Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/29239
Title: Live animal predictions of carcass components and marble score in beef cattle: model development and evaluation
Contributor(s): McPhee, M J  (author); Walmsley, B J  (author); Dougherty, H C  (author)orcid ; McKiernan, W A (author); Oddy, V H  (author)orcid 
Publication Date: 2020-08
Early Online Version: 2020-03-16
DOI: 10.1017/S1751731120000324
Handle Link: https://hdl.handle.net/1959.11/29239
Abstract: Until recently, beef carcass payment grids were predominantly based on weight and fatness categories with some adjustment for age, defined as number of adult teeth, to determine the price received by Australian beef producers for slaughter cattle. With the introduction of the Meat Standards Australia (MSA) grading system, the beef industry has moved towards payments that account for intramuscular fat (IMF) content (marble score (MarbSc)) and MSA grades. The possibility of a payment system based on lean meat yield (LMY, %) has also been raised. The BeefSpecs suite of tools has been developed to assist producers to meet current market specifications, specifically P8-rump fat and hot standard carcass weight (HCW). A series of equations have now been developed to partition empty body fat and fat-free weight into carcass fat-free mass (FFM) and fat mass (FM) and then into flesh FFM (FleshFFM) and flesh FM (FleshFM) to predict carcass components from live cattle assessments. These components then predict denuded lean (kg) and finally LMY (%) that contribute to emerging market specifications. The equations, along with the MarbSc equation, are described and then evaluated using two independent datasets. The decomposition of evaluation datasets demonstrates that error in prediction of HCW (kg), bone weight (BoneWt, kg), FleshFFM (kg), FleshFM (kg), MarbSc and chemical IMF percentage (ChemIMF%) is shown to be largely random error (%) in evaluation dataset 1, though error for ChemIMF% was primarily slope bias (%) in evaluation dataset 1, and BoneWt had substantial mean bias (%) in evaluation dataset 2. High modelling efficiencies of 0.97 and 0.95 for predicting HCW for evaluation datasets 1 and 2, respectively, suggest a high level of accuracy and precision in the prediction of HCW. The new outputs of the model are then described as to their role in estimating MSA index scores. The modelling system to partition chemical components of the empty body into carcass components is not dependent on the base modelling system used to derive empty body FFM and FM. This can be considered a general process that could be used with any appropriate model of body composition.
Publication Type: Journal Article
Source of Publication: Animal, 14(S2), p. s396-s405
Publisher: Cambridge University Press
Place of Publication: United Kingdom
ISSN: 1751-732X
1751-7311
Field of Research (FOR): 070204 Animal Nutrition
070103 Agricultural Production Systems Simulation
Socio-Economic Objective (SEO): 830301 Beef Cattle
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Animal Genetics and Breeding Unit (AGBU)
Journal Article
School of Environmental and Rural Science

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