Asian-Australasian Journal of Animal Sciences

  • 제29권9호
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  • Pages.1215-1221
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  • 2016
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  • 1011-2367(pISSN)
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  • 1976-5517(eISSN)
아세아태평양축산학회 (Asian Australasian Association of Animal Production Societies)
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Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers

  • Lee, Jooyoung (College of Animal Life Science, Kangwon National University) ;
  • Won, Seunggun (College of Animal Life Science, Kangwon National University) ;
  • Lee, Jeongkoo (College of Animal Life Science, Kangwon National University) ;
  • Kim, Jongbok (College of Animal Life Science, Kangwon National University)
  • 투고 : 2015.09.09
  • 심사 : 2015.11.26
  • 발행 : 2016.09.01
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초록

The prediction of carcass composition in Hanwoo steers is very important for value-based marketing, and the improvement of prediction accuracy and precision can be achieved through the analyses of independent variables using a prediction equation with a sufficient dataset. The present study was conducted to develop a prediction equation for Hanwoo carcass composition for which data was collected from 7,907 Hanwoo steers raised at a private farm in Gangwon Province, South Korea, and slaughtered in the period between January 2009 and September 2014. Carcass traits such as carcass weight (CWT), back fat thickness (BFT), eye-muscle area (EMA), and marbling score (MAR) were used as independent variables for the development of a prediction equation for carcass composition, such as retail cut weight and percentage (RC, and %RC, respectively), trimmed fat weight and percentage (FAT, and %FAT, respectively), and separated bone weight and percentage (BONE, and %BONE), and its feasibility for practical use was evaluated using the estimated retail yield percentage (ELP) currently used in Korea. The equations were functions of all the variables, and the significance was estimated via stepwise regression analyses. Further, the model equations were verified by means of the residual standard deviation and the coefficient of determination ($R^2$) between the predicted and observed values. As the results of stepwise analyses, CWT was the most important single variable in the equation for RC and FAT, and BFT was the most important variable for the equation of %RC and %FAT. The precision and accuracy of three variable equation consisting CWT, BFT, and EMA were very similar to those of four variable equation that included all for independent variables (CWT, BFT, EMA, and MAR) in RC and FAT, while the three variable equations provided a more accurate prediction for %RC. Consequently, the three-variable equation might be more appropriate for practical use than the four-variable equation based on its easy and cost-effective measurement. However, a relatively high average difference for the ELP in absolute value implies a revision of the official equation may be required, although the current official equation for predicting RC with three variables is still valid.

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참고문헌

  1. Anderson, B. B., H. Busk, J. P. Chadwick, A. Cuthbertson, G. A. J. Fursey, D. W. Jones, P. Lewin, C. A. Miles, and M. G. Owen. 1983. Comparison of ultrasonic equipment for describing beef carcass characteristics in live cattle (report on a joint ultrasonic trial carried out in the U. K. and Denmark). Livest. Prod. Sci. 10:133-147. https://doi.org/10.1016/0301-6226(83)90030-1
  2. Cannell, R. C., J. D. Tatum, K. E. Belk, J. W. Wise, R. P. Clayton, and G. C. Smith. 1999. Dual-component video image analysis system (VIASCAN) as a predictor of beef carcass red meat yield percentage and for augmenting application of USDA yield grades. J. Anim. Sci. 77:2942-2950. https://doi.org/10.2527/1999.77112942x
  3. Choy, Y. H., S. B. Choi, G. J. Jeon, H. C. Kim, H. J. Chung, J. M. Lee, B. Y. Park, and S. H. Lee. 2010. Prediction of retail beef yield using parameters based on Korean Beef Carcass Grading Standards. Korean J. Food Sci. Anim. Resour. 30:905-909. https://doi.org/10.5851/kosfa.2010.30.6.905
  4. Dikeman, M. E., L. V. Cundiff, K. E. Gregory, K. E. Kemp, and R. M. Koch. 1998. Relative contributions of subcutaneous and intermuscular fat to yield and predictability of retail product, fat trim, and bone in beef carcasses. J. Anim. Sci. 76:1604-1612. https://doi.org/10.2527/1998.7661604x
  5. Drennan, M. J., M. McGee, and M. G. Keane. 2008. The value of muscular and skeletal scores in the live animal and carcass classification scores as indicators of carcass composition in cattle. Animal 2:752-760.
  6. Greiner, S. P., G. H. Rouse, D. E. Wilson, L. V. Cundiff, and T. L. Wheeler. 2003a. Accuracy of predicting weight and percentage of beef carcass retail product using ultrasound and live animal measures. J. Anim. Sci. 81:466-473. https://doi.org/10.2527/2003.812466x
  7. Greiner, S. P., G. H. Rouse, D. E. Wilson, L. V. Cundiff, and T. L. Wheeler. 2003b. Prediction of retail product weight and percentage using ultrasound and carcass measurements in beef cattle. J. Anim. Sci. 81:1736-1742. https://doi.org/10.2527/2003.8171736x
  8. Griffin, D. B., J. W. Savell, H. A. Recio, R. P. Garrett, and H. R. Cross. 1999. Predicting carcass composition of beef cattle using ultrasound technology. J. Anim. Sci. 77:889-892. https://doi.org/10.2527/1999.774889x
  9. Herring, W. O., S. E. Williams, J. K. Bertland, L. L. Benyshek, and D. C. Miller. 1994. Comparison of live and carcass equations predicting percentage of cutability, retail product weight, and trimmable fat in beef cattle. J. Anim. Sci. 72:1107-1118. https://doi.org/10.2527/1994.7251107x
  10. Hickey, J. H., M. G. Keane, D. A. Kenny, A. R. Cromie, and R. F. Veerkamp. 2007. Genetic parameters for EUROP carcass traits within different groups of cattle in Ireland. J. Anim. Sci. 85:314-321. https://doi.org/10.2527/jas.2006-263
  11. Johnson, D. D. and A. L. Rogers. 1997. Predicting the yield and composition of mature cow carcasses. J. Anim. Sci. 75:1831-1836. https://doi.org/10.2527/1997.7571831x
  12. Kauffman, R. G., M. E. Van Ess, R. A. Long, and D. M. Schaefer. 1975. Marbling: Its use in predicting beef carcass composition. J. Anim. Sci. 40:235-241. https://doi.org/10.2527/jas1975.402235x
  13. Kim, J. B. and C. Lee. 2000. Historical look at the genetic improvement in Korean cattle - Review -. Asian Australas. J. Anim. Sci. 13:1467-1481. https://doi.org/10.5713/ajas.2000.1467
  14. Koh, D., J. Lee, S. Won, C. Lee, and J. Kim. 2014. Genetic relationships of carcass traits with retail cut productivity of Hanwoo cattle. Asian Australas. J. Anim. Sci. 27:1387-1393. https://doi.org/10.5713/ajas.2014.14170
  15. Lee, J. M., Y. M. Yoo, B. Y. Park, H. S. Chae, D. H. Kim, Y. K. Kim, and Y. I. Choi. 2005. Study on the carcass yield grade of Hanwoo. J. Anim. Sci. Technol. 47:261-270. https://doi.org/10.5187/JAST.2005.47.2.261
  16. Lee, J. M., K. H. Hah, J. H. Kim, S. H. Cho, P. N. Seong, M. O. Jung, Y. Cho, B. M. Park, D. H. Kim, and C. N. Ahn. 2008. Study on the carcass yield grade traits and prediction of retail product weight in Hanwoo beef. Korean J. Food Sci. Ani. Resour. 28:604-609. https://doi.org/10.5851/kosfa.2008.28.5.604
  17. Maeno, H., K. Oishi, T. Mitsuhashi, H. Kumagai, and H. Hirooka. 2014. Prediction of carcass composition and individual carcass cuts of Japanese Black steers. Meat Sci. 96:1365-1370. https://doi.org/10.1016/j.meatsci.2013.11.017
  18. May, S. G., W. L. Mies, J. W. Edwards, J. J. Harris, J. B. Morgan, R. P. Garrett, F. L. Williams, J. W. Wise, H. R. Cross, and J. W. Savell. 2000. Using live estimates and ultrasound measurements to predict beef carcass cutability. J. Anim. Sci. 78:1255-1261. https://doi.org/10.2527/2000.7851255x
  19. McPhee, M. J., J. W. Oltjen, J. G. Fadel, D. Perry, and R. D. Sainz. 2008. Development and evaluation of empirical equations to interconvert between twelfth-rib and kidney, pelvic, and heart fat respective fat weights and to predict initial conditions of fat deposition models for beef cattle. J. Anim. Sci. 86:1984-1995. https://doi.org/10.2527/jas.2008-0840
  20. Minchin, W., F. Buckley, D. A. Kenny, M. G. Keane, L. Shalloo, and M. O'Donovan. 2009. Prediction of cull cow carcass characteristics from live weight and body condition score measured pre slaughter. Ir. J. Agric. Food Res.(check abbr.) 48:75-86.
  21. MAFRA (Ministry of Agriculture, Food and Rural Affairs). 2009a. The grading standards for livestock products. Official Announcement 2009-344 (In Korean), Seoul, Korea.
  22. MAFRA (Ministry of Agriculture, Food and Rural Affairs). 2009b. Standards for fabrications of saleable meat products. Official Announcement 2009-49 (In Korean), Seoul, Korea.
  23. Realini, C. E., R. E. Williams, T. D. Pringle, and J. K. Bertrand. 2001. Gluteus medius and rump fat depth as additional live animal ultrasound measurements for predicting retail product and trimmable fat in beef carcasses. J. Anim. Sci. 79:1378-1385. https://doi.org/10.2527/2001.7961378x
  24. Shackelford, S. D., L. V. Cundiff, K. E. Gregory, and M. Koohmaraie. 1995. Predicting beef carcass cutability. J. Anim. Sci. 73:406-413. https://doi.org/10.2527/1995.732406x
  25. Tedeschi, L. O. 2006. Assessment of the adequacy of mathematical models. Agric. Syst. 89:225-247. https://doi.org/10.1016/j.agsy.2005.11.004
  26. Williams, R. E., J. K. Bertrand, S. E. Williams, and L. L. Benyshek. 1997. Biceps femoris and rump fat as additional ultrasound measurements for predicting retail product and trimmable fat in beef carcasses. J. Anim. Sci. 75:7-13. https://doi.org/10.2527/1997.7517

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