Common factors method to predict the carcass composition tissue in kid goats

Abstract

-The objective of this work was to analyze the interrelations among weights and carcass measures of the longissimus lumborum muscle thickness and area, and of sternum tissue thickness, measured directly on carcass and by ultrasound scan. Measures were taken on live animals and after slaughter to develop models of multiple linear regression, to estimate the composition of shoulder blade, from selected variables in 89 kids of both genders and five breed groups, raised in feedlot system. The variables considered relevant and not redundant on the information they carry, for the common factor analysis, were used in the carcass composition estimate development models. The presuppositions of linear regression models relative to residues were evaluated, the estimated residues were subjected to analysis of variance and the means were compared by the Student t test. Based in these results, the group of 32 initial variables could be reduced to four variables: hot carcass weight, rump perimeter, leg length and tissue height at the fourth sternum bone. The analysis of common factors was shown as an effective technique to study the interrelations among the independent variables. The measures of carcass dimension, alone, did not add any information to hot carcass weight. The carcass muscle weight can be estimated with high precision from simple models, without the need for information related to gender and breed, and they could be built based on carcass weight, which makes it easy to be applied. The fat and bones estimate models were not as accurate.

Multivariate modelling to estimate carcase characteristics and commercial cuts of Boer goats

“…Through factor analysis, it was possible to observe that the first factor extracted by the method of Kaiser (1974), was responsible for accumulating 62% of the total variance of the studied characteristics (Table 7), that is, most of the variation was explained with the first factor, with reduced sample space. Gomes et al (2013), when evaluating the carcase characteristics of five genetic groups of goats in Brazil, selected the first four factors that explained 77% of the total variance of the data.…”

“…Gomes et al . (2013), when evaluating the carcase characteristics of five genetic groups of goats in Brazil, selected the first four factors that explained 77% of the total variance of the data.…”

Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep

“…Therefore, multiple regression analysis is a commonly used prediction model for interpretation between a dependent variable and two or more independent variables, however, this method has some disadvantages. The development of multiple regression models using independent variables with high correlations may present limitations in their inference and accuracy, and are likely to have serious effects on the estimates of regression coefficients and the overall applicability of the estimated model (Gomes et al, 2013), due to the problem of multicollinearity.…”