Hierarchical Bayesian semi-parametric models for measurement error correction in determining optimal fertilizer application levels

Abstract

Measurement errors present a substantial challenge in accurately determining optimal fertilizer application levels, directly impacting agricultural efficiency and cost-effectiveness. This study examines the use of Hierarchical Bayesian Semi-Parametric (HBS) models to correct these errors, thereby improving precision in agricultural decision-making. By applying these models to a decade of data from Uasin Gishu County, Kenya, we evaluated key variables including maize yield, land size, and fertilizer levels. The results indicate that the HBS models effectively mitigate both systematic and random errors, leading to more accurate fertilizer recommendations. This advancement supports better resource management and higher crop yields. Our findings underscore the value of Bayesian methods in agricultural data analysis and highlight the critical role of accurate measurement and correction in achieving optimal outcomes. The implications of this research extend to improved decision-making processes and more sustainable agricultural practices.