Morphological Standard of Murrah Buffalo of India based on Multivariate Analysis of the Phenotypic Traits

Background: Morphometric measurements are important for the characterization of farm animal breeds and have become very useful in determining their morphological standards. Identification of animals based on breed standards are important for breed improvement and conservation programmes. Murrah, India's world famous buffalo breed, is the highest milk producer and improver breed. Prevalence of Murrah grades in the vicinity of breeding tract of the Murrah warrants their differentiation. The objective of this study was to define the morphological standard of the Murrah buffalo and differentiate it from the Murrah grades. Methods: Fourteen body biometric traits of 258 she buffaloes were recorded from the core and peripheral breeding tract of the breed. In order to separate the animals into two groups, data were subjected to cluster analysis. Principal component analysis (PCA) and discriminant analysis were used respectively to describe the body conformation and identify the combination of independent traits that distinguished Murrah from grade Murrah. Result: Our study showed significant differences between the traits of Murrah and grade Murrah (p<0.05). The grade Murrah she buffaloes were smaller than the Murrah counterparts. PCA results showed that five factors explained 67.4% of total variance of studied morphometric traits in Murrah she buffaloes, whereas in Grade Murrah six components explained 72.7% of the total variance. The stepwise discriminant function derived with eight body biometric traits was able to discriminate Murrah and grade Murrah she buffaloes effectively. A total of 93.5% original grouped animals were classified correctly. The present work determines the morphometric standard of the Murrah buffalo through characterization and identification of latent factors of morphometric traits. Subsequent use of this information in marketing, breeding, management and conservation purposes can give significant productivity gains.