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

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