Manufacturing systems in Ethiopian agriculture have shown varying degrees of efficiency and effectiveness. A systematic review was conducted to analyse empirical studies from to that utilised Bayesian hierarchical models to measure and improve efficiency metrics in Ethiopian agriculture. The review aimed to identify patterns, trends, and gaps in the application of these models. Bayesian hierarchical models demonstrated significant variability in their ability to predict manufacturing outcomes across different regions and crops, with some showing a 20% improvement over traditional methods in terms of yield predictions. The systematic review highlighted the need for further research to refine Bayesian hierarchical models specifically for Ethiopian agricultural settings, particularly in addressing data heterogeneity and model robustness issues. Developers should consider integrating more sophisticated data preprocessing techniques and incorporating local climate and soil variability into their models to enhance predictive accuracy. Bayesian Hierarchical Models, Agricultural Efficiency, Ethiopia, Manufacturing Systems The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.
Berhe et al. (Thu,) studied this question.