ArticleAnimals : an open access journal from MDPI2026
Evaluation of Growth and Performance Traits in Limousin Weaned Bull Calves Using K-Means Clustering.
Article in Animals : an open access journal from MDPI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Despite the growing use of k-means clustering in cattle breeding research, its application to weaning performance traits in Limousin young bulls remains limited. This study evaluated the effectiveness of k-means clustering for identifying distinct groups based on age, live weight and morphometric records in a Hungarian Limousin nucleus herd. A total of 146 bulls weaned at an average age of 213 ± 2.03 days and live weight of 263 ± 3.31 kg (mean ± S.E.M.) were analysed. Age, live weight, withers height, rump height, back length, shoulder width, hip width and pin width were evaluated using k-means clustering. The optimal number of clusters was determined using the Elbow method, Average Silhouette Width analysis, the Calinski-Harabasz index, and the Davies-Bouldin index. Three clusters were identified, representing low (213 kg), intermediate (260 kg), and high (309 kg) body size groups. Live weight showed the strongest contribution to clustering (F = 387.3), followed by rump height (F = 85.6) and withers height (F = 82.8). Back length (F = 13.7) and pin width (F = 12.6) contributed the least. The greatest separation was observed between Cluster 1 and Cluster 3 (Euclidean distance = 99.26). Cluster 3 showed the highest within-cluster homogeneity, with no outliers and the lowest dispersion (SD = 10.67). These findings support data-driven phenotypic preselection strategies; however, future research should incorporate additional herds, economically relevant traits, and genomic data to enhance generalisability.
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