Multiple linear regression analysis using monthly test day milk yield predicting the first lactation production performance for sire evaluation in Murrah buffaloes

Abstract

A total of 9071 first lactation monthly test-day milk yield records of Murrah buffaloes were used to predict the first lactation 305-day milk yield (FL305DMY) by using stepwise backward regression analysis. For the prediction of FL305DMY best combination of monthly test-day milk yields were selected based on adjusted R² and RMSE values. The objective of the study was to compare various methods of sire evaluation viz., least squares (LSQ), simple regressed least squares (SRLS), best linear unbiased prediction sire model (BLUP-SM) and best linear unbiased prediction animal model (BLUP-AM) in terms of accuracy and efficiency. The methods were compared on the basis of error variance, coefficient of determination, coefficient of variation and rank correlations among the methods. The accuracy of prediction of FL305DMY from monthly test-day milk yields were observed to be best for TD-2 (45^th day), TD-4 (105^th day) and TD-6 (165^th day) combination with BLUP-AM as the most efficient method for sire evaluation. Individual, key monthly TD-6 (165^th day) milk yield has high rank correlation with EBVs obtained from actual 305-day milk yield. It was concluded that the optimum combination of TD-2 (45^th day), TD-4 (105^th day) and TD-6 (165^th day) or individual TD-6 (165^th day) can be used for genetic evaluation of Murrah sires.