Bayesian Hierarchical Model Assessment of Process-Control Systems in Kenyan Agricultural Yield Improvement: A Comparative Study

The study focuses on assessing process-control systems in Kenyan agricultural yield improvement through a Bayesian hierarchical model. A Bayesian hierarchical model is employed to analyse data from multiple fields, with uncertainty quantified via credible intervals. The analysis revealed that a specific control system increased crop yield by an average of 15% compared to traditional farming practices in the region. Bayesian hierarchical models provide robust insights into process-control systems' efficacy and can guide future agricultural policy and practice improvements. Policy-makers should consider implementing these enhanced control systems to improve agricultural productivity, particularly in regions with similar climatic conditions. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.