Pantnagar Journal of Research

Abstract

Principal Component Analysis is a mathematical procedure employed to transform a set of correlated variables into a smaller set of uncorrelated variables, thereby reducing the dimensionality. By applying principal component analysis, a comprehensive data set comprising various production and reproduction parameters such as milk yield, age at first calving, and calving interval etc. can be effectively analyzed. The study aimed to analyze the principal component of production and reproduction traits in Frieswal cattle, data spanning from 2013 to 2021, comprising of production and reproduction traits of 1163 cattle across six different field units, were collected and subjected to PCA to explain the performance in Frieswal. Factor analysis with varimax rotation uncovered three principal components, collectively explaining 74.30% of the total variance. The first principal component accounted for 35.54% of the variance, followed by the second and third components, which explained 23.37% and 15.38% of the variance, respectively. The communality values ranged from 0.247 (average fat %) to 0.972 (calving interval) across all performance traits. These findings indicate that PCA can serve as a valuable tool in breeding programmes, allowing for a significant reduction in the number of production and reproduction traits while still effectively capturing performance trends in Frieswal cattle.