Estimation of mutton carcass components using two predictors.

Area of Science:

• Agricultural Science
• Animal Science
• Meat Science

Background:

• Accurate estimation of mutton carcass composition is crucial for effective pricing and utilization.
• Existing models may have limitations in predicting specific lean meat percentages.

Purpose of the Study:

• To evaluate the efficacy of carcass weight and GR measurement in predicting mutton carcass components.
• To assess the accuracy of models for estimating different visual lean specifications, particularly trunk meat.

Main Methods:

• Utilized carcass weight and GR measurement (fat depth) as predictor variables.
• Developed regression models to estimate weights of various carcass components.
• Analyzed 557 mutton carcasses with weights ranging from 9.2 to 43.8 kg and GR values from 0 to 41.0 mm.

Main Results:

• Carcass weight and GR explained moderate to high variation (r(2) = 0.47-0.93) in most component weights.
• Model accuracy was notably lower for 50% visual lean trunk meat (r(2) = 0.15).
• Observed chemical lean percentages were less than expected for 50% and 80% visual lean categories.

Conclusions:

• Carcass weight and GR are valuable predictors for general mutton carcass composition.
• Current models require refinement for accurate prediction of specific lean meat percentages, especially for trunk meat.
• Findings have implications for carcass pricing models based on derived lean meat values.