Spatial Dynamics of Farmland Rental Prices in Corn Belt: A Geographically Weighted Regression Approach Integrating Economic and Agricultural Indicators

Understanding the forces that shape farmland rental prices in major agricultural regions such as the U.S. Corn Belt is essential for evaluating the economic and environmental resilience of agricultural regions. This study develops an integrated framework that combines spatial modelling with uncertainty-aware spatial analysis to examine how macroeconomic conditions influence rental dynamics across the core Corn Belt. Using geographically weighted regression, the analysis captures spatial variation in the sensitivity of rental prices to oil prices, interest rates, and economic activity, revealing substantial geographic heterogeneity in macroeconomic exposure. The results reveal pronounced spatial heterogeneity in rental price responses, with geographically weighted models consistently outperforming global linear specifications. Despite strong spatial variation in rental sensitivities, neither prediction uncertainty nor maize yield volatility displays a clear regional pattern, indicating that production stability and model reliability are highly localised. By linking spatially varying rent sensitivities with indicators of economic pressure and production instability, this study provides new insights into agricultural sustainability risk. The findings highlight the importance of place-based policy and region-specific risk management under increasing macroeconomic volatility.