Application of Multivariate Statistics to Optimize the Selection of Breeding Bulls

The article presents a methodology for cluster typification of Holstein breeding bulls based on a comprehensive set of breeding traits, including productivity, health, exterior, fertility, and adaptability indices. The purpose of the study was to identify stable selection types of bulls using the Kmeans clustering algorithm followed by principal component analysis (PCA). The dataset included 2,141 Austrian Holstein bulls, standardized and analyzed across twenty selection indices. Four stable clusters with distinct genetic profiles were identified. Analysis of variance (ANOVA) confirmed statistically significant differences (p < 0.001) among clusters for most traits, including the total breeding index (RZG), productivity (RZM), health (RZОko), and fertility (RZN). Visualization via PCA validated the biological basis and stability of the identified groups. The results indicate a high level of genetic heterogeneity in the imported Holstein population and provide a foundation for developing a national typification system of breeding bulls, aimed at improving selection efficiency and adapting foreign genetic material to Russian livestock production conditions.