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Common factors method to predict the carcass composition tissue in kid goats
  • Universidade Federal de Mato Grosso
  • Universidade Estadual Paulista (UNESP)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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.
Issue Date: 
Revista Brasileira de Zootecnia. Sociedade Brasileira de Zootecnia, v. 42, n. 3, p. 193-203, 2013.
Time Duration: 
Sociedade Brasileira de Zootecnia
  • co-linearity
  • correlation
  • evaluation
  • goat
  • regression
Access Rights: 
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Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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