Short Communication: Prediction of body weight using morphometric measurements in Creole goats from Peru

MIGUEL PAREDES-CHOCCE (National Institute of Agrarian Innovation. Av. La Molina 1981, La Molina, Lima 15024, Peru)
EMMANUEL SESSAREGO (National Institute of Agrarian Innovation. Av. La Molina 1981, La Molina, Lima 15024, Peru)
LUCINDA TAFUR (National Institute of Agrarian Innovation. Av. La Molina 1981, La Molina, Lima 15024, Peru)
VICTOR TEMOCHE (National Institute of Agrarian Innovation. Av. La Molina 1981, La Molina, Lima 15024, Peru)
JORGE SALINAS (National Institute of Agrarian Innovation. Av. La Molina 1981, La Molina, Lima 15024, Peru)
IRENE ACOSTA (National Institute of Agrarian Innovation. Av. La Molina 1981, La Molina, Lima 15024, Peru)
JOSE RUIZ (National Institute of Agrarian Innovation. Av. La Molina 1981, La Molina, Lima 15024, Peru)
JUANCARLOS CRUZ (National Institute of Agrarian Innovation. Av. La Molina 1981, La Molina, Lima 15024, Peru)
FRITZ TRILLO (National Institute of Agrarian Innovation. Av. La Molina 1981, La Molina, Lima 15024, Peru)

Biodiversitas Journal of Biological Diversity
Vol. 26 No. 7 (2025)

Abstract. Paredes-Chocce M, Sessarego E, Tafur L, Temoche V, Salinas J, Acosta I, Ruiz J, Cruz J, Trillo F. 2025. Short Communication: Prediction of body weight using morphometric measurements in Creole goats from Peru. Biodiversitas 26: 3193-3198. Goats are an important component of smallholder family farms along the coast and highlands of Peru. The weight of an animal is an important indicator of the production and economy of farmers in rural areas. Therefore, this study aimed to develop predictive models for Body Weight (BW) using Morphometric Measurements (MM) of Creole goats (Capra hircus) in Perú. BW and five MM were collected from 356 goats from the coast and highlands of Peru. Variables were analyzed using correlation and stepwise regression analysis to select the best model based on the coefficient of determination (r2), adjusted r2, Residual Standard Error (RSE), and Akaike Information Criterion (AIC) using the RStudio statistical software. The highest correlation was found between BW and TG (0.76), followed by RW (0.67), and RH (0.65). The combinations of MM selected as predictors of BW by stepwise regression were TG, RH, and RW, with r2: 0.640. The selected candidate model met all established tests and, upon validation, reached an r2 of 0.66 (p<0.001), indicating that the model can adequately predict the BW of Peruvian Creole goats and serve as a practical tool to support selection programs, feeding strategies, and market decision-making in smallholder systems.