Prediction of second parity milk yield of Kenyan Holstein-Friesian dairy cows on first parity information using neural network system and multiple linear regression methods

Abstract

Artificial neural networks (ANN) have been used for prediction in many fields of knowledge and currently they have been used in agriculture field. The objective of this study was to investigate the usefulness of ANN in the prediction of second parity 305-day milk yield (SLMY305) of Kenyan Holstein-Friesian dairy cows based on first parity information. The ANN was compared with multiple linear regression (MLR) method. From a total of 2808 records of first and second parities Holstein-Friesian, 1685 records were trained using back propagation neural network and the rest were used for validation and testing sets. The network architecture was optimized by testing several types of structures. The model efficiency and accuracy were measured based on root mean square (RMSE) and regression coefficient (R2). Multilayer perceptron with the best ANN structure was determined as 8-1-1 with RMSE=682 and R2=0.86 with tangent sigmoid transfer function for hidden layer. The correlation coefficients between the observed and the predicted SLMY305 for the two estimation methods were generally high (>0.80) although ANN had higher figure but the difference was not statistical significant (P<0.05). Results illustrated the potential of ANNs in predicting SLMY305 in dairy cows. The implication is that dairy cow farmers could make selection decisions of prospective productive cows early hence increasing genetic potential of dairy herds.