Prediction of second parity milk yield and fat percentage of dairy cows based on first parity information using neural network system

Abstract

Neural network system can be used as a decision making support system in dairy industry as well as other industries. It can help breeders to predict future yield of dairy cows based on uncorrelated and orthogonalized available information and making selection decisions. Data from 4 medium to large sized dairy farms in Isfahan, Iran, were used. From 1880 available records of first and second parities, 1850 records were used for training a back propagation artificial neural network system and 30 randomly chosen records (not used in the system training step) were introduced to the trained neural network system for its evaluation. The results of the simulation showed that there was no significant difference between the observed and the predicted second parity milk yield and fat percentage (p>0.05). The major use of this predictive process is to make accurate selection decisions which are based on prior knowledge of the outcomes. © 2007 Asian Network for Scientific Information.