Toward More Resilient Agricultural Enterprises: Integrating Agricultural Environment Information into Explainable Financial Distress Prediction

Financial vulnerability in agriculture is shaped by both enterprise-specific factors and the agricultural systems in which firms operate, challenging financial distress prediction models based exclusively on firm-level financial metrics. This study assesses the predictive performance of logistic regression (LR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and category boosting (CatBoost), examines whether country-level agricultural environment characteristics improve prediction beyond traditional financial indicators, and uses Shapley additive explanations (SHAP) to analyze predictor contributions. The empirical analysis is based on 28,745 firm-year observations of agricultural enterprises in the Visegrad Group (V4) countries over 2019–2024. Adding agricultural environment variables produced a modest but consistent improvement in predictive performance, increasing AUC from approximately 0.854–0.855 in firm-level boosting models to 0.864–0.865 in integrated specifications. All three gradient boosting algorithms outperform conventional LR, although differences among XGBoost, LightGBM, and CatBoost remain statistically insignificant. SHAP analysis identifies liquidity, leverage, profitability, and firm size as the primary predictors while revealing nonlinear and threshold-dependent relationships not captured by conventional linear models. Integrating agricultural environment characteristics with explainable machine learning (ML) therefore supports more comprehensive, transparent, and context-aware early-warning systems for agricultural financial distress.