Machine Learning Insights on Farm Exits: Enhancing Resilience in Wisconsin’s Dairy Industry

Identifying farms at risk of exiting the dairy industry remains a major challenge, particularly due to data scarcity and the limitations of traditional econometric models such as logit and probit. This study applies machine learning (ML) techniques to predict dairy farm exit intentions in Wisconsin using data from the 2024 DATCP Dairy Producer Survey. Using a broad set of accessible survey based variables, including farm demographics, operations, environmental practices, and perceived challenges, we compare the performance of Lasso, Ridge, Random Forest, and Extreme Gradient Boosting (XGBoost) models. XGBoost outperforms all others in both overall accuracy and sensitivity, effectively identifying farms at risk of exit while maintaining strong performance in predicting continuation. Furthermore, SHAP (SHapley Additive exPlanations) analysis highlights succession planning, operators age, investment behavior, labor constraints, and conservation practices as key predictors. These findings demonstrate the practical utility of ML models for early risk detection and offer actionable insights for policymakers, industry stakeholders, and extension services aiming to sustain Wisconsin’s dairy sector.