Predicting Agribusiness Insolvency Risk in North Carolina: A Random Forest Approach to Crop Insurance Indemnities

This study investigates the financial distress within the North Carolina crop sector caused by sudden climatic shocks. Traditional insurance models often fail to anticipate catastrophic losses because they rely on linear historical averages that smooth over biological tipping points. This research addresses this gap by developing an early warning system that identifies the specific environmental and financial thresholds that push county-level insurance claims past the break-even point. Using a 15-year panel of North Carolina agricultural data, the study compares the predictive performance of a non-linear ensemble framework against traditional benchmarks to evaluate the reliability of the current crop insurance safety net. The results demonstrate that machine learning significantly improves the detection of extreme loss events by identifying financial scarring, the path dependency of previous losses, as the primary driver of current farm-level fragility. The analysis reveals that extreme heat waves and cumulative thermal stress act as triggers that push financially weakened counties into regional insurance shortfalls. These findings suggest that transitioning from reactive disaster relief to proactive, threshold-based risk management can stabilize the agricultural credit market and protect the savings of rural communities. This study provides a scalable framework for farmers, lenders, and insurers to mitigate the economic damage caused by climate-driven volatility in regional agribusiness sectors.