Comparison of linear model and Artificial Neural Network to Prediction of Milk Yield Using First Recorded Parity

Precise prediction of milk yield is essential for management and modeling of farmer’s income in analysis of cost-benefit. Such that, accurate prediction of future records can decrease recording time. Early estimation of bull breeding value using partly records of parity can lower generation interval and increase selection intensity and genetic progress. Linear model is the most commonly used modeling method in research on different field of science. Artificial Neural Network (ANN) is based on artificial intelligent that uses working principles of human brain. Ease of application and power to model complex functions and relationships is factor of wide use of Artificial Neural Network. Revolutionized use of artificial neural network modeling in different aspects of science in the last two decades, is indication of successful application of this powerful technique to solve wide range of problems in different scientific issues. Therefore, object of current research is prediction milk yield of different parity milk production of dairy cattle using production of first recorded parity and artificial neural networks modeling. Furthermore, results of artificial neural network model compared with linear model. In current research, two sequential records of 2460 dairy cattle of a herd were investigated. Pedigree of used data set contained 2517 individuals. Data divided into two sub data of training and testing, to fitting Artificial Neural Network model. Artificial Network model learned the relationship between output and inputs of training data set. Adequacy parameters of the model investigated using model predicted outputs of testing data set and original outputs of the data. Network structure with the beast adequacy parameters were used for Artificial Neural Network model. Finally, linear model was fitted and compared with artificial neural network model. The best structure of Neural Network had 8 inputs, 4 neuron at first hidden layer, 2 neuron at second hidden layer and output of milk production that inputs were breeding value of average milk yield of first recorded parity, parity, sire group, age at first registered parturition, number of records for each parity and mean, minimum and maximum of recorded days in milk for each parity. The used artificial neural network model, predicted the parity milk production with RMSE and R2 of 7.94 and 0.625, respectively. R2 and RMSE of considered linear model was 0.39 and 26.63, respectively. The applied model of artificial neural network appropriately predicted the subsequent parity production using precedent parity data. This research indicated that use of artificial network model can be beneficial for decreasing recording period for dairy cattle genetic evaluation specially in sire evaluation and will decrease generation interval. The results showed that incomplete data can be used for genetic evaluation using artificial neural network model. Comparison of the results with past reports indicated that use of effective inputs for milk production can increase accuracy and precision of the ANN model.