Genetics R&D: Phenotypic and genetic relationships between retail beef yield, live animal, and carcase traits Final Report
Did you know, saleable meat yield is a key economic driver of the beef industry, improvement of which is underpinned by the retail beef yield (RBY) estimated breeding value (EBV) in BREEDPLAN.
| Project start date: | 01 October 2017 |
| Project end date: | 17 June 2021 |
| Publication date: | 04 July 2022 |
| Project status: | Completed |
| Livestock species: | Grass-fed Cattle |
| Relevant regions: | National |
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Download Report
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Summary
This project aimed to generate Retail Beef Yield (RBY) phenotypes on at least 1000 fully pedigreed and genetically described Angus cattle for use in re-estimating BREEDPLAN RBY parameters to provide more accurate carcase RBY Estimated Breeding Values (EBVs) aligned to modern beef cattle. Phenotypes for RBY and other production and carcase traits were collected on 1036 cattle, and have been submitted to Angus BREEDPLAN. Live and carcase trait relationships with retail beef yield were assessed.
Objectives
The aim of this project was to generate retail beef yield (RBY) phenotypes on at least 1000 fully pedigreed and genetically described Angus cattle suitable for use in re-estimating BREEDPLAN RBY parameters to provide more accurate carcase RBY EBVs align with the modern beef cattle population. A further aim was to assess the role visual muscle score (MS) has in the prediction of RBY, and to examine the effects of the 821del11 myostatin mutation in cattle with a broader genetic base than the NSW Department of Primary Industries (DPI) muscling selection line herd.
Key findings
• EMA and MS provide important information for predicting RBY but they provide different information to assist that prediction.
• The value of using fat measurements for predicting RBY varies with time from slaughter with the most useful being provided by carcass fat traits, and little value provided by fat traits measured at feedlot entry.
• Little relationship was observed between RBY and other carcase and productions traits, indicating that selection for improved RBY can be undertaken with little impact on other traits influencing profitability.
• Weak relationships between RBY and IMF, marble scores and MSA Index suggest that increases in RBY can be achieved while also improving meat quality.
• This research has been conducted outside the NSW DPI muscling herd using industry relevant animals and the findings suggest that previous findings
Benefits to industry
• Data will be available to re-estimate genetic parameters for RBY in the Angus BREEDPLAN genetic evaluation.
• Once this data has been analysed for BREEDPLAN parameter estimation, RBY EBV accuracy and associated selection index accuracy for the ASBP sires and related animals is expected to increase resulting in opportunities for increased rates of genetic gain for commercially relevant traits.
• As the progeny and sires have genomic profiles and phenotypes available, this will seed BREEDPLANs genetic evaluation with quality RBY phenotypes collected on animals which are well linked to the current Angus population.
MLA action
Implementation of the recommendations and future research highlighted in the final report.
Future research
The findings from this research support the need to collect high quality RBY phenotypes into the future including the development of technologies to reduce the cost and increase the efficiency of collecting such data in the abattoir. The findings from this research also support the need to further develop, and integrate into the beef industry, objective live animal assessment tools for recording both EMA (developments in ultrasound scanning) and MS (development of 3D camera technology) to aid both genetic evaluation and on-farm management decisions to produce future improvements in carcass yield in association with meat quality and other on-farm profit drivers (calving ease, growth, fertility, temperament, etc). On-going collection of RBY in other breeds of cattle to estimate genetic parameters and underpin genomic prediction is also recommended.
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For more information Contact Project Manager: Peta Bradley |
