Using multivariate adaptive regression splines and classification and regression tree data mining algorithms to predict body weight of Nguni cows

The study was performed to determine the association between body weight and biometric traits and to examine the effect of biometric traits on the live body weight of Nguni cows using Multivariate Adaptive Regression Splines (MARS) and Classification and Regression Tree (CART) data mining algorithms. In total, 105 Nguni cows aged three to four years were used for body weight (BW) and biometric traits viz; head width (HW), head length (HL), ear length (EL), body length (BL), rump height (RH), withers height (WH), sternum height (SH), body depth (BD), bicoastal diameter (BCD), rump width (RW), rump length (RL) and heart girth (HG). Coefficient of determination (R2), adjusted coefficient of determination (Adj.R2), root-mean square error (RMSE), standard deviation ratio (SD ratio) and Pearson correlation between actual and predicted values were predicted to find out the best fit models. MARS models in prediction of BW presented as the best fit as compare with CART model with higher R2 = 0.993 and Adj.R2 = 0.991 with the lowest RMSE = 5.97 and SD ratio = 0.081. In this study, MARS models established are the significant statistical tools that might be used for describing studied breed standards for breeding purposes.

Abstract licence: CC BY-NC

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