Keywords

1 Learning Objectives

  • To showcase the importance, functionality, and advantages of utilizing wheat crop models for wheat production decision making and assistance in the development of breeding strategies.

  • Outline future trends and areas of improvement needed in wheat crop models to mitigate future agricultural challenges.

2 Introduction

The global demand for food is continuously increasing due to the growing population and agricultural production strategies must continue to improve to ensure future global food security (see Chap. 4). Wheat (Triticum spp.) is the most important food crop in the world due to its high nutrition content contributing to approximately 20% of calories and protein in the human diet [1]. This chapter discusses potential agricultural strategies for improving wheat production by using crop simulation models as instruments for adaptation. Crop simulation models are computational tools used to determine crop growth and development using quantitative knowledge and input data from agronomic systems. Crop models produce dynamic simulations of crop phenology and growth based on the combined principles of crop physiology, soil science, and agrometeorology. Most crop models use daily maximum and minimum temperature, rainfall, solar radiation, atmospheric CO2 concentration, soil attributes, cultivar characteristics, and crop management as inputs to simulate crop phenological development, biomass and yield accumulation, water use, and nutrient uptake.

Wheat crop modeling emerged in the 1980s based on the computational integration of mathematical relationships between environmental interactions and wheat growth developed in the 1960s and 1970s. In the 1990s, models began merging into crop modeling platforms, i.e., software that combines multiple models of various crops to facilitate evaluation and application for users [2]. Over the past decades, individual crop models and crop modeling platforms have received many technological and functional improvements which has promoted the development of new modeling approaches and new models. Currently, there are more than 30 wheat crop models used by modeling groups across the world [3]. These present-day crop models have progressed to become helpful tools in understanding how crops develop, grow, and yield in various agricultural scenarios which makes them advantageous for projecting how internal (e.g., genes, traits) and external (e.g., climate, crop management) factors impact crop production [4]. Thus, crop models are often integrated into new agricultural research activities, such as the improvement of breeding strategies. However, as knowledge and understanding of agricultural systems increases, model improvement is necessary to address the new challenges that arise when simulating complex agricultural systems interactions.

This chapter outlines the benefits and limitations of current wheat crop models for applications of wheat production regarding environmental sustainability, climate change, and breeding.

3 Assisting Breeding with Crop Modeling

Crop models can extrapolate data beyond field experimentation through simulation of different agronomic treatments with many variables over extended periods, allowing for agricultural systems studies across a wide range of spatial and temporal scales [5]. For example, wheat crop models can simulate the effects between climate and natural resources management (NRM) using various inputs (e.g., sowing dates, N fertilizer amounts, irrigation rates, etc.) to assist in the development of sustainable best management practices. This allows model users to maximize economic return at specified locations while managing spatial variability, environmental impact, and natural resources availability. Wheat crop models can also use seasonal forecasts and historical weather data to simulate a wide range of management practices for optimization of management strategies for predicted season types (e.g., increasing N fertilization in a predicted wet season) to improve efficiency and profitability of wheat production [5]. Additionally, wheat crop models are often paired with Global Climate Models (GCMs) to assess the impact of future climate change on wheat production under different Representative Concentration Pathway (RCP) scenarios of projected rainfall, temperatures (including extreme events), elevated atmospheric CO2 concentrations, and increased tropospheric ozone (O3) concentrations at local and global scales [6].

In addition to adaptive crop management, another strategy to increase global wheat production is through wheat breeding and genetic engineering of new stress resilient cultivars with improved resource use efficiencies [7, 8]. Breeding new wheat cultivars with desired traits such as heat, drought, or O3 tolerance can mitigate the negative effects of climate change and improve overall agricultural productivity (see Chap. 7); however, producing a new wheat cultivar requires about 8–12 years to develop [9]. Wheat crop models can assist breeders to accelerate the development of improved cultivars by simulating many treatments of breeding programs in current and future environments with various crop management practices [10]. Figure 31.1 illustrates the interactions between breeding and crop modeling. Combining wheat breeding strategies with wheat models allows for the (1) characterization of wheat growing environments to better understand environmental variability, (2) assessment of physiological trait performance in targeted environments to focus on favorable traits, (3) evaluation of the potential effects of genetic controls on wheat yield in specified environments, (4) improved understanding of genotype × environment (G × E) interactions in statistical models, and (5) utilization of high-throughput phenotyping to identify traits of interest [4].

Fig. 31.1
figure 1

Diagram describing the role of crop modeling in breeding. Crop models can be used to (1) dissect and characterize complex traits into simpler components traits, (2) simulate the impact of traits and genetic controls on crop growth and yield, (3) characterize environments of the target population of environments to identify the main environment types and their frequency, and (4) inform breeding via integrated analyses with breeding system models. (Reprinted with permission from Ref. [4])

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3.1 Cultivars and Traits

Interactions between physiological traits within a wheat cultivar cause seasonal variations in wheat fecundity and nutrition. Wheat crop models can estimate the effect of single and combined agronomic traits on wheat growth and yield and can approximate the degree of additivity or reduction of the traits. Models can simulate multiple trait combinations of wheat grown in various in silico environments to observe if the traits will have positive or negative effects on crop growth, yield, or protein content [10]. The use of wheat models in breeding facilitates the testing of new wheat ideotypes, i.e., sets of wheat cultivar parameters that mimic the genotype for an “ideal wheat type.” Wheat models can simulate potential ideotypes under various treatments so that they are optimized for specified locations and future adaptation needs, while using less time and resources than field experiments [11]. Wheat models have been used to simulate grain yields for possible wheat ideotypes under future climate scenarios and found combinations of specific traits that could lead to large yield increases in certain environments [12]. However, appropriate understanding and caution should be exercised when using wheat models to simulate ideotypes because (1) models cannot account for all the interactions among traits, (2) variations in the simulated traits may not represent existing genetic variability, and (3) simulated combinations of traits may not be physiologically or genetically possible in the field [4].

3.2 Simulating Genotype × Environment × Management (G × E × M) Interactions

For agricultural productivity to increase in a changing climate, the interactions between genotype, environment, and management (G × E × M) on crop development, growth, and yield must be considered. Wheat crop models can simulate wheat growth, development, and yield using G × E × M interactions to assess the benefits and risks of different adaptation strategies in different environmental scenarios (Fig. 31.2). Wheat models are ideal tools for determining the benefits of various genetic improvement strategies, but models are often limited by uncertainties related to processes and parameters when simulating genetic variations [11]. This is because modeling wheat phenology requires algorithms with cultivar-specific parameters, but parameter estimation for large numbers of genotypes can be time-consuming and costly [13], limiting the rate at which new genotypes are incorporated into models. Some studies have linked parameters with genetics, such as modifying a wheat model to incorporate gene effects into the estimation of wheat heading time [14]. This resulted in a gene-based model that showed new longer-season cultivars may flower later when sown early in a season leading to potential yield increases. Recently, a multiscale (gene to globe) modeling framework has been developed to assess different adaptation strategies, including genetic improvement, under climate change at larger scales [8].

Fig. 31.2
figure 2

Example of using crop models to simulate G (genotype) × E (environment) × M (management) interactions for climate change impact assessment. When optimizing existing and designing new agricultural adaptation strategies, lessons from both crop productivity and environmental sustainability assessments should be used as guidance. Brown lines and arrows indicate guidance from crop productivity assessment and green lines and arrows indicate guidance from environmental assessment. (Reprinted with permission from Ref. [8])

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3.3 Integrating Genotype-to-Phenotype Interactions

A crop phenotype is the observable expression of the genes, i.e., specific traits/characteristics such as crop structure, development, physiological properties, phenology, or behavior, resulting from the interaction of the crop genotype (genetic structure) and the environment (G × E interactions). Understanding wheat genotypes and phenotypes allows breeders to develop new and/or improved wheat cultivars, such as abiotic stress-tolerant cultivars, for improved agricultural productivity. Linking genotype-to-phenotype relationships in crop modeling is a novel area, but there is considerable potential to agricultural and breeding improvement strategies because wheat crop models can simulate multiple agronomic traits in various environmental and management conditions [14]. Incorporating the principles of genetics and genomics into wheat models enables genotype-to-phenotype prediction so agronomists and breeders can select the ideal cultivar for target locations and needs. The advantages to modeling with integrated physiological traits are that (1) model parameters may be more closely linked to Quantitative Trait Loci (QTLs) and genes, and (2) complex traits may be better represented as emergent properties arising from interactions between component traits and the environment [4]. To integrate genotype-to-phenotype interactions, some wheat models attempted a ‘top-down’ approach to simulate dynamics and phenotypic outcomes from genetic variation at the whole-crop scale with course granularity while considering phenotyping capabilities, measurement errors, and prediction accuracy [15]. Other models have attempted a ‘bottom-up’ approach to integrate single biological processes (e.g., photosynthesis) operating at different temporal and spatial scales by explicitly simulating gene network regulation, metabolic reactions, and metabolite transport to be scaled to the whole-crop level [16]. However, before scaling the ‘bottom-up’ approach to the whole-crop level, challenges must be addressed in the model integration of component modules and representation of phenotypic responses to G × E × M interactions. Studies have suggested multiscale modeling framework for combining these two modeling approaches to simulate the whole-crop level impact of trait manipulation at finer (gene-to-cell) scales while retaining model predictability [8].

3.4 Improvements for G × E × M and Genotype-to-Phenotype Interactions in Crop Models to Assist Agronomists and Breeders

The ability of wheat crop models to simulate G × E × M interactions and support genotype-to-phenotype predictions offers large potential to facilitate genetic improvement (e.g., dissection and understanding of complex traits) and the development of breeding and management adaptation strategies. However, challenges remain when simulating G × E × M and genotype-to-phenotype interactions in current wheat crop models because of (1) uncertainty in the representation of key physiological processes leading to accumulated uncertainty in simulated resource transport, growth, and yield, (2) differences between model parameters and underlying genetic interactions, (3) difficulty in quick and accurate phenotyping for model parameters, (4) limited availability of detailed quantitative data of the interaction between the genetic controls (genotype) and physiological processes in response to environmental and management changes, and (5) limited information on gene/QTL function, the genotypes characterized for specific gene/QTL, and their interactions [17, 18].

Current wheat crop models can be generalized into different levels to show similarities and differences in model processes and parameterization across the levels (Fig. 31.3) [17]. Most wheat models are considered ‘level 3’ cultivar models, where models describe cultivar differences of species using cultivar specific parameters. Models that link model parameters to gene effects and QTL for limited genotypes or traits are considered ‘level 4’ genotype models [14]. There have been several attempts to represent gene effects on phenological development as a ‘level 5’ model, e.g., ‘top-down’ or ‘bottom-up’ approaches or through capturing the interactive effects of specific genes on wheat vernalization [19]. Models that are considered ‘level 4’ or ‘level 5’ provide a better representation of genetic interaction with the environment and can assist in the development of breeding strategies. To increase the number of wheat models in ‘level 4’ and ‘level 5’, Wang et al. [18] outlined three stages of model improvement: (1) improving physiological understanding and modification of physiological algorithms to simulate subprocesses for trait prediction and evaluation, (2) linking model parameters to phenotypic responses of genetic variation using genomic data and identification of QTLs, and (3) modifying model structure to better represent physiological feedback and gene-level understanding. Figure 31.3 illustrates the incorporation of these areas of improvement at each model level. In order to incorporate these improvements, a new software design, improved crop model (that accurately represents crop responses to different environmental and management conditions), and statistical model (that links crop model parameters to genetic information) are recommended.

Fig. 31.3
figure 3

Different levels of crop models categorized by their similarities and differences in physiological processes and parameters, with additional details on the three interactive stages to improve current process-based wheat crop models for simulation of gene effects and G × E × M interactions. Dotted lines with arrows indicate information feeding loops, and solid lines with arrows indicate model development loops. (Modified with permission from Ref. [18])

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Improving genotype-to-phenotype prediction requires understanding and development of algorithms that represent the underlying genetic structure to generate a phenotype of a crop based on simulated dynamics. Recent development of new technologies for high-throughput phenotyping in both controlled environments and in the field (see Chap. 27) will open additional possibilities to improve genotype-to-phenotype prediction in models [20]. Additionally, breeding support systems, such as the Genomic Open-source Breeding informatics initiative (GOBii) (http://cbsugobii05.biohpc.cornell.edu/wordpress/) or the Integrated Breeding Platform (IBP) (https://www.integratedbreeding.net/), are developing databases and software tools to maintain and organize large quantities of genomic data to facilitate efficient crop cultivar selection which will also benefit genotype-to-phenotype model development. However, the added rigor to improve genotype-to-phenotype predictions should balance simplicity and model complexity so that parameterization does not become problematic [15].

3.5 Identifying Target Regions for Breeding

Climate change is likely to shift the target population of environments (TPE), i.e., the areas and seasons in which cultivars produced by breeding programs will be grown (see Chap. 3), for wheat cultivars which could limit the genetic gain from breeding programs [4]. Integrated modeling approaches that characterize TPE for projected future climates would help breeders target genotypes, traits, and regions of interest for new cultivars. Wheat crop models paired with GCMs can simulate the G × E × M interactions of wheat cultivars for TPE under future climate scenarios. Additionally, wheat models can characterize TPE at large-scales and/or over long periods of time to estimate spatial and temporal variability. For example, studies in the North China Plain using wheat models found that the addition of new winter wheat cultivars could prolong the growing period which reduced the negative warming effects from climate change in this region [21]. Also, dynamic in-season modeling can target specific stress patterns of interest to improve relevant field phenotyping [22].

4 Limitations and Improvements in Crop Model Performance

The expanding ability of wheat crop models to assess agricultural management, environmental, climate change, and breeding adaptation strategies on various scales (e.g., points-regions-global) has advanced agricultural science and opened new areas of interdisciplinary research. This expansion of research has raised many novel questions and challenges, such as the issue of climate change and food security on a global scale. Currently, the main challenges facing wheat modeling are improving model development to (1) incorporate the phenotypic effects of genotypes on physiological processes, (2) enhance simulations of management consequences (e.g., leaching of nitrate and pesticides) and soil constraints (e.g., acidity, salinity, access water), (3) enhance simulations of physiological responses to compound climate factors (e.g., CO2 × temperature interactions), (4) enhance simulations of the impacts from extreme climatic events (e.g., heat shocks, drought, elevated CO2, frost), (5) incorporate biotic stresses such as weeds, pests, and diseases, and (6) incorporate grain quality aspects (e.g., grain protein content and composition) and nutrition (e.g., Zn, Fe). Additionally, incorporation of detailed physiological processes (e.g., respiration costs, role of hormones in signaling environmental factors, and partitioning of carbon, especially to the roots) may help to improve current and future model performance; however, inclusion of additional model parameters does not always improve model precision [23]. Therefore, these challenges require improvements in model processes and interactions determined through model testing and evaluation with comprehensive and detailed observations. Addressing these challenges will help wheat models to continue providing a key role in guiding future adaptation advancements of wheat crop systems.

4.1 CO2 × Temperature Interactions

Additional understanding of the interactions between climate factors with other environmental factors, extreme events, and crop feedback (e.g., source-sink relationships) is needed to simulate wheat production. Before assessing the impacts of these combined interactions, the impact of climate variables on wheat yield should be separated and tested individually. It is well known that elevated atmospheric CO2 concentrations stimulate crop growth through improved photosynthetic capacity and transpiration efficiency (TE), and that higher temperatures decrease wheat productivity (see Chaps. 10 and 22). Most wheat crop models can simulate the interactions between atmospheric CO2 and temperature on wheat growth; however, multi-model grain yield simulations have been shown to diverge under higher temperatures [3] or elevated atmospheric CO2 [24], which highlights the need for model improvement in response to both high temperatures and elevated atmospheric CO2. A challenge for many wheat models is the lack of sensitivity to short-term stresses of one to two days related to extreme events that can affect yield-determining processes. Therefore, it is necessary to test and improve wheat model processes with data from field experimentation examining the impacts of climate factors at different developmental stages. In addition to simulating combined climate factors, it is important for wheat models to consider single or infrequent extreme events such as hailstorms, floods, or wind gusts as these can severely limit wheat yields. Climate change adds an additional modeling challenge because predicting the frequency of extreme events is difficult and extreme events are projected to become more variable [25]. Improving climate model projections of extreme events and the simulated physiological effects of individual and combined climatic factors on wheat growth within wheat models is necessary for developing future adaptation strategies.

4.2 Frost Stress

Severe yield losses in wheat production can be caused by extreme low temperatures or frost/chilling stress, an extended period of low temperatures (<2 °C), especially at the reproductive stage. Several wheat crop models include a “frost-kill” threshold, where simulated wheat crops start to fail when daily minimum temperatures fall below a critical low temperature threshold (e.g., <−10 °C) [2]. However, many models do not account for the effects of accumulated frost stress under low temperatures. It is necessary to incorporate these effects from frost stress into wheat models as future climate change can cause high variability in seasonal and daily temperatures adding potential risk to certain wheat producing areas [25]. Models that include frost stress functionality have been able to assess the potential impact from frost stress, such as quantifying the risk from frost in the major wheat growing areas of China [26].

4.3 O3 Stress

O3 is a ubiquitous secondary pollutant that can negatively impact wheat development and yield, especially since wheat is the most sensitive crop to O3 stress [27]. Future global O3 concentrations are projected to increase due to increased amounts of O3 precursor emissions [25]. Recently, crop modeling studies have shown that the negative impact from future O3 concentrations on wheat production can be comparable to, or larger than, the combined climate change impact from atmospheric CO2, temperature, and rainfall depending on location [6]. However, many wheat models do not consider the effects from O3 stress (or the combined interactions between O3 and water and/or CO2) on development and growth. These effects are often not included in models because of limited O3 data availability. However, the new Tropospheric Ozone Assessment Report (TOAR) database contains the world’s most extensive collection of global O3 observations from 1970 to present and could alleviate O3 data limitations [28]. Additionally, the Agricultural Modeling Intercomparison and Improvement Project (AgMIP) Ozone modeling community facilitates O3 data collection and multi-model ensemble studies, which can help incorporate O3 effects into models [27]. The inclusion of O3 effects in wheat models will improve climate change impact assessment and the development of future adaptation strategies [6].

4.4 Weeds, Pests, and Diseases

Biotic factors such as weeds, pests, and diseases can severely limit wheat health and yield quality causing global losses in wheat production of approximately 28% [29]. A major challenge in global wheat production is limiting the negative effects and spread of weeds/pests/diseases such as Fusarium head blight (FHB, also known as scab). Wheat is highly susceptible to infection from FHB and other diseases, especially in warm and wet climates experiencing frequent precipitation, high humidity, or heavy dews (see Chaps. 8 and 9). Additionally, the increased weather variability caused by climate change creates another challenge when determining disease risk areas [30]. Combining wheat crop models with weed/pest/disease models provides a method to simulate the effects of weeds/pests/diseases on crops while accounting for future climatic changes. However, modeling the occurrence, movement, and dynamics of weeds/pests/diseases in relation to crops and their dynamic interactions is still a challenge which is why few crop models estimate the effects of weeds/pests/diseases.

Several studies have been conducted to link weed/pest/disease models to wheat crop models through modification of crop model processes and algorithms. Wheat crop models have been combined with weed competition models to evaluate model performance and estimate the effect of weeds in dryland and irrigated treatments in Australia [31]. Wheat models have been linked with disease models to estimate the seasonal consequences from FHB on wheat production under variable climatic conditions in southern Brazil [32]. In addition to FHB, framework for linking wheat crop models to other pest population models has been developed to estimate impacts from other major pests and diseases such as grain aphids (Sitobion avenae), eyespot, and rust (Puccinia striiformis) [33]. To improve simulated impacts caused by weeds/pests/diseases in crop models, several steps have been suggested: (1) improvement in weed/pest/disease data quality and availability for model inputs and evaluation, (2) improvement in integration of weed/pest/disease and crop physiological interactions, (3) development of standard criteria for weed/pest/disease model evaluation, and (4) development of a community for weed/pest/disease modelers for sharing of information and resources [30]. Although there are still many challenges in linking weed/pest/disease population models with wheat crop models, the ability of a wheat model to simulate G × E × M interactions while also accounting for weed/pest/disease risk provides an opportunistic goal to assist breeders in developing pest or disease resistant wheat cultivars for the future.

4.5 Grain Quality

Climate change will affect grain yield quality (e.g., grain protein concentration; see Chap. 11) which poses a major challenge for global food security [34]. Grain protein concentration, the ratio of grain protein amount to grain yield, is an important characteristic for evaluating the nutritional quality of wheat yield since wheat contributes about 20% of protein for global human consumption [1]. Grain protein concentrations depend on a combination of G × E factors, e.g., grain protein concentrations are negatively affected by elevated atmospheric CO2 concentrations but increase under higher temperatures and drought stress due to lower starch accumulation. Wheat crop models could help determine the nutritional quality of grain yield under different G × E × M conditions. Some wheat models can simulate grain protein, but many models require a better understanding and/or integration of the physiology of yield quality components [10]. Recently, studies using wheat models have shown that climate change adaptation strategies that benefit grain yield may not be beneficial for grain quality depending on the environmental and input conditions [7].

5 Collaborative Global Crop Modeling Networks

Individual wheat crop models are powerful tools and provide a useful method to determine wheat growth in various environments and conditions as previously described. However, models are abstract representations of reality and many uncertainties and limitations exist within the processes and simulated interactions of each model. The use of multiple crop models in multi-model ensemble studies can improve overall accuracy and reduce uncertainty of simulated results [35]. In addition to improved accuracy and reduced uncertainty, another major benefit of using multi-model ensembles is that the comparison and evaluation of multiple models simulating the same scenario can highlight limitations or issues within individual crop models leading to model improvement. This improvement of individual models will then improve the overall accuracy of future multi-model ensemble studies.

Using multiple crop models in model intercomparison programmes for climate impact assessments is an auspicious method for projecting future crop productivity and for comparing results between modeling groups. The AgMIP (www.agmip.org) is a major international collaborative effort to combine interdisciplinary modeling communities with state-of-the-art information technology for the goal of significantly improving climate impact projections, crop models, and economic models for agricultural advancement and sustainability at local and global scales [36]. The AgMIP initiative established detailed protocols for simulating crop models, emissions scenarios, and GCMs on a global scale to facilitate the use of multi-model ensemble studies in climate change impact assessments. The multi-model impact assessment studies produced by global collaborative efforts, such as AgMIP, allow farmers, scientists, stakeholders, and policymakers to make improved decisions and adaptation strategies for future agricultural challenges.

6 Case Study – Using Crop Models to Determine the Effects of Genetic Adaptations

An increasing amount of agricultural studies are examining the impacts of climate change on grain yield, but few focus on the impacts on the nutritional content of the grain [34]. As mentioned earlier, grain protein concentration is an important characteristic for evaluating the nutritional quality of wheat yield (Sect. 31.3.5). The recent study by Asseng et al. [7] estimated the effects of climate change on wheat protein concentration for the main wheat producing areas across the world as part of the AgMIP. The study used a multi-model ensemble of 32 wheat crop models to simulate the combined effects of temperature, CO2, water, and nitrogen (N) on wheat protein concentration while including a trait adaptation option of delayed anthesis with an increased grain filling rate. Sixty major wheat-growing locations were examined, thirty high-rainfall or irrigated locations (simulated with no N or water limitations) and thirty low-rainfall locations, to represent total global wheat production. The wheat models used projected climate data from 2040 to 2069 provided by 5 GCMs under RCP8.5. Model testing was done using outdoor chamber and Free-Air Carbon dioxide Enrichment (FACE) experiments to evaluate model performance of heat shocks, increased temperature, and elevated atmospheric CO2 concentrations. The trait adaptation option of delayed anthesis with increased grain filling rate was determined from a wide range of observed field experiments at different locations across the world.

After confirming that the multi-model ensemble median produced acceptable results, the multi-model median impact of climate change on grain and protein yield at the sixty locations was determined with and without the genotypic adaptation option (Fig. 31.4). The study found that grain yields were improved in most locations with the trait combination of delayed anthesis and increased grain filling rate (Fig. 31.4b). However, the response of grain protein concentration was more variable and dependent upon the growing season and location. It was found that climate change and the combined trait adaptation could lead to an increase in grain protein concentration at low-rainfall locations, particularly where yield was projected to decline. After aggregating the sixty locations to the global scale, it was determined that the inclusion of the trait combination of delayed anthesis and increased grain filling rate could increase global yield 7% and protein yield 2% by 2050. However, this inclusion of the trait combination would decrease grain protein concentration by a relative change of −4%. This study shows the complex relationship of climate change and genetic adaptations on crop yield and illustrates the robust benefits of linking crop modeling and breeding disciplines for the development of adaptation strategies.

Fig. 31.4
figure 4

Simulated multi-model ensemble projection under climate change of global wheat grain yield (left half) and protein yield (right half), (a) without genotypic adaptation and (b) with genotypic adaptation. Relative climate change impacts for 2036–2065 under RCP8.5 compared with the 1981–2010 baseline. Impacts were calculated using the medians across 32 models (or 18 for protein yield estimates) and five GCMs (circle color) and the average over 30 years of yields using region-specific soils, cultivars, and crop management. (Reprinted with permission from Ref. [7])

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7 Key Concepts

  • Utilization of wheat crop models can assist agronomists and breeders in the development of new wheat ideotypes and breeding strategies through dynamic simulations of many agronomic treatments across various spatial and temporal scales.

  • Incorporation of G × E × M and genotype-phenotype interactions into wheat crop models is an emerging area of interest with high potential agricultural benefits.

  • Wheat crop models are robust instruments for agricultural adaptation that have steadily improved over recent decades, but further improvement is needed to address future agricultural challenges.

8 Conclusion

The challenge of supplying food to the expanding global population in an increasingly hostile climate with decreasing resources and cropping area will require advancement in many agricultural practices. Crop simulation models are powerful tools that produce dynamic simulations of crop growth and environmental interactions across various spatial and temporal scales using the principles of crop physiology, soil science, and agrometeorology. These simulations of agronomic treatments help farmers, scientists, and policymakers develop adaptation strategies to increase agricultural productivity and sustainability for projected climatic and environmental scenarios.

The emerging linkage between the breeding and crop modeling communities has accentuated the ability of crop models to assist in defining target ideotypes for novel breeding strategies. Wheat models simulate environment and management interactions well, and some models have incorporated genotypic variation of select phenotypes, e.g., flowering time [14]. However, simulating differences across genotypes and genotypic variations of phenotypes is still a challenge. This is mainly because of (1) uncertainty within model physiological processes, (2) differences between integration of genetic interactions and current model structure, (3) limited availability of detailed genomic data, and (4) cost and time constraints for large-scale, rapid phenotyping of complex traits (Sect. 31.3.4). The burgeoning improvements of high-throughput phenotyping allow researchers to focus on many new physiological traits, which will improve model development, testing, and evaluation of G × E × M interactions.

Collaborative global networks, like AgMIP, and multi-model ensembles improve overall model performance and accuracy allowing for improved agricultural decision making. Multi-model ensemble studies can highlight limitations within individual wheat crop models and addressing these limitations will further enhance decision making for wheat production and breeding. Crop models are successfully applied in agricultural research, decision making, and policy support, but future applications in agronomy, NRM, climate change, and breeding will require model improvement through improved (1) understanding of G × E × M and genotype-to-phenotype interactions, (2) integration of G × E × M and genotype-to-phenotype interactions into model processes, and (3) availability of high quality, detailed genomic, climatic, and management data sets. Tackling these challenges will improve crop model performance and assist farmers, agronomists, breeders, and policymakers to develop improved agricultural adaptation strategies.