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

Md Azhar Uddin

No 362687, 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO from Agricultural and Applied Economics Association

Abstract: 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.

Keywords: Teaching/Communication/Extension/Profession (search for similar items in EconPapers)
Pages: 22
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/362687/files/Script%204_AAEA.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:aaea25:362687

DOI: 10.22004/ag.econ.362687

Access Statistics for this paper

More papers in 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO from Agricultural and Applied Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().