needs to import 14 M t of maize for its growing livestock sector. Indonesia is the leading maize producer (26 M t in 5.3 Mha) in the ASEAN Economic Community, followed by the Philippines (7.5 M t) and Vietnam (5.2 M t). Indonesia needs to import maize, primarily for livestock. The main constraint to Indonesian production is the lack of enough agricultural land, which is balanced with forested lands. South Africa produced 16 M t of maize in 2.6 Mha during 2018–19. It is one of the main African producers together with Nigeria, Egypt, and Ethiopia (10 M, 8 M, and 7.8 M t, respectively). The crop is cultivated primarily in the north and north-east of the country. The South African provinces of Guateng, north-west, Mpumalanga, and Orange Free generate the highest maize GYs of the country. This crop is sown between September and December and is harvested between April and June. The rest of the world (141 countries) accounts for the production of the remaining 140 M t to complete 1147 M t in 2018 (based on FAO, 2019).

1.3  Maize in rotations: Suitability of previous and consequences for following crops Being a C4 plant, a high proportion of maize in the cropping sequence assures large biomass productivity and high water and radiation use efficiencies to the crop system (Caviglia et al., 2013). Maize has positive effects on soil carbon (C) ­balance and soil physical properties (e.g. infiltration, stability of aggregates) compared to other crops because it produces a high amount of stubble with a high C/N ratio. The effect of maize on soil carbon content and on the soil carbon content equilibrium depends on management practices and GY as well as on soil and climate. The annual soil carbon balance is positively related to maize GY and negatively associated with soil carbon content. For maize GYs higher than 10 t ha− 1 in the US Corn Belt, soil carbon balance was negatively associated with soil carbon percent, and the balance was positive at soil carbon percent below 3% (Lucas et al., 1977). For the south-east of the Pampas region of Argentina, crop sequences with maize caused smaller reductions in soil organic matter along the years when compared with rotations without maize (Studdert and Echeverría, 2002). The most relevant short-term effects of the preceding crop in the rotation are related to biotic adversities and the availability of water and nutrients. Maize reduces N availability for the next crop because of its high nutrient demand and high total N harvested with the grain, which leaves a large amount of residues with a high C/N ratio. Accordingly,

6  Crop Physiology: Case Histories for Major Crops

GY of non-fertilised wheat following maize is penalised and has a high response to N fertilisation when compared with wheat following other crops (Echeverría et al., 1992). Hence, a nitrogen (N) fixing crop or a crop with low N requirement is convenient after maize (Domínguez et al., 2001). Additionally, where water recharge of the soil profile during fallow and after sowing is limited and rainfall during the growing season is insufficient, the effect of the preceding crop on water availability becomes relevant. In this sense, maize usually consumes more water than the other summer crops (Della Maggiora et al., 2002). Growing maize in consecutive seasons reduces GY (Berzsenyi et al., 2000). Repeating maize in the cropping sequence reduces its productivity when compared with maize in rotation after pasture or soybean (Domínguez et al., 2001; Farmaha et al., 2016; Sindelar et al., 2015). This positive rotation effect is usually because of the increased N supply to maize following soybean or pasture (Domínguez et al., 2001). Fertiliser, however, does not fully compensate the described positive crop rotation effect, which could be also attributed to (1) improvements in soil water availability, soil structure, and soil microbial activity, (2) reductions in weed, insect, and disease incidence, and (3) absence of phytotoxic compounds and/or presence of growth-promoting substances originated from crop residues (Bullock, 1992; Karlen et al., 1994). Nevertheless, in semi-arid regions like those in the north of Argentina, the rotation soybean-maize requires a minimum of 50% of maize to adequately supply carbon to the soil through residues and roots, improving water capture efficiency, soil physical characteristics, and tolerance to erosion (Martini and Angeli, 2017).

1.4  Multiple cropping An option to intensify the use of agricultural land consists of sowing two or more crops per season as double crops, relay crops, or intercrops (Caviglia et al., 2004; Coll et al., 2012; Neto et al., 2010). These intensified systems have been implemented in many regions worldwide (Fischer et  al., 2014). They increase annual land productivity and water, radiation, and nutrient productivity, mainly through improved resource capture (Andrade et al., 2015; Caviglia and Andrade, 2010). The increase in resource capture results in (1) reduced surface run-off, percolation, erosion, water contamination, and water table level and (2) increased stubble inputs, soil carbon content, soil protection, and better weed control. Including maize in intensified systems also ensures high resource use efficiency (Caviglia and Andrade, 2010) and biomass production (Monzon et al., 2014), which contribute to maintain soil carbon balance (Oelbermann and Echarte, 2011). Examples of intensified systems that include maize are (1) winter wheat–summer maize double cropping in the North China Plain, which provides about 33% of the nation’s maize production (Wang et al., 2012; Yu et al., 2006), (2) maize as a second crop after soybean harvest (safrinha) in Mato Grosso and Parana, Brazil, with more than 3.3 Mha in 2013, (3) maize grown as double crop after field pea or other winter crop as an alternative to the widespread wheat/soybean double crop in Argentina (Andrade et al., 2017; Mercau and Otegui, 2015), and (4) soybean as double crop after maize harvest or as relay intercrop from 40 to 60 days after maize sowing (Monzon et al., 2014). Xu et al. (2020) estimated the worldwide average land equivalent ratio of the maize/soybean intercropping in 1.32 ± 0.02, underscoring the high potential of intercropping over sole crops. The productivity of such intensified option depends on the frost-free period, temperature regime, and water budget of each environment (Andrade and Satorre, 2015; Caviglia et al., 2013, 2019; Monzon et al., 2014; Van Opstal et al., 2011). The length of the growing season may limit the adoption of these alternatives in several regions. Accordingly, the increase in annual land productivity for maize–soybean double crop and relay intercrop was directly associated with the length of the growing season (Monzon et al., 2014).

2  Crop structure, morphology, and development 2.1  Main phenological events Ritchie et al. (1986) summarised visual maize development in the most widely used phenological scale (Fig. 1.3a). The VT-R1 period (VT: tasselling; R1: silking) splits the crop between vegetative (Vn, with n representative of leaf number) and reproductive (Rn, with n spanning from visible silks in R1 up to physiological maturity in R6) phases. Shoot meristems, however, follow a microscopic differentiation pattern that was described and illustrated in detail for the first time by Bonnett (1940, 1948). This pattern includes (1) floral initiation (FI) of the main stem apex (Fig. 1.3a), which represents the shift from leaf primordia differentiation to male differentiation (FIM) and (2) FI at axillary meristems (Fig. 1.3a), which represents the shift from husks differentiation to female differentiation (FIF). The former will produce the panicle (described as tassel) responsible for pollen production. The latter will produce the spikes (described as ears), which will bear the ovaries (Fig. 1.3b). The FIM sets total leaf number (TLN) in the main stem and usually takes place when the number of visible leaf tips is one-third of TLN, reflecting the tight coordination between the number of leaves initiated by the apical meristem and





Leaf area index .........









ML BL milky Start ear Kernel Active Phisiological Silking elongation Harvest set Grain Filling Maturity Critical period

Inducible phase Juvenile phase 300














DAS Scale


70-90 R2



Reproductive Stages (R)

Vegetative Stages (V)







Kernel weight (mg) .........



1 2





FIG. 1.3  (a) Schematic representation of the maize growth cycle. The x axis includes the scale proposed by Ritchie et al. (1986) together with cycle duration in thermal time (TT, °Cd) and in days after sowing (DAS) and main developmental events and phases. The span of the horizontal arrows represents (1) the period of crop response to temperature and photoperiod, and (2) the extension of the juvenile phase, the inducible phase, and the critical period for kernel number per plant (KNP) determination. Vertical arrows indicate the time of TLN and maximum leaf area index (LAIMAX) establishment, as well as of KNP, individual kernel weight (KW), and grain yield (GY) establishment. Data of cycle duration, leaf area index (LAI; m2 of green leaves per m2 of soil), and KW are representative of temperate hybrids with a TLN of ≈ 20 and a relative maturity (RM) of ≈ 120. Images of just extruded silks and a mature kernel with the characteristic black layer (BL) at the placental region are representative of the silking and physiological maturity stages, respectively. The latter is preceded by a schematic representation of a kernel at the half-milk stage (90%–95% of final KW), with the milk line (ML) separating the starchy from the milky endosperm. FIM: male floral initiation; FIF: female floral initiation. (b) Illustrative images of ear development between the start of active ear elongation (≈ V14) and silking (R1). In the example, the ear length increases from ≈ 1.5 cm in (1) to ≈ 8.5 cm in (6), depending upon the genotype and growing conditions. At the start of active ear growth, spikelets at the base of the ear have completely developed florets with silks of less than 1 mm (circle in 1). These silks will reach 14.5 cm at silking (the bottommost red arrow in 6). Floret development and silk elongation continues acropetally along the ear from (1) to (6). Active silk elongation can be observed in florets at the middle of the ear during 5–7 days before silking (4), when silks at bottommost positions are 4 cm long and have reached the tip of the ear. The bottommost ovaries will be 10 cm long during 1–2 days before silking (5), whereas those from the middle of the ear will reach silking on the same date with silks of only ≈ 10 cm length (6). Almost all florets along the ear will reach complete floret development at silking. Pictures obtained by M.E. Otegui on maize crops grown a Pergamino (33° 56′ S, 60° 33′ W), Argentina. Reference scale is in mm.

8  Crop Physiology: Case Histories for Major Crops

the number of visible leaves up to FIM (Padilla and Otegui, 2005). This coordination holds across maize hybrids of different maturity groups, with TLNs between 15 (short maturity) and 25 (long maturity). There are no axillary buds in the topmost leaves between the apical meristem and apical ear bud because apical dominance arrests the acropetal differentiation of axillary buds at the time of FIM (Lejeune and Bernier, 1996). Therefore, FIF is delayed with respect to FIM (Otegui and Melón, 1997), and it occurs first at the last differentiated, topmost axillary bud in the so-called ear-leaf. The ear leaf is among the largest leaves of the plant and is located at approximately one-third of the TLN counting downwards along the stem (Dwyer et al., 1992). This proportion, however, is under genetic control and consequently, may vary across genotypes (Li et al., 2015), with the concomitant effect on leaf area distribution above and below the ear. The FIF continues downwards, which explains the existence of more than one grain-bearing ear per plant (i.e. prolificacy > 1) depending upon the genotype (i.e. prolific vs. non-prolific), the resource availability per plant, and their interaction (Otegui, 1995; Tetio-Kagho and Gardner, 1988a). Buds at the lowermost nodes do not elongate and do not become reproductive. These buds may develop into tillers, particularly for some hybrids grown at very low stand densities in arid environments (Kapanigowda et al., 2010). From FI onwards, reproductive buds undergo the developmental processes that shape the characteristic morphology of maize tassel and ears (Bonnett, 1966). The final results of these processes are the events of anthesis (i.e. anthers dehiscence and pollen shed by tassels) and silking (silks extrusion from the husk by one or more ears per plant). At this stage, all differentiated leaves have completed their expansion and the crops have reached its maximum leaf area (Fig. 1.3a), whereas most florets along the ear of maize are totally developed and have started silk elongation (Fig. 1.3b). Contrary to other cereal crops as wheat, floret development per se does not represent a constraint to maize kernel set in most field conditions (Otegui et al., 1995a, 1995b; Otegui, 1997; Otegui and Melón, 1997; Rattalino Edreira et al., 2011), but environmental constraints that cause a delay in ear morphogenesis may affect the silking pattern (Rossini et al., 2012). A slight protandry (i.e. anthesis anticipation respect to silking of the topmost ear) is common in maize, producing an anthesis-silking interval (ASI) of a few days in standard growing conditions. This effect of apical dominance is enhanced when crops are exposed to stressful environments around flowering, increasing the ASI (Bolaños and Edmeades, 1993; D’Andrea et al., 2009; Edmeades et al., 1993; Hall et al., 1982), and reducing the total number of exposed silks (Debruin et al., 2018; Messina et al., 2019; Rossini et al., 2020) with the concomitant negative effect on kernel set. In contrast, some genotypes may express protogyny, i.e. anticipation of silking with respect to anthesis (Haskell, 1953), particularly when grown under no abiotic/biotic constraint at low stand density (Borrás et al., 2009). This condition usually improves pollination synchrony within and between ears (Uribelarrea et al., 2002), promoting increased prolificacy and kernel set (Cárcova et al., 2000). There is an important variation for prolificacy expression in maize (De Leon and Coors, 2002), with no clear benefit between prolific and non-prolific hybrids in many cropping conditions (Otegui, 1995). The exception to this trend is in drought-prone environments for which reduced stand density is recommended, where prolific hybrids have higher GY stability across years (Ross et al., 2020). Ovary fertilisation along the ear proceeds between the start of silking (R1) and the start of active kernel growth of the early-fertilised ovaries (R2), which usually represents a limit to successful kernel set among the late-fertilised ones (Cárcova and Otegui, 2007). Early kernel growth is chiefly linked to water influx (Borrás et al., 2003b), whereas active biomass accumulation starts at R2 and continues up to R6 (Fig. 1.3a). Biomass accumulates steadily in kernels along this period in parallel with a steady decline in kernel water content. Maximum kernel weight (KW) is reached at physiological maturity (Fig. 1.3a), when a black layer of necrotic tissue is evident at the placental region of the kernel (Daynard and Duncan, 1969) and its water content has dropped to ≈ 30%–40% (Borrás et al., 2004; Gambín et al., 2007). A good surrogate to anticipate R6 is to follow the ML development (Fig. 1.3a). This line represents the interface between the solid (starchy) and liquid (milky) matrices of the maturing endosperm (Afuakwa and Crookston, 1984) and progresses from the crown to the base of the kernel. A 50% ML (or half-milk stage) corresponds to ≈ 40% kernel moisture and ≈ 90%–95% of final GY, whereas there is no milk remaining when the black layer becomes evident. Anticipation of R6 is particularly important in the production of maize silage (Ma et al., 2006).

2.2  Genotypic and environmental drivers of maize development 2.2.1 Temperature For a given genotype, the duration in days of the above-described cycle is primarily modified by temperature (Fig. 1.3a), which affects the extension of all developmental stages in most evaluated environments (Capristo et al., 2007; Kumudini et al., 2014; Shim et al., 2017; Tsimba et al., 2013). In the absence of photoperiodic effects and abiotic constraints, such as water or N deficiencies, maize developmental rate during the vegetative period (i.e. pre-VT) increases between a base temperature (Tb) of ≈ 8–10 °C and an optimum temperature (Top) of ≈ 30–35 °C, whereas temperatures > Top reduce the developmental rate (Cicchino et al., 2010a; Gilmore and Rogers, 1958; Kiniry, 1991). References for Tb and Top are less

Maize Chapter | 1  9

accurate for the grain-filling period (Kumudini et al., 2014), being 8 °C in CERES-Maize model (Kiniry, 1991) but assumed as 0 °C by other authors (Sinclair et al., 1990; Tsimba et al., 2013). Within Tb and Top, the TT (in °Cd) requirement for completing a given phase is represented by the inverse of the slope in the model fitted to the response of the developmental rate to temperature; i.e. it is a genotype-dependent constant (Ellis et al., 1992). On the basis of the TT concept, several models have been developed to predict cycle duration that are used in crop simulation models such as CERES-Maize (Jones and Kiniry, 1986) and APSIM (Keating et al., 2003). These models differ in cardinal temperatures and the associated functions for computations. Therefore TT requirements reported in the literature must be taken with care for comparisons because they may vary depending upon the approach (Dwyer et al., 1999a; Kumudini et al., 2014). Predictions of these models usually focus on TT to anthesis, silking, and physiological maturity and do not include the period of kernel desiccation up to commercial harvest maturity. The only methods that include differences in kernel desiccation rate are the ones developed by FAO and the comparative RM or simply RM. Originally (Jugenheimer, 1958), the former used a kernel moisture content of 34%–36% as reference, which dropped to less than 20% in the 1990s (Marton et al., 2008). The latter is an adaptation of the originally known as Minnesota Relative Maturity Rating (MRMR) because of its origin (Peterson and Hicks, 1973), and it is the most widely used all across the Americas. The RM method ranks hybrids comparatively so that two RM units represent a 1% difference in kernel moisture at harvest. As a general reference, hybrids that range between 1014 and 1453°Cd for the whole cycle and represent RMs of 75–110 or FAO of 100–500 are recommended for latitudes between 48º and 39ºN, respectively, in Canada and the USA (Dwyer et al., 1999a; Troyer, 2001). The TT requirement rises to 1200–1700°Cd for the whole cycle (Fig. 1.3a) among hybrids recommended for mid-latitude environments (30–38ºS) as those representative of the Pampa region in Argentina (Capristo et al., 2007; Otegui et al., 1996), which correspond to RMs of 110–128 or FAO 500–600 (Di Matteo et al., 2016; Luque et al., 2006; Troyer, 2001). TT requirements (> 1500°Cd) and RMs (125–140) and FAO rankings (600–800) increase for subtropical and tropical conditions as those representative of Brazil (Bergamaschi et al., 2013; Tojo Soler et al., 2005) or India (Thimme Gowda et al., 2013). Despite this conceptual framework, TT requirements reported in literature must be taken with care for comparisons because they may vary widely depending upon the approach used for computation (Dwyer et al., 1999a, 1999b; Kumudini et al., 2014). Expansion of all differentiated leaves takes place between crop emergence (VE) and tasselling-anthesis (Fig. 1.3a) at a relatively constant phyllochron of usually 37–42°Cd leaf− 1 among reported hybrids (Hesketh and Warrington, 1989) but may extend up to 75°Cd leaf− 1 among hybrids in the CERES-Maize database of DSSAT (Hoogenboom et al., 2017) and between 33 and 62°Cd leaf− 1 among inbreds (Giauffret et al., 1995). The TT requirement for leaf primordia differentiation at the apical meristem (i.e. plastochron) may range between 24.3 and 36.4°Cd leaf− 1, with Tb between 4.0 and 8.1 °C, depending upon the genotype and based on soil temperature at 5-cm depth (Padilla and Otegui, 2005).

2.2.2 Photoperiod As a short-day plant (i.e. highest developmental rate under short photoperiod), the TLN may vary depending upon (1) the photoperiod during the inducible phase and (2) the degree of photoperiod sensitivity. Two sub-phases can be recognised during the vegetative phase of maize apical meristem, during which only leaf primordia are differentiated. The first one is a juvenile (i.e. photoperiod insensitive) phase, which is followed by an inducible (i.e. photoperiod sensitive) phase (Fig. 1.3a). The latter is subsequently followed by a realisation phase, when the meristem has already initiated reproductive differentiation and is no longer sensitive to photoperiod (Kiniry et al., 1983b). The FIM sets the limit between the inducible and realisation phases, and it is usually evident as an elongation of the apical meristem (Bonnett, 1966; Stevens et al., 1986). No similar morphological change identifies the transition between the juvenile and the sensitive phases of the meristem. Detection of a juvenile period in three photoperiod-sensitive hybrids was possible by exposing plants to alternating short and long photoperiods at different intervals during the cycle and recording tassel emergence date (Kiniry et al., 1983b). These experiments in controlled conditions allowed the detection of a juvenile phase that ended 4–8 days before FIM when plants were grown at short photoperiods (i.e. highly inducible). Therefore the inducible phase lasts 4–8 days under optimum, short photoperiods and is expected to be ≥ 4–8 days for photoperiod-sensitive genotypes grown at photoperiods longer than a threshold. Subsequent experiments (Kiniry et al., 1983a) in controlled conditions allowed the estimation of the main parameters (Major, 1980) of maize developmental response to photoperiod (BVP: basic vegetative phase, sum of the juvenile and inducible phases under optimum photoperiods; TP: threshold photoperiod; PS: photoperiod sensitivity) for hybrids of different maturity groups. These parameters ranged between 139 and 344°Cd for BVP, 10 and 13.5 h for TP, and 0 and 36°Cd h− 1 for PS. The BVP (equivalent to earliness per se presently used in wheat, see Chapter 3: Wheat, Section 2.2) was the shortest for the early-maturity group (≤ 139°Cd), and longest for the late-maturity group (≥ 251°Cd). Duration of the juvenile phase presently documented at the CERES-Maize database of DSSAT range between 110 and 458°Cd, whereas photoperiod sensitivity ranges between 0 and 5 d h− 1 (Hoogenboom et al., 2017). The use of days for the inducible phase

10  Crop Physiology: Case Histories for Major Crops

a­ llows for a variable TT duration of this phase, which may or may not modify TLN depending upon temperature. This subtle change is key to capture genotype × environment effects that are usually evident as a variation in the TLN in field conditions because of variable temperature regimes (Tollenaar and Hunter, 1983). Such effects are commonly registered even for the same hybrid sown in the same site and sowing date across years (Cirilo and Andrade, 1994a; Otegui et al., 1995b). Knowledge of the previously described genotype × temperature × photoperiod interactions is critical for matching genotype and environment under two main premises. Firstly, total cycle duration must fit within the length of the growing season and must avoid excessively low temperatures at early and late growth stages. These situations are frequent in exceptionally early (Otegui et al., 1996) and late sowings (Bonelli et al., 2016; Mercau and Otegui, 2015). In extremis, early frosts may cause seedling mortality and reduce stand density and uniformity because maize cannot compensate for plant losses. Uneven stands are particularly critical in maize (Liu et al., 2004) because they promote hierarchies among plants (i.e. dominant and dominated individuals) with negative effects on the final number of kernels m− 2 and GY (Maddonni and Otegui, 2004). Late frost may prematurely arrest grain filling with direct penalisation of GY (Baum et al., 2019; Bonelli et al., 2016; Mercau and Otegui, 2015). Such penalisation may also take place with excessively low irradiance late in the cycle, which reduces plant growth per set kernel (Cirilo and Andrade, 1996; Maddonni et al., 1998), may hasten reserve deployment (Jones and Simmons, 1983; Uhart and Andrade, 1991) and may shorten the grain-filling period and increase grain moisture at physiological maturity (Sala et al., 2007). Secondly, sowing date and genotype selection should pursuit the concurrence of the critical period for kernel set with the optimum conditions for crop growth (Section 4.1) in order to (1) avoid negative effects of the environment on the ASI (in days), which affects kernel set (Bolaños and Edmeades, 1993; Hall et al., 1982), and (2) maximise the duration of the critical period for kernel set (Otegui and Bonhomme, 1998) and crop growth rate (CGR) during the critical period (Andrade et al., 1999) for maximising the number of kernels m− 2 (Section 4.1).

3  Growth and resources 3.1  Capture and efficiency in the use of radiation 3.1.1  Canopy size and light interception The total shoot biomass (BT) produced in a period of n days can be expressed as the cumulative daily product of incident photosynthetically active radiation (IPAR), the efficiency of the canopy for intercepting IPAR (ei), and the radiation use efficiency (RUE) (Eq. 1.1).

   IPAR  e  RUE  , with IPARi  IPAR  e

BT g m 2 






During early growth, maize canopies invest a large proportion of resources (photoassimilates and nutrients) in leaves, which in turn promotes ei. From seedling emergence (VE) to flowering (VT-R1), the canopy will develop a leaf area three to five times greater than the land area it covers, a relationship known as LAI (Fig. 1.3a). In the absence of plant stress, CGR increases with increasing ei, peaking at ei = 0.95. The LAI required to achieve this ei is the critical LAI (LAIC). In maize, a typical LAIC is 3–4. Differences in LAIC can be related to contrasting leaf habit (e.g. erectophile or planophile) between hybrids (Maddonni and Otegui, 1996) and sowing patterns (Maddonni et al., 2001, 2006). Then, ei is an exponential function of LAI, with a curvature that depends upon an extinction coefficient k (Eq. 1.2) that usually ranges between 0.4 and 0.6 for maize. e i  1  exp

 k  LAI 


Leaf area is a function of leaf area growth and senescence. Leaf area growth depends on leaf number and size. Leaf number is mainly regulated by the genotype (Dwyer et al., 1992) and by the environmental conditions, depending on the crop response to photoperiod and temperature (Section 2.2). Leaf area per plant may be greater under long photoperiods that favour more leaves in sensitive hybrids (Bonhomme et al., 1991; Kiniry et al., 1983b). Individual leaf development and expansion, however, are mainly driven by temperature (Section 2.2; Ritchie and Nesmith, 1991). Leaf appearance rate increases between 8 and 34 °C and decreases at higher temperature (Kiniry, 1991). Leaf expansion rate (LER) is a direct function of temperature (greatest between 22 and 32 °C; Ritchie and Nesmith, 1991), whereas duration of the expansion period is inversely related to temperature (Hay and Walker, 1989). Owing to total or partial compensation between these two processes, the canopy achieves maximum leaf area when the average daily temperature is 21 °C (Hardacre and Turnbull, 1986; Reid et al., 1990; Wilson et al., 1973). A consequence of these characteristics is the

Maize Chapter | 1  11

increase in LAI in response to the delay in sowing date registered for spring sowings among temperate hybrids grown at the same stand density under no abiotic constraint (Maddonni and Otegui, 1996). LER is highly sensitive to water and nutrient deficits, as discussed in Sections 3.2.3 and 3.3.2. From crop emergence to anthesis, generation and expansion of leaves dominate leaf area dynamics, with little contribution of senescence except in extreme situations (e.g. high plant population density and shortage of N). Early vigour and maintenance of active leaf area ensure high capture of radiation and thus high biomass production. Maximum green leaf area is attained at anthesis (Fig. 1.3a). Afterwards, changes in leaf area are a direct function of the senescence rate (Borrás et al., 2003a). Leaf senescence is the series of biochemical and physiological events comprising the final stage of development, from the fully expanded state until leaf death. Leaf senescence is genetically determined and modulated by environmental factors (Thomas and Ougham, 2015). For instance, maize expressing the sunflower transcription factor HaHB11 exhibits improved GY chiefly through delayed leaf senescence (Raineri et  al., 2019). Key environmental factors accelerating senescence include low radiation, low red:far-red ratio, water and nutrient deficiencies, vascular and leaf diseases, and unbalanced source–sink ratio (Borrás et al., 2003a; Rajcan and Tollenaar, 1999; Sadras et al., 2000).

3.1.2  Radiation-use efficiency and its response to environmental factors Maize average RUE during the season is higher than for other summer grain crops such as soybean and sunflower (Andrade et al., 2005). For maize growing under adequate conditions, RUE is between 2 and 4 g MJ− 1 (Andrade et al., 1992; Hao et al., 2016; Lindquist et al., 2005; Otegui et al., 1995b; Westgate et al., 1997), when obtained from spot measurements at midday on sunny days and expressed on shoot biomass and a photosynthetically active radiation (PAR) basis. The superior RUE of maize results from (1) its C4 metabolism (Hesketh, 1963), with leaf photosynthetic rate 30%–40% higher than C3 species such as soybean, (2) its lower extinction coefficient (Eq. 1.2) that allows a more uniform distribution of the incoming radiation within the crop canopy, and (3) the low energy cost of carbohydrate-rich plant tissues (cellulose during vegetative growth and starch during grain filling) compared to protein and fat-rich tissues (Varlet-Grancher et al., 1982). Sinclair and Muchow (1999) and Stöckle and Kemanian (2009) reviewed the sources of variation in RUE for different species. Low temperatures (Andrade et al., 1993; Westgate et al., 1997), water deficits (Muchow, 1989a), and nutrient deficiencies (Muchow and Davis, 1988; Uhart and Andrade, 1995a) reduce RUE. In most crop environments, low temperatures during early vegetative growth in early sowing and during grain filling in late sowing reduce RUE (Cirilo et al., 1992; Cirilo and Andrade, 1994a; M.E. Otegui et al., 1995b; Wilson et al., 1995)(Cirilo et al., 1992; Cirilo and Andrade, 1994a; Wilson et al., 1995). The effect of water and nutrient deficiencies on crop biomass production, however, is largely accounted for by the reduction in the amount of radiation intercepted by the crop because the decline in RUE is generally less important (Boyer, 1970; Gifford et al., 1984; Uhart and Andrade, 1995a). This is the consequence of leaf expansion being much more sensitive to water and nutrient deficits than photosynthetic rate per unit leaf area (Muller et al., 2011; Sadras and Milroy, 1996; Salah and Tardieu, 1997). Reductions in RUE as a result of water deficits are explained by stomatal or non-stomatal factors (Farquhar and Sharkey, 1982), according to the direction of the changes in CO2 concentration in the stomatal cavity (decreases or increases, respectively). High source–sink ratio during grain filling can reduce RUE (Borrás and Otegui, 2001; Rajcan and Tollenaar, 1999), possibly because of photosynthetic feedback inhibition.

3.1.3  Crop growth rate and growth duration in response to management practices As it was already indicated, CGR is a function of IPARi and RUE, which depend on temperature and on the water and nutrient status of the crop. In turn, growth duration is determined by factors controlling phenological development, mainly temperature (Section 2.2). Rate of growth and growth duration are integrated into conceptual variables largely correlated with total biomass accumulation, i.e. photothermal quotient and growth per unit TT (Andrade et al., 1999; Fischer, 1985). Maize canopies, as in all annual species, do not profit from all the IPAR during the growing season. The proportion intercepted generally ranges between 59% and 79% of total IPAR (Otegui et al., 1995b). This limitation can be overcome in part by (1) early sowing, which does not improve the proportion of IPAR that is intercepted along the season but improves the total amount of light intercepted by the crop (Bonelli et al., 2016; Cirilo and Andrade, 1994a; Otegui et al., 1995b); (2) long season hybrids (Otegui et al., 1995b); (3) increasing plant population density (Westgate et al., 1997), and (4) reducing row spacing (Andrade et al., 2002a; Maddonni et al., 2006). These practices increase IPARi because they promote rapid canopy closure and/or increase the amount of IPAR. The benefits of early ground cover do not translate into increased GY if they do not improve ei at the critical stages of GY determination (Maddonni et al., 2006; Westgate et al., 1997; Section 4.1). Delaying sowing hastens vegetative growth and development because of high temperatures. Vegetative growth, however, is accelerated to a greater extent, so late-sown plants are generally larger than those sown early (Cirilo and Andrade, 1994a; Knapp and Reid, 1981; Maddonni and Otegui, 1996). Under these conditions, crops achieve maximal light i­nterception

12  Crop Physiology: Case Histories for Major Crops

in a shorter period from emergence (Bonhomme et al., 1994; Cirilo and Andrade, 1994a; Maddonni and Otegui, 1996). However, shortening of the growing cycle in late sowings decreases the total amount of radiation intercepted by the crop and, thus crop dry matter at harvest (Cirilo and Andrade, 1994a; Otegui et al., 1995b; Srivastava et al., 2018). Delays in sowing result in deterioration of some environmental conditions (i.e. reduced incident radiation and reduced RUE related to reduced temperatures) during the critical period for grain number determination and mostly during grain filling (Bonelli et al., 2016; Cirilo and Andrade, 1994b; Tsimba et al., 2013; van Roekel and Coulter, 2011), as analysed in Section 4. Plant density is the practice with the greatest impact on LAI and hence on light interception of maize canopies (Overman and Scholtz, 2011). LAI decreases markedly in response to reductions in plant density (Cox, 1996; Maddonni et al., 2001; Tetio-Kagho and Gardner, 1988b) because leaf area per plant does not vary much when resources per plant increase (Andrade et al., 2005). This lack of vegetative plasticity in maize is the consequence of a very stable leaf size, a nearly constant leaf number (Vega et al., 2000), and a low capacity for tillering (Doebley et al., 1997). Thus radiation interception in maize is highly responsive to plant density (van Roekel and Coulter, 2011). This decrease in ei with reduced plant densities contrasts with the response of other crops. Decreasing plant density results in large reductions in IPARi at the critical period for grain number determination in maize, which results in reduced CGR at flowering and, in turn, in lower number of grains per unit area (Andrade et al., 1999). Decreasing row spacing at equal plant densities leads to more equidistant plant distribution, hence reducing plant-toplant competition for water, nutrients, and light, and increases intercepted radiation and biomass (Barbieri et al., 2008, 2012, 2013; Bullock et al., 1988). It also reduces the LAI required to intercept 95% of the incident radiation because of a higher light extinction coefficient (Flénet et al., 1996; Riahinia and Dehdashti, 2008). In the absence of significant water and nutrient deficiencies, however, the benefits of decreasing row spacing are variable. Some researchers report GY increases (Bullock et al., 1988; Olson and Sander, 1988; Scarsbrook and Doss, 1973), while others do not (Ottman and Welch, 1989; Westgate et al., 1997; van Roekel and Coulter, 2012). GY responses to decreased distance between rows are inversely proportional to ei achieved with the wide row control treatment during the critical period for grain number determination (Andrade et al., 2002a). Thus GY increase in response to narrow rows is closely related to the improvement in ei during the critical period for grain set. Full light interception can probably not be achieved when (1) short-season and/or erect-leaf cultivars are grown (Bavec and Bavec, 2002); (2) plants are defoliated by frost, hail or insects; or (3) plants are subjected to water or nutrient stress at vegetative stages (Barbieri et al., 2000). Because drought or nutrient deficiencies during vegetative periods limit leaf area expansion (D’Andrea et al., 2006; Salah and Tardieu, 1997; Uhart and Andrade, 1995a), they would increase the probability of response to reduced row spacing. Early sowing in maize could also increase the response to reductions in row spacing because this practice leads to smaller plants with fewer leaves (Duncan et al., 1973; Maddonni and Otegui, 1996) (Section 2.2). The length of the growing cycle is critical in matching genotype and environment (Capristo et al., 2007; Wilkens et al., 2015). In general, the longer the growing season, the longer the maturity group of adapted cultivars (Section 2.2). At low latitudes, temperature and radiation do not vary much along the year, and long-season hybrids are generally more suitable because they compensate reductions in cycle duration because of high mean temperature with more leaves (Section 2.2); this phenotype enables to capture more incoming radiation than short-maturity hybrids in those environments (Lafitte and Edmeades, 1997). Contrarily, at high latitudes, radiation and temperature decrease markedly during grain filling (Maddonni et al., 1998). Therefore, GY usually decreases when sowing is delayed and hybrid maturity class is increased above the limit set to total cycle duration by the frost-free period (Baum et al., 2019). A short-season hybrid with low leaf area per plant and low vegetative plasticity may not achieve full light interception at the critical stages (Eq. 1.2) and therefore is more likely to benefit from higher plant density and reductions in row spacing than a long-season cultivar (Assefa et al., 2016; Lindsey and Thomison, 2016; Sarlangue et al., 2007). The detrimental effects of delayed sowing in maize are, in general, more pronounced in long-season hybrids. These hybrids benefit most from early sowings and show the largest reductions in GY in response to delayed sowing (Olson and Sander, 1988; Tsimba et al., 2013). The benefit of late-sown early-maturity hybrids depend on the magnitude of the delay and the potential length of the growing season (Baum et al., 2019; Lauer et al., 1999).

3.2  Capture and efficiency in the use of water 3.2.1  Environmental patterns of water supply and demand Within the broad range of temperatures and frost-free periods for maize outlined in Section 1, crops are mostly grown where rainfall exceeds the 250 mm y− 1, with no rainfed commercial production and with rainfall below 150 mm during the

Maize Chapter | 1  13

warm season (Shaw, 1988). This distribution is linked to the high sensitivity of maize GY to water deficits. For instance, Meng et al. (2016) estimated a decline of 0.17% mm− 1 in relative GY (i.e. quotient between rainfed and irrigated GY) when rainfall dropped below 462 mm in the Chinese maize belt during the growing season, with no GY record for rainfall ≤ 240 mm. Globally, Rattalino Edreira et al. (2018) estimated a marked decline (64%) in rainfed water productivity (i.e. GY per unit of potential crop evapotranspiration under water-limited conditions) when average evaporative demand increased from 3 to 7 mm d− 1 or when the fraction of soil evaporation to potential rainfed crop evapotranspiration increased from less than 20% to more than 40%. Both low evaporative demand and low soil evaporation usually corresponded to humid, cool high-latitude environments typically represented by European countries and the north-central USA. In contrast, high values corresponded to arid, warm low-latitude environments well represented by sub-Saharan Africa and the western US corn belt. It has been well documented that maize GY reductions are particularly pronounced when water deficits take place around flowering (Claassen and Shaw, 1970; Hall et al., 1982). In these conditions, GY can be more affected than biomass production, bringing a marked decline in harvest index (HI, grain to total biomass ratio) and water productivity (Sinclair et al., 1990). Despite these characteristics, Rattalino Edreira et al. (2018) estimated that maize global GY gaps because of non-water limitations (i.e. the difference between potential water-limited GY and on-farm GY) could be equivalent to those produced by water scarcity (i.e. the difference between potential GY and potential water-limited GY), even in climatic zones traditionally considered as highly water-limited as the sub-Saharan Africa. This paradigm shift is expected to modify, at least partially, systems analysis of crop management to improve water productivity.

3.2.2  Root expansion and senescence, root size, architecture, and functionality Typical for gramineous plants, maize main root system corresponds to nodal or crown roots that arise from basal nodes (i.e. older) (Gregory, 2006). These nodes do not elongate and produce the adventitious root system that comprises multiple root axes and their laterals. The system is of fibrous, finely distributed appearance. Additional aerial roots from higher nodes may grow into the soil and contribute to water and nutrient uptake and plant anchorage. McCully (1999) summarised the characteristics of field-grown maize roots, indicating that first-order laterals are short (less than 3 cm) when compared with lab-grown roots, most of them reach final length in less than 2.5 days, and they usually persist along the whole cycle. In this system, however, maturation for adequate water transfer does not take place until 15–40 cm from the root tip for large xylem vessels, whereas for small vessels, this distance is reduced to 4–9 cm and for the very narrow protoxylem to about 1–2 cm. Tips of lateral roots have early and rapid senescence, which progresses towards its older, basal part (Fusseder, 1987). Although only one-third of these roots produce second-order laterals, overall laterals represent × 30 the length of the axial roots. In this system, root hairs are key to water and nutrient uptake, and their life-span ranges between a few days and 1–3 weeks (Fusseder, 1987), depending upon the method used for the analysis (cytoplasmic intactness or nuclear staining, respectively). The profuse maize root system develops primarily between sowing and R2 (McCully, 1999), when it reaches its maximum depth (Fig. 1.4a). For soils with no permanent limitation to root growth, the evolution of root depth follows a general sigmoid pattern (Fig. 1.4a). Higher soil bulk density (e.g. silty clay loam soils of the Argentine Pampas respect to loam soils of Iowa) and reduced temperature (high latitude with respect to low latitude) may delay maize root penetration across soil layers, modifying the root front velocity (RFV; Fig. 1.4a). Estimated maximum RFVs for maize ranged between 2.4 and 3.4 cm d− 1 and were reached between 43 and 56 days after sowing in the evaluated temperate environments. In a given environment, hybrids with extended cycle duration (i.e. high RM) are expected to have deeper roots than their short-cycle counterparts, whereas those with acute root angles are expected to have enhanced RFV with respect to those with wide angles (Hammer et al., 2009). Maximum root depth (Fig. 1.4b) and root abundance at each soil layer (Fig. 1.4c) vary with growing conditions. For instance, maize rooting depth can reach 2.25 m on loam soils (Typic haplustols) when exposed to terminal drought in a mid-latitude environment (Dardanelli et al., 1997). In contrast, no water uptake was detected below 1.65 m under similar drought conditions when crops were growing on silty clay loams (Typic argiudols) of the same region (Fig. 1.4b). Roots can traverse dense layers when layers are wet or bypass them through cracks when soils dry (Dardanelli et al., 2004). However, root proliferation within a soil layer is severely affected by increased bulk density (Fig. 1.4c), which promotes root clumping and the concomitant negative effect on water extraction (Fig. 1.4b and d). This restriction may also compromise root proliferation in subsequent soil layers (Dardanelli et al., 2004), which may not reach the critical root density for maximum water extraction rate, reducing the actual amount of extractable soil water in deep soil layers (Carretero et al., 2014). Similarly, management practices with a negative effect on soil structure also affect maize root proliferation, as observed for long respect to short cropping periods in a rotation (Cárcova et al., 2000) and for soil densification related to tillage (Díaz-Zorita et al., 2002; Taboada and Alvarez, 2008).

14  Crop Physiology: Case Histories for Major Crops

Relative PASW Argentina Corn belt (33°33’S, 60°20’W) Argentina temperate cool (37°47’S, 58°18’W) US Corn belt (42°01’N, 93°46’W)







Root front velocity (cm day–1)

3 –50 2 1


Clayey layer for squares



0 25 50 75 100 125 150 175 200




100 DAS

–125 –150 –175 –200 –225

(a) 0.0

Soil depth (cm)

Soil depth (cm)

0.00 0



Root abundance 40 60

(b) 80

100 0

Water uptake (%) 20


–0.1 –0.2 –90

Soil depth (cm)


Clayey layer

–0.4 –0.5



–0.6 –0.7 –0.8 –0.9 –1.0


FAO Arenosol

USDA Entisol








Water deficit –180


FIG. 1.4  Maize root system: expansion, size, and functionality. (a) Evolution pattern of the root front of maize crops growing in different environments (lower panel) and derived RFV (upper panel). The former was estimated from depletion of soil water (Argentina) or in situ measurements (USA). DAS: days after sowing. (b) Maximum rooting depth based on relative plant available soil water (PASW) at physiological maturity (dashed line) of maize crops grown on contrasting soil types (circles: Haplustol soil; squares: Typic argiudol soil) in the Pampas of Argentina. The solid lines correspond to initial soil water content (0: permanent wilting point; 1: field capacity). (c) Root abundance (as proportion of in situ evaluated soil blocks with at least one visible root) at R1 of one maize hybrid grown on different soils of central France. Vertical dashed lines indicate the presence of massive structures (arenosols and planosols) or shrinkage cracks (vertisols). (d) Proportion of total water uptake from different soil layers during the silking-20 days (solid lines) and silking + 20 days (dashed lines) periods of maize crops exposed to contrasting water regimes. a: Adapted from Cárcova, J., Maddonni, G.A., Ghersa, C.M., 2000. Long-Term Cropping Effects on Maize on Maize: Crop Evapotranspiration and Grain Yield. Agron. J. 92, 1256–1265. agronj2000.9261256x; Otegui, M.E., 1992. Incidencia de una sequía alrededor de antesis en el cultivo de maíz. Consumo de agua, producción de materia seca y determinación del rendimiento. Tesis MSc. Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Planta; Ordóñez, R.A., Castellano, M.J., Hatfield, J.L., Helmers, M.J., Licht, M.A., Liebman, M., Dietzel, R., Martinez-Feria, R., Iqbal, J., Puntel, L.A., Córdova, S.C., Togliatti, K., Wright, E.E., Archontoulis, S. V., 2018. Maize and soybean root front velocity and maximum depth in Iowa, USA. F. Crop. Res. 215, 122–131. https://; b: Adapted from Dardanelli, J.L., Bachmeier, O.A., Sereno, R., Gil, R., 1997. Rooting depth and soil water extraction patterns of different crops in a silty loam haplustoll. F. Crop. Res. 54, 29–38.; Otegui, unpublished; c: Adapted from Nicoullaud, B., King, D., Tardieu, F., 1994. Vertical distribution of maize roots in relation to permanent soil characteristics. Plant Soil 159, 245–254.; d: Adapted from Otegui, M.E., Andrade, F.H., Suero, E.E., 1995a. Growth, water use, and kernel abortion of maize subjected to drought at silking. F. Crop. Res. 40, 87–94.

Maize Chapter | 1  15

3.2.3  Crop water use and canopy conductance as related to canopy architecture, stomatal conductance, and canopy-atmosphere coupling Common to all crops, biomass production (Eq. 1.1) and the amount of water transpired by maize canopies are tightly related to the amount of solar radiation (SR) intercepted along the cycle (McNaughton and Jarvis, 1991), and both processes depend upon leaf differentiation (Section 2) and expansion (Fig. 1.5). Crop capacity to intercept light (Eq. 1.2) depends chiefly on a high leaf elongation rate (LER) to achieve the critical LAI (LAIC; Section 3.1.1) at the start of the critical period for kernel set (Section 4.1; Fig. 1.5d and e). In these conditions, LER depends on temperature in the 10–35 °C range 1.2

0.8 0.6


0.4 Irriqated Control Water Deficit 1 Water Deficit 2

0.2 0.0










Y1(MPa) –0.6

0.4 0.2 0.0













5 4 3 2









(d) 1000 900 800 700 600 500 400 300 200 100 0


Free Water Reference

Transpired Water (mm)

Light Interception Efficiency




(c) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0


6 Leaf Area Index


(b) LER (mm h–1)

Sl on Expansion Processes

(a) 1.0

Daily ETc/PET



1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0




90 DAS





500 1000 1500 2000 2500 Accumulated Intercepted SR (MJ m–2)

FIG. 1.5  Maize crop water use. DSSAT (Hoogenboom et al., 2017) simulated results of processes conducive to the amount of water transpired by crops grown under contrasting water regimes in Spain (based on Cavero et al., 2000). DAS: days after sowing. (a) Water regimes and their effects on the fraction of transpirable soil water (FTSW), (b) crop (ETc) to potential (PET) evapotranspiration ratio, (c) degree of water stress (SI: stress index) on tissue expansion processes, and (d) LAI along the cycle. Effects on LAI summarise the response of LER to pre-dawn leaf water potential (Ψ) (c inset; Chenu et al., 2008). (e) Large effects on LAI did not affect to the same extent maximum light interception efficiency (ei; Eq. 1.1) of stressed crops but (f) did affect their capacity to hold it along the reproductive phases, with the concomitant variation in the amount of SR intercepted by canopies that drive plants transpiration. Short black arrows in (a–e) indicate silking date on ca. 80 DAS. Squares in (d) correspond to observed LAI at silking and maturity. Long vertical arrows in (d) and (e) indicate the date when ‘control’ and ‘water deficit 1’ plots reached ≈ 95% ei, which corresponded to LAI ≅ 4.5 (horizontal arrow in d). Plots under water deficit 2 reached a maximum LAI of 3.45 and ei = 0.87.

16  Crop Physiology: Case Histories for Major Crops

(Sadok et al., 2007) and varies with leaf position along the stem (Andrieu et al., 2006; Dwyer et al., 1992). Therefore and provided all other variables remain uniform (e.g. VPD, mineral nutrition, stand density), the effects of soil water deficits (Fig. 1.5a and c) on maize LER (Fig. 1.5c, inset; Tanguilig et al., 1987) and LAI (Fig. 1.5d; Cavero et al., 2000) are directly driven by fluctuations in the soil–leaf water potential continuum that affect cell turgor (Boyer, 1970; Reymond et al., 2003) and plant hydraulic conductivity. These fluctuations depend primarily upon the crop water regime (Fig. 1.5a) and can be influenced by management practices (row spacing, tillage system, soil mulching) that affect the partitioning of crop evapotranspiration (ETc) between transpiration (T) and soil evaporation (Es) (Al-kaisi et al., 1989; Villalobos and Fereres, 1990). Reductions in intercepted SR because of water shortages will depend upon the extent of effects driven by LAI reductions on ei (Fig. 1.5e), which include the pre-silking period of canopy expansion and canopy senescence (Fig. 1.5d). Water deficit reducing LAI reduces both cumulative intercepted SR (Muchow, 1989a) and transpiration (Muchow and Sinclair, 1991; Tanguilig et al., 1987). The relative response, however, differs among water regimes (Fig. 1.5f), being larger for water loss (up to 57% in the example) than for SR captured (up to 32% in the example). This uneven response is because of the additional reduction of transpiration in response to the decline in canopy conductance (gc) in drying soils. As an isohydric species (Tardieu and Simonneau, 1998), maize tends to keep a high leaf water potential (Ψ) by an efficient stomata control through hydraulic and non-hydraulic signals. The latter is mediated by xylem abscisic acid (ABA) (Bahrun et al., 2002), for which there is still some controversy about its origin in the plant (Christmann et al., 2007; Jackson, 1997). Main effects of increased xylem ABA on growth of maize plants exposed to water deficits are related to stomatal control (Tardieu et al., 2010). Increased ABA reduces leaf expansion (Cramer and Quarrie, 2002) and enhances stomatal closure, both conducive to reduced transpiration. Stomatal closure reduces gc, which maintains the plant water status (i.e. prevents a large drop inΨ) and improves plant hydraulic conductivity. Moreover, ABA effects on overall improved water status seem partially mediated by enhanced aquaporins activity, as reported by Parent et al. (2009) for maize lines divergently transformed in the NCED (9-cis-epoxycarotenoid dioxygenase) gene. Plants of ‘sense lines’ (i.e. those with enhanced ABA production with respect to the ‘antisense’ ones) had improved expression of mRNA for aquaporin PIP genes and related protein contents. Variations in gc may also relate to fluctuations in atmospheric vapour pressure deficit (VPD). Increased VPD can promote stomatal regulation through upregulation of foliar ABA biosynthesis triggered by reduced leaf turgor (McAdam and Brodribb, 2016). Maize hybrids vary in transpiration response to both soil water content (Fig. 1.6a) and VPD (Fig. 1.6b) (Gholipoor et al., 2013).

3.2.4  Water use efficiency As a summer crop with broad adaptation (Sections 1 and 3.2.1), maize is usually exposed to periods of water scarcity, hence the agronomic importance of water use efficiency (WUE) for biomass and GY. Owing to difficulties for separation between T and Es in the field (Villalobos and Fereres, 1990), accurate assessment of transpiration-based WUE for biomass production (WUEB,T) is usually limited to controlled conditions, with ranges between 4.1 and 8.4 mg g− 1 (equivalent to 41–84 kg ha− 1 mm− 1). Part of this variation is explained by differences in VPD (Fig. 1.6c). Most field surveys define WUE as biomass (WUEB,ET) or GY in relation to ETc (WUEGY,ET), traits that are more variable (Table 1.1) than WUEB,T. Reasons for the large variation in WUE based on ETc are primarily linked to the inclusion of unproductive water loss from the soil (Fig. 1.6d), evidenced by the marked increase in both WUEB,ET (Otegui et al., 1995a) and WUEGY,ET (Yu et al., 2018) obtained when soil mulching minimises Es (Table 1.1). When GY is considered, variations in WUE may be also linked to differences in HI. The frontier line (FL) fitted to data with maximum biomass per unit ETc in Fig. 1.6d has a slope close to maximum WUEB,T in Fig. 1.6c and the maximum in Table 1.1 and represents potential maize water productivity for biomass in field conditions for the evaluated data set (Grassini et al., 2009).

3.2.5  Management practices under water deficits When the probability of water deficit at flowering is high, a decrease in maize stand density reduces plant-to plant competition and avoids plant growth rate at flowering close to threshold values for ear growth suppression (Section 4.1). This practice would also result in a conservative use of soil water during the vegetative period and thus in higher water availability for reproductive growth (Curin et al., 2020; Hao et al., 2019). Wide rows would also result in a conservative use of water during the vegetative period, but as in low plant density, the combination of hybrid, stand density, and row spacing can modify crop response markedly, depending upon their relative effect on water loss from plants and soil along the cycle. For instance, Barbieri et al. (2012) reported enhanced use of soil water reserves early in the cycle under narrow rows, which may produce an unfavourable distribution between vegetative and reproductive stages of the type described by Passioura (2006) for wheat crops exposed to terminal drought in Mediterranean environments. The low stability of maize HI in response to available resources per plant (Echarte and Andrade, 2003) indicates that there is a need for careful adjustment of stand density to environmental conditions and input level (Al-Naggar et al., 2015;

Maize Chapter | 1  17

FIG. 1.6  Maize soil–plant–atmosphere coupling and water use efficiency. Transpiration response of two maize hybrids to the (a) fraction of transpirable soil water (FTSW) and (b) vapour pressure deficit (VPD). EOT, end of treatment. Adapted from (a) Ray and Sinclair (1997) and (b) Gholipoor et al. (2013). (c) Response to VPD for biomass production based on plant transpiration (WUEB,T); data correspond to hybrids included in (a). (d) Biomass and GY response to crop evapotranspiration (ETc); the FL was fitted to data with increased biomass per unit increase in ETc. Adapted from (c) Ray et al. (2002) and (d) several references of Table 1.1.

Hernández et al., 2014), particularly in drought-prone environments (Rotili et al., 2019). Some new hybrids, however, have a more stable response of GY to variations in stand density than others (Di Matteo et al., 2016; Mansfield and Mumm, 2014). Varying sowing date is a recommended strategy to move a crop stage away from constraints such as seasonal drought, heat stress, frost, and biological adversities. In many maize production areas, delaying sowing is recommended to avoid or alleviate water deficits and heat stress at the critical flowering period (Maddonni, 2012; Mercau and Otegui, 2015; Rotili et al., 2019), provided the decline in the photothermal environment and early frosts (Bonelli et al., 2016; Maddonni, 2012) do not penalise GY excessively because of the anticipated arrest of grain filling (Section 2.2).

3.3  Capture and efficiency in the use of nutrients N, phosphorus (P), potassium (K), sulphur (S), and zinc (Zn) are the most widespread limiting nutrients for maize in the main producing regions (Table 1.2). Additionally, some of these regions are characterised by acid soils (e.g. Brazil) owing to naturally high (toxic) aluminium (Al) levels together with very low availability of macronutrients. Such conditions demand liming to produce (Table 1.2). Here we address the most important features of mineral nutrition, with emphasis in N.

18  Crop Physiology: Case Histories for Major Crops

TABLE 1.1  Estimated maize water use efficiency (kg ha− 1 mm− 1) for biomass or grain yield based on crop evapotranspiration. WUE Biomass

Source of variation


Year and Sowing date (Irrigated crops)

Otegui et al. (1995a)

Year and Sowing date (Rainfed crops + soil mulch)

Otegui et al. (1995a)


Soil amendments

Faloye et al. (2019)


Water regimes

Zhang et al. (2017)


Year (variable degree of stress)

Tolk et al. (2016)


Year (Rainfed crops)

Kresović et al. (2016)


Year (Full irrigation)

Kresović et al. (2016)

42–53 65–83





Hybrid × Stand density

Curin et al. (2020)


Environment × Stand density

Curin et al. (2020)


Farming systems (Continuous maize vs rotation)

Hussain et al. (2019)


Soil amendments

Faloye et al. (2019)


Water regimes

Zhang et al. (2017)


Year (variable degree of stress)

Tolk et al. (2016)


Year (Rainfed crops)

Kresović et al. (2016)


Year (Full irrigation)

Kresović et al. (2016)



Hybrids, water regimes, and years

Nagore et al. (2017)


Hybrids × stand density

Curin et al. (2020)


Environment × stand density

Curin et al. (2020)


Hybrids (under water deficit)

Hao et al. (2019)


Water regime

Hao et al. (2019)


Stand density

Hao et al. (2019)


N level

Hernández et al. (2015)


Climate zones and soil textures (current climate)

Rodrigues Pinheiro et al. (2019)


Climate zones and soil textures (future climate)

Rodrigues Pinheiro et al. (2019)


Mulching and tillage systems

Yu et al. (2018)


Water regime and salinity level

Yuan et al. (2019)

In each group, bold indicates maximum and underlined data indicate minimum. a

For the period under complete ground cover.


Hybrids of different breeding eras.

3.3.1  Nutrient absorption, assimilation, accumulation, and remobilisation Under potential growing conditions (Section  3.1), the maximum N accumulation in shoots of maize hybrids grown at optimal stand density varies between 240 and 320 kg ha− 1, depending upon the hybrid, with maximum accumulation rates between 2.7 and 3.7 kg ha− 1 d− 1. These high rates remain relatively constant between V5 and R4-R5 (Fig. 1.7b), although peaking slightly before flowering in concurrence with the increase in ei (Eq. 1.2) that drives CGR and total biomass (Fig. 1.7a). The amount of N accumulated at flowering varies between 60% and 74% of the total N at harvest (Ciampitti and Vyn, 2013a), although this proportion has tended to decrease among modern hybrids (55%), highlighting the increasing importance of post-flowering N absorption (Ciampitti and Vyn, 2012; Mueller and Vyn, 2016). This trend is in agreement with the improved post-silking biomass production of modern hybrids (Luque et al., 2006). N is accumulated in vegetative parts until 10–15 days after flowering and then partially mobilised to the grains. The source–sink ratio (capacity to provide assimilates to the grains/capacity of the grains to accumulate the assimilates) is the main influence on N mobilisation

Maize Chapter | 1  19

TABLE 1.2  Soil fertility characteristics of the main maize production zones. Country



Soil fertility

Organic matter










































































Ukraine, Russia







































Indonesia, Philippines, Vietnam













South Africa, Egypt, Nigeria Ethiopia

























Very severe


Adapted from Bardy Prado et al., 2012; Choudhary et al., 2014; FAO-UNESCO, 1974; Gonzalez Castorena, 2010; Hengl et al., 2015; INEGI, 2007; Jones et al., 2013; Leenaars et al., 2014; Li et al., 2016; Plaza et al., 2018; Schmidt et al., 2011; SEMARNAT y CP, 2003; SEMARNAT y UACh, 2003; Teixeira Guerra et al., 2014; Tian et al., 2010.

(Borrás et al., 2002; Uhart and Andrade, 1995b). Source limitations (e.g. drought, defoliation, etc.) lead to low assimilate availability for N absorption and reduction during the grain-filling period, increasing N mobilisation and reducing kernel N concentration. Contrarily, adequate assimilate availability per kernel during grain filling decreases N mobilisation and increases N absorption and its concentration in kernels (Borrás et al., 2002; Uhart and Andrade, 1995b). During grain filling, the crop can mobilise between 28 and 100 kg N ha− 1, according to the source/sink ratio (Uhart and Andrade, 1995b). These values represent between 18% and 42% of the N in vegetative biomass at 15 days after flowering. Hybrids vary in N mobilisation capacity (Ciampitti and Vyn, 2014; Mueller and Vyn, 2016; Uhart and Andrade, 1991). The contribution of N mobilisation to N in kernels during grain filling can range between 15% and 70% (Ta and Weiland, 1992; Uhart and Andrade, 1995b). The dynamics of N accumulation and mobilisation in the plant are reflected in the N HI (N in grain/N in shoot), which varied between 0.59 and 0.73 (Table 1.3), according to the source–sink ratio and the hybrid (Ciampitti et al., 2013b; Liu et al., 2017; Uhart and Andrade, 1995b). Therefore in maize for grain production, 41%–27% of the N in shoot remains in the stubble if it is not used as forage or source of bioenergy (Table 1.3). P starts accumulating in the plants at maximum rate after V5–V6. At flowering, the crop has accumulated between 45% (Fig. 1.7c) and 55% (Bender et al., 2013) of the total P at harvest. The P HI ranges between 75% and 80%. The dynamics of K accumulation in the crop is anticipated compared to N and P. Almost all K uptake is usually completed by flowering, and there may be a partial decline respect to the maximum at physiological maturity (Fig. 1.7d) because of some loss of senesced leaves and panicles. The K HI varies between 23% and 33% (Bender et al., 2013; Ciampitti et al., 2013a), so most of the absorbed nutrient returns to the soil with crop residues. Other macronutrients (Table 1.3) have low HI (e.g. Ca 7%, Mg 28%) as several micronutrients (e.g. B 25%, Fe 36%, and Cu 29%), except Mo (63%–65%) and Zn (50%–55%).

20  Crop Physiology: Case Histories for Major Crops


2500 N0

2000 52%

1500 1000

60% 500

N content (g m–2)

Biomass (g m–2)




5 4 3


2 45%




K content (g m–2)

P content (g m–2)




20 74%

15 10
















20 15


10 5

0 1500 2000 (d) 0 500 Thermal time from VE (°Cd)




FIG. 1.7  Evolution from crop emergence to maturity of (a) BT and its content of (b) N, (c) P, and (d) K of maize crops grown at a stand density of 79,000 plants ha− 1 and two N rates (N0: no N added; N224: 224 kg N ha− 1). The arrow indicates the mean date of silking, and the percent next to each line is the value at silking with respect to the total amount at maturity. Redrawn from Ciampitti, I.A., Camberato, J.J., Murrell, S.T., Vyn, T.J., 2013a. Maize Nutrient Accumulation and Partitioning in Response to Plant Density and Nitrogen Rate: I. Macronutrients. Agron. J. 105, 783–795. https://doi. org/10.2134/agronj2012.0467.

3.3.2  Effects of nutrients on crop development, growth, and grain yield Owing to large requirements for crop production, even mild N deficiencies may reduce maize biomass (Ciampitti et al., 2013b), HI (D’Andrea et  al., 2009) and grain quality (Section  4.4). N deficiencies do not reduce leaf number (TLN, Section 2) and consequently have little (if any) effect on crop development (Uhart and Andrade, 1995a) but may cause large reductions in leaf expansion and leaf persistence (Uhart and Andrade, 1995a), affecting the LAI and consequently, ei (Eq. 1.1). Both IPARi and RUE (Eq. 1.1) decrease under N deficiency, reducing CGR and both shoot and root biomass (Uhart and Andrade, 1995a). Reductions in N availability can also increase the ASI with variable effects across genotypes (D’Andrea et al., 2013; Debruin et al., 2018; Rossini et al., 2020), supporting the value of this secondary trait for adaptation to poor soil N (Lafitte and Edmeades, 1994). Reduction in seed set (Section 4.1) does not lead to proportional decreases in forage quantity and quality because the excess of assimilates can be stored as reserves in the stems, which increase between 6% and 38% under N deficiency (Uhart and Andrade, 1995b). Failures up to 90% of seed set reduced total dry matter 23% and increased stem biomass 55%–60%, without a significant effect on dry matter digestibility or total protein percent of the forage at harvest (Dalla Valle et al., 1998, 2008).

3.3.3  Nutrients diagnosis and fertilisation requirements Nitrogen Supply–demand balance This method, summarised in Eq. (1.3), has been widely used in maize N diagnosis. b  GY  N initial  N mineralised  N stubble  N fertiliser  N loss


with b = N absorbed t− 1 of grain, GY = grain yield goal, N initial = mineral soil N at sowing, Nmineralised = N mineralised during the crop cycle, Nstubble = N supplied by the stubble, Nfertiliser = N from fertilisers, and Nloss = N losses from the system. The maize crop absorbs 15–22 kg of N per t of grain, and a total amount of absorbed N larger than 266 kg N ha− 1

TABLE 1.3  Traits describing macro and micronutrient use by a maize crop with grain yield of 12 t ha− 1. Macronutrients Traits measured at physiological maturity




Micronutrients Mg




kg ha− 1




g ha− 1

Total requirement

286 (266–307)

114 (100–133)

202 (181–225)

59 (52–66)

26 (24–28)

498 (448–563)

542 (496–793)

83 (67–101)

1376 (1224–1569)

141 (132–155)

Harvested with grain

166 (145–188)

90 (73–108)

66 (57–78)

17 (15–20)

15 (13–16)

308 (269–353)

72 (62–87)

19 (13–32)

248 (218–285)

41 (30–49)

Nutrient harvest index (%)

58 (51–62)

79 (70–82)

33 (27–37)

29 (25–33)

57 (52–60)

62 (60–65)

13 (11–16)

23 (17–31)

18 (17–22)

29 (21–33)

Data correspond to the average of six hybrids grown at two locations (DeKalb and Urbana, IL, USA) during 2010 and are expressed as the mean and the range between maximum and minimum values (in parenthesis). Nutrient harvest index represents the quotient between nutrient harvested with grain and total requirement (in %). Adapted from Bender, R.R., Haegele, J.W., Ruffo, M.L., Below, F.E., 2013. Nutrient uptake, partitioning, and remobilization in modern, transgenic insect-protected maize hybrids. Agron. J. 105, 161–170. https://doi. org/10.2134/agronj2012.0352.

22  Crop Physiology: Case Histories for Major Crops

has been documented for producing a GY of 12 t ha− 1 in the US (Table 1.3; Ciampitti and Vyn, 2012; Djaman et al., 2013). Large N requirements are indicative of a low N use efficiency (NUE), which recognizes different components. Agronomic NUE (NUEA, kg of grain kg− 1 applied N) is the product between the physiological or internal NUE (NIE, kg of grain kg− 1 absorbed N) and the fraction recovered from fertiliser (NRE, kg N in biomass kg− 1 applied) (Ciampitti and Vyn, 2014; Novoa and Loomis, 1981). As N availability decreases, NIE increases from approximately 43 to 56 kg of grain kg− 1 of N absorbed, whereas NRE usually varies between 0.70 and 0.40 (Ciampitti and Vyn, 2012). Variation of NIE among hybrids may range between 37 and 70 kg of grain kg− 1 of N absorbed (Ciampitti and Vyn, 2014). The synchrony between crop N demand and indigenous N supply is the main variable that explains changes in NRE (Ciampitti and Vyn, 2014). Ninitial and Nmineralised during the season depend on the amount and composition of soil organic matter, soil temperature, and water availability. Water effects on N use are particularly critical in a summer crop as maize (Djaman et al., 2013). Initial soil N can be measured, whereas organic matter mineralisation can be measured or estimated with models based on potential conditions (N0) affected by soil moisture and temperature. From evaluations in wheat and maize fields performed in the Pampa of Argentina, Nmineralised can vary from 22 to 232 kg N ha− 1, depending on soil organic matter, soil moisture, and temperature (Reussi Calvo et al., 2018). Measurement of N-ammonium released during soil anaerobic incubation improved the estimation of maize N needs by 29% and 46% for the cool southeast Pampas region and North Humid Pampa of Argentina, respectively (Orcellet et al., 2017). The preceding crop modifies N availability (30–100 kg N ha− 1) depending on the stubble C/N ratio, temperature, and soil moisture (Ranells and Wagger, 1996). N losses include leaching, denitrification, and volatilisation. Low soil buffer capacity, high soil pH, urease activity, temperature, wind speed, and soil moisture promote N volatilisation generating losses of applied N from 2 to 50%. Denitrification occurs in anaerobiosis, high C availability, and presence of denitrifying bacteria, with losses of applied N estimated in 2.5%–70% (Nieder et al., 1989). Their correct estimation is critical for robust Eq. (1.3) outputs. The efficiency factor (N absorbed/N available-applied) for Ninitial and Nfertiliser is similar (0.4–0.6) while for Nmineralised is ≈ 0.7–0.8 (Meisinger, 1984). Soil determinations Probably the most widespread method for the diagnosis of maize N needs is based on soil N-nitrate content at sowing (0–60 cm) plus N added with fertilisation. For the main maize producing region of Argentina, Alvarez (2008) defined a critical value of 180 kg N ha− 1 for GYs close to 8 t ha− 1 using data from the 1980s (when traditional tillage systems predominated), whereas Correndo et al. (2018) recently reported critical values of 293–304 kg N ha− 1 for GYs of 11–14 t ha− 1 in the same region, mostly under no-till. A common weakness of this approach is the large dispersion of data (r2 ≤ 0.50), with N thresholds strongly modified by GY potential, soil texture, Nmineralisation, and previous crop (Orcellet et al., 2017). Plant determinations Among plant determinations, N dilution curves capture the allometric relationship between critical N, i.e. the concentration of N in shoot to maximise growth, and biomass (Gastal et al., 2015). A nitrogen nutrient index (NNI) is defined as the ratio between actual and critical N concentration. Analysing the N dilution curves for different hybrids and regions of the world, a single equation was obtained. Values of NNI = 0.85 at V5 and NNI = 0.80 at V15 (15 d before flowering) associated with maximum GY (Ciampitti and Vyn, 2013b; Greenwood et al., 1990). Djaman and Irmak (2017) estimated the critical NNI of 0.9 near physiological maturity. The relationship between relative GY and nutrient concentration in leaves and stems responds usually to linear + plateau function. On the basis of field studies between 2010 and 2016 in the US Corn Belt, Kovács and Vyn (2017) established a N threshold of 30 g kg− 1 in the ear leaf at silking for achieving 95%–100% of maximum biomass (range of 8–30 t ha− 1) and GY (range of 4–18 t ha− 1). For maximising GY in the Humid Pampa of Argentina, Uhart and Echeverría (2000) reported thresholds of 28 g kg− 1 for leaves at V6, 25 and 13 g kg− 1 for leaves and stems at V15, and 3.5 g kg− 1 for stems and 12 g kg− 1 for grains at harvest. Iversen et al. (1985) estimated 30 days after emergence as the optimum sampling time for nitrate concentration in the stem as indicative of plant N status and established a critical concentration between 11 and 16 g N-NO3 kg− 1 in the base of the stalk to achieve 95% of maximum GY. Sainz Rozas et al. (2001) determined critical values that varied between 4.3 and 10.4 g N-NO3 kg− 1 at V6, whereas a threshold of 0.4 g N-NO3 kg− 1 at R6 was reported in Argentina and the USA (Blackmer et al., 1997; Echeverria et al., 2001). These thresholds need local calibration for fertilisation recommendations elsewhere. Chlorophyll concentration in leaves is common among plant determinations and is usually linked to soil plant analysis development (SPAD) measurements. In a study in 93 sites in Iowa (rainfed) and Nebraska (irrigated), USA, Schepers et al. (1992) established 43.3 as the critical SPAD 502 threshold as representative of possible N deficiency at anthesis. Similar

Maize Chapter | 1  23

values were determined by Rashid et  al. (2005), whereas Ziadi et  al. (2008) acknowledge strong year and site effects. Hybrids may differ substantially in SPAD readings even when exposed to similar soil N supply, but genotypic variability in the chlorophyll/protein relationships should be considered when using chlorophyll-based measurements for N-status assessment (Antonietta et al., 2019). The prediction accuracy can be improved using the N sufficiency index (NSI) based on a well-fertilised tester. The sensitivity of this method is high enough after V6. The NSI ranged between 0.92 and 0.99 at V8, V10, V15, and R1 (Sainz Rozas et al., 2019). Simulation models Several models (CERES-Maize, APSIM, WHCNS, CropSyst) are presently used to explore N restrictions to crop GY, which have been locally used for management decisions, including fertilisation (He et al., 2012; Liu et al., 2011; Mercau and Otegui, 2015; Monzon et al., 2014; Morris et al., 2018). Their basis is the combination of climates, soils, water recharge, crop management, N availability, etc. to simulate GY. The results are synthesised in probabilistic (i.e. based on historic or simulated weather records) GY response curves for different soil N supplies (i.e. Ninitial + Nfertiliser). Tovihoudji et  al. (2019) found that the DSSAT model adequately estimated the positive effects of N fertilisation on the long-term average maize GY when compared with checks. Puntel et al. (2016) tested the APSIM model to simulate maize GY and the economic optimum N rate (EONR) using a large dataset (16-year field experiments) from central Iowa with good results for the long-term response of GY to N but great uncertainty for the EONR (relative root mean square errors of 12.3% and 36.6%, respectively). Remote sensing Remote sensing contributes to characterise intra- and inter-plot variability and, together with field determinations, improve the understanding of environmental limitations to crop production. After reviewing 108 studies, Griffin et al. (2005) reported benefits for the use of sensor-based variable fertiliser rate for sugar beet, maize, and wheat (in 80%, 72%, and 20% of cases, respectively). Similar trends were obtained for maize and wheat in Argentina (Bongiovanni and LowenbergDeBoer, 2006). Holland and Schepers (2010) generated a N fertiliser recommendation based on spatially variable inseason remote sensing data and established the EONR. Their model accommodates management zones, pre-sowing N applications, manure mineralisation, legume credits, nitrate in irrigation water, and crop growth stage. Ransom et  al. (2020) have conducted simultaneous comparisons of multiple N fertiliser rate recommendation tools, including canopy reflectance sensing using RapidSCAN CS-45 (Holland Scientific, Lincoln, NE), across 49 sites in eight US Midwest states and three growing seasons. Tool performances were compared to the EONR. Only 10 of 31 tools (mainly soil nitrate tests and canopy reflectance) produced N rate recommendations that correlated at least weakly with the EONR (r2 ≤ 0.20). In general, remote sensors are complementary to the other N assessment methods, simplifying the field monitoring (Morris et al., 2018).  Other nutrients Phosphorus P-Bray soil nutrient deficiency thresholds for maize varied between 13 and 20 mg kg− 1 (Bray y Kurtz I, Mehlich 3, Olsen) in Argentina, USA, China, and Switzerland (Cadot et al., 2018; García et al., 2015; Leikam and Mengel, 2007; Wang et al., 2016). The thresholds could be affected by P mineralisation (Pmin), soil texture, and previous crop. There are no methods to estimate Pmin. Fine-textured soils have a lower P threshold than coarse-textured soils, and the previous crop could release 2.8–16.5 kg of P ha− 1 (Correndo, 2018; Maltais-Landry et al., 2014; Varela et al., 2014). Sulphur S-SO4 soil nutrient deficiency threshold (0–20 cm) for maize ranges between 7 and 10 mg kg− 1 (Carciochi et al., 2016). Variability of S-SO4 thresholds could be related to SO4 content in deep layers of the soil and water table presence and to S mineralisation during the growing cycle. Carciochi et al. (2016) reported that mineralisable N determined by short-term anaerobic incubation (Nan) was associated with S mineralisation and explained 62% of the variation in the response to S fertilisation in maize. The threshold of Nan was established at 54 mg N kg− 1. Potassium The threshold for interchangeable K soil deficiency varies between (1) 100 and 120 mg kg− 1 for soils with a cation exchange capacity (CIC) of 10–15 meq 100 g− 1 and (2) 150–170 mg kg− 1 for CIC > 10–15 meq 100 g− 1 (Eckert, 1994; Leikam and Mengel, 2007). K has a negative interaction with Ca and Mg (Ca + Mg/K  1 and the vertical arrow the threshold plant growth rate for plant barrenness, whereas values in the upper box represent the evaluated stand densities (SD, in plants m− 2). In (c), the vertical arrow indicates the mean optimum SD across the 13 evaluated hybrids. In (d), all data are relative values respect to controls signalled by arrows at the (0,0) ordered pair; fitted model in dark grey ± 10% interval in light grey. Adapted from (a): Andrade, F.H., Vega, C., Uhart, S., Cirilo, A., Cantarero, M., Valentinuz, O., 1999. Kernel Number Determination in Maize. Crop Sci. 39, 453–459. 039000200026x; (b): Andrade, F.H., Echarte, L., Rizzalli, R.H., Della Maggiora, A., Casanovas, M., 2002b. Kernel number prediction in maize under nitrogen or water stress. Crop Sci. 42, 1173–1179.; Andrade, F.H., Cirilo, A.G., Uhart, S.A., Otegui, M.E., 1996. Ecofisología del cultivo de maíz. Dekalb Press, p. 292; (c): Hernández, F., Amelong, A., Borrás, L., 2014. Genotypic differences among argentinean maize hybrids in yield response to stand density. Agron. J. 106, 2316–2324.; (d): Borrás, L., Slafer, G.A., Otegui, M.E., 2004. Seed dry weight response to source–sink manipulations in wheat, maize and soybean: a quantitative reappraisal. F. Crop. Res. 86, 131–146.

of the method (Pagano et al., 2007; Rattalino Edreira and Otegui, 2013), particularly in association with flowering dynamics models (Borrás et al., 2007, 2009), which are crucial for improving the nick between male and female inbreds in seed production (Hallauer et al., 1988). Increased broad stress tolerance of new hybrids appears to be related to an increase in partitioning of assimilate to the developing ear from a very early stage, which probably reduces PGRcp and EGRcp thresholds to prevent plant barrenness and a steeper initial slope (Echarte et al., 2000) and a shorter ASI (Campos et al., 2004).

4.2  Kernel weight GY also depends on KW and partial trade-offs between kernel number and weight occur but do not impair increases in KN to translate into improved GY. Maize source–sink ratio during grain filling (i.e. the assimilates availability to fill the kernels) is well represented by the quotient between plant growth during the effective grain filling period and the number of kernels established during the critical period (Borrás and Otegui, 2001; Cirilo and Andrade, 1996; Maddonni et  al., 1998). Grain-filling duration could be reduced when the source is strongly limited during kernel growth (Egharevba et al.,

Maize Chapter | 1  27

2010; Jones and Simmons, 1983). This shortened grain filling reflects the high dependency on current assimilates of maize kernels (Borrás et al., 2004), although some genotypic differences exist in the capacity to hold high KW when assimilates available per kernel are reduced (Borrás and Otegui, 2001). Decreases in the source–sink ratio during the effective grain filling period reduced KW, as observed for the intensifying negative effects of soil N deficiency (Hisse et al., 2019). In contrast, increasing the ratio had minimum positive effects (Fig. 1.8d). This lack of KW response to increased assimilate availability during the effective grain-filling period indicates that maize plants set an individual kernel sink potential early in grain filling, which cannot be increased by improved growing conditions in subsequent stages but can be severely reduced by an unfavourable environment (Borrás et al., 2004; Gambín et al., 2006). Differences in maize potential KW because of genotypes or environments are related to the source–sink ratio established early in grain filling and are associated with changes in kernel growth rate (KGR) during the effective grain-filling period. This rate is usually the main determinant of potential KW in maize (Borrás et al., 2003b; Saini and Westgate, 1999) and depends on the sink capacity established early during development (Jones and Schreiber, 1996; Reddy and Daynard, 1983). Then, PGRcp per kernel estimates this source–sink ratio, underscoring that the importance of the critical period is not limited to KN determination, because it also modulates the potential KW and consequently GY. Alvarez Prado et al. (2013) analysed the genetic bases of physiological processes determining KW in field-grown maize using 245 RILs derived from the IBM Syn4 population (B73 × Mo17). They established positive (and consistent across environments) genetic correlations between KW, KGR, and kernel maximum water content on maize chromosomes 2, 6, 9, and 10 respective, whereas only one consistent quantitative trait locus (QTL) was found for KW, kernel filling duration, and kernel desiccation rate. No common consistent QTL was detected for KGR and kernel filling duration. They highlighted that the co-localisation of consistent QTL for KW, KGR, and maximum water content suggests a common genetic basis for these critical secondary traits. In contrast, Mandolino et al. (2016) found a common QTL for potential KW, KGR, and kernel filling duration on chromosome 5 after mapping 181 RILs derived from a dent × flint Caribbean cross, alerting on the need to explore different physiological strategies for KW determination in different genetic backgrounds.

4.3  Biomass partitioning Genetic GY gains for maize in the past decades in Argentina associated with improved KN m− 2 at optimum stand density, enhanced post-silking biomass, and enhanced biomass allocation to reproductive sinks (Echarte et al., 2004; Luque et al., 2006). Physiological traits representative of pre-silking growth (i.e. canopy development and intercepted radiation up to silking) had almost no effect on these gains; differences among hybrids arose at the start of the critical period and were evident as improved RUE, PGRcp, and biomass partitioning to reproductive organs during that period (EGRcp/PGRcp). Kernel number responded to these trends in biomass production and partitioning. Improved biomass after silking allowed for an almost constant source–sink ratio during active grain filling (Lee and Tollenaar, 2007), which avoided a trade-off between GY components (Sadras, 2007), with the concomitant improvement in GY (Luque et al., 2006). Biomass allocated to kernels may come from current photosynthesis or from reserves stored as stem water soluble carbohydrates (SWSC). The relative contribution of each source has traditionally been estimated by comparing individual KW and the plant growth per kernel during the grain-filling period (PGKgf) (Borrás and Otegui, 2001; Cirilo and Andrade, 1996). When KW ≈ PGKgf, it is assumed that stored reserves were not used during the grain-filling period nor accumulated in other organs. Reserve use per set kernel increases when KW > PGKgf, whereas reserves are accumulated when PGKgf > KW (D’Andrea et al., 2016). Reserve use during active grain filling can vary widely in response to growing conditions that modify the initial reserves at R2 and their subsequent demand (Rattalino Edreira et al., 2014). Hybrids with large kernels combined with high KNP represent an enhanced demand of assimilates and consequently, an enhanced dependence on reserves that might explain the reduced KW stability among modern maize hybrids in certain environments (Echarte et al., 2006). This response is substantially affected by growth conditions during active grain filling, which modify current plant growth and the actual dependence of kernels on reserves (Borrás et al., 2004; Cirilo and Andrade, 1996; Hisse et al., 2019; Rattalino Edreira et al., 2014). Adverse photothermal environments during this stage are particularly critical for crops sown late in spring (Bonelli et al., 2016) and/or those grown at high latitudes (Ruget, 1993), where declining irradiance is accompanied by low temperatures that may affect RUE (Andrade et al., 1993) and therefore crop growth (Tsimba et al., 2013), increasing the dependence on reserves (Kiniry and Otegui, 2000). Maize breeding has improved stay green and canopy health (Tollenaar and Aguilera, 1992) and RUE (Curin et al., 2020; Luque et al., 2006). It has also produced a delay in the age-related decline in photosynthetic rate, especially under N stress (Tollenaar and Lee, 2011). This improved photosynthetic performance could be responding to the larger sink size in modern hybrids (i.e. feedforward effect) and highlights the importance of breeding for an increased photosynthetic activity during grain filling. This effort, however, should not be limited to the effective grain-filling period. The enhanced demand of reserves in high-yielding modern hybrids suggests

28  Crop Physiology: Case Histories for Major Crops

that high photosynthetic activity during late stem elongation, pollination, and early grain growth is essential for assimilate provision to the ear (Schussler and Westgate, 1994) and for reserves accumulation in the stem to cope with grain filling and reduced lodging risk. High accumulation of SWSC may be particularly important in environments prone to terminal stress (Ouattar et al., 1987; Rattalino Edreira et al., 2014).

4.4  Grain quality Phenotypic plasticity of KW is usually small, reflecting a strong genetic control (Hallauer et al., 1988; Prado et al., 2014). Nevertheless, KW responds to reductions in assimilate availability during grain filling as mentioned earlier, which may impair kernel composition (Borrás et al., 2002) and quality properties for industrial purposes (Cirilo et al., 2011; Mayer et al., 2014; Tanaka and Maddonni, 2008).

4.4.1  Kernel hardness Kernel hardness is related to bulk density, storability, insect damage of stored grain, breakage susceptibility, milling characteristics, dry and wet milling yields, and production of special foods (Pomeranz et al., 1986). Maize dry milling industry demands high kernel hardness to maximise yield of coarse fractions (flaking grits) during grinding (Chandrashekar and Mazhar, 1999). The wet-milling market demands intermedium kernel hardness to obtain higher starch yield (Eckhoff, 2004). For animal feed, soft endosperm with higher digestibility is preferred (Rooney et al., 2005). Maize endosperm is composed of vitreous and floury portions, and kernel hardness and density result from the balance between these portions. Flint maize kernels with hard endosperm are still highly demanded for breakfast cereals, snacks, polenta, and brewing (Chandrashekar and Mazhar, 1999; Lee et al., 2007). Argentina is presently the single exporter of nonGMO flint maize to the EU, where special import permits allow its use provided grain quality attains specific standards. The non-GMO flint maize has physicochemical characteristics that make it the preferred raw material for dry milling (Litchfield and Shove, 1990; Rooney et al., 2005). It is highly demanded because of its high milling yield of large endosperm grits and the particular quality that provides to a wide variety of end-use products (Macke et al., 2016). Starch and protein are the main components of maize endosperm, and both have been mechanistically related to kernel hardness (Gayral et al., 2016). Starch granules are embedded within a protein matrix that stretches while starch granules grow. Upon kernel desiccation, the protein matrix is torn in sections where it is thin and labile, and air-filled spaces appear resulting in the floury endosperm. Instead, polyhedral starch granules grow embedded within thicker and stronger sections of the endosperm protein matrix to yield the vitreous endosperm fraction. Within this part, no void spaces are formed during kernel desiccation, thus leading to a compact and vitreous appearance and a hard texture (Watson, 2003). Cerrudo (2018) reported that higher kernel protein concentrations are commonly observed among flint type hybrids with greater proportion of vitreous endosperm (mean of 10.3%; range 9.3%–12.0%) when compared to dent types with predominant floury endosperm (mean of 9.4%; range 8.4%–10.5%). In particular, zeins concentration (a prolamin-type protein found in the corn endosperm) is correlated with kernel hardness (Gerde et al., 2016; Kljak et al., 2018; Robutti et al., 1997). Starch concentration and composition are also central to endosperm hardness. Endosperms with high amylose proportion are harder and denser than endosperms with high amylopectin proportion, suggesting that increased amylose concentration provides more amorphous regions, thus resulting in more compressible polyhedral starch granules that characterise the vitreous endosperm (Dombrik-Kurtzman and Knutson, 1997; Robutti et  al., 2000). Amylose/starch ratio would be modified by changes in starch branching enzyme activity. Lenihan et al. (2005) hypothesised that low temperature would increase this enzyme activity, while high temperature would have the opposite effect, affecting amylose proportion in the starch. Analysing several environments in Argentina, Martínez et al. (2017) reported that air temperature during the effective grain-filling period of hybrids with different endosperm texture was the environmental factor that better accounted for the variation in kernel starch composition, modifying the proportion of amylose in the starch (amylose/starch ratio). Moreover, they confirmed that increases in ear temperature explained the increases in amylose/starch ratio in maize kernels, particularly for treatments applied early during grain filling (Martínez et al., 2019). Dry milling performance is directly associated with kernel hardness, which can be expressed as its mechanical resistance to milling (Wu et al., 2010). Kernel coarse-to-fine ratio of particles derived from the mill is an excellent indicator of hardness (Blandino et al., 2013). A high coarse-to-fine ratio is typical of hard kernels and is associated with elevated dry milling yields. Genetic determinants are the main contributors to common field variations in endosperm hardness. Heritability values of 0.49, 0.65, and 0.80 were established for flaking-grit yield, dry-milling efficiency, and test weight, respectively, as estimators of kernel hardness (Macke et  al., 2016). However, the growing environment can also affect kernel hardness (Cerrudo et al., 2017; Mayer et al., 2019; Tamagno et al., 2016). Enhanced grain dry-milling quality was

Maize Chapter | 1  29

e­ stablished for flint maize exposed to environments or management that improved the source–sink ratio during the postsilking period (Cirilo et al., 2011). For instance, an improved ratio is strongly associated with enhanced incident SR and increased biomass as related to early respect to late sowings at mid-latitudes (Bonelli et al., 2016; M.E. Otegui et al., 1995b). According to this, while no sowing date effects on kernel hardness were found at low latitudes (Abdala et al., 2018; Gerde et al., 2016), Cerrudo et al. (2017) reported decreases in the coarse-to-fine ratio under late sowings in environments with short and cool summers that promote low photosynthetic source capacity during grain filling.

4.4.2  High-oil maize and acidic specialties Maize kernels with high oil concentration are preferred for livestock and poultry feed rations because of their energy value and as a substitute for animal fats (Thomison et al., 2003). Traditional maize hybrids produce kernels with an oil concentration of ≈ 6% (Maddonni and Otegui, 2006), but kernel oil concentration exhibits genetic variability that enables breeding for this trait (Laurie et al., 2004). Hence maize hybrids with a high kernel oil concentration could be obtained by crossing parental lines selected for this trait. Unfortunately, GY and other agronomic characteristics of these high-oil populations are poor, so they are not used in commercial production (Laurie et al., 2004). An alternative way to achieve high oil maize production is based on the exploitation of the xenia effect on kernel composition (Letchworth and Lambert, 1998), which depends upon the use of high-oil parents as pollen donors. This strategy, described as top-cross (Thomison et al., 2002), does not modify GY and KW of the maternal genotype but changes embryo and endosperm growth rates and embryo oil deposition, improving kernel oil concentration (Tanaka and Maddonni, 2008). This attribute in maize kernels shows a strong stability for a wide range of post-flowering source–sink ratios because of a stable embryo–kernel ratio and embryo oil concentration (Tanaka and Maddonni, 2008). Modifying source–sink ratio with thinning and intensity of shading during the effective grain-filling period, Tanaka and Maddonni (2008) found that only severe shading at early stages of kernel growth reduced the final embryo–kernel ratio and the embryo oil concentration. Then, maize kernel oil concentration seemed to be commonly sink-limited. Vegetable oil quality is linked to fatty acid composition; see Chapter 16: Sunflower, Section 4 for comparison. Oleic acid is nowadays the preferred fatty acid for edible purposes because it combines a hypo-cholesterolemic effect and a high oxidative stability (Mensink and Katan, 1989). Temperature during kernel formation accounts for most of the variation in fatty acid composition across years, locations, and sowing dates, and a linear response was detected for oleic acid percent to daily mean temperature in traditional (Izquierdo et al., 2009) and in high oleic-acid hybrids (Zuil et al., 2012). In both types, the oleic acid percent also responded to the amount of intercepted radiation per plant during grain filling (Izquierdo et al., 2009; Zuil et al., 2012). Then, increasing daily mean temperature and/or intercepted SR per plant (up to a saturation level) increased the proportion of oleic acid at the expense of linoleic and/or linolenic acid. Consequently, management practices that increase temperature and intercepted SR per plant during grain filling (e.g. sowing date, plant density, location, and fertilisation) could increase oleic acid percent in maize oil, irrespective of the genotype.

5  Concluding remarks: Challenges and opportunities In this chapter, we outlined the main maize production areas and the role of this crop in farming systems and presented the factors and mechanisms that control development, growth, capture, and efficiency in the use of resources, GY, and kernel quality. A distinctive aspect of maize production was the marked increase during the past two decades when compared with the second half of the 20th century. The outstanding feature was that its production increase has been primarily sustained by area expansion rather than by improved GY, defying the predominant paradigm of supporting production in the latter rather than in exploiting fragile lands. Maize is presently the crop that covers the largest cultivated area, has the largest production, and is an important component in many relevant productions systems. In this scenario, the biggest challenge for the next decades will be to satisfy two contrasting social demands: to increase food production in amount and quality and to decrease the environmental impact linked to input-based agriculture. The pressure will be particularly important for maize, a species characterised by high potential GYs as much as by its sensitivity to resource availability to make those GYs possible. Improved resource use efficiency as much as GY stability across environments would be the main target of future breeding and crop management efforts. Thus the knowledge and quantification of the ecophysiological factors and mechanisms underlying maize development, growth, and GY determination are valuable to (1) design knowledge-intensive crop management strategies for specific genotype and environment combinations oriented to a high and sustainable production in quantity and quality and (2) to understand the differential responses of maize to management practices among cultivars, environmental conditions, and

30  Crop Physiology: Case Histories for Major Crops

production systems. This approach constitutes a low-cost technology that helps matching crop demands and ­environmental offer and is expected to improve the efficiency in the use of environmental resources and inputs. It would also help to solve some input shortage problems that are presently threatening maize production in less developed countries as much as environmental constraints. The identification and analysis of factors and processes that regulate maize growth and GY in interaction with the environment can also provide conceptual and practical tools to improve maize breeding efficiency by identifying relevant traits for increasing GY potential and GY stability across environments, disentangling complex genotype by environment interactions and interactions among relevant traits. The success of this enterprise is highly dependent upon the development of rapid, accurate, and affordable phenotyping methods to assist breeders and to extend modelling predictions from gene expression to agroecosystems. Improved crop modelling is also expected to help redesign cropping and breeding strategies and to guide public policies for sustainable land use.

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Image source: Shiv Mirthyu from Pixabay

Chapter 2

Rice Shu Fukai and Len J. Wade University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD, Australia

1 Introduction In the LT Evan’s book ‘Crop physiology – some case studies’, Murata and Matsushima (1975) contributed the rice (Oryza sativa L.) chapter and described crop physiological understanding of rice at the time on dry matter production, generative growth, and yield limitation by grain storage capacity and assimilate supply during grain filling, with emphasis on the influence of timing of nitrogen (N) fertilisation. Their work was almost solely limited to fully irrigated conditions and mostly in temperate environments. For the intervening 45 years since the publication of the book, crop physiological understanding of rice increased greatly, including growth under water-limiting environments in the tropics. Global rice yield roughly doubled in the period, and high yielding varieties have been grown with different management. This chapter summarises recent advances in crop physiology of rice through sections on crop development, growth and resources, grain sink and source availability determining yield, response to abiotic factors, and effects of crop management on grain yield and quality. The concluding section suggests priority areas for further research. This first section introduces the global significance of rice, classification of rice cropping systems based on water availability, and key management options for rice establishment, water saving technologies, and mechanisation that are taking place in many rice-growing areas.

1.1  Global significance of rice More than half the population in the world consumes rice as a staple food, and 90% of the rice production and consumption take places in Asia. The most common system of rice culture is in irrigated lowlands where the crops are grown with standing water during most of the season in paddy fields with bunds to store and secure water supply. This unique rice-growing environment is not conducive to most other crops, and hence rice is commonly grown as a sole crop, and intercropping or mixed cropping is very limited. In Asia, rice farmers had traditionally grown the crop to provide sufficient food for the family, and this subsistence nature continues to the present time in many countries, and hence the rice traded at market is smaller when compared to other major cereals such as wheat. Nevertheless, rice produced for international markets has increased. According to FAO statistics for 2017, world-wide rice area is 167 Mha, production 770 Mt, with average yield of 4.60 t ha− 1. In 1961, rice area was 115 Mha, production 216 Mt, and yield 1.87 t ha− 1. Thus in the 56 years, yield increased 2.5 times, area 1.45 times, and production 3.56 times (Fig. 2.1). The rice productivity increase in the early period coincided with the green revolution where semi-dwarf high-yielding varieties were grown with increased fertiliser, particularly N in the irrigated fields. The increase continued with further development and adoption of new technologies, although there is now a sign of reduced rate of increase. As in other crops, rice has experienced climate change in recent years, and this change is likely to continue. Atmospheric CO2 concentration has increased, and this is expected to have contributed to increased dry matter production of rice as in other crops (Wang et al., 2015). Global mean surface temperature increased by 0.85°C from 1880 to 2012 and is expected to increase by 1.0–3.7°C by 2100 according to IPCC (2013). The magnitude of temperature increase depends on both the location and time of the year (Shimono, 2011). Increased temperature has hastened phenological development and generally had adverse effect on rice growth and yield. The trend in rainfall patterns is less clear, although there are changes in seasonal rainfall in some areas (Prabnakorn et al., 2018). Section 4.2 addresses rice responses to abiotic factors, which can form a basis for estimation of rice production under possible climate change scenarios. Crop Physiology: Case Histories for Major Crops. Copyright © 2021 Elsevier Inc. All rights reserved.


46  Crop Physiology: Case Histories for Major Crops

Global rice area, producon and yield since 1961 9 8 7 6 5 4 3 2 1 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017


area (x100 mha)

producon (x100 mt)

yield (t/ha)

FIG. 2.1  Change in global rice area, production, and yield since 1961. Source: FAOSTAT.

1.2  Rice ecosystem classification with emphasis on water availability Rice ecosystems are commonly classified based on water availability. The most common ecosystem is irrigated lowland rice where rice paddy field is flooded with shallow water depth of less than 0.3 m after the crop is established until just before harvesting. In some areas, rice fields may be flooded with relatively shallow stagnant water (0.3–0.5 m for most of the season) or deeper water, and deep-water and floating rice ecosystems may be adopted. Another major ecosystem is rainfed lowland rice, which is practised in areas of no or limited irrigation water, where crops may experience stress because of soil water deficit at some stages. In the irrigated and rainfed lowland ecosystems, there are bunds to maintain standing water in rice paddies, and crops are commonly grown anaerobically with saturated soil. Rainfed lowland rice may experience anaerobic conditions during the growing period, but in the period of low rainfall, the crop is grown aerobically. Lowland fields are mostly located in plains or towards lower parts of sloped land, and with heavy rainfall, lowland crops may be submerged, i.e. water level exceeds the plant height, and they can be severely damaged. In these lowland areas and also deep water and floating rice areas, often only rice crops can be adapted to the excess water conditions, particularly during wet season; without rice, the area would be wetland. In areas where water is in short supply, upland rice ecosystem may be practised. Upland rice is grown mostly in hilly areas and commonly without irrigation. Upland fields do not have bunds to store water above the soil surface, excess water may be lost as run-off, and crops are grown mostly aerobically. Aerobic rice is a relatively new term for rice mainly grown in irrigated lowlands but in aerobic soil without standing water. The aerobic rice has been researched recently in an attempt to maintain high irrigated yield with minimal water deficit while reducing the high irrigation water requirement of lowland rice ecosystem. Rao et al. (2017) and Chauhan et al. (2017) further describes rice ecosystems. Rice is a C3 plant adapted to warm temperate–tropical environments. It is grown widely from the tropics to the temperate areas. Often indica varieties are grown in hot–warm areas, and japonica varieties are common in warm temperate areas. Rice is grown as a single crop in summer in temperate areas, while in the tropics, it is grown commonly in the wet season but can also be double cropped in longer wet seasons, or wet season plus dry season, or even triple cropped, where irrigation water is available. It can be grown in rotation with non-rice crops: for example, rice-wheat rotation is widely practised in the Indian Subcontinent and in the Central East of China. Fischer et al. (2014) identified several Mega Environments (ME) for rice. Irrigated lowland rice consists of three major MEs, and their cropping systems are determined mostly by temperature; one ME for temperate area where only one rice crop is grown in a year, another with rice double cropped with other crops in the warm tropics and subtropics, and the third for warm to hot tropics where double cropping of rice is practised. In this chapter, we emphasise water availability as a key factor determining growth of the rice crop, and management options are considered in relation to the growth environment, particularly water availability. With secured water supply available in irrigated areas, rice is commonly grown with high input, and high yields can be achieved with high yielding varieties, and harvested crops are sold commercially, except some that are kept for home consumption. On the other hand, growth and yield of rainfed rice depends strongly on seasonal rainfall, and with high risk of not achieving high yield, input is generally low, and yield may not be high even in a season of high rainfall. Genotypes differ in their adaptation to water availability, including excess water, and suitable genotypes have been developed and are grown under particular land and crop management (Wade et al., 1999b). Water shortage is a serious threat to the rice industry in many growing regions, and development of management methods to use limited water efficiently is a major challenge (Prasad et al., 2017b).

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1.3  Crop management There are several management options available for rice growers. This section concentrates on crop establishment, water saving, and mechanisation. The effect of crop management on crop development, growth and resource requirement, and grain yield and quality is described in Sections 2, 3, and 4.3, respectively. Other management options such as selection of varieties, time of planting, fertiliser rates, and timing are integrated into different sections throughout the chapter.

1.3.1  Crop establishment The most common method for crop establishment, particularly in Asia, is transplanting where seedlings are raised in a nursery under more controlled conditions and transplanted to the main fields using commonly 15–40-day-old seedlings. Transplanting is not common in some countries, for example the USA and Malaysia, as described by Kumar and Ladha (2011) in their review of direct seeding. Transplanting is labour-intensive requiring often about 30 people to transplant 1 ha in 1 day, including the time of pulling seedlings, carrying them from the nursery to the main field, and transplanting (Fukai et al., 2019; Xangsayasane et al., 2019b). Different types of crop establishment and some of their main characteristics are listed in Table 2.1. Broadcasting, the most common method of direct seeding, particularly in Asia, requires only one to two people to sow 1 ha (Fukai et al., 2019) and is adopted when there is a lack of labour for manual transplanting. There are different methods in direct seeded rice (Kumar and Ladha, 2011). Wet direct seeding is practised when the soil is saturated with water and commonly, the soil puddled before sowing, and thus land preparation is similar to transplanting. On the other hand, in dry direct seeding, cultivation takes place while soil is still dry. These methods are often related to water availability; in irrigated fields, wet direct seeding is commonly practised, while in rainfed lowland where water environment is not favourable, dry direct seeding is more common. In some areas, particularly where red rice is grown, weedy rice is a problem, and water seeding is practised where seeds are broadcast in standing water (Kumar and Ladha, 2011). Transplanting is common when soil in the main field is saturated with water, and this may often take 2 months after the onset of the wet season. On the other hand, direct seeding particularly dry direct seeding is often practised earlier in the season. This method may also be adopted when there is not sufficient rain during the early part of the wet season and seedlings become too old for transplanting, so as a last resort, farmers may broadcast seed with the hope that rain will come soon after sowing. For flood-prone areas where taller seedlings are needed for transplanting into standing water, late transplanting or double transplanting may be needed (Sharma, 1995; Satapathy et al., 2015). In addition to transplanting and direct seeding, rice can be established from ratooning, as is common in sugarcane (see Chapter 21: Sugarcane). Ratooned rice is established as a regrowth of the crop when it is harvested, and the crop establishment cost is greatly reduced. Commonly rice is ratooned only once, but there is a possibility that ratooning can be extended for additional cycles, especially with perennial rice.

1.3.2  Water-saving methods Owing to increasing water scarcity and low water use efficiency (WUE), water-saving technologies have been developed in some irrigated rice systems. For example, water table depth has decreased in many parts of the world, particularly in China and India, promoting the need for water-saving technologies in the affected areas (Prasad, 2011). In rainfed lowland areas TABLE 2.1  Common rice establishment methods. Transplanting

Direct seeding

Manual transplanting



Cultivation condition





Dry water seeding


Row pattern

Rows not common

Rows (around 25 cm)

No rows

No rows

No rows

Rows (20–25 cm common)

Labour requirement








Seed drill

Labour requirement (the number of people required for planting 1 ha in 1 day) is for Lao PDR. From Xangsayasane, P., Phongchanmisai, S., Vuthea, C., Ouk, M., Bounphanousay, C., Mitchell, J., Fukai, S., 2019b. A diagnostic on-farm survey of the potential of seed drill and transplanter for mechanised rice establishment in Central Laos and Southern Cambodia. Plant Prod. Sci. 22, 12–22.

48  Crop Physiology: Case Histories for Major Crops

with no irrigation water, water-saving technologies are generally not available. One possibility for both irrigated and rainfed lowlands is the use of dry direct seeding, which decreases the total water requirement when compared with puddled fields with transplanted crop because as high water loss through the puddling process is removed. Alternate wetting and drying irrigation (AWD) has recently gained popularity. Rice is grown in irrigated lowland fields, and water level is allowed to drop to commonly 15 cm below the soil surface before irrigation to bring the water level to above the surface, and this is repeated throughout the season. This is now practised where the full amount of irrigation water cannot be secured or is too costly (Bouman et al., 2007). Aerobic rice is grown with no standing water in the field in Brazil (Pinheiro et al., 2006), parts of China (Prasad, 2011), and incipiently in northern Australia. Aerobic rice is grown with ample irrigation to minimise potential water stress effects. Rice fields may not be puddled, and the crop may be planted with a seed drill and grown aerobically. The aerobic system saves water when compared to fully flooded or AWD and allows for rotation with non-rice crops. Land preparation, planting, irrigation, and harvesting of rice may be repeated in a similar manner to the non-rice crop, which follows after the rice in the wet season is harvested. Advantages/limitations of these water-saving methods are described further in Sections 3.2 and 4.3.2.

1.3.3 Mechanisation Traditionally, rice was managed manually, particularly in south and southeast (SE) Asia, but with increased labour cost and labour shortage, mechanisation is increasing where suitable machinery is available and affordable. Mechanisation is commonly adopted for land preparation, planting, and harvesting. This may be based on a two-wheel tractor (hand tractor) that the farmer owns or on a contracting service. It is common for farmers in Asia to own a two-wheel tractor for land preparation, for planting using a seed drill, and harvesting using a reaper. Because of labour shortage and its cost, the transplanter has been used in Eastern Asia, while the seed drill has started to be used in south and SE Asia (see Table 2.1). Mechanical planters can improve crop establishment. A common feature of a mechanical seeder, such as the seed drill, transplanter, and drum seeder is seeding in rows, which ease subsequent crop management. In some areas such as in California and Australia, rice seed is aerially sown in flooded paddies. A combine harvesting service has become available in many rice growing areas and is well adopted by farmers. The adoption of mechanised rice production affects some aspects of crop growth and grain yield and quality (Section 4.4). Mechanisation is often adopted as rice cropping moves from subsistence to commercial agriculture (Clarke et al., 2018). Grain quality, particularly milling quality, becomes important for marketing purposes, so the broken rice fraction should be avoided. A key factor for assessing milling quality is head rice yield (head rice recovery), the proportion by weight of unbroken milled rice (80% of maximum length retained after milling) to weight of rough paddy rice, is used in this chapter. Other grain quality parameters for chemical composition, such as amylose content and protein content, are also important, and they are reviewed in Fitzgerald et al. (2009).

2  Crop structure, morphology, and development This section discusses crop establishment first, followed by phenological development with emphasis on flowering, and concludes with an outline of shoot and root development.

2.1  Germination and seedling emergence Transplanting has an advantage of good establishment with seedlings raised in areas where water may be more controlled, and often fertiliser is applied for healthy seedlings suitable for transplanting. When the main fields are saturated with water, seedlings are transplanted, and water level is raised as seedlings become taller. Older seedlings are used commonly in rainfed lowland areas because farmers may need to wait for main paddy fields to be saturated with water for transplanting. Also in rainfed lowlands where water control is not readily available, young seedlings risk submergence with heavy rainfall after transplanting. For transplanted rice, seed beds provide favourable conditions for germination and early growth. This protection is not available for direct seeded rice, and germination, seedling emergence, and early vigour are important, particularly for direct seeded rice in both lowland and upland ecosystems, including aerobic rice. Water control may be available for irrigated lowland or aerobic rice but may not be available for rainfed lowland rice. Even where irrigation water is available, delicate water management is required not to inundate germinating seed and young seedlings yet to provide sufficient water for their growth. Thus Kato and Katsura (2014) consider early vigour to be one of most important genotypic characteristics for successful aerobic culture. Rapid leaf development to secure a quick increase in leaf area index (LAI) is required for aerobic culture. This could be partly achieved by appropriate genotypes such as tropical japonica types.

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2.1.1  Importance of seedbed in direct seeded rice Direct seeding requires precise water management for emergence, crop establishment, and weed management. Often broadcasted crops are not as even as transplanted ones (Hayashi et al., 2009), particularly when land preparation is not thorough and land level is uneven (Rickman et al., 2001). There could be large areas in which seedlings fail to emerge, and sometimes farmers transplant seedlings into such areas. In addition, broadcasted crops may have seed buried to different soil depths, when broadcasting is followed by harrowing, as is commonly practised. Weeds may grow where rice plants are missing or slow to emerge. Thus it is important to have good rice seed with high germination rate and vigour for quick establishment, and in some cases, seed priming may be used to promote quick establishment (Farooq et al., 2011). Similarly, coating of seeds with calcium peroxide is used to assist good establishment in wet seeding or water seeding (Yamauchi and Chuong, 1995). Iron coating appears useful in anchoring seed in water seeding (Yamauchi, 2017). Germination is important particularly for direct seeded crop in rainfed lowlands where water control is limited, and often the fields are affected by flooding or water logging after seeding. Low oxygen availability in the soils reduces germination and could result in failure of establishment. While rice can grow well under flooded conditions with the development of aerenchyma tissues that transport oxygen through the plant under standing water, it is susceptible to excess water during germination and early growth (Yamauchi et al., 1993). Some genotypes are more tolerant of flooding (anaerobic germination (AG)) because they are able to break starch into simple sugars (Ismail et al., 2009). Genotypes IR64-AG131 and IR64-AG132 introgressed with quantitative trait loci (QTL) for AG (AG1) were able to germinate, and seedlings emerged from the field at more than 200% higher rate than IR64 even after 21 days flooding following seeding (Lal et al., 2018). The improved establishment in genotypes with AG1 increased effective tillers m− 2 and grain yield in three establishment ­methods: dry direct seeding, wet direct seeding using the drum seeder, and broadcasting. Lal et al. (2018) showed that higher seed rate and fertilisation, particularly phosphorus (P), improved crop establishment.

2.1.2  Lodging in broadcasted rice The position of the crown in broadcasted crops is commonly shallow. When compared with transplanted crops, broadcasted crops often require higher number of seeds, resulting in a higher established plant density. These conditions are inductive for plant lodging, and the percent of lodged crops is commonly higher in broadcasted crops than in transplanted crops (Xangsayasane et al., 2019a). Thus lodging resistant varieties are required for broadcasted crops. Lack of labour to transplant seedlings or lack of early season rain with unsaturated soil for manual transplanting may direct farmers to direct seeding with suitable varieties that can emerge quickly from the soil and that do not lodge.

2.1.3  Deep planting Deep planting can compromise rice seedling emergence. However, Hanviriyapant et al. (1987) showed the benefit of deep seeding using a seed drill or by dibbling when surface soil dry out after a rainfall event in rainfed lowland or with delayed planting after an irrigation event. They found that top soil (0–5 cm) dried out quickly, while the 5–10 cm layer was wetter at 10 days after irrigation. This soil moisture condition resulted in the optimal planting depth of 4–6 cm for cv Pelde in northern Australia when irrigation was applied on the same day and shifted to 6–8 cm and 8 cm on 10 and 15 days after irrigation, respectively. The maximum emergence decreased from about 80% to 50%–40% with delay in sowing after irrigation. Seedling vigour as measured by shoot dry weight at 43 days after planting followed the pattern of seedling emergence. An advantage of delayed sowing after irrigation was better control of weeds with cultivation before planting. Using a seed drill, seed placement can be deeper than broadcasted crops. In rainfed lowlands in the Philippines, mean seed depth of broadcasted rice was 16–35 mm in a 2-year study under the experimental conditions of Ohno et al. (2018) and 16–23 mm for Bautista et al. (2019), while seed depth with a seed drill mounted on a two-wheeled tractor was 16–32 mm. This depth can be lowered further with the seed drill if required. Thus with a seed drill, planting depth can be below that of the broadcasted crop, with higher water availability in the deeper soil assisting establishment of drill-planted crops, even when establishment of broadcasted crops may be poor or even fail (Xangsayasane et al., 2019b). The drill-planted crops are not only better in establishment but also form rows that are easier for weed control when compared with broadcasting (Kumar and Ladha, 2011). However, with deep planting, varieties that can emerge from deeper soils are required (Fukai et al., 2019; Xangsayasane et al., 2019b). Early vigour would also be important for drilled crops. There is a large genotypic variation for seedling emergence from deep soil. Lee et al. (2017) found this variation to be associated with mesocotyl length for seed sown at 5 cm depth. Using 57 rice genotypes, they found indica genotypes had the longest mesocotyl, while japonica genotypes had the longest coleoptile. Using chromosome segment substitution lines (CSSL) of Nipponbare and Kasalath, they identified two QTL that are associated with mesocotyl length. Similarly, Ohno

50  Crop Physiology: Case Histories for Major Crops

et al. (2018) found variation in mesocotyl length and seedling emergence among varieties. When sown at 85 mm depth, emergence ranged from greater than 80% to less than 1% in popular varieties.

2.2  Phenological development There are three main growing phases in rice: vegetative, reproductive, and grain filling. The vegetative phase covers from planting to panicle initiation (PI), and its duration varies greatly depending on growing environment and genotypes, particularly their photoperiod sensitivity. Reproductive stage commences with PI and ends with anthesis, while grain filling covers the period from anthesis to maturity. In irrigated lowlands, time of seeding and transplanting may be selected to suit crop growth and high yield. However, as time of seeding and transplanting may be dictated by the availability of water, this could affect crop phenological development and subsequently yield formation. In these cases, photoperiod-sensitive varieties may be selected to give more flexibility, although they may not be high yielding (Ouk et al., 2007).

2.2.1  Drivers of phenological development Time to flowering is strongly controlled by photoperiod and/or temperature during the vegetative stage before PI. Time to flowering is mostly determined by photoperiod with some interaction with temperature in photoperiod-sensitive genotypes, while temperature drives phenological development of photoperiod-insensitive varieties. Other factors that contribute to time to flowering are described at the end of this section. Rice is a short-day plant, and some varieties respond strongly to photoperiod; in this case, flowering date may be similar despite large differences in planting time. Others respond weakly to photoperiod, and in this case, delay in planting results in some delay in flowering. Photoperiod-insensitive varieties flower after a certain number of days if temperature is constant during early growth. Thus often photoperiod-sensitive varieties such as KDML105 in Thailand are considered to flower on the calendar date of 25 October, while the insensitive varieties are often expressed as the number of days to flower or maturity. Photoperiod sensitivity may be expressed as a photoperiod sensitivity index (PSI), calculated as 1- (delay in flowering)/(delay in planting). Thus if a variety is strongly photoperiod-sensitive and always flowers on the same date, delay in flowering with late planting is 0 day, and hence PSI = 1.0. On the other hand, if flowering is delayed by 15 days with 60 days delay in planting, PSI = 0.75. In the insensitive genotypes, 60 days delay in planting results in 60 days delay in flowering, assuming temperature is constant during the growth period, PSI = 0.0. The range of PSI in rainfed lowland rice varieties in Thailand and Laos was 0.23–0.82 (Fukai, 1999). The period from planting to flowering for photoperiod-sensitive varieties is commonly divided into three phases; the basic vegetative phase (BVP) or juvenile phase when the plants do not respond to photoperiod, the photoperiod-sensitive phase (PSP), and post-photoperiod-sensitive phase (PPP) (Yin et al., 1997). The BVP is longer under lower temperatures (Collinson et al., 1992). A base temperature of 8°C is often assumed in rice phenology models (Fukai, 1999). However, more recent work has shown that photoperiod has some effect even after PI, and this can affect the flowering date up to 10 days in some varieties (Yin and Kropff, 1998). In earlier experiments in temperature-controlled rooms, the effect of temperature on time to flower was considered as of heat-sum/thermal time type defined by three cardinal temperatures (the base temperature, the optimum temperature at which the rate of development to flower is the highest, and the high-temperature limit), and the rate of development between cardinal temperatures was interpolated from linear regressions (Summerfield et al., 1992). More recent work that examined photothermal effects simultaneously indicated that the responses were not linear but better described by a skewed bellshaped curve (beta function); hence the use of a bilinear heat sum model overestimated the rate of development to flower when temperature was below 30°C (Awan et al., 2014). Modelling by Yin et al. (1997) based on their earlier experiments indicated that minimum time to flowering of 17 varieties varied between 35 and 74 d, with japonica varieties tending to flower earlier (35–44 days). For these varieties, BVP ranged from 12 to 40 days under these optimum conditions. Optimum day temperature ranged from 28 to 35°C among varieties, and this was 2–3°C higher than optimum night temperature for most varieties. These optimum temperatures were commonly a few degrees lower in japonica varieties. Their model contained five parameters and was able to predict flowering date of 12 varieties in 3 countries. Because temperatures before flowering are more or less constant and often around the optimum in the tropics in the wet season, e.g. lowland rice in the Mekong region, the effect of temperature before flowering on time to flower may be small. In this case, photoperiod has profound effect on flowering time as indicated earlier. However, shoot apex is underwater in small plants, hence water rather than the air temperature affects rice phenological development. Water temperature is more stable and often a few degrees higher during night than air temperature, and this

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should promote development. Experimental data support the use of water temperature until the panicle is exposed to the air during the booting stage (Shimono et al., 2005). Flowering time of rice is sensitive to drought (Puckridge and O'Toole, 1980), and severe stress causes a long delay (Lilley and Fukai, 1994). Longer delay in flowering of a variety is an indication of its susceptibility to drought and is related to panicle water potential (Jongdee et al., 2002). Under an infertile and acidic sandy soil (Aeric Paleaqualt) common for rainfed lowland rice in Thailand, flowering was delayed, and application of farmyard manure promoted flowering by 4–7 days (Wonprasaid et al., 1996). Rice flowering is accelerated by mild N stress but impeded by severe N stress.

2.2.2  Global warming effect Recent warming has hastened rice phenological development. For example, in Punjab and Pakistan, mean air temperature during the transplanting-maturity period of rice increased by 0.5–1.2°C decade− 1 between 1980 and 2014, with an associated decrease in mean time between transplanting and maturity by 6.4 days decade− 1, which was significant in 8 of 10 locations (Ahmad et al., 2019). Effect of temperature increase in 1981–2009 on rice phenological development was examined for 202 stations across China (Zhang et al., 2016). Mean temperature increase during emergence to maturity was about 0.45°C decade− 1. Mean growth duration from emergence to maturity decreased in 92% of cases, and a significant negative correlation between growth duration and mean air temperature was obtained in 54% of the stations. In some stations, the change in varieties towards later maturing during the study period resulted in no significant effect of temperature increase on growth duration. However, the effect of climate change varied between single crop per year in the northeast and north region and double crops per year in the humid south region in China (Zhang et al., 2014a, 2016). Zhang and Tao (2013) mentioned that temperature variability also increased in northeastern China, and this may have prolonged the growing season. Shimono (2011) examined the change in temperature for 1961–2010 for northern Japan where cold often affects grain yield. During these years, temperature increased up to June, and this resulted in heading occurring 0.7–1.9 days decade− 1 earlier. However, there was no significant temperature increase during reproductive growth in July–August, and this resulted in lower temperature at booting when cold has severe effect on spikelet sterility. Current heading date is slightly earlier than the optimal heading date, and with increased temperature, heading would occur even earlier. Crop management could be altered, but the development of cold-tolerant varieties is also required for the region.

2.2.3  Crop establishment methods Crop establishment methods affect flowering date, particularly in rainfed lowland rice. Transplanting of seedlings delays flowering because plants take several days to recover from uprooting and transplanting. This delay was particularly prolonged when old seedlings were used in photoperiod-insensitive varieties in rainfed lowland, and Immark et al. (1997) considered this seedling age effect to be more important than the sowing date effect in varieties considered almost photoperiod insensitive (PSI = 0.23). In their work, delay in transplanting of seedlings from 25 to 45 days old delayed flowering by 5–9 days. Thus while the use of old seedlings will reduce the time the crop spends in the main paddy fields, the saving is partially offset by the delay in flowering. The growing period of transplanted crops in the main field is reduced by a few weeks, although total duration including nursery time may be somewhat larger, and this reduced main field period can be particularly advantageous in marginal environments for rice; for example, in temperate areas where the seedling nursery can be protected from cold weather in spring before transplanting, and seedlings are transplanted to the main fields when the weather is warm so the crop matures before the onset of cold weather (Hoshikawa et al., 1995). It should be noted that crops can be direct-seeded well before the field is ready with saturated soil water for transplanting, particularly with dry direct seeding. Thus with early sowing, the time of harvesting of direct-seeded rice crop may be earlier than the transplanted crops. Seed drill for dry direct seeding requires early planting, before the soil is saturated with water (Xangsayasane et al., 2019b). With the earlier time of sowing and emergence when dry direct seeding is used, the crop may be harvested earlier if photoperiod insensitive varieties are used. With such conditions of varying time of planting, photoperiod-sensitive varieties are often advantageous to avoid flowering in a heavy rain period (Fukai et al., 2019; Xangsayasane et al., 2019b). This can provide an opportunity for a short-duration post-rice crop such as mungbean (Samson et al., 2020). Recently, Vote et al. (2019) explored pond water availability to subsistence farmers in Champassak Province of Lao PDR, and limited supplementary water from small farm ponds could be used to secure rainfed rice or to improve family nutrition with a small area of short-duration post-rice vegetables or grain legumes.

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2.2.4  Crop ripening and maturity Grain-filling duration is affected by temperature and could range from 25 days in the tropics to 50 days in temperate areas. Physiological maturity is the time of maximum grain yield, but grain moisture content keeps decreasing after physiological maturity. Time of harvesting is an important decision for farmers, particularly when rice is produced for marketing. Early harvesting during grain ripening, particularly before physiological maturity, reduces both grain yield and milling quality with a high proportion of immature grain. Early harvesting with high moisture content may also result in extra cost of drying. On the other hand, as the grain ripens and grain moisture declines, fissured grain percent at harvest increases, resulting in increased broken rice percent after milling, and hence head rice yield (unbroken milled rice/rough paddy rice) decreases (Bunna et al., 2019a,b). A heat sum of 450–500oCd (base temperature = 10°C) appears to be the optimum for harvesting with maximum head rice yield for both wet and dry season crops in Laos (Xangsayasane et al., 2019c). The grain moisture content at maximum head rice yield appears to be high at 25%–26% for wet season rice in Cambodia (Bunna et al., 2019b) when compared to southern USA, for example, 21% at Arkansas (Siebenmorgen et al., 2013). High-yielding, quick-maturing, photoperiod-insensitive varieties have contributed to cropping system intensification in Asia. Thus varieties grown in the dry season are mostly photoperiod-insensitive in the Mekong region (Fukai and Ouk, 2012). Quick maturity is often required because the season before the main wet season is often limited because of cold weather or insufficient water. This limitation is further constrained owing to shift from transplanting to direct seeding, for example, in Central China (Xu et al., 2018).

2.3  Shoot development and growth Shoot development is affected by crop establishment, and often increased seed rate promotes shoot development of the canopy. In the work of Lal et al. (2018), direct-seeded rice established from a seed rate of 60 kg ha− 1, compared to 40 kg ha− 1, had higher seedling population, taller plants, more tillers m− 2, higher LAI (5.4 vs. 4.2) at flowering, and more panicles (269 vs. 168 panicles m− 2) but small difference in grain yield. The number of tillers increases during the tillering stage for about 1 month from transplanting, and the maximum tiller number depends on growing conditions such as plant spacing, N availability, and genotype. However, not all tillers produce panicles, and commonly, late tillers senesce particularly under poorer conditions. The number of panicle-bearing tillers, but not maximum tiller number, was correlated with mean daily solar radiation for 6 weeks after transplanting, for crops grown under favourable conditions across locations in Japan (Murata and Matsushima, 1975). Genotypes with fewer tillers, such as the New Plant Type (NPT), would reduce the wasteful non-productive tillers, but because of the limitation in tillering capacity, a closer spacing may be required (Peng et al., 2008). Rice tillers profusely and high-yielding varieties tend to have a high proportion of productive tillers. Kato and Katsura (2014) showed tiller production was reduced when seedlings were grown under flooded than under aerobic conditions, resulting in higher panicle numbers in aerobic, although this may be related to the higher number of plants established when compared to transplanted crops. A medium level of standing water covering the tillering nodes would suppress tiller emergence. Broadcasted crops with higher plant density than transplanted crops maintain higher tiller density and higher shoot biomass from tillering to maturity, but plant height may be similar or even slightly lower (Naklang et al., 1996). Leaf area development depends on environmental and management factors, such as seed rate mentioned earlier, and N availability (Murata and Matsushima, 1975). New leaves are produced and often appear at a constant rate (phyllochron) on each tiller. The flag leaf is the last leaf to appear before heading and is generally smaller than leaves produced earlier. Leaves commonly senesce and LAI decreases during grain filling (San-oh et al., 2004), which is accelerated by high temperature (Kim et al., 2011). Some genotypes have an ability to maintain leaf area often called ‘stay-green’ characteristics; for example, one genotype lost leaf area between heading and maturity at the rate of 0.02 LAI d− 1, while two others lost at the rate of around 0.06 LAI d− 1 (Huang et al., 2019). Varieties with maintenance of high green leaf area are important for assimilate production during grain filling (Peng et al., 2008). Premature leaf senescence may occur in varieties with high N concentration and high N content in grain (Wei et al., 2017a). Once PI is achieved, the young panicle starts to develop. Bracts, primary branches, and secondary branches are differentiated, and at about 10–12 days after PI, spikelets are differentiated in secondary branches (Murata and Matsushima, 1975). The potential number of spikelets per panicle is determined during this early reproductive stage, while some spikelets are aborted during the late reproductive stage (Kato and Katsura, 2010). In the later reproductive stage, florets are differentiated, and maturation of pollen ends the reproductive stage. The period of pollen development to flowering is susceptible to

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environmental stresses such as extreme temperatures and drought, causing male sterility, and subsequently, reduced spikelet fertility, and often, reduced grain yield. Flowering takes place over several days within a panicle. A spikelet bears only one floret in rice, and this reduces flexibility to adjust to the environments that are available relative to most other cereal crops. There are six stamens in the rice floret. At anthesis, the lodicules become turgid, and their increased volume causes the anther to dehisce and pollen to shed, some of which are intercepted by the stigma. Spikelets developed early and mostly located on primary branches, often called superior spikelets, flower early than inferior spikelets on secondary branches, which often fail to become fully filled grain (Mohapatra et al., 2011). Detailed morphological development of above-ground organs is described in Yoshida (1981).

2.4  Rood development and growth Rice root system differs greatly under anaerobic and aerobic conditions. There are two distinct types of roots in flooded lowland rice; one is a superficial dense hairy root system in the top soil layer (< 1 cm), where oxygen is still available. The other type is a nodal root-lateral root complex that develops below the aerobic soil-flood water interface (Ladha et al., 1998). The nodal roots are thick (> 0.5 mm) and aerenchymatous, and oxygen is transported through the aerenchyma to lateral roots that are mostly fine and less than 0.1 mm in diameter. These lateral roots take up most water and nutrients. Aerenchyma formation in the root cortex is promoted under anaerobic conditions. When water is ponded, development of root aerenchyma is essential for passage of oxygen, with the sclerenchyma barrier to radial oxygen loss essential to keeping oxygen available to growing root tips in flooded soils (Ismail, 2018). Root growth is reduced after PI in both upland and lowland conditions (Naklang et al., 1996), resulting in a decrease in root–shoot ratio with root mass at maturity below 20% of total mass, particularly in lowlands. Kato and Okami (2010) found in Japan that root weight increased to around flowering in flooded and aerobic conditions, but the root mass was less than 10% of total biomass at maturity.

2.4.1  Shallow root system One common feature of lowland rice is a shallow root system under anaerobic conditions. For example, Pantuwan et al. (2002) described the top 15 cm soil containing 84%–87% of total root weight. Similarly, Naklang et al. (1996) showed most roots were in the top 10–15 cm layer under lowland conditions. In the experiments of Naklang et al. (1996), not only shoot growth but also root growth was higher in broadcasted crops than transplanted crops, particularly in the shallow soil layer of 0–10 cm, while differences in shoot and root growth were generally small among the four varieties they examined. Root growth is particularly sensitive to soil water availability. In saturated soil, root growth may be limited to the surface layer where a large number of fine roots develop (Kato et al., 2013). The general lack of deep roots in lowland conditions may be because of lack of oxygen in lower soil layers, but other physiological factors also affect deep root growth (Gowda et al., 2011). Lack of deep water may not cause much problem in irrigated lowland rice because sufficient water is taken up from the surface roots except around midday. However, this could be an issue in rainfed lowland rice where standing water often disappears, but roots are not able to grow deeper perhaps because of the hard pan that may have developed after continuous puddling (Pantuwan et al., 2002). Samson et al. (2002) reported genotypic differences in ability for roots to grow into deeper layers as water deficit proceeded in Rajshahi Bangladesh. Root plasticity was considered important under the fluctuating soil water conditions in the rainfed lowlands (Kano-Nakata et al., 2011). Dry direct seeding or aerobic system could minimise this problem. The large number of surface roots common in lowland rice may decrease sharply under aerobic conditions in which the number of nodal roots is reduced, resulting in a higher proportion of total roots in deeper soil in the study of Kato and Okami (2010) in a temperate area. In their study, aerobic root growth was limited during early stages, and the crop was susceptible to water stress showing stomatal closure. When the soil surface dried, the aerobic crop was able to extract water from subsurface layers, but stomatal aperture decreased sharply with decrease in soil water potential below − 50 kPa at 20 cm soil depth. Siopongco et al. (2008, 2009) demonstrated chemical and hydraulic root signals for stomatal and transpirational adjustments in rice as soils dried. When soil surface water is reduced to around field capacity, roots may start to descend (Kato et al., 2013). However, once soil water content decreases further, root growth may be reduced sharply. Soil strength as measured by penetrometer increases sharply with decrease in soil water potential after an irrigation event in aerobic rice, and this would impair root elongation (Kato et al., 2013).

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In upland-grown rice without irrigation, water stress may affect both shoot and root growth (Naklang et al., 1996). In a comparison of four varieties in various upland and lowland conditions over 3 years, upland crops had slightly lower total root mass and a higher proportion of deep roots (> 30 cm) in upland conditions (Naklang et al., 1996). Variety difference in root dry matter was small, except that lowland-adapted IR20 had more surface roots than others under transplanted lowland conditions.

2.4.2  Deep roots Unlike lowland conditions, upland rice, including aerobic, can develop functionally important deep roots. Root angle is important in determining the vertical distribution of roots, as demonstrated by Kato et al. (2006) and further by Ramalingam et  al. (2017). Thus the proportion of roots with a steep angle (i.e. 45–90 degrees from the horizontal) was associated with deep roots (> 30 cm soil layer). The expression of deep root characteristics was less pronounced in compacted soils (Ramalingam et al., 2017), although water may be available below the hardpan (Yano et al., 2006). Recent studies have shown genotypic variation in root angle or deep-root ratio under aerobic conditions (Kato et al., 2013) or upland conditions (Kitomi et al., 2015; Ramalingam et al., 2017); thus it may be feasible to develop rice varieties with deeper root systems for aerobic conditions with reduced requirements for frequent irrigation. Wade et al. (2015) reported two genomic hotspots for root growth under drought and grain yield in rainfed lowland conditions. Ramalingam et al. (2017) mentioned four QTL that were associated with deep root angle. Ishimaru et al. (2017) found that when compared with IR64, its introgression line with IR64 background (YTH183) with higher yield potential had steep-angle deeper roots, accessing more water from deeper soil layers under AWD. Kato et al. (2011) also found YTH183 had higher root length density at 15–30 cm and 30–70 cm soil layers but not in the 0–15 cm layer. The importance of steep-angle roots with larger deep root ratio has been found by others in water limiting conditions, and deep root gene dro 1 was identified (Uga et al., 2013). Deep root as drought progressed was the implication in Samson et al. (2002), Clark et al. (2002), and Wade et al. (2015). See Shashidhar et al. (2012) for root methods. However, it should be noted in most studies (Naklang et al., 1996; Kato et al., 2013) that roots are considered deeprooted when they are in soil depth of only 30–45 cm (Naklang et al., 1996), 15–30 cm (Kato et al., 2013), and 30–60 cm (Ramalingam et al., 2017). Fukai and Inthapan (1988) showed that water uptake in aerobic rice was limited to 60 cm depth, and this was shallower than in maize (Chapter 1: Maize, Section 3.2.2) or sorghum.

3  Growth and resources 3.1  Capture and efficiency in the use of radiation Radiation interception and radiation use efficiency (RUE) are used to dissect the growth of irrigated rice in response to crop management, crop physiological status (e.g. leaf N concentration), and genotype. RUE is commonly calculated as the ratio of shoot biomass and total amount of radiation intercepted during a growth period. In this chapter, RUE is expressed on the basis of intercepted photosynthetically active radiation (PAR); here, we use a factor of two to transform RUE from shortwave radiation to PAR.

3.1.1  Crop growth analysis with radiation interception Radiation interception increases with LAI almost linearly for small canopies, but the rate of increase in radiation interception decreases with larger LAI and eventually reaches a maximum. A similar pattern applies when variation in LAI is created by agronomic treatments such as plant density and fertiliser N rate. For example, Weerakoon et al. (2000) examined the effect of N rate under two CO2 concentrations and showed that radiation interception increased sharply to 50% with LAI ≈ 2, but the rate of increase in interception decreased with the maximum interception obtained at LAI ≈ 6. Increased N rate from 0 to 200 kg ha− 1 increased LAI and radiation interception greatly, while there was also an increase in RUE. Radiation interception during early growth may be influenced by seed rate and planting pattern such as row spacing. Wide rows may reduce radiation interception during early growth, and this is a possible reason for reported disadvantages from the transplanter machine; increased plant density per row may then increase dry matter production and grain yield (Xangsayasane et al., 2019b). Differences in yield between a tropical environment at International Rice Research Insstitute (IRRI) in the Philippines and a cooler environment in Hubei, China associated with lower RUE under higher temperature (Wang et al., 2016a). For a common set of varieties, total dry matter (TDM) was higher in Yunnan (southern China) with higher incident radiation and higher total radiation intercepted than in Kyoto (Japan) with no changes in RUE (Katsura et al., 2008).

Rice Chapter | 2  55

In this analysis, radiation interception was estimated from the extinction coefficient k and LAI, and variation in soil and management may have also differed. Firm conclusions can be obtained when comparison is made at the same location, with experimental manipulations such as plant density and varieties. Alternatively, by considering comparable management, the hierarchy of limitations over locations or ecosystems can be evaluated in cross-site analyses (Wade et al., 1998; Stuart et al., 2016). Deshmukh et al. (2017) showed that lower biomass production in upland conditions in the wetter year was mostly because of lower RUE (2.3 vs. 3.0 g MJ− 1) with no difference in total radiation intercepted. On the other hand, Katsura et al. (2010) showed aerobic rice in temperate Japan produced higher biomass than flooded rice in three of four comparisons, and this was associated with similar or slightly higher RUE in aerobic rice (2.5–3.0 g MJ− 1) than in flooded lowland rice (2.4–2.7 g MJ− 1). This high RUE in aerobic rice was associated with higher N uptake exceeding 200 kg ha− 1 when compared with that under flooded condition (150–170 kg ha− 1). Thus well-irrigated and well-fertilised, such as 180 kg N ha− 1 in the work of Katsura et al. (2010), aerobic rice appears capable of similar radiation interception and similar or higher RUE when compared to the flooded lowland rice. However, lower RUE in aerobic condition than in flooded condition was reported in experiments at IRRI (Clerget et al., 2014). Radiation interception may be reduced under drought because of reduced leaf area growth, leaf rolling, and leaf death (Boonjung and Fukai, 1996a). In upland experiments in which crops of different ages were subjected to drought periods of 23–35 days, the drought effect on RUE depended on the crop age (Boonjung and Fukai, 1996a). When the crop was 33 days old at the commencement of drought, water requirement was small, severe water stress did not develop, and the crop adapted through reduced LAI and radiation interception, with almost no effect on RUE. In the older crop with radiation interception exceeding 60% and hence higher water demand, leaf water potential decreased rapidly, and the reduction in RUE from 2.3 to 1.2 g MJ− 1 mostly contributed to the reduction in biomass production.

3.1.2  Radiation use efficiency as reflection of leaf photosynthesis rate Rice RUE is commonly about 2.8 g MJ− 1 under favourable conditions (Sinclair and Horie, 1989; Sinclair and Muchow, 1999). However, RUE is affected by weather and soil. For example, water stress often reduces the rate of photosynthesis and hence RUE (Boonjung and Fukai, 1996a), as mentioned earlier. Thus in an aerobic condition that has induced slight water stress, RUE may be reduced. Similarly, soil N availability and leaf N status affect photosynthesis and RUE (Sinclair and Horie, 1989). Experiments in Korea showed that increased N increased both RUE as related to leaf photosynthesis and intercepted radiation as related to LAI, with higher biomass production during early growth (Xue et al., 2016). In experiments in China, integration of higher plant density, late split N application, and post-anthesis shallow alternate wetting and drying (AWD) increased grain yield of early and late sown rice by 33%–37% over present practice (Qin et al., 2013). These yield gain related to both increased radiation interception and increased RUE. Qin et al. (2013) suggested that the late N application at PI-flowering and post-anthesis shallow AWD improved late N availability, maintaining photosynthesis. Total N uptake at flowering and N recovery at maturity were higher in these superior management practices. Usefulness of late N application was also noted in Peng et al. (2006). As leaves age and start to senesce, the rate of photosynthesis decreases, resulting in reduced RUE towards maturity (Horai et al., 2013). Thus maintaining a high photosynthetic capacity and hence high RUE during grain filling appears critical for high yield. Stay-green varieties that maintain green leaf area during grain filling would be expected to maintain higher rate of photosynthesis and RUE to meet the assimilate requirement to fill grain. On the other hand, leaf senescence may be associated with the transport of N and C to meet the grain filling demand; in this case, RUE would decrease. Yield advantage of ‘super’ hybrid rice is partially associated with its high RUE in late growth stages (Huang et al., 2016). Experiments in Guangxi Province, China showed that higher RUE was associated with higher photosynthesis at heading and milk grain in the field. Glasshouse experiments show that the ‘super’ hybrid had higher chlorophyll a and Rubisco contents than the ordinary hybrid. While the yield advantage of the ‘super’ hybrid was also demonstrated in subtropical areas of Hunan Province in China, this was more related to a slightly longer growth duration and higher intercepted radiation, while there was no significant variety difference in RUE (Zhang et al., 2009c). They also found that these hybrids did not necessarily require more N fertiliser.

3.1.3  Radiation use efficiency as related to canopy structure While RUE often reflects the leaf photosynthetic rate (Sinclair and Horie, 1989), it is also affected by canopy structure in rice. Higher RUE can be related to high leaf inclination angle from the horizontal, i.e. erect leaves. For a canopy of mostly horizontal leaves, radiation is mostly intercepted by the top leaves, and light flux density at the leaf surface may be above or close to light saturation for photosynthesis with lower efficiency of conversion of solar radiation. With more erect, ­radiation

56  Crop Physiology: Case Histories for Major Crops

is more evenly distributed on large leaf surface areas with higher efficiency of conversion. It is well known in rice that high leaf inclination angle with smaller extinction coefficient k allows better penetration of radiation into canopy depth and with reduced mutual shading, so canopy photosynthesis is higher (Monsi and Saeki, 2005). Leaf inclination angle may vary because of crop management or varieties. Rice planted with one plant per hill had better canopy structure when compared to three plants per hill at the same plant density (San-oh et al., 2004). During reproductive and grain filling stages, these crops intercepted more than 90% of incident radiation, but crop growth rate was higher in the more even plant distribution with higher leaf inclination angle. San-oh et al. (2004) showed that more even distribution of plants with one plant hill− 1 resulted in larger leaf inclination angle (e.g. flag leaf 79–81 degrees) with smaller extinction coefficient (k = 0.46–0.50) when compared with the three plants per hill (74–75 degrees, k = 0.74–0.82). Transplanted crops and dibbled direct-seeded crops were also compared, and under good crop establishment with no weed problems, direct-seeded crops with even plant distribution and one plant per hill produced the highest LAI, light interception, dry matter, and grain yield (San-oh et al., 2004). In the experiments of Gu et al. (2017), AWD outyielded flooded because of increased RUE and reduced k in AWD. Root oxidation activity was higher in AWD than in flooded conditions, and this was associated with increased cytokinin production. The authors considered this may be a reason for increased N allocation to the higher canopy position contributing to higher RUE. The importance of maintaining N in the top canopy layer for high canopy photosynthesis was also suggested by Ladha et al. (1998), but they considered that with most N already in the top layer, there is limited scope for further improvement of this trait. Comparison of a historic set of 14 varieties in Middle Reaches of Yangtze river showed improvement in pre-heading RUE from 2.2–2.7 g MJ− 1 to 3.1–3.5 g MJ− 1 over the period of 1936–2005 (Zhu et al., 2016). This is a mean annual increase of 0.43%, which was smaller than the mean annual increase in grain yield of 1.17%, implying that factors other than RUE contributed to yield improvement. The importance of canopy structure has been highlighted in hybrid rice; ‘super’ hybrid rice varieties have high inclination angles in three top leaves (Peng et al., 2008). These leaves are long and narrow with their LAI exceeding 6 and are also more erect compared to the standard hybrid, with a smaller k allowing more solar radiation to penetrate the deeper canopy, contributing to increased canopy photosynthesis. This, together with panicle position within a canopy, is considered to be advantageous, with high conversion efficiency of intercepted radiation. Comparison of 3D canopy structure can be made more readily with the availability of digital technology (Hua et al., 2016). Hua et al. (2016) demonstrated the effect of Prostrate Growth 1 gene which was more prevalent in wild rice; YIL18 with the gene had about 10 degrees lower leaf inclination angle than the variety Teqing, which is known for erect growth habit. Prostrate type growth with large tiller angle from the main stem favours rapid canopy spread and competition with weeds. However, gradually, this trait was lost during rice domestication. Thus selection of rices with improved seedling vigour and weed competitiveness may be useful for direct-seeded conditions where weeds are a concern (Zhao et al., 2006; Kumar et al., 2009). Conversely, improved rices have more erect tillers, and tiller angle-controlling genes have been identified recently (Dong et al., 2016). It may be advantageous to combine prostrate early growth for weed suppression and erect growth later for yield potential, perhaps using tissue-specific promoters.

3.2  Capture and efficiency in the use of water Crop growth may be analysed from the amount of water taken up by the crop and efficiency of water use for biomass production (Passioura, 1977); this analysis provides information that could help reduce water use in irrigated rice and reduce the effect of water stress in rainfed rice. WUE or water productivity in this chapter is defined as the ratio of grain yield to total water used in the rice field, unless stated otherwise.

3.2.1  Water balance in lowlands Fig. 2.2 shows the water balance in a rice paddy field on a sloped land. One common issue with lowland crop is its high water requirement because deep percolation and seepage are large components of the water balance. Seepage can be high, although this water lost from a field may be utilised in a lower field, and hence on a whole catchment basis, water loss may be smaller than expected from single-field estimates. Owing to evaporation from the water surface, well-irrigated rice may have slightly larger evapotranspiration than crops grown without standing water. However, rice fields may lose water through deep percolation, particularly where the soil is not clay or porous; subsoil compaction can be used to reduce percolation loss on sandy soils (Sharma et al., 1995), and puddling will also help to reduce this loss, which is commonly practised in transplanted and wet direct-seeded rice. However, a hard pan to reduce deep percolation loss favours waterlogging and compromises other crops in the rotation (Mitchell et al.,

Rice Chapter | 2  57

FIG. 2.2  Water balance in lowland rice paddy field on a sloped land. Source: Courtesy of Dr. Mitsuru Tsubo.

2013). This may be a particular problem when heavy machinery is used in cultivating rice. Deep ripping can be practised to reduce the hard pan strength, and this can increase the growth and yield of non-rice crops (Vial et al., 2013). Inthavong et al. (2011b) developed a water balance model for lowland rice fields in which deep percolation rate was estimated from clay content. Shallow root depth in lowland rice crops predisposes them to drought when rainfall is limited and irrigation water is not available. These rainfed lowland fields are often located on sloping land (Fig. 2.2), and upper fields lose water more quickly than lower fields (Inthavong et al., 2012). Thus it is common to plant early maturing varieties late in upper fields and harvest early and to plant late maturing varieties in lower fields early and harvest late (Fukai and Ouk, 2012), with lower fields having greater soil water holding capacity, longer ponded water duration, and higher yield than upper fields (Samson et al., 2004).

3.2.2  Water requirement and water use efficiency  Effect of crop establishment methods Transplanting of seedlings commonly takes place when soil is saturated with water and shallow standing water may just cover the soil surface. From the onset of the wet season, paddy fields are often wet cultivated (puddled), and significant amount of water may be lost by the time of transplanting. In a study of porous soil, 10% of total irrigation may be saved by dry seeding (Sudhir et al., 2014). Thus direct seeding, particularly dry direct seeding that could be planted early at the beginning of wet season, could save water that is otherwise required before transplanting. However, in some fields with high rate of deep percolation, more water may be lost without puddling in dry direct seeding (Sudhir et al., 2011a,b). Dry seeding required less water in the first month when compared to water seeding but did not save water for the whole growth period when the same flooded condition was maintained after the first month (Linquist et al., 2015). On the other hand, transplanted crops spend fewer weeks in the main paddy fields, and this would save some water. In the study of Sudhir et al. (2014) at IRRI in the Philippines, water use was 2817 mm in the wet direct-seeded rice, 2315 mm in the transplanted crops, and only 2141 mm in dry direct-seeded rice crops. Kumar and Ladha (2011) examined 44 studies from different countries where irrigation water use was estimated for direct seeded and transplanted crops. Transplanted fields used an average of 1372 mm of irrigation water, while this was reduced by 12% in wet direct seeding and 21% in dry direct seeding. Water saving and water productivity of irrigated directseeded rice is reviewed by Farooq et al. (2011). They showed examples of increased water productivity in direct-seeded rice.

58  Crop Physiology: Case Histories for Major Crops  Effect of water-saving methods The main characteristics of dry direct seeding, together with two other methods of water saving, AWD and aerobic rice, are summarised in Table 2.2. Water saving in AWD is owing to at least partly to reduced percolation and seepage when water level is reduced from fully flooded conditions. Just before irrigation when soil water level was lowest in AWD-mild (or shallow AWD), stomatal conductance was reduced and leaf transpiration rate was about 20% lower than the flooded conditions in experiments in Jiangsu Province, China (Zhang et al., 2009b), indicating water saving was partly because of reduced transpiration. Meta-analysis of Carrijo et al. (2017) concluded that in AWD-mild where watering was applied to maintain soil water potential at 15 cm above − 20 kPa, WUE increased by about 25% with a corresponding decrease in water use. Saving of irrigation water may be even larger in the wet season when overall irrigation requirement is lower. However, water saving can also be large in dry seasons. In dry season, AWD experiments in the Philippines, soil water tension was about 10–15 kPa when water level declined to 15 cm, while at 30 cm, the water tension often exceeded 20 kPa (Lampayan et al., 2015b). In these experiments, where water saving was about 50% in any treatments, growth was slightly affected by an AWD treatment for irrigation at a water level of 30 cm below soil surface. Water productivity (yield per unit irrigation + rainfall) almost doubled over the flooded control in AWD for irrigation at 25 cm below soil surface. Deshmukh et al. (2017) reported total biomass under AWD was similar to that in flooded lowland in a wetter year, but it was about 25% lower in AWD in drier year. On the other hand, biomass under upland condition was 25%–40% lower than that under flooded condition. Water stress would have reduced biomass production and water productivity in these conditions. In very leaky lowland soils in Tokyo, water productivity was very low (0.16–0.17 kg m− 3), but AWD increased it to 0.48–0.68 kg m− 3 and upland conditions to 0.62–0.71 kg m− 3 (Deshmukh et al., 2017). In other experiments at the same location, flooded lowland rice used more than 3000 mm of water, while aerobic rice used 800–1300 mm with resultant water productivity of 0.23–0.26 and 0.75–0.84 kg m− 3, respectively (Kato et al., 2009). They conducted another set of experiments in the lowland fields where water use was about 1500 mm and water productivity was 0.54 kg m− 3. Aerobic rice used 790–910 mm of water, with water productivity of 0.78–0.96 kg m− 3. Thus aerobic rice saved a large amount of water and water productivity increased substantially over the flooded lowland culture. In Brazil, Reis et al. (2018) found water productivity in aerobic rice was about double that of flooded rice.  Other factors Under non-flooded irrigated, dry direct seeding in NW India (Pal et al., 2017), where four lines were planted three times in each of 2 years, grain yield and water use differed among genotypes and sowing time combinations. High-yielding genotype and shorter growing duration had higher water productivity (1.40 kg m− 3) than other combinations (range 0.85– 1.32 kg m− 3). For late-sown crops, higher yielding varieties (HYVs) had higher TDM and N uptake at anthesis and higher translocation of assimilates during grain filling with high harvest index. Lampayan et al. (2015a) compared water use of three seedling ages, 14, 21, and 30 days old at transplanting. Water requirement was reduced with older seedlings as the time the crop spent in the main field was reduced. The 21 day-old seedlings produced the highest yield and the highest water productivity. TABLE 2.2  Main characteristics of water-saving methods. Method

Cropping system

Dry direct seeding

Lowland-irrigated or rainfed, directseeded


Aerobic rice

Puddling (wet cultivation)

Water level

Water saving

Potential issues

Not puddled

Maintenance of standing water desired

Water saving in land preparation but possibly increased percolation

Percolation water loss, water stress development, weeds, and poor establishment

Irrigated lowland, transplanted or direct seeded

Puddled or not puddled

Water level fluctuate as designed

Water saving when water level is reduced

Increased cost and labour for water management

Irrigated in lowland area, direct-seeded

Not puddled

No standing water desired

Water saving with reduced percolation and seepage

Weeds and poor establishment

Rice Chapter | 2  59

3.3  Capture and efficiency in the use of nutrients 3.3.1 Nitrogen Availability of affordable N fertiliser and of semi-dwarf varieties was critical to the green revolution in the 1960s and 1970s, greatly increasing rice production, particularly in irrigated lowlands. However, the recent increases in N fertiliser costs, and the environmental concerns with excess N fertilisation, have resulted in research to improve the efficiency of N use so that smaller amount of N fertiliser could be applied to produce the same or even higher yield. Sections and are mostly for irrigated rice, while Section is for water stress conditions. Definitions used in this section include relationships between crops with no N fertiliser (0 N) and N-fertilised crops (N applied); supply of other nutrients is the same in both 0 N and N applied treatments. We define the following: N recovery efficiency  RE   100   shoot N uptake in N applied  N uptake in 0 N  / amount of N applied


Agronomic NUE   grain yield in N applied  grain yield at 0 N  / amount of N applied


Internal NUE = grain yield / total N uptake


Physiological NUE   grain yield in N applied  grain yield at 0 N  /  N uptake in N applied  N uptake in 0 N 


Partial factor productivity of applied N  PFPN   grain yield / amount of N applied


Similar definitions apply to other nutrients, as discussed for P and K further.  Plant N uptake and the fate of N in the field Nitrogen uptake and plant N concentration In main paddy fields after transplanting or direct seeding, the rates of seedling growth and N uptake are low. This is followed by rapid dry matter production and high N uptake that tend to maintain high plant N concentration. Over 60% of total plant N is located in the leaves for around 60 days after transplanting (Ladha et al., 1998). N is critical for leaf expansion and light interception and photosynthesis and RUE (Section 3.1.2). The rate of total plant N uptake starts to decline often before heading, while crop growth rate for TDM production may still be at its maximum. Thus N concentration in the plant decreases with time, but N concentration at a given growth stage varies depending on N availability to the plant. Thus shoot N concentration declined from about 3.5% after transplanting to just below 2% towards maturity under favourable N conditions, but this decline was from 2.5% to about 1% under severely N-limiting conditions for japonica rice in east China (Ata-Ul-Karim et al., 2013). Critical N concentration (Nc) is the shoot N concentration below which growth is affected. Decline in N concentration may be quantified against shoot dry matter, and such Nc dilution curve is available for rice (Sheehy et al., 1998; Ata-Ul-Karim et al., 2013). The Nc dilution curve can be used as a diagnostic indicator of N fertiliser requirement, and subsequent grain yield can be estimated from N concentration determined at PI or even earlier (Tahir Ata-Ul-Karim et al., 2016; Ata-Ul-Karim et al., 2017). The proportion of N in the leaves declines as leaves senesce and translocate N to grain, with leaf N proportion below 10% by maturity (Ladha et al., 1998). Leaves senesce from those in lower canopy, but as those in the upper position start to senesce, canopy photosynthesis decreases and the rate of assimilate translocation to grain declines, and this reduced source supply could become a limiting factor for high grain yield, particularly when grain sink demand is high (see Section 4). The proportion of N stored in leaves earlier and translocated to grain is much greater than from other organs. While translocation of carbon also takes place during grain filling, particularly under unfavourable conditions for photosynthesis, carbon in grain is mainly derived from current photosynthesis that prevails during grain filling (Kumar et al., 2006). Nitrogen losses from the field For crops grown with standing water, ammonium (NH4+) is the main N form in the soil available for rice plants. Lateral roots that are finer with high root length density, rather than primary nodal roots, take up most N from the soil (Ladha et al., 1998). Fertiliser N is commonly broadcast just before transplanting or wet direct seeding and incorporated into the puddled soil and also during growth as top dressing, often at tillering and PI or heading. Fertiliser N is typically NH4+-based or urea that hydrolyses to NH4+ within a few days of application and moves in the water. N concentration in the water spikes for several days before returning to a very low level. NH4+ in the water may be lost by volatilisation of NH3, particularly when

60  Crop Physiology: Case Histories for Major Crops

solution pH is high, while lateral roots may take up NH4+ from the soil (Ladha et al., 1998). NH3 volatilisation is the main form of gaseous loss from rice fields as losses from denitrification are small (Cassman et al., 1996). Ammonia volatilisation occurs in both flooded and non-flooded periods, and the loss can be commonly as high as 24%–32% when fertiliser is applied under non-flooded conditions (Linquist et al., 2013). Ammonia volatilisation can reduce germination and seedling growth when urea fertiliser is applied at sowing in dry direct-seeded rice (Qi et al., 2012). When standing water is lost and the bulk of soil becomes aerobic, NH4+ is quickly converted to NO3−, and with subsequent flooding, N can be denitrified and lost as N2; AWD, where aerobic condition is followed by anaerobic condition, regularly favours these processes. In lowland, N leaching is small because most N is in the form of NH4+, which is attracted to negatively charged soil particles, and puddled rice soils have low water permeability (Linquist et al., 2013). However, estimates with soil WHCNS (water heat carbon nitrogen simulator) showed greater loss from leaching than volatilisation (Liang et al., 2019). Some N fertiliser applied may be immobilised and may not be available to the present rice crop. In rice, global N RE (Eq. 2.1) is about 46% and agronomic NUE (Eq. 2.2) about 22 kg kg− 1 (Ladha et al., 2005). Wang et al. (2018) using 15N showed that low RE did not necessary mean that all N was lost because nearly 15% of applied N remained in the 0–20 cm soil layer. In their experiments in China, RE varied from 21% to 36%. At maturity, 51% of 15N was in the plant and soil, and hence 49% was considered to be lost as volatilisation and denitrification. They showed that RE depends on the inherent soil N level; if it is high and grain yield of control without N application is high, fertiliser N applied may not be taken up at the maximum rate, and RE may be low. On the other hand, if soil N is low, then the plants rely more on applied N, and RE may be high. The low recovery and NUE can be improved with better fertiliser management and other methods. Cassman et al. (1996) estimated that commonly 10%–65% of applied N fertiliser is lost in rice. One method to improve RE is to reduce N losses through ammonia volatilisation and other processes. Ammonia volatilisation loss is higher when urea is broadcast to flood water than when it is incorporated into the soil, and deep placement of urea reduced the N loss by 33% and increased the yield by 10% (Yao et al., 2018). Different types of controlled-release fertiliser are now available, which can reduce the N loss and increase the grain yield (see further).  Nitrogen use efficiency under favourable conditions This section describes NUE under irrigation, and the next section considers NUE under water deficit. Under favourable conditions where no other factors are limiting yield to any large extent, often grain yield increases non-linearly with increase in applied N, until the point where yield levels off at the optimum N rate for grain yield. For example, under dry direct seeding in northern India, grain yield increased sharply with 60 kg N ha− 1, and the maximum grain yield was achieved at 120 kg N ha− 1, with no further yield increase at 180 kg N ha− 1 (Mahajan et al., 2012). Agronomic NUE decreased from 19.3 to 13.0 and further 8.3 kg kg− 1 with the increase in N rate from 60 to 120 and then to 180 kg ha− 1. The optimum N rate for the maximum yield is generally higher than that for the maximum return to N application, and this difference in N rate depends on the cost of N fertiliser and farm gate rice price. As a result of increased N rate, grain yield increased in many countries in recent time, but agronomic NUE generally decreased. In China, which accounts for 37% of global inorganic N consumption for rice, rapid increase in N rate resulted in decline in agronomic NUE; 15–20 kg kg− 1 in 1958–63, 9.1 kg kg− 1 in 1981–83, and in Zhejiang province to 6.4 kg kg− 1 in more recent times (Peng et al., 2006). According to Peng et al. (2006), about 7% of total N produced in the world was applied to rice in China, and on average, rice farmers applied 145 kg N ha− 1 in 1997, and a high proportion was applied within 10 days of transplanting. Timing of fertiliser application affecting NUE A number of papers demonstrate over-fertilisation in China. Under irrigation, there has been major progress in N management that reduces N input and risk of environmental pollution without reducing yield. Peng et al. (2006) found that farmers in China could reduce N input in early stages without sacrificing grain yield in experiments at four major rice-producing provinces comparing farmer’s practice and modified practice. The farmer’s practice was set at 180–240 kg N ha− 1 of which 56%–85% was applied as basal fertiliser within 10 days of transplanting. In their modified practice, the total was reduced to 130–170 kg N ha− 1 by reducing the proportion of basal to 38%–79% but keeping the amount of N top dressing the same at each site. The mean grain yield in modified practice was higher than the farmer’s practice (7.6 vs. 7.2 t ha− 1). Mean total N uptake was slightly lower in the modified practice (178 vs. 194 kg N ha− 1), resulting in higher internal NUE. In these experiments, mean control yield (the same fertiliser input except N which was 0 kg ha− 1) was high (6.4 t ha− 1), and mean N uptake of the control was also high (95 kg ha− 1), resulting in PFPN about 50% higher in the modified practice (52 vs. 35 kg kg− 1). The highest PFPN of 124 kg kg− 1 was achieved when total N rates of 60 kg ha− 1 were split with 35% applied at basal, 20% at mid-tillering, 30% at PI, and 15% at heading; the mean yield of this treatment was 7.4 t ha− 1, which was not

Rice Chapter | 2  61

significantly different from the 7.6 t ha− 1 of the modified practice. Thus shifting N application towards reproductive stage can save a large amount of N. Generally, early N application, i.e. basal and top dressing at tillering, has lower N fertiliser recovery than that applied later in reproductive stage (Wang et al., 2018). However, they also showed that N applied basally tend to remain in the soil at maturity, and it could be available to subsequent crops. Since Peng et al. (2006) was published, similar work on modified farmer’s practice was examined at different locations/ years with overall similar results; for example, the work of Sui et al. (2013) at seven different sites in Jiangsu Province. In Chen et al. (2015b), multi-split application of 15 kg ha− 1 every week increased grain yield with the lowest total N input but with the highest N recovery of 64%–79% in four locations among all N application treatments. Sometimes modified farmer’s practice treatment reported from China included, in addition to modified rate and timing, increased hill density or plants per hill, increased P and K rate, and AWD. While the yield, NUE, and economic benefit may increase in the modified practice and is of great practical use for local farmers, it is difficult to pinpoint exactly the cause of such improvement, e.g. Hubei Province ((Zeng et al., 2012; Chen et al., 2015a; Chen et al., 2015b), Jiangsu Province (Zhang et al., 2018), and Hunan Province (Xie et al., 2019)). These modified practices, with an increase in the amount of N applied in the reproductive stage but a decrease in the vegetative stage together with other changes in agronomy, often resulted in increased recovery of applied N and increased sink size (spikelet number m− 2), which appeared to be responsible for increased yield. The work at IRRI, Philippines showed that late N application was also productive in drill-planted irrigated lowland rice (Liu et al., 2019). When compared to the current practice of equal amounts of split N application at seedling, mid-tillering, and PI (standard), shifting the application from seedling to heading increased the yield by 6%–7%, with the same total N rate. Total N uptake was higher with equal splits at mid-tillering, PI, and heading stages than the standard N treatment. Thus replacing the seedling stage application with the heading stage application increased N uptake in both wet and dry seasons, with apparent N recovery exceeding 70% in the former. The N that was taken up after heading increased yield in association with higher LAI and crop growth rate during grain filling, higher grain set (filled grain percentage), and higher harvest index. In these examples mentioned earlier, predetermined fixed amounts of N were applied at particular times, regardless of plant N status, thus there was no variation in the N rate in different areas with varied soil N status. This could be modified to tailor fertilisation to plant N status. Commonly, colour chart, SPAD meter, and N dilution curves (Ata-Ul-Karim et al., 2013, 2017) are used to improve NUE and grain yield; the former can be readily used by farmers. Direct determination of leaf N concentration can be used, such as practised in Australia at PI. Site-specific N management Site-specific N management (SSNM) considers the site-specific indigenous N supply using the information obtained from unfertilised controls. SSNM may be considered as an empirical model to optimise N, P, and K application (Dobermann et al., 2002). The NPK supply at a site or region is determined from indigenous N, P, and K supply plus nutrients from fertiliser assuming fertiliser recovery rate for N, P, and K, for example, 50% for N. Nutrient demand is estimated to achieve maximum yield at the site, often 80% of estimated potential yield. Thus SSNM intends to match the application with the plant N demand (Cassman et al., 1996). In-season adjustment is then made from N concentration determination to improve NUE. The concept was tested in irrigated fields in seven locations in six countries in Asia, and the overall result was 7% increase in grain yield over farmers practice on fertiliser application. Peng et  al. (2006) tested SSNM with grain yield without N application set at 5 t ha− 1. SSNM (fixed time application) produced the highest yield of 7.7 t ha− 1, with total N application of 110–130 kg ha− 1, resulting in agronomic NUE of 11.8 kg kg− 1. The yield and agronomic NUE were significantly higher than the farmer’s practice (7.2 t ha− 1 and 3.6 kg kg− 1) but not significantly higher than the modified practice mentioned earlier. SSNM has shown to be advantageous over farmer’s practice in N management in subsequent experiments in China. For example, about 5%–8% increase in grain yield was observed over the farmer’s practice treatment, with a higher increase in AWD than in continuously flooded conditions, in the experiments conducted in Jiangsu Province (Liu et al., 2013). With decreased total N rate in SSNM, total N uptake was less, but internal N use efficiency and PFPN were higher. However, N application time was different between these treatments, and hence both the rate and timing were confounded making it difficult to specify the benefit of determination of plant N status before determining N application rate. Controlled-release N fertiliser One way of applied N fertiliser matching with the timing of plant N demand is the use of controlled-release fertiliser. Fertiliser is coated with water-insoluble protective materials so that N is released slowly to better match with crop growth and its N requirement. Alternatively, urease inhibitors can reduce NH3 volatilisation and nitrification ­inhibitors

62  Crop Physiology: Case Histories for Major Crops

can reduce nitrification–denitrification. Meta-analysis by Linquist et al. (2013) showed that urease inhibitors, nitrification inhibitors, and controlled-release N fertiliser modestly increased yield by about 10% in alkaline (pH > 8.0) but not in acidic soils (pH  japonica conventional (JC) inbred > IH (Wei et al., 2017a). The lower yield of IH (mean 9.9 t ha− 1 vs. 11.6 t ha− 1 in JIH) was associated with lower spikelets m− 2 despite single grain weight being the highest among the three groups. JIH had more spikelets panicle− 1, which was almost double that in JC and more than 50% greater than in IH. Thus increased sink size in JIH with increased panicle size resulted in overall higher yield. This was accompanied by JIH producing the highest biomass during grain filling; thus the high sink size was matched with its capacity to supply assimilate source. They also had the longest grain filling duration ensuring sufficient time to meet the demand to fill in the larger sink. Most panicle dry weight increase was met by current assimilate during grain filling; stem weight decreased slightly during grain filling (less than 100 g m− 2), and contribution of NSC was small with a maximum of about 10% in IH. Shorter grain filling in IH with lower grain yield was associated with high grain N concentration; Wei et al. (2017a) suggested this high N concentration and higher total N content in grain caused premature leaf senescence because of greater N translocation from leaves to grain, with shorter grain filling resulting in lower grain yield in IH. Huang et al. (2019) compared three varieties, JIH, IH, and inbred indica, with different sink sizes under low N input across 3 years in the Middle Reaches of Yangtze River in China. They showed that JIH had (1) high yield associated with high sink size, and the latter was associated with its ability to produce more spikelets for given biomass or N uptake. JIH had more primary and secondary branches in a panicle with a large number of spikelets differentiated and becoming fertile; (2) JIH also produced more assimilate and absorbed more N during grain filling. The higher biomass production was associated with higher RUE and NUE and better canopy structure of JIH; and (3) JIH also headed earlier than others, and biomass at heading may be less than others, but grain filling duration was longer.  Other factors affecting genotypic variation in grain yield One key factor determining genotypic variation in grain yield is RUE, according to the study of 12 varieties in two planting densities in wet and dry seasons in the Philippines (Dingkuhn et al., 2015). RUE was higher in varieties with lower specific leaf area but was also affected by the variety’s ability to maintain green leaves during grain filling. Thus RUE was higher in varieties with stay-green traits. Modelling confirmed the benefit of partial stay-green to potential yield but also showed that some degree of leaf senescence was required for N translocation from leaves to grain. There was genotypic variation in maintaining chlorophyll content during grain filling in a population developed from a bi-parental cross, and several QTL were identified (Yamamoto et al., 2017). There are cases of genotypic variation in use of available assimilates to fill grain that has caused variation in yield. For example, Okamura et al. (2018) showed a high-yielding variety depleted all the carbohydrate reserves at maturity, whereas a lower-yielding counterpart had unused residual carbohydrates in the stem at maturity and low grain set of 53% across 4-year experiments. Use of a larger amount of the reserve to fill the grain in a HYV was also noted in the work of Ishimaru et al. (2017), where IR64 and introgression line YTH183, which was derived from the cross between a large panicle sized NPT and IR64 were compared in 14 experiments under fully flooded and water-saving conditions across three Asian countries in the tropics. Varieties were similar in total biomass. The -yielding ability of YTH183 was noted for two countries earlier (Kato et al., 2011). Under flooded conditions, they found no difference in spikelets panicle− 1 nor grain set, but YTH183 produced heavier grain and had a higher harvest index. Similarly, Ishimaru et al. (2017) found YTH183 out-yielded IR64 by 1.2–1.3 t ha− 1 or 22.8%–32.3% in fully flooded and water-saving conditions, respectively, because of larger single grain weight and harvest index in most of the 14 environments. The heavier grain was related to greater grain width and thickness. Grain growth curves showed that the advantage in grain growth in YTH183 occurred during the latter part of grain filling, particularly in the bottom part of the panicle. NSC in the stem at heading was similar between the two varieties but decreased during grain filling more greatly in YTH183, indicating its ability to mobilise stored reserves resulting in heavier grain. They also found that YTH183 had more vascular bundles with

Rice Chapter | 2  71

larger panicle neck diameter contributing to the translocation of assimilates during grain filling. The NSC available in the stem at heading was not fully utilised to fill grain in IR64, and thus availability of NSC at heading may not be critical for its subsequent use. Zhang et  al. (2013b) demonstrated that three high-yielding japonica varieties that differed in panicle size, that is, Yangfujing-8 (YFJ8), Lianjing-7 (LJ7), and Huaidao-9 (HD9) with small, medium and large panicles, differed in their response to timing of N application (PI, spikelet differentiation, and heading) in China. HD9 had fewer panicles than YFJ8 and LJ7. HD9 produced higher yield with N application at spikelet differentiation and heading than at PI (10.4–10.7 vs. 9.5 t ha− 1). In this variety, total spikelets produced with early N application at PI appeared excessive, so source was insufficient and grain set and single grain weight were lower, resulting in lower yield. NSC availability spikelet− 1 at heading was also the lowest in this variety, and NSC availability spikelet− 1 was associated with grain set and activity of SUS enzyme. Total biomass production at maturity also tended to be greater with early N application at PI, although the rate of leaf photosynthesis remained higher when N was applied later at heading. Thus the variety with large panicle (HD9) required more assimilate during grain filling, which became available with N application at spikelet differentiation—heading. The small panicle variety (YFJ8) produced the highest yield with earlier N application (10.2, 9.2, and 7.9 t ha− 1 with N application at PI, spikelet differentiation, and heading, respectively), and the medium panicle variety (LJ7) produced higher yield with application at PI and spikelet differentiation than at heading (9.8–9.8 vs. 8.2 t ha− 1). With YFJ8 and LJ7, N application at PI and spikelet differentiation stages increased spikelet number m− 2 without greatly affecting grain set and single grain weight. Thus there was a clear genotype by environment by management interaction (G × E × M) in the high-yielding environment (about 10 t ha− 1). Time of N application may need to be adjusted with genotype, in that a variety with a large sink size and a large panicle size may require N application later in the reproductive stage, to ensure sink size does not become too large, and source supply is maintained to fill grain.

4.2  Response to abiotic factors Section 4.2.1 describes rice response to water deficit that may develop in rainfed lowland or upland conditions. Submergence, a problem unique to rice-growing ecosystems, is the focus of Section 4.2.3, while effect of increased CO2 is described in Section 4.2.2. Sections 4.2.4 and 4.2.5 deal with rice responses to extreme temperatures, and genotypic and management adaptations, and Section 4.2.6 describes response to salinity.

4.2.1  Water deficit Rice is quite sensitive to even mild soil water deficit, and hence maintaining favourable soil water condition is essential for high yield. Unless soil is saturated with water, maintaining high water potential at shallow soil depth is required. For example, in upland conditions, soil water potential at 20 cm and 40 cm depth may be maintained at around − 10 kPa and − 30 to − 40 kPa, respectively, for maximum growth (Kato et al., 2013). Kato and Okami (2011) have shown thresholds from − 15 kPa to − 25 kPa at 20 cm soil depth for maintaining transpiration. Yield of upland rice is sensitive to mild soil water deficit when compared with other crops such as sorghum and maize (Inthapan and Fukai, 1988). This is partially related to rice’s shallower root system, which becomes even shallower when confined to a small soil volume by ponding of water. The effect of drought on grain yield depends on the timing, intensity, and duration of the stress in relation to crop development. Water deficit during establishment may compromise transplanting; stress during tillering may reduce number of tillers and panicles; stress during reproductive stage reduces spikelet number; and stress during flowering may increase spikelet sterility and reduce grain set (Boonjung and Fukai, 1996b). We thus outline different types of drought as a framework to identify management and varieties to improve adaptation.  Types of drought and genotype × management options For rainfed lowland rice, three types of drought are considered: (1) early season drought that compromises transplanting; (2) intermittent drought between rainfall events, and (3) terminal drought that develops during growth and continues until the crop matures or is killed. Of 32 observations in 3 years across northeast Thailand, four fields had continuous drought throughout the growing period, terminal drought developed very frequently, most fields lost standing water by 1 week after flowering, and intermittent drought developed in 20 fields (Monkham et al., 2018). These patterns would be similar in the Mekong region, although the frequency of drought may be somewhat less in neighbouring countries in the region (Tsubo et al., 2009; Inthavong et al., 2012). In the Mekong, drought is more severe in higher topo-sequence positions with reduced water availability (Fukai and Ouk, 2012).

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Management options (Table 2.3) should be tailored to type of drought. Knowledge of the phenological development of candidate varieties and of the local environment would help in identifying the appropriate time of planting. Yield is reduced if standing water disappears before flowering (Jearakongman et al., 1995; Tsubo et al., 2009), and hence planting time, particularly of photoperiod-insensitive or mildly sensitive varieties, needs to be sufficiently early to escape from late season drought, which is quite common in rainfed lowland rice in Thailand and elsewhere (Fukai and Ouk, 2012; Monkham et al., 2018). On the other hand, early planting, particularly of photoperiod-sensitive varieties, results in a long time between planting and flowering, and crops may lodge under high soil fertility (Fukai, 1999). Strongly photoperiod-sensitive varieties commonly flower late, and this predisposes to late season drought. In some countries such as Cambodia, sowing of late-flowering strongly photoperiod-sensitive varieties has been discouraged, and earlier flowering varieties have been developed. Early season drought is also common, but farmers may be able to transplant using older seedlings or younger seedlings produced during the drought period. Other farmers may broadcast after the dry period if there is still sufficient time for crop growth to maturity. However, they may seed drill and plant deeper in the soil where water is still available. Varieties with early vigour (Sandhu et al., 2015) may be used for quick emergence and overcoming the common problem of weeds in direct-seeded fields, and those with early vigour together with an ability to emerge from deep sowing need to be developed. Intermittent drought is also common, and breeding efforts are being made to develop adapted varieties (Xangsayasane et al., 2014; Monkham et al., 2015). Sahbhagi Dhan is a drought-tolerant variety with stable yield released in South Asia; it has often shown high emergence rate under dry soil and a high proportion of total root length as lateral roots (Anantha et al., 2016). Similarly, line IR57514-PMI-5-B-1-2 is a drought-tolerant donor (Fukai and Ouk, 2012) and was used as a parent of varieties subsequently released in SE Asia. Because of the complexity of drought development at different toposequence positions in different seasons, often there is strong genotype and environment interaction for grain yield in rainfed lowland rice; for example, in Thailand and Laos (Cooper et al., 1999) or across rainfed lowlands worldwide (Wade et al., 1999c). Acuna et al. (2008) used G × E analysis to show adaptation to one type of drought did not necessarily provide adaptive advantage in other kinds of drought. This necessitates repeating multilocation trials before adapted varieties could be selected and slows down rice improvement.  Adaptive traits Rice is considered to be a drought avoider (Kamoshita et al., 2008), with key avoidance traits, including ability to maintain a higher leaf water potential and better root systems for water uptake. In rainfed lowlands, high-yielding varieties under drought maintain high leaf water potential (Pantuwan et al., 2002) and turgor that minimises delay in heading (Homma et al., 2004). Turgor maintenance was associated with higher osmotic adjustment in leaves (Kamoshita et al., 2004). Similarly, in upland conditions, varieties that maintained leaf water potential had lower spikelet sterility and higher grain yield under drought (Jongdee et al., 2002). Maintenance of leaf water potential was associated with larger xylem diameter in the stem and hence its ability to transport water within the plant (Sibounheuang et al., 2006).

TABLE 2.3  Types of drought in rainfed lowland rice and management and variety options available. Types of drought

Option 1

Option 2

Option 3


Drill seeding

Dry broadcasting



Early vigour

Early vigour

Short duration or photoperiod-sensitive


Optimum nutrition


Drought avoidance


Early planting


Short duration, insensitive

Photoperiod sensitive

Drought avoidance

Early season

Mild intermittent

Terminal drought

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Roots play a major role in drought adaptation. Henry et al. (2011) showed in rainfed lowland experiments at IRRI that varieties with high root length density at 30–45 cm extracted more water and maintained cooler canopy, which was consistent with earlier results from Bangladesh, in which genotypes differed in the ability to increase root length density below 15 cm soil depth as water deficit progressed (Samson et al., 2002). Gowda et al. (2011) reviewed root characteristics associated with drought resistance in rice. Deep and coarse roots were key traits for drought adaptation in upland conditions, but adaptive traits were not so straightforward in rainfed lowlands. This was partly related to the hard pan resulting from puddling that helped reduce deep percolation but impeded root penetration to deep soil layers, especially as the soil dried. Hence coarse nodal roots could help deeper root growth and access to stored water. Another issue was the functionality of deeper roots dependent on the transport of oxygen through aerenchyma. Large vascular bundle diameter could help maintain high hydraulic conductivity and hence high plant water status under drought. Aquaporins may be involved in hydraulic conductance. Genotypic variation in root traits such as root number, root diameter, and root growth plasticity have been listed and their roles tested under controlled conditions (Azhiri-Sigari et al., 2000; Kamoshita et al., 2000), but the functional implications of these traits for yield under drought are not clear (Gowda et al., 2011).

4.2.2  Effect of increased CO2 concentration  Crop growth As rice is a C3 plant, the rate of photosynthesis and dry matter production increase with increase in CO2 concentration over current and expected ranges. Findings in controlled environments were confirmed by free air CO2 enrichment experiments (FACE) (Sakai et al., 2001; Yang et al., 2006b). Meta-analysis by Wang et al. (2015) showed about a 25% increase in shoot dry matter of rice in response to increase in mean CO2 to 630 μL L− 1, although the increase was smaller in FACE than in the controlled environments. The analysis also showed a 17% mean increase in light-saturated photosynthesis and a proportionally similar decrease in stomatal conductance. The effects of CO2 enhancement are greater at vegetative stage and decrease with time. For example, in FACE at Jiangsu, 200 μL L− 1 increase in CO2 concentration increased TDM by 40%, 30%, 22%, 26%, and 16% at tillering, PI, heading, mid ripening, and grain maturity, respectively (Yang et al., 2006a). Measurement of canopy CO2 exchange also showed a 33% increase in growth with increased CO2 concentration in the first 3 weeks of growth but a diminishing effect that disappeared by heading (Sakai et al., 2001). With CO2 enhancement, LAI increased, particularly when high N was applied, and this faster leaf area development also helped to increase TDM production when the leaf canopy was still small. At later stages, leaf area is often fully developed, and solar radiation should be fully intercepted, so that the effect of enhanced CO2 is based only on increased rate of photosynthesis. If sufficient N is not applied, N supply from the soil may not meet the increased N requirement for greater TDM in the later growth stage, resulting in diminished effect of CO2 enhancement. Wang et al. (2015) showed N was critical for crop response to elevated CO2. Total N uptake increased with CO2 enhancement, but the TDM increase was also large, so N concentration in the plants, and particularly in leaves decreased, indicating N becomes a more limiting factor for growth with CO2 enhancement. Fertiliser N application required to maximise the effect of CO2 enhancement is influenced by variety (Hasegawa et al., 2019). If on the other hand, the leaf N concentration during grain filling was maintained above 15 mg g− 1, which was considered the threshold affecting RUE (Ata-Ul-Karim et al., 2013), crop growth may not be limited by N as shown by De Costa et al. (2006).  Grain yield and quality In their meta-analysis of 125 studies, Wang et al. (2015) showed 20% increase in mean grain yield under increased CO2 concentration and smaller effects in FACE than in growth chambers. Yield gains with increased CO2 are usually larger under non-limiting N, with some exceptions (Yang et al., 2007; Shimono et al., 2008). The mean response of hybrids was greater than indica and japonica inbred varieties in terms of biomass production, but the increase in tiller number and panicle number was smaller and HI decline was greater than others. Japonica inbred varieties responded most in terms of tiller production and tended to have more response in grain yield. It may be that these hybrids had limited capacity to respond to increased assimilate availability. In cool areas, warm temperature may increase the effect of CO2 enhancement. For example, Shimono et  al. (2008) reported that the positive effect of CO2 on growth was 6% in 1 year when mean air temperature was 18.4°C, while it was 17% in another year when temperature was 19.9°C. In a larger study covering 11 years, Hasegawa et al. (2016) showed the effect of CO2 enhancement on grain yield varied by 0%–21% with nil or reduced response under low and high temperatures. The grain yield increase because of CO2 enhancement was associated mostly to increased panicle number (12%) and grain number (15%), a slight (5%) increase in spikelet fertility, and no significant increase in single grain weight

74  Crop Physiology: Case Histories for Major Crops

(Wang et al., 2015). The FACE by Zhang et al. (2015) showed that increased yield associated with enhanced CO2 was mediated by increased grain set for spikelets at the bottom of the panicle. Increased assimilate availability in vegetative stage to panicle development stage increased panicle number and spikelet number m− 2 while that after heading increased grain set (Sakai et al., 2001; De Costa et al., 2006; Yang et al., 2006b). Enhanced CO2 also effected grain quality. Elevated CO2 decreased protein content and increased grain chalkiness. Yang et al. (2007) showed decreased head rice yield (the ratio of whole milled grain/paddy rice) by 13% with CO2 enhancement, and hence the positive effect on grain yield reported in the literature may be overestimated when milled whole grain for marketing is considered. They reported a reduction in amylose content, and an increase in peak viscosity as a result of reduced grain hardness, and hence improved eating quality under elevated CO2.

4.2.3 Submergence Submergence is a major problem in many rice-growing countries in SE and South Asia, particularly along the coast of Bangladesh, India, and Vietnam. Flooding has two distinct classes and mechanisms: flash flooding of short duration, in which cessation of elongation and energy-saving allows growth to restart when floodwaters retreat (e.g. QTL SUB1); and deep-water flooding to several metres for several months, in which plant elongation is required for foliage to remain above the water to allow respiration and photosynthesis (e.g. QTL Snorkel) (Sripongpangkul et al., 2000; Das et al., 2005; Hattori et al., 2011). Transient complete submergence of less than 2 weeks caused by flash flood with heavy rainfall is common and affects over 20 Mha of rice (Mackill et al., 2012). Submergence is common during germination—establishment in direct-seeded rice fields, but also causes severe problems after seedling emergence in both transplanted and direct-seeded crops when the plants are still short. Submergence effects on germination and seedling emergence are discussed in Section 2.1. Submergence reduces both the radiation available for photosynthesis and the oxygen level. The damage to the submerged plants is exacerbated with increased submergence depth, higher temperature, and increased turbidity because these conditions reduce photosynthesis and increase respiration requirement. When susceptible rice plants are submerged, the level of ethylene increases, plants elongate, and chlorophyll is degraded. This consumes energy and the carbon and hastens plant mortality under transient submergence, particularly when the photosynthetic rate is reduced severely (Mackill et al., 2012). Some landraces can withstand submergence for up to 2 weeks, while susceptible varieties commonly die in complete submergence after 1 week. Owing to the frequency of the problem in these areas, improved varieties with high-yield potential are not grown because of their susceptibility to submergence. From one of the tolerant land races in India, FR13A was selected, and the QTL for submergence tolerance (SUB1) was identified on chromosome 9 (Mackill et  al., 2012). Expression of SUB1 is induced by ethylene, which inhibits plant elongation and hence saves energy within the plants resulting in survival for about 2 weeks. SUB1 is effective from 4 days after germination to 2 weeks before flowering. Markerassisted backcrossing was used to introgress the gene into several mega varieties in South and SE Asia. These SUB1 varieties behave like the mega varieties when there is no flood in terms of grain yield and grain quality but survive submergence much longer. Comparisons of the mega varieties with or without SUB1 show the yield advantages of SUB1 varieties from 1 to more than 3 t ha− 1 (Ismail et al., 2013). These SUB1 introgression varieties were adopted very rapidly, covering areas exceeding 1 Mha by 2012. In addition to flash flooding, long-term partial stagnant flooding is common in low-lying areas. There are some varieties tolerant to long-term partial stagnant flooding, but this is not related to SUB1 (Singh et al., 2011). Deep-water and floating rices require an entirely different strategy because prolonged flood depth and duration precludes a tolerance strategy. Here, stems must elongate with the rising floodwater so that oxygen can continue to be transported to roots through aerenchyma (Sripongpangkul et al., 2000; Das et al., 2005; Hattori et al., 2011). Similarly, tolerance for submergence during germination (AG) is independent of, but compatible with, SUB1 (Mackill et al., 2012). Development of multi-stress tolerant varieties is continuing, for example, pyramiding SUB1 and brown planthopper resistance into Thai jasmine rice KDML105 (Korinsak et al., 2016).

4.2.4  High temperature  Reproductive growth Increased temperature has large effect on phenological development as described in Section  2, but it also has adverse effect on growth through reduction in RUE (see Section  3.1), reproduction, and yield. Similar to the effect of drought (Section 4.2.1), the effect of high temperature depends on timing, intensity, and duration of hot spells. Rice is susceptible to heat about 9 days before anthesis when the male gametophytes develop (Satake and Yoshida, 1978). High temperature damage in rice is particularly severe to the male reproductive organs. The damage may be reduced with deep standing water, which may buffer the effect on young developing panicles from high daytime air temperature

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(Jagadish et al., 2015). Thus one of the challenges of water-saving technologies such as aerobic rice is to ensure the plants cope with the exposure of young panicles to extreme temperatures in the absence of standing water. Rice is considered most susceptible to heat on the day of flowering because it causes sterility and hence affects yield directly. Several reviews are available on this topic (Jagadish et al., 2015; Arshad et al., 2017; Prasad et al., 2017a). The heat-damaging temperature in rice at flowering is considered 35°C (Satake and Yoshida, 1978) and is lower than that of other tropical and sub-tropical cereals (Prasad et al., 2017a). Heat-induced spikelet sterility is common with high daily temperature at flowering. For example, spikelet sterility was about 11% when average maximum temperature at around heading was below 33°C but increased to over 23% when temperature exceeded 37°C, with the variety TDK1 in the dry season in Lao PDR (Ishimaru et al., 2016). When the crop was shaded during flowering, the air temperature decreased by more than 2°C, and the sterility was decreased. In Tamil Nadu, spikelet sterility of cv. Coimbatore 51 was below 10% with average maximum temperature at heading below 36.5°C, but it increased to over 33% when air temperature exceeded 38°C. However, in southern Australia where rice is grown under irrigation in the dry season in inland areas with high potential evapotranspiration and large day and night temperature variation, air temperature exceeding 40°C at flowering did not induce spikelet sterility in cv. Langi (Matsui et al., 2007). Owing to the low relative humidity of around 20%, high evaporative cooling lowered panicle temperature by almost 7°C below air temperature. The number of anthers that dehisce and the distance of dehiscence at anthesis are important in determining the number of shed pollen, and long dehiscence at the base of the thecae of this variety contributed to heat tolerance, probably through the release of large number of pollen grains onto the stigma (Matsui and Omasa, 2002). High temperature at flowering reduces anther dehiscence (Matsui and Kagata, 2003), the number of pollen grains intercepted by the stigma and spikelet fertility (Matsui et al., 1997). Growing with standing water in lowlands secures high water uptake by the roots and hence continuous transpiration, unless stomata close at midday, and a high transpiration rate reduces leaf temperature by evaporative cooling, and hence this acts as heat avoidance mechanism. This ability would be reduced in humid areas where transpiration would be lower. Because of the evaporative cooling effect, adverse effects of heat may be less severe in irrigated than in rainfed conditions, and also in dry than in humid conditions, for a given air temperature. In the work of Matsui et al. (1997) at IRRI using open-top chambers in the field in the dry season, a 4°C increase over ambient temperature reduced spikelet fertility from 85% to 78%. Spikelet fertility varied daily according to the maximum temperature. Enhanced CO2 concentration by 300 μL L− 1 had no effect on spikelet fertility, but when it was combined with increased temperature, spikelet fertility decreased from 87% to 66%. This interaction appears associated with enhanced CO2 reducing stomatal conductance, and the resultant reduced transpiration increased canopy temperature. Thus CO2 enhancement reduced the critical temperature, above which spikelet fertility is significantly reduced, by 1–2°C. Pollen germination and pollen tube elongation may be affected by high temperature causing spikelet sterility (Jagadish et al., 2015). However, opening of spikelets within a plant takes a few days, and the spread of spikelet opening further increases in a plant community. Thus an extended heat period causes a stronger effect on spikelet sterility percent. An example of the effect of heat duration is seen in 3-year outdoor potted experiments that examined the heat stress duration effect at anthesis and during grain filling on spikelet fertility (Shi et al., 2016). When the plants were at anthesis and also at 12 day after anthesis, they were brought into temperature-controlled rooms with varied day/night temperature at 32/22°C, 35/25°C (close to the field conditions), 38/28°C, and 41/31°C for 2, 4, and 6 days, respectively (Shi et al., 2016). While spikelet fertility and grain yield were not affected for 4 days at 35/25°C when compared with 4 days at 32/22°C at anthesis, spikelet fertility decreased at the higher temperatures. Increased heat duration from 2 to 6 days at anthesis reduced spikelet fertility. Spikelet fertility and yield were also reduced when temperature treatments were applied at 12 days after anthesis, although the effect was less pronounced compared to the treatments at anthesis. Sun et al. (2018) extended the work of Shi et al. (2016) mentioned above to measure spikelet sterility in response to timing of heat during grain filling. Spikelet sterility at extremely high temperature (44/34°C) for 6 days was the highest when it was imposed at anthesis (65% vs. 8% in control), was reduced at 6 days after anthesis (25%), and was small at 12 days after anthesis (12%). This timing effect was associated with the time of flowering of spikelets at different positions within a panicle. The temperature threshold for spikelet sterility was 35–36°C depending on the variety. The temporal spread of flowering in the controlled room (about 8 days) was important in determining the sensitive time to heat stress. However, the flowering pattern in the field appears even more spread; for example, 6.4 days from the first flower to 50% flowering when dry and wet season data of 292 varieties were pooled at IRRI (Bheemanahalli et al., 2017).  Grain yield In comparing responses of different crops to a wide range of temperatures in sun-lit temperature-controlled rooms, Boote et al. (2005) found rice to have an optimum temperature of 25°C for grain yield. The response of total biomass was less

76  Crop Physiology: Case Histories for Major Crops

pronounced than grain yield, and the response of harvest index followed the pattern of grain yield with a threshold temperature of 36°C, above which HI was 0. This threshold was lower than most crops they examined. While some other crops adjusted to temperature extremes by producing a smaller grain size, rice maintained similar grain size, but grain set responded greatly to temperature. Higher temperature commonly reduced grain filling duration, and this often reduces grain yield with lower assimilate partitioning to grain. For example, in the temperature-controlled experiment by Kim et al. (2011), moderately high temperature after heading (31/23°C) compared to 25/17°C and 19/11°C accelerated grain filling, maturity, and leaf senescence. The variety used, japonica variety Ilpumbyeo, matured before full leaf senescence, and hence assimilate was still available when grain filling was completed, and all grains were filled. They obtained similar results in a field experiment in Korea by comparing two crops planted 44 days apart, where mean daily temperature during grain filling was 24.4°C and 21.9°C. Thus the advantage of maturing in cooler condition was longer grain filling and increased assimilate partitioning to panicles. In the meta-analysis of enhanced CO2 effect, Wang et al. (2015) showed the interaction of enhanced CO2 and high temperature among 19 studies. For increased temperature alone, there was a large negative effect on yield (33% reduction), associated with reduced spikelet fertility and HI. When both CO2 and temperature were increased, yield decreased by 7%. However, this interaction also depended on the ambient temperature; thus 1°C increase resulted in decrease of 9%–10% in warm areas (mean ambient temperature of around 25°C or 30°C) but not in the cooler area with a mean temperature around 20°C. Importance of night-time temperature Field experiments that directly compared the effect of daytime temperature on rice yield are limited. Shah et al. (2014) used heaters and blowers in open fields with plastic walls to increase daytime temperature alone, night-time alone, and both day- and night-time temperature by about 2°C over the ambient temperature from booting to maturity in a set of nine varieties, four japonica and five indica. The ambient temperature varied greatly during the experiment period in 2 years, but the maximum ambient temperature reached 32–34°C, and the minimum ambient temperature was about 16°C towards maturity in both years. Heating day and night reduced yield by 21%–30% on average of 9 varieties. Decrease in grain yield was less than 10% when daytime temperature alone was increased, while warmer night-time temperature alone reduced yield by 5% in the first year and 31% in the second year. Warming reduced yield more in japonica than in indica varieties. The high day- and night-time temperatures increased spikelet sterility (16% in both indica and japonica in years 1 and 12 and 40% in indica and japonica, respectively, in year 2) and lower harvest index (15% and 19% in indica and japonica, respectively, in year 1 and 17% and 42%, respectively, in year 2). While high daytime temperature at anthesis may induce spikelet sterility (Section, high night-time temperature is often more detrimental to grain yield than daytime temperature (Peng et al., 2004). While night-time temperature of 25°C compared to 21°C during vegetative stage had almost no effect on plant growth, the higher night-time temperature commencing at PI increased dark respiration and promoted degeneration of spikelets that had been differentiated, resulting in fewer spikelets and hence reduced sink size (Laza et al., 2015). High night-time temperature during grain filling may be considered unfavourable with increased respiration losses and reduced assimilate partitioning to grains. An example of high temperature reducing assimilate partitioning can be seen in a temperature-controlled experiment during grain filling where assimilate production at high temperature (32°C) compared to 22°C was not reduced despite increased respiration loss, but assimilate distribution to panicle was reduced, and this caused reduction in grain yield (Cheng et al., 2010). For a critical view on the effect of temperature on respiration and growth, see Chapter 19: Cassava, Section 3.1.5. At IRRI, six hybrid and two inbred varieties were compared in field experiments with two night-time temperatures: 23°C in controls and 29°C in crops heated with closed chambers; chambers were open during the day (Shi et al., 2016). Mean grain yield reduction was 13.4% in dry season and 18.6% in wet season, and head rice yield was also generally decreased, while chalkiness increased with increased night-time temperature. Yield decrease was more pronounced in hybrids than inbred varieties. Using the same field chamber system, Shi et al. (2017) tested three varieties in two N levels in both wet and dry seasons. Increasing N application rate did not alleviate the adverse effect of high night-time temperature. Higher night-time temperature did not affect spikelet sterility in the field experiment (Shi et al., 2013), and this contrasts with temperature-controlled studies where often higher night-time temperature was used, and the effect was severe causing increased spikelet sterility (Jagadish et al., 2015). Genotypic variation There is large genotypic variation in heat tolerance in rice (Pasuquin et al., 2013). Prasad et al. (2006) grew 14 varieties of different species (O. sativa, Oryza glaberrima, and interspecific varieties), ecotype-subspecies (tropical indica, temperate

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japonica, and tropical japonica) in temperature-controlled glasshouses where mean temperature was 5°C higher than ambient temperature. They found no negative effect of high temperature on vegetative growth, but grain yield decreased sharply at high temperatures in most varieties. The grain yield variation was associated with spikelet sterility and harvest index, but there was no clear trend of high temperature tolerance among different species or subspecies. The variation in spikelet sterility was related to pollen production and pollen interception by stigma. Variation in anther dehiscence also contributed to the variation in pollen interception by stigma (Matsui and Omasa, 2002). Using 292 diverse indica varieties at IRRI, 33°C was the threshold beyond which spikelet sterility increased (Bheemanahalli et al., 2016). Spikelet sterility increased at a rate of 0.26% per oC h with base temperature of 33°C. As temperature increases in the morning when rice plants flower, indica varieties with early morning flowering show lower spikelet sterility (Bheemanahalli et al., 2017). Ishimaru et al. (2010) identified a QTL for early morning flowering from wild rice Oryza officinalis, and it was further fine mapped (Hirabayashi et al., 2015). Incorporating such a trait into breeding populations is promising in reducing spikelet sterility under high temperature. Future global warming effect In the global meta-analysis of many simulated studies of the effect of temperature change in the future, Challinor et al. (2014) indicate that rice yield, and wheat and maize yields, would decrease with a global warming of 2°C or greater without adaptation, but rice yield can be maintained or enhanced with crop adaptation. It should be, however, pointed out that different simulations by different groups produced quite variable results, and hence the close monitoring of actual temperature change and crop response is required. Effective adaptation would include the change in time of planting and varieties (Osborne et al., 2013). With increased temperature, growth duration may become too short for effective interception of solar radiation and crop growth, resulting in reduced grain yield, and thus varieties with larger thermal requirement are required for adaptation to global warming.

4.2.5  Low temperature Cold is a problem in some temperate areas and also in high altitude areas in the tropics. Rice is most susceptible to cold at the young microspore stage resulting in male sterility (Satake and Hayase, 1970). Thus in these areas, rice is planted at a time that will result in the young microspore stage, which is in about 14 days before flowering, taking place in the warmest time of the year, and often protected by increasing standing water to cover young panicles (Farrell et al., 2006b). When mean minimum temperature for a month is below 17°C, male sterility could develop. When cold weather develops during vegetative and reproductive stages, floral development is delayed, and this could also affect fertilisation. In addition to the cold problem during reproductive stage, low temperatures at planting and towards harvesting often become limiting, and quick maturing varieties are required that fit well in the period of warm temperature. Transplanting can be advantageous because a protected seedling nursery can be heated up with the sun, and seedlings can be transplanted when the temperature is high enough before transplanting to the main fields (Hoshikawa et al., 1995). Optimum time of planting is considered to be the time that would minimise the chance of cool weather occurrence for a given variety. Thus optimum time of planting is determined from the knowledge of genotypic flowering time response and historical local weather pattern, as demonstrated by Farrell et al. (2006b) for the Riverina region in southern Australia. While indica varieties are generally more susceptible to cold at any growth stage, there is genotypic variation against cold at young microspore stage (Ye et al., 2009). Genotypes may be screened using cold water or cold air and by determining spikelet sterility. Cold tolerance mechanisms at young microspore stage and flowering are illustrated in Fig. 2.4. Cold temperature at young microspore stage reduces the number of pollen grains. Cold temperature at flowering may reduce (1) anther dehiscence, mediated by reduced pollen water uptake and pollen swelling, (2) interception efficiency by stigma, and (3) pollen germination (Mitchell et al., 2016). Often genotypes with a large number of pollen in the anther are coldtolerant at the young microspore stage (Farrell et al., 2006a). Those with a higher pollen number tend to have more pollen intercepted by the stigma, increasing the chance of fertilisation. Anther dehiscence appears important for securing a larger number of pollen intercepted by the stigma in the young microspore under cold conditions, and genotypic variation has been found in responses to cold temperature at this stage (Susanti et al., 2019). While Fig. 2.4 is based on the findings of the effect of cold, the effect of other stresses in rice follows a similar pattern of processes that lead to male sterility, resulting in reduced grain number panicle− 1. Increased N application often makes the plants more susceptible to cold at the young microspore stage. Increased N causes more tillering and increased panicle number, which could result in a smaller number of pollen grains in each anther (Gunawardena et al., 2003b). Gunawardena et al. (2003a) showed that removing tillers increased pollen number per anther and reduced spikelet sterility.

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FIG. 2.4  Diagram of cold stress at booting or flowering on development of male sterility in rice.

4.2.6 Salinity Saline environments for rice may range from flash flood and stagnant flood in flood-prone regions, through tidal saline with fluctuating water depths in coastal and delta regions, to dryland salinity in drought-prone regions. Salinity is a complex problem because plants may be exposed to osmotic stress, and to Na+ and Cl− ion toxicity, as salt concentrations rise, sometimes in conjunction with flooding and heavy metal toxicities (Ismail and Horie, 2017). Various strategies have been identified for salinity tolerance, including salt uptake and exclusion, compartmentation, compatible solutes, reduced distribution to shoots, dilution through continued transpiration and growth, and combinations of these (Ismail and Horie, 2017). Physiological studies suggest salinity tolerance is most associated with low shoot sodium concentration, compartmentation to older leaves, tolerance within leaves, and greater plant vigour (Yeo et al., 1990). Crops rarely tolerate salt concentrations greater than one-third seawater, so for differences in performance (not only survival), ion transport mechanisms in conjunction with continued growth were essential (Flowers, 2004). In contrast, halophytes were able to tolerate high tissue concentrations of Na+ and Cl−, through regulation of membrane transport, synthesis of compatible solutes, and ability to deal with reactive oxygen species (Flowers and Colmer, 2015). Chapter 20: Sugar Beet (Section 3.3.2) is an illustrative contrast whereby a halophyte plant absorbs and utilises sodium physiologically. The first release of a salinity-tolerant rice cultivar, PSB-Rc50 ‘Bicol’, was derived from anther culture by combining the high yield of IR5657-33-2 with the salinity tolerance of IR4630-22-2-5-1-3 (Senadhira et  al., 2002). Bangladeshi cultivars BRRIdhan-47, BRRIdhan-61, and BINAdhan-8 performed better under dry-season salinity, so were preferred by farmers (Islam et al., 2016). ABA priming was considered effective against saline/alkaline stress (Wei et al., 2017b), while multi-environment screening separated tolerance to salinity from tolerance to sodicity (Krishnamurthy et al., 2017). Although genetic control of salinity tolerance was complex, the evidence nevertheless suggested a few QTL of major effect could be pyramided using molecular approaches (Ismail et al., 2007). For example, Thomson et al. (2010) demonstrated the Saltol gene from the salt-tolerant cultivar Pokkali was able to control shoot Na+/K+ homeostasis. These authors advocated a backcrossing programme because multiple tolerant alleles were closely associated with the Saltol locus. Aala and Gregorio (2019) screened tolerant lines from Bangladesh and Philippines and reported a diversity of alleles from the tolerant-check FLA78, offering promise of further recombination. Likewise, in examining the correlation of salinity-induced senescence with whole-plant and leaf-blade sodium concentration, Platten et al. (2013) found seven major and three minor alleles closely associated with the gene OsHKT1;5. Rahman et al. (2016) explored novel genetic sources and found successful landraces effectively limited sodium transport to the shoot, including for seedling tolerance. These mechanisms were associated with reduced accumulation of Na+, increased accumulation of K+, and lower Na+/K+ ratios in leaves. These reports seemed consistent with the many sources, many genes, but

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single mechanism philosophy for rice performance under salinity (Ismail et al., 2007; Platten et al., 2013). Leaf relative water content also showed promise for screening rice for salinity tolerance, because it was highly correlated with low Na+/K+ ratio and was fast, simple, cheap, and quantitative (Suriya-Arunroj et al., 2004). Comprehensive reviews of physiological and molecular mechanisms to salinity tolerance, how to combine multigenic salinity tolerance in a coordinated manner, and rice responses to salinity stress have recently been provided by Islam et al. (2019), Gupta et al. (2019), and Riaz et al. (2019), respectively. Radanielson et al. (2018) used Oryza V3 model to examine trait combinations for enhanced rice performance under salinity tolerance and reported three promising strategies: (1) shortduration rice to escape late exposure to increasing salinity as water availability declined (or late irrigation of long-duration rices for the same purpose), (2) include salt tolerance traits bTr and bPN above 12 dS m− 1, and resilience trait aSalt of 0.11 for 60%–70% yield in up to 16 dS m− 1, and (3) increase the value of the tolerance parameter b by 1% would increase yield by 0.3%–0.4%. Despite the extensive knowledge of critical mechanisms for salinity tolerance, there has been limited progress in breeding and especially the successful release of tolerant rice cultivars, suggesting a need to better utilise molecular and genomic approaches to combine these complex strategies into a single salinity-tolerant rice cultivar (Ismail and Horie, 2017).

4.3  Crop management for yield and quality 4.3.1  Crop establishment Crop establishment methods include manual transplanting, mechanical transplanter, broadcasting, seed drill, and ratooning, and their suitability depends on environmental conditions, availability of labour, and infrastructure.  Comparison of direct seeding and transplanting Yield In Thailand where broadcasting has increased gradually to about 50% of total rice areas over the last 50 years, the yield of broadcasted crops was lower than that of transplanted crops in earlier years, but it has become similar more recently because farmers have been adopting suitable technology for direct seeding (Suwanmontri, 2018). If crop establishment is good and weeds are not an issue, the broadcasted crop may out-yield the transplanted crop (Naklang et al., 1996), but often different establishment methods produce similar yields (Xangsayasane et al., 2019b). Broadcasting often results in low or uneven establishment and lower yield than transplanted crops (Naklang et al., 1996) or drill-planted crops (Kumar and Ladha, 2011). Weedy fields may not be suitable for direct seeding, particularly for broadcasting (Kumar and Ladha, 2011). A higher seed rate in broadcasting could help under such conditions because the rice competes more strongly against weeds (Basnayake et al., 2006). Seed drilling produced better yield than broadcasting in extremely dry conditions, where broadcasted crops almost failed completely (Xangsayasane et al., 2019b). Comparison of yield between direct-seeded and transplanted rice is reviewed by Farooq et al. (2011). Kumar and Ladha (2011) compared the yield of transplanted and direct-seeded crops from six Asian countries; yield was similar in most cases, although in India and Pakistan, the mean yield of dry direct-seeded rice was lower than transplanted crop, while in Bangladesh and the Philippines, wet direct seeding produced higher yield than transplanted crops. However, farmers may choose a particular method of establishment to maximise their financial return rather than crop yield, resulting in their opting for broadcasting, particularly if they can readily control weeds (Fukai and Ouk, 2012). Sometimes, they use older seedlings for transplanting, particularly if the soil is not saturated with water, and the potential yield may be reduced as transplanting is delayed. Weeds Commonly transplanted fields are wet-cultivated and during this process, some weeds that may have emerged are cultivated out. Transplanting with seedlings of often 10–20 cm height has an advantage in competing against weeds that have emerged after last harrowing conducted often just before transplanting. This size difference gives the rice a competitive advantage over the weeds. Another reason is that after transplanting, rice fields have standing water, with its depth being increased as rice seedlings grow taller, and anaerobic conditions suppress germination and emergence of weeds. Thus transplanted rice fields commonly have less weeds when compared with direct-seeded fields. For details, see a few reviews (Kumar and Ladha, 2011; Matloob et al., 2015; Rao et al., 2017). Direct-seeded fields are prone to weed problems, and yield of direct seeded rice can be reduced greatly by weeds. There was a negative correlation between rice yield and weed seed number in a research station experiment at CARDI, Cambodia (Kamoshita et al., 2016). Ikeda et al. (2008) showed that yield reduction started when weed dry weight exceeded 48 g m− 2, and yield was reduced by 22%, with each 100 g m− 2 increase in weed weight with maximum yield reduction of 54%.

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With the change to direct seeding, particularly in dry direct seeding, often herbicide use increases sharply, as found in Thailand where the use of herbicides in dry-seeded rice increased from 36% to 92% between 1998 and 2009 (Pandey et al., 2012). This often results in changes in weed composition in rice fields, resulting in increases in grassy weeds such as weedy rice (red rice) that are difficult to control with any selective herbicide (Kumar and Ladha, 2011). With drill sowing, however, banding of fertiliser with the seed increases early seedling vigour, and young ducklings can be used to reduce early weed competition, with benefits to grain yield (Sengxua et al., 2019). Ratooning Ratooning, whereby the rice crop is allowed to regrow after harvest from tiller buds, has been practised in China for over 1700 years (Wang et al., 2020). Historically, grain yields have been low (Santos et al., 2003), but with careful management, yields of 28.6%–64.3% of the plant crop can be attained (Wang et al., 2019). With increasing water scarcity and climate change, interest in ratooning as a green, resource-efficient technology is increasing, for sustainable systems with reduced environmental impact (Yuan et al., 2019). Ratooning reduces costs by savings in labour for transplanting and crop establishment and reduced fertiliser requirements. Less greenhouse gas is produced because less fossil fuel is consumed, soil is submerged for less time, and organic matter is reduced. Although yields are lower, grain quality remains high, and returns to the farmer are improved (Wang et al., 2020). Attaining consistently higher ratoon yields requires careful management of the crop to minimise lodging and carryover of pests and diseases. Dry- or wet-direct seeding saved additional labour over transplanting in establishing the plant crop (Dong et al., 2017). Improved ratoon performance was associated with application of 100 kg N ha− 1 at 15 days after heading in the plant crop to encourage tiller buds and of 100 kg N ha− 1 at 1–2 days after stubble cutting in the ratoon crop to promote their growth (Wang et al., 2019). At tiller bud regeneration, a shallow water depth is essential for regrowth and tiller survival (or 69% yield loss), which is also aided by shorter stubble and timely N application (Bahar and Dedatta, 1977; Nakano and Morita, 2008). Shorter stubble encourages regeneration from basal nodes for stronger tillers and panicles (Harrell et al., 2009). Regeneration rates in direct-seeded rice were higher in hybrid rice than in inbred cultivars because of greater dry matter per stem at the first harvest (Chen et al., 2018). Ling et al. (2019) added a reserve pool submodule to the ORYZA-v3 model to investigate critical factors for rice ratooning. Consistent with the experimental results above, the most sensitive phases were initial ratoon tiller development and early production and allocation of dry matter in the ratoon crop. Ziska et al. (2018) used models to demonstrate ratoon rice could be used as an adaptive management tool under climate change by allowing rice systems to migrate along a south– north transect in the southern Mississippi Valley. Wang et al. (2020) suggested a specialised harvester may be needed to reduce rolling of stubble during ratoon harvest, although timely soil drying in mid-late grain filling before harvest can reduce this. Greater attention to breeding rice varieties for enhanced ratoon performance was advocated, along with improved mechanical harvesters (Wang et al., 2020). A change to government policy in China was also encouraged to strengthen research on mechanisms for high ratoon yields to secure sustainable increases in rice production for food security and environment benefit (Lin, 2019).  Perennial rice With global population increasing, pressure on the resource base and impact of climate change, even marginal lands, which currently support 50% of the world’s population, are at risk of degradation under annual cropping, and they must be farmed sustainably in future to meet the ever-increasing demands for food and livelihood (Eswanan et al., 1999; Tilman et al., 2011). Perennial grains show promise in meeting these conflicting needs for protection of fragile lands while also allowing farmers to support themselves and their families (Glover et al., 2010). To do so, perennial grains must stabilise land and soil resources while at the same time contributing to grain and/or forage in mixed crop-livestock systems (Batello et al., 2014). In rice-based systems, with populations rising rapidly, favourable land with access to irrigation is largely utilised, and marginal lands of low fertility that are dependent on rainfall and vulnerable to climate change are being increasingly targeted to meet the food gap, the need to develop perennial rice as a component of sustainable farming systems is urgent (Wade, 2014). Following successful hybridisation between O. sativa (L.) and Oryza longistaminata (Tao and Sripichitt, 2000), efforts to develop perennial rice commenced (Hu et al., 2003; Sacks et al., 2006; Zhang et al., 2014b), with the long-term goal of breeding perennial rice to stabilise fragile soils in rainfed lowland and rainfed upland rice-based systems. Four papers have specifically reported on performance of perennial rice in the field (Zhang et al., 2017, 2019; Huang et al., 2018; Samson et al., 2018), indicating perennial rice may have promise in a number of rice-based systems. Perennial rice derivatives were reported to survive, regrow, and yield successfully across a diverse range of environments in southern China and Lao PDR, with perennial rice PR23 identified as a prime candidate for release to farmers,

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based on its broad adaptation and high yield over environments (Zhang et al., 2017). Other genotype groups showed preferential adaptation to dry season, wet season, or more tropical conditions. The paper concluded that regrowth success and maintenance of spikelet fertility over regrowth cycles were important for adaptation of perennial rice, especially to low minimum temperatures at higher altitude and rainfall deficit in lower altitude sub-humid conditions. Huang et al. (2018) then examined the suitability of PR23 for release to farmers under irrigated paddy conditions by comparing perennial rice PR23 with two seasonally replanted annual rice genotypes, RD23 and HXR7, across nine ecological regions in southern Yunnan Province of China, and across scales, from experimental plots to smallholder fields to commercial areas. Overall, the grain yield of PR23 was similar to those of the preferred annual rice cultivars in these conditions, but the economic analysis indicated substantial labour savings for farmers by growing the perennial instead of the annual rice varieties. PR23 was considered acceptable in grain size and grain quality, so farmers were keen to grow PR23 because of reduced costs and especially labour savings. Samson et al. (2018) extended these comparisons to rainfed lowland environments in the subhumid tropics of Lao PDR. While yields were lower in the ratoon crop, all perennial rice derivatives were able to survive the dry season with access to life-saving irrigation. This was promising because the annual rice RD23 was unable to ratoon under these conditions and had to be resown. Ratoon grain yields of several perennial rice lines were comparable to replanted annual RD23, which was also promising under those wet-season rainfall-deficit conditions. Recently, Zhang et al. (2019) reported a combination of high-yield potential, strong regrowth, and earlier maturity resulted in higher performance over environments and regrowth cycles, with PR23 outstanding and able to perform similarly to the seasonally replanted annual check, BN21, over up to six growth cycles. Further understanding of longevity in perennial rice is required. How many ratoon cycles can be grown before replanting is needed? Is there systematic yield decline over regrowth cycles? If so, can any such decline be arrested through improved management or improved disease resistance? What trade-offs may occur as a result of the perennial growth habit, and can they be compensated by any improved resource capture in the perennial varieties? Are there benefits from including perennials, such as improved sustainability, biodiversity, soil health, or livestock integration? Some of these challenges may be best addressed using long-term experiments, to ensure valid comparisons, as is presently being implemented in China.

4.3.2  Water-saving technologies  Alternate wetting and drying irrigation In AWD irrigation, adding water when soil water level reaches to 15 cm below the soil surface is considered to be neutral for yield (Bouman et al., 2007). In their meta-analysis of AWD irrigation, Carrijo et al. (2017) concluded that AWD-mild where field was irrigated to maintain soil water potential above − 20 kPa at 15 cm, yield was not affected in most cases. However, when the soil dried below − 20 kPa (AWD-severe), grain yield was reduced on average by 23%. AWD, particularly AWD-severe, was found to exhibit less adverse effect on yield under higher soil organic carbon. Mahajan et al. (2012) also showed an interaction effect in AWD in experiments in northwest India; without N application, the effect of mild water stress (at − 20 kPa over − 10 kPa) reduced yield from 5.90 to 3.77 t ha− 1, but when N was applied at 60 kg ha− 1 or greater amount, there was no effect of water stress. Whether grain yield under AWD can be maintained the same as the yield under flooded conditions also depends on the varieties (Sandhu et al., 2017). For example, Chu et al. (2018) found that AWD adapted varieties can produce the same yield but not those that are adapted only to flooded paddy conditions. Under flooded paddy conditions, the two varieties produced similar yield and expressed physiological attributes such as root biomass, but under AWD, the adapted variety produced higher root dry matter, root density, root oxidation activity, and higher activities of enzymes that convert sucrose to starch in grains. Recent work from China shows that the timing of AWD can be important and mild AWD during the grain-filling period can increase grain yield over the flooded crop. Gu et al. (2017) in Jiangsu Province compared flooded and AWD with constant N management. Nitrogen uptake and hence N recovery was about the same at 37%–38%, but AWD increased panicles m− 2, grain set, and yield (12%–14%), with agronomic NUE increasing from about 13 kg kg− 1 to 18 kg kg− 1. Grain yield increase in AWD-mild over the flooded control appeared to be associated with improved root aeration and increased concentration of cytokinins in roots and shoot (Zhang et al., 2009b) (see Chapter 16: Sunflower, Section 3.1.2 for the role of root cytokinins on canopy senescence). Zhang et al. (2009b) demonstrated in experiments in Jiangsu, China, that after wetting, stomata conductance was increased to the same as, but leaf photosynthesis exceeded, that in the flooded conditions, contributing to increased shoot and root dry matter, particularly during grain filling. Enzyme activities that were associated with sucrose to starch conversion in grain were also higher in AWD-mild, and this also contributed to its 11% higher grain yield. A similar yield advantage in AWD-mild was further demonstrated in 3 N rates (100, 200 and 300 kg ha− 1) also in Jiangsu (Wang et al., 2016b).

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Despite the advantage of saving water and no yield penalty of AWD-mild, Carrijo et al. (2017) indicated AWD was not adopted well except in China where a similar system of draining in the mid-season has been practised. A secure source of irrigation water is required where irrigation water is costly for wide adoption of AWD. Lampayan et al. (2015b) demonstrated an economic benefit of AWD and mentioned technology adoption in Vietnam, Bangladesh, and the Philippines. Nitrous oxide emissions are significant in rice in flooded soil conditions but are reduced when ponded water disappears, as in rainfed lowland, AWD, and especially, aerobic and upland conditions (Kirk et al., 2019; Kirk and Kronzucker, 2005).  Aerobic rice Aerobic rice can produce similar or even higher yield as demonstrated in temperate areas (Kato et al., 2009). Harvest index and yield components were mostly similar under aerobic and flooded conditions. In the work of Kato et al. (2009) in two locations in 2 years, the first aerobic rice was grown in upland areas after growing soybean and wheat for the previous 5 years, while in lowland fields, rice was grown once a year and fallowed between the rice crops. Thus it is possible that rotation with non-rice crops might have provided more favourable conditions to aerobic rice when compared to continuous cropping of flooded rice. Kato and Katsura (2014) review aerobic rice production and list high yields achieved at different locations. Aerobic rice in temperate areas often produced yields exceeding 9 t ha− 1, while yield in the tropics appears limited to about 8 t ha− 1. Prasad (2011) suggested partially aerobic rice system, including AWD and saturated soil culture, may be more suitable in the tropics. In soils of high percolation across 3 years in Brazil, Reis et al. (2018) compared four water management treatments at planting (flooded, AWD, saturated soil, and aerobic). Rainfall was sufficient for rice growth, and the aerobic rice with no standing water did not require any irrigation other than that at the time of fertiliser application. They showed the highest yield in the aerobic conditions where N uptake was the highest during early growth. The yield advantage of aerobic rice was expressed mostly through increased panicle density and to lesser extent through increased grain number panicle− 1, and assimilate availability during grain filling was sufficient to meet the large demand created by the large sink size. The advantage of aerobic over flooded rice was also demonstrated by Katsura and Nakaide (2011). In their work, the NSC reserve available in the shoot and the dry weight increase during grain filling was higher in aerobic than flooded conditions. They found that root oxidation activity was greater in the aerobic conditions because of higher oxygen availability, while it decreased during grain filling in the flooded conditions, and this, together with higher soil N availability, appeared to have helped production of more assimilate to meet the higher demand by the larger sink size. Aerobic rice may have higher N uptake than flooded rice, resulting in leaves maintaining high photosynthetic capacity, in turn providing assimilates to support more spikelets and a larger sink for high yield potential in temperate areas (Kato and Katsura, 2010) but not in the tropics (Clerget et al., 2014). Kato and Katsura (2014) mentioned the genotypic variation in spikelet production efficiency, the spikelets produced per unit N uptake, a key character associated with high yield. However, they suggested high sink capacity of aerobic rice does not always result in high yield because there may not be sufficient source supply to fully fill all the spikelets produced, and hence yield may become source limited. As mentioned in Section 4.2, rice is sensitive to mild soil water deficit, and without standing water, aerobic rice can be water stressed more readily and growth can be reduced any time during crop growth. With mild stress and with reduced evaporative cooling as a result of stomatal closure, high temperature can damage rice badly. If water stress develops during mid-panicle development to flowering, it can affect not only panicle exertion and grain set (see Section 4.2) but also potential grain size through the reduction in husk size (Katsura and Nakaide, 2011), reducing grain yield when compared to flooded rice. Genotypes with resistance to mild soil water deficit are required for sustainable aerobic rice, and this is incorporated in most aerobic rice breeding programmes in the world. Pinheiro et al. (2006) described the aerobic rice breeding in Brazil; blast resistance, drought resistance, high-yield potential, and lodging resistance were gradually incorporated into new varieties, and these varieties out-yielded other semi-dwarf or upland type varieties under favourable aerobic conditions. High grain quality was then added as a breeding objective. Kato and Katsura (2014) listed characteristics of major aerobic rice breeding systems presently practised. They noted that tropical japonica type was well adapted to aerobic culture, and indica-japonica crosses were commonly used for aerobic rice breeding. Prasad (2011) also described major breeding objectives for aerobic rice in different countries; a common objective was for drought tolerance and the other, responsiveness of grain yield to high input such as fertiliser. Thus drought-tolerant upland varieties and high-yielding lowland varieties are also commonly used to develop varieties suitable for aerobic conditions. These breeding programmes produced a number of varieties well adapted to aerobic conditions such as Apo. Table 2.4 lists some key issues, management options, and desirable traits associated with aerobic rice. Incorporation of these traits is expected to increase adaptation to aerobic conditions. Deep rooting may be important in securing water from depth under water-saving methods (Section 2.4). Other related issues are discussed in Section 2.1 (germination and emergence) and Section 4.3.2 (water saving).

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TABLE 2.4  Key issues, management options, and variety traits associated with aerobic rice. Issues




Soil water content may be low

Planting time window, deep sowing

Long mesocotyl, quick germination


With no standing water, weeds may compete with rice

Weed control measures, land preparation, herbicide

Early vigour

Mild intermittent stress

Stress may develop between irrigation events

Water at prescribed soil water potential

Deep and extensive roots, stomata open in mild stress conditions

Nutrient availability

P may be less available in aerobic soils, particularly low pH soils

Soil amendments

High P use efficiency

Soil water availability

Conversion of lowland fields

Hard pan disruption, avoid heavy clay soils

Deep penetration ability with thick roots

Extreme temperatures

Without standing water, plants are exposed to ambient temperature

Possibly change the time of planting to escape from extreme temperatures

Heat tolerance and cold tolerance

Without transplanting and no standing water to control weeds, aerobic rice could be severely affected by weeds. In the aerobic rice study of weed control in the Philippines in wet and dry seasons, Chauhan and Johnson (2010) showed 94%– 96% yield loss if weeds were not controlled. Weed control was required for a longer time period in 30 cm rows compared with 15 cm rows, with uncontrolled weed biomass being about 10% greater in wide rows. The critical period of weeding to achieve 95% weed-free field was from 15 to 64 days after sowing for 30 cm rows and from 17 to 56 days after sowing in 15 cm rows in the dry season. The critical period was slightly shorter in both row spacings in the wet season. Higher rice plant density also helped to reduce weed problem in aerobic rice (Chauhan et al., 2011). Aerobic and anaerobic flooded conditions provide different soil physical and chemical conditions. Thus there are cases where aerobic rice experience unfavourable conditions compared to flooded rice. For example, in low pH soils, aerobic rice may encounter difficulty in P uptake because P is more immobilised in aerobic soil conditions, and fine roots are reduced with the loss of standing water (Kato and Katsura, 2014). Similarly, excess Al, which may not be an issue in lowland rice where ample water reduces the potential impact of these ions, could cause toxicity under aerobic conditions. Continuous rice cropping in aerobic conditions can cause severe disease and insect pest issues, such as soil-borne root nematode; the topic is well reviewed in Prasad (2011). Prasad (2011) showed cases where the yield of aerobic culture relative to flooded culture decreased as the number of continuous crops of aerobic rice increased. Pinheiro et al. (2006) showed 1 year of soybean with 4 years of rice doubled the yield in a 5-year continuous rice (1.16 vs. 2.58 t ha− 1), while 2 years of rice and 3 years of soybean produced a rice yield of 4.32 t ha− 1. While there may be yield decline with continuous lowland rice production, the detrimental effect of continuous rice cropping is much more severe in upland aerobic conditions, with root-knot nematodes implicated as an important factor in upland rice (Prot and Matias, 1995). Root damage was reduced by soil flooding and was eliminated by soil fumigation.

4.4 Mechanisation Mechanisation such as the use of mechanical planters and combine harvesters may not greatly affect crop growth, development, and yield, but it can often affect the gross margin of the farmers (Fukai et al., 2019), while reducing labour requirement, and reducing drudgery, especially for women and children. The advantages of machinery are better captured with varieties that are suitable for mechanised rice production (Table 2.5, Fukai et al., 2019). Field capacity of the combine harvester was reduced by 38% in lodged crops, and hence varieties with lodging resistance are required, particularly when rice was established from broadcasting (Xangsayasane et al., 2019b). The seed drill has an advantage over broadcasting when the soil surface is dry because of its ability to plant deeper in the soil (Xangsayasane et al., 2019b). Rice varieties suitable for deep planting need to be identified. As drill is used before soil is saturated with water, often planting takes place much earlier than the time of transplanting. This could cause a problem if photoperiod-sensitive varieties are planted too early because flowering may take place during the peak rainy season. Thus there is a need to identify genotypes suitable for use with seed drills.

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TABLE 2.5  Variety characteristics required for mechanised rice production (Fukai et al., 2019). Characteristics

Type of operation


Lodging resistance, reduced canopy bulkiness

Combine harvesting

Particularly broadcasted crops

Shattering resistance

Combine harvesting

Particularly old indica varieties

Photoperiod sensitivity

Seed drill

Avoiding flowering at peak rainy period from early planting

Seedling’s ability to emerge

Seed drill

Seed may be planted at depth in moist soil

Canopy spread

Seed drill, transplanter

Filling initial gap quickly, weed control

Combine harvesting coupled with an artificial dryer can improve grain quality and marketability (Fukai et al., 2019). When rice is grown for subsistence, i.e. home consumption, farmers may not have concern on grain quality as long as the variety meets their preferred type such as glutinous/non-glutinous, aroma, colour, and hardness. When farmers are harvesting rice manually, sun drying of the plants in the field is common and is often acceptable for home consumption. On the other hand, marketing often requires consistent physical and milling quality such as low broken rice percent, and this can be achieved more readily with combine harvesting and artificial drying. As a combine harvests the crop and also threshes it to produce paddy rice with high moisture content, sun drying in the field requires more resources when compared with manually harvested rice crops, and the milling after sun drying commonly produces a lower head rice yield than artificial dryers (Xangsayasane et al., 2019c). Different types of artificial dryers are now available, and the best drying practice depends on the dryer’s energy efficiency, greenhouse gas emissions, and cost–benefit (Nguyen Van et al., 2019).

5  Concluding remarks: Challenges and opportunities With the changes in external factors, rice cropping is changing, and this provides challenges and opportunities. Here, we identify six aspects that need the attention of rice crop physiologists, agronomists, soil scientists, and others.

5.1  Adaptation mechanisms to reduced water input in irrigated system Shortage and affordability of water supply have become a major limiting factor in many rice-growing areas, and water-saving methods are required. In these areas, irrigation water may be available, but the traditional flooding system may not be sustainable, and different methods have been examined, including dry direct seeding, AWD, and aerobic rice.

5.1.1  Dry direct seeding Alternative planting systems such as dry direct seeding are changing soil characteristics, with the likelihood of greater root access to deeper soil layers, to provide additional plant demands in rainfed lowland and other ecosystems. Under drill seeding, root access to deeper soil layers should be improved, offering prospects for rice in the rainfed lowlands to access additional resources from below the surface layer during dry periods.

5.1.2 AWD AWD was developed to save water in irrigated lowlands and seems to work well in different countries. There are some reports that indicate AWD produce yield that can exceed the traditional flooded rice, particularly AWD applied during grainfilling period. This advantage could be because of increased N availability or increased aeration that vitalise root activities. Understanding the mechanisms could lead to appropriate N and water management, with possible improvement in grain yield, NUE, and WUE.

5.1.3  Aerobic rice The sensitivity of rice to mild soil water deficits limits the use of aerobic system. This is partially because of varieties developed for flooded system, which may not be adapted to the aerobic system. For example, shallow root system suits flooded conditions, but deep roots are often required for the aerobic system. In transplanted rainfed lowlands, the hard pan limits root growth, and adapted varieties may not always possess deeper root

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systems. In aerobic s­ ystems, the hard pan does not commonly develop, and water in deeper soil should be more accessible to varieties with deeper roots. Adapted varieties in rainfed lowland system possess ‘avoidance’ mechanism, and they can maintain high plant water potential (Kamoshita et al., 2008). This is an adaptation strategy suitable in systems where water availability during growth is uncertain. However, in aerobic system, irrigation water is available, and hence ideally available soil water is fully exploited with stomata open so that crop growth continues at maximum rate. Thus introduction of some ‘tolerance’ strategy would achieve this, possibly, including stomata that are not strongly sensitive to mild soil water deficit, so manipulation of root signals may be promising. The water management practice would consider the sensitivity of stomata and effective root depth. Similar to AWD, exposure to aerobic condition may be advantageous for N uptake and root health, and management practices may require changes; for example, fertiliser N rate, timing, and form.

5.2  Adaptation mechanisms for drought avoidance in rainfed lowland rice Genotypic variation in leaf water potential and the advantage of varieties that maintain high water potential is well documented, but the underlying mechanisms are not well understood. Usefulness of a deep root system in extracting more water has been suggested, but little experimental evidence is available on the extent of the advantage of deep root system, e.g. to what access to deep-soil water explains the variation in leaf water potential? High-throughput, reliable, and affordable methods to quantify root system and water extraction in lowland field are required, especially for direct-seeded systems. Improved understanding in conductance of water in the plant is also required, as well as osmotic adjustment, dehydration tolerance, and root signals in assisting maintenance of leaf water potential.

5.3  Adaptation mechanism for mechanised rice farming 5.3.1  Direct seeding, particularly drill planting While farmers are increasingly adopting direct seeding, crop establishment is inferior relative to transplanted crops. Drill planting provides generally better establishment as long as seed is placed in moist soil and not too deep in the soil that allows seedling emergence. Genotypes appear to differ in their ability to emerge from deep soil, but clear understanding of the underlying mechanisms will allow us to find the interrelationship among soil depth, moisture content, and genotype. Screening genotypes for deep placement and lower soil moisture content than field capacity may be required for identification of genotypes suitable for direct seeding in water-limiting conditions. Good and quick establishment helps to minimise the damage by weeds, which is a major issue in direct-seeded rice, including drill-planted crops. Well-adapted varieties would allow earlier planting at the onset of wet season, and new cropping system could evolve.

5.3.2  Combine harvesting Lodging is a major problem for combine harvesting. Lodging tends to be more of a problem in direct-seeded crops, particularly broadcasted crops. Understanding mechanisms of lodging and identifying screening methods for lodging resistance would be useful.

5.4  Factors determining grain set Grain set is sensitive to internal and external factors and plays a key role in grain yield (Section 4.1). The fate of spikelets during grain filling requires a detailed study. While this may be determined mostly by availability of assimilate, source supply alone may not be sufficient in determining grain set. The extent of other limiting factors such as assimilate transport through phloem and starch synthesis requires further work.

5.5  Enhancing yield potential Grain yield in rice has increased greatly in the past 50 years with application of appropriate inputs and with improved genotypes. The latter has achieved varieties adapted to biotic and abiotic stresses and those with higher yield potential. Improving yield potential has been achieved mostly through improved grain sink capacity rather than improvement in CO2 assimilation. Research on improving leaf photosynthetic rate would be worthwhile. This may be through improved carboxylation capacity such as C4 rice or through improved CO2 transport system. Enhancing yield potential is likely to have a direct impact on rice productivity, particularly in irrigated lowlands and favourable rainfed lowlands.

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5.6  Head rice yield Head rice yield influences the commercial value of the crop. It varies greatly depending not only on harvesting time and postharvest management but also rice fissuring at the time of harvest (Bunna et al., 2019b; Xangsayasane et al., 2019c). Genotypes differ in head rice yield (Vongxayya et al., 2019), but plant factors determining head rice yield are not well understood. Grain response to milling time or degree of milling appears different among varieties, but this could be further investigated.

Acknowledgement We thank Amelia Henry, Yoichiro Kato, Toshihiro Hasegawa, Abdel Ismail, Shaobing Peng, and Guy Kirk for their helpful comments on the manuscript during its development and Mitsuru Tsubo for the figure on water balance (Fig. 2.2).

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Agronomic responses of ratoon rice to nitrogen management in Central China. Field Crop Res. 241. Wang, W.Q., He, A.B., Jiang, G., Sun, H.J., Jiang, M., Man, J.G., Ling, X.X., Cui, K.H., Huang, J.L., Peng, S.B., Nie, L.X., 2020. Ratoon rice technology: a green and resource efficient way for rice production. Adv. Agron. 159, 135–167. Weerakoon, W.M.W., Ingram, K.T., Moss, D.N., 2000. Atmospheric carbon dioxide and fertilizer nitrogen effects on radiation interception by rice. Plant Soil 220, 99–106. Wei, H., Meng, T., Li, C., Xu, K., Huo, Z., Wei, H., Guo, B., Zhang, H., Dai, Q., 2017a. Comparisons of grain yield and nutrient accumulation and translocation in high-yielding japonica/indica hybrids, indica hybrids, and japonica conventional varieties. Field Crop Res. 204, 101–109. Wei, L.X., Lv, B.S., Li, X.W., Wang, M.M., Ma, H.Y., Yang, H.Y., Yang, R.F., Piao, Z.Z., Wang, Z.H., Lou, J.H., Jiang, C.J., Liang, Z.W., 2017b. Priming of rice (Oryza sativa L.) seedlings with abscisic acid enhances seedling survival, plant growth, and grain yield in saline-alkaline paddy fields. Field Crop Res. 203, 86–93. Wei, H., Hu, L., Zhu, Y., Xu, D., Zheng, L., Chen, Z., Hu, Y., Cui, P., Guo, B., Dai, Q., Zhang, H., 2018a. Different characteristics of nutrient absorption and utilization between inbred japonica super rice and inter-sub-specific hybrid super rice. Field Crop Res. 218, 88–96. Wei, H., Meng, T., Li, X., Dai, Q., Zhang, H., Yin, X., 2018b. Sink-source relationship during rice grain filling is associated with grain nitrogen concentration. Field Crop Res. 215, 23–38. Wissuwa, M., Ae, N., 2001. Genotypic variation for tolerance to phosphorus deficiency in rice and the potential for its exploitation in rice improvement. Plant Breed. 120, 43–48. Wissuwa, M., Yano, M., Ae, N., 1998. Mapping of QTLs for phosphorus-deficiency tolerance in rice (Oryza sativa L.). Theor. Appl. Genet. 97, 777–783. Wonprasaid, S., Khunthasuvon, S., Sittisuang, P., Fukai, S., 1996. Performance of contrasting rice cultivars selected for rainfed lowland conditions in relation to soil fertility and water availability. Field Crop Res. 47, 267–275. Xangsayasane, P., Jongdee, B., Pantuwan, G., Fukai, S., Mitchell, J.H., Inthapanya, P., Jothiyangkoon, D., 2014. Genotypic performance under intermittent and terminal drought screening in rainfed lowland rice. Field Crop Res. 156, 281–292.

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Xangsayasane, P., Phongchanmisai, S., Bounphanousai, C., Fukai, S., 2019a. Combine harvesting efficiency as affected by rice field size and other factors and its implication for adoption of combine contracting service. Plant Prod. Sci. 22, 68–76. Xangsayasane, P., Phongchanmisai, S., Vuthea, C., Ouk, M., Bounphanousay, C., Mitchell, J., Fukai, S., 2019b. A diagnostic on-farm survey of the potential of seed drill and transplanter for mechanised rice establishment in Central Laos and southern Cambodia. Plant Prod. Sci. 22, 12–22. Xangsayasane, P., Vongxayya, K., Phongchanmisai, S., Mitchell, J., Fukai, S., 2019c. Rice milling quality as affected by drying method and harvesting time during ripening in wet and dry seasons. Plant Prod. Sci. 22, 98–106. Xie, X., Shan, S., Wang, Y., Cao, F., Chen, J., Huang, M., Zou, Y., 2019. Dense planting with reducing nitrogen rate increased grain yield and nitrogen use efficiency in two hybrid rice varieties across two light conditions. Field Crop Res. 236, 24–32. Xu, L., Zhan, X., Yu, T., Nie, L., Huang, J., Cui, K., Wang, F., Li, Y., Peng, S., 2018. Yield performance of direct-seeded, double-season rice using varieties with short growth durations in Central China. Field Crop Res. 227, 49–55. Xue, W., Lindner, S., Nay-Htoon, B., Dubbert, M., Otieno, D., Ko, J., Muraoka, H., Werner, C., Tenhunen, J., Harley, P., 2016. Nutritional and developmental influences on components of rice crop light use efficiency. Agric. For. Meteorol. 223, 1–16. Yamamoto, T., Suzuki, T., Suzuki, K., Adachi, S., Sun, J., Yano, M., Ookawa, T., Hirasawa, T., 2017. Characterization of a genomic region that maintains chlorophyll and nitrogen contents during ripening in a high-yielding stay-green rice cultivar. Field Crop Res. 206, 54–64. Yamauchi, M., 2017. A review of iron-coating technology to stabilize Rice direct seeding onto puddled soil. Agron. J. 109, 739–750. Yamauchi, M., Chuong, P.V., 1995. Rice seedling establishment as affected by cultivar, seed coating with calcium peroxide, sowing depth, and water level. Field Crop Res. 41, 123–134. Yamauchi, M., Aguilar, A.M., Vaughan, D.A., Seshu, D.V., 1993. Rice (Oryza-sativa L) germplasm suitable for direct sowing under flooded soil surface. Euphytica 67, 177–184. Yang, J.C., Zhang, J.H., 2010. Grain-filling problem in 'super' rice. J. Exp. Bot. 61, 1–4. Yang, L., Huang, J., Yang, H., Dong, G., Liu, G., Zhu, J., Wang, Y., 2006a. Seasonal changes in the effects of free-air CO2 enrichment (FACE) on dry matter production and distribution of rice (Oryza sativa L.). Field Crop Res. 98, 12–19. Yang, L., Huang, J., Yang, H., Zhu, J., Liu, H., Dong, G., Liu, G., Han, Y., Wang, Y., 2006b. The impact of free-air CO2 enrichment (FACE) and N supply on yield formation of rice crops with large panicle. Field Crop Res. 98, 141–150. Yang, L., Wang, Y., Dong, G., Gu, H., Huang, J., Zhu, J., Yang, H., Liu, G., Han, Y., 2007. The impact of free-air CO2 enrichment (FACE) and nitrogen supply on grain quality of rice. Field Crop Res. 102, 128–140. Yano, K., Sekiya, N., Samson, B.K., Mazid, M.A., Yamauchi, A., Kono, Y., Wade, L.J., 2006. Hydrogen isotope composition of soil water above and below the hardpan in a rainfed lowland rice field. Field Crop Res. 96, 477–480. Yao, Y., Zhang, M., Tian, Y., Zhao, M., Zhang, B., Zhao, M., Zeng, K., Yin, B., 2018. Urea deep placement for minimizing NH3 loss in an intensive rice cropping system. Field Crop Res. 218, 254–266. Ye, C., Fukai, S., Godwin, I., Reinke, R., Snell, P., Schiller, J., Basnayake, J., 2009. Cold tolerance in rice varieties at different growth stages. Crop Pasture Sci. 60, 328–338. Yeo, A.R., Yeo, M.E., Flowers, S.A., Flowers, T.J., 1990. Screening of rice (Oryza-sativa-L) genotypes for physiological characters contributing to salinity resistance, and their relationship to overall performance. Theor. Appl. Genet. 79, 377–384. Yin, X.Y., Kropff, M.J., 1998. The effect of photoperiod on interval between panicle initiation and flowering in rice. Field Crop Res. 57, 301–307. Yin, X., Kropff, M.J., Nakagawa, H., Horie, T., Goudriaan, J., 1997. A model for photothermal responses of flowering in rice II. Model evaluation. Field Crop Res. 51, 201–211. Yoshida, S., 1981. Fundamentals of Rice Crop Science. International Rice Research Institute, Los Banos, the Philippines. Yoshinaga, S., Takai, T., Arai-Sanoh, Y., Ishimaru, T., Kondo, M., 2013. Varietal differences in sink production and grain-filling ability in recently developed high-yielding rice (Oryza sativa L.) varieties in Japan. Field Crop Res. 150, 74–82. Yuan, S., Cassman, K.G., Huang, J.L., Peng, S.B., Grassini, P., 2019. Can ratoon cropping improve resource use efficiencies and profitability of rice in Central China? Field Crop Res. 234, 66–72. Zeng, X.M., Han, B.J., Xu, F.S., Huang, J.L., Cai, H.M., Shi, L., 2012. Effects of modified fertilization technology on the grain yield and nitrogen use efficiency of midseason rice. Field Crop Res. 137, 203–212. Zhang, S., Tao, F.L., 2013. Modeling the response of rice phenology to climate change and variability in different climatic zones: comparisons of five models. Eur. J. Agron. 45, 165–176. Zhang, H., Xue, Y., Wang, Z., Yang, J., Zhang, J., 2009a. Morphological and physiological traits of roots and their relationships with shoot growth in “super” rice. Field Crop Res. 113, 31–40. Zhang, H., Xue, Y.G., Wang, Z.Q., Yang, J.C., Zhang, J.H., 2009b. An alternate wetting and moderate soil drying regime improves root and shoot growth in rice. Crop Sci. 49, 2246–2260. Zhang, Y., Tang, Q., Zou, Y., Li, D., Qin, J., Yang, S., Chen, L., Xia, B., Peng, S., 2009c. Yield potential and radiation use efficiency of “super” hybrid rice grown under subtropical conditions. Field Crop Res. 114, 91–98. Zhang, H.M., Xu, M.G., Shi, X.J., Li, Z.Z., Huang, Q.H., Wang, X.J., 2010. Rice yield, potassium uptake and apparent balance under long-term fertilization in rice-based cropping systems in southern China. Nutr. Cycl. Agroecosyst. 88, 341–349. Zhang, Q.C., Wang, G.H., Feng, Y.K., Qian, P.Y., Schoenau, J.J., 2011. Effect of potassium fertilization on soil potassium pools and rice response in an intensive cropping system in China. J. Plant Nutr. Soil Sci. 174, 73–80. Zhang, Y., Zhang, C.C., Yan, P., Chen, X.P., Yang, J.C., Zhang, F.S., Cui, Z.L., 2013a. Potassium requirement in relation to grain yield and genotypic improvement of irrigated lowland rice in China. J. Plant Nutr. Soil Sci. 176, 400–406.

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Zhang, Z., Chu, G., Liu, L., Wang, Z., Wang, X., Zhang, H., Yang, J., Zhang, J., 2013b. Mid-season nitrogen application strategies for rice varieties differing in panicle size. Field Crop Res. 150, 9–18. Zhang, S., Tao, F.L., Zhang, Z., 2014a. Rice reproductive growth duration increased despite of negative impacts of climate warming across China during 1981-2009. Eur. J. Agron. 54, 70–83. Zhang, S., Wang, W.S., Zhang, J., Ting, Z., Huang, W.Q., Xu, P., Tao, D., Fu, B.Y., Hu, F.Y., 2014b. The progression of perennial rice breeding and genetics research in China. In: Batello, C., Wade, L.J., Cox, T.S., Pogna, N., Bozzini, A., Chopianty, J. (Eds.), Perennial Crops for Food Security. FAO, Rome, pp. 27–38. Zhang, G., Sakai, H., Usui, Y., Tokida, T., Nakamura, H., Zhu, C., Fukuoka, M., Kobayashi, K., Hasegawa, T., 2015. Grain growth of different rice cultivars under elevated CO2 concentrations affects yield and quality. Field Crop Res. 179, 72–80. Zhang, S., Tao, F., Zhang, Z., 2016. Changes in extreme temperatures and their impacts on rice yields in southern China from 1981 to 2009. Field Crop Res. 189, 43–50. Zhang, S.L., Hu, J., Yang, C.D., Liu, H.T., Yang, F., Zhou, J.H., Samson, B.K., Boualaphanh, C., Huang, L.Y., Huang, G.F., Zhang, J., Huang, W.Q., Tao, D.Y., Harnpichitvitaya, D., Wade, L.J., Hu, F.Y., 2017. Genotype by environment interactions for grain yield of perennial rice derivatives (Oryza sativa L./Oryza longistaminata) in southern China and Laos. Field Crop Res. 207, 62–70. Zhang, H., Yu, C., Kong, X., Hou, D., Gu, J., Liu, L., Wang, Z., Yang, J., 2018. Progressive integrative crop managements increase grain yield, nitrogen use efficiency and irrigation water productivity in rice. Field Crop Res. 215, 1–11. Zhang, S.L., Huang, G.F., Zhang, J., Huang, L.Y., Cheng, M., Wang, Z.L., Zhang, Y.N., Wang, C.L., Zhu, P.F., Yu, X.L., Tao, K., Hu, J., Yang, F., Qi, H.W., Li, X.P., Liu, S.L., Yang, R.J., Long, Y.C., Harnpichitvitaya, D., Wade, L.J., Hu, F.Y., 2019. Genotype by environment interactions for performance of perennial rice genotypes (Oryza sativa L./Oryza longistaminata) relative to annual rice genotypes over regrowth cycles and locations in southern China. Field Crop Res. 241. Zhao, D.L., Atlin, G.N., Bastiaans, L., Spiertz, J.H.J., 2006. Developing selection protocols for weed competitiveness in aerobic rice. Field Crop Res. 97, 272–285. Zhu, G.L., Peng, S.B., Huang, J.L., Cui, K.H., Nie, L.X., Wang, F., 2016. Genetic improvements in rice yield and concomitant increases in radiation- and nitrogen-use efficiency in middle reaches of Yangtze River. Sci. Rep. 6. Ziska, L.H., Fleisher, D.H., Linscombe, S., 2018. Ratooning as an adaptive management tool for climatic change in rice systems along a north-south transect in the southern Mississippi valley. Agric. For. Meteorol. 263, 409–416.

Image source: Manfred Richter from Pixabay

Chapter 3

Wheat Gustavo A. Slafera, Roxana Savinb, Dante Pinochetc, and Daniel F. Calderinid a

ICREA, Catalonian Institution for Research and Advanced Studies, and Department of Crop and Forest Sciences, University of Lleida—AGROTECNIO Center, Lleida, Spain, bDepartment of Crop and Forest Sciences, University of Lleida—AGROTECNIO Center, Lleida, Spain, cInstitute of Agricultural Engineering and Soil Science, Universidad Austral de Chile, Valdivia, Chile, dInstitute of Plant Production and Protection, Universidad Austral de Chile, Valdivia, Chile

1 Introduction 1.1  Wheat origin, production, and yield Wheat is one of the most widely cultivated crops in the world (Leff et al., 2004; Fischer et al., 2014; Fig. 3.1a and b); grown from Japan in the east to the US plains in the west; from Scandinavia and Canada in the north to the Patagonia and New Zealand in the south; from the sea level in many countries to more than 1700 m.a.s.l. in Nepal. It contributes about 20% of energy and protein in human diets worldwide (Braun et al., 2010) and is therefore critical to food security (Reynolds et al., 2012). Global wheat area averaged 220 Mha over the past 5 years (Section 1.2). About one-third of wheat is irrigated (Frenken and Gillet, 2012). Wheat is the second most irrigated cereal after rice, and 37% of the irrigated wheat area is in Asia. In the context of climate change and water scarcity, sustainability of wheat cropping systems is a challenge for farmers, scientists, and policymakers (Section 3.2). Wheat originated in the Levant (Vavilov, 1940—English version 1992; Abbo and Gopher, 2020). This area of the Middle East features a large diversity of Triticum L. species; for example, T. aethiopicum, T. araraticum, T. boeoticum, T. dicoccoides, T. dicoccum, T. carthlicum, T. ispahanicum, T. karamyschevii, T. macha, T. monococcum, T. sinskajae, T. spelta, T. timopheevii, T. turanicum, T. urartu, T. vavilovii, and T. zhukovskyi and related species such as Aegilops spp. The evolution of domesticated wheat was characterised by interspecific hybridisation events, showing positive correlation between increased ploidy and agricultural achievement (Dubcovsky and Dvorak, 2007). In the first hybridisation event, the diploid Triticum urartu (2n = 2x = 14, AA genome) and presumably Aegilops speltoides (2n = 2x = 14, BB genome) generated the tetraploid Triticum turgidum spp. durum (2n = 4x = 28, AABB genome). In the second hybridisation event, the tetraploid wheat and the diploid Aegilops tauschii (2n = 2x = 14, DD genome) formed the hexaploid Triticum aestivum (2n = 6x = 42, AABBDD genome) (Dubcovsky and Dvorak, 2007; Matsuoka, 2011). Aegilops spp. has provided additional genetic variability captured in synthetic wheats developed by the International Maize and Wheat Improvement Center (CIMMYT). A retrospective analysis on the development and utilisation of synthetic hexaploids in the CIMMYT Global Wheat Programme found that 20% of the lines sampled in two international yield trials were synthetic-derived with an average contribution of 15.6% (Rosyara et al., 2019). Owing to its global importance, this chapter focuses on T. aestivum with occasional comments on durum wheat Triticum turgidum spp. durum, mainly in Section 5 with a focus on grain quality.

1.2  Trends in production, area, and yield The global wheat production increased almost linearly at a rate of 8.7 Mt y−  1 during the past 60 years (Fischer et al., 2014; Fig. 3.1c). Although a linear trend is apparent (R2 = 0.96; P < .001), changes in slope indicate three periods (Fig. 3.1b): (i) a first linear period 1961–82, with a rate of 11.3  ±  0.54a Mt y−  1, (ii) followed by a lower rate increase between 1983 and 2002 with a rate of 5.7  ±  0.92 Mt y−  1 with a peak of 591 Mt in 1990, and (iii) a final period between 2003 and 2018, showing a recovery with a rate of 11.6  ±  1.21 Mt y−  1. Wheat production over the last 5 years averaged ~ 750 Mt. The major wheat producers are China (124.9 Mt y−  1), India (91 Mt y−  1), Russia (60.2 Mt y−  1), the US (56.7 Mt y−  1), and France (37.5 Mt y−  1). These five countries produce 52% of global wheat, and China and India together account for almost one-third of it (Fig. 3.1c). a. Standard error, unless otherwise specified. Crop Physiology: Case Histories for Major Crops. Copyright © 2021 Elsevier Inc. All rights reserved.


100  Crop Physiology: Case Histories for Major Crops

FIG. 3.1  (a) Wheat production, (d) harvested area, and (g) grain yield across the world averaged between 2009 and 2018; (b, e, h) trends from 1961 to 2018; and (c, f, i) averages from 2009 to 2018 for the top five countries in each of the three variables. Data from: FAOSTAT, 2020. Food and Agriculture Organization of the United Nations.

In the first half of the 20th century, the acreage cultivated with wheat increased in the Americas, Australia, and parts of Africa and contributed to increased global production (Calderini and Slafer, 1998). Since the 1960s, global wheat acreage stabilised between 200 and 240 Mha (Fig. 3.1e). Presently, the largest areas of wheat are in India, Russia, China, the US, and Australia, accounting collectively for half of the global wheat growing area (Fig. 3.1f). Grain yield has been the major driver of wheat production increase since the 1960s, in turn driven by improved varieties and management. Despite the strong linear trend for yield between 1961 and 2018, changes in slope indicated three periods, similar to those for production: 1961–82 with a rate of 407  ±  22.3 kg ha−  1 y−  1, 1983–2003 with a rate of 332 ± 23.4 kg ha−  1 y−  1, and 2004–18 with a rate of 507 ± 43.8 kg ha−  1 y−  1 (Fig. 3.1h). The apparent levelling-off between 1990 and 2003 at global level (Fig. 3.1h) was also documented at national scales (e.g. Calderini and Slafer, 1998). In addition to yield, trends in yield variability are also an important issue and will be even critical in the near future because of the challenge of the increasing food demand in a changing climate (Section 4.1.3). In a detailed study evaluating yield trends and variability of 168 crops in 224 countries from 1961 to 2014, Arata et al. (2020) concluded that wheat yield increased in 75% of the areas considered in the study (e.g. North America, Western Europe, etc.) and showed no trends in 17% and decrease in 8% of the area. Regarding yield stability across the crops, only 27.6% of the series showed increased variability, with the highest and lowest yield variability found in the West Asia and North Africa (WANA) region and in Western Europe and North America, respectively. Differences in climate variability and management development seem to be the most likely causes explaining the differential yield variability (Arata et al., 2020). In the most productive areas, accounting for 75% of wheat global production, climate variability explained 36% of the year-to-year yield variation (Ray et al., 2015). For example, in China and India, the top wheat producers (Fig. 3.1c), 32% of their yield variation was associated to precipitation and both temperature and precipitation variability, respectively (Ray et al., 2015). Present average global yield is ~ 3400 kg ha−  1 (FAOSTAT, 2020). The countries with the higher yields—Ireland, the Netherlands, Belgium, New Zealand, and UK—average 7900–9200 kg ha−  1 (Fig. 3.1i) but do not have large growing areas and therefore do not coincide with those leading production (cf. Fig 3.1i, f, and c).

Wheat Chapter | 3  101

Grain weight (mg grain -1)

Average grain weight


Number of plants (m-2)

Number of spikes per plant

Number of spikes per m2

Number of grains per spikes

Number of grains (spikelet-1)

Number of plants per m2

Number of grains per m2

Number of grains (spike-1)

Number of spikes (plant-1)

Number of grains (m-2)

Number of grains per spikelet Number of spikeletsper spike

Number of spikelets (spike-1)

Number of spikes (m-2)

FIG. 3.2  Yield determination as a product of yield components with relationships illustrating frequent negative relationships between yield components for wheat grown under field conditions and in dense stands. Modified from Slafer, G.A., Savin, R., 2006. Physiology of crop yield. In: Goodman, R. (Ed.), Encyclopedia of Plant and Crop Science. Taylor & Francis Group, NY, USA; Slafer, G.A., Savin, R., Sadras, V.O., 2014. Coarse and fine regulation of wheat yield components in response to genotype and environment. Field Crop Res. 157, 71–83.

2  Crop structure, morphology, and development 2.1  Yield determination Yield is the most important trait designing breeding strategies and management practices and is also the most complex trait because it is the final outcome of multiple interactions between developmental and growth processes, the focus of this chapter.

2.1.1  Yield components Owing to its complexity, crop physiologists, breeders, and agronomists have tried to decompose yield into simpler components. The most usual approach considers yield as the product of the number of grains per m2 and average grain weight (GW). The former is the product of spikes per m2 and grains per spike. Spikes per m2 is in turn the product of plants per m2 and spikes per plant, and grains per spike is the product of spikelets per spike and grains per spikelet (Fig. 3.2). Despite its popularity, this approach has a major drawback: components are not independent of each other, particularly under field conditions of dense stands, and they commonly relate negatively to each other (Fig. 3.2). This approach is useful retrospectively but is unsuitable to predict yield response to particular management or breeding interventions because the mechanisms for the trade-offs between components are only partially understood. For the component number of grains per m2, feedbacks determine a true compensation, likely related to their simultaneous generation (Section 2.2); therefore resources allocated to one component could be detracted from its complementary. Then, improving one component may not result in a net yield increase (Slafer, 2003). The negative relationship between grains per m2 and average GW is less likely to represent feedback processes because the crop first sets the grains and then fills them (Section 2.2). Two elements that contribute to a true feedback are the overlap between the determination of grain number and potential GW (i.e. the size of the ovaries; Section 2.1.3) and the overlap between grain set and storage of water-soluble carbohydrates, which in turn can contribute to fill the grains. Indeed, virtual lack of feedback does not mean that the negative relationship between average GW and grain number may not represent competition. Competition could arise under short supply of assimilates to satisfy the demand of growing grains; that is, final GW would reflect the availability of resources per grain set, and grain growth would continue or would have a higher rate should not assimilate availability during grain filling be restrictive. But that lack of simultaneity between the setting and the effective growth of the grains indicates other likely causes for the negative relationship, not involving competition for resources amongst growing grains during effective grain filling (Miralles and Slafer, 1995; Acreche and Slafer, 2006). The simplest noncompetitive cause would be that any additional grain, increasing grain number per m2, is bound to be constitutively smaller because it belongs to a lower hierarchy; that is, more distal position within spikelets or in lower-rank spike.

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Owing to the relevance of the negative relationship between average GW and grains per m2, many studies investigated whether grains are source- or sink-limited during the effective period of grain filling. The most common relationship between yield and the number of grains per m2 over large yield ranges (Slafer et al., 2014) would support that grains hardly compete amongst them; otherwise, compensation would result in a scattered relationship between yield and grain number. However, this interpretation overlooks the possibility that management and breeding interventions may increase simultaneously and similarly, the strength of both sinks and sources during grain filling and then the positive relationship between yield and grain number would hold. The noncompetitive explanation for the negative relationship between grain number and GW is further supported by the limited response of average grain size to decrease (e.g. shading or defoliation) or increase (e.g. degraining and thinning plants in the plot) the source–sink ratio during grain filling in wheat (e.g. Borrás et al., 2004; Calderini et al., 2006; Pedro et al., 2011; Serrago et al., 2013; González et al., 2014).b This is also consistent with (i) the downregulation of photosynthesis because of weak sink during grain filling (e.g. Acreche and Slafer, 2009; Serrago et al., 2013) and (ii) substantial amounts of reserves of water-soluble carbohydrates that often remain in vegetative tissues at physiological maturity (e.g. Serrago et al., 2013). All in all, it seems that wheat (and other grain crops) evolved a conservative strategy conducive to high source:sink ratio (Reynolds et al., 2005; Serrago et al., 2013; Borrill et al., 2015) that would ensure grain fill and viable seed size in most circumstances (Sadras, 2007; Sadras and Slafer, 2012). Therefore yield could be increased if the number of grains were improved without penalties in the potential size of the grains and vice versa, if potential grain size were improved without penalties in grain number. In this scenario, it is imperative to understand the determination of grain number per m2 and potential grain size. Both traits are largely determined in a relatively short phenological window that is, therefore, known as the critical period for yield determination.

2.1.2  Grain number determination The number of grains per m2 is as complex as, and more laborious to determine than, yield itself. Hence we need to identify simpler traits putatively related to this major yield component. As mentioned earlier, yield numerical components are unsuitable for prospective analyses because of feedback processes (Section 2.1.1). An alternative approach to identify critical determinants of grains per m2 has been outlined in the pioneering work of Tony Fischer. Fischer (1985) compiled experiments in which crop growth was reduced by sequential, brief shading periods (Section 3.1) and high-temperature accelerating development (Section 2.2.1) at different stages of development. He reported that the number of grains per m2 and yield were particularly sensitive to stress imposed close to anthesis but not for stress in earlier or later periods (Fig. 3.3a), although components of grain number are produced throughout the whole growing season to slightly after anthesis (see also Section 2.2). This pattern has been verified for different cultivars and growing conditions (e.g. Fischer and Stockman, 1980; Savin and Slafer, 1991; Slafer et al., 1994; Abbate et al., 1995, 1997; 1 Yield sensitivity to source-strength

50% anthesis

Grains per m2 (% of unshaded control)






20 -60 -40 -200 Days to Anthesis




Anthesis Time from sowing to harvest


FIG. 3.3  (a) Number of grains per m2 was sensitive to shading during a narrow developmental window from ~ 30 days before to ~ 10 days after anthesis; this is roughly from the onset of stem elongation to the lag phase of postanthesis development when grain set is determined. (b) Dynamics of sensitivity of yield to source-strength from sowing to maturity. Modified from (left panel) Fischer, R.A., 1985. Number of kernels in wheat crops and the influence of solar-radiation and temperature. J. Agric. Sci. (Camb.) 105, 447–461; (right panel) Slafer, G.A., Savin, R., 2006. Physiology of crop yield. In: Goodman, R. (Ed.), Encyclopedia of Plant and Crop Science. Taylor & Francis Group, NY, USA. b. Exceptionally, GW is reduced with reductions in source strength during grain filling under conditions such as severe leaf diseases (Serrago et al., 2011), extreme lodging (Acreche and Slafer, 2011), or extreme shading (Beed et al., 2007; Sandaña et al., 2009). However, there is a lack of symmetry, whereby this reduction in final GW in response to extreme source reduction does not imply that grain growth in the unstressed control was source-limited (Serrago et al., 2013).

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FIG. 3.4  (a) Timing of spike growth before anthesis, in a narrow developmental window before anthesis, indicating on the ordinate the SDWa. Dashed lines are two hypothetical aims of breeding and management to increase sink strength and yield: improving growth or partitioning over the same period or extending the duration of the spike growth period maintaining growth rate and partitioning. (b) Relationship between grains per m2 and SDWa. Dashed line represents the improvement from either of the two mechanisms mentioned in a, and dotted line represents an improved sink strength because of an increased fruiting efficiency (FE), represented by the slope of the relationship. Based on: (a) Kirby, E.J.M., 1988. Analysis of leaf, stem and ear growth in wheat from terminal spikelet stage to anthesis. Field Crop Res. 18, 127–140; González, F.G., Slafer, G.A., Miralles, D.J., 2003. Floret development and spike growth as affected by photoperiod during stem elongation in wheat. Field Crop Res. 81, 29–38. (b) Fischer, R.A., 1985. Number of kernels in wheat crops and the influence of solar-radiation and temperature. J. Agric. Sci. (Camb.) 105, 447–461; Abbate, P.E., Andrade, F.H., Culot, J.P., 1995. The effects of radiation and nitrogen on number of grains in wheat. J. Agric. Sci. 124, 351–360; Demontes-Meynard, S., Jeuffroy, M-H., Robin, S., 1999. Spike dry matter and nitrogen accumulation before anthesis in wheat as affected by nitrogen fertilizer relationship to kernels per spike. Field Crop Res. 64, 249–259; Dreccer, M.F., Schapendonk, A.H.C.M., Slafer, G.A., Rabbinge, R., 2000. Comparative response of wheat and oilseed rape to nitrogen supply: absorption and utilitarian efficiency of radiation and nitrogen during the reproductive stages determining yield. Plant Soil 220, 189–205.

Demontes-Meynard et al., 1999; Dreccer et al., 2000; Demontes-Meynard and Jeuffroy, 2004; Ugarte et al., 2007; Prasad and Djanaguiraman, 2014). Furthermore, the relationship between crop growth and partitioning in this developmental window often accounts for yield responses to breeding and management (Section 3.1). Thus although crop growth is virtually always source-limited (increasing source strength increases growth and vice versa), wheat yield is source-limited only during this developmental period from ~ 3 weeks before to ~ 7 days after anthesis (Fig. 3.3b). The period of determination of grain number coincides with the period of active spike growth before anthesis (Fig. 3.4). Floret primordia develop within the juvenile spikes before anthesis, determining sequentially the number of fertile florets and the number of grains (Section 2.2). This explains the strong relationship between the number of fertile florets or grains and the spike dry weight at anthesis (SDWa; Fig. 3.4). Fruiting efficiency explains the scatter in the relationship between grain number and spike dry weight (Box 3.1). Although not always completely independent (e.g. Dreccer et al., 2009; Lázaro and Abbate, 2012), the potential tradeoff between fruiting efficiency and SDWa can be avoided (Bustos et al., 2013; García et al., 2014; Elía et al., 2016; Ferrante

Box 3.1  Fruiting efficiency as a determinant of grain number Fischer (1984) developed a physiological model of grain number based on the strong relationship between grain number and SDWa and recognised that there would be some variation in the slope of that relationship. This slope was first termed ‘spike fertility index’ (Fischer, 2011 and references therein) and was overlooked because of the large impact of Rht genes and nitrogen (N) fertilisation on spike dry weight. More recently, this trait gained in relevance because of the difficulties in increasing spike dry weight, with no further reducing plant height and the need to limit N fertilisation. It was then renamed ‘fruiting efficiency’ (e.g. González et al., 2011, 2014; Pedro et al., 2011; Ferrante et al., 2012; Reynolds et al., 2012; Bustos et al., 2013; García et al., 2014; Foulkes and Reynolds, 2015) because other indices are defined as unitless ratios like harvest index (HI) or leaf area index (LAI), whilst efficiency is reserved to define output-to-input ratios (e.g. water or nitrogen use efficiency (NUE)), as discussed by Slafer et al. (2015). Attention to fruiting efficiency increased during this century (Fischer, 2011; Slafer et al., 2015). The physiology of fruiting efficiency is related to that of the developmental dynamics of floret primordia (as described in Section 3) and the fate of pollinated ovaries (to set a grain or to abort), as recently described elegantly by Pretini et al. (2020). As a cleistogamous plant, wheat, most fertile florets set grains, and commercial cultivars show low rates of grain abortion. Therefore differences in fruiting efficiency in modern cultivars are normally related to the rates of survival of floret primordia determining the number of fertile florets at anthesis. (Continued)

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BOX 3.1  Fruiting efficiency as a determinant of grain number—cont’d Accelerating development of labile florets improves fruiting efficiency. As the survival of floret primordia initiated responds to availability of assimilates, there are two alternative ways to improve fruiting efficiency. The two mechanisms are (i) diminishing the carbohydrate demand of individual florets and (ii) improving the partitioning between structural parts of the inflorescence and florets. The former would be useless to improve yield because the resulting florets would have smaller ovaries determining a reduction in potential grain size (see issue 2.3). On the other hand, improved partitioning of the resources in favour of growing florets within the juvenile spike at the expense of structural parts would not lead to a trade-off between fruiting efficiency and GW. In this case, a noncompetitive reduction in average grain size may arise by a higher proportion of constitutively smaller grains (Fig. 3.B1). Several recent studies have screened elite materials for fruiting efficiency that could be exploited in breeding. A relatively large degree of variation has been reported whenever modern cultivars or elite lines were compared (see Table 2 in Slafer et al., 2015), and it has been shown that selecting for fruiting efficiency (or a proxy to it) would effectively increase grain number and yield in wheat (Pedro et al., 2011; Alonso et al., 2018). The genetic bases for this trait are emerging (Basile et al., 2019; Gerard et al., 2019). A validation of QTLs for fruiting efficiency in independent F2 populations has been reported (Pretini et al., 2020).

FIG. 3.B1  Schematic representation of two alternative explanations for a negative relationship between average grain weight and fruiting efficiency. Left: a constitutive reduction in floret size bringing about a trade-off between weight of the grains (not any average weight) and fruiting efficiency resulting in no yield gain from increased fruiting efficiency. Right: a nonconstitutive alternative hypothesis in which the size of individual grains is not affected but the proportion of grains of smaller size potential is increased, and then increasing fruiting efficiency does produce yield gain. Reproduced with permission from Slafer, G.A., Elia, M., Savin, R., García, G.A., Terrile, I.I., Ferrante, A., Miralles, D.J., González, F.G., 2015. Fruiting efficiency: an alternative trait to further rise wheat yield. Food Energy Secur. 4, 92–109; Slafer, G.A., Kantolic, A., Appendino, M., Tranquilli, G., Miralles, D.J., Savin, R., 2015. Genetic and environmental effects on crop development determining adaptation and yield. In: Sadras, V.O., Calderini, D.F. (Eds.), Crop Physiology Applications for Genetic Improvement and Agronomy, second ed. Elsevier, Amsterdam, The Netherlands. pp. 285–319.

et al., 2017). Consequently, fruiting efficiency and SDWa are useful for the prospective analysis of management and breeding aiming to increase grains per m2. For this reason, this developmental window of time from ~ 3 weeks before to ~ 7 days after anthesis is known as the ‘critical period’ for yield determination because it is the period when major changes in yield are expected, associated to the number of grains set and their potential weight (see further and Section 2.2.1). Breeding and management shall, therefore, aim to improve crop growth and/or partitioning in that particular window.

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2.1.3  Determination of potential grain weight Average GW is a major component of yield and is an important quality trait (Section 5.1). Agronomic characteristics such as seedling vigour and crop establishment may be affected by grain/seed and embryo size (Richards and Lukacs, 2002). As discussed earlier, average GW is related to grain weight potential (GWP). Therefore this section is focused on the determination of GWP, whilst the interaction with environmental and management factors influencing the realisation of GWP will be considered in Sections 3 and 4. Bremner and Rawson (1978) defined GWP as ‘the intrinsic capacity of grains to accumulate dry matter’. For an operational definition, we paraphrase the definition of potential yield (Evans and Fischer, 1999): GWP is the GW of a cultivar when grown in environments to which it is adapted, with nutrients and water nonlimiting and pests, diseases, weeds, lodging, and other stresses effectively controlled. In contrast to indeterminate and semideterminate crops such as pulses and canola where grain growth overlaps with vegetative growth (e.g. Chapter 8: Soybean; Chapter 11: Peanut; Chapter 15: Faba bean, and Chapter 17: Canola), the growing grain is the dominant sink of wheat after anthesis. Wheat leaves, stems, and roots end their growth at anthesis, except for the accumulation of soluble carbohydrates in stems, which can extend to 1–2 weeks after anthesis (Ehdaie et al., 2006; Dreccer et al., 2009). In wheat and other determinate crops, the grain growing period is defined between anthesis and physiological maturity. Considering the time-course of grain growth, and that a grain exists as such after ovule fertilisation, most efforts to understand GWP determination focused on grain filling. The simplest approach to study GWP has been through the characterisation of the rate and duration of dry matter accumulation in grain in absence of growth restrictions. In general, genetic variations in GWP seemed more related to the rate of grain filling than to the duration from anthesis to maturity (e.g. Asana and Williams, 1965; Millet and Pinthus, 1984; Loss et al., 1989; Wardlaw and Moncur, 1995; Miralles et al., 1996; Calderini and Reynolds, 2000). However, the underlying causes of differences in grain growth rate are poorly understood. Indeed, if grain growth is mainly sink-limited (Section 2.1.1), GWP should be established even before the start of grain growth, and differences in rate of grain growth may well be the consequence, rather than the cause, of differences in the capacity of the grains to grow. A number of studies have challenged the assumption that GWP is determined between the onset of grain filling and physiological maturity (Ugarte et al., 2007; Hasan et al., 2011; Simmonds et al., 2016). Indeed, treatments imposed before anthesis such as high temperature (Wardlaw, 1994; Calderini et al., 1999) or removal of florets from central spikelets (Calderini and Reynolds, 2000) modified potential and actual GW. These studies showed that the determination of both grain number and GWP overlap during a wider period than previously assumed, that is, at least between booting and a week after anthesis (Calderini et al., 2001; Parent et al., 2017). The effect of preanthesis treatments affecting GWP was also found in barley and triticale (Ugarte et al., 2007), sorghum (Yang et al., 2009) and sunflower (Cantagallo et al., 2004; Lindström et al., 2006; Castillo et al., 2017). As a consequence, the understanding of GWP determination in wheat should consider the processes immediately before and after anthesis. In addition, GWP has related to either inner or outer tissues of the grain in wheat (Brinton and Uauy, 2019). The association between final GW and endosperm cell number (Brocklehurst, 1977; Gleadow et al., 1982; Nadaud et al., 2010) supports the assumption that the capacity of the grain to accumulate carbohydrates into the inner tissues regulates GWP. On the other hand, the relationship between final GW and the volume of the floret cavity raised the hypothesis that maternal tissues could delimit a volume available for the growth of the endosperm (Millet, 1986). More recently, three grain traits have been identified as key drivers of GWP in wheat: carpel weight, grain length, and stabilised water content, that is, water content at the water plateau (Hasan et al., 2011). The effect of preanthesis conditions on GW of wheat has been ascribed to carpel/ovary weight at anthesis (Calderini and Reynolds, 2000; Calderini et al., 2001; Simmonds et al., 2016; Xie et al., 2015; Reale et al., 2017). The association between final GW and carpel weight at anthesis had been previously reported for barley (Scott et al., 1983); linear relationships between these traits are evident in wheat (Fig. 3.5) and have been reported through the assessment of wheat genotypes (Hasan et al., 2011; Yu et al., 2015; Simmonds et al., 2016). This association was also found in sunflower (Cantagallo et al., 2004; Castillo et al., 2017) and sorghum (Yang et al., 2009). The association between final GW and carpel/ovary weight supports the hypothesis that GWP is regulated by the maternal grain tissues, taking into account that the ovary becomes the pericarp. This hypothesis is supported by (i) the sensitivity of GWP to treatments during fast carpel growth between booting and anthesis (Calderini and Reynolds, 2000; Ugarte et al., 2007) and (ii) the association between final GW and the maximum dry matter of pericarp reached early after anthesis (Herrera and Calderini, 2020). Moreover, the relationships between final GW and both ovary size and endosperm cell number as determinants of GWP are not mutually exclusive because division of endosperm cells is a centripetal process, starting from the pericarp. Therefore the number of endosperm cells could be linked to the size of the pericarp (the ovary wall) and

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FIG. 3.5  Relationships between grain weight and (a and b) carpel weight at pollination and (c and d) stabilised water content of 105 doubled haploid lines plus the parental cultivars GW carried out in two experimental years (year 1: a and c; year 2: b and d) in Valdivia, Chile. Data from: Hasan, 2011. Ph.D. thesis: Physiological bases of grain weight determination and associated QTL markers in wheat (Triticum aestivum L.). Universidad Austral de Chile. p. 102.

the internal volume delimited by pericarp, as has been hypothesised (Hasan et al., 2011; Brinton and Uauy, 2019; Kino et al., 2020). An important implication of the correlation between size of ovaries and grain is that improvement in fruiting efficiency should not be at the expense of a reduction in resource demand by florets (Box 3.1). Grain length was positively associated with length of the pericarp cells across ploidies, including bread, durum, and diploid wheat genotypes (Muñoz and Calderini, 2015); this has been validated by Brinton et al. (2017) with wheat NILs for a QTL associated with GW in chromosome 5A. The line carrying this QTL had both heavier grains and larger pericarp cells than the line lacking this QTL. However, grain width was also related to final GW when mapped populations, elite cultivars, and ancestral wheat species were compared (Gegas et al., 2010). The association between GW and maximum water content has been widely recognised in wheat (Borrás et al., 2004 and references therein). Therefore although our understanding of GWP determination is incomplete, few traits seem key in building up sequentially GWP and final GW (Fig. 3.6). These traits will be considered when discussing the effects of environmental, breeding, and management practices on GW (Sections 3 and 4). Our knowledge of genes associated with GWP has advanced noticeably over the past decade in wheat (Brinton and Uauy, 2019) and other crops, mainly rice (e.g. Ma et al., 2019; Yuan et al., 2019). The gene GW2 has been validated as a negative regulator of grain size and weight (Yang et al., 2012; Simmonds et al., 2016; Wang et al., 2018; Zhang et al., 2018). However, most attempts to increase GWP with this gene in wheat have not improved yield because of a strong trade-off with grain number per m2 (Brinton et al., 2017; Wang et al., 2018; Song et al., 2007). For instance, triple mutant lines of TaGW2 gene increased GW by 20% over the wild types, with no impact on yield (Wang et al., 2018). This may reflect increases in GWP at the expense of reductions in fruiting efficiency owing to having fertile florets with larger carpels (Ferrante et al., 2015), a trade-off that should be avoided when identifying useful genes to increase yield through increasing GWP [alike useful traits to improve yield through increasing fruiting efficiency should avoid constitutive reductions in GWP (Box 3.1)]. Expansins and XTH genes influence grain size through their effect on cell wall loosening (McQueen-Mason et  al., 1992; Cosgrove and Jarvis, 2012; Calderini et al., 2020a). For example, expansins and others pericarp cell-wall genes were down-regulated in response to high postanthesis temperature with reduction in actual GW (Kino et al., 2020). The expression of these genes has been associated with cell and grain length in wheat (Lizana et al., 2010; Muñoz and Calderini, 2015), barley (Radchuk et  al., 2011), and sunflower (Castillo et  al., 2018). Recently, it was found a putative gene on chromosome 5A associated with a QTL region for grain length, which would encode an expansin protein in durum wheat

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FIG.  3.6  Grain weight determination in wheat. From booting to anthesis, florets develop and the ovaries grow at their highest rate. Expansin gene expression increases from booting until a peak during the lag phase and declines afterwards. From anthesis to physiological maturity, the pericarp cells elongate, as affected by expansins, endosperm cell division, and water inflow. Grain dry mater accumulation rate firstly increases and then decreases along the grain-filling phase (responsible for the typical sigmoid curve describing grain dry weight along time after anthesis). At physiological maturity, grain dry matter accumulation ends and grains dehydrate further. Modified from: Calderini, D.F., Castillo, F., Arenas, A., Molero, G., Reynolds, M.P., Craze, M., Bowden, S., Milner, M.J., Wallington, E.J., Dowle, A., Gomez, L.D., McQueen-Mason, S.J., 2020a. Overcoming the trade-off between grain weight and number in wheat by the ectopic expression of expansin in developing seeds leads to increased yield potential. New Phytol. (accepted).

(Mangini et al., 2020). Additionally, Choi et al. (2018) showed association between GW2 and an expansin gene regulating GW in rice. On the other hand, the natural variation associated with long glume and lemma has been mapped to a single semidominant P1 locus on chromosome 7A in T. polonicum, and studies confirmed the linkage of P1 locus with grain size (Watanabe et al., 1996; Okamoto and Takumi, 2013). This information about genes linked to GW is important to connect physiological processes and traits with their molecular bases to improve wheat GWP (Brinton and Uauy, 2019). However, as we discussed throughout the three subsections of Section 2.1, understanding potential trade-offs is critical. Overlooking trade-offs can lead to misleading conclusions; for example, the association between TaTPP-6AL1 and GW lead the authors to conclude that ‘…TaTPP-6AL1 and its functional marker are valuable to improve yield in wheat breeding’, whereas yield and grain number were not measured (Zhang et al., 2017). On the other hand, the ectopic expression of expansins seems to overcome the trade-off between GW and grains per m2 (Calderini et al., 2020a), but more studies are needed.

2.2  Crop phenology In addition to changes in grain retention and threshability, changes in phenology have been critical in domestication of crop species (Doebley et al., 2006; Gao et al., 2017; Haas et al., 2019; Lu et al., 2020) and remains critical to match crop and

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environment for two reasons (Sadras and Dreccer, 2015). Firstly, extreme events (e.g. frost, heat) during the critical period disrupt reproduction and impair floret development, grain set, and yield. Secondly, yield is proportional to the duration of the critical period, and the crop growth rate and partitioning to reproductive structures during the critical period. Hence manipulating sowing date and cultivar phenological type to match critical stages with favourable environmental conditions is central to risk management for yield (e.g. Richards, 1991; Araus et al., 2002; Flohr et al., 2017, 2018; Dreccer et al., 2018; Hunt et al., 2019). The large developmental plasticity of wheat accounts for its broad geographical spread (Section 1.2). Agronomic and genetic studies generally focus on the period from sowing to anthesis (or heading) as a single trait, but time to anthesis includes phenotypically distinct phases in terms of generation of particular organs, responsiveness to environmental factors, and genetic modulation. In this section, we describe (i) the phases of wheat development and the organogenesis that takes place in these phases with emphasis on yield components and (ii) environmental and genotypic factors determining the length of the developmental phases. Fig. 3.7 illustrates the time course of wheat development, highlighting the initiation, appearance, and growth of shoot organs and the timing of production of yield components. Spikes per m2 and grains per spike feature phases of generation (tillering and floret initiation) and degeneration (tiller mortality, floret death) whose balance determines their final number. Although crop development is a continuous process, we frequently divide the crop cycle into phases to analyse the dynamics of organogenesis and the effects of genetic and environmental factors altering the rates of development. Time to anthesis is conveniently divided into a vegetative phase from sowing to floral initiation and a reproductive phase from floral initiation to anthesis (e.g. Slafer and Rawson, 1994; Kirby et  al., 1999), the latter frequently divided into earlyreproductive and late-reproductive phases, with the stage of terminal spikelet initiation as a cut-off (e.g. Ochagavía et al., 2017). A complementary approach considers the number of leaves initiated in the main shoot during the vegetative phase and the phyllochron, that is, the time between the appearance of successive leaves, plus the time from appearance of flag leaf to anthesis (e.g. Jamieson et al., 1998). Time to anthesis plus the duration of grain filling complete the growing cycle. In the rest of this section, we first describe organogenesis generically (Section 2.2.1) during particular phases of development (Section 2.2.2) and then discuss environmental (Section 2.2.3) and genetic (Section 2.2.4) sources of variation in duration of those phases and on rate of development of particular organs.

2.2.1  Generation, appearance, and growth of organs  Initiation of leaves, spikelets, and florets Seeds of wheat have the embryo with a plumule (the embryonic shoot) and a radicle (primary root). The plumule includes the coleoptile, approximately four leaf primordia and the dome-shaped shoot apex that will be responsible for the development of more leaves first and reproductive organs later. Indeed, development of an individual includes leaf initiation started in the mother plant (Fig. 3.7) (Kirby and Appleyard, 1987; Hay and Kirby, 1991). Immediately after sowing, seed imbibition takes place and leaf primordia initiation is resumed. The initiation of new leaf primordia, as single ridges on opposite and alternating sides of the apex, follows an almost linear dynamic (Fig. 3.9a; Kirby et al., 1987; Delécolle et al., 1989; Kirby, 1990). The time between the initiations of two consecutive leaf primordia is called the leaf plastochron and can be estimated as the reciprocal of the rate of leaf initiation. Leaf initiation continues, still as single ridges, until the apex switches from initiating leaf primordia to initiate spikelet primordia at the time of floral initiation; this switch is paralleled by a morphological change of the apex from dome-shaped to a cylindrical, more elongated structure (but still differentiating single ridges). The number of initiated leaves is the final leaf number in the main shoot and depends on the duration from sowing to floral initiation and the leaf plastochron of each genotype in the specific conditions of growth. Indeed, final leaf number reflects variation in phenology with genotype (G), environment (E) and their interaction G × E. At floral initiation, the meristematic apex starts differentiating spikelet primordia. Again, the dynamic of spikelet initiation is linear from floral initiation to the formation of the last spikelet (the terminal spikelet of the spike). The maximum number of spikelets per spike is a function of both the duration of the period from floral initiation to terminal spikelet and the spikelet plastochron, that is, the time between the initiations of two consecutive spikelet primordia, which is estimated as the reciprocal of the rate of spikelet initiation. The linear relationship between spikelet primordia number returns a single rate of spikelet initiation that is faster than the rate of leaf initiation (Fig. 3.9b). Traditionally, it has been assumed that the stage of double ridge evidences the transition from vegetative to reproductive apex, which is the reason for considering this stage as critical for understanding wheat development. But the first spikelets are initiated as single ridges before double ridge and are morphologically undistinguishable from leaf primordia (Delécolle et al., 1989; Kirby, 1990; Fig. 3.9a). The exact time of floral initiation can only be dated a posteriori, when the accumulated number of primordia is determined from

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FIG. 3.7  Key developmental stages of wheat from sowing to harvest in an arbitrary time scale. Boxes underneath illustrate (i) the appearance of the apex/ spike, (ii) the four major component phases, (iii) the timing of differentiation or growth of organs, and (iv) the timing of formation and definition of yield components. Modified from: Slafer, G.A., Rawson, H.M., 1994. Sensitivity of wheat phasic development to major environmental factors: a re-examination of some assumptions made by physiologists and modellers. Aust. J. Plant Physiol. 21, 393–426; Miralles, D.J., Slafer, G.A., 1999. Wheat development. In: Satorre, E.H., Slafer, G.A. (Eds.), Wheat: Ecology and Physiology of Yield Determination. Food Product Press, New York, USA, pp. 13–43; Slafer, G.A., Kantolic, A., Appendino, M., Tranquilli, G., Miralles, D.J., Savin, R., 2015. Genetic and environmental effects on crop development determining adaptation and yield. In: Sadras, V.O., Calderini, D.F. (Eds.), Crop Physiology Applications for Genetic Improvement and Agronomy, second ed. Elsevier, Amsterdam, The Netherlands. pp. 285–319.

seedling emergence and the final leaf number is determined from the appeared leaves over the growing season: we can determine the time when the first primordia in excess of final leaf number was initiated. Central spikelets develop first, and further differentiation progresses acropetally and basipetally. By the time of terminal spikelet, the individual florets would have been already started to develop in the firstly initiated spikelets (in the middle third of the spike; Fig. 3.8). Floret initiation within each of the spikelets progresses acropetally from the floret most proximal to the rachis (e.g. Sibony and Pinthus, 1988; Kirby, 1988; Miralles et al., 1998; González et al., 2003, 2005b; Ferrante et al., 2010, 2013a). Associated with this hierarchy, the carpels forming the ovary at anthesis and the mature grain are larger for proximal florets than for more distal positions. Floret initiation within each spikelet is indeterminate and continues approximately until flag leaf emergence or early booting (Kirby, 1988; González et al., 2003, 2005b; Ferrante et al., 2010, 2013a), reaching a maximum of 6–12 floret primordia per spikelet, depending on the spikelet position within the spike (Sibony and Pinthus, 1988; Youssefian et al., 1992; Miralles et al., 1998). However, unlike leaf and spikelet primordia that always survive, only a small proportion (15%–30%) of all the initiated florets reaches the stage of fertile florets at anthesis (Fig. 3.8). Massive flower and fruit abortion is a universal feature of angiosperms (Stephenson, 1981).

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Spikelet primordia

Terminal spikelet

Carpel of proximal floret

W 3.5 W 5 W9 W 10 Floret developmental stages




Spikelets within the spike

Floret within the spikelet







W5 3.5


.5 W7



Spike at Grain set. anthesis soon after anthesis



(com ent ) W 9 velopm l florets e a d mal proxim r o N in

Late degeneration (commonly in intermediate florets) Early degeneration (commonly in distal florets)

FIG. 3.8  Wheat floral development as shown (a) schematically and (b) photographically from early spikelet primordia differentiation to anthesis, with selected floret developmental stages. (c) Floral development in the scale of Waddington (W#) with time (upward arrow) for florets that develop normally to achieve the fertile floret stage at anthesis and setting grains afterwards. The downward arrows show floral degeneration either early or late during development. The drawings and photographs are not to scale; as a reference, the width of a floret is 0.10 mm at W3.5, 0.15 mm at W5 0.15 mm, 0.30 mm at W7.5, and 1.60 mm at W10. Reproduced with permission from Ferrante, A., Savin, R., Slafer, G.A., 2010. Floret development of durum wheat in response to nitrogen availabilities. J. Exp. Bot. 61, 4351–4359.

This period from terminal spikelet to anthesis coincides with that in which the juvenile spikes, in which florets are developing, do actually grow (Fig. 3.9b). This supports the idea that floret death reflects a possible competition for assimilates because increasing the availability of resources for spike growth increases the rate of development of labile florets, reduces floret death, and increases the number of fertile florets (González et al., 2005a; Ghiglione et al., 2008; Ferrante et al., 2010, 2013a, 2020). In addition, the onset of floret mortality that had been proposed to be a developmental process (e.g. Bancal, 2009) has also been proven to be related to the growth of the spikes (e.g. González et al., 2011; Ferrante et al., 2013b). Thus the more the spike can grow at these critical stages, the more florets can reach the stage of fertile florets and grains afterwards, irrespective of whether this growth is dependent on crop growth or partitioning or whether it is because of agronomy or genetic drivers (Section 4.1); see Slafer (2003) and Slafer et al. (2005) for a more comprehensive discussion on this issue.  Appearance of leaves and tillering and growth of stems, spikes, and grains The wheat shoot is a succession of vegetative phytomers. Each phytomer is composed of a node, an internode, a leaf comprising sheath and blade or lamina, and an axillary tillering bud. A wheat plant at anthesis is composed of a variable number of phytomers that depends on the final leaf number, although not all organs grow in all phytomers. The leaves grow in all phytomers, but internodes do not expand in the first phytomers, hence the lack of a true stem until well advanced the growing cycle. From seedling emergence to the onset of stem elongation, a false stem is formed by successive leaf sheaths, whilst only the first phytomers have tillers that will actually grow in agronomic conditions. Seedling emergence is the appearance of the tip of the first leaf through the coleoptile, which is adapted to push through soil whilst protecting the first leaf that will emerge through its extreme. From then on, leaves appear at more or less constant rate (Fig. 3.9b) if final leaf number is relatively small (e.g. < 8 leaves), until the appearance of the flag leaf. The phyllochron

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FIG. 3.9  Dynamics of (a) leaf, spikelet, and floret primordia initiation, (b) leaf appearance, tillering and tiller mortality, and growth of stems and spikes, and (c) grain growth, including the dynamics of grain volume and grain moisture, both in absolute terms and as a percentage of grain fresh weight. Arrows show timing of key developmental/morphological stages.

is longer than the plastochron, implying that the higher the number of leaf primordia initiated (frequently related to longer periods of leaf initiation), more leaves will have to appear after floral initiation (Hay and Kirby, 1991) and consequently, the longer the reproductive phase from floral initiation to anthesis. When the number of leaves initiated exceeds a certain threshold (e.g. > 8), the phyllochron of early leaves is shorter and the rate of leaf appearance faster than that of late appearing leaves (e.g. Jamieson et al., 1995; Calderini et al., 1996; Slafer and Rawson, 1997; González et al., 2005a; Ochagavía et al., 2017). The dynamics of leaf appearance is important because it largely determines the duration of the time to anthesis and also because it is related to other developmental processes (e.g. Kirby, 1990). Tillering, a particular type of lateral branching in grasses (see also Chapter 4: Barley; Chapter 6: Oat; Chapter 2: Rice; Chapter 5: Sorghum), is partially related to the dynamics of leaf appearance, at least whilst the availability of resources, or environmental signals related to that availability, does not limit their growth. The coleoptile is a modified leaf with a tiller bud. The first tiller is the coleoptile’s, but it may not emerge through the soil (Baker and Gallagher, 1983). All other tillers emerge through the sheath of the leaf of that phytomer. These ‘leaf tillers’ start appearing in coordination with leaf appearance (e.g. Klepper et al., 1983; Rickman et al., 1983; Masle, 1985), which makes it predictable and relevant for crop simulation (e.g. Porter, 1985; McMaster et al., 1991). There is a lag of ~ three phyllochrons for the starting of tillering from seedling emergence. From then on, one additional primary tiller (those emerging through main shoot leaf sheaths) will emerge from the following leaves at ~ one phyllochron interval. The rate of tiller emergence per leaf appeared (or per phyllochron) is constant for primary tillers. Tiller buds are also initiated in phytomers of the tillers; therefore primary tillers may produce secondary tillers, emerging from the leaf sheaths of the tiller leaves; tertiary tillers are also potentially possible (although unexpected in plants grown at normal crop densities), emerging from leaves of the secondary ones. The increase in tiller number is linear only for a short period and exponential (following a Fibonacci series; Masle, 1985), when higher order tillers start to appear. Under agronomic conditions, this free tillering proceeds as expected only until a restriction in availability of resources inhibits the growth of new tillers (Fig. 3.9b); that is, only in the earliest part of the cycle, when plants are rather isolated. When resources become scarce mainly because of interplant competition, the rate of tillering diminishes, and frequently, some tillers die. The mortality of tillers starts with the younger tillers, contributing to the convergence and synchrony in flowering and maturity of the crop (Hay and Kirby, 1991). There is not a developmental stage marking the end of tillering and the onset of tiller mortality, but in agronomic conditions, this roughly coincides with the onset of stem elongation (Fig. 3.9b; Rawson, 1971). The reason for such coincidence is that elongating internodes became a dominant sink at this stage, reducing the availability of assimilates to alternative sinks (including tillers to appear and very young tillers that have not produced the structures necessary not to require assimilates from others shoots). Tiller death continues until reaching self-supporting tillers and then stabilises, resulting in a certain number of tillers that corresponds to the number of tiller spikes of the crop (Fig. 3.9b). The top ~ four to six internodes elongate from the onset of stem elongation to anthesis (Fig. 3.9b). The elongating process is related to the dynamics of leaf growth and appearance (Kirby et al., 1994), and the elongating internode at any time is two phyllochrons delayed respect to the leaf of the same phytomer (McMaster et al., 1991). The length of each elongated internode is inversely proportional to its order: the later the longer, being the last elongating internode, the spike peduncle.

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Spike growth takes place in very few (~ 3) weeks immediately before anthesis (Kirby et al., 1987; González et al., 2003). Although highly developed, the spike has not accumulated much dry matter until well entered the period of stem growth (Fig. 3.9b). In this very short period of spike growth, florets complete their development (as described earlier) returning a strong correlation between the number of fertile florets or grains and spike dry matter at anthesis (Section 2.1.2). After anthesis, grains became the dominant sink. In many cases, grains grow more than the crop, highlighting the contribution of reserves accumulated beforehand to fill the grains. The grain-filling period can be divided into four phases, characterised by the dynamics of grain growth after pollination (Fig. 3.9c; e.g. Bewley and Black, 1985; Loss et al., 1989; Stone and Savin, 1999). The first phase is known as either ‘lag phase’ (referring to a delay in starting growth) because it features active grain development but negligible growth, ‘grain set phase’ because pollinated fertile florets may abort in this period immediately after anthesis (Fig. 3.7), or ‘watery ripe’ because grain water uptake drives a rapid increase in volume, reaching > 70% moisture (and then, if pressed with the fingers, it breaks easily and release a watery fluid). In this phase, cell division and development are incipient for endosperm first and embryo later. The endosperm creates the sink strength for each individual grain, through producing the endosperm cells where dry matter will be accumulated during effective grain filling. It is then the period when GWP, which was initially established by the size of the ovary, is finally determined associated with the number of endosperm cells and the volume of the grain (Section 2.1.3). The second phase shows a strongly linear GW increase at maximum rates, during which most of GW is realised (Fig. 3.9c). Grain water content stabilises reaching a ‘grain water plateau’, that is, a period in which the amount of water in the grain remains more or less unchanged, and the water percentage is linearly reduced. Most, 70% or more, of the dry matter is starch (Section 5.1) and therefore the driving force for grain growth. Protein is a small proportion of dry matter but critical for grain quality (see Section 5.1). Complex carbohydrates, relevant as a source of dietary fibres (mainly arabinoxylans; Shewry et al., 2020 and references quoted therein), are accumulated in this phase. This stage is known as ‘milky ripe’ (Fig. 3.9c) because if pressed with the fingers, it breaks easily and release a white fluid resulting from the mixture of starch and still high water content in the grain. The third phase of ‘dough grain’ is actually the last of grain growth, when accumulation of dry matter occurs at decreasing rates and water content diminishes, reducing the relative water content of the grains (then, when pressing the grains with the fingers, it breaks with increasing difficulty because grain growth progresses releasing a content similar to a dough, until towards the end of the phase the grain cannot be broken by hand, although the pericarp is still soft and pressing with nails leave an indentation). This phase ends at ‘physiological maturity’ when dry matter peaks and the grain enters a quiescent state (Bewley and Black, 1985). Water content at physiological maturity is still high, ~ 37% (Calderini et al., 2000), but the grain is hard (cannot be indented by pressing with the nails). In the last phase from physiological maturity to commercial harvest of the crop, grain water content decreases, first quickly and then more slowly because the driving force for this water loss is the difference in water potential between the grain and the surrounding air (Fig. 3.9c).

2.2.2  Phenological phases and scales There are two main approaches to divide the cycle into phases, which are similar for postanthesis but differ strongly before anthesis. The most common approach recognises the external morphology of the plants and splits vegetative and reproductive periods shifting at flowering. In wheat, there are distinct morphological processes before flowering like appearance of leaves, tillering, and the elongation of internodes with a certain degree of overlapping. Therefore the scale of development frequently used for agronomic research and farming decisions (e.g. spraying of agrochemicals, fertilisation) considers two digits in the ‘decimal code’ of Zadoks et al. (1974). The first digit, from 0 to 9, refers to the main stage or organ, and the second digit quantifies the advancement of that stage or number of organs. Thus the scale of Zadoks describes development into stages from 00 and 99. Main stages from 0 to 3 refer to vegetative organs (0: germination, 1: leaves, 2: tillers, and 3: internodes), from 4 to 6 refer to the condition of the spike (4: booting, 5: heading, and 6: anthesis), and from 7 to 9 to stages of grain growth and development. The earlier scale of Feeks (1941), popularised by Large (1954), is less detailed with only 1 digit for each of the stages (from pretillering and tillering, stages 1–5; through stem extension and heading, stages 6–10 to ripening, stage 11). The scale of Haun (1973) focuses on appearance of leaves; that is the stage of Haun is a number describing the number of leaves (and the fraction thereof) that have appeared in the main shoot. All in all, considering the external morphology, wheat can be divided into four main phases, the first three comprising development to anthesis. These phases are (i) crop establishment, from sowing to the onset of tillering, when the seedlings would approximately have three expanded leaves (Section 2.2.1), (ii) tillering, from the appearance of the first tiller to the

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onset of stem elongation, commonly coinciding with the cessation of tillering (Section 2.2.1), (iii) stem elongation, from first node detectable above the soil to anthesis, embracing relevant stages, such as flag leaf appearance, booting, and heading, and (iv) grain filling, describing the progress in relation to the water content of the grain from watery through milky and dough to hard (Section 2.2.1). This approach is practical but has two significant limitations. First, phases are not clearly defined, for example, tillering may cease before or after the onset of stem elongation, depending on availability of resources and plant density (Section 2.2.1). Secondly, and more relevant, it does not reflect the actual physiological stage of the apex, where development does take place. For that reason, in this chapter, we described the development in wheat divided in four phases considering the developmental features of the apex: the vegetative, early reproductive, late reproductive, and grain-filling phases (Figs 3.7 and 3.10). Vegetative phase is, when the apex develops vegetative organs, characterised by leaf initiation. It starts with seed imbibition (in practical terms, with sowing) and lasts until floral initiation. The morphology of the apex changes from a dome to an elongated cylinder, in all cases producing single ridges corresponding to leaf primordia. In this phase, all leaves are initiated, and final leaf number is determined. Early reproductive phase is from floral initiation, through double ridges, glume primordium, and lemma primordium, to terminal spikelet stages. The apex shape changes considerably from cylindrical with single ridges corresponding to the first spikelets initiated to the formation of double ridges and then widening with development of organs at each particular spikelet. In this phase, all spikelets are determined. Quantitative scale based on the morphogenesis of the spike, ovary and pistil of florets

Spikelet detail

Developmental score

Description Transition apex.


Early double ridge stage. Double ridge stage.


Glume primordium present.


Lemma primordium present.


Floret primordium present. Stamen primordium present.


Pistil primordium present. Carpel primordium present.


Carpel extending round three sides of ovule.


Stylar canal closing; ovarian cavity enclosed on all sides but still open above.


Stylar canal remaining as a narrow opening; two short round style primordia present.


Stylar begin elongating.


Stigmatic branches just differentiating as swollen cell on styles.


Unicellular hairs just differentiating on ovary wall; stigmatic branches elongating.


Stigmatic branches and hairs on ovary wall elongating.


Stigmatic branches and hairs on ovary wall continue to elongate; stigmatic branches from a tangled mass. Styles and stigmatic branches erect; stigmatic hairs differentiating.


Styles and stigmatic branches spreading outwards. Stigmatic hairs well developed.















W3.5 W4.5







Styles curved outwards and stigmatic 10 branches spread wide; pollen grains on welldeveloped stigmatic hairs.







waddington et al., 1983

FIG.  3.10  Floral developmental stages according to the Waddington scale (Waddington et  al., 1983), with pictures taken from the experiments of Ferrante et al. (2013a) from early spikelet initiation at the transition of the apex to reproductive development to the stage of the fertile floret (W10), with details of selected developmental stages focused on the development of the spike until the initiation of the terminal spikelet (W3.5) in each individual floret primordium from then onwards. Reproduced with permission from Oxford University Press: Ferrante, A., Savin, R., Slafer, G.A., 2013a. Floret development and grain setting differences between modern durum wheats under contrasting nitrogen availability. J. Exp. Bot. 64, 169–184.

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Late reproductive phase is of floret development and stem elongation. Florets are developed inside each spikelet initiated in the previous phase. The development of florets includes the initiation and growth of each of the floret organs of both androecium (pistil) and gynoecium (ovary). Through their development, florets reach the stage of fertile floret at anthesis or stop developing and die (Section 2.2.1 and Fig. 3.8). Grain-filling phase is when GWP is first established and grain growth follows. It is further subdivided into stages considering the consistency of the grains revealing the relative water and starch contents, and then reflecting the advancement in grain growth from anthesis to physiological maturity: watery, milky, dough and hard stages (Section 2.2.1). The decrease in grain water content through grain filling could be used as a reliable and simple method to more quantitatively estimate the degree of grain development (Box 3.2). Owing to their relevance, different scales for apex and floret development have been produced (e.g. Gardner et al., 1985; Waddington et al., 1983), and guides have been popularised (mainly Kirby and Appleyard, 1987). The scale of Gardner divides apex development into eight stages, from the vegetative apex to terminal spikelet. The scale of Waddington focuses more on the further development of the florets (Fig. 3.10).

Box 3.2  Quantifying grain development through its moisture content


Days after anthesis


Physiological maturity

Weight or water % ofaverage grains

Grain water percentage (%)

The most common characterisation of postflowering development progress towards maturity has been qualitative, dividing the development into loosely defined grain stages, such as ‘watery’, ‘milky’, ‘dough’, and ‘hard’ grain. As this characterisation is based in the proportion of water in grains, it is possible to put forward a quantitative developmental estimate based on the actual grain water content, reflecting the proportion of the time to maturity already elapsed at any time the moisture content of growing grains is measured. For this to be realistic, there must be a steady change in this variable during the whole postflowering period, and for it to be of universal application (a developmental scale applicable to all genotypes of a particular species and to different crop managements), there should be uniform performance across cultivars and environmental conditions. The scheme (Fig. 3.B2) indicates that grain growth and grain moisture content dynamics are strongly variable depending on the genotype and the environment, determining large differences in final GW. However, the relationship between grain growth and its water content seem much more stable because there is a positive relationship between the rate of grain growth and the rate of water percentage reduction (the higher the slope of grain dry matter gain, the smaller—more negative—the rate of water percentage in grains). If the final GW is normalised (by referring in each case the GW at any time between anthesis and maturity as a percentage of the final GW), there seems to be a universal sharp negative relationship between the grain moisture percentage and GW normalised; so that disregarding profound differences in final GW and in the dynamics of grain growth, all crops within a particular species reached physiological maturity at a rather similar water content in the grains. Evidences in maize (Saini and Westgate, 2000; Borrás et al., 2003; Borrás and Westgate, 2006), wheat (Schnyder and Baum, 1992; Calderini et al., 2000), sorghum (Gambín and Borrás, 2005), soybean (Swank et al., 1987), and sunflower (Rondanini et al., 2007) have shown that final GW is achieved at, or near to, a particular moisture content, irrespective of the actual size of the grains (affected by genetic or environmental factors)*, revealing that dry matter accumulation in developing grains and the concurrent loss of water are closely related phenomena.

Moisture content at physiological maturity

Grain weight relative to final

FIG. 3.B2  Schematic representation of the dynamics of grain weight and water content considering three contrasting grain-filling environmental conditions (a), and relationship between grain growth (normalised as grain weight at any time between anthesis and maturity as a percentage of the final grain weight) and its relative water content (b).

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BOX 3.2  Quantifying grain development through its moisture content—cont’d Thus it seems that duration of grain filling is determined by the interaction between reserve depositions and declining cellular water content, where deposition of reserves such as starch replaces water until critical minimum moisture content is reached. As, for each crop, (i) water percentage at flowering and at maturity are rather constant (for a wide range of grain-growing conditions and of final GWs), and (ii) it decreases linearly across the range from flowering to physiological maturity, and it can be proposed that the progress of grain development towards maturity may be trustworthily based on the water content of the grains. For instance, if for wheat, the limits are ~ 80% water content just after anthesis and ~ 40% at physiological maturity (Calderini et al., 2000), it can be directly established what proportion of the grain-filling period has elapsed at any time we measure grain moisture content in the field. This quantitative assessment allowing determining how much of the grain filling has been already completed may be instrumental in management decisions such as when to apply a desiccant to the crop to advance harvest without losing yield (e.g. Calviño et al., 2002). *In some extreme conditions, moisture content at maturity may also be affected within a crop, although assuming a constant value for a particular crop seems justified for realistic agronomic conditions. Reproduced with permission from Slafer, G.A., Kantolic, A., Appendino, M., Tranquilli, G., Miralles, D.J., Savin, R., 2015. Genetic and environmental effects on crop development determining adaptation and yield. In: Sadras, V.O., Calderini, D.F. (Eds.), Crop Physiology Applications for Genetic Improvement and Agronomy, second ed. Elsevier, Amsterdam, The Netherlands. pp. 285–319.

2.2.3  Environmental factors affecting wheat development Comparing the phenology of a particular genotype across sowing dates or locations highlights the environmental effect. Whereas wheat phenology can respond to water, nutrients, and radiation (e.g. Rawson, 1993; Rodriguez et al., 1994; Arisnabarreta and Miralles, 2004; Angus and Moncur, 1977), the responses are small and inconsistent (Slafer, 1995; Hall et  al., 2014). Phenology of indeterminate crops such as quinoa (Chapter  7: Quinoa, Section  2.2.2.) and pulses (Chapter 10: Chickpea, Section 2) is responsive to soil stress, including drought and salinity. The main environmental factors affecting wheat phenology are temperature, including temperature per se and vernalisation, and photoperiod (Slafer and Rawson, 1994).  Temperature per se The positive effect of temperature per se on the rate of development is ‘universal’ in that all phases for all genotypes are similarly sensitive to temperature (Aitken, 1974; Miralles and Slafer, 1999). Furthermore, this effect is the same in other crops (e.g. Parent and Tardieu, 2012) and other ectotherm organisms (Gillooly et al., 2002). The effect is ‘positive’ because, at least for a large range of temperatures, the rate of development is accelerated, and the duration of the phenological phases is reduced, when plants are exposed to higher temperatures (Slafer and Savin, 1991; Miralles and Slafer, 1999; Porter and Gawith, 1999, and references quoted therein). The most common model of developmental response to temperature features a linear increase in developmental rate between the base Tb and the optimum temperature To and a linear decline between To and the maximum temperature Tm (Fig. 3.11a).c The TT model (Monteith, 1984) is the calendar time weighted by the thermal conditions in which plants are developing with units of degree days (°Cd). When the temperature increases, there is a proportional acceleration of developmental processes resulting in a reduction of the calendar time required for the completion of the phase (Fig. 3.11a). Considering this effect, the duration of the phase becomes invariable in terms of TT, resulting from multiplying the calendar time by the temperature affecting the development, instead of days. Indeed, the physiological robustness of the concept relies on this linear relationship whose slope represents the reciprocal of the TT required for completion of this phase at any temperature ranging from the base and the optimum thresholds. Effective temperatures calculated as the daily mean minus the base temperature, with daily mean calculated from hourly temperatures or as the mean of the maximum and minimum daily

c. The literature is inconsistent in naming these thresholds. We use ‘optimum’ for the independent variable when the dependent variable is negatively affected if exposed to values of the independent variable either below or above that threshold (like in the case of temperature in Fig. 3.10). On the other hand, we use ‘critical’ for the threshold at which the dependent variable reaches its maximum, and any values below the critical negatively affects the dependent variable, but values above the critical are all equally ‘optimum’ (like in the case of photoperiod or vernalisation in Fig. 3.10; but also for the relationship between radiation interception and LAI, see Section 3.1).

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FIG. 3.11  Relationship between the rate of development and (a) temperature and (b) vernalisation or photoperiod. (c) Timing during the crop cycle when wheat is sensitive to these three factors: temperature per se, solid line; vernalisation, dashed line; photoperiod, dotted line. In A, rate of development increases with temperature between the base (Tb) and optimum temperatures (To) and declines between To and Tm. In B, the rate of development increases linearly with vernalisation (duration of a period with vernalising temperatures) or photoperiod and plateaus after a critical photoperiod or critical vernalisation threshold. In this example, the rate of development refers to either the whole phase of sowing to anthesis or to any particular phase that is sensitive to these factors. In the relationship with temperature, the reciprocal of the slope of the linear regression between Tb and To is the thermal time (TT−  1) required to complete the phase considered at any temperature between these thresholds and using the estimated Tb. The slope of the relationship with photoperiod or vernalisation is the sensitivity of the cultivar to these factors. From Slafer, G.A., 2012. Wheat development: its role in phenotyping and improving crop adaptation. In: Reynolds, M.P., Pask, A.J.D., Mullan, D.M. (Eds.), Physiological Breeding I: Interdisciplinary Approaches to Improve Crop Adaptation. CIMMYT, Mexico DF, pp. 107–121.

t­ emperature. It has been suggested that for early developmental phases, when the internodes are underground, soil temperature should be used instead of air temperature (Jamieson et al., 1995; Vinocur and Ritchie, 2001), but the advantage of using soil temperature is unclear (McMaster et al., 2003; McMaster and Wilhelm, 2003). Empirically, the process consists in ‘accumulating’d daily mean temperatures above the base, and through estimating duration of phases through TT, the duration becomes independent of temperature, and therefore the observed TT differences could be used to analyse sensitivity to the other factors (photoperiod and vernalisation). The base temperature cannot be accurately determined experimentally (by definition, the duration of the phase—and therefore of the experiment—would be infinite), and therefore it is always indirectly estimated. Although the model is applicable to all phenological phases and genotypes, the actual base and optimum temperatures vary with genotype and phase (e.g. Angus et al., 1981; Slafer and Savin, 1991; Rawson and Richards, 1993; Slafer and Rawson, 1995a; Porter and Gawith, 1999). As temperature accelerates not only the rate of development but also the rate of primordia initiation, there are not clear effects of temperature per se on the final number of leaves initiated; and advanced anthesis under higher temperatures is related to the increased rate of leaf appearance. Vernalisation Vernalisation is the requirement for an exposure to a period of low temperatures to allow (qualitative response) or accelerate (quantitative response, i.e. to shorten the phase) development. The effect is mainly effective in early stages of development and is a mechanism evolved in temperate species to avoid flowering and grain filling during periods of high risk of frost. Fulfilment of vernalisation requirements dominates the response to photoperiod (see further) ensuring that even if wheat is sown early in autumn (with still relatively long days and warm temperature), anthesis will not occur until the following spring (Dubcovsky et al., 2006; Hemming et al., 2008). The stimulus is perceived directly by the active shoot apex (Chouard, 1960; Amasino, 2004), that is, from seed imbibition onwards; indeed, vernalisation may take place during grain filling in the mother plant. ‘Winter’ wheat genotypes have a strong sensitivity to vernalisation, which is lacking or reduced in ‘spring’ genotypes (Slafer and Rawson, 1994; Valle et al., 2009). Normally, winter wheats are sown in fall, their vernalisation requirements avoid them to advance in development until after the winter, and flower in spring soon after the risk of late frosts has been minimised. In locations where winter wheats would need to vegetate through very harsh winters, spring wheats are the alternative, and as these wheats do not d. Plants do not accumulate temperature anywhere! Researchers, agronomist, breeders, modellers, and others interested in assessing and/or predicting the progress of development accumulate temperatures over days as a practical tool for quantitatively taking into account the large effect it has on the rates of development (Fig. 3.10).

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require vernalisation when sown in spring and, as they do not require the exposure to a period of low temperatures, they can flower in early summer. Vernalisation occurs in a wide range of temperatures, from −  1°C to 15°C (Porter and Gawith, 1999), but most effective temperatures are between ~ 1°C and 8°C (Flood and Halloran, 1984; Brooking and Jamieson, 2002). The rate of development of a sensitive genotype increases (and the duration of the phase decreases) with longer exposure to vernalising temperature and saturates after a threshold (Fig.  3.11b). The slope of the response represents the sensitivity to vernalisation, and the rate of development at critical or longer vernalisation periods represents the earliness per se (Section 2.2.4) of the genotype; spring genotypes are mostly insensitive to vernalisation, and the development vs vernalisation response is normally a horizontal line with intercept representing its earliness per se. Unlike temperature per se, wheat is sensitive to vernalisation only in early developmental stages (Fig. 3.11), which is expected from the evolutionary interpretation of the trait, that is, to avoid reproduction before being exposed to winter. Thus vernalisation affects mainly the rate of vegetative development and that of the early reproductive phase (Slafer and Rawson, 1994; and references quoted therein). As vernalisation affects the rate of development of the vegetative phase much more than the rate of primordia initiation, exposure to vernalisation reduces the final number of leaves initiated (and then wheats sown in fall normally initiate many more leaves, normally > 12, than those sown in spring, normally seven to nine). Photoperiod In addition to vernalisation, plants evolved the capacity to use photoperiod as a cue to speed up or slow down development towards flowering; strictly, plants respond to the duration of the dark period (e.g. Pearce et al., 2017). Leaves detect photoperiod by changes in the isomer form of phytochrome (Legris et al., 2017 and references quoted therein). Under inductive photoperiods (long days in wheat), leaves produce a signal (florigen; a sort of hormone) that moves to the apex where it accelerates the rate of development, inducing flowering (Wigge et al., 2005; Zeevaart, 2006). The response to photoperiod cannot start until at least seedling emergence (when leaves start to perceive the length of the day) and normally starts immediately after emergence because wheat lacks a ‘juvenile phase’e (Hay and Kirby, 1991; Slafer and Rawson, 1995b) and continues during the late reproductive phase (Miralles et al., 2000; Whitechurch and Slafer, 2002; González et al., 2003, 2005a; Fig. 3.11c). In genotypes with no vernalisation requirement, long photoperiod at seedling emergence can induce reproductive development immediately, and final leaf number would be the number of leaves primordia in the embryo plus those initiated from sowing to seedling emergence (both together ~ six to seven leaves) (Hay and Kirby, 1991). Wheat is a long-day plant, which implies that development slows down when photoperiod is shorter than the critical (Fig. 3.11b), and therefore the phase becomes longer if the response is quantitative, as in commercial cultivars, or prevented if response is qualitative, virtually inexistent in agronomically adapted materials (Major, 1980; Slafer and Rawson, 1994). As photoperiod affects more the rate of development of the phase than the rate of primordia initiation, photoperiods shorter than the critical generally increase the number of primordia initiated (e.g. Rawson, 1993; Major, 1980; Slafer and Rawson, 1996; González et al., 2002, 2003; Miralles et al., 2003). The response to photoperiod is characterised by at least three parameters: a critical photoperiod, the sensitivity to photoperiod (represented by the slope), and earliness per se (Fig. 3.11b). Within the available variation, even within commercial cultivars or elite material, it is possible to find genotypes that are insensitive (the rate of development is that determining the earliness per se at any photoperiod) and a large degree of sensitivity levels (magnitude of the slope in Fig. 3.11b) (Slafer and Rawson, 1994; Ochagavía et al., 2017; Pérez-Gianmarco et al., 2018).

2.2.4  Genotypic differences and main genetic factors Genetic variation is associated with three groups of genes. Two of them, the photoperiod (Ppd) and vernalisation sensitivity genes (Vrn) account for the majority of genotypic variation in phenology and are primarily responsible for coarsetuning adaptation (Griffiths et al., 2009). However, when genotypes are screened for phenology under long days and after being vernalised, there is ‘residual’ variation that by definition is independent of photoperiod and vernalisation sensitivities (Appendino and Slafer, 2003). These relatively minor differences are ascribed to genes of earliness per se (Eps; Slafer, 1996; Snape et al., 2001), responsible for fine-tuning adaptation (Griffiths et al., 2009; Zikhali et al., 2014; Ochagavía et al., 2018).

e. An initial phase in which a photoperiod-sensitive genotype is insensitive to photoperiod, determining a longer minimum duration of vegetative development and therefore a relatively higher minimum number of leaves that must be initiated before the apex becomes reproductive (such as in the case of maize, e.g. Kiniry et al., 1983; Chapter 1: Maize).

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Ppd, Vrn, and Eps may alter different phases of development. However, most frequently, these genes were identified, and their effects quantified, considering time to heading or anthesis as a single phase. Recent efforts have been made to determine the effect of individual developmental genes (or their interaction) on particular phenological phases and associated dynamics of primordia initiation (e.g. González et al., 2005c; Ejaz and von Korff, 2017; Ochagavía et al., 2017, 2018, 2019; Pérez-Gianmarco et al., 2018, 2019). Results are incipient and more work would be required to reach general ­conclusions. For that reason, in this section, we focused on the genetic factors controlling developmental rates considering time to heading or anthesis. We will comment only superficially on the most critical genes involved in genotypic variation in development; for a more comprehensive view and discussions on gene action pathways, please see the recent review on the issue by Hyles et al. (2020). Genotypic variation in vernalisation sensitivity in bread wheat is largely controlled by the Vrn-1 family of major genes. However, other Vrn genes have been identified (e.g. Cockram et al., 2007). These include Vrn-2 that is more relevant in diploid wheat and barley but not in commercial bread wheat (Dubcovsky et al., 2006), Vrn-B3 in chromosome 7B, formerly Vrn5, but not very relevant in determining genetic variation within commercial wheats (Yan et al., 2006), and Vrn-D4 (Yoshida et al., 2010). Thus most studies have focused on this family of genes, integrated by Vrn-A1 (formerly, Vrn1), Vrn-B1 (Vrn2), and Vrn-D1 (Vrn3) located in chromosomes 5A, 5B, and 5D, respectively (Flood and Halloran, 1986; Snape et al., 2001). Each of these genes has a dominant (Vrn-1a) and recessive allele (Vrn-1b) that confer insensitivity and sensitivity, respectively. Winter wheats would have the recessive alleles in all three Vrn-1 genes. Vrn-A1 has stronger effects than Vrn-B1 and Vrn-D1, and therefore genotypes with the Vrn-A1a are spring wheats (e.g. Appendino and Slafer, 2003; Yan et al., 2004). Vrn-1 genes are expressed in the apex regulating the transition from vegetative to reproductive stages, and in leaves, expression that is relevant to allow the photoperiod response to occur in photoperiod-sensitive winter wheats (see Fig. 2 in Hyles et al., 2020). Major photoperiod-sensitivity genes are Ppd-1 located in chromosome 2. These are Ppd-A1 (formerly, Ppd3), Ppd-B1 (Ppd2), and Ppd-D1 (Ppd1) located in chromosomes 2A, 2B, and 2D, respectively (Scarth and Law, 1984; Snape et al., 2001; Beales et al., 2007). Each of these genes has a dominant (Ppd-1a) and recessive allele (Ppd-1b) that confer insensitivity and sensitivity, respectively. In general, Ppd-D1a has the strongest effect (Snape et al., 2001; Yang et al., 2009; Bentley et al., 2013; Kiss et al., 2014; Jones et al., 2017; Ochagavía et al., 2017), although not always this superior strength is evident (e.g. Stelmakh, 1998; Tanio and Kato, 2007; Bentley et al., 2011), and Ppd-A1 and Ppd-B1 are also recognised as important factors controlling photoperiod sensitivity in wheat (e.g. Bentley et al., 2013). This variability reflects that the interaction with the background or the source of the dominant allele (Ochagavía et al., 2017) may affect the strength of the effect. Also the insensitivity is normally clearer with the joint action of two or more genes, that is, the effects of these genes would mainly be additive (Shaw et al., 2012; Ochagavía et al., 2017). Despite the Ppd-1 are the main genes recognised for photoperiod sensitivity, there must be other genes also contributing to this sensitivity (Bloomfield et al., 2018; Hyles et al., 2020). Earliness per se are a set of heterogeneous genes (Worland et al., 1994), each one independent of the others and are considered in conjunction only because of their final effect on time to anthesis: all of them affect the rate of development independently of the photoperiod and vernalisation and are responsible for relatively minor differences in phenology when plants are grown under saturating photoperiod and vernalisation (Slafer, 1996). They cannot be easily identified in other conditions because their effects can be masked by Vrn and Ppd genes (Sukumaran et al., 2016; Zikhali et al., 2014). Owing to their relatively small effect, Eps genes can be critical for fine-tuning adaptation (Griffiths et al., 2009; Gomez et al., 2014). They are a large number of genes reported to exist in virtually all chromosomes (Kamran et al., 2014; Lopes et al., 2015). In principle, the term used to designate these genes (per se) was based on the idea that they affected the rate of development independently of the environment (see references in Slafer, 1996). But it has been hypothesised that at least some Eps genes would actually be temperature-sensitivity genes (Slafer, 1996). The lack of insensitivity to temperature (Section 2.2.3) does not preclude variation in the degree of sensitivity. In the absence of photoperiod and vernalisation effects, Slafer and Rawson (1995c) showed genotypic variation in responsiveness to temperature and genotypic differences in maximum rate of development responsive to temperature (Slafer and Rawson, 1995d). More recently, the Eps × temperature interaction was explicitly reported, firstly in T. monococcum (Bullrich et al., 2002; Appendino and Slafer, 2003) and later in T. aestivum (Ochagavía et al., 2019).

3  Capture and efficiency in the use of resources 3.1  Capture and use efficiency of radiation Photosynthesis returns high-energy organic compounds (C6H12O6) from CO2 and H2O with radiation as the source of energy. Yield is the product of shoot biomass and HI (Eq. 3.1), and biomass is a function of incident photosynthetic a­ ctive

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radiation (PARi), the fraction of that radiation intercepted by the canopy (fRI) and the radiation use efficiency (RUE) (Monteith, 1977; Grifford et al., 1984): Yield  Biomass  HI


Biomass  IPARi  fRI  RUE


3.1.1  Dynamics of radiation interception Grain yield is closely associated with shoot biomass, mainly when environment and management are the driving forces of yield variation (Fischer, 1993; Cossani et al., 2009), although genotypic variation in yield is also related to that in biomass, particularly comparing lines of wheat that are all semidwarf and elite germplasm (e.g. Bustos et al., 2013; García et al., 2013). Grain yield is closely associated with shoot biomass across genotypes, environmental and management conditions (e.g. Sadras and Slafer, 2012; Bustos et al., 2013; García et al., 2013), as illustrated in Fig. 3.12. As yield is mostly source-limited during the critical period for grain number determination (Section 2.1.2 and Fig. 3.3), reaching full radiation interception at the onset of the critical period (Fig. 3.13) is crucial to maximise the crop growth rate, grain number, and yield. For example, a large amount of intercepted radiation in the critical period is one of the key environmental conditions to explain the high yield (≥  12 t ha−  1) achieved in southern Chile (e.g. Sandaña et al., 2009; Bustos et al., 2013; Box 3.3).

FIG. 3.12  Relationship between yield and shoot biomass at harvest for wheat, barley, and triticale crops grown in Valdivia, Chile. The cumulative PAR intercepted by the crop up to anthesis is shown for wheat. Data from: Quiroz, J., 2010. Rendimiento y producción de biomasa de trigo, cebada y triticale bajo riego y secano durante el llenado de grano en sur de Chile (MSc. thesis). Universidad Austral de Chile, p. 71.

FIG. 3.13  Fraction of intercepted PAR of spring wheat during the crop cycle. The arrow shows anthesis and the horizontal line shows 95% of radiation interception. Phenological phases are: S, sowing; Em, seedling emergence; DR, double ridge; An, anthesis; PM, physiological maturity. Reproduced with permission from Mera, M., Lizana, X.C., Calderini, D.F., 2015. Cropping systems in environments with high yield potential of southern Chile. In: Sadras, V.O., Calderini, D.F. (Eds.), Crop Physiology: Applications for Genetic Improvement and Agronomy, second ed. Academic Press, Elsevier, pp. 111–140.

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Box 3.3  Potential yield of winter vs spring wheat The assumption that potential yield is higher in winter than in spring wheat is common and influences farmer decisions. However, potential yield is similar in spring and winter wheats in high-yielding environments. Despite a short season (~  4.5 months, from late August to mid-January), spring wheat cultivars and lines yielded from 12 to 16 t ha−  1 in southern Chile (Sandaña et al., 2009; Bustos et al., 2013), which is similar to high-yield potentials achieved in North Europe with winter wheats. In this environment, spring canola produced 8 t ha−  1, with 50% of grain oil concentration (Calderini et al., 2020b). How could spring wheat (and spring types of other temperate crops) have potential yields similar to those of the winter types? Why these similar potential yields are not seen in other regions, particularly in the northern hemisphere? A major difference is the severe winter in the North and rather mild winter in the South. Therefore in most wheat-growing regions of the northern hemisphere, it is not possible to sow wheat in winter, whilst wheat is commonly sown in winter in the southern hemisphere (Fig. 3.B3). Thus in the northern hemisphere, delaying sowing of spring wheat until the soil temperature allows a normal seedling emergence (generally, towards early spring) and returns a low photothermal quotient during the critical period for grain number determination with respect to that of winter wheats. On the other hand, in the southern hemisphere, winter wheats are sown in late autumn-early winter, whilst spring wheats are sown over the winter, so that in all cases, the critical period of winter and spring wheats overlaps. Therefore the different sowing windows between hemispheres strongly conditions the potential and actual yield of spring vs winter wheat; but when they have similar photothermal conditions during the grain set and grain filling, potential yield seems to be similar between both wheat types. This reinforces the relevance of the critical periods for yield determination (Section 2.1.2).

FIG. 3.B3  Winter and spring wheat crop cycles in the northern and southern hemispheres. Note that sowing and maturity dates are a broad average example. Dotted vertical lines show the solstices and equinoxes.

The fraction of radiation intercepted by the canopy fRI depends on the radiation attenuation coefficient k and LAI: fRI  1  exp

 k  LAI 


In wheat, k ranges between 0.33 and 0.46 (Calderini et al., 1995) and varies with cultivar (Bustos et al., 2013) and crop geometry (Abichou et al., 2019). This trait is affected by the canopy optical properties, chiefly the angle of insertion of the leaves and tillering, and consequently, it changes with phenology. Wheat NILs of contrasting height and canopy architecture varied in k, with k = 0.8 for a double dwarf line (Miralles and Slafer, 1995), which is similar to planophile crops such as sunflower (Chapter 16: Sunflower, Section 3.1.3). The time-course of LAI in an expanding canopy typically conforms to a logistic pattern with a lag phase after seedling emergence depending on the phyllocron (Fig. 3.9) and leaf expansion. At the beginning of tillering (Section 2.2 and Figs 3.7 and 3.9), LAI grows fast and could reach 6 or more, well above the critical LAI required to achieve maximum radiation interception ~ 95% (LAIc; that normally ranges between 3 and 4), in crops well supplied with water and nutrients. In unstressed crops, maximum LAI is often reached around booting (Zadoks 4.5), when flag leaves had been already fully expanded. Leaf senescence starts before anthesis by tiller mortality (Section 2.2 and Fig. 3.7) but is more evident during grain filling. Postanthesis senescence is rarely a constraint because wheat yield is sink-limited during grain filling (Section 4.1). Hence the relevance of stay green for yield may be associated with other traits such as cooler canopy rather than with supply of assimilates. Interception of radiation varies with cultivar and management, as illustrated in Fig. 3.14. For instance, cultivar Otto intercepted more radiation early in the season than Quijote and Pumafen (Fig. 3.14a) despite that all had similar k. This differential capacity to intercept radiation at earlier stages would be related to early vigour, in turn related to differences

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FIG. 3.14  Fraction of intercepted PAR of (a) three spring wheat cultivars, (b) a spring wheat at optimum (S1) and delayed (S2) sowings in Valdivia, southern Chile, and (c) a spring wheat sown at conventional (300 pl m−  2) and low (45 pl m−  2) plant densities. The arrows show anthesis and the horizontal line 95% of radiation interception. Data from Mera, M., Lizana, X.C., Calderini, D.F., 2015. Cropping systems in environments with high yield potential of southern Chile. In: Sadras, V.O., Calderini, D.F. (Eds.), Crop Physiology: Applications for Genetic Improvement and Agronomy, second ed. Academic Press, Elsevier, pp. 111–140; Bustos, D.V., Hasan, A.K., Reynolds, M.P., Calderini, D.F., 2013. Combining high grain number and weight through a DHpopulation to improve grain yield potential of wheat in high-yielding environments. Field Crop Res. 145, 106–115; and unpublished data.

in phyllochron, tillering, and leaf expansion rates (Section 2.2.1). Crops sown at the optimum date reached LAI close to LAIc at least 20 days before anthesis even in spring wheat cultivars grown in the southern hemisphere (Box 3.3), whilst a late-sown crop developed faster and did not reach full radiation interception at anthesis (Fig. 3.14b). Seasonal radiation interception was 1619 MJ m−  2 in S1 and 1232 MJ m−  2 in S2 (Fig. 3.14b), with a corresponding difference in shoot biomass at harvest (S1 = 21.6 and S2 = 18.2 t ha−  1). Well-managed crops under two contrasting plant population densities are compared in Fig. 3.14c. Accumulated intercepted radiation up to anthesis was 879 MJ m−  2 in the conventional plant density compared to 677 MJ m−  2 at low plant density. The effect of nutrient availability on radiation interception has been widely reported in wheat and is largely mediated by LAI (e.g. Fischer, 1993; Salvagiotti and Miralles, 2008; Sandaña et al., 2012). Fischer et al. (1993) reported a range of maximum LAI between 0.5 and 9 in response to nitrogen supply. LAI and radiation interception fully accounted for the impact of the combined N and sulphur (S) supply on crop growth rate and shoot biomass of wheat (Salvagiotti and Miralles, 2008). Similarly, phosphorus deficiency reduced LAI with no effect on k (Sandaña et al., 2012).

3.1.2  Radiation use efficiency RUE is a measure of crop-level photosynthesis often calculated as the slope of the zero-intercept regression between biomass and intercepted radiation (Monteith, 1977; Verón et  al., 2005). Leaf photosynthesis increases nonlinearly with irradiance (Fig. 3.15a) and saturates at ~ 1000 μmol PAR m−  2 s−  1, although this depends on the position of the leaf on the canopy and the distribution of both radiation and nitrogen in the canopy (Dreccer et al., 2000). Crop photosynthesis is also asymptotic with irradiance, although the levels of irradiance saturating canopy photosynthesis are much higher than those saturating single leaf photosynthesis (Fig. 3.15b). This is because the extinction of light in the canopy means leaves in lower layers of the canopy rarely saturate; hence crop photosynthesis normally increases linearly with intercepted solar radiation

FIG.  3.15  Relationships between (a) leaf photosynthesis and irradiance, (b) crop photosynthesis and irradiance, (c) crop photosynthesis and radiation interception, and (d) shoot biomass and accumulated intercepted PAR of wheat crop in southern Chile under optimum management. Data from: (d) Quiroz, J., 2010. Rendimiento y producción de biomasa de trigo, cebada y triticale bajo riego y secano durante el llenado de grano en sur de Chile (MSc. thesis). Universidad Austral de Chile, p. 71.

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(Fig. 3.15c); most leaves (those below the upper layer) would never experience saturation, and upper-layer leaves may only be saturated in the central hours of the day. RUE of an unstressed, well-managed wheat crop was 3 g MJ−  1 PAR in the high-yielding environment of southern Chile (Fig. 3.15d). This RUE corresponded with a crop growth rate of 300–320 kg ha−  1 d−  1 during the linear phase of shoot biomass accumulation. A range of 2.4–3.0 g MJ−  1 PAR was reported for wheat as achievable efficiencies (Sinclair and Muchow, 1999; Stockle and Kemanian, 2009). Wheat RUE varies with ontogeny and commonly decreases from anthesis to maturity (Sinclair and Muchow, 1999; Stockle and Kemanian, 2009). The fall in postanthesis RUE has been ascribed to a higher crop respiration, the ageing of the photosynthetic tissues, and leaf senescence processes. Even before viewing the starting of senescence by naked eye, leaves start remobilising N to the grains, and photosynthesis is intimately related to leaf N concentration (del Pozo et al., 2007; Moreau et al., 2012). In addition, because grain growth is commonly sink-limited (Section 4.1.1), a weak sink may downregulate photosynthesis during grain filling (e.g. Acreche and Slafer, 2009; Serrago et al., 2013). Indeed, breeding and introgressing semidwarf genes (both increasing postanthesis sink strength) have reduced the gap between pre and postanthesis RUE (Calderini et al., 1997; Miralles and Slafer, 1997), in line with the suggestion that postanthesis sink strength in wheat is the main driving force for postanthesis growth (Reynolds et al., 2005). Therefore very high-yielding wheats with a strong sink during grain filling showed similar RUE after and before anthesis (Bustos et al., 2013), leading to the hypothesis that increasing postanthesis sink strength would concomitantly result in indirect improvements in source-strength owing to this feedback, maintaining postanthesis RUE at similar levels of preanthesis (Bustos et al., 2013). Comparisons of historic collections of genotypes revealed improved RUE with selection for yield over decades in UK and Australia (Shearman et al., 2005; Sadras et al., 2012). Improved RUE of wheat in Australia was independent of leaflevel photosynthesis and associated with higher nitrogen uptake and a relaxation in the extinction of nitrogen relative to the extinction of radiation—newer varieties with higher RUE have greener leaves and more radiation at the bottom of the canopy (Sadras et al., 2012). Consistently, Richards et al. (2019) showed that erectophile lines yielded 13% more than planophile lines, and most of this yield advantage was associated with a higher shoot biomass (11%), although they did not measure RUE. Furthermore, G × E for visual scores of canopy architecture was low, and significant QTL associated with canopy architecture were identified on most chromosomes (Richards et al., 2019). NILs of different height had RUE from 2.14 g MJ−  1 PAR in double dwarf lines with poor radiation distribution within the canopy to 2.88 g MJ−  1 PAR in semidwarf lines with optimal height (Miralles and Slafer, 1997). A genome-wide association study (GWAS) showed that traits associated with RUE and final biomass at various growth stages that explained 7%–17% of phenotypic variation in yield (Molero et al., 2019). Similarly, lines with higher RUE before and after anthesis produced 20% more biomass than the best parental and current cultivars, reaching RUE of 3.8 g MJ−  1 PAR during the preanthesis period, that is, 380 g m−  2 d−  1 (Bustos et al., 2013; García et al., 2013); the reasons for the high RUE are unknown in this case. High vapour pressure deficit (VPD) reduces RUE in wheat (Kemanian et al., 2004; Dreccer et al., 2018; Rodriguez and Sadras, 2007). Kemanian et al. (2004) attributed changes of RUE between 1.6 and 3.2 g MJ−  1 of PAR to differences in VPD. Correcting RUE by VPD may be useful for comparisons amongst experiments. High proportion of diffuse radiation increases RUE (Sinclair and Muchow, 1999; Stockle and Kemanian, 2009), and crop simulation models account for it (Asseng et al., 2015). In a north–south transect in eastern Australia, with a range of diffuse radiation from 0.4 to 0.6, RUE increased 1.6-fold per unit increase in diffuse radiation (Rodriguez and Sadras, 2007). Changes in RUE from 1.7 to 2.14 g MJ−  1 of PAR have been reported between nitrogen fertilised and control treatments in wheat (Caviglia and Sadras, 2001). This result is in agreement with the link between N availability and RUE (García et al., 1988; Fischer, 1993). Salvagiotti and Miralles (2008) found no effect of N and S fertilisation on wheat RUE. Differences in N effect on RUE are likely because of soil N content of the control treatments, highlighting the differential sensitivity of RUE from radiation interception in wheat and in other crop species (e.g. Chapter 4: Barley, Section 3). Similarly, Sandaña and Pinochet (2011) did not find changes in RUE under different soil phosphorous (P) concentrations even though the crop growth rate ranged between 257 and 425 kg ha−  1 d−  1. A negative effect was found only for severe P deficiency reducing shoot biomass > 60% relative to the control, when RUE decreased 30%–40% relative to the control. Consistently, RUE of wheat was insensitive to Al toxicity in a range of Al saturation between 0% and 35%, with the reduction in shoot and root biomass ascribed to LAI and radiation interception (Valle et al., 2009).

3.2  Capture and efficiency in the use of water Water deficit is the main abiotic stress affecting wheat yield worldwide (Ding et al., 2018; De Oliveira Silva et al., 2020b). The balance between reference evapotranspiration and rainfall varies amongst environments, as illustrated in Fig. 3.16. In Valdivia, rainfall often meets or exceeds evaporative demand (Fig. 3.16a), hence the water-limited yield potential Yw is close to yield potential Yp (Fischer, 2015). In this environment, yield potential is high because of the favourable ­photothermal

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FIG.  3.16  Cumulative rainfall and evapotranspiration (ET0) during the growing season in two contrasting environments: Valdivia (Chile) (a) and Roseworthy (Australia) (b). Bars show the regular spring wheat season from sowing (S), depicting the time of anthesis (At) and physiological maturity (PM) in each environment. Data of rainfall and ET0 corresponding to 2018–19 season. Data from: Valdivia ( and Roseworthy (

regime during grain set and grain filling (Mera et  al., 2015), and yield of farmers in this region averaged ~  8 t ha−  1 in 2018 (ODEPA, 2019). Furthermore, volcanic soils with high water-holding capacity, 180 mm of available water (Dörner et al., 2015), buffer occasional dry spells between rainfall events, mainly in late grain filling. On the other hand, evaporative demand typically exceeds rainfall at Rosewothy (Fig.  3.16b), especially during key phases for yield determination (Section  2.1). Owing to the combination of rainfall seasonality and soils with low water-holding capacity, stored soil water at sowing is a small source of water for crops compared to in-season rainfall in environments like this (Sadras and Rodriguez, 2007). Hence the gap between Yw and Yp is large (van Ittersum et al., 2013), with yield averaged ~ 4 t ha−  1 in 2018 (Crop and Pasture Report South Australia, 2019). Therefore there is a wide range of conditions where management of water has high impact on wheat yield, as shown for regions of Australia (Dreccer et al., 2018). In this regard, environmental characterization of water stress has been developed during the last years, which is relevant for both breeding and crop management. Drought patterns have been identified by using crop simulation models over long-term climatic records. For example, Chenu et al., (2013, 2018) identified four major drought patterns over space and time for the wheat-belt of Australia (see Chapter 4: Barley). Passioura (1977) advanced a water-based model of crop yield: Y  ET  WUE  HI


where Y is yield, ET is seasonal evapotranspiration, Water use efficiency (WUE) is biomass per unit ET, and HI is harvest index. The three terms of the identity are not unrelated, but the model remains useful to assess breeding and management strategies (Passioura, 2006; Araus et al., 2008; Reynolds and Tuberosa, 2008).

3.2.1  Crop evapotranspiration Crop evapotranspiration depends on water availability in the soil, evaporative demand, and the capacity of the crop to use soil water. Crops rely on water stored in the soil before sowing and rainfall during the growing season (Fig. 3.17). Runoff, deep drainage, and soil evaporation are unproductive losses. In addition, soil physical and chemical constraints may restrict root growth and function. Fallow is a common practice to store water in soil in different wheat cropping systems (e.g. Fischer, 2009; Savin et al., 2015; Zhang et al., 2015) and has been reviewed by Passioura and Angus (2010), Hunt and Kirkegaard (2011), and Pittelkow et al. (2015). A metaanalysis showed the impact of no-till on wheat yield depended mainly on three variables: aridity index (the degree of dryness of the climate), irrigation, and N rate (Pittelkow et al., 2015). Consequently, contrasting effect of no-till has been reported on average wheat yield relative to conventional tillage. In Mediterranean climates, wheat yield is higher under no-till, especially in water-stressed years. In the US, the positive effect of no-till is apparent for wheat often grown in semiarid areas. In the Mediterranean environment of central Chile, no-till improved water infiltration and storage in the soil, but the effect on yield depended on the precipitation because no-till favoured yield in dry years but penalised it in wet years (Brunel et al., 2013; Brunel-Saldias et al., 2018). The metaanalysis showed that the average wheat yield across all the studied cases decreased 2.6% in no-till wheat relative to when the crop follows conventional tillage (Pittelkow et al., 2015).

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FIG. 3.17  Variables and processes driving crop water uptake. Based on Passioura, J.B., Angus, J.F., 2010. Improving productivity of crops in waterlimited environments. Adv. Agron. 106, 37–75.

Direct seeding in doubled-cropped wheat–soybean in Argentina, Brazil, and the US (Calviño and Monzón, 2009) and in rice–wheat systems in the Indo-Gangetic Plains (Bhushan et al., 2007) effectively improves capture of water. In these no-till doubled-cropped systems, the capture and efficiency in the use of resources, especially water, have been improved (Caviglia et al., 2004; Bhushan et al., 2007). For example, Caviglia et al. (2004) calculated that the rainfall capture efficiency increased from 0.51 (dimensionless) in sole crops to 0.71 in double crops. Crop water uptake depends on the depth and distribution of the root system. The usual pattern of wheat rooting depth and density has been extensively reviewed (Thorup-Kristensen and Kirkegaard, 2016). The wheat root system grows fast during the vegetative period, reaches almost half of its final biomass by double ridge stage, and peaks around anthesis. The growth of the root system is in turn related to the early vigour in wheat crops (Turner and Nicolas, 1987). Early vigour not only correlates with deeper root systems but also improves water use (WU) by reducing the proportion of water lost by direct evaporation from the soil, and thus increases the proportion of top-soil water used in transpiration (Richards, 1991; Rebetzke and Richards, 1999). The main attributes conferring early vigour appear to be low-specific leaf weight (i.e. thin leaves) and large embryo (LópezCastañeda et al., 1996). Other factors contributing to variation in the early vigour include the rate of leaf appearance (Whan et al., 1991) and the associated pattern of tillering, which also includes the possibility of coleoptile tillers that can improve early leaf area and ground cover (Liang and Richards, 1994; Bort et al., 2014; Rebetzke and Richards, 1999; Zhao et al., 2019). Nutrient deficiencies may restrict water availability for crops (Angus and van Herwaarden, 2001; Sadras and Roget, 2004; Cossani et al., 2012; De Oliveira Silva et al., 2020a). Early fertilisation improves early vigour that often increases water capture by the crop. For instance, Wang et al. (2018) showed that N fertilisation increased dryland wheat root length density and water uptake in deeper layers. Therefore the healthier and well-nourished the crop, the greater is its water extraction capacity (Angus and van Herwaarden, 2001). Other root traits that should be considered for increasing crop water uptake are: (i) root architecture (Lynch, 2007, 2019), such as lateral branching, thinner roots, length, and density of root hairs (useful also for capturing low mobile nutrients such as phosphorus) and (ii) the reduction of root metabolic cost, which has been pointed out for favouring soil exploration and water uptake (Lynch, 2007). Several workers around the world are presently studying these traits, and various attempts to develop high-throughput screening facilities are being developed and genetic markers for traits (ThorupKristensen and Kirkegaard, 2016), although allocation of resources to root growth may depend more on management than breeding (e.g. Allard et al., 2013). Improvements in water capture associated with larger root systems in semiarid regions need to be considered cautiously. This proposition assumes that the root system of current varieties is insufficient to capture available water. A number of studies challenge this assumption. Passioura (1983) advanced the notion of redundant root systems for Australian wheat

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varieties. In a comparison of wheat released over five decades, Aziz et al. (2017) found selection for yield associated with smaller root system in winter-rainfall environments of Australia. Fourteen bread wheat genotypes covering 100 years of Swiss wheat breeding were grown in 1.6 m tall columns in the greenhouse under well-watered and drought conditions. Rooting depth diminished with year of release under well-watered conditions but not under early water stress (Friedli et al., 2019). In semiarid region of Shaanxi province in China, breeding favoured larger root systems with no increase in water capture (Sun et al., 2020). In a study with pot-grown plants, modern wheat had less root redundancy and higher yield in water-limited environments than an older counterpart (Zhu and Zhang, 2013). Indeed, root pruning may increase yields of winter wheat in semiarid conditions (e.g. Hu et al., 2015, 2019).

3.2.2  Water use efficiency WUE could be defined from short-term gas exchange (mol of CO2 mol of H2O) to biomass or yield per unit seasonal ET. At crop level, wheat WUE for biomass ranges from 29 to 105 kg ha−  1 mm−  1 and for yield, from 5.4 to 24 kg ha−  1 mm−  1 (French and Schultz, 1984; Barraclough et al., 1989; Passioura, 1996; Abbate et al., 2004; Steduti and Albrizio, 2005; Sadras and Angus, 2006; Sadras and Lawson, 2013; Fan et  al., 2018). WUE is commonly estimated from successive shoot biomass samples at different phenological stages and ET estimates using soil water balance or lysimeter. Carbon isotope discrimination (Δ13C) has been used as a surrogate for WUE in wheat breeding (Condon et al., 2002, 2004). The principle behind this trait is that open stomata associated with a high availability of CO2 and high discrimination against the heavier C isotope (13CO2) and the contrary is true when stomata are partially or totally closed (Farquhar and Richards, 1984; Araus et al., 1993). However, the classical model of C discrimination has shown inconsistent results mainly under low photosynthesis conditions, but new approaches seem to reinforce this indirect method to quantify WUE (Busch et al., 2020). Oxygen discrimination (18O/16O) has been associated with yield and stomatal conductance in irrigated wheat (Barbour et al., 2000). WUE declines with increasing VPD (Abbate et al., 2004; Sadras and Angus, 2006). Although the atmospheric water demand could be neutralised when WUE is corrected by VPD, the WUE is still dependent on water availability because the lower the water availability, the higher the WUE (Fig. 3.18). The CO2 concentration also affects WUE, and the increasing CO2 can positively impact on WUE of C3 species such as wheat (Asseng et al., 2015). This was confirmed for wheat in plot experiments under terminal stress at high CO2 concentration, that is, 700 ppm (Dias de Oliveira et al., 2013). But even though increased CO2 concentration can improve WUE of wheat under water stress, it cannot offset the thermal increase and increased leaf temperature (Lopes et al., 2012). Management of crop residue, row spacing, and irrigation can reduce soil evaporation and increase both transpiration and WUE (Hatfield and Dold, 2019). The change from furrow to micro-irrigation has been proposed and reviewed recently (Fan et al., 2018), adjusting WU to optimising WUE and balancing crop water traits and yield. However, maximum yield and maximum WUE are not always compatible goals, and the compromise between crop and water production has been

FIG.  3.18  Relationship between wheat shoot biomass (DW) and WU weighted by the VPD under high (open symbols) and low (closed symbols) water availabilities in different locations of Argentina. Reproduced with permission from: Abbate, P.E., Dardanelli, J.L., Cantarero, M.G., Maturano, M., Melchiori, R.J.M., Suero, E.E., 2004. Climatic and water availability effects on water-use efficiency in wheat. Crop Sci. 44, 474–483. https://doi. org/10.2135/cropsci2004.4740.

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proposed (Fereres et  al., 2014). The trade-off between crop production and WUE has been reviewed by Fereres et  al. (2014), who emphasised the importance of environmental conditions such as temperature, VPD, and solar radiation on this trade-off and a lower impact by the genotype.

3.2.3  Harvest index Stresses at early stages have little impact on HI (Section 2.1), but HI is sensitive to stress, especially thermal and water stress, at later stages (Unkovich et al., 2010). Therefore there is room to increase yield by reducing the negative impact of water stress on HI, which is a common feature in Mediterranean conditions. Studies with plants in containers to manipulate the dosage of water showed a positive relationship between HI and the ratio between WU after anthesis and seasonal WU (e.g. Passioura, 1977; Richards and Townley-Smith, 1987; Sadras and Connor, 1991). The association between HI and partitioning of WU seems weaker in the field (Unkovich et al., 2010). In a recent study assessing the variation of HI in Australia, water shortage and high temperature were the main variables affecting HI; HI was negatively associated with floret and stem sterility, spike density and GW (Porker et al., 2020), reinforcing the importance of the key periods for yield discussed in Section 2.1. For crops relying on stored soil water, limiting water uptake during the vegetative growth could favour HI and yield (Passioura, 2006). Terminal drought affects GW, and maintenance of grain filling would mitigate the negative impact of water stress on both GW and HI. Carbohydrate reserves can buffer shortage of current photosynthesis, but trade-offs between reserves and grain number and between reserves and root growth (Lopes and Reynolds, 2010; del Pozo et al., 2016; Ovenden et al., 2017) have been reported, and the association between yield and water-soluble carbohydrates has not been confirmed (Sadras et al., 2020); therefore further research is needed to understand the physiological role of stored carbohydrate reserves.

3.3  Capture and efficiency in the use of nutrients Macronutrients such as nitrogen (N), phosphorus (P), and potassium (K) are usually applied as fertilisers for high yielding wheat; however, yield response to N supply has been found even at lower yields (Fig. 3.19). Indeed, as discussed in the previous section (Section 3.2), there is an interaction between nutrients and water so that under water limited yields there could be a yield response to fertilisation as the availability of nutrients would alleviate the level of water stress by increasing water capture and WUE in water stressed low-yielding environments (Angus and van Herwaarden, 2001; Sadras and Roget, 2004; Cossani et al., 2012; Wang et al., 2018; De Oliveira Silva et al., 2020b). Underfertilisation of N mines organic matter from soils (Angus and Grace, 2017) and overfertilisation contributes reactive nitrogen to the environment (Fageria and Baligar, 2005; Yang et al., 2017). P overfertilisation can also cause en-

FIG. 3.19  Relationship between grain yield with and without fertiliser under rainfed conditions for wheat (triangles) and barley (circles) in Morocco, Jordan, Italy, and Spain. Dotted line is the relationship 1:1. Reproduced with permission from Elsevier Savin, R., Slafer, G.A., Cossani, C.M., Abeledo, L.G., Sadras, V.O., 2015. Cereal yield in Mediterranean-type environments: challenging the paradigms on terminal drought, the adaptability of barley vs wheat and the role of nitrogen fertilization. In: Crop Physiology, second ed. Applications for Genetic Improvement and Agronomy, pp. 141–158.

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vironmental issues such as eutrophication (Schindler et al., 2016). Therefore a balance between the crop requirements and availability of nutrients should be considered when assessing the required levels of fertilisation in order to increase the sustainability of the agroecosystem.

3.3.1  Nutrient absorption, assimilation, accumulation, and mobilisation Crop uptake can be calculated as: Crop uptake  Available nutrient  BD  H  NuUpE


where available nutrient is the amount of nutrient available in the soil (usually 20 cm of depth for immobile nutrients, and root depth for mobile nutrients ~ 100 cm in wheat) as indicated by chemical indexes; BD, is the bulk density of the soil (g cm−  3), H is the soil rooting depth from where the nutrient is captured by the crop (dm), and NuUpE (nutrient uptake efficiency) quantifies the ability of the crop to capture a particular nutrient from the soil (kg kg−  1).  Nutrient uptake efficiency NuUpE depends on the crop root traits and nutrient mobility in the soil (Thorup Kristensen, 2001). Regarding the root characteristics, when nutrients being analysed are mobile in the soil, there are two complementary traits characterising this efficiency: the capacity of the root to explore the soil profile and the efficiency of roots in capturing N (i.e. N uptake per unit root length). A surrogate often used to quantify the former is the rate of root soil penetration (Rasmussen et al., 2015). Wheat rooting depth penetration rate ranges from 1.0 to 1.5 mm d−  1, compared with 1.5 and 2.3 mm d−  1 in nonlegume dicots. Winter and spring wheat had a similar rate (1.3 mm d−  1) but winter wheat reaches a depth of 2.2 m, twice that of spring wheat, due to longer growth period (Thorup-Kristensen et al., 2009; Rasmussen et al., 2015). Breeding would have increased rooting depth under limiting availability of soil resources (e.g. Friedli et al., 2019). Regarding N uptake per unit of root length, it would be negatively related to root thickness (Melino et al., 2015; Corneo et al., 2016). Then it is possible to improve NuUpE through selecting for thinner roots which would have more capacity to extract nutrients (Aziz et al., 2017). For nutrients with low mobility such as P, root length density, root hairs and associated traits in the upper 20 cm of the soil are a key (Goos et al., 1993; Lynch, 2007). Topsoil foraging can be improved through greater production of axial roots, shallower axial root growth angles, greater lateral root density, reduced root metabolic cost, and greater root hair length and density (Lynch, 2019). However, these traits can be overridden by other crop singularities such as organic acid secretions that solubilise P in soil (Sandaña and Pinochet, 2014). The relative importance of the acquisition of nutrient by wheat roots is evidenced in three main mechanisms: (i) the mass flow, which is largely dependent on water flow and soil solution concentration, (ii) diffusion, which is dependent on soil characteristics particularly of the soil buffer capacity, porosity tortuosity, water content and the concentration gradient from soil particles to root surface, and (iii) the rhizosphere effect, which is particularly important for the interaction between crop roots, soil and microorganisms (Table 3.1). Nutrients are taken up by plants roots in a regulated manner and are distributed along the plant according to the crop demand during the crop cycle, where requirements and sinks change with phenology (Barracough et al., 2014 and Section 2.2). Three phases can be distinguished in the process of nutrients uptake and distribution during the ontogeny (Fig. 3.20): (a) a first phase ruled by the space colonisation where root and canopy are growing and the crop behaves more as an individual plant than a population, colonising the space in both ways above and below ground, (b) a second phase of nutrient accumulation, mainly in leaves and stems, and (c) a final phase where nutrients are mobilised from vegetative sources to reproductive sinks as spikes and mainly grains after anthesis. During the first phase, nutrients came from the seeds reserves (mainly form the endosperm) and is expressed as vigour and initial capacity to produce carbohydrates after emergence and the initial colonisation of space, which relies on fast growth and root contact with the soil solution. At this stage, the explored soil volume is small and the seminal roots start the soil colonisation (Fig. 3.20); meanwhile the aboveground space is limited by crop cover to capture radiation (Section 3.1). As plants cover the soil and roots grow into deeper soil layers, wheat reaches its maximum nutrient uptake, and the efficiency of this process is one of the major determinants of crop growth together with water capture (Section 3.2). At heading, nutrient translocation from vegetative organs becomes more important and increases as seeds grow. This last phase is important for grain and seed quality (Section 5.2). To achieve optimal nutrient use efficiency, crops need to maximise the uptake and then the internal process of nutrient cycling or recycling.

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TABLE 3.1  Relative influence of the mechanisms of nutrient acquisition by wheat.


Mass flow


Rhizosphere effect (wheat roots-soil)




Bacterial association



Mycorrhizas, organic acids



Organic acids


pH changes


pH and Eh changes


Bacterial association


pH changes, mycorrhizas


pH changes






























Modified from Gregory, P.J., Crawford, D.R., McGowan, M., 1979. Nutrient relations of winter wheat: 2. Movement of nutrients to the root and their uptake. J. Agric. Sci. 93, 495–504.; Hinsinger, P., Bengough, A.G., Vetterlein, D., Young, I.M., 2009. Rhizosphere: biophysics, biogeochemistry and ecological relevance. Plant Soil 321, 117–152.; Giehl, R.F.H., von Wirén, N., 2014. Root nutrient foraging. Plant Physiol. 166, 509–517.; Lynch, J.P., 2019. Root phenotypes for improved nutrient capture: an underexploited opportunity for global agriculture. New Phytol. 223, 548–564.

FIG. 3.20  The three phases of wheat crop nutrition. The scale is Zadoks et al. (1974).

3.3.2  Effects of nutrients on wheat growth  Nutrient uptake and partitioning Fig. 3.21 shows the time-course of N, P, and K accumulation and partitioning in wheat during the crop cycle. The maximum rate of nutrient uptake occurred between tillering and stem elongation, and the maximum amount of nutrient remobilisation from vegetative to reproductive organs (when grain nutrient accumulation exceeds that of the crop) is from the end of the grain lag phase to physiological maturity depending on the environmental conditions, especially temperature and water availability (Malhi et al., 2006; Maillard et al., 2015). Nutrient accumulation in wheat tissues could be divided into two groups considering their time-course relative to the biomass accumulation. N, P, K, S, Ca, and Fe accumulate in advance to biomass and, on the contrary, Mg, Zn, Cu, Mn, and B are delayed, especially at the early developmental stages, taking into account that the nutrient time-course is affected by the availability of the element in the soil. At anthesis, around 70%, 80%, and 90% of the total uptake of N, P, and K, respectively, occurs (Malhi et al., 2006; Clarke et al., 1990; Fig. 3.21 and Table 3.2). Nutrient accumulation in high-yielding wheat (>  8 t ha−  1) ranges from 240 to 300 kg N ha−  1, 35 to 40 kg P ha−  1, 160 to 200 kg K ha−  1, 50 to 60 kg Ca ha−  1, 15 to 20 kg Mg ha−  1, and 15 to 20 kg S ha−  1. This productivity requires maximum

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FIG. 3.21  Relative dry matter, N, P, and K accumulation and partitioning amongst leaves, stems spikes, and grains during the wheat crop cycle (from emergence to maturity) in Valdivia, Chile. The scale is Zadoks et al. (1974). Data from Sandaña, P., Pinochet, D., 2014. Grain yield and phosphorus use efficiency of wheat and pea in a high yielding environment. J. Soil Sci. Plant Nutr. 14.; Clunes, J., Pinochet, D., 2020. Effect of slow‐release nitrogen on the nitrogen availability in an andisol and the critical nitrogen concentration in wheat. Agron. J. 112, 1250–1262.

a­ccumulation rates up to 6.5 kg N ha−  1  d−  1, 1.2 kg P ha−  1  d−  1, and 3.2 kg K ha−  1  d−  1 from the end of tillering to anthesis. However, at yield ~  6 t ha−  1, the maximum nutrient uptake rate declines to 3.2–5.7, 0.3–0.6, 3.9–7.0, and 0.45–0.6 kg ha−  1 d−  1 for N, P, K, and S, respectively (Malhi et al., 2006). Most of total nutrient uptake at maturity is in grains because they accumulate over 70% of the N and P and 31%–64% of the S, Mg, Mn, and Zn. Less than 20% corresponds to K, Ca, Na, Cl, and Fe (Hocking, 1994). Vegetative tissues provide a substantial amount of the nutrients to grain: almost 100% of K is remobilised from stems and leaves, over 70% of the N and P, and between 15% and 51% of S, Mg, Cu, and Zn. Mobilisation of Ca, Fe, Mn, Na, and Cl from vegetative tissues is negligible.  Crop nutrient demand Crop nutrient demand can be estimated as:

Crop nutrient demand kgha 1  Yield 100 kgha 1  Nc  1  Wc  / HI


where yield is the yield target (i.e. the estimated expected yield should the crop not experience deficiencies of the nutrient under consideration), Nc is the nutrient critical concentration measured at harvest or when the maximum of the nutrient is reached (expressed as kg 100 kg−  1), Wc is the water content of grain at harvest (dimensionless), and HI is the harvest index. In general, all the factors from the right part of this equation are unified in a ‘factor of Demand’ (fDem), which is fDem = Nc × (1  −  Wc)/(HI) rearranging as:

Crop nutrient demand kgha 1  Yield t ha 1  fDem kg t 1 agronomic product


fDem is the relationship between actual yield and crop nutrient uptake at its minimal optimal nutrition. This value can be obtained from experiments where different quantities of N available in soil are supplied and maximum yield (Ymax or 90%–95% of Ymax) is obtained with the minimal amount of the nutrient available for the crop.

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TABLE 3.2  Most typical values for fDem and extraction factors by wheat. Extraction factors Typical Nutrient


Grain Typical


Straw Typical




3.5–9.3 0.4–1.0

−  1

Macronutrients (kg t ) N
































−  1

Micronutrients (g t ) Fe




































From: FAO, 1971. A Study on the Response of Wheat to Fertilizers. FAO Soils Bulletin 12. Series number 0253-2050. Food and Agricultural Organization of the United Nations, Rome, Italy 131 p; GRDC (Grain Research & Development Corporation), 2016. Section 5. Wheat—Nutrition and Fertilisers. au/__data/assets/pdf_file/0029/373907/GrowNote-Wheat-South-05-Nutrition.pdf; Fan, M.S., Zhao, F.J., Fairweather-Tait, S.J., Poulton, P.R., Dunham, S.J., McGrath, S.P., 2008. Evidence of decreasing mineral density in wheat grain over the last 160 years. J. Trace Elem. Med. Biol. 22, 315–324. jtemb.2008.07.002; Murphy, K., Reeves, P.G., Jones, S.S., 2008. Relationship between yield and mineral nutrient concentration in historical and modern spring wheat cultivars. Euphytica 163, 381–390.; Marles, R.J., 2017. Mineral nutrient composition of vegetables, fruits and grains: the context of reports of apparent historical declines. J. Food Compos. Anal. 96, 93–103.

fDem is the inverse of the nutrient utilisation efficiency (NuUtE); that is, amount of grain produced per unit of N uptake expressed in kg of yield to total kg of nutrient in the crop (Moll et al., 1982). This trait requires periodic updates because it changes with genetic improvement of both yield and nutrient use efficiency (Clarke et al., 1990; de Oliveira Silva et al., 2020b). Wheat breeding in the long term has consistently improved NUtE (e.g. Calderini et al., 1995). More recently, approaches from the genetic perspective have shown high variation in the component of the NuUE in wheat (Gaju et al., 2011; Guo et al., 2012), which gives an opportunity to improve the crop nutrient use. The critical nutrient concentration is the minimum concentration for maximum growth (Greenwood et al., 1986; Lemaire and Gastal, 1997; Sadras and Lemaire, 2014; Justes et al., 1994). This concept is useful to characterise the nitrogen status of the crop through the estimation of the nitrogen nutrition index. Curves relating shoot critical N concentration (Nc, g kg−  1) and shoot dry matter (DM, t ha−  1) have the form: Nc  a  DM  b


where Nc is the critical concentration of nitrogen in the shoot biomass, and DM is the shoot biomass of the crop. The a and b parameters that characterise the dilution curve are species-dependent (Lemaire et al., 2008). With a 38.5 to 53.5 and b 0.44 to 0.59 for wheat (Justes et al., 1994). Cadot et al. (2018) proposed a similar approach for P, showing similar behaviour and relationships than N, demonstrating also that N and P are both associated by the N:P stoichiometry in wheat and other crops (Sadras, 2006; Lemaire et al., 2019). However, Hoogmoed and Sadras (2016, 2018) showed that both water-soluble carbohydrates and crop water status can affect the dilution curve in wheat. This is in agreement with Zörb et al. (2018), who found a decrease of grain N grain concentration with the increase of the crop yield (dilution effect, see Calderini et al., 1995), in agreement with

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de Oliveira et al. (2020a), who showed the risk of extrapolating the dilution curve across different agroecosystem and environments. Nevertheless, the crop demand factor or NuUtE can be useful to estimate wheat nutrient demand for an expected yield considering modern wheat cultivars with high NUE.

4  Yield responsiveness to management and breeding 4.1  Yield responsiveness to management and breeding 4.1.1  How management practices affect yield Wheat yield is the product of biomass and HI (Eq. 3.1) and is the emergent of the interaction amongst G × E × M (genotype, environment, and management). Wheat yield is closely associated with shoot biomass (Fig. 3.12), with the exception of wheat breeding (Section 4.1.2) and terminal drought (Section 3.2). Both biomass and yield are modified by the growing environment (weather and soil conditions) and management. The same is true for the key window of yield determination (Fig. 3.3), which affects both the set of grain number and potential GW (Sections 2.1.2 and 2.1.3). Management practices impact on yield through affecting yield determinant traits have been discussed earlier. To illustrate this, we selected a few management practices reviewing briefly their impact on yield determination.  Sowing date, density, and arrangement Wheat yield shows an optimum response to sowing date where suboptimal dates have a penalty on yield by exposing the crop to unfavourable conditions such as frost and supra-optimal sowing dates negatively affect yield by shorter crop cycle, lower photothermal quotient (PTQ) during the critical period and/or unfavourable conditions during grain filling, decreasing shoot biomass, and especially, HI. One of the main objectives when sowing date is scheduled is to match key developmental phases for yield determination (Section 2.1) and favourable environmental conditions avoiding stresses. Winter, facultative, and spring wheats cultivars should be sown at the time to assure the highest PTQ during the critical period for grain number determination (Fischer, 1985; Savin and Slafer, 1991; Ortiz-Monasterio et al., 1994; Menéndez and Satorre, 2007). The need to consider PTQ when radiation interception is maximised makes it necessary to displace the critical period in spring wheat grown in the N hemisphere to times of the year with decreasing PTQ; something that is not an issue in the southern hemisphere (or other wheat regions with mild winters) in which it is possible setting the critical period of winter and spring wheats at the most favourable PTQ, that is, to concur anthesis date in winter and spring wheats because spring wheat in these conditions can be sown in winter (Box 3.3). Therefore sowing date sets the crop Yp and the gap with attainable (Yat) and actual (Yac) yields (van Ittersum et al., 2013). These gaps are because of the occurrence of water and nutrient stresses, plagues, diseases, weeds competition, frost, lodging, sprouting, and hail, amongst others. To narrow the gap between Yp and both Yat and Yac, the crop should reach the LAIc at the beginning of the critical period, that is, ~ 20 days before anthesis or earlier (Figs 3.13 and 3.14b). The cultivar and sowing date choices are two key decisions to be made by farmers before the crop is sown. Additionally, but associated with these choices, plant density is an important complementary decision because they will affect LAI and radiation interception dynamics (Fig. 3.14c). In wheat, it is generally accepted that GY stabilizes ~ 100 plants m−  2 following an asymptotic shape equation for well-adapted cultivars and at optimum sowing date (Frederick and Marshall, 1985; Spink et al., 2000; Whaley et al., 2000; Lloveras et al., 2004; Valério et al., 2009; Dai et al., 2013). However, recent studies demonstrated that similar yield could be reached at lower plant rates. Fischer et al. (2019) showed that yield of 7 t ha−  1 was recorded under both conventional (200–300 plants m−  2) and very low (20 plants m−  2) plant rates. This corroborates previous studies such as that of Darwinkel (1978), who showed that seed rates lower than 100 plant m−  2 achieved similar GY than higher densities and Bustos et al. (2013), showing similar yield between conventional (350 plants m−  2: 11 t ha−  1) and low plant rate (44 plants m−  2: 11.2 t ha−  1). Hasan et al. (under review) confirmed these results, supporting that low seed rate (20–44 plants m−  2) do not penalise wheat yield in a range from 7 to 12 t ha−  1 across different cultivars and environments (Bustos et al., 2013; Fischer et al., 2019; Hasan et al., under review). Under low plant density, lower radiation interception is expected, as demonstrated by Bustos et al. (2013 and Fig. 3.14c), which is apparently compensated by a higher RUE through the crop cycle, that is, 2.5 and 3.8 g MJ−  1 in conventional and low plant rate, respectively, averaged across cultivars (Bustos et al., 2013). Also, higher spikes per plant, grains per spikelet, and occasionally, higher thousand GW compensated the lower number of plants challenging the consensus assuming an uneven trade-off amongst yield components at plant rates lower than 100 plants m−  2. Undoubtedly, changes in plant density go hand in hand with plant distribution on the ground. Lower plant rate decreases rectangularity (the ratio between plant distance across and within rows), increasing the Red:Far red ratio and neighbours perception (Evers et al., 2006; Abichou et al., 2019). This enhances tillering, reduces plant height, increases HI, and allows higher grain number per spike and sometimes, improved thousand GW.

132  Crop Physiology: Case Histories for Major Crops  Fertilisation and irrigation Nitrogen fertilisation is required not only for reaching high yields, undoubtedly in highly productive environments, but also to reduce the gap between actual and attainable yield in low-yielding conditions (Fig. 3.19). Therefore N fertilisation is a common farmer’s practice that modifies the offer of resources impacting on crop growth rate. N fertilisation improves yield through increasing the crop growth rate during the critical period of grain number (and potential GW) determination. This is mainly because higher N availability accelerates the expansion of LAI advancing then the levels of radiation interception by the crop (not affecting the extinction coefficient of the canopy; Fischer, 1993). As N fertilisation increases LAI, at moderate fertilisation doses, the extra N uptake is normally diluted in more LAI, and therefore leaf N concentration does not change much, and consequently, RUE is also maintained. At higher doses where N availability increases more than what is required for LAI responses, there is also an increase in RUE, although the magnitude is normally smaller than that on radiation interception (Fischer, 1993). Across experiments, N affected the crop growth rate during the critical period affecting the biomass accumulated in the reproductive organs, and higher SDWa and more fertile florets were measured under increased N availability (Fischer, 1993; Demontes-Meynard and Jeuffroy, 2004; Prystupa et al., 2004; Ferrante et al., 2013a, 2017; Fig. 3.10). And any effects of N at earlier stages of development, although affecting early growth, do not affect yield if the crop growth rate during the critical period is not compromised; that is why there are no penalties in postponing N fertilisation until late tillering even when growth until then had been penalised by N stress (Fischer, 1993). It is based on this physiological determination of yield that N-fertilisation practices start delaying the application to late tillering or even to the onset of stem elongation in wheat (which is convenient as it is easier to estimate the expected yield when part of the season has elapsed, and it may also reduce N losses if applied too early and winter is rainy). Noteworthy, the impact of P fertilisation showed similar effects on wheat yield and its physiological determinants than N with only little differences. In two independent studies assessing P availability on wheat carried out in Argentina (Lázaro et al., 2009) and Chile (Sandaña and Pinochet, 2011), P fertilisation increased LAI, radiation interception, and crop growth rate. Across the experiments and P treatments, a unique and positive association was found between grain number per unit area and SDWa and between the last and the CGR during the critical period (Fig. 3.22). Other crop traits such as k and RUE were not modified (Lázaro et al., 2009; Sandaña and Pinochet, 2011). Irrigation is also a key management strategy for reducing the gap between Yp and Yat  −  Yac. In India, the study of historical data of irrigated wheat showed that yield increased 13% powered by irrigation between 1970 and 2000 (Zaveri and Lobell, 2019). These authors also showed that irrigated wheat was less sensitive to heat than the rainfed crops. Nevertheless, yield increments had slowed during past years. In addition, as pointed out in Section 1.1, in the context of water scarcity, to maintain irrigated wheat cropping systems is a big challenge regarding that irrigated wheat accounts for ~ 70 Mha the second most irrigated cereal area after rice (FAOSTAT, 2020). The direct effect of irrigation is on water uptake increasing the crop evapotranspiration and transpired water increasing biomass production, however negatively affecting WUE (Fig.  3.18). In a Mediterranean environment, differences between irrigated and rainfed conditions consistently showed linear associations between yield and grain number for barley, bread, and durum wheat (Cossani et al., 2012). Positive associations were also found between yield and shoot biomass and between grain number and PTQ during the critical period calculated as the intercepted PAR (Cossani et al., 2012).

FIG. 3.22  Relationship between (A) grain number and spike dry weight at anthesis and (B) spike dry weight at anthesis and crop growth rate during the critical period for grain number determination in treatments with (closed symbols) or without (open symbols) P fertilisation from experiments carried out in Argentina (squares; Lázaro et al., 2009) and Chile (circles; Sandaña and Pinochet, 2011). Reproduced with permission from Elsevier Sandaña, P., Pinochet, D., 2011. Ecophysiological determinants of biomass and grain yield of wheat under P deficiency. Field Crop Res. 120, 311–319.

Wheat Chapter | 3  133  Management of other constrains In addition to nutrient and water scarcity, management of other constrains are highly important in wheat cropping systems. For example, acidic soils (pH < 5.5) account for about 30% of the world’s land (~ 3950 Mha), excluding ice areas, and it has been estimated that over 50% of the world’s potential arable lands are acidic (von Uexküll and Mutert, 1995). An asymptotic shape response has been reported for wheat yield to pH in Oklahoma soils where grain yield stabilised at soil pH 5.8 (Lollato et al., 2019). Wheat sown in acidic soils is often affected by Al toxicity (Kariuki et al., 2007; Lollato et al., 2013), decreasing yield 30% or more depending on Al concentration (Costa et al., 2003; Kariuki et al., 2007). Two main management strategies are used by farmers to neutralise the negative impact of Al toxicity: (i) liming and (ii) cultivar choice. Regarding the latter, there is a wide range of variability in sensitivity to Al. Whilst yield of Al sensitive cultivars could be affected by relatively low soil Al concentrations, Al-tolerant cultivars could grow without important yield penalties when grown in soils with up to ~ 0.5 cmol (+) kg−  1 of exchangeable Al in acidic soils and andisoils, which is equivalent to ~ 16% of Al saturation (Al concentration expressed as a percentage of total exchangeable base cations), depending on the soil type (Coutinho, 1990; Valle et al., 2009). On the other hand, the application of liming depends on soil pH, soil buffer capacity, and Al concentration. For example, in soils with pH 5, the quantity of liming to apply is about 4 t ha−  1, but this depends on soil characteristics (because the type and quantity of clays and other colloids determine the buffer capacity). Lime application improves the root growth, which is the wheat organ most impacted by Al toxicity, allowing the capture of water and nutrients facilitating the shoot growth by increasing LAI and radiation interception (Valle et al., 2009, 2011). As in previous sections, grain yield across a wide range of soil Al concentrations associated with shoot biomass and grain number (Tang et al., 2003; Kariuki et al., 2007; Valle et al., 2009), and the capture of radiation has been found as the main cause of the penalty of Al toxicity on wheat shoot biomass and yield as under other soil constraints such as nitrogen deficiency and compactness (Abbate et al., 1995; Sadras et al., 2005). Lately, silicon has been proposed as a soil Al toxicity amendment (Vega et al., 2020 and references therein). Other soil constrains are saline and sodic soils, estimated from 830 to 932 Mha, more than 6% of the world’s land, which is rising (Acosta-Motos et al., 2017; Genc et al., 2019). Indeed, it is estimated that over 50% of global arable land will be salinised by 2050 (Jamil et al., 2011). Soil salinisation emerges as an important constraint, particularly in arid and semiarid regions of the world with hotspots in Pakistan, China, US, India, Argentina, Sudan, central and western Asia, and in the Mediterranean coastline (Cuevas et al., 2019). Yield reductions of 50% in durum wheat under dryland salinity (James et al., 2012), 88% in bread wheat under irrigation with high salinity water (Jafari-Shabestari et al., 1995), and 70% under sodicity have been reported (Rengasamy, 2002). In controlled conditions experiments, transpiration accounted for 90% of the variation of shoot growth in barley and wheat (Harris et al., 2010). Bread wheat is moderately salt-tolerant (Munns et al., 2008), being the threshold by 100 mM NaCl (about 10 dS m−  1). Durum wheat is less salt-tolerant than bread wheat, that is, 6.8–8.6 dS m−  1 (Francois et al., 1986). Other soil constrains such as Bo toxicity has been recently reviewed (Landi et al., 2019).

4.1.2  Impact of wheat breeding on grain yield and next steps Improving yield is a permanent aim of wheat breeding, and this objective has been reinforced in the present century by the challenge of an increasing world population and food demand (Ray et al., 2013), which has been recently estimated to peak in 2064 when global population reaches 9.73 billion (8.84–10.9) people (Vollset et al., 2020). The aim of improving potential yield was achieved by wheat breeding in most of the countries along the 20th century (Slafer et al., 1994; Calderini et al., 1999; Foulkes and Reynolds, 2015). In Fig. 3.23, the relative genetic gains of yield of several countries, and also different studies for the same country, are shown. Across the studies, average gain of yield was 0.74% y−  1. Only five countries (Brazil, Chile, China, England, and Mexico) and nine studies showed genetic gains ≥ 1% y−  1. However, it is important to take into account that the shorter the evaluated period, the higher the genetic gain estimated (Fig. 3.23). For example, the average genetic gain from studies evaluating ≤ 20 years was 1.74% y−  1, whilst the average of studies analysing ≥ 50 years was 0.59% y−  1 (Fig. 3.23). Additionally, the later the period, the higher the gain because genetic gain increased slowly in the first half of the 20th century and higher during the second half (see Fig. 2 in Calderini and Slafer, 1999). The Green Revolution, led by Nobel laureate Dr. Norman Borlaug, was a quantum leap increasing both potential and actual yield, but wheat yield was increased step by step during the 20th century, even before the 60s (e.g. Austin et al., 1980; Slafer and Andrade, 1989; Siddique et al., 1989; Fig. 3.23). Plant height was slowly decreased by wheat breeding since 1900 (see Fig. 16.2 in Calderini et al., 1999), and Nazareno Strampelli in Italy was a forerunner of wheat plant height reduction and yield improvement. Even though, plant height reduction was jumped up by the introgression of the Rht alleles from Norin 10 in the 60s, which consolidated the wheat yield improvement worldwide, with few exceptions as in low-yielding

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FIG. 3.23  Relative genetic gain in yield reported for different countries and periods of bread (closed circles) and durum (open triangles) wheat. This figure only included studies in which genetic gains were evaluated through growing side-by-side cultivars released at different times in the same experiment. Genetic gains were calculated as the ratio between the absolute genetic gain and the average yield as a percentage and they are shown by the colour and intensity of the bars representing the period analysed in each study, and exact values are explicit at the right of the bars. Drawing by Gabriela Carrasco-Puga.

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environments, such as in some areas of Australia (Hyles et al., 2020). Main changes because of the introgression of the Rht alleles associated to the improved yield were the increase of HI with similar shoot biomass and augmented grain number per land unit without change or a small negative effect on GW (Gale and Youssefian, 1985; Slafer et al., 1994; Calderini et al., 1999; Foulkes and Reynolds, 2015). Consistently, linear associations between yield and HI were reported across countries when wheat cultivars released at different times since the first half of the 20th century were evaluated together in the same experiment (e.g. Austin et al., 1980; Calderini et al., 1995; Fischer et al., 1998; Brancourt-Hulmel et al., 2003; Shearman et al., 2005; Royo et al., 2008; Acreche et al., 2008; del Pozo et al., 2014; Mondal et al., 2020). A positive association between HI and the yield-plant height ratio was also reported (Calderini et al., 1999). The plant height reduction allowed a rearrangement of shoot biomass towards the reproductive organs as shown in Table 3.3. Different authors found an optimal response of wheat yield to plant height (Richards, 1992; Miralles and Slafer, 1995; Flintham et al., 1997), ranging between 0.70 and 0.90 m (Fig. 3.24). This parabolic response was explained by the tradeoff between biomass and HI in NILs for plant height, where biomass production was affected by lower RUE in the double dwarf line and HI was lower in the standard height in both cases compared against the semidwarf wheat line (Miralles and Slafer, 1995). The same is true for HI, which was estimated to have an upper threshold of c. 62% (Austin et al., 1980). Having modern cultivars already an optimum plant height and a HI, which is close to the threshold, it implies that tools and criteria successfully exploited in the past would have little value to further improve yield, highlighting that future reductions of stem biomass seem to be an uphill battle for wheat breeding (as shown in Table 3.4), where the calculated value of 15% of stem plus sheaths biomass by Austin et al. (1980) has not been reached. Therefore the strategy of continuing increasing wheat yield by stressing plant height is not feasible, and the realistic way is to improve biomass or other alternative was to further improve spike growth before anthesis (Rivera-Amado et al., 2019). Likely, the opportunity of still improving wheat TABLE 3.3  Grain yield and stem dry weight in two wheat cultivars released at different times in Argentina. Year of release

Grain yield (g m−  2)

Stem weight (g m−  2)









−  342.7

Data from: Calderini, D.F., Dreccer, M.F., Slafer, G.A., 1995. Genetic improvement in wheat yield and associated traits. A re‐examination of previous results and the latest trends. Plant Breed. 114, 108–112.

FIG. 3.24  Schematic representation of the optimum wheat yield in response to plant height. Based on Richards, R.A., 1992. The effect of dwarfing genes in spring wheat in dry environments. I. Agronomic characteristics. Aust. J. Agric. Res. 43, 517–527.; Miralles, D.J., Slafer, G.A., 1995. Individual grain weight responses to genetic reduction in culm length in wheat as affected by source–sink manipulations. Field Crop Res. 43, 55–66; Flintham, J.E., Börner, A., Worland, A.J., Gale, M.D., 1997. Optimizing wheat grain yield: effects of Rht (gibberellin-insensitive) dwarfing genes. J. Agric. Sci. 128, 11–25.

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TABLE 3.4  Biomass partitioning into grain, chaff, leaf lamina, stem, plus sheaths. Austin et al. (1980) (four most modern cultivars)

Austin et al. (1980) (theoretical maximum HI)

Consort Herefordshire, UK (mean 1996/1997 and 1997/1998)

Crop component

g m−  2


g m−  2


g m−  2







1 103









Leaf lamina







Stem + sheaths







Measured and calculated data. Data from: Foulkes, M.J., Hawkesford, M.J., Barraclough, P.B., Holdsworth, M.J., Kerr, S., Kightley, S., Shewry, P.R., 2009. Identifying traits to improve the nitrogen economy of wheat: recent advances and future prospects. Field Crop Res. 114, 329–342.

yield by plant height is only feasible for some low-yielding environments where taller semidwarfs have been proposed (Hyles et al., 2020). In historic sets of wheat cultivars, few studies found a positive association between grain yield and shoot biomass, RUE, or photosynthesis (Calderini et al., 1997, Fischer et al., 1998; Shearman et al., 2005; Reynolds et al., 2007; Sadras and Lawson, 2011; Xiao et al., 2012). In Argentina and Mexico, RUE increased after anthesis, apparently driven by the increase of sink size (Calderini et al., 1997; Reynolds et al., 2007). In UK and Australia, selection for yield increased preflowering RUE (Shearman et al., 2005; Sadras et al., 2012). Shearman et al. (2005) speculated the improvement of the intrinsic photosynthetic rate as the likely cause of higher RUE cultivars released in UK after 1983. In Australia, the increase of the preanthesis RUE was apparently driven by changes in nitrogen uptake and distribution of nitrogen and radiation into the canopy profile (Sadras et al., 2012). Shifts in canopy architecture and improvements in leaf photosynthesis or associated traits have been found as a consequence of wheat breeding in China and Chile (Xiao et al., 2012; Sun et al., 2014; del Pozo et al., 2016). However, the phenotypic response of biomass to wheat breeding has been either negligible or modest across the studies, likely because genotypes with higher biomass were obtained by indirect selection for grain yield. To accelerate the development of higher biomass cultivars in the future, key traits have been proposed, that is, from the enhancement of leaf photosynthesis (Parry et al., 2011) to the increase of RUE (Reynolds et al., 2012) as aims per se. The biomass increase of wheat seems physiologically feasible, and genetic variability for this trait has been reported from long ago (Sharma, 1993) to recently (Molero et al., 2019). However, trade-off between HI and shoot biomass has been reported during past years (Duan et al., 2018; Molero et al., 2019; Rivera-Amado et al., 2019) preventing from a simple strategy and view of increasing wheat biomass. Grain number is a key yield component (Section 2.1) and was linearly associated with wheat yield improvement across the world as mentioned earlier. This was possible by the higher dry matter of the spikes at anthesis and more florets reaching the floret fertile category as a consequence of plan height reduction and higher shoot biomass partitioning to the spike until anthesis (e.g. Gale and Youssefian, 1985; Slafer et al., 1994; Miralles et al., 1998). Therefore changes in the spike growth rate and/or the spike-growing period would improve this key trait (Figs 3.4 and 3.7–3.10). Photoperiod sensitivity during the late reproductive phase (Fig. 3.7) has been explored as a way to enlarge the spike fast-growing period and in turn increase fertile floret and grain number (Slafer et al., 2001; Miralles and Slafer, 2007). In addition to spike dry matter, fruiting efficiency (Section 2.1.2 and Box 3.1) could also contribute to the increase of grain number. Recently, Pretini et al. (2020) identified 37 QTL for this trait in a double haploid population of hexaploid wheat. GW has been much less affected by wheat breeding showing no change or even a slight decrease (Siddique et al., 1989; Sayre et al., 1997) across wheat cultivars released in different eras. However, GW increase has been found mainly in cultivars released after the 80s. For example, the positive impact of wheat breeding on GW was found in Argentina (Calderini et al., 1995), Australia (Sadras and Lawson, 2011), and in areas of China (Zheng et al., 2011). Nevertheless, contrasting results could be pointed out in Argentina because the GW increase after 1980 (Calderini et al., 1995) did not continue as it was shown in a recent study updating Argentinian wheat breeding until 2011 (Lo Valvo et al., 2018; Fig. 3.23). On the other hand, in Australia, GW showed two phases: a first period when this trait decreased from 1957 to 1982 and a following phase of GW increase (Sadras and Lawson, 2011). In China, negligible changes in GW have also been reported (Xiao et al., 2012), and the impact of breeding on GW seems to be dependent on the China’s province and breeding programme. Although the results are contrasting, GW is a trait to be considered for improving wheat yield. As described in Section 2.1.3, genes

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a­ ffecting GW have been reported, especially during the past 10 years and QTL associated with this component (Lizana et al., 2010; Yang et al., 2012; Wang et al., 2018; Zhang et al., 2018; Brinton and Uauy, 2019; Mangini et al., 2020), but a concern to bear in mind is the trade-off between GW and grain number reported recently for different wheat populations (Quintero et al., 2018; Molero et al., 2019). However, a recent report seems to have overcome the trade-off between GW and grains per m2 (Calderini et al., 2020a). Wheat breeding also impacted on the use of resources, especially nutrients and water. Higher NUE was a common effect found across countries when NUtE is calculated as the ratio between yield and absorbed N (Fischer and Wall, 1976; Feil and Geisler, 1988; Slafer et al., 1990; Calderini et al., 1995; Ortiz-Monasterio et al., 1997), although few exceptions were also reported (Acreche and Slafer, 2009; Sadras and Lawson, 2013). Similar results were found for PUE (Calderini et al., 1995). On the other hand, N uptake was not modified by wheat breeding in most of the countries (Calderini et al., 1995; Foulkes and Reynolds, 2015). However, improvement of N uptake or N uptake efficiency was also found in CIMMYT, Spain, and Australia (Ortiz-Monasterio et al., 1997; Muurinen et al., 2006; Acreche and Slafer, 2009; Sadras and Lawson, 2013), and genetic variability for both N uptake and N utilisation has been demonstrated (Le Gouis et al., 2000). The rate of N uptake increase ranged between 0.12 and 1.20 kg N ha−  1 y−  1 (Austin et al., 1980; Ortiz-Monasterio et al., 1997; Giunta et al., 2007; Sadras and Lawson, 2013). As a result of the balance between N grain yield and N uptake, grain N concentration was decreased across most of the wheat breeding studies owing to the dilution of N because the rate of yield dry matter was higher than the rate of grain N increase. In addition to the challenge of increasing yield, wheat breeding is also defied by improving the efficiency of nutrients uptake and use. Evidences of the feasibility of them have been shown during the past wheat breeding (Ortiz-Monasterio et al., 1997; Sadras and Lawson, 2013), and deep roots, higher root length density, and specific root density have been recommended for improving the uptake of mobile nutrients in the soil such as N (Cormier et al., 2016). Leaf and canopy photosynthesis per unit of N, the distribution of nitrogen and radiation into the canopy profile, and N remobilisation have been considered for increasing NUE (Cormier et al., 2016). Importantly, the co-limitation between N and water (Sadras, 2004; Cossani et al., 2010; Cossani and Sadras, 2019) supports that selection for yield positively affects both water and nitrogen economies. For immobile nutrients in the soil as P and K, the focus is on the topsoil, where these nutrients are available. Positive associations between P uptake and the length of surface basal roots were found and the higher the basal root angle, the lower the P uptake, especially in soils of low P content (Lynch, 2007). This author suggested that a promising breeding strategy for improving the uptake of these nutrients would be the same than used for tolerance of abiotic stress, that is, to assess a wide range of genotypes for the expression of specific tolerance traits. However, a trade-off between the root architecture proposed for immobile nutrients uptake and water uptake was pointed out by Lynch (2007). Fewer studies evaluated the impact of wheat breeding on water uptake and WUE. In Australia, no trends were found for wheat evapotranspiration in cultivars released between 1958 and 2007 (Sadras and Lawson, 2013). However, when the authors plotted data from different Australian experiments, a linear association was found between yield per transpiration unit (WUE) and year of release from 1918 to 2007. Water-associated traits were evaluated in CIMMYt’s cultivars released between 1962 and 1988, where modern cultivars showed higher stomatal conductance and lower canopy temperature than older ones (Fischer et al., 1998); however, water uptake and WUE were not measured. As in nutrients, roots were the aim for increasing water uptake, and deep roots is a need for this objective not only for water-limited environments but also for high rainfed environments, where N and other nutrients are leaching (Thorup-Kristensen and Kirkegaard, 2016). The recent release of a transgenic wheat apparently tolerant to water deficit, carrying a mutated version of the gene HaHB4 from sunflower (González et al., 2019), opens the opportunity for improving both WUE and Yw.

4.1.3  Perspectives of wheat under climate change Climate change is one of the main challenges for agriculture in this century (Sadras and Calderini, 2009, 2015; Ray et al., 2013; Fischer and Connor, 2018), accounting for global temperature increase (both in daytime and night-time temperatures and variations between winters and summers), rainfall variation, CO2 increment, and higher frequency of extreme events; for example, heat spells producing heat shocks and heavy rains producing waterlogging (IPCC, 2018). Climate change has already affected wheat yields as was reported by Iizumi et al. (2018) through a counterfactual analysis, where the authors found that climate change decreased global mean yield of wheat by 1.8% between 1981 and 2010. Amongst the projected environmental changes, the fact that crops will be exposed to higher temperatures is rather accurately predicted (e.g. Challinor et al., 2014). Temperature increase ranges from 1.3°C to 1.7°C by averaging this century (2046–65) and between 1.8°C and 3.1°C by the end of the century (2080–99) (IPCC, 2018). This global warming will be heterogeneous along the world, predicting higher increases in the northern hemisphere than in the southern hemisphere and also high in tropical regions (Fig. 3.25). Large areas of major wheat producing countries will be affected by global warming such as North America, the Mediterranean Basin, the Indian subcontinent, eastern Asia, and Australia (Figs 3.1 and 3.25).

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FIG. 3.25  Scenarios of global warming for years 2046 (left panel) and 2081–2100 (right panel) and present wheat cropping countries. Temperature change is indicated by colours and wheat country production, the size of grey circles. Modified from: IPCC-AR5 2013, Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L, Fichefet, T., Friedlingstein, P., Gao, X., Gutowski, W.J., Johns, T., Krinner, G., Shongwe, M., Tebaldi, C., Weaver A.J., Wehner, M., 2013. Long-term climate change: projections, commitments and irreversibility. In: Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J. , Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA; FAOSTAT, 2020. Food and Agriculture Organization of the United Nations.

Predictions based on assembles of a large number of different simulation models showed that wheat production is estimated to be penalised significantly by raising temperatures (in average by c. 6% °C−  1) (Asseng et al., 2015). Recognising the detrimental effect of raising temperatures, that the problem is even more complex, because not only the average temperatures will increase but also the frequency of heat waves (Meehl and Tebaldi, 2004; Seneviratne et al., 2014); and the increase in frequency will not only occur in regions already exposed to heat waves but also in cooler regions (Semenov, 2007). This is a rather relevant distinction because heat waves produce yield penalties much stronger than expected from the raise in average temperature not only accelerating development and reducing growth but also more directly impairing reproductive processes (Slafer and Savin, 2018). Additional information accounting for the effect of climate change on wheat and other cereal crops in different areas of the world has recently been reported by O’Leary et al. (2018). The forecast of rainfall and cloudiness, affecting two key environmental factors for crops: water availability and radiation, has been less precise than temperature, especially rainfall. However, climate models simulate that global mean precipitation will increase with global warming but with high variation along the globe. Increases in precipitation are predicted for high latitudes and decreases in mid-latitude, mainly in summertime, except in eastern Asia. In addition, decreases in rainfall

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in subtropical areas are estimated by climate multimodel ensemble. In agreement, cloud cover is expected to decrease in low and middle latitudes and substantial increases at high latitudes (IPCC, 2018). In this context, the response of wheat to climate change has been estimated by crop simulation models (e.g. Meza and Silva, 2009; Makowski et al., 2020, and references therein), but especially by ensemble models, which has been demonstrated as more accurate than single models when crop temperature response is simulated (Asseng et al., 2015). Early simulations of ensemble models, considering only temperature increase, showed a negative impact of global warming on wheat yield as expected in a range of 15–32°C. Across different wheat cropping areas, simulated grain yield decreased 5% °C−  1 of temperature increase. This negative impact was mainly because of the increase in the developmental rate (Section 2.2.3), reducing biomass and grain number per unit area because of lower accumulated intercepted radiation (Sections 2.1.1 and 3.1). However, few positive impacts were also found in water- and nitrogen-limited conditions by avoiding the stresses ending the crop cycle (Asseng et al., 2015). More recently, Asseng et al. (2019) simulated wheat yields in 60 world areas considering two temperature increases (2°C and 4°C) and two CO2 concentrations (360 and 550 ppm) for the period 2040–69. Generally, negative impacts on yield were simulated for low‐ and mid‐latitude locations and positive effects in high‐latitude locations. Most of the yield responses ranged between −  10% and 10%. Variability in wheat yield by the impact of climate change was also shown in other studies (e.g. Rosenzweig et al., 2014). However, we must be careful when extrapolating the conclusions from studies using crop simulation models that still need to consider the magnitude of effects from heat waves compared to that from constantly higher temperatures (Slafer and Savin, 2018). Field experiments were also carried out to evaluate the impact of climate change on wheat, mainly global warming. As it was shown by modelling, field experiments reported negative impact of increasing temperature on wheat yield. Most of the field experiments increased temperature by portable chambers equipped with heaters (e.g. Savin et al., 1996). These studies generally focused on key phases for yield and components, taking into account that heat waves are forecasted for the future, and the impact of temperature on crop yield depends on the developmental stage of the crop at which the thermal increase occurs. Also, higher night temperatures were studied in field conditions by this methodology. Increased temperatures during key periods for yield determination (Section 2.1) showed yield decrease by 4.5%–5% C−  1 (Ugarte et al., 2007; Lizana and Calderini, 2013; García et al., 2016). When night temperature was increased in wheat and barley, similar results were found, that is, 7% °C−  1 (García et al., 2015). These studies also shown that when yield components were considered, grain number reduction ranged between 4% and 6% °C−  1 and GW by 1%–4% °C−  1 (Lizana and Calderini, 2013; García et al., 2015, 2016). The effect of the temperature increase on grain number was analysed in more detail by García et al. (2015) that the impact of increased temperature was because of the higher developmental rate, shortening the duration of the critical period, reducing radiation capture with negative consequences for biomass production. Similar explanation was found in other experiments where the higher developmental rate was the cause of yield penalty under increased temperature (Lizana and Calderini, 2013). Few studies combined temperature and CO2 increases in field plot experiments. Dias de Oliveira et al. (2013) evaluated the response of wheat to increased temperature (2°C, 4°C, and 6°C) and CO2 concentration (700 mL L−  1) showing that in irrigated plots, grain yield was increased 9% (averaged across the two assessed cultivars) under increased CO2 and temperature (2°C), but this reversed when temperature was increased by 4°C (GY: ~7%) and 6°C (GY: ~10%). In this experiment, terminal drought was also tried. The effect of both increased temperature and terminal drought was counterbalanced by higher CO2 by increasing yield by 30% and 18.5% (averaged across cultivars) under 2°C and 4°C, respectively. On the contrary, CO2 increase was unable of compensating drought under the higher thermal increase (6°C) decreasing yield by 7.5% (similar in both cultivars). Although these results are useful, the positive impact of increased CO2 concentration on wheat would be lower until the middle of this century because CO2 concentration for this time is expected to reach 550 ppm (RCP8.5), and values of 700 ppm or higher are projected for the end of the century ( ddc_co2.html).

5 Quality 5.1  Grain quality traits Wheat is used for various types of breads, noodles, biscuits, cakes, pasta, etc., and in nonfood applications such as starch, vital dry gluten, biodegradable plastics, and ethanol, amongst others (Day et al., 2006; Uthayakumaran and Wrigley, 2010). Wheat quality has, therefore, different meanings depending on the end-use, and the step in the value chain from breeding, production in the field, commercialisation, manufacture of end product, and consumer (Rondanini et al., 2019). The diversity of markets and of uses means that breeders must provide for a similarly wide variety of products when they select for quality type (Wrigley, 1994).

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Carbohydrate, mostly starch, accounts for 70% of the weight in the mature wheat grain (Stone and Savin, 1999; Shewry, 2009), with protein accounting for 8%–22% (Peña et al., 2002; Shewry, 2007). Carbohydrates and protein are synthesised during the grain-filling phase described in Section 2.2.2. The timing when these components are synthesised and deposited during grain growth are described elsewhere (e.g. Stone and Savin, 1999; Shewry et al., 2012). The most common grain quality traits for commercialisation are test weight, moisture content, protein content, and particular grain defect limits (sprouted, fungal, or insect damage) or weed seed limits and other contaminant limits (Wrigley, 1994). These attributes are used for segregation or grading wheat by grain buyers and traders to separate sound wheat, suitable for human consumption, from weather-damaged and disease- or drought-affected grain of lesser value. Some countries have more complex system of grading than others, but there are many common attributes and values amongst the different standards (Delwiche, 2010). Some quality traits can be used to predict end-product quality. The ultimate test of the suitability of wheat for any end product is to manufacture the end product, using scale test methods. However, as manufacture of the end product is timeconsuming and usually requires a large sample, research has been performed to link quality traits and end-product traits. To estimate the end-quality product dough, rheological measurements and small-scale laboratory tests are often used in bread wheat mills and bakery industry (Fu et al., 2020). Wheat quality can be simplified to three key traits: grain hardness, grain protein concentration, and dough or protein quality. The relationships between these three traits have been summarised graphically by Moss (1973) and are widely used (Fig. 3.26). For the production of a particular quality end product, there is a relatively narrow range of grain protein concentration of each endosperm hardness type (Fig. 3.26). Grain endosperm hardness is a measure of the resistance to deformation (Turnbull and Rahman, 2002). It is determined by the way starch granules and proteins are packed in the endosperm cells. Common hexaploid wheat (T. aestivum L.) endosperm texture ranges from very soft to hard, whereas the tetraploid durum wheat (T. turgidum L. ssp. durum) presents the hardest grains across all ploidies (Pauly et al., 2013). Hardness is largely controlled by genetic factors (e.g. values of 89% narrow-sense heritability are usually reported, Jernigan et al., 2018), but it can be affected by the environment and factors such as moisture, lipid, and pentosan content (Turnbull and Rahman, 2002). Endosperm hardness affects the particle size after milling, water absorption by the flour, and milling yield. On the other hand, for the bakery industry, the endosperm hardness is a predictor of the suitability for a particular end product (Fig. 3.26).

FIG.  3.26  Relationship between grain protein content, endosperm type, and end uses of wheat flour. Reproduced with permission from Australian Institute of Agricultural Science & Technology Moss, H.J., 1973. Quality standards for wheat varieties. J. Aust. Inst. Agric. Sci. 39, 109–115.

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Grain protein content varies depending on environment and crop management practices (Peña et al., 2002; Shewry, 2007), and these variations are much larger than variations due to genotype (Aguirrezábal et  al., 2015). Grain protein concentration is mainly due to variations in the quantity of carbon compounds (i.e. starch, Jenner et  al., 1991), whilst the quantity of nitrogen compounds (i.e. proteins) per grain is relatively stable. The relationship between carbon and nitrogen compounds leading to a final concentration of protein in the grain can most simply be explained by the effects of environmental factors during the grain-filling period on the rate and duration of accumulation of starch, oil, and protein (Aguirrezábal et al., 2015). And, as shown by Jenner et al. (1991), their depositions in the grain are relatively independent from each other and are controlled differently. Although protein content is usually used for grade and market quality (Wrigley and Bekes, 2004), there is not a linear association with dough quality because protein composition is different from protein content (Stone and Savin, 1999; Wrigley and Bekes, 2004).

5.2  Grain proteins, nutrients, fibre, and healthy traits Around 85% of the endosperm protein is gluten, a very large complex primarily composed of polymeric (multiple polypeptide chains linked by disulphide bonds) and monomeric (single chain polypeptides) proteins, known as glutenins and gliadins, respectively (MacRitchie, 1994; Peña et  al., 2002). These storage proteins are controlled by over 100 genes located at different loci (Shewry and Halford, 2002), coding for high molecular-weight-glutenin subunits (HMW-GS), low-­molecular-weight-glutenin subunits (LMWGS), α/β-gliadins, γ-gliadins, and ω-gliadins. The chemical description and classification of the different proteins involved in gluten can be found in comprehensive reviews by Shewry and Halford (2002) and Peña et al. (2002). When gluten is hydrated and mixed and or kneaded, it forms a continuous network of proteins, which provide the cell structure to a loaf of bread (Stone and Savin, 1999). Glutenins confer elasticity, whilst gliadins confer viscous flow and extensibility to the gluten complex. Thus gluten is responsible for most of the viscoelastic properties of wheat flour doughs and is the main component conditioning the use of a wheat variety in bread and pasta-making. Gluten viscoelasticity, for end-use purposes, is commonly known as flour or dough strength (Peña et al., 2002).  Grain nutrients, fibre, and healthy traits Wheat products contribute to dietary fibre, which in turn promotes human health. For instance, the pentosan content (mainly arabinoxylan and to a lower degree β-glucans) reduces the risk of chronic diseases, such as diabetes, cardiovascular diseases, and colorectal cancer (De Munter et al., 2007; Vitaglione et al., 2008; Aune et al., 2011), which are particularly relevant in countries with ageing populations. Consumption of these dietary fibres by the European population is well below the recommended levels (Shewry et al., 2014). Minor components, including lipids, terpenoids, phenolics, minerals, and vitamins, are dietary important (Shewry et al., 2013). These components differ in their distribution within the grain. For instance, the starchy endosperm (recovered as white flour on milling) contains low contents of cell wall components, minerals, and phytochemicals, whereas the pure bran, that is, aleurone layer, outer layers of the grain, and the embryo, lack in starch and are enriched in minor components with nutritional and health benefits (Shewry et al., 2013).

5.3  Sensitivity of grain quality traits to environmental stresses GW and composition depend on the genotype, but most of the quality traits are highly conditioned by the environment and by the genotype–environment interaction (Gooding and Davies, 1997; Savin and Molina-Cano, 2002; Wrigley and Bekes, 2004; Aguirrezábal et al., 2015). The two major environmental stresses that alter grain composition and quality are high temperature and drought. The effect of heat stress on wheat grain quality is well documented (Stone, 2001 and references therein), and the occurrence of heat stress will likely increase with climate change. Global climate models predict an increase in mean ambient temperature between 1.8°C and 5.8°C by the end of this century (IPCC, 2018), and also, more intense and extremes temperatures are predicted (Meehl and Tebaldi, 2004; Seneviratne et al., 2014). Grain quality is likely to be affected by higher mean temperatures, higher maximum (Meehl and Tebaldi, 2004; Seneviratne et al., 2014), moderately high temperatures (in the c. 20–32°C range, Wardlaw and Wrigley, 1994) and higher night temperatures (Shi et al., 2010), as illustrated in Table 3.5. However, most reports are from experiments in controlled conditions and are thus inconclusive (Slafer and Savin, 2018). In general brief periods of heat stress with temperatures higher than 32–35°C may alter flour, dough, and baking quality (Blumenthal et al., 1993); these effects have been related to an increased gliadins/glutenins ratio (Blumenthal et al., 1991; Triboi et al., 2000) and a decrease in the proportion of the larger molecular size glutenins (Wardlaw et al., 2002). On the other hand, moderately high temperatures of 20–32°C have a positive effect on dough properties (Randall and Moss, 1990; Wrigley et al., 1994) and have been reported to lead to changes in the gliadin fraction composition (Daniel and Triboi, 2000, 2001).

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TABLE 3.5  Examples of the effect of different type of heat stress on the major grain quality attributes in wheat. Grain quality attributes Type of heat stress

Grain weight

Starch content/ quality

Protein content

Protein quality

Dough quality

Daily maximum or heat waves








Randall and Moss (1990)


Blumenthal et al. (1995)


Graybosh et al. (1995)


Stone et al. (1997)


Wardlaw et al. (2002)


Spiertz et al. (2006)





Stone et al. (1997)


Daniel and Triboi (2000)


Wardlaw et al. (2002)





Prasad et al. (2008)


García et al. (2016)

✓ ✓

Moderately high



Negative, positive, and equal symbols indicate a reduction, an increase, and no significant modifications compared to the unheated control, respectively. The question mark symbols indicate that there is no report so far on the effects of that particular type of stress on grain quality attributes.

Regarding dietary fibre, minerals, and the other minor components, differences in the amount have been found amongst genotypes (Ortiz-Monasterio et al., 2007; Rakszegi et al., 2008; Shewry et al., 2013; De Santis et al., 2018), and also the concentration may vary under heat stress and drought (Zhang et al., 2010; Rakszegi et al., 2014) or N availability (Shi et al., 2010).

5.4  Grain quality and crop management Grain yield and quality are determined throughout the growing season, but important decisions that will strongly affect them should be taken before planting. Some grain attributes with high heritability will be decided by the genotype chosen, such as the colour of the wheat grain (white, yellow, purple), which is strongly determined by the ability of genotype to accumulate lutein or anthocyanin, the endosperm hardness (Turnbull and Rahman, 2002), or the composition of the starch; although commercial wheat genotypes usually contain ∼  25% amylose and ∼  75% amylopectin, at present, a large number of mutants have been discovered and used as specialty quality genotypes. For example, a high-amylose type contains 55%–70% amylose and 45%–30% amylopectin, and a waxy mutant contains almost 100% amylopectin and no amylose (Hung et al., 2006; Bird and Regina, 2018). The starches of these wheats have unique characteristics that promote specific application for food processing and enhance nutritional quality of staple foods (Hung et al., 2006; Bird and Regina, 2018). Although final protein content and the amount of certain proteins are modulated by the environment and the G × E × M interaction, differences amongst wheat varieties in gluten viscoelastic properties (i.e. strength and extensibility) are mainly associated to different combinations of high and low molecular glutenins (Bonafede et al., 2015). The development and utilisation of gene-specific markers for these glutenins and gliadin alleles have dramatically improved the selection efficiency of breeding materials with desirable genes. However, most low-molecular glutenins and gliadin genes comprise complex populations of gene with high allelic variation; therefore their contributions in bread quality still need to be solved (Rasheed et al., 2014).

Wheat Chapter | 3  143  Nitrogen and other nutrient fertilisers Nitrogen fertilisation is one of the most widely applied management practices in grain crops worldwide. In many regions, crops are frequently well fertilised to maximise productivity. Addition of nitrogen fertiliser also affects grain quality (Stone and Savin, 1999), modifying not only the protein percentage but also the gliadin:glutenin ratio (Saint Pierre et al., 2008). However, it is important to understand the key aspects for obtaining a particular outcome: the amount of initial nitrogen content in the soil, the water availability throughout the growing season, the timing of nitrogen application, and the potential yield and quality of the genotype. As elegantly shown by Fischer et al. (1993), starting from a low level of nitrogen availability, the first increment of nitrogen fertiliser increases the yield and protein content in the grain, but the response of starch is usually the greater (Jenner et al., 1991; Calderini et al., 1999; Aguirrezábal et al., 2015). Therefore nitrogen application increases the yield but decreases the protein concentration when grain yield is in the linear response to nitrogen. This is generally reported as the negative relationship between protein concentration and grain yield (see Stone and Savin, 1999; Aguirrezábal et al., 2015, Fig. 3.27, left panel). Before the critical level of nitrogen is attained, the response of starch and protein accumulation enters a second region of response, in which additional nitrogen fertiliser will often have a reduced (but still positive) effect on starch accumulation and a proportionally greater impact on protein accumulation. The net effect of nitrogen in the second region of response is therefore an increase in yield, and a comparatively large increase in protein percentage (Fig. 3.27, left panel), which is not always a profitable management option. If additional fertiliser is applied, the amount of starch in the grain may not be affected because the maximum possible yield is attained by that particular environment and genotype, and also a maximum genetic amount of protein percentage will be attained (Stone and Savin, 1999). An interesting summary of the effects of environmental variables at a given nitrogen supply and source–sink ratio can be found in Aguirrezábal et al. (2015, see Fig. 17.8 therein) Thus the final decision on the amount of nitrogen fertiliser to add should come from the expected yield responses at each site (source–sink relationship) and also depend on the temperature, water availability, and the end use product required by the local industry and profitability. Often, when nitrogen fertiliser increases and/or timing of application in near heading or anthesis, protein percentage increases between 0.5% and 6% units (Fischer et al., 1993), resulting in an increase in both gliadins and glutenins (Fig. 3.27, right panel), but gliadins increase preferentially over glutenins, and consequently the gliadin:glutenin ratio increases (Stone and Savin, 1999; Triboi et al., 2000; Dupont and Altenbach, 2003; Saint Pierre et al., 2008; Fig. 3.27). These variations may result in decrease in gluten strength and mixing properties. However, other researchers have reported that increasing nitrogen fertilisation did not affect the relative amount of gliadins and glutenins (Dupont and Altenbach, 2003; Johansson et al., 2013) or a reduction in the size of glutenin polymers (Naeem et al., 2012; Johansson et al., 2013). These differences could be attributed to genotype and allocation of nitrogen to the different protein subunits but also to different environmental backgrounds. Martre et al. (2003, 2006) successfully simulated crop traits on both grain yield and protein concentration. In fact, important advances have been made in modelling protein components using a modified Sirius Quality model, which partitioned N into structural/metabolic and major storage proteins within the routine, which provided prediction of the gliadin and glutenin fractions. Further discussions in this issue can be found in Aguirrezábal et al. (2015) and Nuttall et al. (2017). An additional aspect regarding nitrogen fertilisation is the increase in some micronutrients concentration (Shi et al., 2010). Proper nitrogen fertilisation may enhance Zn, Cu, and Fe concentrations and could be regarded as a way to reduce

FIG. 3.27  Relationship between grain protein content and grain yield (a), and total grain protein and the percentage of monomeric and polymeric proteins (b). Modified from (a) Fischer, R.A., Howe, G.N., Ibrahim, Z., 1993. Irrigated spring wheat and timing and amount of nitrogen fertilizer. I. Grain yield and protein content. Field Crop Res. 33, 37–56. (b) Saint Pierre, C., Peterson, C.J., Ross, A.S., Ohm, J.B., Verhoeyen, M.c., Larson, M., Hoefer, B., 2008. White wheat grain quality changes with genotype, nitrogen fertilization, and water stress. Agron. J. 100, 414–420. agronj2007.0166.

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human malnutrition (Shi et al., 2010). Grain sifting could also be a strategy for selecting high-micronutrient concentration grains because proximal grains from the spike (which also are larger in size) have higher micronutrient concentration than distal and smaller grains (Calderini and Ortiz-Monasterio, 2003). Another nutrient that has been found to reduce gluten quality is S (Wrigley et al., 1980; Moss et al., 1981; Flæte et al., 2005) because the proportion of S-poor to S-rich prolamins is dependent on S availability (Moss et al., 1981) and then, a change in the nutritional quality owing to a major reduction in the proportion of all the essential amino acids present (Wrigley et al., 1980). In addition, changes owing to S deficiency were associated with a decrease in dough extensibility, an increase in resistance to extension, and a consequent deterioration of baking quality (Moss et al., 1981); whilst loaf volume was increased significantly by S in some experiments (Zhao et al., 1999). N:S ratio have been found to be a better indicator of loaf volume than protein concentration alone (Moss et al., 1981; Zhao et al., 1999).

6  Concluding remarks: Challenges and opportunities Wheat has been part of the human history and a key crop in the Neolithic Revolution supporting the dawn of civilisation. From the Middle East, wheat has spread all over the world, and nowadays, it is the crop with the larest acreage across the continents. Everything suggests that wheat will continue as one of the most important staple food crops in the future in spite of the big challenge of balancing intensification and sustainability in a context of climate change. In this chapter, we described wheat development and growth characteristics considered in general and focused on the critical periods for yield determination (corresponding to the periods when grain number and potential size of the grains is determined), which is common across the different types of wheat and across ploidies and species. Regarding developmental patterns, we considered the organogenesis of the major organs determining sources and sinks for the crop and the environmental and genetic factors affecting crop development, which is essential for crop adaptation and productivity, considering the three types of wheats regarding the developmental patterns: winter, alternative, and spring. Regarding growth, the importance of radiation interception as a determinant process and a key for biomass production has been shown. When constrains were reviewed in Sections 3 and 4.1, a common feature emerged, namely, the uptake of resources and their use efficiency. About radiation, although the capture of this fundamental source of energy for photosynthesis has explained differences in biomass and yield production across plant densities, water and nutrients availabilities and even under constrains such as Al toxicity, the future improvement of radiation capture seems to be little promising because k is very conservative in wheat, and the possible gain in soil cover by LAI is narrow. As a result, most efforts are being focused on RUE. Genetic variability exists in biomass and RUE, and some examples were shown in this chapter; however, the trait has been elusive for genetic improvement in biomass production of cultivars. Another strategy consisted in increasing growth specifically during the critical period for yield determinantion, discussed in Section 4.1.2, through modifying the length of the period when the spike grows to increase grain number. Photoperiod sensitivity has been proposed for this aim, but more work is still needed to make this hypothesis real. Looking for higher GW, a huge work has been carried out in wheat and other crops searching for both QTL and genes controlling this yield component. The gain in knowledge of physiological and molecular bases of GW determination has showed a quantum leap during the past 20 years and lines with higher GW than wild types have become real; however, this increase was not conveyed in higher yield because of the trade-off between grain number and weight. Therefore several bottlenecks should be solved during the next years to successfully increase wheat production to match the increment of food demand. Climate change is a central challenge for agriculture and natural systems. The knowledge and certainty on magnitude of climate variables change is variable. CO2 and temperature increases are more accurately predicted than rainfall. As shown in Section 4.1.3, the increase in temperature has a negative impact on wheat yield; however, less clear is the impact of both temperature and CO2 increases, although yield increases cannot be denied, especially for northern areas, where the crop cycle could be extended. We also call the attention to the fact that most predictions are based on the expected impact of increasing average temperature, but the likely effects of heat waves may be more damaging and are mostly ignored so far. Finally, grain quality is an important trait. Quality traits are highly affected by G × E ×  M interaction. These aspects were extensively discussed in Section 5.3 and the main management practices conditioning wheat grain quality. The industrial requirements depending on the wide type of products derived from wheat grains were also presented, highlighting the properties of glutenins and gliadins, whose balance is key for the bread industry. In addition to the traditional quality aspects, we also covered superficially nutraceutical properties. Protein and elements (Fe and Zn) content have become a central issue in breeding programmes and research (Biofortification Challenge Program, CGIAR). Opportunities for further increasing grain quality aspects will increase with the understanding of their determination.

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Image source: Authors

Chapter 4

Barley Daniel J. Mirallesa, L. Gabriela Abeledob,c, Santiago Alvarez Pradob,c,d, Karine Chenue, Román A. Serragob,c, and Roxana Savinf a

Department of Plant Production, School of Agriculture, University of Buenos Aires and IFEVA-CONICET, Buenos Aires, Argentina, bDepartment of Plant Production, School of Agriculture, University of Buenos Aires, Buenos Aires, Argentina, cCONICET, Buenos Aires, Argentina, dIFEVA, Buenos Aires, Argentina, eUniversity of Queensland, Brisbane, QLD, Australia, fDepartment of Crop and Forest Sciences, University of Lleida—AGROTECNIO Center, Lleida, Spain

1 Introduction 1.1  Global trends in harvested area and yield Barley is the fourth most cultivated cereal in the world. Of the 680 Mha cultivated with cereals, about 47 Mha or 7% correspond to barley (FAOSTAT, 2019). About 70%–85% of barley is used for animal feed, 15%–20% for malting, and around 5% is retained for seed (Fischbeck, 2001). In diverse regions of the world, barley is predominantly grown as feed grain where other crops such as maize cannot be cultivated because of short growing season, cool spring, rainfall deficiency, and high atmospheric evaporative demand. In these areas, especially in developed countries, barley straw is used for feeding and animal bedding. The world production of barley has increased from the beginning of the 1960s to the beginning of the 1990s at a rate of 3.4 Mt. y− 1. However, world production was reduced at the rate of 3.3 Mt. y− 1 from 1990 to 2000, then stabilised at ca. 140 Mt. after 2000 (Fig. 4.1a). A similar trend was observed in harvested area because it increased from the 1960s to the beginning of the 1980s at a rate of 1.46 M ha y− 1. However, from the 1980s until now, harvested area was reduced at rate of 1.07 M ha y− 1 (Fig. 4.1b). In contrast to harvested area, yield has consistently increased from the 1960s at a rate of 25 kg ha− 1 y− 1, reaching a current average world yield of about 3 t ha− 1 (Fig. 4.1c). Considering only malting barley, total production in 2018/19 was 141 Mt. (, with 40% of the total production in the European Union, followed by Russia (12%), Canada (6.2%), Australia (5.5%), Ukraine (5.4%), Turkey (5.2%), Kazakhstan (3%), Argentina (3%), USA (2.4%), and Iran (2.2%) (FAOSTAT, 2019). Most projections estimate that cereal production must increase by 50% or even more by 2050 to ensure food security (Fischer et al., 2014). Therefore the understanding of key processes determining crop development, growth, and yield in cereals, including barley, is relevant to facilitate crop breeding and improve management practices. Moreover, it is crucial to understand how physiological processes interact with the environment, especially given challenges related to climate change. In this context, characterising barley responses to climate change scenarios is required to develop adaptation strategies and guarantee sustainable food production for a growing global population (Howden et al., 2007).

2  Crop structure, morphology, and development Two botanical types of barley can be distinguished, two- and six-row genotypes, depending on the number of fertile and well-developed spikelets at each node of the rachis. Test weight, kernel weight, and kernel plumpness are normally higher in two-row types than in six-row barley (Ullrich, 2002). Often, six-row barley has higher grain protein concentration than two-row, although this highly depends on crop management. Cultivars of both types suit the requirements of the malting and brewing industries. Traditionally, two-row malting barley has been used in Europe, Australia, and South America, whilst six-row malting barley has been more commonly used in North America (Savin et al., 2012). Double cropping using the wheat–soybean rotation is a common practice in many countries around the world to promote the intensification of crops within rotations. However, late harvest times in wheat result in delayed sowing and yield penalty in soybean (Calviño et al., 2003). In this context, barley represents a better option than wheat preceding soybean in crop rotation because it is

Crop Physiology: Case Histories for Major Crops. Copyright © 2021 Elsevier Inc. All rights reserved.


Production (M t)

166  Crop Physiology: Case Histories for Major Crops

200 180 160 140 120 100

80 60 40 20 0





1980 1990 Year




Harvest area (M ha)

90 80

70 60 50

40 30 20 10 0





1980 1990 Year





Yield (t ha-1)

3.0 2.5

2.0 1.5

y = 0.025x - 47.5 R² = 0.92

1.0 0.5


0.0 1950



1980 1990 Year




FIG. 4.1  Time trends of (a) production, (b) harvested area, and (c) yield of barley globally (from 1960 to 2018). In (c), the dotted line represents the linear regression. Data From FAOSTAT, 2019. Verified February 2020.

harvested earlier than wheat. Alvarez Prado et al. (2013) demonstrated that earlier field release by barley is mainly owing to earlier flowering time because the grain-filling and drying periods of both wheat and barley were similar. Crop development is crucial to crop adaptation, and in particular to yield and grain quality, in a specific environment. Thus it is important to understand the physiology of crop development and identify the genetic and environmental factors affecting the duration of vegetative and reproductive phases during which yield components are formed. In this section, we describe: (1) the stages, development phases, the interaction between the different phases, and how yield components are determined, (2) the dynamics of the initiation and appearance of both vegetative and reproductive organs, and (3) the role of temperature, vernalisation, and photoperiod as the main environmental drivers of crop development.

2.1  Differentiation of vegetative and reproductive organs The development of barley can be partitioned into three major phases, i.e. vegetative, reproductive, and grain filling (Fig. 4.2). The timing of developmental phases can be associated with a particular yield component (Fig. 4.2). However, some developmental phases are more important than others for grain yield (GY) determination. In cereals, including barley (Arisnabarreta and Miralles, 2008a; Cossani et al., 2009), GY is better explained by the number of grains per unit of land area than other traits (Miralles and Slafer, 1999; García et al., 2013; Ferrante et al., 2017), and weaker relationships have been reported between GY and average grain weight. Key reproductive phases occur from the beginning of spikelet initiation to heading as critical (Arisnabarreta and Miralles, 2008a). Final grain number depends more on grain and/or spike survival than on the initiation of structures that may potentially produce a grain. In two-row barley, the survival of spikes per unit area is more important than grains per spike to determine

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Stages Sw E







Grain filling Phase

Reproductive Phase



Hd BGF Spike and stem grow th

Leaf intiation

Development processes

Spikelet initiation Tillers initiation

Spikelet mortality Tillers mortality

Grain set

Leaf intiation

Leaf appearance -2

Plants m


Grains m





Grains spikelet-1

Spikelets spike -1

Yield components

Environmental control



Grain filling

Grain weight

Photoperiod Vernalization Temperature

FIG. 4.2  Barley development throughout the crop cycle: Sowing (Sw), seedling emergence (E), floral initiation or ‘collar’ stage (FI), double ridge (DR), triple mound (TM), maximum number of total primordia initiated in the apex (MP), heading (Hd), beginning of grain-filling period (BGF), physiological maturity (PM), and harvest (Hv). Boxes indicate different phases, developmental processes, and yield components formation. Environmental factors that control the length of different phases are also indicated. Adapted from Slafer, G.A., Rawson, H.M., 1994a. Sensitivity of wheat phasic development to major environmental factors: a re-examination of some assumptions made by physiologists and modellers. Aust. J. Plant Physiol. 21, 393–426.

the final number of grains, while in six-row barley, both spikes per unit area and grains per spike contribute highly to the number of grain per unit area (Garcia del Moral et al., 1991b; Arisnabarreta and Miralles, 2008a, 2015).

2.2  Dynamics of initiation and appearance of vegetative and reproductive organs 2.2.1  Leaf and spikelet initiation into the apex The mature barley embryo contains three to four leaf primordia (Kirby and Appleyard, 1986). Fig. 4.3 shows the transformation of the apex in the main shoot across plant development immediately after germination. From germination to seedling emergence, the apex initiates one to three new leaf primordia, depending on sowing depth because this practice modifies the thermal time between sowing and emergence and thereby the number of leaves initiated in the apex. At seedling emergence, the shoot apex has five to seven leaf primordia initiated. The initiation of leaf primordia in the apex occurs at a single rate for a particular genotype under constant environmental conditions. The interval between the initiation of two consecutive leaf primordia (viz. plastochron) is commonly around 40–50°Cd (degree days), above a base temperature of 0°C (Kirby et al., 1987; Delécolle et al., 1989; Miralles and Richards, 2000). During the vegetative phase, the apical meristem produces single ridges, which will become actual leaves. At the beginning, the apex has a conical shape (dome) of about 0.2 mm in length that later elongates (Fig. 4.3). The maximum number of leaves in the main shoot is reached at the time of cessation of leaf initiation, when the apex changes from vegetative to reproductive, thus starting the initiation of spikelet primordia. The first visual evidence of the change from vegetative to reproductive apex is the appearance of a double ridge (Fig. 4.3). This double ridge corresponds to both a smaller primordium of the last leaf primordium, which does not develop further, and the upper and bigger ridge that will become the first developed spikelet of the spike (Bonnett, 1966; Kirby and Appleyard, 1986). The apex at this time is about 0.5 mm long, and this stage is usually referred to as the beginning of floral initiation (FI in Fig. 4.2). However, it should be noted that

168  Crop Physiology: Case Histories for Major Crops

Reproductive stages

Vegetative stages

Grain filling

Lemma Primordium Spikelet Primordium


Leaf Primordium


Leaves initiation



Spikelet initiation

Spikelet abortion

Grain setting

Grain Filling

Floret development Anther primordium


Fertile Floret

Floret primordium

FIG. 4.3  Morphological changes in the apex of main stem barley throughout developmental stages. See Fig. 4.2 for abbreviations.

about half of the total number of spikelet primordia have already started to develop by this time (Kirby and Appleyard, 1986). Even though it is generally accepted that the visual evidence of the change from vegetative to reproductive stage is the appearance of the ‘double ridge’, the true change is named ‘collar’ stage that occurs well before the appearance of the first visible double ridge in most agronomic conditions (Fig. 4.3). Spikelet primordia are initiated faster than the leaf primordia. When the total number of primordia (leaves + spikelets) is plotted against thermal time, there is an inflection point that generally coincides with the time of collar stage (i.e. floral initiation in Fig. 4.3). Owing to the lack of unequivocal morphological change in the apex to identify the collar stage, the occurrence of this stage can be determined only by subtracting final leaf number from the accumulated number of primordia. The next important apical stage in barley is ‘triple mound’, in which the spikelet primordium differentiates three protuberances corresponding to the central and lateral spikelets. At this stage, two- and six-rowed varieties look similar, but the lateral spikelets do not develop further in two-rowed varieties (Bonnett, 1966). Spikelet initiation ceases when awn primordia on the most advanced spikelets are evident (Fig. 4.4). The number of spikelet primordia initiated in barley varies between 10 and 45, depending on genotype and environment (Kirby, 1977; Kitchen and Rasmusson, 1983). Maximum number of spikelet primordia coincides with the beginning of stem elongation, and the young spike is about 3 mm long and still at the ground level or slightly below ground (Fig.  4.4). After the maximum number of spikelets is reached, a proportion of later initiated primordia at the tip of the shoot apex does not progress to mature spikelets. Thus, about 30%–40% of the maximum number of spikelet primordia initiated abort before ear emergence (Kirby and Faris, 1972; Kernich et al., 1996). The number of fertile florets per spike is closely related to the number of spikelet survival in both two- and six-row barley, and thereby the higher the rate of spikelet primordia mortality, the lower the spikelet survival (Arisnabarreta and Miralles, 2004).

2.2.2  Leaf emergence All the leaf primordia initiated (those initiated in the mother plant and new primordia initiated after seed imbibition) must appear between germination and the emergence of the spike from the sheath of the last appeared leaf (flag leaf). Thus the time to heading strongly depends on the number of leaves initiated in the main shoot and the rate at which these leaves emerge. The phyllochron, defined as the period between the appearance of two successive leaves, is estimated as the reciprocal of the rate of leaf appearance. Therefore the duration from seedling emergence to heading can be calculated as (1) the product of the total number of leaves initiated (i.e. leaves differentiated in the embryo plus leaf differentiating during the vegetative period up the collar stage) and the phyllochron, plus (2) the time from the flag leaf appearance to heading, frequently assessed to last ca. two phyllochrons. The phyllochron can be affected by genetic and environmental factors.

Number of primordia (leaves+spikelets)

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Maximum Number of spikelets


Hd Rate of spikelet initiation

Collar Final Leaf Number

DR Rate of leaf initiation

Leaf primordia diferentiated in the embryo

Time after emergence (days or °Cd) FIG. 4.4  Schematic relationship between cumulative primordia in the apex (leaves and spikelets) and time after emergence. See Fig. 4.2 for abbreviations.

Genetic variability in phyllochron ranges from 50 to 97°Cd in barley (Frank and Bauer, 1995; Kernich et al., 1995a). The environmental conditions also affect the rate of leaf emergence, especially when measured in days per appeared leaf (Kirby et al., 1985a; Kirby and Perry, 1987; Masle et al., 1989). Using thermal time to account for temperature (Klepper et al., 1982; Cao and Moss, 1989; Slafer and Rawson, 1994b; Frank and Bauer, 1995), the phyllochron from seedling emergence to flag leaf emergence is more or less constant (Baker et al., 1980; Kirby et al., 1985b; Cao and Moss, 1989). Some studies reported changes in phyllochron with crop ontogeny, with higher rates of appearance for early leaves than for later leaves (Stapper and Fischer, 1990; Jamieson et al., 1995a; Hotsonyame and Hunt, 1997; Slafer and Rawson, 1997; Miralles and Richards, 2000). This generally occurs when the final number of leaves is increased in response to short photoperiods or the lack of vernalisation requirements in sensitive genotypes such as winter type barley (Slafer and Rawson, 1997; Miralles and Richards, 2000). Thus a single genotype could show a single or two phyllochron values depending upon the final number of leaves initiated. Other factors such as photoperiod, carbohydrate reserves, and moderate water and nutrients stresses have little effect on the phyllochron in wheat and barley (Cutforth et al., 1992; Frank and Bauer, 1995; Hall et al., 2014).

2.2.3 Tillering The number of spikes established per unit area is the most important numerical component of final number of grains per unit area and yield, especially in two-row genotypes (Garcia del Moral et al., 1991b; Dofing and Knight, 1992; Garcia del Moral and Garcia del Moral, 1995; Alvarez Prado et al., 2013). The tillering process can be divided into three phases: (1) tillering appearance, (2) maximum number of tillers per plant, and (3) net tiller mortality, and at the end of the tiller mortality, the final tiller number is established. Under non-restrictive conditions, the first tiller appears when the third or fourth leaf emerges on the main stem. Once the first tiller has emerged, the next tillers appear following a synchrony of a tiller (or even more) per phyllochron depending on the available resources (Salvagiotti and Miralles, 2007). However, it has been reported that differences prevail between wheat and barley in tillering dynamic because barley initiates more tillers per leaf than wheat (Alzueta et al., 2012). Tiller numbers increase rapidly during the first few weeks after the emergence of the first tiller, reach a maximum shortly after floral initiation, and it is assumed that the end of tiller appearance is related to the beginning of stem elongation (Garcia del Moral and Garcia del Moral, 1995; Miralles and Richards, 2000), which signals the beginning of tiller mortality (Borràs-Gelonch et al., 2012). The rate of tiller initiation is closely associated with the supply of resources (nutrients, water and radiation) (Evers et al., 2006) and determines, together with the duration of the tillering cycle, the maximum number of tillers (Alzueta et al., 2012). Cessation of tillering appearance could be related to (1) stem elongation because most of the carbohydrates are allocated to stem growth and/or (2) changes in intensity and quality of radiation inside the canopy. Evers et al. (2006) reported that tillering cessation in wheat occurred when the fraction of photosynthetically active solar radiation intercepted by the canopy exceeds a threshold (0.40–0.45) and red:far-red ratio drops below 0.35–0.40. During the period of tiller appearance, the rate of tiller appearance is the main driver of maximum tiller number; however, there is also a strong negative correlation between final number of tillers and tiller mortality rate (Alzueta et al., 2012). Tiller mortality often begins after floral initiation in main shoots because developing tillers compete with

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l­imited success for available assimilates against developing spikelets and florets on the main stem (Lauer and Simmons, 1988; Garcia del Moral and Garcia del Moral, 1995). The proportion of tillers that senesces without contributing to GY varies with the cultivar and environment (Simmons et al., 1982; Garcia del Moral and Garcia del Moral, 1995; Salvagiotti et al., 2009; Alzueta et al., 2012). Barley cultivars vary in both maximum tiller production (Kirby, 1967; Cannell, 1969a; Garcia del Moral and Garcia del Moral, 1995) and tiller mortality (Garcia del Moral and Garcia del Moral, 1995). In general, two-rowed barleys are higher tillering than six-rowed cultivars, and the winter types generally produce more tillers than the spring ones (Kirby and Riggs, 1978; Garcia del Moral and Garcia del Moral, 1995). Long photoperiod, high temperature, and high plant density reduce tillering (Cannell, 1969b; Simmons et al., 1982; Garcia del Moral and Garcia del Moral, 1995), while high radiation intensity and high water and nitrogen availabilities promote formation and growth of secondary tillers (Cannell, 1969b; McDonald, 1990; Salvagiotti et al., 2009; Alzueta et al., 2012) or at least reduce their mortality. Competition among shoots for nutrients, radiation, and water seems to be one of the principal causes for tiller mortality in barley. Tillers that had at least three fully emerged leaves at jointing (Kirby and Jones, 1977) or were over a third the height of the main stem (Garcia del Moral and Garcia del Moral, 1995) are more likely to survive.

2.3  Genotypic and environmental drivers of barley development The major environmental drivers of barley development are temperature (both temperature per se and low temperature associated with the vernalisation) and photoperiod (Ellis et al., 1988; Slafer and Rawson, 1994a). Temperature per se affects all developmental phases, while photoperiod and vernalising temperatures affect the rate of development in particular phases. Other factors related to level of nutrients in the soil, water availability, plant density, and radiation have small or null effect on the time to heading (Miralles and Slafer, 1999; Hall et al., 2014) and will not be discussed here.

2.3.1 Temperature Temperature affects all genotypes and every developmental phase, from seed imbibition to maturity. The duration of crop phases responds non-linearly to temperature (Fig. 4.5a). The rate of development, calculated as the reciprocal of the duration of the phase, increases linearly with temperature up to a maximum and decreases linearly for higher temperatures; parameters of the rate-temperature model include three cardinal temperatures: base, optimum, and maximum (Fig. 4.5b). The reciprocal of the slope of the linear relationship between rate of development and temperature is the thermal time measured in °Cd that determine the duration of a phase at any temperature within the range between the base and optimum (Fig. 4.5).

2.3.2 Vernalisation

Duration of the Phase (d)

Rate of development (d-1 )

Although spring barley generally lacks vernalisation requirements, the transition from vegetative to reproductive stage in winter type barley cultivars requires vernalising temperatures, typically in the range between 3°C and 12°C (Trione and Metzger, 1970). Vernalising temperature, contrary to photoperiod, is experienced directly by the apex meristem and the embryo of the imbibed seed. Vernalisation mainly affects the length of the vegetative phase and hence the final leaf number (Figs 4.2 and 4.6a). In fact, the number of leaves is commonly used as an indicator of vernalisation sensitivity once the other requirements were satisfied (Kirby et al., 1985b; Rawson and Zajac, 1993). Although it is widely recognised that vernalisation acts during the vegetative phase, the vernalisation effects during the spikelet initiation phase have been reported for wheat (Rahman, 1980).


Tempertaure (ºC)





Tempertaure (ºC)

FIG. 4.5  Relationship between (a) duration of a particular phase and (b) the reciprocal of the duration (i.e. the rate of development) with temperature. Base, optimum, and critical temperatures are indicated.

Barley Chapter | 4  171

2.3.3 Photoperiod

Rate of development (d -1)

Barley is a quantitative long-day species. Increasing photoperiods accelerate development phases and reduce the length of each phase up to the optimum threshold. Increases in photoperiod over the threshold do not modify the length of the developmental phase (Fig. 4.6b). Photoperiod stimulus is perceived by phytochrome of the leaves, and the signal is transmitted to the apex (Evans, 1987). Barley plants respond to photoperiod once the tip of the first leaf emerges. As many other crops (soybean—Collison et al., 1993; maize—Kiniry et al., 1983; Chapter 1: Maize, in this book; and sunflower—Villalobos et al., 1996), barley may exhibit a juvenile phase of insensitivity to photoperiod, during which cultivars do not respond to inductive photoperiods (Takahashi and Yasuda, 1970; Yasuda, 1982; Roberts et al., 1988). However, this point is not completely clear as some cultivars growing under long photoperiods exhibit only ca. six leaves and do not seem to feature a significant juvenile phase (Hay and Ellis, 1998; Miralles and Richards, 2000). The phase from sowing to floral initiation is sensitive to photoperiod. Changes in the duration of this phase, together with the duration of juvenile phase, modify the number of final leaves initiated in the apex (Fig. 4.2). Thus, long days reduce the number of leaves formed on the main shoot, while short days extend the period of leaf initiation increasing the final leaf number. Although daylength modifies the duration of the vegetative phase by altering the final leaf number, photoperiod does not affect the rate of leaf initiation (Miralles and Richards, 2000). Conversely, once the apex changes from vegetative to reproductive, photoperiod influences the rate of spikelet initiation. Although some positive effects of long photoperiod on the rate of spikelet initiation have been reported, they are smaller than those on the rate of phasic development during spikelet initiation (i.e. reciprocal of the phase duration). Consequently, these opposing effects when photoperiod is extended (i.e. increases in the rate of spikelet initiation and reduction in the duration of the initiation phase) are not fully compensated, resulting in fewer spikelets initiated under long days (Miralles and Richards, 2000; Perez-Gianmarco et al., 2018). In addition, daylength affects not only the duration of vegetative phase but also the duration of the late reproductive phase of stem growth, during which a variable number of previously initiated spikelets dies. Experiments based on reciprocal photoperiod transfers (i.e. expose reciprocally the plant to contrasting photoperiods) at the time of stem growth initiation demonstrated that the duration of the late reproductive phase was largely determined by the daylength to which they were exposed at that time (Kernich et al., 1996; Miralles and Richards, 2000). As photoperiod affects the duration of reproductive

Duration of the Phase (ºCd)





Intermediate High


Vernalisation (weeks)

Photoperiod Sensitvity(°Cd h-1 )

Earliness “per se”


Photoperiod (h)

FIG. 4.6  Relationship between (a) rate of development and vernalisation for different vernalisation requirements (no requirement, low, intermediate, high) and (b) duration of the phase for different photoperiods. TV (vernalization threshold) and TP (photoperiod threshold) indicate the respective thresholds at which vernalisation and photoperiod requirements are saturated.

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period, and as the number of grains per unit area is defined during the late reproductive (when spikelet and tiller mortality occur), increasing the duration of reproductive phases could be used to promote a higher number of grains through increasing the assimilates for the spikes (Slafer et al., 1996; Miralles et al., 2000).

3  Growth and resources 3.1  Capture and efficiency in the use of radiation 3.1.1  Canopy size and radiation interception GY can be described as the product of shoot biomass produced during the crop cycle and the proportion of biomass allocated to grain (i.e. harvest index (HI)). In barley, biomass is usually a larger source of variation in yield than HI. Biomass accumulation depends on: (1) the intercepted solar radiation and (2) the radiation use efficiency (RUE) (Monteith, 1977; Gardner et al., 1985). According to the equation derived from Beer’s Law (Monteith and Unsworth, 1990), the fraction of incident solar radiation intercepted by the crop (RI) is a function of leaf area index (LAI) and the extinction coefficient k, which is determined by canopy architecture. The value of LAI at which 95% of intercepted radiation is reached can be considered as the ‘critical LAI (LAIc)’ (Fig. 4.7). RI  t   1  exp

  k LAI   t


Intercepted radiation (%)

LAI, and hence the fraction of intercepted radiation, increases from crop emergence to the end of flag leaf expansion. In unstressed crops, leaf area dynamics is mainly driven by the expansion of leaves with little or no contribution of senescence until flag leaf emergence. Hence in well-watered barley crops, the maximum proportion of intercepted radiation is normally achieved just before or during the flag leaf expansion. As was stated earlier (see Fig. 4.7), the LAI to achieve 95% radiation interception is defined as the critical LAI (LAIC) and depends on canopy architecture characterised by the extinction coefficient (Fig. 4.7). Barley canopies with higher extinction coefficient (planophile canopies) require lower LAIC than crops with more erectophile canopies (see Fig. 4.7). Accordingly, adjusting crop management to ensure high LAI and photosynthesis by the crop for as long as possible allow crops to intercept more radiation and produce more biomass during their crop cycle. The dynamics of LAI is driven by leaf senescence at more advanced crop stages. Senescence is a complex process modulated by environmental and genetic factors. It involves two important crop-level events: (1) the turnover of the photosynthetic apparatus and the concomitant (2) mobilisation of nitrogen (N) from the leaves to the grains. The rate of senescence and the mobilisation of leaf N are related to the source–sink ratio and to the N status of the plant (Masclaux et al., 2000). Depending on the genotype, barley could mobilise up to 90% of the N from the leaves to the grains. Soil N content has a strong influence on the senescence rate (Schildhauer et al., 2008). High N levels in the soil generally delay crop senescence, while low N enhances senescence (Martre et al., 2006). Stay-green traits—their physiology, genetics, and environmental modulation—are well understood in sorghum (Chapter 5: Sorghum, Sections 3.2.1, 3.3.3, 4.3.1) and to a lesser extend in wheat (Christopher et al., 2016, 2018). LAI accounts for the total leaf area of the crop and could be separated in two components: leaf number and leaf size. Leaf size is highly sensitive to growing conditions. Usually the first leaves (represented in Fig. 4.8 as the bottom layer L5) are smaller than the leaves that appeared during jointing. However, flag leaf in barley (indicated as L1 layer in Fig. 4.8) is smaller than the rest of the leaves. Fig. 4.8 illustrated wheat and barley leaves are index for different layers throughout the


Critical LAI

Leaf Area Index FIG. 4.7  Relationship between intercepted radiation and leaf area index. Lines show barley crops contrasting in the light extinction coefficient (k), i.e. higher (solid line) and lower (dotted line) coefficients. The arrows show the critical LAI.

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Leaf layers

Top L1 L2 L3 L4 L5 Bottom


0.5 1.0 1.5 Leaf area index of each layer


FIG. 4.8  Leaf area index of different layers in barley (dashed) and wheat (solid) canopies with similar total leaf area index (LAI = 6). L1 and L5 represent the top and bottom layers, respectively. The sum of the different layers represents the total LAI. Source: R. Carretero and L. Iriarte (unpublished).

profile of the canopy around anthesis. The barley leaf size profile is different to wheat, where the flag leaves is often larger than the others (Fig. 4.8). The number of leaves per m2 is the product of number of plants per m2, number of leaves per tiller, and number of tillers per plant. The number of plants is represented by the plant density, usually chosen by the farmers previous to sowing. The number of leaves per tiller is a function of plastochron (usually 40–50°Cd per leaf in barley) and the duration of leaf differentiation (i.e. from seedling imbibition to double ridge). The number of tillers, however, is a consequence of a complex process involving initiation and degeneration of structures occurring during the tillering phase (see the previous section). The tillering phase can be divided into four stages (Fig. 4.9): (1) tillering appearance, (2) maximum number of tillers per plant, (3) tiller death, and (4) at the end of tiller death phase is defined the final number of tillers. Six-rowed barley genotypes generally establish fewer tillers per plant than two-rowed types with similar time to flowering (Kirby and Riggs, 1978; Garcia del Moral and Garcia del Moral, 1995; Arisnabarreta and Miralles, 2004). The capacity to produce more tillers per plant is important for radiation capture during the pre-flowering period, especially in low input productions systems (i.e. low N applications, late sowings, and short maturity genotypes). Increases in N promote tiller appearance rate determining a higher maximum number of tillers per plant (Prystupa et al., 2003; Abeledo et al., 2004). However, evidences have demonstrated that the higher the tillers initiated, the lower the tillers survival as was described in the previous section of this chapter, causing a partial counterbalance between the rates of tiller initiation and tiller mortality (Berry et al., 2003; Alzueta et al., 2012).

3.1.2  Radiation-use efficiency (RUE)

Number of tillers per plant

RUE for non-stressed barley crops varies from ca. 1.8 to ca. 3.0 g MJ− 1, similar to wheat (Sinclair and Muchow, 1999). Water stress, N deficit, and low temperature can reduce RUE (Jamieson et al., 1995b; Gallagher and Biscoe, 1978; Andrade et al., 1993; Kemanian et al., 2004). However, leaf expansion is usually more sensitive to water and nutrient stress than leaf photosynthesis (Sadras and Milroy, 1996; Salah and Tardieu, 1997). Thus the initial negative effect of water and nutrient stress on crop biomass is generally related to reduction in the radiation intercepted more than in RUE.




(iv) Thermal time from emergence (°Cd)

FIG. 4.9  Time-course of tillers per plant: (1) tiller initiation phase, (2) plateau once maximum tiller number is reached, (3) tiller mortality phase, and (4) final fertile tiller number, which agree with the number of spikes per plant (and per unit area).

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3.2  Capture and efficiency in the use of water 3.2.1  Environmental characterisation of water stress Crop yield improvement relies on the identification of genotypes better adapted to their production environment. However, complex genotype–environment interactions (GEI) typically contribute to yield variability, hindering the identification of superior genotypes, especially where complex abiotic stresses, such as drought, are frequent (Chenu et al., 2013; Chenu, 2015). In such situations, characterisation of the crop environment is important to understand GEI (e.g. Löffler et al., 2005; Chenu et al., 2011). In a global analysis from ICARDA-CIMMYT, 750 barley GY trials in 75 countries were analysed, grouping sites across years that represent similar selection environments (Hernandez-Segundo et al., 2009). The authors clustered environments into three main groups: (1) cool with intermediate precipitation; (2) warmer and drier; and (3) cool with the highest average precipitation (Hernandez-Segundo et al., 2009). While this approach identifies mega-environments across the world, it does not help in understanding which cultivars can be grown within the geographical area targeted by a breeding programme, i.e. the target population environments (TPE) (Comstock, 1977). This is partly because of the lack of information on the timing and intensity of environmental variables such as rainfall and the vapour pressure deficit (VPD). Using crop modelling, Chenu et  al. (2009) looked at water stress experienced by barley crops over the crop cycle for representative management practices. Over the long-term, they identified four major drought patterns for barley in North-East Australia (Fig. 4.10a): ET1 comprised situations where the crop was not water-limited by or only experienced short-term stresses; ET2 was characterised by late stresses, starting around flowering and relieved around mid-grain filling; ET3 had stresses beginning during the vegetative period and relieved during grain filling; and ET4 had stresses beginning a bit later than ET3 but continuing through to crop maturity. For the studied area, the frequency of occurrence of the ­environment types varied greatly over time and spatially. Management practices also had a strong impact on the environment type. Delayed sowing tended to have the opposite impact to an increase in initial soil water, with a decrease

FIG.  4.10  (a) Relationship between water-stress index and thermal time around flowering for four drought environment types in the North-eastern barley-growing region of Australia. Overall, the frequency of environment types ET1, ET2, ET3, and ET4 was 16%, 53%, 10%, and 21%, respectively. (b) Frequency of occurrence of the different environment types for each combination of sowing date and initial soil water used in the simulations. Sowing date increased from the earliest (1) to the latest (5) in 2-week intervals. Initial soil water increased from the lowest (1: most severe conditions) to the highest (5: less severe conditions), each representing 20% of the initial soil water encountered in the first set of simulations performed for each site over 119 years. Frequency data correspond to all the simulations performed (all sites over 119 years). Adapted from Chenu, K., Mcintyre, K., Hammer, G., 2009. Environment characterisation as an aid to improve barley adaptation in water limited environments, in: Barley Symposium, pp. 1–9.

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in the frequency of ET1 and ET2 and an increase in the frequency of ET3 and ET4 (Fig. 4.10b). The impact of initial water tended to increase with later sowing for ET2, ET3, and ET4, while the proportion of ET1 became more marginal. The frequency of occurrence of the four environment types varied across regions and years (Fig. 4.11), being generally correlated with seasonal rainfall. For example, while the Lockyer Burnett region received marginal rainfall and had a high proportion of low-mild ET1–2 stresses (ca. 80%) and a relatively low proportion of severe ET3–4 stresses (ca. 20%), other

FIG. 4.11  Frequencies of drought environment type (pies) in the different regions of the north-eastern barley-growing area of Australia, and simulated yield distribution for each environment type (box plots). Data are based on simulations over 119 years. The environment types are presented in Fig. 4.10a. The size of the pie is proportional to the barley planted area in the associated region. Adapted from Chenu, K., Mcintyre, K., Hammer, G., 2009. Environment characterisation as an aid to improve barley adaptation in water limited environments, in: Barley Symposium, pp. 1–9.

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regions with significantly higher cumulative rainfall (northern Darling Downs and the Dubbo regions) had a lower proportion of low-mild ET1–2 stresses (71% and 68%, respectively) and a higher proportion of severe ET3–4 stresses (29% and 32%, respectively; Fig. 4.11). This highlights the value of this type of approach for environment characterisation for barley production.

3.2.2  Root architecture and functionality Insufficient soil water availability or a high environmental demand, even in well-watered plants, can change plant water status resulting in a water deficit situation (Tardieu et al., 2018). This depends on both the capacity of the root system to supply water to shoots (Lobet et al., 2014) and the potential of shoots to transpire, which combines evaporative demand and shoot characteristics (Monteith, 1977). Roots play a vital role in resource uptake, provide anchorage, and interact with organisms in the soil. Defined as the spatial distribution of roots throughout the soil space, the root system architecture is complex and depends on many underlying processes, such as root elongation, curving and branching (Lynch, 1995; Rich and Watt, 2013). Furthermore, the root system architecture of a crop is influenced by the environment and do influence the efficiency and timing of water capture and extraction in cereals (Kondo et al., 2000; Manschadi et al., 2006; Pennisi, 2008). Root system of barley comprises seminal roots and nodal or secondary roots (Forster et al., 2007). The number of seminal roots was increased through domestication (Grando and Ceccarelli, 1995), with current varieties showing from 3.6 to 6.5 seminal roots (Robinson et al., 2016, 2018). Root angle for seminal roots has an ample variation ranging from 12 to 89 degrees and has been proposed as a proxy to study the genetic variability of the root system architecture (Robinson et al., 2018). Both traits showed a highly variable genetic correlation with GY, mainly explained by the environment. Roots interact with environmental factors such as soil type and strength (Bingham and Bengough, 2003; Rich and Watt, 2013), nutrient heterogeneity and availability (Drew, 1975), and management practices that influence crop water use (Richards et al., 2002). Part of the interaction with the soil type is related to secondary traits such as root hairs and mucilage exudation, which play an important role in the uptake of water from drying soils by increasing the contact surface (Carminati et al., 2017) and the soil water retention, maintaining the rhizosphere wet (Ahmed et al., 2014). Other important root traits, directly related with water extraction, are root depth and density (Fig. 4.12). Both traits influence the amount of soil water that can be supplied to the shoot (Manschadi et al., 2006). Crops frequently fail to extract all available water in the lower half of the root zone because of low root density at deeper layers (Barraclough and Weir, 1988). These differences in root length density at different depths may be associated with the speed at which roots elongate to depth or may be related to proliferation rate at each soil layer (Fig. 4.12). In barley, as in other cereals, maximum root length occurs at around anthesis (Lugg et al., 1988), reaching between 1.5 and 2 m under no water restrictions.

FIG. 4.12  (a) Extractable soil water of barley growing in two experiments in Queensland, Australia. (b) Profiles of root length density of barley at the end of the crop cycle. Based on Thomas, S., Fukai, S., Hammer, G.L., 1995. Growth and yield response of barley and chickpea to water stress under three environments in southeast Queensland. II.* Root growth and soil water extraction pattern. Aust. J. Agric. Res. 46, 35–48. AR9950035.

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3.2.3  Scaling from leaf to canopy: From stomatal conductance to water use efficiency Many physiological mechanisms triggered in plants by water deficit act in a short term, such as the stomatal conductance, impacting over more complex traits such as transpiration rate. For instance, an increase in transpiration rate because of the increase in the environmental demand tends to cause partial stomatal closure (Mott and Parkhurst, 1991), thereby stabilising transpiration rate. Because hourly plant-level transpiration rates correlate with leaf stomatal conductance (Alvarez Prado et al., 2018; Chenu et al., 2018), it is possible that genotypes and species differ in plant-level transpiration rates related to differences in leaf stomatal conductance. Barley is an anisohydric species, meaning that it cannot prevent leaf water potential to drop when soil dries (Tardieu and Simonneau, 1998). It has been proposed that differences between isohydric and anisohydric behaviours mainly result from how stomatal pores at the leaf surface close under water deficit and control plant transpiration (Buckley, 2005). In this regard, reductions in stomatal conductance are observed in barley under water stress (Fig. 4.13a), which can lead to reduced GY (González et al., 1999). Reductions in stomatal conductance are directly related to soil water depletion (González et al., 1999; Fig. 4.13b) and vary depending on the genotype, with those with slight reductions in stomatal conductance between well- and limited-water conditions showing a high level of osmotic adjustment (Fig. 4.13c).

FIG. 4.13  (a) Relationship between stomatal conductance and time during the water-stress period for a control (white points) and a water stress (black triangles) condition. (b) Relationship between stomatal conductance and soil water content. (c) Relationship between the difference in stomatal conductance between well-watered and water stress conditions in eight barley genotypes. (a and c) From González, A., Martı́n, I., Ayerbe, L., 1999. Barley yield in water-stress conditions. Field Crop. Res. 62, 23–34; (b) Adapted from Borel, C., Simonneau, T., This, D., Tardieu, F., 1997. Stomatal conductance and ABA concentration in the xylem sap of barley lines of contrasting genetic origins. Aust. J. Plant Physiol. 24, 607–615.

178  Crop Physiology: Case Histories for Major Crops

Mechanisms involved in the stomatal response to environmental conditions are multiple and have different quantitative effects on stomatal conductance. Scaling up to the whole plant or canopy cannot be considered as a sum of individual mechanisms whose weight would be independent of environmental conditions. All leaves in a canopy are not identical and do not experience identical microclimate (McNaughton and Jarvis, 1991). Small changes in stomatal or canopy conductance have a variable impact on transpiration at the canopy scale, depending on how well the saturation deficit at the leaf surface is coupled to that of the ambient air. In general, the degree of sensitivity of transpiration from a single leaf to changes in conductance of that leaf varies according to the exposure to wind and so can vary according to whether the leaf is located in a glasshouse, in a leaf chamber, or out in the field (Jarvis and Mcnaughton, 1986). However, transpiration does decline as a result of stomatal closure when soil water supply becomes limiting (Jarvis and Mcnaughton, 1986) or when evaporative demand is high (Chenu et al., 2018). In the context of crop improvement, it is advisable to identify component traits that underpin the phenotypic expression of more complex traits (such as yield) and are more suitable for selection by virtue of a reduced environmental dependency, reduced GEI, and closer alignment to underpinning genetics (Hammer et al., 2006). Despite the dependence of transpiration efficiency on environmental conditions and its complex nature, transpiration efficiency itself was suggested as a trait for largescale phenotyping because it is a proxy of the stomatal conductance (Chenu et al., 2018). Transpiration efficiency, defined at the plant level, corresponds to the plant dry biomass (with or without the root system) produced per unit of water transpired (Fig. 4.14a). It is generally measured in sealed containers that exclude soil evaporation and deep drainage and differs from the crop water use efficiency (WUE), which typically includes soil evaporation and deep drainage and excludes root biomass. Biomass accumulation in barley is linearly related to cumulative transpiration (Kemanian et  al., 2005; Fig.  4.14a), with important variations in the slope (Albrizio et al., 2010; Kemanian et al., 2005), principally associated with VPD. As observed in other crops (Abbate et al., 2004), the normalisation of transpiration by daytime VPD in barley decreased the scatter of the data standardising them in a single relationship (Fig. 4.14b). VPD encapsulates the combined effects of air temperature and relative humidity and is the main driving force of the whole-plant transpiration rate (Monteith, 1995). In natural environments, both temperature and relative humidity contribute to the variation in VPD. On a sunny day, VPD typically increases as temperature increases and relative humidity decreases progressively throughout the day. In dry environments, this increase takes place during most of the day, with VPD increasing three- to four-fold over a few hours. Because both transpiration and CO2 intake occur through the stomata, transpiration rate responses to increasing VPD have been linked both theoretically and experimentally to WUE (Fig. 4.14b) and yield under different water regimes. For a given VPD, the variation in short-term WUE of the leaf tissue (WUEph, i.e. the ratio of CO2 assimilation to transpiration), and in the long-term whole-plant transpiration efficiency (on GY basis or on total biomass basis), arises from difference in photosynthetic rate and/or stomatal conductance. Under water deficit, stomata would close, limiting transpiration rate more than photosynthetic CO2 uptake, which in turn increases WUEph (Larcher, 1995). Under moderately high temperature, a high VPD has a positive effect on barley GY per plant, leading to a larger transpiration efficiency on GY basis (Sanchez-Diaz et al., 2002). A selection criterion based on transpiration efficiency of plants is the carbon isotope discrimination (Δ13C) of plant dry matter. It provides time-integrated information of plant performance during the crop cycle (Sanchez-Diaz et al., 2002). Despite its accuracy, this technique is expensive and not widely adopted.

FIG. 4.14  (a) Relationship between biomass and cumulative transpiration and (b) cumulative transpiration normalised by the air VPD of spring barley for an early (first) and late (second) sowing date (SD). (c) Relationship between transpiration-use efficiency (g biomass kg− 1 H2O) and VPD of the air. Kemanian, A.R., Stöckle, C.O., Huggins, D.R., 2005. Transpiration-use efficiency of barley. Agric. For. Meteorol. 130, 1–11, Reproduced with the permission of Elsevier.

Barley Chapter | 4  179

3.3  Capture and efficiency in the use of nutrients In this section, we focus on N, with a brief mention for other nutrients. N is generally the most important nutrient in terms of deficiencies in production systems around the world. Excess N is also a problem in malting barley because high grain protein content is an undesirable trait for the industry.

3.3.1  Soil nitrogen acquisition Soil is the source of N for plants. Only a small fraction (nearly 5%) of the soil N is in inorganic forms, primarily as ammonium (NH4+), nitrite (NO2−), and nitrate (NO3−). The NH4+ ion is the preferred form in which plants uptake N because it requires less energy for reduction. However, owing to the high nitrification rate, N is more commonly available as nitrate in well-aerated soils. Nitrate in the soil solution moves rapidly to the roots by mass flow (Oyewole et al., 2013) and is absorbed by root cells mediated by a low-affinity transport system (LATS) that is constitutive and a high-affinity transport system (HATS) that is regulated by intracellular nitrate consumption (Orsel et al., 2002). The barley HATS for nitrate uptake is similar to those reported in other species such as Zea mays L., although maize and barley feature quantitatively different response to the external nitrate level (concentration dependence is rare in barley; Glass et al., 1992). Once nitrate is absorbed by roots, it can be assimilated by the roots or translocated to aerial organs through the xylem. Studies with 15NH4 estimated that roots contributed ca. 20%–30% of whole-plant nitrate reduction (Gojon et al., 1986). Before its incorporation as amino acid, the nitrate is reduced to nitrite by the nitrate reductase enzyme and then to NH4+ by the nitrite reductase enzyme.

3.3.2  Efficiency in the use of nitrogen and its partitioning to the grains Barley yield increases asymptotically in response to soil N availability, as also observed in other crops (Fig. 4.15). The maximum yield achieved and the N level that saturates the response depends on the cultivar and the environmental conditions throughout the growing season. The higher the soil water availability, the higher the soil N level that saturates the yield response, especially in cultivars with high yield potential (Abeledo et al., 2011). The efficiency in the use of N for yield (NUEY, kg kg− 1 N; Table 4.1) can be interpreted as the GY achieved per unit of N available in the soil, and in barley, it varies between 15 and 55 kg kg− 1 N. NUEY depends on: (1) the amount of N absorbed by the crop per unit of N available in the soil (N uptake efficiency, NUpE, kg N kg− 1 Nsoil), and (2) the N utilisation efficiency for yield (NUtEY, kg kg− 1 Nabsorbed), which represents the ratio between yield and N absorbed (Eq. 4.2; Table 4.1). Thus barley yield (GY) can be expressed as the product between soil N availability (Nsoil + fertiliser) and the efficiency at which that N is absorbed by the crop and converted into yield:

GY kg ha 1  Nsoil kg N ha 1  NUpE kg kg 1 N  NUtEY kg kg 1 N


Grain yield (Mg ha-1)



4 NUEY 2

0 0

40 80 120 160 Soil nitrogen availability (kg N ha-1)


FIG. 4.15  Relationship between grain yield and soil nitrogen availability at sowing for barley. The slope of the linear phase represents the efficiency in the use of nitrogen for grain yield (NUEY). Based on Abeledo, L.G., Calderini, D.F., Slafer, G.A., 2011. Modelling yield response of a traditional and a modern barley cultivar to different water and nitrogen levels in two contrasting soil types. Crop Pasture Sci. 62, 289–298.

180  Crop Physiology: Case Histories for Major Crops

Similarly, the shoot dry biomass at maturity (SHB) depends on N uptake efficiency and the N utilisation efficiency for shoot biomass (NUtEB):

SHB kg ha 1  Nsoil kg N ha 1  NUpE kg kg 1 N  NUtEB kg kg 1 N


In barley, NUpE ranges between 0.35 and 0.51 kg kg− 1 N and NUtEY from 31 to 67 kg kg− 1 N, while NUtEB is stable around 100 kg kg− 1 N (Abeledo et al., 2008; Bingham et al., 2012). NUpE is strongly dependent on the availability of water in the soil (Liu et al., 2018). Modern barley cultivars have a significantly higher NUtEY than older cultivars (Abeledo et al., 2008; Bingham et al., 2012). The agronomic efficiency in the use of nitrogen for grain yield (ANUEY, kg kg− 1 N; Table  4.1) is the increase in yield per each unit of fertiliser applied. ANUEY for barley varies between ca. 3 and 60 kg kg− 1 N (Muurinen et al., 2006; Anbessa and Juskiw, 2012; Cossani et al., 2012; González et al., 2019). The variation in the ANUEY values results from the combined variation in NUpE, NUtEY, and the proportion of the N available for the crop from the fertiliser in relation to the native soil N, with low amount of native soil N improving ANUEY; Gaju et al., 2011). Type, timing, and mode of fertiliser application also modified ANUEY (Sieling et al., 1998; Lázzari et al., 2005). In addition, the capture and efficiency in the use of a nutrient is conditioned by the presence of other nutrients (e.g. Prystupa et al., 2004). For example, ANUEY can be increased with an increased availability of zinc in the soil when that nutrient was deficient in the soil (González et al., 2019). The total N absorbed by a crop at maturity (kg N ha− 1) is determined by both the N available in the soil and the N uptake efficiency. The ability to capture resources changes in barley throughout the ontogeny. In barley, the N uptake up to heading is approximately 80% of the total N seasonal absorption (Boonchoo et al., 1998; Lázzari et al., 2005). Therefore N content in grains depend on the efficiency of N mobilisation towards the grains from N stored in the vegetative biomass at heading (Przulj and Momcilovic, 2001; Muurinen et al., 2007; Abeledo et al., 2008). Environmental variations throughout the crop cycle modify the proportion of N absorbed by the crop at heading in relation to that assimilated throughout the whole growing season. Under potential growing conditions, the amount of N uptake during the pre-heading period is the main source of variation between cultivars (Feingold et al., 1990; Przulj and Momcilovic, 2001). Grain nitrogen yield is defined at maturity and can be explained as:

GNY kg N ha 1  BNm kg N ha 1  NHI


where GNY is the grain nitrogen yield, BNm is the amount of N absorbed by the crop in shoot biomass at maturity, and NHI is the nitrogen harvest index (i.e. the ratio of N in grains to total N in shoot biomass). NHI varies between 55% and 85% (Lázzari et al., 2005; Abeledo et al., 2008; Bingham et al., 2012).

TABLE 4.1  Traits related to the efficiency in the use of nitrogen. Trait




N use efficiency for yield

GY (kg ha− 1) per unit of N available in the soil (kg N ha− 1)


kg kg− 1 N

N uptake efficiency

Amount of N uptake by the crop (kg N ha− 1) per unit of N available in the soil (kg N ha− 1)


kg N kg− 1 N

N utilisation efficiency for yield

GY (kg ha− 1) per unit of N absorbed by the crop at maturity (kg kg− 1 N)


kg kg− 1 N

N utilisation efficiency for shoot biomass

Shoot biomass (kg ha− 1) per unit of N absorbed by the crop at maturity (kg kg− 1 N)


kg kg− 1 N

Agronomic N use efficiency for yield

GY (kg ha− 1) per unit of N added by fertilisation (kg N ha− 1)


kg kg− 1 N

Adapted from Muurinen, S., Slafer, G.A., Peltonen-Sainio, P., 2006. Breeding effects on nitrogen use efficiency of spring cereals under northern conditions. Crop Sci. 46, 561–568.

Barley Chapter | 4  181

Differences in the concentration of N in the grains and, therefore, in N harvested per unit area, depend on the amount of N absorbed by the crop throughout the crop cycle and the proportion of N that is partitioned to the grains (Eqs 4.2–4.4). Variations in NUtEY modify grain N concentration because increases in NUtEY determine decreases in the concentration of N in grains (Sadras, 2006).

3.3.3  Critical nitrogen dilution curve The concentration of N in shoot biomass decreases with increasing crop biomass. Dilution curves relate critical N concentration Nc (the minimum concentration of shoot N for maximum growth rate) and shoot biomass W (Lemaire and Gastal, 2009):

Nc  %   a  W  b Mg ha 1


where parameters a and b are species-dependent (Lemaire et al., 2008). In barley, a = 4.76 and b = 0.39 were determined for shoot biomass above 1.79 Mg ha− 1, while a constant Nc = 3.77% was determined for biomass below this threshold (Zhao, 2014). The critical N dilution curve is useful to characterise the N status of the crop through the estimation of the nitrogen nutrition index (NNI), defined as the ratio between the actual N concentration Na and Nc. NNI below 1 indicates N limits growth, and NNI above 1 indicates luxury consumption of N (Fig. 4.16). The inverse of the critical N concentration indicates the amount of shoot biomass produced per unit of N uptake by the crop and allows to characterise NUtEB (Eq. 4.3).

3.3.4  Relationship between grain yield and grain protein concentration The concentration of protein in grains (GPC) is calculated by the concentration of nitrogen in the grain (GNC) and affected by a conversion factor: GPC  %   GNC  %   CF


where CF is a conversion factor of N into protein, typically 6.25. However, Mariotti et al. (2008) suggested CF = 5.60, while FAO uses 5.83 for barley (FAO, 2003). In grain crops, there is often a trade-off between GY and grain N concentration. This is important for both malting barley, which requires grain protein concentration in a narrow range of 10%–12%, and feed barley, which requires higher protein content. To analyse the relationship of barley between GY and the concentration of N, we compiled a data set from the literature (Fig. 4.17). Just in few cases, it was possible to combine high GYs with the range of GNC demanded by the brewing industry (Fig. 4.17). The trade-off between GNC and GY in grain crops also applies for barley as well (Fig. 4.14a and b). However, exceptions can be found in which there was no trade-off (Fig. 4.14c and d). Most likely, the level of soil N availability mediates on whether or not this trade-off is expected. Those conditions in which the trade-off was not observed may correspond to luxury consumption of N for the environment under study.

FIG. 4.16  Relationship between nitrogen concentration in shoot biomass and crop shoot biomass in barley. Based on Zhao, B., 2014. Determining of a critical dilution curve for plant nitrogen concentration in winter barley. Field Crops Res. 160, 64–72.

182  Crop Physiology: Case Histories for Major Crops

FIG. 4.17  Relationship between grain nitrogen concentration and grain yield reported in four studies. The dotted lines represent the range of GNC demanded by the malting industry. The figure was built considering the references that are indicated in each figure. Holm, L., Malik, A.H., Johansson, E., 2018. Optimizing yield and quality in malting barley by the governance of field cultivation conditions. J. Cereal Sci. 82, 230-242. jcs.2018.07.003; Marinaccio, F., Reyneri, A., Blandino, M., 2015. Enhancing grain yield and quality of winter barley through agronomic strategies to prolong canopy greenness. Field Crops Res. 170, 109-118.; Prystupa, P., Ferraris, G., Ventimiglia, L., Loewy, T., Couretot, L., Bergh, R., Gómez, F., Gutierrez Boem, F.H., 2018. Environmental control of malting barley response to nitrogen in the Pampas, Argentina. Int. J. Plant Prod. 12, 127–137.

GNY can be calculated with Eq. (4.4), and it can also be defined as the product between GY and GNC:

GNY kg N ha 1  GY kg ha 1  GNC  % 


GNY is the amount of nitrogen that is extracted from the paddock with harvest, and varied between 10 and 210 kg N ha− 1, with a common value around 100 kg N ha− 1.

3.4  Requirement of other nutrients Table 4.2 shows barley nutrient requirement per unit GY, and Fig. 4.18 represents total requirement as a function of yield. Barley has a high demand for N and potassium (K) per unit of yield. N is the nutrient with the highest extraction rate in grain (i.e. the highest nutrient through the harvest process). In contrast, barley has low phosphorus (P) extraction rate in grains and low P requirements despite its high PHI (Table 4.2). Sulphur (S) is another nutrient with a high HI (associated with the constitution of S amino acids in the barley grain). TABLE 4.2  Barley nutritional requirement per unit of grain yield, the rate of extraction in grain, and the nutrient harvest index (NuHI) for nitrogen (N), phosphorus (P), potassium (K), sulphur (S), calcium (Ca), and magnesium (Mg). Trait











Extraction in grain (kg t )












Total uptake per unit yield (kg t − 1

− 1


From Ciampitti, I.A., García, F.O., 2007. Requerimientos Nutricionales. Absorción Y Extracción De Macronutrientes Y Nutrientes Secundarios I. Cereales, Oleaginosos E Industriales (in Spanish). Archivo Agronómico # 11. IPNI.

Barley Chapter | 4  183

Nutrient requeriment (kg ha-1)






Mg 140 70 0 0






G rain yield (Mg ha ) FIG. 4.18  Relationship between total nutrient uptake per unit of harvested area (i.e. nutrient requirement) and grain yield for barley crops, for nitrogen (N), phosphorous, potassium (K), sulphur (S), calcium (Ca) and magnesium (Mg). Based on Ciampitti, I.A., García, F.O., 2007. Requerimientos Nutricionales. Absorción Y Extracción De Macronutrientes Y Nutrientes Secundarios I. Cereales, Oleaginosos E Industriales (in Spanish). Archivo Agronómico # 11. IPNI.

4  Grain yield and quality As in other crops, GY can be expressed as a function of accumulated biomass during the crop cycle (BT) and the partitioning of shoot biomass to grain (i.e. HI): GY  BT  HI


HI is considered a conservative trait varying between 0.38 and 0.48 (Arisnabarreta and Miralles, 2015). GY often has a closer association with aboveground biomass than with HI (Arisnabarreta and Miralles, 2015). GY can also be described throughout its numerical components: grain number (GN) and grain weight (GW). Moreover, GN can be described through other physiological components, including the crop growth rate and partitioning to reproductive structures as the spike as will be described further.

4.1  Grain number and the critical period As stated earlier, GY is closely correlated to the number of grains per unit area. This yield component is defined from midstem elongation to the beginning of the awn appearance (or first spikelets appearance in the case of awnless cultivars) over the flag leaf ligule. The period comprised between those stages (i.e. from mid-stem elongation to awn appearance) is named ‘critical period’ (Arisnabarreta and Miralles, 2008a) because both the number of fertile tillers and the number of fertile spikelets per spike are defined, and thereby the number of grains per unit area is finally established. Developmentally, the critical period of barley is similar to that of wheat and oat (Fig. 1 in Preface). Grain number can be divided in different yield components as was proposed by Fischer (1984) in wheat: GN  SDWHD  FE HD




CGR   PARia  RUE  / Ds


where SDWHD is the spike dry weight at heading; FEHD is the fruiting efficiency, i.e. number of grains per unit spike dry weight at heading; Ds is the duration of the spike growth period, which in barley is defined between maximum number of spikelet primordia (MNP) and heading (HD) stages (Arisnabarreta and Miralles, 2008b); CGR is the crop growth rate around the spike growth period; Ps is the proportion of dry weight partitioned to the spike; and PARia is the accumulated photosynthetically active radiation intercepted between MNP and HD. Grain number shows different associations with the components described earlier. For instance, grain number per unit area was significantly associated with SDWHD and PARia. In the same way, CGR is significantly correlated with PARia

184  Crop Physiology: Case Histories for Major Crops

(Arisnabarreta and Miralles, 2015). Studies in barley showed that the proportion of biomass partitioned to the spike was positively correlated with the size of the spike at the beginning of stem elongation, and the partitioning to reproductive organs is increased when nutrients (e.g. N) are increased, promoting a higher spike:stem ratio for the same spike dry weight at the beginning of stem elongation. Under potential growing conditions, greater amounts of assimilates allocated to the spike during the pre-heading phase have a strong impact on the number of fertile florets and grains per unit area (Slafer and Rawson, 1994a; Miralles et al., 1998, 2000; González et al., 2003; Prystupa et al., 2004; Arisnabarreta and Miralles, 2008b, Arisnabarreta and Miralles, 2015). Therefore any stress altering the crop physiological status (i.e. growing conditions below the potential) during that phase has an important negative impact on grain number per unit area, and thereby on yield, more so than during other phases of the crop cycle (Fischer, 1985). This is why a strong positive association between grain number and photothermal quotient, Q (i.e. the ratio between PAR intercepted by the crop and temperature during the pre-flowering period - critical period) was found in barley by Arisnabarreta and Miralles (2008a).

4.2  Grain filling As in many other grain crops, the rate of dry matter accumulation in barley grains is initially slow, increasing to a nearly constant rate up to physiological maturity (Gallagher et al., 1976; Alvarez Prado et al., 2013). Usually, grain growth rate ranges from 0.9 to 2.2 mg d− 1 depending on the supply of assimilates and position of the grain within the spike, with grains in the middle of the spike having the highest growth rates (Gallagher et al., 1976; Scott et al., 1983). Final grain size relates to both the rate of growth and duration of grain filling. It is important to highlight that the environmental conditions immediately before anthesis affect the potential size of the grain (Scott et al., 1983; Ugarte et al., 2007). In two-rowed barley, grains are similar in size between both rows. In six-rowed varieties, all three spikelets at each node of the rachis are fertile, and while the central grains are symmetrical, the lateral grains are asymmetric to a greater or lesser extent, each with a right-handed or left-handed bias (Briggs, 1978). Thus the stability of two-row barley grain weight has great significance for malting because penalties apply if grains size does not meet the industry requirements. Dynamic of grain filling is correlated with the dynamic of grain water content (Borras and Westgate, 2006; Alvarez Prado et al., 2013); see for comparison Chapter 1: Maize, Section 2.1 and Chapter 16: Sunflower, Section 2.1. Developing grains accumulate more water in absolute term (mg water) than reserves immediately after flowering until water content is maximised relatively early during the grain-filling period and remains stable during a ‘hydric plateau’ period. The hydric plateau is a short period during middle of grain filling during which the entry and exit of water into the grain are equalised. Once the hydric plateau is finished, grains start to lose water until harvest moisture is reached. The water loss rate during grain filling is negatively associated with the duration of grain filling (Gambín et al., 2007) and can be assumed that grain moisture (in relative terms) at physiological maturity in barley is close to 50% (Bingham et al., 2007; Alvarez Prado et al., 2013). The causes that determine the end of growth in the grain of cereals are still unclear but could be related to a diminishing ability for starch synthesis, caused by enzyme dehydration, rather than to a lack of carbohydrates (Biscoe et al., 1973). The rapid loss of water in the barley grain during ripening seems to be associated with a raise in the concentration of abscisic acid in the endosperm that could increase pericarp permeability, causing grain dehydration (King, 1976; Mounla, 1979).

4.3  Barley uses and grain quality As it was already pointed out, barley has three distinct uses: malting, feed, and food. Nowadays, the most important uses of barley worldwide are feed and malting (Edney, 2010). However, barley was initially used as human food in many parts of the world (Baik and Ullrich, 2008), but it was transformed into animal feed or beer-making material because wheat and rice gained importance (Newman and Newman, 2006). In fact, barley is still the major food in some countries from Asia, Africa, and the Andean region of Ecuador, Perú, and Bolivia. Recent studies have shown that barley has great nutritious quality for human and animal health (e.g. Wood, 2007; Arcidiacono et al., 2019). As a consequence, there is a renewed interest in the USA, Canada, and Europe for food uses of grain barley (Baik and Ullrich, 2008). Most research and breeding effort on barley quality has focused on malting because it has a premium price over feed barley (Ullrich, 2002). In general, physical characteristics (test weight or plump uniform grains) influence feed barley price, and in fact, feed types are designated because they do not reach malting standards (Ullrich, 2002). In this section, we focus on malt as an ingredient for alcoholic beverages and, in particular, for beer production. In barley (as in other field crops), the end-product quality has traditionally been related to the composition and structure of the seed at harvest maturity, as determined by the genotype, the environment, and the management practices (Rondanini et al.,

Barley Chapter | 4  185

2019). There is no simple, clear group of variables that are unanimously regarded as defining the barley quality of grain or malt. Quality requirements in malting barley represent a consensus of the specifications required by commercial brewers to efficiently produce their products consistently with desired properties or traditional methodologies (Savin and MolinaCano, 2002). For brewing, quality traits align fundamentally on the target type of beer (Edney, 2010). Two processes can be recognised: (1) the transformation of barley grains into malt (malting) and (2) the brewing of the malted grains. Briefly, malting is a controlled germination process in which, under adequate humidity and temperature, the enzymes responsible for the degradation of cell walls and the protein matrix are synthesised to facilitate the access of amylolytic enzymes of yeast to starch. In this stage, the grain components are transformed into soluble sugars. The manufacture of beer consists in producing a sugary wort that in the presence of yeast produces an alcoholic fermentation of soluble sugars (Edney, 2010). Therefore the assessment of malt barley quality begins after harvest and continues after malting. The analyses cover physical, biochemical, and metabolic characteristics (Table 4.3). Standardised protocols are followed with strict criteria depending on the malting and brewing products. Assessments are often divided in barley quality prior to malting and the quality of the malt (Table 4.3). A high-quality malt barley cultivar must have a series of physical and biochemical characteristics that favour high malt extract for a given malting process. Varietal purity and germination are the most important quality requirements (Edney, 2010). Each malt variety may differ in its potential and processing characteristics and must germinate uniformly to avoid malting problems. Also, the weight and screening percent of the grains are important for malting uniformity (mainly during germination). The requirement is that the proportion of grains with a screening > 2.5 mm exceeds 85%–90% of the sample (Table 4.3). Protein content is one of the main quality attributes; Section 3.3.4 analysed the trade-off between grain protein and yield. In general, there is a negative relationship between protein content and malting quality that defines a target of 10%–12% protein for malting. Although high percents of proteins reduce malt quality, some protein is necessary to obtain adequate levels of enzymes during the industrial process. β-glucans should not exceed 3.5% as they may cause reductions in starch degradation and other problems in brewing, such as reduced rates of wort separation and beer filtration and the formation of hazes and precipitates. From a biological point of view, malt extract represents a measure of the solubility of the different components of the grain. It is a complex character that results from the interaction of various biochemical processes that are controlled by several genes (Edney, 2010) and usually must be at least 80% according to the industry requirements (Table 4.3). In addition, the presence of high levels of proteins in the malt causes problems when precipitating during brewing (Smith, 1990), but as in grain, minimum levels of protein are necessary to ensure enzymatic production. High levels of βglucans in malt means an incomplete degradation of cell walls, which decrease the quantity of the extract produced (Fincher and Stone, 1993) and, on the other hand, form high viscosity aqueous solutions that cause problems in the filtering process during brewing (Fincher and Stone, 1993). Free amino nitrogen (FAN) is an indication of available nitrogenous compounds for yeast nutrition (Edney, 2010), and diastatic power is a quantification of starch-degrading enzymes required for distilling (Edney, 2010). All in all, the final quality of the malt is influenced by environmental and managements factors that affect the growth and development of the grain and the malting process (Savin et al., 2004). The malting and brewing processes act on the raw material, i.e. barley grains, the quality of which is strongly dependent on their composition. The environment during

TABLE 4.3  Physical and chemical traits and standards for barley grain and malt. Traits

Requirement for industry


Colour Varietal purity Germination Screening > 2.5 mm Optimum total proteins β-glucans

Yellowish, disease-free 100% At least 95% 85%–90% 10%–12%  80% 4%–5.5% >150 ppm   35°C) during reproductive phase (Prasad et al., 2006, 2009; Djanaguiraman et al., 2014; Singh et al., 2016). Short episodes of high temperatures between 36°C and 38°C around flowering have been reported to be damaging to a sorghum crop in Australia. However, genetic diversity can be utilised to breed varieties with improved tolerance to heat stress. Genotypic differences in heat tolerance do exist in sorghum (Nguyen et al., 2013). The effect of high temperature on seed set operates for about 12–15 days between flag leaf and the start of grain filling. This effect on seed set is cumulative and is a function of both intensity and duration. There are two aspects: (a) threshold temperature and (b) tolerance above the threshold. Singh et al. (2015) reported genotypic differences in seed set percentage for both the threshold temperature (36–38°C) and the tolerance to increased maximum temperature above that threshold. Similar threshold temperatures of 36°C for maize (Dupuis and Dumas, 1990) and 38°C for rice (Tenorio et al., 2013) have been reported. APSIM simulations have indicated that increasing the threshold temperature is more important than the temperature above that threshold for sorghum, where an increase in threshold temperature minimised the adverse yield effects significantly. But selection of genotypes for increased heat tolerance above the threshold temperature is also important because 1–5°C temperature increase is predicted in future (CSIRO, 2007). The US sorghum breeding programme reports lower thresholds (i.e. 33°C) and lack of genetic diversity for heat tolerance (Tack et al., 2017). However, in this study, heat stress was assessed by the yield reduction in a large metaanalysis of trials across a wide range of temperature conditions. However, this analysis did not allow the effects of heat stress on the reproductive biology to be separately assessed. In any case, the main effect of high temperature appears to be on the reproductive biology, especially pollen germination and seed set (Prasad et al., 2006), whereas the effects on plant growth and photosynthesis are considered to be minor (Jain et al., 2007; Prasad et al., 2008; Nguyen et al., 2013). Heat stress resulting in poor seed set can be compensated by increased mass of the grain (Yang et al., 2012), but in another study, reduced seed set in heat-susceptible genotypes was not compensated by increased seed mass (Singh et al., 2015). Similarly, Prasad et al. (2006) reported no effect on seed mass, although seed set percentage significantly declined as temperatures increased from 32°C to 36°C. In fact, decreased seed mass did not eventuate until temperatures reached 40°C. Findings of these studies, and increasing incidents of short episodes of high temperatures > 35°C around flowering, reinforce that the adverse effects of climate change on grain yield in sorghum crops are more likely to be owing to increased incidence of heat stress rather than drought. Therefore more emphasis on tolerance to heat stress is warranted in breeding programmes (Lobell et al., 2015). Sorghum originated in the semiarid tropics and is generally sensitive to low-temperature stress (Yu et  al., 2004). However, in growing regions of Australia, this is not an issue unless late-sown sorghum is maturing in decreasing autumn temperatures in northeastern Australia. Low-temperature stress ( 300 000 plant per ha). As a result, yield response to increasing plant density tends to flatten at relatively low densities (Board, 2000; De Bruin and Pedersen, 2009; Cox et al., 2010). In stressful conditions (e.g. soil water deficit), branching and leaf expansion temporarily cease; in contrast, primordia formation and leaf appearance are largely unaffected. Similarly, LAI development is modest when the crop cycle length is too short (Edwards and Purcell, 2005). Hence increasing plant density may be needed in these situations to ensure a reasonable leaf area at the time of the critical period. Row spacing can also be used to increase absorption of solar radiation. Narrowing row width in soybean fields leads to earlier canopy closure, which may increase capture of incoming solar radiation during the critical period for yield determination (Andrade et al., 2002). In favourable environments in the Corn Belt, narrowing row width (from 76 to 38 cm) is a management option to increase capture of solar radiation when LAI development is insufficient for near-full absorption of the incident radiation during the critical period. For example, Andrade et al. (2019) found a positive yield response to narrowing row width in environments where LAI development was limited by a short duration of the VE-R3 phase owing to late sowing and/or use of early MG cultivars. In contrast, yield response to narrow row width was nil in optimally managed soybean sown early using a full-season MG. In Argentina, narrowing row width from 52 to 35 cm is also common when growing an early-maturing soybean cultivar in early sowings or when soybean is sown after

FIG. 8.11  (a) Fraction of absorbed photosynthetically active radiation (fA) as a function of green leaf area index. The fA was estimated as the ratio between absorbed and incident photosynthetically active radiation (PAR). (b) Seasonal dynamics in the fraction of PAR that was absorbed by the green canopy (fA), transmitted to the soil (fT), and reflected back to the atmosphere (fR). (c) Aboveground dry matter (including abscised leaves) as a function of accumulated absorbed PAR during the crop season. The linear regression was fitted within the range in which increasing radiation resulted in increased dry matter accumulation; slope of the relationship represents the radiation-use efficiency. Data from seven high-yield experiments (range: 5.3–6.7 Mg ha− 1) conducted in Nebraska (USA) at 76-cm row spacing during two-crop seasons (2016 and 2017). See text for explanation on calculation of developmental stages. Adapted from Cafaro La Menza et al. (2020).

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harvest of a winter cereal crop; in both cases, the goal is to compensate for the shortening of the vegetative phase and the low water availability early in the season (late sowing). Narrowing rows also helps weed control. As documented in the 1960s by Shibles and Weber (1965), dry matter accumulation in soybean is proportional to the amount of IPAR, with the slope of the relationship (with zero-intercept) representing the radiation-use efficiency (RUE). Despite its general stability, RUE can vary with temperature, CO2 concentration, total radiation, proportion of diffuse solar radiation, leaf N content, soil water deficit, and O3 level (Sinclair and Muchow, 1999 and references cited therein). Measured RUE in optimally managed irrigated soybean experiments in Nebraska was 2.19 g MJ− 1 APAR (or 2.14 g MJ− 1 IPAR) for most part of the crop season (Fig. 8.11c), which falls within the range reported in the literature (Andrade, 1995; Sinclair and Muchow, 1999; Van Roekel and Purcell, 2014 and references cited therein) and is well below that reported for maize (3.8 g MJ− 1 APAR; Lindquist et al., 2005). The RUE differential between soybean and maize reflects differences in photosynthetic pathway (C3–C4), canopy architecture, and energy content of both vegetative and reproductive organs. Slightly lower RUE, observable in just the early and late parts of the season, may be attributable to early low temperature and lower leaf photosynthetic capacity and the late decline in leaf N content in senescing canopies (Rochette et al., 1995; Sinclair and Muchow, 1999). Rattalino Edreira et al. (2020) estimated average conversion of the total (VE–R7) incident PAR into seed yield to be ca. 0.8% in producer soybean fields in the Corn Belt, with an upper limit between 1.0% and 1.2%. In the 1980s, soybean breeders measured canopy apparent photosynthesis (CAP) using closed field chambers to examine the correlation between CAP and yield. The correlation was relatively strong (r = 0.6), and newer cultivars released in the 1980s had higher CAP than older cultivars released in the prior decade, but not many cultivars were examined (Boerma and Ashley, 1988; Ashley and Boerma, 1989). In theory, selection for higher CAP would be expected to enhance yield, but CAP is an unsuitable trait for breeding applications. About 25 years later, comparison of a historical set of 24 MG III cultivars released from 1923 to 2007 showed that all three traits in the Monteith (1977) equation, efficiency in light interception, RUE, and harvest index (HI), were higher in modern cultivars (Koester et al., 2014, 2016). Total daily leaf CO2 uptake (A′), maximum rate of Rubisco carboxylation (Vc,max), electron transport (Jmax), and night respiration rate were measured for the same set of cultivars on 14 different days spanning V5–R6 in each growing season. Maximum photosynthetic capacity (based on Jmax and Vc,max) and night respiration rates did not change consistently with year of cultivar release. On 8 of the 14 measurement days, linear regression of A′ on cultivar release year was statistically significant, totalling a + 12% total increase in A′ over 85 years (calculated based on those measurement days with a significant trend). Higher A′ in newer– older cultivars was associated with greater rates of leaf photosynthesis in the afternoon during the R3–R6 phase as a result of greater stomatal conductance when soil water content was high. Seed yield has nearly double during the same 80-year time span, suggesting a very modest contribution of increased leaf photosynthesis to the overall yield gain. Transgenic approaches have been effective at deploying cultivars with tolerance to herbicides; these cultivars have been massively adopted by producers in USA, Argentina, and Brazil during the past 25 years, facilitating weed control and reducing labour. An unintended consequence has been build-up of herbicide-resistant weeds. More recently, insectresistant cultivars have been released in South America to control Lepidoptera. Increasing yield through radical changes in leaf photosynthesis efficiency through the use of transgenic approaches has received considerable attention (and funding) in recent years (Zhu et al., 2007, 2010). Despite expectations to deliver commercially available cultivars with 50% higher yield potential within 10–15 years (Long et al., 2006), these efforts have not yet resulted in any cultivar release with one or more transgenic changes leading to a proven superior yield. Changes in leaf photosynthesis do not necessarily translate into changes in CAP as it has been documented by Pettigrew et al. (1989) when comparing chlorophyll-deficient soybean isolines–their normal pigmented wild type. Stimulated by this apparent ‘overinvestment’ of N in chlorophyll, there have also been recent efforts to develop reduced-chlorophyll cultivars. Proponents claim that these cultivars would exhibit better light distribution within the canopy, which, together with reallocation of more plant N to rubisco, might lead to higher RUE and yield (Ort et al., 2011, 2015). This approach has been unsuccessful so far at developing cultivars with higher (or even similar) yield performance than normal pigmented cultivars as a result of lower IPAR early in the season, without any detectable change in RUE (Slattery et al., 2017).

3.2  Capture and efficiency in the use of water 3.2.1  Capture of water The soybean root system is characterised as diffuse but has three distinct morphologically defined components: the primary tap root that originates as the radicle from a germinating seed, the lateral roots, often referred to as secondary roots that emerge from the taproot, and the tertiary roots that originate from lateral roots (Lersten and Carlson, 2004; Torrion et al., 2012). The primary root is strongly geotropic and typically has a large diameter (Mitchell and Russell, 1971). Similar to

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other crops with taproot systems, soybean has nearly two-thirds of the roots in the upper 30 cm of the soil profile (Fan et al., 2016). Despite qualitative differences in root system type between soybean and maize (taproot–fibrous), distribution of root dry matter and root length with depth is remarkably similar between the two crops (Nichols et al., 2019). In soil without physical or chemical constraints, the rate of root growth is modulated by soil temperature and ceases during pod setting and early seed filling (Fig. 8.10b). Reported daily rates of root growth ranged from 1.2 to 3.9 cm d− 1 (Ordóñez et al., 2018 and references cited therein). The upper range of daily root growth rates is consistent with the maximum rate of 4 cm d− 1 proposed by Calmon et al. (1999). Once the roots reach a soil layer and attain a critical root length density in the layer, soil water content decreases quickly with time, especially when the transpiration demand is greater than the available soil water (Dardanelli et al., 2003, 2004). The rate at which water content declines in a given soil layer also depends upon physical factors that impede a uniform root distribution (e.g. high clay content and soils with vertic properties). In deep soil layers, water extraction rate is lower as a result of insufficient time to develop the root system. Soybean root systems can reach a maximum depth from 150 to 220 cm (Bland, 1993; Borg and Grimes, 1986; Dardanelli et al., 1997, 2004; Kaspar et al., 1978; Fan et al., 2016; Ordóñez et al., 2018). Cultivar MG, physical or chemical soil constraints, and presence of water table influence root distribution, maximum root depth, and the time when the latter is attained. Ordóñez et al. (2018) found that maximum rooting depth in soybean grown across 10 site-sowing date combinations in Iowa, USA varied from 88 to 154 cm and was closely related with the depth of water table near the time when root growth ceased. Similarly, low pH and high Al concentration reduces root grow in tropical weathered soils; these soils require periodic lime or gypsum application to alleviate acidity to ensure deeper root systems, greater water and nutrient extraction, and sustain high crop yields (Marsh and Grove, 1992; Caires et al., 2008; Pivetta et al., 2011; Battisti and Sentelhas, 2017). Another example of physical constraints to root depth is a petrocalcic horizon (‘caliche’) in the southeastern Pampas that limits crop water extraction, increasing the chances of water stress in the middle of the summer crop season (Calviño and Sadras, 1999; Sadras and Calviño, 2001). As a result, yield of soybean and other summer crops decreases with reduced soil depth. Despite its relatively late critical period for yield determination (Fig. 8.8) and greater plasticity to tolerate episodic water stresses, soybean is not the best option for this environment because it consumes most of the available soil water before the critical period. More suitable management alternatives to deal with shallow soils include (1) switching to winter crops that grow during a time of the year with substantially lower evaporative demand (winter and spring) and (2) sowing determinate summer crops (e.g. maize) at low plant density to reduce transpiration during the vegetative phase and deferred soil water to the critical period for yield determination (Calviño and Cerrudo, personal communication). Transitory water stress can be detrimental to soybean yield and the differential sensitivity of yield to water stress in relation to crop development has been widely investigated (e.g. Korte et al., 1983; Kadhem et al., 1985; Andriani et al., 1991). In these studies, the yield penalty owing to water stress was minimal or nil when water stress occurred at R1–R2 but high and consistent when it coincided with R3–R6. These findings were consistent with the critical period shown in Fig. 8.8a. A practical application of these results is that, in environments with high available soil water at sowing, as it is the case of most agricultural soils in Nebraska, irrigation can be deferred until the onset of R3 with little risk of water stress, generating yields similar to yields of fully irrigated crops during the whole season but using less irrigation (Torrion et al., 2014). Whilst these studies focused on water stress owing to insufficient water supply, transitory waterlogging events owing to excessive precipitation, presence of shallow water tables, and/or poor soil drainage can also lead to yield reduction, especially when timing of water excess coincides with reproductive stages (Scott et al., 1989; Linkemer et al., 1998; Nosetto et al., 2009). Underlying physiological mechanisms include impaired root capacity to absorb water and nutrients, reduced N fixation, lower leaf expansion and photosynthetic rates, and earlier leaf senescence (Oosterhuis et al., 1990; Bacanamwo and Purcell, 1999; Boru et al., 2003). Not surprisingly, it has been challenging to incorporate soybean to lowland rice-based systems (Bajgain et al., 2015; Theisen et al., 2017).

3.2.2  Water use efficiency Leaf stomata must be open to allow the entry of CO2 into the leaf for photosynthesis, thus allowing the simultaneous exit of H2O (i.e. transpiration) from a humid leaf interior. Dry matter accumulation and consequently, seed yield are inextricably linked to transpiration (Passioura, 1977; Sinclair et al., 1984). Indeed, a linear response of total plant biomass to seasonal transpiration has been documented for most annual crops and, in general, there is minimal variation in water-use efficiency (WUEDM; dry matter per unit of transpiration) within C3 or C4 species (Sinclair et al., 1984). Daytime VPD influences WUE with lower VPD increasing WUE (Tanner and Sinclair, 1983; Sinclair et al., 1984). When WUEDM is normalised to a seasonal daytime VPD of 1 kPa, and roots are accounted for its calculation, the resulting WUEDM (typically referred to ‘kd’ in the literature) for soybean is relatively stable across environments, with values ranging from 4.0 to 4.4 kPa (Tanner and

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Sinclair, 1983; Suyker and Verma, 2010; Connor et al., 2011), which are roughly half of those reported for maize. Hence early sowing in the Corn Belt not only allows greater capture of the seasonal available solar radiation but also shifts the crop cycle towards a time of the year with lower VPD and hence higher WUEDM (Purcell et al., 2003). Ultimately, seed yield will depend, in addition to the amount of crop transpiration and daytime VPD, on the partitioning of dry matter to seed (see Section 3.4). Using producer-reported data from rainfed and irrigated soybean fields in Nebraska, Grassini et  al. (2015) derived a boundary function with x-intercept = 73 mm and slope = 9.9 kg ha− 1 mm− 1 for the relationship between yield and water supply (Fig.  8.12a). These two parameters are biophysically meaningful: the x-intercept gives a coarse estimate of the seasonal soil evaporation, whilst the slope estimates WUEY. The boundary function defined for Nebraska appears also to provide a reasonable upper limit for rainfed and irrigated soybean crops in other environments (Fig. 8.12b). The estimated soybean WUEY of 9.9 kg ha− 1 mm− 1 was similar to reported values for other legume and oilseed crops, ranging from 7 to 12 kg ha− 1 mm− 1 (Sadras et al., 2011; Connor et al., 2011 and references cited therein). In contrast, soybean WUEY was lower than those reported for cereal C3 crops such as wheat and barley (range: 20–30 kg ha− 1 mm− 1), primarily because of higher synthesis cost of seed biomass and differences in seasonal VPD (Specht et al., 1999, 2014). Likewise, soybean WUEY was well below maize WUEY of 30–40 kg ha− 1 mm− 1, which is also explained, in addition to seed biomass composition, by their respective C3–C4 photosynthetic pathways. The distribution of data points in Fig. 8.12a indicates no further yield increase for water supply ≥ 650 mm, suggesting that such amount of water supply, when adequately sustained by well-distributed precipitation or irrigation events during the growing season, should be sufficient to satisfy crop water requirements for highest yields in the western Corn Belt. Many irrigated fields exceeded this threshold, suggesting ample room to reduce irrigation without hurting yield by adjusting irrigation scheduling based on crop water requirements (Gibson et al., 2019). Interestingly, for any given water supply, producer yield was typically higher in irrigated than in rainfed fields, and this was especially notable for fields with abundant water supply (> 650 mm) (Fig. 8.12a). An ill-distributed precipitation pattern during the growing season might expose rainfed crops to transitory water stress during the critical period, even in fields with seasonal water supply exceeding 650 mm. Similarly, intense precipitation events early in the season would favour unproductive water losses. Other nonwater related factors also limited rainfed productivity. For example, fields located in best soils and flat terrain are typically allocated to irrigated production. Likewise, rainfed fields received less intensive management (less frequent P fertilisation, foliar fungicide) and were sown later when compared with irrigated fields, which reduces crop cycle length and exposes the critical period to less favourable environmental conditions (Fig. 8.9). Although much research effort has been expended in the past to improve legume crop WUEY through breeding (Vadez et al., 2014), those efforts have had, so far, only minimal impact. For example, use of carbon isotope discrimination (CID) as a proxy for WUEY has generated mixed results that, when coupled with a high per-sample cost of CID, has not persuaded many soybean breeders to routinely use it. Another reason is that cultivars exhibiting slower soil water extraction (commonly refer to as ‘slow wilting’ phenotypes) can, in principle, ‘save’ water that can be used at a later crop stage (assuming the saved water is not lost via soil evaporation). However, slow wilters are notorious slow growers and typically have

FIG.  8.12  (a) Relationship between producer-reported soybean yield and seasonal water supply in rainfed and irrigated fields. Water supply was estimated as the sum of available soil water at sowing, in-season precipitation, and total irrigation. Parameters of the boundary function are shown. (b) Relationship between soybean yield and seasonal crop evapotranspiration based on data reported in the literature for 116 rainfed and irrigated crops grown in experimental field plots in the western Corn Belt (Elmore et al., 1988; Specht et al., 1989; Payero et al., 2005; Suyker and Verma, 2009; Aiken et al., 2011), Argentina (Dardanelli et al., 1991; Della Maggiora et al., 2000), and Italy (Casa and Lo Cascio, 2008). Dashed black line in (b) indicates the linear regression for the pooled data (r2 = 0.70). Adapted from Grassini et al. (2015).

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s­ ignificantly lower RUE than fast wilters (Ries et al., 2012). Slow wilters are thus best used in production systems with a high frequency of terminal drought, where yield is already severely limited by insufficient water. Low RUE genotypes do not fare well in the high-yield soybean production regions of the Corn Belt, where protracted droughts are infrequent, although short intermittent periods of precipitation scarcity do occur. In the past decade, the focus on WUE improvement has shifted towards genotypes in which the transpiration response to VPD is not linear (which is common in most high-yielding commercial cultivars) but instead exhibits a linear-plateau (L-P) response in which leaf transpiration ceases to increase after the L-P breakpoint is reached (Fletcher et al., 2007; Gilbert et al., 2011; Sinclair et al., 2017). The physiological foundation for this L-P pattern has not been fully characterised but in essence is a mid-day type of stomatal closure triggered when the VPD reaches a genotype-dependent specific value. This is essentially a water-saving trait, but it is now termed an ‘effective water use’ strategy (i.e. shifting crop water use from earlier to later in the season). Proponents claim that the conservation of soil water through this shift would improve crop growth because the water shift should enhance HI (Sinclair, 2018). However, it remains to be seen if genotypes exhibiting limited transpiration also suffer from a lower RUE that slow wilters exhibit owing to stomatal closure lessening photosynthetic carbon gain. A thorough review on conservative water use strategies in legume crops can be found elsewhere (Blessing et al., 2018). The foregoing approaches are effectively designed to lessen stomatal conductance, thereby improving WUE by reducing the WUE denominator. Despite this effort, commercial successes are rare. In a key paper documenting the change in transpiration parameters over the course of 80 years of breeder selection focused solely on yield, Koester et al. (2016) documented that modern soybean cultivars could upregulate their stomatal conductance in the days after soil water content was increased as a result of a precipitation event (or an irrigation event), compared to the older cultivars that could not do that. This suggested that intense breeder selection for yield generated another correlated response that amounted to a more opportunistic stomatal behaviour in modern varieties (i.e. respond with open stomata when soil water was available). In reality, this is an under-appreciated inductive stomatal response that leads to greater carbon gain in modern cultivars grown with intermittent rather than protracted or terminal drought and soils with high plant-available water holding capacity. So-called conservative water saving strategies typically exhibit a constitutive nonresponse to greater soil water availability and thus continue to save water that could have been used for more plant growth. This discovery of stomatal upregulation follows on the heels of intensive review of greater stomatal conductance and its impact on crop productivity (Roche, 2015), and the fact that modern soybean cultivars have cooler canopies because of their greater stomatal conductance (Keep et al., 2016). Similar findings have been reported for cotton (Radin et al., 1994) and wheat (Amani et al., 1996; Fischer et al., 1998).

3.3  Capture and efficiency in the use of nitrogen Soybean has ca. four times higher N requirements per unit of seed mass than does maize. Total N assimilation is proportional to dry matter accumulation: a soybean crop accumulates ca. 33 and 80 kg N ha− 1 Mg− 1 of aboveground dry matter and seed yield, respectively (Salvagiotti et al., 2008; Bender et al., 2015; Tamagno et al., 2017) (Fig. 8.13a). Hence a total N uptake of 240 kg N ha− 1 would be required to sustain current yield in the USA, Argentina, and Brazil of ca. 3 Mg ha− 1. About 75% of accumulated N in soybean is absorbed after R3. Soybean exhibits an attenuated dilution curve when compared with other C3 crops (Divito et al., 2016) (Fig. 8.13b), which is independent from variation in management and environmental factors (Divito et al., 2016). Analyses of dilution curves in soybean only considered samples collected before R5 because the intense N remobilisation from nonseed organs to seeds during the seed filling modifies the dilution pattern (see Section 3.4). Soybean rarely receives N fertiliser in producer fields, except for (sometimes) a small application as ‘starter’ at sowing. Hence soybean relies on: (1) absorption of N that is available in the soil from organic matter mineralisation, residual soil inorganic N left unused by previous crop, and dry and wet atmospheric deposition and (2) reduction of atmospheric N2 by rhizobia (Bradyrhizobium japonicum) in the root nodules. Soybean nodules are determinate because cell division ceases early during nodule development and the final spherical shape results from cell enlargement rather than cell division (Hirsch, 1992). Reduced N compounds are exported out of the nodule as ureides and transported through xylem to the shoots where they are catabolised (McClure and Israel, 1979; Atkins et al., 1982). Symbiotic N fixation in legumes involves substantial changes in morphology and physiology, with the root nodule representing an added sink for carbon that competes with other plant organs. The practice of inoculation involves coating the seed with inoculum produced from cultured bacteria. In fields without history of soybean cultivation, it is important to have seeds inoculated over a number of years (Dunigan et al., 1984; Brutti et al., 1998). For example, soybean was a relatively new crop in the Cerrados, requiring seed inoculation with proper strains for adequate nodulation for N fixation (Alves et al., 2003). In contrast, inoculation is no longer practised in areas with long history of soybean cultivation where Bradyrhizobium strains are well established, as it is the case of the Corn Belt (De Bruin et al., 2010; Leggett et al., 2017).

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FIG. 8.13  (a) Seasonal dynamics for total (circles) and fixed N (triangles) in aboveground dry matter (ADM) in soybean. Inset shows the physiological nitrogen-use efficiency derived from the slope of the relationships between ADM or seed yield (at 13% moisture content) and accumulated N at physiological maturity (R7 stage) based on Cafaro La Menza et al. (2019). (b) Relationship between shoot N concentration and ADM. The Generic N dilution curve proposed by Greenwood et al. (1990) for C3 crop species is shown for comparison purposes (dashed curve; y = 5.67 × − 0.5). Average shoot nitrogen concentration and ADM at R1, R3, and R5 stages are shown. Soybean data were collected in five high-yield experiments (range: 5.3–5.8 Mg ha− 1) conducted in Nebraska (USA) during two-crop seasons (2016 and 2017). Crops did not receive N fertiliser during the crop season. See text at the beginning of Section 3 for explanation on calculation of developmental stages. Adapted from Cafaro La Menza et al. (2020).

The symbiosis between the host plant and the bacteria occurs early in the crop cycle: it can be recognised from nodules on the fine roots at V3. After a lag phase with small N fixation, the nodule starts to supply N to the host plant, but it is not until pod setting (R3–R5) that N supply from fixation becomes significant, supplying most of the N accumulated during seed filling (Fig. 8.13a). In high-yield fields in fertile soils in Nebraska, N fixation has been estimated to contribute with ca. 65% of total N accumulated in aboveground biomass at R7 (Cafaro La Menza et al., 2020), which is consistent with other reports from the Corn Belt and elsewhere (Salvagiotti et al., 2008; Ciampitti and Salvagiotti, 2018; Córdova et al., 2019). Although any factor that affects crop growth would invariably impact N fixation, soil variables with greatest influence on N fixation include N and water content, P supply, pH, and temperature (Cassman et al., 1980; Zhang et al., 1995; Purcell et al., 1998; Collino et al., 2015; Santachiara et al., 2019). The contribution of N fixation to total N uptake correlates negatively with the indigenous N supply (Santachiara et al., 2017; Tamagno et al., 2018; Cafaro La Menza et al., 2019), which is consistent with the observation that high soil N prevents formation of new nodules and, especially, reduces N fixation of existing nodules (Norman and Krampitz, 1946; Thornton, 1947; Allos and Bartholomew, 1955; Pate and Dart, 1961; Streeter, 1988; Denison and Harter, 1995; Arrese-Igor et al., 1997). N fixation is more sensitive to soil water deficit or excess compared with other plant physiological processes such as leaf expansion, photosynthesis, and uptake and assimilation of inorganic soil N (Purcell et al., 1998, 2004; Bacanamwo and Purcell, 1999). One of the implications of these differentials in sensitivity to water stress is that soybean will rely more on soil mineral N when water becomes limiting or excessive. The role of P supply on N fixation is discussed in Section 3.5. As soybean yield continues to increase, it is uncertain the degree to which indigenous soil N and N fixation can meet the proportionally higher N requirements. For example, yields ranging from 4.5 to 6.0 Mg ha− 1 had an associated N uptake requirement from 360 to 480 kg N ha− 1. Given that indigenous soil N typically ranges between 100 and 150 kg N ha− 1 across producer fields in the Corn Belt, a question of concern is whether N fixation can fully meet crop N requirements. Cafaro La Menza et al. (2017, 2019) investigated this issue in irrigated experiments in USA and Argentina, following a meticulous protocol involving a ‘control’ treatment (hereafter called ‘zero-N’) that forced the crop to rely on site-specific biological N fixation and indigenous soil N supply and a ‘full N’ treatment specially designed to provide the crop with an ample N supply to optimally match seasonal crop N demand. These authors found a clear N limitation in high-yield environments exceeding 4.5 Mg ha− 1, with the yield difference between treatments averaging 0.6 Mg ha− 1 as a result of differences in N uptake. Recent research has aimed to close this N-driven yield gap in a cost-effective way by evaluating the yield response to different combinations of N-fertiliser type, amount, placement depth, and timing, and its associated economic profitability (e.g. Salvagiotti et al., 2009; McCoy et al., 2018). In general, yield response to N fertiliser application has been inconsistent and not cost-effective as a result of the trade-off between N fixation and soil N absorption (Salvagiotti et al., 2008; Mourtzinis et al., 2018a; Tamagno et al., 2018). Opportunities to alleviate this trade-off have been proposed, although a proof-of-concept is pending and the timeline for impact is uncertain (Denison, 2015). Without

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changes in N fixation efficiency, N supply will become (if not already) a yield-limiting factor in high-yield soybean production environments (> 4.5 Mg ha− 1) as producers in those systems continue to fine-tune their agronomic practices and adopt high-yielding cultivars. Experimental evidence indicates that new cultivars accumulate more N than older ones. Specht et  al. (1999) and Kumudini et al. (2002) noted that total N accumulation was greater in higher yielding modern Canadian cultivars as a result of greater N accumulation during seed filling but not earlier in the crop cycle. The same authors could not detect statistical differences in N harvest index (NHI). Interestingly, their data also indicated that this arose from proportionally more N provided from fixation than from soil N uptake, which is consistent with the notion that, as N requirement increases with higher yields, more N needs to come from fixation (Santachiara et al., 2017). In a separate study, Rotundo et al. (2014) assessed 17 biomass and N traits in a pool of elite cultivars—25 from Argentina and 65 from USA. Consistent with previous studies, the authors found larger N uptake as the key driver explaining high yields. Interestingly, some highyield cultivars had higher N uptake during the vegetative phase (VE–R1) and seed filling (R5–R7), whilst others relied mostly on N uptake during the R1–R5 phase. Highest-yielding cultivars differed in the physiological strategies to attain maximum yield. For example, some of them combined high NUE and HI traits, whilst others exhibited high NHI and similar seed N concentration.

3.4  Dry matter and nitrogen partitioning Dry matter and N accumulation in soybean follow a sigmoidal pattern, with maximum rates during pod setting and early seed filling (Fig. 8.14). In near-optimal conditions, ca. 75% of total dry matter and N accumulation occurs after R3. Early in the season, roots, leaves, and stems have priority for the new plant dry matter. Subsequently, partitioning to roots declines compared with the amount of dry matter that is allocated to growing leaves, petioles, and stems. During seed filling, most of the new dry matter is partitioned to the growing pods whilst dry matter of stems and petioles declines, indicating some remobilisation of nonstructural carbohydrates to the growing seeds as documented in earlier studies (Hume and Criswell, 1973; Stephenson and Wilson, 1977; Yamagata et al., 1987). Back-of-the-napkin calculations on the efficiency to remobilise nonstructural carbohydrates for seed production have yielded estimates of ca. 0.5–0.6 g seed g− 1 (Borras et al., 2004). In near-optimal conditions, the ratio between seed and shoot biomass at R7, that is HI, is ca. 40%, which is lower compared with cereal crops such as wheat and maize (HI range: 0.45–0.55).b This differential reflects the higher construction costs associated with synthesis of seed oil and protein (Amthor et al., 1994). When evaluated on a glucose equivalent basis, soybean HI averaged 0.49, which is comparable to the HI reported for cereal crops (Cafaro La Menza et al., 2019). Although HI is relatively stable across a range of G, E, and M practices (Spaeth et al., 1984; Egli et al., 1985; Egli, 1988), it can change when vegetative growth is favoured over reproductive growth or vice versa. For example, HI tends to decline with increasing duration of the crop cycle owing to more vegetative growth (Schapaugh Jr. and Wilcox, 1980; Egli, 2011). Similarly, HI can change as a result of water deficit, with direction and magnitude of the change in HI depending upon timing of water stress in relation with vegetative and reproductive growth (e.g. Pandey et al., 1984a; Specht et al., 1986; Andriani et al., 1991). Before seed filling, most N resides in leaves, stems, and petioles, mostly as a constituent of the rubisco enzyme (Fig. 8.14). Nitrogen remobilisation from nonseed organs to the growing seed is larger compared with dry matter remobilisation. During the seed filling, ca. 60% of the N accumulated in leaves, stems, and petioles at R5 is remobilised to seed (Ortez et al., 2019; Cafaro La Menza et al. 2020), leading to quick leaf senescence and decline in leaf N and RUE, which has been referred to as ‘self-destruction’ (Sinclair and de Wit, 1975, 1976). So, whilst the number of seeds set during R3–R6 imposes an upper limit to yield, the capacity of the crop to meet the carbon and N demand of the growing seeds ultimately determines the final yield, which is consistent with the source–sink co-limitation proposed for soybean by Borras et al. (2004). At R7, ca. 70% of total aboveground N is located in the seed biomass (i.e. NHI). The large N requirement per unit of seed yield, together with a high NHI, typically leads to a negative N balance in soybean fields, with the latter estimated as N fixation minus grain N removal (Giller and Cadisch, 1995; Santachiara et al., 2017). This apparent negative N balance (which does not account for N in root and nodule biomass and from rhizodeposition) may be partially alleviated when soybean is rotated with cereal crops receiving N fertiliser inputs as is the case in the dominant maize–soybean rotation in the Corn Belt (Tenorio et al., 2020).


Estimated HI based on aboveground biomass and seed yield on a dry-matter basis; aboveground biomass included abscised leaves and petioles (Cafaro La Menza et  al., 2017, 2019). Many studies do not account for abscised leaves and petioles, leading to an overestimation of HI as discussed by Schapaugh Jr. and Wilcox (1980).

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FIG. 8.14  (a and b) Dry matter and nitrogen (N) in roots, leaves, stems plus petioles, pod walls, and seeds, expressed as a fraction of the total dry matter or accumulated N at R7 stage. (c and d) Derivatives from the lines fitted in (a) and (b) indicating the relative rate in dry matter and N accumulation. Positive rates indicate accumulation, and negative rates indicate remobilisation. See text at the beginning of Section 3 for explanation on the calculation of developmental stages. Adapted from Cafaro La Menza et al. (2020). Root biomass was estimated following Setiyono et al. (2010).

3.5  Other nutrients Seasonal patterns of accumulation of other mineral nutrients (P, K, S, Ca, Mg, Fe, Mn, Zn, B, and Cu) follow sigmoidal patterns similar to the ones reported for dry matter and N in Section 3.4 (Harper, 1971; Hanway and Weber, 1971; Bender et al., 2015; Gaspar et al., 2017a, 2018). Maximum rates of nutrient uptake occur around R3, except for K that peaks earlier (R1–R3). Nutrients with high HI (besides N) include P, S, and Cu, ranging from 60% to 80% of total nutrient accumulation at R7 (Bender et al., 2015; Gaspar et al., 2017a, 2018). Leaves are the major source of remobilised N, P, and Cu, whilst stems and petioles are major sources of remobilised K (Bender et al., 2015; Gaspar et al., 2017a, 2018). In contrast, Ca, Mg, Mn, B, and Fe exhibited remarkable low HI (10%–40%), whilst K and Zn showed intermediate HI (40%–50%).c Nutrient deficiencies affect both plant growth and N fixation as it has been reviewed elsewhere (e.g. Divito and Sadras, 2014; Gonzales-Guerrero et al., 2014). Soybean has relatively large P and K requirements and large removal of these two nutrients occurs via the harvested seed (Bender et al., 2015; Gaspar et al., 2017a). Based on nutrient accumulation in aboveground dry matter, Tamagno et al. (2017) established mean N:P ≈ 11 and N:K ≈ 2. Given these ratios, a crop that produces 3 Mg ha− 1, which is similar to average yields in USA, Argentina, and Brazil, would require uptakes of 22 kg P ha− 1 and 120 kg K ha− 1. Unlike P and N, which are mostly (ca. 75%) absorbed after R3, ca. half of accumulated K at R7 is absorbed before R3 (Bender et al., 2015; Gaspar et al., 2017a). Phosphorus and K are mobile in the plant, and deficiency symptoms are observed in older leaves. Plants with P deficiency exhibit reduced growth and smaller leaflets (Chiera et al., 2002; Gutierrez-Boem and Thomas, 1999). Soybean is sensitive to soil P deficiencies because nodules are formed at the expense of root length density, which, in turn, is needed for efficient P uptake (Cassman et al., 1980). Likewise, there is a direct effect of P on the growth and survival of rhizobia and their capacity for nodulation and fixation (Cassman et al., 1981b; Singleton et al., 1985). Symptoms of K deficiency are well defined (chlorosis followed by necrosis), beginning at the leaflet margins and moving inwards over the leaflet (Sinclair, 1993). Insufficient K reduces photosynthesis, growth, and partitioning to seed (Huber, 1984; Parvej et al., 2015; Singh and Reddy, 2017); K-deficient plants tend also to be more susceptible to pathogens and insect pests (Amtamann et al., 2008).


Estimated nutrient HI was calculated based on nutrient amount in aboveground and seed biomass.

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Raising P availability to meet crop requirements and support N fixation through application of P fertiliser is needed in highly weathered tropical soils to overcome their large P fixation capacity (Cassman et al., 1993). Identification and alleviation of soil constrains was critical for soybean expansion into the Cerrados (Sanchez, 2019). Under natural vegetation, these soils have low pH (range: 4–5), low cation exchange capacity, and high concentration of Al and Fe oxides with a large capacity to fix P. Hence, periodic lime application is needed to increase pH, reduce Al activity, and alleviate Mg and Ca deficiencies. Likewise, after conversion into agriculture, a large amount of P fertiliser is needed to overcome the high fixation capacity of Al and Fe oxides before P can be made available for crops; smaller amounts are needed in subsequent years. Periodic fertiliser K application is also needed. Application of fertiliser P (but not K) is also common in the Pampas (but with smaller rates compared with the Cerrados). In addition to fertilizer, second-crop soybean also relies on residual soil P from previous wheat crop (ReTAA, 2019). Fertiliser P application is less common in the Corn Belt than in the Cerrados and Pampas, with ca. 40% of the fields receiving P fertiliser; K fertilizer is applied in ca. 40% of the fields, which are mostly concentrated in the central and eastern parts of the Corn Belt (USDA-ERS, 2019). Application of P and K fertiliser is guided by soil nutrient tests before sowing, although there have been efforts to use foliar analyses to diagnose nutrient deficiencies and estimating fertiliser requirements (Cassman et al., 1981a; Yin and Vyn, 2004). Reports on S deficiency in soybean have increased in recent decades probably associated with the reduction in atmospheric sulphate deposition from industrial pollution and increasing use of high-analysis fertilisers with less incidental S (e.g. Gutierrez Boem et al., 2007; Hitsuda et al., 2008; Salvagiotti et al., 2012; Kaiser and Kim, 2013). Main symptom of S deficiency is small, yellow–green leaves at the top of the plants, resembling those produced by other immobile nutrients (Sinclair, 1993). Besides yield penalty owing to reduced leaf area expansion and photosynthesis (Sexton et al., 1997), S limitation can also lead to changes in protein composition (Hitsuda et al., 2008). Main S source is mineralisation from soil organic matter; not surprisingly, responses to S fertiliser addition is more likely in soils with low organic matter and/or fields subjected to environmental and management factors that reduce mineralisation rates. Because soil S tests are unreliable, nutrient ratios have been proposed as an alternative to identify S-deficient fields (Hitsuda et al., 2004; Salvagiotti et al., 2012; Divito et al., 2015). A thorough review about S nutrition in soybean is available elsewhere (Hitsuda et al., 2008). Highly productive soils usually contain sufficient quantities of micronutrients for optimum crop growth (Mallarino et al., 2017). Besides their role on the physiological processes that are common to all plants, B, Mo, Co, Fe, Zn, and Ni are important for N fixation (Munns, 1977; Evans and Russell, 1971; Klucas et al., 1983; Gonzales-Guerrero et al., 2014). Seed and foliar fertiliser applications are common to overcome micronutrient deficiencies in the Cerrados, mostly from B, Cu, Zn, and Mn (Fageria and Baligar, 2001; Campo et al., 2009; de Jesus Lacerda et al., 2017). In contrast, micronutrient deficiencies are rare in agricultural soils in the Corn Belt and Pampas, except for specific environments (Mallarino et al., 2017). For example, Mn and Fe are two common micronutrient deficiencies in alkaline soils of the Corn Belt. Because both Mn and Fe are immobile in the plant, the deficiency occurs in the youngest upper leaves, with the leaves turning yellow and veins remaining green (Sinclair, 1993). Symptoms of Fe deficiency are typically referred to as iron deficiency chlorosis (IDC). Soil or foliar fertiliser application can help correct Mn deficiency (Randall et al., 1975), whilst selection of tolerant cultivars and/or use of iron chelates are options to mitigate IDC (Kaiser et al., 2014; Liesch et al., 2011). A thorough review on micronutrients deficiencies in soybean production in the Corn Belt is available elsewhere (Mallarino et al., 2017).

4  Yield and quality 4.1  Yield potential and yield gaps The yield gap is the difference between average on-farm yield and the yield potential defined by solar radiation, temperature, and genotype, and also by water availability in rainfed crop systems (Lobell et al., 2009; van Ittersum et al., 2013; Global Yield Gap Atlas, 2019). This definition of yield potential reflects an upper biophysical limit to what might be attainable for any recent crop cultivar grown on a given field; hence, the magnitude of the yield gap estimates the degree of yield improvement that could still be captured with adjustments in crop management. Specht et al. (1999) and Sinclair and Rufty (2012) reported that a yield potential of ca. 6 Mg ha− 1 can be used as a ‘functional’ upper limit for on-farm soybean yields in favourable environments in the Corn Belt. Experiments in high-yield environments in the Corn Belt and elsewhere have confirmed this upper limit (Spaeth et al., 1987; Van Roekel and Purcell, 2014; Cafaro La Menza et al., 2017, 2019; Zanon et al., 2016). On-farm soybean yields of ca. 6 Mg ha− 1 are currently achieved in the Corn Belt but only under the best possible G × E × M interaction across a large geographic area (see Rattalino Edreira et al., 2020a, b). Hence, this fixed value of yield potential is not meaningful for fields located in other climates and soil types or where water supply is not sufficient to meet crop water requirements. Based on producer data from six regions in Nebraska during 8 years (2004–11), Grassini et al. (2014b) estimated the average yield gap for irrigated soybean using the 95th percentile of the yield d­ istribution in

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each region-year as a proxy to yield potential. These authors found that average yield gap ranged from 12% to 20% of the estimated yield potential, with the latter ranging from 4.5 to 5.0 Mg ha− 1 across the six regions. In subsequent studies, Grassini et al. (2015) and Rattalino Edreira et al. (2017) combined crop modelling and boundary-function analysis, based on site-specific weather and current management practices (sowing date, cultivar MG, and plant density) from farmer soybean fields over many seasons, to determine a yield gap that represents 22% and 13% of the yield potential estimated for rainfed (4.8 Mg ha− 1) and irrigated soybean (5.7 Mg ha− 1) in the Corn Belt. Aramburu Merlos et al. (2015) reported an overall yield gap of 32% of the simulated national yield potential of 3.9 Mg ha− 1 for rainfed soybean in Argentina. Early attempts to estimate yield potential for rainfed soybean in Brazil relied on record yields from contest winning fields (Battisti et al., 2018) or agro-ecological crop models (Sentelhas et al., 2015). Recent efforts to estimate these parameters using wellvalidated process-based models and best available weather and soil databases have yielded an estimated national average yield potential of 5.4 Mg ha− 1 for rainfed soybean in Brazil, with the yield gap representing 45% of the simulated yield potential (Global Yield Gap Atlas, 2019). Underpinning causes for current yield gaps in soybean have received comparably less attention than in cereals. In the Corn Belt, Grassini et al. (2015), Rattalino Edreira et al. (2017, 2020b), and Mourtzinis et al. (2018b), identified late sowing as the key management practice explaining current yield gap in soybean. Their approach, based on analysis of producer survey data, revealed other factors explaining yield gaps, including foliar fungicide and/or insecticide application, tillage method, and P fertiliser application. Following a similar approach, Calviño and Sadras (1999) and later Di Mauro et al. (2018) identified sowing date, previous crop, row spacing, and foliar fungicide and P fertiliser application as potential causes for yield gaps for rainfed soybean in Argentina. Corroborating experimental evidence from the Corn Belt, Di Mauro et al. (2018) also found reduced yield in soybean after soybean compared with soybean rotated with maize (see Section 1). Identification of the causes for yield gaps is needed but not sufficient to close them. In some cases, the solution to close the gap may simply not exist or be too costly. For example, Cafaro La Menza et al. (2017) estimated that N limitation can explain about half of the yield gap measured for irrigated soybean in Nebraska, where current yield potential is 5.7 Mg ha− 1 and average producer yield is 4.5 Mg ha− 1. As explained in Section 3.3, cost-effective measures to close the N-driven gap do not currently exist. In other cases, there may be behaviour factors slowing adoption of a given agronomic technology (Kuehne et al., 2017). For example, whilst optimisation of sowing date seems relatively easy to implement in the Corn Belt, there are many reasons why producers may still be reluctant to sow soybean earlier. The first constraint is a combination of farm logistics and cultural preference as many producers only have one planter, and they prefer to use it for sowing maize first. The second limitation is associated with biophysical factors (i.e. water excess, cold weather) that could often delay sowing. Finally, farmers tend to overestimate the risk associated with seed chilling injury, killing frost, and seed and/or plant stand loss associated with early sowing despite the well-documented benefits of early sowing and associated measures to reduce risk, for example, by using seed treatments and monitoring of soil temperature (e.g. Esker and Conley, 2012; Vossenkemper et al., 2015; Tenorio et al., 2016; Gaspar et al., 2017b). Minimising the yield gap of an individual crop should not compromise the productivity of the entire cropping system and/or increase risk (Guilpart et al., 2017). For example, there is a yield penalty when soybean is sown earlier, or shorter MGs are used in relation to those recommended to maximise soybean yield (as a single crop per year) in the Cerrados (Fig. 8.3). Still, producers prefer early sowings, or shorter MGs when soybean is followed by second-crop maize to minimise the risk of terminal drought in maize and maximise the crop-system productivity (da S. Andrea et al., 2018; Noia Junior and Sentelhas, 2019). Considering trade-offs between closing yield gaps and benefits from other non-yield related factors is also important when evaluating the overall impact of adopting (or not) an agronomic practice. For example, despite the yield penalty associated with no-till adoption in irrigated soybean (Grassini et al., 2015; Rattalino Edreira et al., 2017), other factors can counterbalance this penalty leading to adoption of no-till in irrigated fields to control soil erosion, better capture of pre- and in-season precipitation leading to lower irrigation water requirements, and lowered fossilfuel use for field operations. Finally, when the yield gaps are small and/or agronomic interventions may be too costly or labour-­intensive, it may be wise to look for opportunities to reduce input use without reducing crop yields, as it has been documented for seeding rates and irrigation amount in the Corn Belt (Gaspar et al., 2017b; Gibson et al., 2019), leading to increases in input-use efficiency and farmer profit.

4.2  Seed quality Breeders have found it difficult to simultaneously improve soybean seed yield and seed protein. Despite many claims of achieving that goal, a successful high-yield, high-protein cultivar release has been elusive. Indeed, it has been long known that yield and oil concentration are typically positively correlated, with both traits negatively correlated with protein

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concentration (Burton, 1987; Wilson, 2004). These negative correlations exist whether the source of variation is genetic (Rincker et al., 2014), or agronomic, in both the USA (Assefa et al., 2018, 2019) and Argentina (Bosaz et al., 2019). A multisite evaluation of soybean yield, protein, and oil of a historic set of 168 cultivars of MG II, III, and IV released from 1923 to 2008 documented increases in yield and oil concentration, but concomitant decreases in protein concentration (Rincker et al., 2014). Similar trends have been found for MG III, IV, and V cultivars released between 1980 and 2015 in Argentina (de Felipe et al., 2016). Agronomic practices to simultaneously improve yield and protein concentration have not achieved a similar goal (Assefa et al., 2018, 2019), though increasing seed protein (with minimal impact on seed oil) via seasonally sequential partial applications of high amounts of N fertiliser during the entire crop season may hold promise (Cafaro La Menza et al., 2017). Despite the efforts of both breeders and agronomists towards alleviating the trade-off between yield