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

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