Evaluating the Accuracy of Current Prediction Standards and the Use of Cutout Data to Predict Subprimal Yield in Show and Commercial Lamb Carcasses

The American lamb industry has experienced a great deal of change over the years in the number of sheep produced, lamb consumed, and the way the lamb industry is managed. Traditional grading systems are a sector that have not changed for decades and may not fully account for differences between show lamb and commercial lamb carcasses. This study aimed to assess the accuracy of current predictive techniques using carcass traits, as well as the use of various predictive methods, including carcass linear measurements and carcass cutout to predict subprimal yield. A total of 61 lamb carcasses (33 show lambs and 28 commercial lambs) were analyzed for differences in muscle-to-bone ratio, lean-to-fat ratio, and key linear measurements. Results from univariate t-tests demonstrated significant differences (p < 0.01) in hot carcass weight (HCW), ribeye area (REA), backfat (BF) and several circumference measurements between the two groups. A Principal Components Analysis (PCA) was performed to reduce multicollinearity and identify the most influential traits contributing to yield through physical linear measurements. Findings indicated that PC1 was the most significant component in the show lamb model (p < 0.01) explaining 58% of variance, while PC2 was significant for commercial lambs (p < 0.01), though overall principal component analysis model significance remained low (p = 0.17, p = 0.31). A linear model was created using the significant principal components to predict subprimal yield for the show and commercial lambs (p < 0.01, R2 = 0.30), (p < 0.01, R2 = 0.31), respectively. The identified differences, in the PCA model provide a prediction of subprimal yield, suggesting that traditional industry methods may still hold value when combined with linear carcass measurements such as carcass length and body circumferences. This study also creates possible new prediction equations related to subprimal (bone-in suprimal cuts/HCW) yield using bi-directional stepwise regressions. Both models creating equations using various carcass traits were significant for show and commercial lamb carcass groups (p < 0.01, R2 = 0.25), and (p < 0.01, R2 = 0.50), respectively, and could play a role in suggesting new methods of yield prediction. Future research should explore these emerging techniques along with advanced technology such as 3-D carcass and Dual Energy X-Ray Absorptiometry (DEXA) imaging to refine prediction models and enhance value-based marketing strategies in the lamb industry.