Regression tree analysis to predict body weight of South African non-descript goats raised at Syferkuil farm, Capricorn district of South Africa

Abstract

Regression tree is the data mining algorithm method which contains a series of calculations that creates a model from collected data. Present study aimed to develop model to estimate body weight (BW) from biometric traits viz. withers height (WH), sternum height (SH), body length (BL), heart girth (HG) and rump height (RH). A total of eighty-three (n = 83) South African nondescript indigenous goats (54 females and 29 males) aged three months and above were used in the study. Pearson's correlations and classification and regression tree (CART) as statistical techniques were used for data analysis. Correlation results indicated that there was a positive highly statistical significant (P < 0.01) correlation between BW and all biometric traits in both males and females, the positive highly statistical significant correlation was observed between BW and WH (r = 0.82) in female goats while in males the highest positive statistical significant correlation was detected between BW and BL (r = 0.83). CART model indicated that the BW mean was 29.868 kilograms (kg) as dependent variable and BL had the highest remarkable role in BW followed by SH, RH while the age had the least remarkable role in BW. This study suggests that BL, SH and RH might be used by South African nondescript goats' farmers as a selection criterion during breeding to improve BW of animal. More completive studies and experiments need to be done using CART to predict BW in more sample size of South African nondescript goats or other goat breeds.

Keywords

• biometric traits
• body length
• heart girth
• rump height
• sternum height