Principal component analysis of biometric traits to explain body conformation in Kajali sheep of Punjab, India

Abstract

Different body biometric traits were analysed in Kajali ewes (395) of Punjab (India) using varimax rotated principal components (PCA) with Kaiser Normalization to explain their body conformation and were subsequently used to predict adult body weight. These traits revealed that Kajali sheep were large in size and suitable for mutton production. The positive and highly significant phenotypic correlations among most of the traits indicates high predictability among these traits. PCA extracted major three components which explained 68.66% of total variation of body biometry. First component described the body size and explained about 36% of total variation. It was represented by high component loadings for chest girth, paunch girth and body weight. The second component described the tail length, height and ear length and explained about 21% variation. The communalities ranges between 0.33 (face length) to 0.87 (chest girth). The lower communality of face length and ear length indicates that these traits are less effective to explain the body conformation. The study suggested that PCA could be used in breeding programme for phenotypic selection of Kajali ewes and PCA is more appropriate compared to multiple regression analysis in predicting adult body weight. In predicting body weight using multiple regression analysis, chest girth alone accounted for 55% of total variation in body weight whereas, use of first principal component as a single predictor explained 79% of total variance and the best prediction equation (R² = 0.83) was obtained after inclusion of second and third component in the model.