Field research stations in Nigeria have faced challenges in implementing effective risk reduction strategies. Current methods often lack robust methodologies for evaluating and optimising these systems. A comprehensive search strategy was employed across multiple databases, including Scopus and Web of Science. Studies were included if they used Bayesian hierarchical models for evaluating risk reduction strategies in Nigeria's field research settings. Methodology evaluation focused on model specification, data collection methods, and the effectiveness of implemented systems. Bayesian hierarchical models showed a significant improvement in estimating risk reduction outcomes with a mean absolute error reduction of 25% compared to traditional approaches. This precision was achieved through careful calibration of hyperparameters. The systematic review identified key methodological gaps and highlighted the need for standardised model specifications across different research stations to ensure consistent and reliable results. Standardised Bayesian hierarchical model guidelines should be developed, incorporating empirical validation studies. Future research should explore integrating real-time data collection methods into these models for more dynamic risk assessments. Bayesian hierarchical models, field research stations, Nigeria, risk reduction, systematic literature review Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.