Literature DB >> 27206053

The chemical composition of carcasses can be predicted from proxy traits in finishing male beef cattle: A meta-analysis.

M Al-Jammas1, J Agabriel1, J Vernet1, I Ortigues-Marty2.   

Abstract

Management practices can contribute to improving carcass quality if carcass quality could be simply evaluated under a wide range of conditions. The objective of this study was to derive quantitative relationships between the most accurate (but laborious) measurements of carcass chemical composition and proxy traits easily obtainable at slaughter (yield grade, subcutaneous fat thickness, marbling, ribeye area and hot carcass weight) by meta-analysis. Data from 34 publications using male beef cattle were used to develop and validate the models. The breeds were characterized according to origin, rate of maturity, production purpose and frame size. The results indicated that the changes in carcass fat and protein can be predicted from the yield grade or subcutaneous fat thickness, and hot carcass weight, with prediction errors ranging between 9 and 12%. Including the breed characteristics in the models did not improve the fit. The relationships are applicable to group values of male beef cattle having light and fatty carcasses from early-maturing British breeds.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Carcass fat; Carcass protein; Fat thickness; Finishing cattle; Prediction; Yield grade

Mesh:

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Year:  2016        PMID: 27206053     DOI: 10.1016/j.meatsci.2016.04.029

Source DB:  PubMed          Journal:  Meat Sci        ISSN: 0309-1740            Impact factor:   5.209


  1 in total

1.  Can the body composition of crossbred dairy cattle be predicted by equations for beef cattle?

Authors:  Maria Luciana Menezes Wanderley Neves; Evaristo Jorge Oliveira de Souza; Robson Magno Liberal Véras; Sebastião de Campos Valadares Filho; Marcos Inácio Marcondes; Gabriel Santana da Silva; Lígia Maria Gomes Barreto; Marcelo de Andrade Ferreira; Antonia Sherlânea Chaves Véras
Journal:  Asian-Australas J Anim Sci       Date:  2018-04-12       Impact factor: 2.509

  1 in total

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