PREDICCIÓN DEL PESO VIVO MEDIANTE VARIABLES MORFOMÉTRICAS EN CAPRINOS DE BAJA CALIFORNIA SUR, MÉXICO

<p><strong>Background.</strong> Regression models based on different morphometric measurements have been used as a practical, minimal cost and highly reliable method to predict live weight (LW) in goats; however, for the northwest region of Mexico there is no information available on the genetic and phenotypic variability of local goat populations, therefore, it is necessary to generate experiences on the efficiency of morphometric measurements to estimate LW in this area. <strong>Objective. </strong>To obtain the best equation for predicting live weight through variables and morphometric indices in goats from Baja California Sur. <strong>Methodology. </strong>Two assessments were carried out; the first one measured the height at the withers (AC), body length (LC), thoracic girth (PT) and LW of 403 Nubian crossbred goats. Morphometric measurements were analyzed by stepwise multiple regression and simple correlation coefficients were obtained; in the second, the efficiency of the best model (soft tape measure) was evaluated <em>vs</em> the weight obtained with a spring balance and the estimated by observation. <strong>Results. </strong>In the first assessment, it was observed that the three morphometric variables presented a positive, high and significant correlation (p < 0.001) with the PV, with the PT being the variable with the highest correlation value (r = 0.97), followed by the LC (r = 0.93) and AC (r = 0.91). The best PV prediction equation (R<sup>2</sup> adjusted = 0.98) was the one that included PT. The inclusion of the three morphometric variables in the model only improved the coefficient of determination by 1 % (R<sup>2</sup> adjusted = 0.99). The efficiency of the estimated weight with a soft tape measure <em>vs</em> a spring balance was similar (p < 0.05). <strong>Implications: </strong>Results indicate that the developed equation can accurately estimate goats PV when a spring balance is not available. <strong>Conclusion.</strong> Under the conditions of this study, the best model that predicted the PV was the one that included PT, which means saving time, money, and simplicity.</p>