This study examines the determinants of organic agriculture adoption in Taiwan, employing machine learning techniques to analyze both internal motivations and farm characteristics. Using data from the "Social and Cultural Survey of Rural Taiwan" conducted in 2019 with supplementary data from 2021, we apply Lasso and Elastic Net regression models to identify key predictors of farmers' willingness to transition to organic agriculture. Our analysis suggests that environmental beliefs may have causal effects on adoption intentions, though important methodological limitations should be considered. The empirical analysis reveals that while formal education levels do not significantly influence adoption decisions, access to agricultural information through expert consultation and internet use plays a crucial role. Complex land tenure arrangements emerge as a significant barrier to organic transition, suggesting the importance of institutional factors in adoption decisions. The Elastic Net model with Bayesian Information Criterion offers one approach to handling high-dimensional data while maintaining interpretability, though different methods may be appropriate depending on research objectives and data characteristics. Additionally, this study explicitly compares the results of the machine learning approach with those of Chang (2025), who applied a principal component factoring (PCF) and multinomial logit analysis to the same dataset, highlighting the methodological differences and the policy implications of each. The comparative analysis offers robust evidence regarding the common and distinct drivers of organic agriculture adoption revealed by both analytical frameworks. The findings provide insights that may be relevant for policymakers in Taiwan, though generalization to other contexts would require careful consideration of local agricultural, institutional, and cultural differences.
