Mohammad Ali Norouzian - Department of Animal and Poultry Sciences, College of Abouraihan, University of Tehran, Tehran, Iran
Hossein Bayatani - Department of Animal and Poultry Sciences, College of Abouraihan, University of Tehran, Tehran, Iran‎
Mona Vakili Alavijeh - Department of Soft Computing, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran

In this study, artificial neural networks (ANNs) were employed to investigate the relationship between locomotion score and production traits. A total number of ۱۲۳ dairy cows from a free-stall housing farm were used in this study. To compare the effectiveness of the ANNs for the prediction of locomotion score, the multiple linear regression (MLR) model was developed using the eight production traits, body condition score, parity, days in milk, daily milk yield, milk fat percent, milk protein percent, daily milk fat yield, and daily milk protein yield as input variables to predict the locomotion score. The ANN predictions gave a higher coefficient of determination (R۲) values with lower mean squared error (MSE) than MLR. The R۲ and MSE of the MLR model were ۰.۵۳ and ۰.۳۶, respectively. However, the ANN model for the same dataset produced much improved results with R۲ = ۰.۸۰ and MSE = ۰.۱۶, respectively. Globally, the results of this study showed that the connectionist network model was a better tool to predict locomotion scores compared to the multiple linear regression.

‎ Dairy cow, ‎ Locomotion score, ‎ Neural network, ‎ Regression models