Comparison of linear and nonlinear models to estimate body weight of Pelibuey ewes from body measurements

The aim of this study was to develop prediction equations for the body weight of Pelibuey ewes from body measurements comparing linear vs nonlinear models. A subsample of 197 ewes was scored for body weight (BW), rump length (RL), rump width (RW), height at withers (HW), chest girth (CG), chest width (CW), chest depth (CD), cannon bone perimeter (CP), and body length (BL). Pearson’s correlation analysis was performed on a sub-data set from 197 ewes to estimate the relationship between body weight and body measurements. Multiple linear regressions were fitted to obtain prediction equations of body weight from the eight body measurements, and prediction equations were obtained from the body measurement that showed the highest correlation with body weight using five nonlinear models allometric, saturation growth, exponential, and incomplete gamma. Data from an independent subsample of 196 ewes was used to validate the equation with the best goodness of fit using linear regression analysis. CG was the body measurement that showed the highest correlation with BW, and based on multiple stepwise regression, in the equation BW = − 60.622 + 1.233CG explained 79% of the body weight variation. Moreover, BW prediction was more accurate when other measurements such CW, BL, and RW were added to the model generating to the equation BW = − 68.875 + 0.845CG + 0.866CW + 0.195BL + 0.601RW (R2 = 0.85, MSE = 15.51). In the case of nonlinear models, incomplete gamma and exponential models generated the equations with the best goodness of fit and precision: BW = 0.077CG1.108exp(0.016CG) (R2 = 0.82, MSE = 18.64) and BW = 3.5759exp(0.0292CG) (R2 = 0.82, MSE = 18.65) respectively.