Prediction of carcass characteristics from body measurements using linear regression and artificial neural network methods in Lori-Bakhtiari sheep

In this study, the relationships between live body weight at slaughter, carcass characteristics (weights of hot carcass, cold carcass, cold half-carcass, lean meat, fat, bone and fat-tail) and body measurements (body length, chest girth, hip width and shoulder width) were investigated in Lori-Bakhtiari sheep. Two methods of linear regression and artificial neural network (ANN) in prediction of live weight and carcass characteristics were compared.
The data of 58 male lambs were collected at the Breeding Station of Lori-Bakhtiari sheep in Shahrekord city of Chaharmahal and Bakhtiari province, Iran. The lambs were weaned at 90 ± 5 days of age and were fattened in three groups of 60, 80 and 100 days. At the end of fattening period, body measurements and after slaughtering and skinning, all carcass characteristics were measured. A total of 696 records were used for prediction of carcass characteristics by using regression models and ANN. The possibility of predicting carcass characteristics and live weight at slaughter through body measurements was evaluated by choosing the most suitable regression model based on the value of the determination coefficient. The selective regression models also were fitted by artificial neural network then; these two methods were compared based on the value of the determination coefficient and the mean square errors.
The estimated phenotypic correlation coefficients between carcass characteristics and live weight at slaughter were generally positive and relatively high (0.58 to 0.99). The phenotypic correlation coefficients between carcass characteristics and body measurements were estimated between 0.29 and 0.69.
Between all body measurements, the highest correlations are observed between chest girth and carcass characteristics and live body weight. Based on relatively high phenotypic correlation coefficients in this research, prediction of carcass characteristics and live body weight can be possible with high accuracy by using body measurements. The results show that, to prediction of live body weight of Lori-Bakhtiari male lambs, body length, chest girth and hip width must be included in the regression model to obtain an accuracy of 79 percent. The regression equation including live body weight and heart girth could predict hot carcass and cold carcass weights with an accuracy of 97 and 96 percent, respectively. Live body weight alone explains the 94 and 72 percent of the variation of half-carcass and fat-tail weight, respectively. Moreover, the results show that the accuracy of ANN model for prediction of some carcass traits was more than regression models.
Based on results of this research, prediction of carcass characteristics and live weight is possible with relatively high accuracy by using some phenotypic body measurements. Also, results indicate that the artificial neural network technique was much better capable of predicting weight and carcass traits in Lori-Bakhtiari sheep compared to linear regression equations. The results of this study can be useful due to the importance of carcass traits in determination of genetic potential and regulation of animal breeding programs for high meat production.