Predicting Carcass Weight of Grass-Fed Beef Cattle before Slaughter Using Statistical Modelling.

Area of Science:

• Agricultural Science
• Animal Science
• Data Science

Background:

• Farm-level data utilization is crucial for enhancing decision-making, production, and profitability in the beef industry.
• Accurate prediction of carcass weight (CW) is essential for effective farm management and market readiness.

Purpose of the Study:

• To develop and evaluate statistical models for predicting the carcass weight (CW) of grass-fed beef cattle at various pre-slaughter stages.
• To assess the accuracy of these predictions using historical cattle data from Northern Australia.

Main Methods:

• Utilized boosted regression trees and multiple linear regression to model CW.
• Employed data from 2995 grass-fed cattle across three properties, considering four pre-slaughter time points (1 month, 3 months, 9-10 months, weaning).
• Included seven predictors: weaning weight, weight gain, time since weaning, breed, sex, weaning season, and property.

Main Results:

• Carcass weight showed a strong association with animal body weight at all pre-slaughter stages.
• Achieved a mean absolute percentage error (MAPE) of 4% (~12-16 kg) for predictions made three months before slaughter.
• Prediction error increased with earlier time points, reaching ~8% (~20-25 kg) at weaning.

Conclusions:

• Statistical models can reliably predict beef carcass weight in advance of slaughter.
• Early CW prediction enables farmers to optimize husbandry practices, inventory control, and financial planning.
• Improved forecasting capabilities contribute to maximizing profitability within the beef industry.