Predicting the impact of extreme weather on agricultural losses in the Delmarva peninsula using multi-step machine learning and financial crop loss data

Abstract The increasing occurrence of extreme weather events due to climate change presents significant challenges for agricultural production. Existing research on climatic impacts to agriculture has predominantly focused on changes in yield for major crops, providing limited insights into overall losses and impacts on diverse regional agricultural systems. This study addresses this gap by using financial crop loss data and crop insurance payouts to gain a more comprehensive understanding of agricultural impacts in diverse agricultural regions. To address the irregular data structure of financial loss data, we developed multi-step machine learning models to quantify the relationship between weather-related financial crop loss and contributing climatic factors. The Delmarva Peninsula in the Eastern United States is used as a case study location to demonstrate this methodology over the period from 1980 to 2018. Multi-step configurations of linear regression, random forest, and support vector machine approaches are compared in terms of their classification and estimation accuracy using a repeated hold-out cross-validation analysis. Results indicate that machine learning methods, particularly random forest, outperform both statistical approaches and our null baseline model, demonstrating superior generalizability in agricultural damage estimation. Multistep configurations that address irregular data distributions are shown to have a significant influence on models’ capacity to detect and estimate damage occurrence. The study reveals a preference for simpler modeling approaches that minimize variance in handling unseen data, as well as the importance of accounting for seasonal patterns, spatial groupings, and persistent weather phenomena in accurately estimating agricultural losses.