Regional Variability of Drought-Crop Sensitivities Across Iowa Using Unsupervised Learning

S M Samiul Islam , Ibrahim Demir, Most Fatematozzohora

Understanding the spatial variability of crop drought sensitivity is critical for improving agricultural resilience in the face of climate change. This study presents a station-level analysis of meteorological and yield data across Iowa from 1998 to 2022 to investigate the relationship between multiple drought indices and detrended yields of Corn and Soybean. Eleven drought indicators were selected to capture both short-term and cumulative drought stress, including SPI at multiple timescales, SPEI, PDSI, EDDI, and CMI. Spearman correlation analysis revealed crop-specific sensitivities: corn yields were more affected by short-term atmospheric dryness (e.g., SPI-1, EDDI), while soybean yields responded more to long-term soil moisture deficits (e.g., SPI-6, PDSI, CMI). To reduce complexity and extract dominant sensitivity patterns, Principal Component Analysis (PCA) was applied, and the first principal component (PC1) was used as a summary indicator of drought sensitivity. Local Indicators of Spatial Association (LISA) revealed statistically significant clusters of vulnerability, with High-High drought sensitivity zones for both crops concentrated in South Central Iowa. In contrast, low-low clusters indicated spatial drought resilience, particularly in North Central and East Central Iowa for Corn and North Central and Northwestern Iowa for soybeans. Moran's I scatterplots confirmed moderate spatial autocorrelation in PC1 values. This study demonstrates the value of combining multivariate statistics, spatial analysis, and unsupervised learning to map crop-specific responses to drought. Identifying regionally coherent sensitivity patterns provides a robust foundation for improving early warning systems, informing localized drought adaptation strategies, and guiding climate-resilient agricultural planning across the U.S. Corn Belt.