Bayesian Hierarchical Model for Measuring Adoption Rates in Smallholder Farms Systems of Ethiopia: A Methodological Evaluation

The adoption rates of new agricultural technologies among smallholder farmers in Ethiopia are essential for improving productivity and sustainability. A Bayesian hierarchical logistic regression model was employed to analyse data from multiple farms. The model accounts for spatial heterogeneity and potential confounding variables such as farmer education levels and access to credit. The analysis revealed significant variations in adoption rates across different regions, with a median adoption rate of 45% overall. The Bayesian hierarchical model provided robust estimates of adoption rates, accounting for spatial and socio-economic influences effectively. Future studies should incorporate additional data sources to enhance the model's predictive power and reliability. Bayesian Hierarchical Model, Adoption Rates, Smallholder Farms, Ethiopia, Logistic Regression The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.