Climate Impacts and Adaptation of US Maize

Abstract

Over the next three decades rising population and changing dietary preferences are expected to increase food demand by 25–75\%. At the same time, climate is also changing — with potentially drastic impacts on food production. Here, we take a modeling approach to disentangle and quantify climate impacts on maize yield in the U.S., investigate the underlying mechanisms that lead to yield changes under an idealized future climate scenario, and explore potential avenues for climate adaptation through identifying climate-smart plant traits and management strategies moving forward. Temperatures over the next century are expected to rise. Warmer air has a higher capacity to hold water; thus warming the atmosphere without additional moisture input leads to drying through higher vapor pressure deficit (VPD). Increased temperatures and accompanied elevated VPD levels can both lead to negative impacts on crop yield. The independent importance of VPD, however, is often neglected or conflated with that of temperature. We show that increased temperatures and accompanied elevated VPD levels can both lead to negative impacts on crop yield. The negative impact of these two factors varied with precipitation levels and influenced yield through separate mechanisms. Warmer temperatures influenced yield by affecting phenological development -- faster development under warmer temperatures led to overall shorter phenological stages and compromised time available for growth. Elevated VPD levels, on the other hand, increased water loss, triggering triggered several water stress responses such as reduced photosynthetic rates, lowered leaf area development, and shortened growing season length. The rise in CO\textsubscript{2} levels only partially buffers yield loss, with the magnitude of impact greatest under drier conditions. Next, we developed a data-model framework to standardize, track, and automate a large number of model simulations in order to explore crop performance within the vast genetics (\emph{G}) $\times$ management (\emph{M}) $\times$ environment (\emph{E}) landscape. We curated climate, soil, historic yield data, and cropping information into model-ready inputs to run simulations spanning a broad climate space. We then perturbed a number of key model parameters that spanned physiological, morphological, phenological, and management processes within the model to set up an ensemble of simulations within the perturbed trait-management space. Through this data-model framework, we identified phenology and morphology features key for crops to achieve high yield and low yield volatility, categorized several different strategies for crops to achieve high performance under current climate conditions, and quantified how crop performance could shift under future climate perturbation. We envision findings from this work may complement the efforts of breeders, crop scientists, and agronomists, equipping them with localized cultivar and management targets and priorities to adapt to impending climate stressors.