Post-mortem prediction of primal and selected retail cut weights of New Zealand lamb from carcass and animal characteristics

• PCA and linear regression model were used for the analysis of 792 lamb carcasses.
• High prediction accuracy for primal cut weight was achieved (adj-R2 up to 0.95).
• Moderate accuracy for key retail cut weight was achieved (adj-R2 range 0.62–0.75).
• Cold weight is the most important predictor, leading to adj-R2 0.9 for primal cuts.
• Linear dimensions have limited impact on carcass weight predictions (5% adj-R2).

Post-mortem measurements (cold weight, grade and external carcass linear dimensions) as well as live animal data (age, breed, sex) were used to predict ovine primal and retail cut weights for 792 lamb carcases. Significant levels of variance could be explained using these predictors. The predictive power of those measurements on primal and retail cut weights was studied by using the results from principal component analysis and the absolute value of the t-statistics of the linear regression model. High prediction accuracy for primal cut weight was achieved (adjusted R2 up to 0.95), as well as moderate accuracy for key retail cut weight: tenderloins (adj-R2 = 0.60), loin (adj-R2 = 0.62), French rack (adj-R2 = 0.76) and rump (adj-R2 = 0.75). The carcass cold weight had the best predictive power, with the accuracy increasing by around 10% after including the next three most significant variables.