Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-27T03:12:00.914Z Has data issue: false hasContentIssue false
  • English
  • Français

Genetic relationships between predicted and dissected carcass composition in Scottish Blackface sheep

Published online by Cambridge University Press:  02 September 2010

S. C. Bishop
S. C. Bishop
Affiliation:
Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS
Get access

Abstract

Carcass composition was measured on 133 Blackface ram lambs from a flock divergently selected for predicted carcass lean proportion. Prediction equations for different carcass components were developed using combinations of live weight and ultrasonic backfat and muscle depth. Both carcass lean and carcass fat proportion were best predicted using only live weight and fat depth, and a genetic transformation of the equation predicting carcass lean proportion was highly correlated (genetic correlation = 0·97) with the index on which the sheep were selected. Weights of carcass tissues were more accurately predicted than proportions. Lean weight was best predicted using live weight and muscle depth, and the weights of different fat components were best estimated using live weight, muscle depth and fat depth.

The equations predicting carcass lean proportion, carcass fat proportion, lean mass and fat mass had heritabilities of 0·29, 0·27 0·20 and 0·23, respectively. Heritabilities for carcass lean and carcass fat proportions, and the subcutaneous and intermuscular fat components were 0·43, 0·48, 0·24 and 0·49, respectively. Genetic correlations of the equation predicting carcass lean proportion with lean and fat proportions were 0·52 (s.e. 0·21) and –0·45 (s.e. 0·22), respectively. The same correlations for the equation predicting carcass fat proportion were –0·47 (s.e. 0·22) and 0·57 (s.e. 0·21). The equations predicting carcass lean and fat proportions were strongly correlated with subcutaneous fat proportion but weakly genetically correlated with intermuscular fat proportion.

Keywords

carcass compositiongenetic parameterssheepultrasonic measurements
Type
Research Article
Information
Animal Production , Volume 59 , Issue 3 , December 1994 , pp. 421 - 427
Copyright
Copyright © British Society of Animal Science 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bennett, G. L. 1992. Predicting lean growth while accounting for correlated traits. Journal of Animal Science 70: 51–56.CrossRefGoogle ScholarPubMed
Bennett, G. L., Meyer, H. H. and Kirton, A. H. 1988. Effects of selection for divergent ultrasonic fat depth in rams on progeny fatness. Animal Production 47: 379–386.Google Scholar
Bishop, S. C. 1993. Selection for predicted carcass lean content in Scottish Blackface sheep. Animal Production 56: 379–386.Google Scholar
Cameron, N. D. and Bracken, J. 1992. Selection for carcass lean content in a terminal sire breed of sheep. Animal Production 54: 367–377.Google Scholar
Fennessy, P. F., Bain, W. E., Greer, G. J. and Johnstone, P. D. 1993. Progeny test of ram lambs selected for low ultrasonic backfat thickness or high post-weaning growth rate. Livestock Production Science 33: 105–118.CrossRefGoogle Scholar
Kadim, I. T., Purchas, R. W., Rae, A. L. and Barton, R. A. 1989. Carcass characteristics of Southdown rams from high and low backfat selection lines. New Zealand journal of Agricultural Research 32: 181–191.CrossRefGoogle Scholar
Lawes Agricultural Trust. 1983. Genstat: a general statistical program. Numerical Algorithms Group Ltd.Google Scholar
Meyer, K. 1985. Maximum likelihood estimation of variance components for a multivariate mixed model with equal design matrices. Biometrics 41: 153–165.CrossRefGoogle ScholarPubMed
Meyer, K. 1989. Restricted maximum likelihood to estimate variance components for animal models with several random effects using a derivative-free algorithm. Génétiaue, Selection et Evolution 21: 317–340.CrossRefGoogle Scholar
Parratt, A. C., Burt, C. M., Bennett, G. L., Clarke, J. N. and Rae, A. L. 1987. Heritabilities, genetic and phenotypic correlations for carcass traits and ultrasonic fat depth of sheep. Proceedings of the sixth Australian Association of Animal Breeding and Genetics conference, pp. 76–78.Google Scholar
Simm, G. and Dingwall, W. S. 1989. Selection indices for lean meat production in sheep. Livestock Production Science 21: 223–233.CrossRefGoogle Scholar
Solis-Ramirez, J., Blair, H. T. and Purchas, R. W. 1993. Direct and correlated responses to selection for high or low ultrasonic backfat depth in Southdown sheep. New Zealand journal of Agricultural Research 36: 133–141.CrossRefGoogle Scholar
Thompson, R., Crump, R. E., Juga, J. and Visscher, P. M. 1994. Estimating variances and covariances for bivariate animal models using scaling and transformation. Genetics, Selection and Evolution In press.Google Scholar
Wilson, D. E. 1992. Application of ultrasound for genetic improvement. journal of Animal Science 70: 973–983.CrossRefGoogle ScholarPubMed
Wolf, B. T., Smith, C., King, J. W. B. and Nicholson, D. 1994. Genetic parameters of growth and carcasscomposition in crossbred lambs. Animal Production 32: 1–7.Google Scholar
Young, M. A. 1989. Responses to selection for leanness in Suffolk sheep. M.Sc. thesis, University of Edinburgh.Google Scholar