Comparison of artificial neural networks and multiple linear regression for ‎prediction of dairy cow locomotion score

Document Type : Original Article


1 Department of Animal and Poultry Sciences, College of Abouraihan, University of Tehran, Tehran, Iran

2 Department of Animal and Poultry Sciences, College of Abouraihan, University of Tehran, Tehran, Iran‎

3 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 123 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 (R2) values with lower mean squared error (MSE) than MLR. The R2 and MSE of the MLR model were 0.53 and 0.36, respectively. However, the ANN model for the same dataset produced much improved results with R2 = 0.80 ‏ and MSE = 0.16, 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.


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