Estimation of carcass weight of Hanwoo (Korean native cattle) as a function of body measurements using statistical models and a neural network

Abstract

30Objectives: The objective of this study was to develop a model for estimating the carcass weight of 31 Hanwoo cattle as a function of body measurements using three different modeling approaches: 1) 32 multiple regression analysis, 2) partial least square regression analysis, and 3) a neural network. 33 Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal 34 Science (NIAS) in South Korea. Among the 372 variables in the raw data, 20 variables related to 35 carcass weight and body measurements were extracted to use in multiple regression, partial least 36 square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo 37 cattle by any of seven body measurements significantly related to carcass weight or by all 19 body 38 measurement variables. For developing and training the model, 100 data points were used, whereas the 39 34 remaining data points were used to test the model estimation.40 Results: The R 2 values from testing the developed models by multiple regression, partial least square 41 regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, 42 respectively, whereas all the methods exhibited similar R 2 values of approximately 0.93 with all 19 43 body measurement variables. In addition, relative errors were within 4%, suggesting that the 44 developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network 45 exhibited the highest accuracy.46 Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using 47 body measurements. Because the procedure and required variables could differ according to the type 48 of model, it was necessary to select the best model suitable for the system with which to calculate the 49 model.50 51 A c c e p t e d A r t i c l e 54 55 Body weight of beef cattle is one of the most important traits affecting price [1] and animal 56 condition [2, 3]. For this reason, accurate estimation of body weight is emphasized to establish 57 adequate management and nutritional approaches for improving conditions for raising beef cattle and 58 maximizing profits [4]. Because body weight is related to the body size of beef cattle, body size 59 measurement is considered the main physical estimator of body weight [5, 6]. Unlike internal traits, 60 such as body composition and genetic characteristics [7], body size is easy to measure; thus, it has 61 been used to evaluate body weight [8-10]. 62 Body weight of beef cattle has been estimated as a function of body measurements according to 63 cattle species, age, and gender. In particular, as image analysis in automated carcass weight 64 measurement has been demanded by the livestock industry, a simple model for predicting body weight 65 has been coded as an algorithm [10]. Heinrichs et al. [8] predicted the body weight of Holstein heifers 66 through body measurements based on a large number of observations and found a greater than 95% 67 R 2 -value. Ozkaya and Bozku...

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