Structural Determinants of Organic Farm Persistence: Evidence from Hungary Using Combined Machine Learning and Statistical Models

Organic farming has gained increasing relevance worldwide due to its environmental benefits and its prominent role in sustainable food systems; however, the persistence of organic farms remains uneven across regions, particularly within the European Union. While the number of organic farms has grown overall in the EU, significant exits from organic production highlight the need to better understand the factors shaping farm survival, especially in newer Member States, where organic conversion and maintenance support schemes are often implemented through area-based CAP payments. This study aims to identify the structural and contextual determinants of short-term organic farm persistence in Hungary within a broader European context. Using farm-level data for the period 2020–2023, including Standard Output (SO) indicators, we applied a combined modelling framework based on Logistic Regression, Decision Trees, and Random Forest algorithms to assess the relative importance of economic, structural, and regional variables. The results show that organic farm persistence is primarily driven by structural characteristics such as farm size, economic scale, degree of conversion to organic farming and regional embeddedness, while production specialization and organizational features play a secondary, conditional role. The convergence of results across modelling approaches indicates that survival is shaped by hierarchical structural constraints rather than isolated management decisions. Our findings suggest that policy measures aiming to stabilize and expand the organic sector should move beyond uniform incentives, such as area-based payments, and should place greater emphasis on the structural conditions of farms and region-specific support mechanisms.