Yield response to climate, management, and genotype: a large-scale observational analysis to identify climate-adaptive crop management practices in high-input maize systems
Sustaining food security under climate conditions expected for the 21st century will require that existing crop production systems simultaneously increase both productivity and resiliency to warmer and more variable climate conditions. In this study, we analyzed observational rainfed maize (Zea mays L.) yield data from major maize production areas of the US Corn Belt. These data included detailed information on crop management and genetics not typically available in observational studies, allowing us to better understand maize yield response to climate under variable management. Spatial variability in management variables across the study domain is coincident with spatial climate gradients. Regularized global and geographically weighted regression models were used to explore maize yield response to climate, management, genetics, and their interactions, while accounting for collinearity among them associated with corresponding scales of spatial variability. In contrast with recent analyses suggesting increased susceptibility to drought stress under higher plant populations in maize production, our analyses indicated that under moisture limitation, higher yields were achieved when high planting rates were coupled with delayed planting date. Maize genetic families that performed best with adequate moisture saw greater yield penalties under moisture limited conditions, while positive response to increased radiation was consistent among family lines. The magnitude of yield response to climate, management, and their interactions was also variable across the study domain, suggesting that information on crop management in spatial yield data can be used to better tailor local management practices to changes in yield potential resulting from agronomic advancements and changing local climate.