Interrelationships between carcass weight, dimensions and tissues thickness measurements on Churro Galego Bragançano and Suffolk Lambs

• Autor(a) principal: Cadavez, Vasco
• Data de Publicação: 2021
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10198/28572
• topic: Lambs; Carcass; Tissue; Measurements; Common Factors; Research Subject Categories::VETERINARY MEDICINE::Animal hygiene

The goal of this study was to identify a reduced pertinent set of variables from an original data set of 18 carcass measurements in order to avoid redundancy and collinearity problems or to simplify data analysis and the development of the linear regression models. Forty-six (46) male lambs, 26 of Churro Galego Bragançano Portuguese local breed and 20 of Suffolk breed were used. Lambs were slaughtered and carcasses weighed approximately 30 min after in order to obtain hot carcass weight (HCW). After cooling at 4žC for 24 h a set of seventeen carcass and measurements were recorded. The data interrelationships common factor analysed following the common factor analysis procedure. Carcass width and perimeter measurements showed high and positive correlations with HCW (from 0.74 to 0.91) and between themselves (from 0.55 to 0.80). However, HCW was lowly correlated with leg length (0.17) and moderately correlated with measurements that characterise carcass lengths and perimeters (from -0.39 to 0.56). Subcutaneous fat thickness measurements made at different anatomical positions were lowly correlated with HCW (lower than 0.20), even though high correlations were observed among the fat thickness measures (higher than 0.67). Four common factors were retained and identified: carcass weight (factor I), breast bone tissue thickness (factor II), subcutaneous fat thickness (factor III) and conformation (factor IV), which account for 81.9% of the variation on the eighteen original variables. This study shows that common factors analysis can be used to condense the information given by large sets of variables, by selecting a reduced number of variables, which avoids collinearity problems and simplifies the development of carcass composition estimation models.