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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.

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 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.

Temperature and vapor pressure deficit (VPD) are two important environmental factors influencing stomatal conductance and transpiration. A limited transpiration rate (TRlim) trait expressed under high VPD has been shown to offer an approach to increase crop yield in water-limited areas. The benefit of the TRlim trait is that it lowers the effective VPD under which plants lose water and so conserves soil water to support crop growth for use during drought periods later in the growing season. Previous studies at moderate temperatures (32 degrees C and lower) identified 12 maize (Zea mays L.) hybrids that express the TRlim trait. A critical question is whether the TRlim trait is also expressed by these hybrids under temperatures up to 38 degrees C, which are relevant in environments where maize may be grown. Five hybrids failed to express the TRlim trait at 38 degrees C but seven hybrids had sustained expression of the trait at 38 degrees C. The loss of expression of the TRlim response in the five hybrids was found to occur in the very narrow range of temperature increase from 36 to 38 degrees C. The genetic differences in water use among these maize hybrids could be useful in selecting hybrids that are especially well adapted for temperature conditions in a targeted production area.

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 lateseason use. Reduced water use, however, may compromise yield potential. The complexity associated with genotype X environment X 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.

Maize (Zea mays L.) is among the most important grains contributing to global food security. Eighty years of genetic gain for yield of maize under both favorable and unfavorable stress-prone drought conditions have been documented for the US Corn Belt, yet maize remains vulnerable to drought conditions, especially at the critical developmental stage of flowering. Optimum AQUAmax (Dupont Pioneer) maize hybrids were developed for increased grain yield under drought and favorable conditions in the US Corn Belt. Following the initial commercial launch in 2011, a large on-farm data set has been accumulated (10,731 locations) comparing a large sample of the AQUAmax hybrids (78 hybrids) to a large sample of industry-leading hybrids (4287 hybrids) used by growers throughout the US Corn Belt. Following 3 yr (2011-2013) of on-farm industry-scale testing, the AQUAmax hybrids were on average 6.5% higher yielding under water-limited conditions (2006 locations) and 1.9% higher yielding under favorable growing conditions (8725 locations). In a complementary study, 3 yr (2010-2012) of hybrid-by-management-by-environment evaluation under water-limited conditions (14 locations) indicated that the AQUAmax hybrids had greater yield at higher plant populations when compared to non-AQUAmax hybrids. The combined results from research (2008-2010) and on-farm (2011-2013) testing throughout the US Corn Belt over the 6-yr period from 2008 to 2013 indicate that the AQUAmax hybrids offer farmers greater yield stability under water-limited conditions with no yield penalty when the water limitations are relieved and growing conditions are favorable.

Limited transpiration rate (TR) under high vapor pressure deficit (VPD) conditions has been proposed as a desirable trait for crop yield improvement. The limited-TR trait has been identified in several single-cross maize hybrids, and among these hybrids, a range in the VPD breakpoint for limited TR was identified. It was hypothesized that the variation in the VPD breakpoint was due to differences in hydraulic conductance in their roots or leaves, or both. Therefore, the objective of this study was to compare relative hydraulic conductance in the roots and leaves across the maize hybrids expressing the VPD breakpoint. It was found that the VPD of the breakpoint was correlated with each of three indices of hydraulic conductance. That is, low VPD breakpoint was associated with low hydraulic conductance in both leaves and roots indicating a common, underlying limiting mechanism in these two tissues. It was hypothesized that expression of similar aquaporin populations influencing hydraulic flow across membranes in the roots and leaves may account for the consistency in results across the indices of hydraulic conductance.

Maize (Zea mays L.) yield is often restricted by low soil water availability, particularly late in the growing season. To increase yields, genetic options for more effective use of available soil water are being explored. One option is to select genotypes that have restricted transpiration rate under high vapour pressure deficit (VPD) conditions so that soil water is conserved for use later in the growing season. While genetic variation for this trait has been identified within several crop species, such variation has never been explored in maize. The objective of this study was to examine transpiration rate of 35 single-cross hybrids to determine whether hybrids can be identified that express limited transpiration under high VPD. Two sets of experiments were undertaken in which plants were exposed to a range of VPD in chambers. A two-phase transpiration response was observed in 11 hybrids in which there was a threshold VPD above which transpiration rate was restricted. The VPD threshold varied from 1.7 to 2.5 kPa among these hybrids. Eight hybrids were included in both sets of experiments, and the same results were obtained in both experiments, indicating that expression of the trait was consistent.

Drought is ubiquitous in rainfed cropping systems and often limits maize yields. The sensitivity of transpiration response early in progressive soil drying is a trait with potential to improve crop drought resistance. Simulation studies demonstrated that increased sensitivity to drying soil leading to restricted transpiration rates results in conservation of soil water during vegetative stages for possible use during grain filling. In contrast to other crops, there have been no studies characterizing genotypic variability for this trait in maize. Experiments in controlled environments were conducted to characterize the fraction of transpirable soil water (FTSW) threshold on drying soil for 36 hybrids selected for variation in the field for drought resistance, regions of adaptation and stay green. While FTSW thresholds varied among hybrids from 0.60 to 0.33, these thresholds were not uniformly associated with level of drought resistance in the field. Nevertheless, this study demonstrated a high FTSW threshold corresponded with drought resistance observed in some modern maize germplasm (hybrids #7, 17, 24, 27 and 32). This knowledge can enable breeding work seeking to exploit this adaptive trait to improved drought tolerance in low threshold FTSW germplasm.

Continuous increase in the yield of maize (Zea mays L.) in the U.S. Corn Belt has involved an interaction with plant density. A number of contributing traits and mechanisms have been suggested. In this study we used a modeling approach to examine whether changes in canopy and/or root system architecture might explain the observed trends. A maize crop model was generalized so that changes in canopy and root system architecture could be examined. A layered, diurnal canopy photosynthesis model was introduced to predict consequences of change in canopy architecture. A two-dimensional root exploration model was introduced to predict consequences of change in root system architecture. Field experiments were conducted to derive model parameters for the base hybrid (Pioneer 3394). Simulation studies for various canopy and root system architectures were undertaken for a range of sites, soils, and densities. Simulated responses to density compared well with those found in field experiments. The analysis indicated that (i) change in root system architecture and water capture had a direct effect on biomass accumulation and historical yield trends; and (ii) change in canopy architecture had little direct effect but likely had important indirect effects via leaf area retention and partitioning of carbohydrate to the ear. The study provided plausible explanations and identified testable hypotheses for future research and crop improvement effort.

Realizing the potential of agricultural genomics into practical applications requires quantitative predictions for complex traits and different genotypes and environmental conditions. The objective of this study was to develop and test a procedure for quantitative prediction of phenotypes as a function of environment and specific genetic loci in soybean [Glycine max (L.) Merrill]. We combined the ecophysiological model CROPGRO-Soybean with linear models that predict cultivar-specific parameters as functions of E loci. The procedure involved three steps: (i) a field experiment was conducted in Florida in 2001 to obtain phenotypic data for a set of near-isogenic lines (NILs) with known genotypes at six E loci; (ii) we used these data to estimate cultivar-specific parameters for CROPGRO-Soybean, minimizing root mean square error (RMSE) between observed and simulated values; (iii) these parameters were then expressed as linear functions of the (known) E loci. CROPGRO-Soybean predicted various phenological stages for the same NILs grown in 2002 in Florida with a RMSE of about 5 d using the E loci-derived parameters. A second evaluation of the approach used phenotypic data from cultivar trials conducted in Illinois. Cultivars were genotyped at the E loci using microsatellites. The model predicted time to maturity in the Illinois variety trials with RMSE around 7.5 d; it also explained 75% of the time-to-maturity variance and 54% of the yield variance. Our results suggest that gene-based approaches can effectively use agricultural genomics data for cultivar performance prediction. This technology may have multiple uses in plant breeding.