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Genetic parameters of Visual Image Analysis primal cut carcass traits of commercial prime beef slaughter animals

Published online by Cambridge University Press:  15 March 2017

K. L. Moore ,
R. Mrode  and
M. P. Coffey
K. L. Moore*
Affiliation:
Animal and Veterinary Sciences, Scotland’s Rural College (SRUC), Peter Wilson Building, Kings Buildings, West Mains Road, Edinburgh EH9 3JG, UK
R. Mrode
Affiliation:
Animal and Veterinary Sciences, Scotland’s Rural College (SRUC), Peter Wilson Building, Kings Buildings, West Mains Road, Edinburgh EH9 3JG, UK
M. P. Coffey
Affiliation:
Animal and Veterinary Sciences, Scotland’s Rural College (SRUC), Peter Wilson Building, Kings Buildings, West Mains Road, Edinburgh EH9 3JG, UK
*
†E-mail: [email protected]
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Abstract

Visual Image analysis (VIA) of carcass traits provides the opportunity to estimate carcass primal cut yields on large numbers of slaughter animals. This allows carcases to be better differentiated and farmers to be paid based on the primal cut yields. It also creates more accurate genetic selection due to high volumes of data which enables breeders to breed cattle that better meet the abattoir specifications and market requirements. In order to implement genetic evaluations for VIA primal cut yields, genetic parameters must first be estimated and that was the aim of this study. Slaughter records from the UK prime slaughter population for VIA carcass traits was available from two processing plants. After edits, there were 17 765 VIA carcass records for six primal cut traits, carcass weight as well as the EUROP conformation and fat class grades. Heritability estimates after traits were adjusted for age ranged from 0.32 (0.03) for EUROP fat to 0.46 (0.03) for VIA Topside primal cut yield. Adjusting the VIA primal cut yields for carcass weight reduced the heritability estimates, with estimates of primal cut yields ranging from 0.23 (0.03) for Fillet to 0.29 (0.03) for Knuckle. Genetic correlations between VIA primal cut yields adjusted for carcass weight were very strong, ranging from 0.40 (0.06) between Fillet and Striploin to 0.92 (0.02) between Topside and Silverside. EUROP conformation was also positively correlated with the VIA primal cuts with genetic correlation estimates ranging from 0.59 to 0.84, whereas EUROP fat was estimated to have moderate negative correlations with primal cut yields, estimates ranged from −0.11 to −0.46. Based on these genetic parameter estimates, genetic evaluation of VIA primal cut yields can be undertaken to allow the UK beef industry to select carcases that better meet abattoir specification and market requirements.

Keywords

Visual Image Analysis (VIA)genetic parameter estimationprime slaughter beef cattlecarcass traits
Type
Research Article
Information
animal , Volume 11 , Issue 10 , October 2017 , pp. 1653 - 1659
Copyright
© The Animal Consortium 2017 

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References

Cantet, RJC, Pedro Steibel, J, Birchmeier, AN and Santa Coloma, LF 2003. Bayesian estimation of genetic parameters for growth and carcass traits of grass-fed beef cattle by full conjugate Gibbs. Archiv für Tierzucht 46, 435–443.Google Scholar
EBLEX 2014. EBLEX UK 2014 cattle yearbook. Retrieved on 1 December 2015 from http://www.eblex.org.uk/wp/wp-content/uploads/2014/07/UK-Yearbook-2014-Cattle-240714.pdf.Google Scholar
Eriksson, S, Näsholm, A, Johansson, K and Philipsson, J 2003. Genetic analyses of field-recorded growth and carcass traits for Swedish beef cattle. Livestock Production Science 84, 53–62.Google Scholar
Gilmour, AR, Gogel, BJ, Cullis, BR and Thompson, R 2009. ASReml user guide, release 3.0. VSN International Ltd, Hemel Hempstead, UK.Google Scholar
Hickey, JM, Keane, MG, Kenny, DA, Cromie, AR and Veerkamp, RF 2007. Genetic parameters for EUROP carcass traits within different groups of cattle in Ireland. Journal of Animal Science 85, 314–321.CrossRefGoogle ScholarPubMed
Hirooka, H, Groen, AF and Matsumoto, M 1996. Genetic parameters for growth and carcass traits in Japanese brown cattle estimated from field records. Journal of Animal Science 74, 2112–2116.CrossRefGoogle ScholarPubMed
Kause, A, Mikkola, L, Stranden, I and Sirkko, K 2015. Genetic parameters for carcass weight, conformation and fat in five beef cattle breeds. Animal 9, 35–42.Google Scholar
Liinamo, AE, Ojala, M and van Arendonk, JAM 1999. Relationships of body weight and carcass quality traits with first lactation milk production in Finnish Ayrshire cows. Livestock Production Science 60, 271–279.CrossRefGoogle Scholar
MLCSL 2014. Beef carcass classification. Retrieved on 1 February 2014 from http://www.mlcsl.co.uk/publications/Beef-carcass-classification.pdf.Google Scholar
Moore, K, Pritchard, T, Wilkinson, S, Mrode, R, Pearston, F, Kaseja, K, Wall, E and Coffey, M 2014. Developments in genetic prediction of carcase merit in Limousin beef cattle in the UK. Proceedings of the 10th World Congress of Genetics Applied to Livestock Production, 17–22 August, Vancouver, Canada, communication 244.Google Scholar
Pabiou, T, Fikse, WF, Amer, PR, Cromie, AR, Näsholm, A and Berry, DP 2011a. Genetic variation in wholesale carcass cuts predicted from digital images in cattle. Animal 5, 1720–1727.Google Scholar
Pabiou, T, Fikse, WF, Amer, PR, Cromie, AR, Näsholm, A and Berry, DP 2011b. Use of digital images to predict carcass cut yields in cattle. Livestock Science 137, 130–140.Google Scholar
Pabiou, T, Fikse, WF, Amer, PR, Cromie, AR, Näsholm, A and Berry, DP 2012. Genetic relationships between carcass cut weights predicted from video image analysis and other performance traits in cattle. Animal 6, 1389–1397.CrossRefGoogle ScholarPubMed
Pabiou, T, Fikse, WF, Näsholm, A, Cromie, AR, Drennan, MJ, Keane, MG and Berry, DP 2009. Genetic parameters for carcass cut weight in Irish beef cattle. Journal of Animal Science 87, 3865–3876.Google Scholar
Parkkonen, P, Liinamo, AE and Ojala, M 2000. Estimates of genetic parameters for carcass traits in Finnish Ayrshire and Holstein-Friesian. Livestock Production Science 64, 203–213.CrossRefGoogle Scholar
SAS Institute 2007. SAS user’s guide in statistics, 9th edition. SAS Institute Inc., Cary, NC, USA.Google Scholar
Todd, D, Woolliams, J and Roughsedge, T 2011. Gene flow in a national cross-breeding beef population. Animal 5, 1874–1886.Google Scholar
Utrera, AR and Van Vleck, LD 2004. Heritability estimates for carcass traits of cattle: a review. Genetics and Molecular Research 3, 380–394.Google Scholar
Van Der Werf, JHJ and De Boer, W 1989. Influence of non additive effects on estimation of genetic parameters in dairy cattle. Journal of Dairy Science 72, 2606–2614.Google Scholar