Germplasm, genetics, phenotyping, and selection, combined with a clear definition of product targets, are the foundation of successful hybrid maize breeding. Breeding maize hybrids with superior yield for the drought-prone regions of the US corn-belt involves integration of multiple drought-specific technologies together with all of the other technology components that comprise a successful maize hybrid breeding programme. Managed-environment technologies are used to enable scaling of precision phenotyping in appropriate drought environmental conditions to breeding programme level. Genomics and other molecular technologies are used to study trait genetic architecture. Genetic prediction methodology was used to breed for improved yield performance for drought-prone environments. This was enabled by combining precision phenotyping for drought performance with genetic understanding of the traits contributing to successful hybrids in the target drought-prone environments and the availability of molecular markers distributed across the maize genome. Advances in crop growth modelling methodology are being used to evaluate the integrated effects of multiple traits for their combined effects and evaluate drought hybrid product concepts and guide their development and evaluation. Results to date, lessons learned, and future opportunities for further improving the drought tolerance of maize for the US corn-belt are discussed.

A successful strategy for prediction of crop yield that accounts for the effects of genotype and environment will open up 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 increase the size of plant breeding programs without the need to expand expensive field testing. The presence of Genotype-by-Environment-by-Management (G×E×M) interactions for yield presents a significant challenge for the development of prediction technologies for both product development by breeding and product placement within agricultural production systems. The integration of a CGM into the algorithm for whole genome prediction WGP, referred to as CGM-WGP, has opened up the potential for prediction of G×E×M interactions for breeding and product placement applications. Here a combination of simulation and empirical studies are used to explain how the CGM-WGP methodology works and to demonstrate successful reduction to practice for applications to maize breeding and product placement recommendation in the US corn belt.

Yield loss due to water deficit is ubiquitous in maize (Zea mays L.) production environments in the United States. The impact of water deficits on yield depends on the cropping system management and physiological characteristics of the hybrid. Genotypic diversity among maize hybrids in the transpiration response to vapor pressure deficit (VPD) indicates that a limited‐transpiration trait may contribute to improved drought tolerance and yield in maize. By limiting transpiration at VPD above a VPD threshold, this trait can increase both daily transpiration efficiency and water availability for late‐season use. Reduced water use, however, may compromise yield potential. The complexity associated with genotype × environment × management interactions can be explored in a quantitative assessment using a simulation model. A simulation study was conducted to assess the likely effect of genotypic variation in limited‐transpiration rate on yield performance of maize at a regional scale in the United States. We demonstrated that the limited‐transpiration trait can result in improved maize performance in drought‐prone environments and that the impact of the trait on maize productivity varies with geography, environment type, expression of the trait, and plant density. The largest average yield increase was simulated for drought‐prone environments (135 g m−2), while a small yield penalty was simulated for environments where water was not limiting (–33 g m−2). Outcomes from this simulation study help interpret the ubiquitous nature of variation for the limited‐transpiration trait in maize germplasm and provide insights into the plausible role of the trait in past and future maize genetic improvement.

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.

High throughput genotyping, phenotyping, and envirotyping applied within plant breeding multienvironment trials (METs) provide the data foundations for selection and tackling genotype × 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 whole‐genome 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.