Prediction of Body Weight of Yearling Boer Goats from Morphometric Traits using Classification and Regression Tree

Abstract

Classification and Regression Tree (CART) is a predictive algorithm method used to explains how the dependent variable can be predicted using independent variables (numerical and characters). The study was conducted to investigate the relationship between body weight and morphometric traits (Body Length (BL), Heart Girth (HG), Rump Height (RH), Rump Width (RW), Ear Length (EL), Cannon Circumference (CC) and Head Width (HW)) and to estimate body weight from morphometric traits in yearling Boer goats. In addition, age and sex were also considered. A total of seventy-one (71) yearling Boer goats (female = 57 and male = 14) between the age of one year and two years old were used. Pearson correlation and CART were used for data analysis. Correlation results indicated that BW of female goats was highly positive significant at (P<0.01) with HG (r =0.828) and BL (r = 0.621) and consistently positively correlated at (P<0.05) with RH (r = 0.558) and HW (r = 0.512), while BW of male goats was highly positive significant at (P<0.01) with BL (r = 0.727), CC (r = 0.642), HG (r = 0.564), RW (r = 0.361) and EL (r = 0.340) and consistently positively significant at (P<0.05) correlated with RH (r = 0.317). CART findings showed that sex played a crucial role on body weight of yearling Boer goats. Correlation results suggest that morphometric traits of yearling Boer goats might be used to improve body weight. CART model developed in this study could be used by breeders to advice resource-limited Boer goats’ farmers which morphometric traits they can use to select their animals in order to improve their herd. However, further studies need to be done to validate the use of CART in prediction of body weight from morphometric traits of yearling Boer goats using large sample size, different area or other goat breeds.