Statistical models and approaches for analyzing the data on production traits and estimation of breeding value

Abstract

This article presents a comprehensive overview of statistical models and analytical approaches used for evaluating production traits and estimating breeding values in livestock. Emphasizing both traditional and advanced methodologies, the discussion includes least squares, Best Linear Unbiased Prediction (BLUP), and contemporary genomic models such as Genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), Bayesian methods, and dominance models. Special attention is given to data challenges in smallholder dairy systems, the importance of contemporary group effects, genetic trend estimation, and accounting for breed composition and population structure. The article further explores software tools, model selection, and the practical implications of genetic evaluations in diverse production systems. This synthesis serves as a valuable reference for researchers and practitioners engaged in animal breeding, genetic evaluation, and livestock improvement under both intensive and resource-constrained conditions.