P Greenwood1, J Finn2, T May3, P Nicholls2
1NSW Department Of Primary Industries, Armidale; 2NSW Department Of Primary Industries, Camden; 3NSW Department Of Primary Industries, Dubbo
Prediction of dressing percentage (DP) and retail meat yield (RMY) using live and carcass assessments are important to objectively value animals and carcasses, and underpin development of value-based marketing. This paper assesses predictive value of live goat and carcass assessments for DP and RMY across goat classifications that included age, sex, fleece characteristics, breed/genotype, live weight (LW) and carcass weight (HSCW) using data (n ≤ 803 goats) from previously reported studies (Greenwood et al. 1992, 2008, 2010). Predictive capacity of live goat assessment for DP and RMY (kg, %LW and %HSCW) were assessed using each of five live body condition scoring (CS) methods independently and with LW and classifications. LW accounted for 5.3% of variation in DP (RSD 3.2 kg) and 7.0% (2.7%) and 9.2% (2.8%) of variation in RMY as %LW and %HSCW, respectively. The best LW plus CS predictions accounted for 20.5% (3.2%) of variation in DP and 38.5% (2.1%) and 45.3% (2.2%) in RMY as %LW and %HSCW. More comprehensive live assessment prediction models including classifications accounted for 67.2% (2.5%), 71.6% (1.9%) and 71.5% (2.1%) of variation in DP and RMY as % LW and % HSCW. Predictive capacity of carcass assessments for RMY (kg and % HSCW) using GR tissue depth (GR), eye muscle depth (EMD), and eye muscle area (EMA) were assessed independently and with HSCW. The best HSCW plus carcass tissue measurement (EMD) prediction accounted for 39.9% (2.9%) of variation in RMY as % HSCW. This paper elucidates predictive models for DP and RMY, including for goat kids suitable for more premium markets, in support of objective production and marketing of goat meat.
Greenwood PL et al. (1992) Anim Prod Aust 19:277-280
Greenwood PL et al. (2008) Aust J Exp Agric 48:910-915
Greenwood PL et al. (2010) Anim Prod Sci 50:533-540
Paul Greenwood is a Senior Principal Research Scientist in the Extensive Livestock Industries Unit within NSW DPI Livestock Systems. He is an Adjunct Professor at the University of New England, and has a joint appointment with CSIRO Agriculture and Food in Armidale where he leads a major research initiative on efficiency at pasture. Paul trained at Sydney and Cornell Universities and worked as an extension officer prior to a lengthy research career with NSW DPI. His research has focused on regulation of growth and development in ruminant species including within Beef and Sheep CRCs.