Models to predict live weight from heart girth in crossbred beef heifers.

The objective of this study was to develop and evaluate linear, quadratic, and exponential mathematical models to predict live weight (LW) from heart girth (HG) in crossbred heifers raised in tropical humid conditions in Mexico. Live weight (363.32 ± 150.88 kg) and HG (166.83 ± 24.88 cm) were measured in 400 heifers aged between 3 and 24 months. Linear and non-linear regression was used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), the Bayesian information criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). In addition, the developed models were evaluated through internal and external cross-validation (k-folds) using independent data. The ability of the fitted models to predict the observed values was evaluated based on the root mean square error of prediction (RMSEP), R2, and mean absolute error (MAE). The correlation coefficient between LW and HG was r = 0.98 (P < 0.001). The quadratic model showed the lowest values of MAE (736.57), RMSEP (27.13), AIC (3783.95), and BIC (3799.91). Additionally, this model exhibited better goodness-of-fit values regarding external and internal validation criteria (higher R2 and lower RMSEP and MAE), thus having better predictive performance. The RMSE represented about 8% of the observed LW. Heart girth is highly correlated (r = 0.98) with LW. The quadratic model showed a high predictive capacity for crossbred heifers kept in tropical conditions.