Exploring body morphometry and weight prediction in Ganjam goats in India through principal component analysis.

Abstract

The body conformations of 262 adult Ganjam goats were subjected to principal component analysis (PCA) with 11 morphometric variables. The results were then used to predict the mature body weight of the goats. Most of the traits were positively correlated, and the correlations were statistically significant. The three main components that the PCA recovered explained 76.12% of the variation in body morphometry overall. The first component accounted for approximately 54.74% of the overall variation and described almost all the traits except ear length and tail length, as indicated by high component loadings. The second component accounted for approximately 11.48% of the variation, mostly accounting for the variation in tail length. The principal component accounted for 9.89% and mostly explained the variation in ear length. The communalities ranged between 0.557 (horn length) and 0.848 (chest circumference) for the first three extracted components. The highest percentage of variability in chest girth was explained by the first three principal components, whereas it was the lowest for the horn length. In the context of predicting body weight through stepwise regression analysis, nine primary variables accounted for 57.3% of the total variance in body weight. Conversely, utilizing the first principal component alongside six additional principal components as independent variables resulted in capturing 56.3% of the variation in the adult live weight of goats while maintaining model comparability with other pertinent parameters. PCA was used efficiently for body weight prediction from major morphometric traits of Ganjam goats addressing the multicollinearity issue.