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

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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 Morphomet...

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Detalles Bibliográficos
Autores: Paredes Chocce, Miguel Enrique, Sessarego Davila, Emmanuel Alexander, Tafur Gutierrez, Lucinda, Temoche Socola, Victor Alexander, Salinas Marco, Jorge, Acosta Granados, Irene Carol, Ruiz Chamorro, Jose Antonio, Cruz Luis, Juancarlos Alejandro, Trillo Zarate, Fritz Carlos
Formato: artículo
Fecha de Publicación:2025
Institución:Instituto Nacional de Innovación Agraria
Repositorio:INIA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.inia.gob.pe:20.500.12955/2826
Enlace del recurso:http://hdl.handle.net/20.500.12955/2826
Nivel de acceso:acceso abierto
Materia:goats
morphometrictraits
regressionmodel
stepwise
cría de cabras
cabras
rasgos morfométricos
modelo de regresión
paso a paso
Goat farming
https://purl.org/pe-repo/ocde/ford#4.01.06
Producción caprina; morfometría; Morphometrics; Morfometría
Descripción
Sumario: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.
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