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Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers

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 cond...

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Detalles Bibliográficos
Autores principales: Lee, Jooyoung, Won, Seunggun, Lee, Jeongkoo, Kim, Jongbok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Asian-Australasian Association of Animal Production Societies (AAAP) and Korean Society of Animal Science and Technology (KSAST) 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5003980/
https://www.ncbi.nlm.nih.gov/pubmed/26954134
http://dx.doi.org/10.5713/ajas.15.0754
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author Lee, Jooyoung
Won, Seunggun
Lee, Jeongkoo
Kim, Jongbok
author_facet Lee, Jooyoung
Won, Seunggun
Lee, Jeongkoo
Kim, Jongbok
author_sort Lee, Jooyoung
collection PubMed
description 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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spelling pubmed-50039802016-09-01 Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers Lee, Jooyoung Won, Seunggun Lee, Jeongkoo Kim, Jongbok Asian-Australas J Anim Sci Article 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. Asian-Australasian Association of Animal Production Societies (AAAP) and Korean Society of Animal Science and Technology (KSAST) 2016-09 2015-11-28 /pmc/articles/PMC5003980/ /pubmed/26954134 http://dx.doi.org/10.5713/ajas.15.0754 Text en Copyright © 2016 by Asian-Australasian Journal of Animal Sciences This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Lee, Jooyoung
Won, Seunggun
Lee, Jeongkoo
Kim, Jongbok
Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers
title Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers
title_full Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers
title_fullStr Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers
title_full_unstemmed Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers
title_short Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers
title_sort prediction of carcass composition using carcass grading traits in hanwoo steers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5003980/
https://www.ncbi.nlm.nih.gov/pubmed/26954134
http://dx.doi.org/10.5713/ajas.15.0754
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