Bayesian Hierarchical Model for Measuring Adoption Rates in Smallholder Farms Systems in Uganda

This study focuses on evaluating adoption rates among smallholder farmers in Uganda's agricultural systems. A Bayesian hierarchical model was developed and implemented using data from smallholder farms. The model accounts for spatial heterogeneity and incorporates uncertainty through robust standard errors. The Bayesian hierarchical model revealed a significant proportion (25%) of farmers adopting improved farming techniques, with substantial variability across regions. The Bayesian hierarchical model provided insights into adoption patterns that were more nuanced compared to traditional models. Future studies should consider longitudinal data for a deeper understanding of adoption dynamics over time. Bayesian Hierarchical Model, Adoption Rates, Smallholder Farms, Uganda The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.