Machine Learning-Based Bioclimatic Suitability Modeling for Maize Cultivation Under Future Projections

Climate-driven heat and water stress are increasingly compromising rainfed maize yields in transition zones, with significant implications for global food security. While continental-scale models of crop suitability exist, they often fail to capture the high-resolution heterogeneity of agricultural landscapes or distinguish between irrigated and rainfed systems in semi-arid regions. This study models the current and future suitability of rainfed maize in Kansas, USA, using a Maximum Entropy (MaxEnt) approach. To accurately isolate biophysical constraints, we employed a novel data-filtering workflow using the USDA Cropland Data Layer (CDL) and Landsat-based Annual Irrigated Datasets (LANID) to train the model exclusively on rainfed occurrences. We projected suitability shifts for the mid- (2041–2070) and end-of-century (2071–2100) periods under two CMIP6 Shared Socioeconomic Pathways (SSP3-7.0 and SSP5-8.5), using high-resolution CHELSA bioclimatic variables. The model, achieving an Area Under the Curve (AUC) of 0.73 and validated against 30 years of historical USDA production records, reveals a distinct spatial contraction of areas climatically suitable for growing maize. Projections indicate a significant decline in suitability across Western and Central Kansas driven by rising temperatures and precipitation variability, with the most highly suitable optimal habitats projected to decline by approximately 90% by mid-century. These findings quantify mounting climate impacts on maize-growing areas of the Great Plains and provide spatially explicit baselines for the development of regional adaptation strategies and groundwater conservation policies.