Prediction of carcass weight at the age of slaughtering in guinea pigs of the Cieneguilla genotype based on a synthesis of body measurements

Descripción del Articulo

The objective of the present study was to predict carcass weight (PC) in guinea pigs (Cavia porcellus) at the age of slaughtering (16±2 weeks), considering their biometric measurements. The following measures and weights were taken before and after the slaughter of 150 male guinea pigs of the Cieneg...

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Detalles Bibliográficos
Autores: Arias, Pablo Rubio, Chávez C., Juan, Febres, Grimaldo, Deza C., Hugo
Formato: artículo
Fecha de Publicación:2018
Institución:Universidad Nacional Mayor de San Marcos
Repositorio:Revistas - Universidad Nacional Mayor de San Marcos
Lenguaje:español
OAI Identifier:oai:ojs.csi.unmsm:article/14476
Enlace del recurso:https://revistasinvestigacion.unmsm.edu.pe/index.php/veterinaria/article/view/14476
Nivel de acceso:acceso abierto
Materia:guinea pig
carcass yield
body measurements
selection
cuy
rendimiento de carcasa
medidas corporales
selección
Descripción
Sumario:The objective of the present study was to predict carcass weight (PC) in guinea pigs (Cavia porcellus) at the age of slaughtering (16±2 weeks), considering their biometric measurements. The following measures and weights were taken before and after the slaughter of 150 male guinea pigs of the Cieneguilla genotype: live body weight (PV), body length (LC), head length (LCA), head width (AC), loin length (LL), loin width (AL), chest girth (PT), thigh perimeter (PM), thigh length (LM), arm perimeter (PB), arm length (LB), and rump middle square (CMG), as well as the carcass weight (PC). The data were analyzed to determine the best regression equation and to establish the best predictive linear model of carcass weight. The «Step-Wise Regression» process of the SAS statistical package was used. The combinations of independent variables in the model revealed that the variables PV, PT, AC, and LL give a better explanation of the CP at the benefit age (R2=0.71; Cp-Mallows=1.63).
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