Prediction of dressing percentage, carcass characteristics and meat yield of goats, and implications for live assessment and carcass-grading systems
Paul L. Greenwood
+ Author Affiliations
- Author Affiliations
NSW Department of Primary Industries, Armidale Livestock Industries Centre, J. S. F. Barker Building, Trevenna Road, University of New England, Armidale, NSW 2351, Australia. Email: paul.greenwood@dpi.nsw.gov.au
Animal Production Science 61(3) 313-325 https://doi.org/10.1071/AN20160
Submitted: 1 April 2020 Accepted: 16 September 2020 Published: 22 October 2020
Abstract
Context: Dressing percentage (DP) and meat yield (MY) predictions using live assessments and carcass measurements enable objective valuation of animals and their carcasses. We hypothesised that distribution of goat carcass tissues affects predictive value of live body condition scoring (CS) methods and carcass measurements for these traits.
Aims: The present paper aimed to assess the value of CS methods for prediction of DP and MY and of carcass measurements for prediction of MY.
Methods: Correlation and regression analyses from a dataset (n = 1014 goats) highly heterogeneous for factors influencing DP and MY were used to assess (1) the value of live-goat assessments and classifications, including five CS methods, age (dentition), liveweight (LW), sex, fleece characteristics and breed or genotype to predict DP and MY, and (2) the value of hot standard carcass weight (HSCW) and carcass GR (soft tissue over the 12th rib) tissue depth, eye-muscle depth and eye-muscle area to predict MY.
Key results: Among kids, LW accounted for 1% (residual standard deviation of 2.6%) of variation in DP, 22% (2.3%) in MY (% LW) and 34% (2.5%) in MY (% HSCW). LW plus the best CS method accounted for 24% (2.3%) of variation in DP, 58% (1.7%) in MY (% LW) and 61% (2.0%) in MY (% HSCW). Among all goats, LW plus CS accounted for up to 21% (3.2%), 39% (2.1%) and 45% (2.2%) of variation in these traits. Regression models that included age, sex, fleece type, breed or genotype, LW and CS accounted for 67% (2.5%), 72% (1.9%) and 72% (2.1%) of variation in DP, MY (% LW) and MY (% HSCW). Among carcass measurements, HSCW plus eye-muscle depth had best predictive value, accounting for 61% (2.3%) of variation in MY (% HSCW) for kids and 40% (2.9%) for all goats.
Conclusions: The body condition-score methods that best relate to DP and MY (% LW or % HSCW) assessed the shape of M. longissimus lumborum (eye muscle) in the lumbar region, which relates to muscularity of goats, rather than subcutaneous fat depth such as assessed at the GR-site.
Implications: The results guide potential targets for future developments in live-goat assessment, carcass classification and grading, and trading languages underpinned by value-based marketing.
Keywords: AUS-MEAT goatmeat language, carcass description, goatmeat, meat quality, meat supply chain.
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