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Crop yield results from the complex interaction between genotype, management, and environment. While farmers have control over what genotype to plant, and how to manage it, their decisions are often sub-optimal due to climate variability. Sub-seasonal climate predictions embrace the great potential to improve risk analysis and decision-making. However, adequate frameworks integrating future weather uncertainty to predict crop outcomes are lacking. Maize (Zea mays L.) yields are highly sensitive to weather anomalies, and very responsive to plant density (plants m-2), thus this variable could be optimized conditional to the seasonal prospects. The aims of this study were to (i) design a model that describes the yield-to-plant density (herein termed as yield-density) relationship as a function of weather variables, (ii) evaluate the predictive performance and analyze the sources of uncertainty, and (iii) provide probabilistic forecasts for predicting the economic optimum plant density (EOPD). We present a novel approach to enable decision-making in agriculture using sub-seasonal climate predictions and Bayesian modeling. This model provides crop management recommendations by accounting for various sources of uncertainty. A Bayesian hierarchical shrinkage model was fitted to the response of maize yield-density trials performed during the 2010-2019 period across 7 states in the United States, identifying the relative importance of key weather, crop, and soil variables. Tercile forecasts of precipitation and temperature from the International Research Institute (IRI) were used to forecast EOPD before the start of the season. The variables with the greatest influence on the yield-density relationship were weather anomalies, especially months with above-normal temperatures. Improvements on climate forecasting may also improve precision, as the coefficient of determination (R2) increased from 0.26 to 0.32 when weather forecasts were correct. This study may contribute to the development of decision-support tools that can trigger discussions between farmers and consultants about management strategies and their associated risks.

In the absence of stress, crop growth depends on the amount of light intercepted by the canopy and the conversion efficiency [radiation use efficiency (RUE)]. This study tested the hypothesis that long-term genetic gain for grain yield was partly due to improved RUE. The hypothesis was tested using 30 elite maize hybrids commercialized in the US corn belt between 1930 and 2017. Crops grown under irrigation showed that pre-flowering crop growth increased at a rate of 0.11 g m–2 year–1, while light interception remained constant. Therefore, RUE increased at a rate of 0.0049 g MJ–1 year–1, translating into an average of 3 g m–2 year–1 of grain yield over 100 years of maize breeding. Considering that the harvest index has not changed for crops grown at optimal density for the hybrid, the cumulative RUE increase over the history of commercial maize breeding in the USA can account for ~32% of the documented yield trend for maize grown in the central US corn belt. The remaining RUE gap between this study and theoretical maximum values suggests that a yield improvement of a similar magnitude could be achieved by further increasing RUE.

Crop adaptation to the mixture of environments that defines the target population of environments is the result from a balanced resource allocation between roots, shoots and reproductive organs. Root growth places a critical role in the determination of this balance. Root growth and function responses to temperature can determine the strength of roots as sinks but also influence the crop’s ability to uptake water and nutrients. Surprisingly, this behavior has not been studied in maize since the middle of the last century, and the genetic determinants are unknown. Low temperatures often recorded in deep soil layers limit root growth and soil exploration and may constitute a bottleneck towards increasing drought tolerance, nitrogen recovery, sequestration of carbon and productivity in maize. High throughput phenotyping (HTP) systems were developed to investigate these responses and to examine genetic variability therein across diverse maize germplasm. Here we show that there is: 1) genetic variation of root growth under low temperature and below 10°C, and 2) genotypic variation in water transport under low temperature. Using simulation, we demonstrate that the measured variation for both traits contribute to drought tolerance and explain important components of yield variation in the US corn-belt. The trait set examined herein and HTP platform developed for its characterization reveal a unique opportunity to remove a major bottleneck for crop improvement, and adaptation to climate change.

Plant physiology can offer invaluable insights to accelerate genetic gain. However, translating physiological understanding into breeding decisions has been an ongoing and complex endeavor. Here we demonstrate an approach to leverage physiology and genomics to hasten crop improvement. A half-diallel maize (Zea mays) experiment resulting from crossing 9 elite inbreds was conducted at 17 locations in the USA corn belt and 6 locations at managed stress environments between 2017 and 2019 covering a range of water environments from 377 to 760 mm of evapotranspiration and family mean yields from 542 to 1,874 g m−2. Results from analyses of 35 families and 2,367 hybrids using crop growth models linked to whole-genome prediction (CGM–WGP) demonstrated that CGM–WGP offered a predictive accuracy advantage compared to BayesA for untested genotypes evaluated in untested environments (r = 0.43 versus r = 0.27). In contrast to WGP, CGMs can deal effectively with time-dependent interactions between a physiological process and the environment. To facilitate the selection/identification of traits for modeling yield, an algorithmic approach was introduced. The method was able to identify 4 out of 12 candidate traits known to explain yield variation in maize. The estimation of allelic and physiological values for each genotype using the CGM created in silico phenotypes (e.g. root elongation) and physiological hypotheses that could be tested within the breeding program in an iterative manner. Overall, the approach and results suggest a promising future to fully harness digital technologies, gap analysis, and physiological knowledge to hasten genetic gain by improving predictive skill and definition of breeding goals.

Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the “breeder’s equation,” which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.

Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential (“risky” stomatal regulation), high xylem hydraulic conductivity, and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance, and low stomatal conductance at low leaf water potential (“conservative” stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits and their interactions (i.e., networks), has potential to improve crop growth predictions in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance.Summary statementOur process-based model uncovered two beneficial but contrasting trait networks for maize which can be understood by their integrated effect on water use/conservation. Modification of multiple, physiologically aligned, traits were required to bring about meaningful improvements in NPP and yield.

Special Issue: Feeding the World: The Future of Plant Breeding Asymmetry of investment in crop research leads to knowledge gaps and lostopportunities to accelerate genetic gain through identifying new sources andcombinations of traits and alleles. On the basis of consultation with scientistsfrom most major seed companies, we identified several research areas withthree common features: (i) relatively underrepresented in the literature; (ii) highprobability of boosting productivity in a wide range of crops and environments;and (iii) could be researched in‘precompetitive’space, leveraging previousknowledge, and thereby improving models that guide crop breeding and man-agement decisions. Areas identified included research into hormones, recombi-nation, respiration, roots, and source–sink, which, along with new opportunitiesin phenomics, genomics, and bioinformatics, make it more feasible to explorecrop genetic resources and improve breeding strategies. The authors acknowledge the following scientists for helping to achieve a consensus on research bottlenecks with significant implications for crop improvement: Yann Manes (Syngenta), Jeremy Derory (Limagrain), Bruno Marty (BASF), and Hans Braun (recent director of the Global Wheat Program, CIMMYT). We acknowledge Renee Lafitte (Bill and Melinda Gates Foundation) for helpful feedback on the manuscript and Fatima Escalante for valuable assistance in coordinating edits to the manuscript and its formatting. M.R. acknowledges the International Wheat Yield Partnership (https://iwyp.org/) for establishing a precedent of integrating different research strands in wheat for targeted prebreeding and the Foundation for Food and Agricultural Research (https://foundationfar.org/) for supporting a translational research and prebreeding pipeline at CIMMYT to identify and stack climate resilience traits in wheat.

Because plants capture water and nutrients through roots, it was proposed that changes in root systems architecture (RSA) might underpin the 3-fold increase in maize (Zea mays L.) grain yield over the last century. Here we show that both RSA and yield have changed with decades of maize breeding, but not the crop water uptake. Results from X-ray phenotyping in controlled environments showed that single cross (SX) hybrids have smaller root systems than double cross (DX) hybrids for root diameters between 2465 µm and 181µm (P, Emerging opportunity to continue long-term genetic gain in maize yield in the US corn belt by improving the balance between canopy, root, and reproductive growth and development.

Increasing temperatures in the US Midwest are projected to reduce maize yields because warmer temperatures hasten reproductive development and, as a result, shorten the grain fill period. However, there is widespread expectation that farmers will mitigate projected yield losses by planting longer season hybrids that lengthen the grain fill period. Here, we ask: (a) how current hybrid maturity length relates to thermal availability of the local climate, and (b) if farmers are shifting to longer season hybrids in response to a warming climate. To address these questions, we used county-level Pioneer brand hybrid sales (Corteva Agriscience) across 17 years and 650 counties in 10 Midwest states (IA, IL, IN, MI, MN, MO, ND, OH, SD, and WI). Northern counties were shown to select hybrid maturities with growing degree day (GDD°C) requirements more closely related to the environmentally available GDD compared to central and southern counties. This measure, termed "thermal overlap," ranged from complete 106% in northern counties to a mere 63% in southern counties. The relationship between thermal overlap and latitude was fit using split-line regression and a breakpoint of 42.8°N was identified. Over the 17-years, hybrid maturities shortened across the majority of the Midwest with only a minority of counties lengthening in select northern and southern areas. The annual change in maturity ranged from -5.4 to 4.1 GDD year-1 with a median of -0.9 GDD year-1 . The shortening of hybrid maturity contrasts with widespread expectations of hybrid maturity aligning with magnitude of warming. Factors other than thermal availability appear to more strongly impact farmer decision-making such as the benefit of shorter maturity hybrids on grain drying costs, direct delivery to ethanol biorefineries, field operability, labor constraints, and crop genetics availability. Prediction of hybrid choice under future climate scenarios must include climatic factors, physiological-genetic attributes, socio-economic, and operational constraints.

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

A Crop Growth Model (CGM) is used to demonstrate a biophysical framework for predicting grain yield outcomes for Genotype by Environment by Management (G×E×M) scenarios. This required development of a CGM to encode contributions of genetic and environmental determinants of biophysical processes that influence key resource (radiation, water, nutrients) use and yield‐productivity within the context of the target agricultural system. Prediction of water‐driven yield‐productivity of maize for a wide range of G×E×M scenarios in the U.S. corn‐belt is used as a case study to demonstrate applications of the framework. Three experimental evaluations are conducted to test predictions of G×E×M yield expectations derived from the framework: (1) A maize hybrid genetic gain study, (2) A maize yield potential study, and (3) A maize drought study. Examples of convergence between key G×E×M predictions from the CGM and the results of the empirical studies are demonstrated. Potential applications of the prediction framework for design of integrated crop improvement strategies are discussed. The prediction framework opens new opportunities for rapid design and testing of novel crop improvement strategies based on an integrated understanding of G×E×M interactions. Importantly the CGM ensures that the yield predictions for the G×E×M scenarios are grounded in the biophysical properties and limits of predictability for the crop system. The identification and delivery of novel pathways to improved crop productivity can be accelerated through use of the proposed framework to design crop improvement strategies that integrate genetic gains from breeding and crop management strategies that reduce yield gaps.

Over the past century of maize (Zea mays L.) breeding, grain yield progress has been the result of improvements in several other intrinsic physiological and morphological traits. In this study, we describe (i) the contribution of kernel weight (KW) to yield genetic gain across multiple agronomic settings and breeding programs, and (ii) the physiological bases for improvements in KW for US hybrids. A global-scale literature review concludes that rates of KW improvement in US hybrids were similar to those of other commercial breeding programs but extended over a longer period of time. There is room for a continued increase of kernel size in maize for most of the genetic materials analysed, but the trade-off between kernel number and KW poses a challenge for future yield progress. Through phenotypic characterization of Pioneer Hi-Bred ERA hybrids in the USA, we determine that improvements in KW have been predominantly related to an extended kernel-filling duration. Likewise, crop improvement has conferred on modern hybrids greater KW plasticity, expressed as a better ability to respond to changes in assimilate availability. Our analysis of past trends and current state of development helps to identify candidate targets for future improvements in maize.

Environmental characterization for defining the target population of environments (TPE) is critical to improve the efficiency of breeding programs in crops, such as sorghum (

Nitrogen (N) metabolism is a major research target for increasing productivity in crop plants. In maize (Zea mays L.), yield gain over the last few decades has been associated with increased N absorption and utilization efficiency (i.e. grain biomass per unit of N absorbed). However, a dynamical framework is still needed to unravel the role of internal processes such as uptake, allocation, and translocation of N in these adaptations. This study aimed to 1) characterize how genetic enhancement in N efficiency conceals changes in allocation and translocation of N, and 2) quantify internal fluxes behind grain N sources in two historical genotypes under high and low N supply. The genotypes 3394 and P1197, landmark hybrids representing key eras of genetic improvement (1990s and 2010s), were grown under high and low N supply in a two-year field study. Using stable isotope

Genetic gain in breeding programs depends on the predictive skill of genotype-to-phenotype algorithms and precision of phenotyping, both integrated with well-defined breeding objectives for a target population of environments (TPE). The integration of physiology and genomics could improve predictive skill by capturing additive and non-additive interaction effects of genotype (G), environment (E), and management (M). Precision phenotyping at managed stress environments (MSEs) can elicit physiological expression of processes that differentiate germplasm for performance in target environments, thus enabling algorithm training. Gap analysis methodology enables design of GxM technologies for target environments by assessing the difference between current and attainable yields within physiological limits. Harnessing digital technologies such as crop growth model-whole genome prediction (CGM-WGP) and gap analysis, and MSEs, can hasten genetic gain by improving predictive skill and definition of breeding goals in the U.S. maize production TPE. A half-diallel maize experiment resulting from crossing 9 elite maize inbreds was conducted at 17 locations in the TPE and 6 locations at MSEs between 2017 and 2019. Analyses over 35 families represented by 2367 hybrids demonstrated that CGM-WGP offered a predictive advantage (y) compared to WGP that increased with occurrence of drought as measured by decreasing whole-season evapotranspiration (ET; log(y) = 0.80(±0.6) − 0.006(±0.001) ×ET;r2= 0.59;df= 21). Predictions of unobserved physiological traits using the CGM, akin to digital phenotyping, were stable. This understanding of germplasm response to ET enables predictive design of opportunities to close productivity gaps. We conclude that enabling physiology through digital methods can hasten genetic gain by improving predictive skill and defining breeding objectives bounded by physiological realities.

ABSTRACTThe timing of crop development has significant impacts on management decisions and subsequent yield formation. A large intercontinental dataset recording the timing of soybean developmental stages was used to establish ensembling approaches that leverage both knowledge-based, human-defined models of soybean phenology and data-driven, machine-learned models to achieve accurate and interpretable predictions. We demonstrate that the knowledge-based models can improve machine learning by generating expert-engineered features. The collection of knowledge-based and data-driven models was combined via super learning to both improve prediction and identify the most performant models. Stacking the predictions of the component models resulted in a mean absolute error of 4.41 and 5.27 days to flowering (R1) and physiological maturity (R7), providing an improvement relative to the benchmark knowledge-based model error of 6.94 and 15.53 days, respectively, in cross-validation. The hybrid intercontinental model applies to a much wider range of management and temperature conditions than previous mechanistic models, enabling improved decision support as alternative cropping systems arise, farm sizes increase and changes in the global climate continue to accelerate.

Maize is considered less drought-tolerant than sorghum, but sorghum is commonly grown as a short triple dwarf (3dwarf) type, so difference in plant height confounds the species comparison. The objectives of this study were to experimentally determine effects of species and plant height differences on transpiration efficiency (TE) and transpiration rate per unit green leaf area (TGLA) and use findings to explain input parameters in a simulation study on the comparative adaptation of 3dwarf sorghum and maize in environments with contrasting water availability. Maize, tall double dwarf (2dwarf) and short 3dwarf sorghum genotypes were grown in two lysimeter experiments in 2011 in SE Queensland, Australia. Each plant was harvested after anthesis and total transpiration, shoot and root dry mass were measured to estimate TE. Daily TGLA was used to compare transpiration rates. Species and height had limited effect on TE, but significantly affected TGLA. This was associated with differences in biomass allocation. The similar TE but higher TGLA in maize compared with 3dwarf sorghum meant it potentially produces more biomass, consistent with published differences in biomass accumulation and radiation use efficiency (RUE). The simulation study, which used similar TE for maize and 3dwarf sorghum, but captured differences in TGLA through differences in RUE, predicted crossover interactions for grain yield between species and total water use. The greater TGLA of maize decreased grain yield in water-limited environments, but increased yields in well-watered situations. Results highlight that similarity in TE and differences in TGLA can influence comparative adaptation to water limitation.

Plant-breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeder’s equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeder’s equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimization of selection in breeding programs.

Over the last decade, society witnessed the largest expansion of agricultural land planted with drought tolerant (DT) maize (Zea maysL.) Dedicated efforts to drought breeding led to development of DT maize. Here we show that after two decades of sustained breeding efforts the rate of crop improvement under drought is in the range 1.0-1.6% yr−1, which is higher than rates (0.7% yr−1) reported prior to drought breeding. Prediction technologies that leverage biological understanding and statistical learning to improve upon the quantitative genetics framework will further accelerate genetic gain. A review of published and unpublished analyses conducted on data including 138 breeding populations and 93 environments between 2009 and 2019 demonstrated an average prediction skill (r) improvement around 0.2. These methods applied to pre-commercial stages showed accuracies higher that current statistical approaches (0.85 vs. 0.70). Improvement in hybrid and management choice can increase water productivity. Digital gap analyses are applicable at field scale suggesting the possibility of transition from evaluating hybrids to designing genotype x management (GxM) technologies for target cropping systems in drought prone areas. Due to the biocomplexity of drought, research and development efforts should be sustained to advance knowledge and iteratively improve models.HighlightCrop improvement rate in maize increased after implementation of drought breeding efforts. Harnessing crop, quantitative genetics and gap models will enable the transition from genetic evaluation to crop design.

Plant breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeder’s equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeder’s equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimisation of selection in breeding programs.

Plants capture soil resources to produce the grains required to feed a growing population. Because plants capture water and nutrients through roots, it was proposed that changes in root systems architecture (RSA) underpin the three-fold increase in maize grain yield over the last century1,2,3,4. Within this framework, improvements in reproductive resilience due to selection are caused by increased water capture1. Here we show that both root architecture and yield have changed with decades of maize breeding, but not the water capture. Consistent with Darwinian agriculture5 theory, improved reproductive resilience6,7 enabled farmers increase the number of plants per unit land8,9,10, capture soil resources, and produced more dry matter and grain. Throughout the last century, selection operated to adapt roots to crowding, enabling reallocation of C from large root systems to the growing ear and the small roots of plants cultivated in high plant populations in modern agriculture.

Nitrogen (N) metabolism is a major research target for increasing productivity in crop plants. In maize (Zea mays L.), yield gain over the last few decades has been associated with increased N absorption and utilization efficiency (i.e. grain biomass per unit of N absorbed). However, a dynamical framework is still needed to unravel the role of internal processes such as uptake, allocation, and translocation of N in these adaptations. This study aimed to 1) characterize how genetic enhancement in N efficiency conceals changes in allocation and translocation of N, and 2) quantify internal fluxes behind grain N sources in two historical genotypes under high and low N supply. The genotypes 3394 and P1197, landmark hybrids representing key eras of genetic improvement (1990s and 2010s), were grown under high and low N supply in a two-year field study. Using stable isotope 15N labelling, post-silking nitrogen fluxes were modeled through Bayesian estimation by considering the external N (exogenous-N) and the pre-existing N (endogenous-N) supply across plant organs. Regardless of N availability, P1197 exhibited greater exogenous-N accumulated in leaves and cob-husks. This response was translated to a larger amount of N mobilized to grains (as endogenous-N) during grain-filling in this genotype. Furthermore, the enhanced N supply to leaves in P1197 was associated with increased post-silking carbon accumulation. The overall findings suggest that increased N utilization efficiency over time in maize genotypes was associated with an increased allocation of N to leaves and subsequent translocation to the grains.

A successful strategy for prediction of crop yield that accounts for the effects of genotype, environment and their interactions with management will create many opportunities for enhancing the productivity of agricultural systems. Crop growth models (CGMs) have a history of application for crop management decision support. Recently, whole genome prediction (WGP) methodologies have been developed and applied in breeding to enable prediction of crop traits for new genotypes, and thus increased the size of plant breeding programs without expanding expensive field testing. The integration of a CGM into the algorithm for WGP, referred to as CGM-WGP, has opened up the potential for prediction of G × E × M interactions for breeding and product placement applications. The main objectives of this study were to extend CGM-WGP methodology to train models using data from multiple environments, and to evaluate, using both synthetic and experimental data from a maize drought breeding program, whether CGM-WGP methodology can enable improved phenotypic prediction when G × E interactions are an important determinant of performance. The CGM-WGP methodology was improved by 1) reformulating the model as a Bayesian generalized linear hierarchical model, and 2) by sampling the posterior distribution using a Metropolis-within-Gibbs sampling algorithm. The increased efficiency of the algorithm enabled the use of multiple environments and larger populations than those used in previous studies. Synthetic datasets included three environments and an empirical dataset included two environments contrasting for drought stress pattern and intensity. The empirical dataset included four double haploid populations expressing different levels of G × E interaction. Collectively, the prediction accuracy results for the empirical study indicate there were realized advantages in prediction accuracy for yield, in both the water limited and the not water limited environments, from the modeling of the G × E interactions by the CGM-WGP methodology relative to the reference method BayesA. Similarly, the difference in CGM-WGP accuracy relative to BayesA increased with decreasing similarity between the environment types utilized for training and evaluating the predictions. The synthesis provided in this work that encompasses crop physiology and modeling, quantitative genetics, genomic prediction and breeding, should stimulate a cross disciplinary dialogue towards building the next generation of prediction methodologies.

Development of predictive algorithms accounting for uncertainty in processes underpinning the maize (Zea mays L.) yield response to nitrogen (N) are needed in order to provide new N fertilization guidelines. The aims of this study were to unravel the relative importance of crop management, soil, and weather factors on both the estimate and the size of uncertainty (as a risk magnitude assessment) of the main components of the maize yield response to N: i) yield without N fertilizer (B0); ii) yield at economic optimum N rate (YEONR); iii) EONR; and iv) the N fertilizer efficiency (NFE) at the EONR. Combining Bayesian statistics to fit the N response curves and a machine learning algorithm (extreme gradient boosting) to assess features importance on the predictability of the process, we analyzed data of 730 response curves from 481 site-years (4297 observations) in maize N rate fertilization studies conducted between 1999 and 2020 in the United States and Canada. The EONR was the most difficult attribute to predict, with an average uncertainty of 50 kg N ha−1, increasing towards low ( 200 kg N ha−1) EONR expected values. Crop management factors such as previous crop and irrigation contributed substantially (∼50%) to the estimation of B0, but minorly to other components of the maize yield response to N. Weather contributed about two-thirds of explained variance of the estimated values of YEONR, EONR, and NFE. Additionally, weather factors governed the uncertainty (72% to 81%) of all components of the N response process. Soil factors provided a consistent but limited (10% to 23%) contribution to explain both expected N response as well as its associated uncertainties. Efforts to improve N decision support tools should consider the uncertainty of models as a type of risk, potential in-season weather scenarios, and develop probabilistic frameworks for improving this data-driven decision-making process of N fertilization in maize crop.

The competing demands of increasing grain yields to feed a growing population and decreasing nitrogen (N) fertilizer use and loss to the environment poses a grand challenge to farmers and society, and necessitates achieving improved N use efficiency (NUE) in cereal crops. Although selection for increased yield in maize has improved NUE over time, the present understanding of the physiological determinants of NUE and its key components hampers the design of more effective breeding strategies conducive to accelerating genetic gain for this trait. We show that maize NUE gains have been supported by more efficient allocation of N among plant organs during the grain filling period. Comparing seven maize hybrids commercialized between 1946 and 2015 from a single seed company in multiple N fertilizer treatments, we demonstrate that modern hybrids produced more grain per unit of accumulated N by more efficiently remobilizing N stored in stems than in leaves to support kernel growth. Increases in N fertilizer recovery and N harvest index at maturity were mirrored by a steady decrease in stem N allocation in this era study. These insights can inform future breeding strategies for continued NUE gains through improved conversion efficiency of accumulated plant N into grain yield.

Reproductive failure under drought in maize (Zea mays) is a major cause of instability in global food systems. While there has been extensive research on maize reproductive physiology, it has not been formalized in mathematical form to enable the study and prediction of emergent phenotypes, physiological epistasis and pleiotropy. We developed a quantitative synthesis organized as a dynamical model for cohorting of reproductive structures along the ear while accounting for carbon and water supply and demand balances. The model can simulate the dynamics of silk initiation, elongation, fertilization and kernel growth, and can generate well-known emergent phenotypes such as the relationship between plant growth, anthesis-silking interval, kernel number and yield, as well as ear phenotypes under drought (e.g. tip kernel abortion). Simulation of field experiments with controlled drought conditions showed that predictions tracked well the observed response of yield and yield components to timing of water deficit. This framework represents a significant improvement from previous approaches to simulate reproductive physiology in maize. We envisage opportunities for this predictive capacity to advance our understanding of maize reproductive biology by informing experimentation, supporting breeding and increasing productivity in maize.

The introduction of winter canola (Brassica napus L.) into rotations with winter wheat (Triticum aestivum L.) in the United States (US) revealed economic and agronomic benefits as well as improved weed and pest control. Canola stand establishment during the fall and plant survival over the winter are critical to the success of this crop. The environment plays a key role influencing survival, but in-depth evaluation of meteorological factors has not been conducted. This research study aimed to: i) identify meteorological factors underpinning winter canola survival, ii) build probabilistic response models based on historical meteorological data for different severities of winter kill across the US, and iii) define areas of adaptation of current germplasm. A winter survival dataset was compiled from the National Winter Canola Variety Trials from 2003 until 2018 (190 site-years) and auxiliary meteorological data over the last 40 years. A regression tree analysis indicated that meteorological variables related to minimum temperature, fluctuating temperatures above and below 0°C, and windchill during the cold period were the main factors accounting for winter kill. Cold periods across all site-years were classified into three clusters: cold periods with high (96%), medium (70%), and low (28%) average plant winter survival. For 94 US sites, the probabilities of these conditions were calculated and summarized in a map that defined areas of adaptation: a large area south of 35° N latitude for the US was identified with greater potential for overwintering success. Based on the response under multiple meteorological conditions, four distinct genotype survival groups were identified (tolerant, semi-tolerant, semi-susceptible, and susceptible). Groups with a greater number of genotypes differ in the impact of meteorological conditions on survival for the medium cluster. In regions with more favorable conditions for overwintering success, farmers may be open to introducing this crop to diversify their farming system.

High throughput genotyping, phenotyping, and envirotyping applied within plant breeding multienvironment trials (METs) provide the data foundations for selection and tackling genotype x environment interactions (GEIs) through whole-genome prediction (WGP). Crop growth models (CGM) can be used to enable predictions for yield and other traits for different genotypes and environments within a MET if genetic variation for the influential traits and their responses to environmental variation can be incorporated into the CGM framework. Furthermore, such CGMs can be integrated with WGP to enable wholegenome prediction with crop growth models (CGM-WGP) through use of computational methods such as approximate Bayesian computation. We previously used simulated data sets to demonstrate proof of concept for application of the CGM-WGP methodology to plant breeding METs. Here the CGM-WGP methodology is applied to an empirical maize (Zea mays L.) drought MET data set to evaluate the steps involved in reduction to practice. Positive prediction accuracy was achieved for hybrid grain yield in two drought environments for a sample of doubled haploids (DHs) from a cross. This was achieved by including genetic variation for five component traits into the CGM to enable the CGM-WGP methodology. The five component traits were a priori considered to be important for yield variation among the maize hybrids in the two target drought environments included in the MET. Here, we discuss lessons learned while applying the CGM-WGP methodology to the empirical data set. We also identify areas for further research to improve prediction accuracy and to advance the CGM-WGP for a broader range of situations relevant to plant breeding.