Morphometric evaluation of guinea pigs (Cavia porcellus) in Southern Peru

• Autores: Quispe Condori, Dennis, Huacani Pacori, Ferdynand Marcos, Mamani Paredes, Javier, Mamani Cato, Ruben Herberht
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:null:20.500.12955/2548
• Enlace del recurso: https://hdl.handle.net/20.500.12955/2548; https://doi.org/10.55214/25768484.v8i4.1049
• Nivel de acceso: acceso abierto
• Materia: Abdominal perimeter; Body weight; Guinea pigs; Head length; Head width; Morphometric characteristics; Neck perimeter; Southern Peru; Stepwise regression; https://purl.org/pe-repo/ocde/ford#4.02.01; Morphometrics; Morfometría; Peso corporal; Cobaya; Head dimensions; Dimensiones de la cabeza; Peru; Regresion de paso cauteloso

The aim of this study was the morphometric evaluation of guinea pigs in southern Peru. The study was carried out at the Agrarian Experimental Station Illpa (AESI) of the National Institute of Agrarian Innovation (NIAI) in Puno at 3824 meters above sea level. 120 guinea pigs were used (females n = 60 and males n = 60) with an average age of 22 days. The morphometric characteristics evaluated were: body weight (BW), chest circumference (CC), abdominal perimeter (AP), neck perimeter (NP), head length (HL) and head width (HW). To evaluate the effect of sex on morphometric characteristics, a completely randomized design was used. To determine the equation that best predicts body weight, stepwise regression was used, and correlations between morphometric characteristics were obtained using Pearson's correlation. The results show that the sex factor does not significantly influence BW, CC, AP, NP, HL, and HW (p≥0.05); likewise, the equation that best predicts the body weight of the guinea pigs was: BW = -530.50 + 21.98(CC) + 12.72(AP) + 10.16(NP) + 57.23(HW), with R2 = 84%. Pearson correlations between morphometric characteristics were of high magnitude, positive, and statistically significant (p<0.001). It is concluded that in conditions of the Peruvian highlands, the sex factor does not influence the morphometric characteristics. It is also possible to predict body weight from CC, AP, NP, and HW, and the correlations were high and positive.