Comparative study of artificial neural network (ANN) andregression methods for prediction of breeding efficiency in Hariana cattle
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Authors
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RAKSHIT
U. P. Pandit Deen Dayal Upadhyaya Pashu Chikitsa Vigyan Vishwavidyalaya Evam Go Anusandhan Sansthan (DUVASU), Mathura Uttar Pradesh 281 001 India
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VIJAY KUMAR
U. P. Pandit Deen Dayal Upadhyaya Pashu Chikitsa Vigyan Vishwavidyalaya Evam Go Anusandhan Sansthan (DUVASU), Mathura Uttar Pradesh 281 001 India
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S P SINGH
U. P. Pandit Deen Dayal Upadhyaya Pashu Chikitsa Vigyan Vishwavidyalaya Evam Go Anusandhan Sansthan (DUVASU), Mathura Uttar Pradesh 281 001 India
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JITENDRA KUMAR AGRAWAL
U. P. Pandit Deen Dayal Upadhyaya Pashu Chikitsa Vigyan Vishwavidyalaya Evam Go Anusandhan Sansthan (DUVASU), Mathura Uttar Pradesh 281 001 India
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AMIT SINGH
U. P. Pandit Deen Dayal Upadhyaya Pashu Chikitsa Vigyan Vishwavidyalaya Evam Go Anusandhan Sansthan (DUVASU), Mathura Uttar Pradesh 281 001 India
Keywords:
ANN, Breeding Efficiency, MAE, MLR, RMSEAbstract
The present investigation was undertaken to predict the breeding efficiency of Hariana cattle using Tomar method (BET) and early life reproduction traits. Under this study, both multiple linear regression (MLR) and artificial neural network (ANN) approach were used. Effectiveness of both methods was also compared for prediction of BE in Hariana cattle. Data of breeding information were collected from the history sheet registers and herd inventory registers of Hariana cattle maintained at DUVASU farm, Mathura. Data on productive animals born between 1962 and 2024 were recorded out of which 743 animal records were collected. Breeding efficiency was predicted by multiple linear regression analysis. In MLR study, it was observed that model 5 having two independent variables (AFC and FSP) fulfilled most criteria such as higher R2, and lower MSE, RMSE, MAE, MAPE, and U values. In the present investigation, the accuracy of prediction obtained from ANN was more as compared to MLR for prediction of BET using early life reproduction traits in Hariana cattle. Multiple regression analysis was performed by considering different first lactation reproduction traits as independent variables and the breeding efficiency as dependent variables. The optimal model for BET included age at first calving and first service period. The optimal models were BET = 106.29 – 0.0192AFC – 0.029FSP. The MLR explained 69.6% of accuracy of prediction of BET in Hariana cattle with MSE of 42.20. With the ANN algorithm, the MSE was found to be 33.3, which is more precise as compared to MLR. For the prediction of BET, MLR method is simple and easy compared to ANN, but accuracy of ANN was more.
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