The rapid expansion of off-grid communities in Kenya has necessitated a methodological assessment to evaluate their systems and identify areas for risk reduction. A comprehensive search strategy was employed using electronic databases such as PubMed and Web of Science. Studies were selected based on predefined criteria related to methodology and relevance to agricultural risk management. The analysis identified a significant trend (p < 0. 05) in the effectiveness of time-series models in predicting crop yield variability, with an R² value of 0. 67 indicating moderate explanatory power. Time-series forecasting models appear to be effective tools for assessing and mitigating agricultural risks in off-grid communities, particularly when applied to data from Kenya's agricultural sector. Further research should focus on validating these findings across different geographic regions and farming systems within Kenya. The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.
Wafula et al. (Thu,) studied this question.