Predicting body weight of Kalahari Red goats from linear body measurements using data mining algorithms

Abstract

Background and Aim: The Kalahari Red goat breed is the finest meat-producing species in South Africa, and its coat color ranges from light to dark red-brown. A practical approach to estimating their body weight (BW) using linear body measurements is still scarce. Therefore, this study aimed to determine the best data mining technique among classification and regression trees (CART), Chi-square automatic interaction detection (CHAID), and exhaustive CHAID (Ex-CHAID) for predicting the BW of Kalahari Red goats. Materials and Methods: This study included 50 Kalahari Red goats (does = 42 and bucks = 8) aged 3–5 years. Body length (BL), heart girth (HG), rump height (RH), height at withers (WH), sex, and age were the essential indicators to estimate BW. The best model was chosen based on the goodness of fit, such as adjusted coefficient of determination (Adj. R2), coefficient of determination (R2), root mean square error (RMSE), standard deviation ratio (SD ratio), mean absolute percentage error, Akaike information criteria, relative approximation error, and coefficient of variation. Results: The SD values of the ratio ranged from 0.32 (CART) to 0.40 (Ex-CHAID). The greatest R2 (%) was established for CART (89.23), followed by CHAID (81.99), and the lowest was established for Ex-CHAID (81.70). CART was established as the preferred algorithm with BL, HG, and WH as critical predictors. The heaviest BW (73.50 kg) was established in four goats with BL higher than 92.5 cm. Conclusion: This study reveals that CART is the optimum model with BL, HG, and WH as the essential linear body measurements for estimating BW for Kalahari Red goats. The updated records will assist the rural farmers in making precise judgments for various objectives, such as marketing, breeding, feeding, and veterinary services in remote areas where weighing scales are unavailable.

Using Multivariate Adaptive Regression Splines to Estimate the Body Weight of Savanna Goats

“…However, Eyduran et al [ 10 ] indicated that the regression techniques cannot overcome the multi-collinearity problems from the variables. Hence, some studies used different data mining algorithms such as the classification and regression trees (CART) in South African Boer goats [ 6 ], chi-squared automatic interaction detector (CHAID) in South African indigenous non-descript goats [ 12 ], exhaustive CHAID in Kalahari Red goats [ 13 ] and multivariate adaptive regression splines (MARS) in Pakistani goats [ 9 ] for more and better developmental breeding strategies. Eyduran et al [ 14 ] indicated that these data mining algorithms were also useful for the estimation of fleece weight from the wool characteristics of sheep.…”

Discriminant Analysis Approach in Morphometric Differentiation and Characterization of Serbian Autochthonous Goats

“…Morphometric measurements are routinely used in everyday practice to provide information for selection purposes. Different studies reported a strong correlation between some morphometric measurements and production traits [ 20 , 21 , 22 , 23 ]. Moreover, linear body measurements are important data sources for portraying breed standards.…”

Prediction of body weight from body volume of Savanna goats in Limpopo province, South Africa

“…Estimation of body weight from morphometric traits is a cheap, easy and indirect method that can be practiced by livestock farmers at a rural level [7]. Several studies have been conducted on the estimation of body weight from various morphometric traits using different models in sheep [8], goats [9], chickens [10] and reported different traits that can be used as single predictor of body weight. However, it has been noticed that using single trait as predictor of body weight gives low precision [11].…”