Principal component analysis of linear type traits to explain body conformation in Murrah buffaloes

Linear type traits are important in terms of reflecting breed standards and in giving information about the developmental ability of the animals. For data analysis, principal component analysis (PCA) is most important technique when variables are correlated. The aim of present study was to make linear type traits unrelated and reduce their number to the extent which could be used in explaining body conformation in Murrah buffaloes. Measurements were recorded on a total of 81 adult Murrah buffaloes maintained at Buffalo Farm, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar for 11 linear type traits (top wedge angle, rump slope, rump width, hip bone distance, navel flap length, brisket distance, height at wither, body length, skin thickness at neck region, skin thickness at ribs region and skin thickness at rump region). Phenotypic correlations were calculated for considered traits and significant positive correlations varied from 0.26 to 0.67 in the present study. All 11 linear type traits were subjected to varimax rotated PCA with Kaiser Normalization to explain body conformation of Murrah buffaloes. Principal component analysis resulted into four components which described 69.522% of total variation and out of this, first component explained 28.678% variation. The communality ranged from 0.882 (rump slope) to 0.390 (naval flap length) and unique factors ranged from 0.118 to 0.610 for 11 different linear type traits. It was concluded that PCA was effective to reduce the number of variables required to explain the body conformation in Murrah buffaloes.