Assessing the impact of climate indices on corn yield in the continental USA using machine learning approach

Climate has a profound impact on crop productivity, but its effects are difficult to measure due to significant spatial and temporal variability. This study explores a wide range of climatic indices that represent the various conditions affecting corn growth across the United States. By employing clustering techniques, we categorized rainfed corn-growing regions into distinct zones based on similar climatic characteristics to evaluate how each index influences crop yields. We identified the most effective combinations of these indices and used machine learning models at the county level to map the relationships between climatic factors and crop yields. Our analysis reveals that temperature-related indices, such as the number of days with temperatures exceeding 30 °C (HD30), Temperature Variance (Tvar), and Extreme Temperature Range (ETR), are the top three factors that negatively impact yields, while SU (number of summer days) has a positive effect. Precipitation-related indices also contribute positively, highlighting the critical role of balanced water availability during key growth stages. Notably, temperature-related indices emerged as the most effective predictors of yield in most regions, demonstrating stronger influence and higher predictive accuracy compared to precipitation indices. At the county level, machine learning models were used to map these relationships, with XGBoost emerging as the most reliable model. It consistently outperformed alternatives like Random Forest, Support Vector Machine, and LASSO, demonstrating superior accuracy and robustness. This was particularly evident during the extreme climatic conditions of 2012, marked by severe drought and heatwaves, where XGBoost accurately captured yield losses without overestimation. Among all factors, HD30 was identified as the most influential climatic driver of yield reductions under heat stress. This study not only enhances our understanding of climatic influences on crop production but also empowers stakeholders like farmers, policymakers, insurers, and agribusinesses to adopt optimized agricultural practices and develop strategic initiatives that enhance agricultural resilience and food security.