Enhancing Agricultural Climate Resilience: A Spatially Heterogeneous Functional Framework for Corn Yield Prediction in the U.S. Midwest

Abstract

Accurate crop yield prediction is paramount for food security amid climate volatility but struggles with complex, nonlinear, and spatially heterogeneous weather–crop interactions. This study develops a novel Spatially Heterogeneous Functional Additive Model (SH-FAM), representing a methodological innovation by uniquely integrating Multivariate Functional Principal Component Analysis (mFPCA) with data-driven climate zoning into a Generalized Additive Model (GAM) framework. The U.S. Midwest was selected as a study area for its pronounced east–west aridity and north–south thermal gradients, forming a natural laboratory for dissecting spatially heterogeneous climate–yield relationships. Unlike traditional models, SH-FAM preserves the continuous temporal structure of weather while allowing nonlinear biological thresholds to vary structurally across distinct agro-climatic zones. Extensive cross-validation shows SH-FAM reduces prediction error by 19% compared to benchmarks and substantially mitigates spatial bias during extreme events like the 2012 drought. We reveal distinct regional sensitivities to Heat and Drought Stress: water-limited western counties face immediate linear yield declines; the high-yielding core exhibits a nonlinear resilience threshold with catastrophic loss beyond a critical tipping point; northern regions show an inverted-U response where moderate warming enhances productivity. These spatially explicit response patterns enable zone-specific adaptation strategies, from drought mitigation in water-limited regions to thermal opportunity exploitation in heat-limited zones, providing actionable guidance for climate-resilient agricultural planning.