Field research stations in Kenya's agricultural ecosystems are crucial for monitoring yield improvement across various crops. A Bayesian hierarchical linear regression model was employed to analyse data from multiple years and stations. The model accounts for spatial and temporal variability, incorporating prior knowledge about station-specific effects and overall trends. The analysis revealed a significant proportion (35%) of yield improvement attributed to the application of recommended agricultural practices in one research station over a five-year period. Bayesian hierarchical models provided enhanced statistical inference compared to traditional methods, demonstrating their utility for monitoring and improving crop yields in diverse environmental contexts. Further studies should validate these findings across more stations and years, with an emphasis on incorporating additional covariates such as weather patterns and soil quality. The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.
Njoroge et al. (Sun,) studied this question.