Prediction of first lactation 305 days milk yield using artificial neural network in Murrah buffalo

Abstract

In the present study, first lactation test day and monthly milk records of 301 Murrah buffaloes were used for prediction of first lactation 305-day milk yield (FL305DMY) using artificial neural network (ANN) and was compared with multiple linear regression (MLR). Models were evaluated on the basis of coefficient of determination and root mean square error (RMSE). Two different input sets (Input set-1 and Input set-2) were used in the study. In input set-1, four test day milk yields (6th, 36th, 66th and 96th day of lactation) along with age at first calving (AFC) and peak yield (PY) were taken together and in input set-2, four monthly milk yields record (1st, 2nd, 3rd and 4th month yield) along with AFC and PY were taken together. The ANN was trained using back propagation (BP) algorithm which is also known as Bayesian regularization (BR). ANN achieved highest accuracy of 82% with lowest RMSE value of 16.46% for input set-1 while MLRs accuracy was 80.53% with RMSE value of 17.48%. Higher accuracy and lower RMSE value for ANN clearly showed its better performance than MLR model. Hence, ANN could be alternatively used as a tool for prediction of FL305DMY in Murrah buffaloes using input set-1 with more than 80% accuracy. So, 96th day test day yield (TD4) can be used for prediction of FL305DMY and as a trait for early genetic evaluation of sires.