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Genetic relationships between carcass cut weights predicted from video image analysis and other performance traits in cattle

Published online by Cambridge University Press:  03 April 2012

T. Pabiou ,
W. F. Fikse ,
P. R. Amer ,
A. R. Cromie ,
A. Näsholm  and
D. P. Berry
T. Pabiou*
Affiliation:
The Irish Cattle Breeding Federation, Highfield House, Bandon, Co. Cork, Ireland Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
W. F. Fikse
Affiliation:
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
P. R. Amer
Affiliation:
AbacusBio Limited, PO Box 5585, Dunedin 9058, New Zealand
A. R. Cromie
Affiliation:
The Irish Cattle Breeding Federation, Highfield House, Bandon, Co. Cork, Ireland
A. Näsholm
Affiliation:
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
D. P. Berry
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
*
†E-mail: [email protected]
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Abstract

The objective of this study was to quantify the genetic associations between a range of carcass-related traits including wholesale cut weights predicted from video image analysis (VIA) technology, and a range of pre-slaughter performance traits in commercial Irish cattle. Predicted carcass cut weights comprised of cut weights based on retail value: lower value cuts (LVC), medium value cuts (MVC), high value cuts (HVC) and very high value cuts (VHVC), as well as total meat, fat and bone weights. Four main sources of data were used in the genetic analyses: price data of live animals collected from livestock auctions, live-weight data and linear type collected from both commercial and pedigree farms as well as from livestock auctions and weanling quality recorded on-farm. Heritability of carcass cut weights ranged from 0.21 to 0.39. Genetic correlations between the cut traits and the other performance traits were estimated using a series of bivariate sire linear mixed models where carcass cut weights were phenotypically adjusted to a constant carcass weight. Strongest positive genetic correlations were obtained between predicted carcass cut weights and carcass value (min rg(MVC) = 0.35; max rg(VHVC) = 0.69), and animal price at both weaning (min rg(MVC) = 0.37; max rg(VHVC) = 0.66) and post weaning (min rg(MVC) = 0.50; max rg(VHVC) = 0.67). Moderate genetic correlations were obtained between carcass cut weights and calf price (min rg(HVC) = 0.34; max rg(LVC) = 0.45), weanling quality (min rg(MVC) = 0.12; max rg(VHVC) = 0.49), linear scores for muscularity at both weaning (hindquarter development: min rg(MVC) = −0.06; max rg(VHVC) = 0.46), post weaning (hindquarter development: min rg(MVC) = 0.23; max rg(VHVC) = 0.44). The genetic correlations between total meat weight were consistent with those observed with the predicted wholesale cut weights. Total fat and total bone weights were generally negatively correlated with carcass value, auction prices and weanling quality. Total bone weight was, however, positively correlated with skeletal scores at weaning and post weaning. These results indicate that some traits collected early in life are moderate-to-strongly correlated with carcass cut weights predicted from VIA technology. This information can be used to improve the accuracy of selection for carcass cut weights in national genetic evaluations.

Keywords

beef cattlegenetic correlationsVIAcarcass cutsprice
Type
Full Paper
Information
animal , Volume 6 , Issue 9 , September 2012 , pp. 1389 - 1397
Copyright
Copyright © The Animal Consortium 2012

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References

Crump, RE, Wray, NR, Thompson, R, Simm, G 1997. Assigning pedigree beef performance records to contemporary groups taking account of within-herd calving patterns. Animal Science 65, 193–198.CrossRefGoogle Scholar
Madsen, P, Jensen, J 2007. A user's guide to DMU. A package for analysing multivariate mixed models, version 6, release 4.7. Tjele, Denmark.Google Scholar
McHugh, N, Fahey, AG, Evans, RD, Berry, DP 2010. Factors associated with selling price of cattle at livestock marts. Animal 4, 1378–1389.CrossRefGoogle Scholar
McHugh, N, Evans, RD, Amer, PR, Fahey, AG, Berry, DP 2011. Genetic parameters for cattle price and body weight from routinely collected data at livestock auctions and commercial farms. Journal of Animal Science 89, 29–39.CrossRefGoogle Scholar
Pabiou, T, Fikse, WF, Cromie, AR, Keane, MG, Näsholm, A, Berry, DP 2011a. Use of digital images to predict carcass cut yields in cattle. Livestock Production Science 137, 130–140.CrossRefGoogle Scholar
Pabiou, T, Fikse, WF, Amer, PR, Cromie, AR, Näsholm, A, and Berry, DP 2011b. Genetic variation in wholesale carcass cuts predicted from digital images in cattle. Animal 5, 1720–1727.CrossRefGoogle ScholarPubMed
Renand, G 1985. Genetic parameters of French beef breeds used in crossbreeding for young bull production II – slaughter performance. Genetic Selection Evolution 17, 265–282.CrossRefGoogle ScholarPubMed
Teuscher, F, Ender, K, and Wegner, J 2006. Growth- and breed-related changes of muscle bundle structure in cattle. Journal of Animal Science 84, 2959–2964.Google Scholar