Evaluating Peak Milk Yield as a Predictor for First Lactation 305-Day Milk Yield in Murrah Buffaloes

Background: Peak milk yield is an important early observable trait that holds immense practical significance in dairy farming. It is often utilized to assess the production potential of dairy animals under field conditions. Therefore, the present study aimed to predict the first lactation 305-day milk yield (FL305DMY) based on peak milk yield (PY) and subsequently, compare its efficiency with monthly test-day milk yield (TD) records alone or in combinations. Methods: Data on 350 PY and 3,850 TD records pertaining to first lactation of 350 Murrah buffaloes that calved in between 1993 and 2017 at ICAR-National Dairy Research Institute, Karnal, India were utilized for the investigation. A total of 11 TD records were taken from each animal at an interval of 30 days, starting from 6th day onwards until 305th day of lactation. The prediction of FL305DMY was performed by univariate and stepwise backward multiple linear regression analysis. The efficiency of the prediction equations was evaluated by Coefficient of determination (R2), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Root Mean Square Error (RMSE). Result: It was observed that the peak milk yield alone could predict the FL305DMY with 64.15% accuracy, which was the highest in univariate linear regression analysis. Furthermore, the regression coefficients and R2 values indicated that mid-lactation monthly test-day milk yield records up to TD-7 could be utilized in conjunction with peak milk yield for early prediction of FL305DMY. The stepwise backward multiple linear regression analysis revealed that the most optimal prediction equation, including peak milk yield as one of the variables, was found to be composed of three variables (PY, TD-4 and TD-7) showed 84.79% R2. The results could be utilized for early selection of genetically superior animals in breeding strategies.