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

SIMPLE SUMMARY: The beef industry plays a crucial role in the livestock supply chain, and data are becoming increasingly vital for informed decision-making. In Australia, significant amounts of data are collected within cattle farms; however, due to a lack of suitable data-driven methods, much of the data go to waste without being effectively utilized. This study developed a statistical model to predict the carcass weight (CW) of grass-fed beef cattle at four different stages before slaughter using farm-level data. Two statistical modelling approaches were used, and results were compared. Four timespans prior to the slaughter, i.e., 1 month, 3 months, 9–10 months, and at weaning, were considered in the predictive modelling. Seven phenotypic features of cattle were used to describe the CW. The results showed that the CW of the cattle was strongly associated with the animal’s body weight at each stage before slaughter. The CW can be predicted with an average error of 4% (~12–16 kg) at three months before slaughter. The predictive error increased gradually when moving away from the slaughter date, where the prediction error at weaning was ~8% (~20–25 kg). The outcomes of this study demonstrate the value of using historical data in optimizing production and improving efficiency in the supply chain. ABSTRACT: Gaining insights into the utilization of farm-level data for decision-making within the beef industry is vital for improving production and profitability. In this study, we present a statistical model to predict the carcass weight (CW) of grass-fed beef cattle at different stages before slaughter using historical cattle data. Models were developed using two approaches: boosted regression trees and multiple linear regression. A sample of 2995 grass-fed beef cattle from 3 major properties in Northern Australia was used in the modeling. Four timespans prior to the slaughter, i.e., 1 month, 3 months, 9–10 months, and at weaning, were considered in the predictive modelling. Seven predictors, i.e., weaning weight, weight gain since weaning to each stage before slaughter, time since weaning to each stage before slaughter, breed, sex, weaning season (wet and dry), and property, were used as the potential predictors of the CW. To assess the predictive performance in each scenario, a test set which was not used to train the models was utilized. The results showed that the CW of the cattle was strongly associated with the animal’s body weight at each stage before slaughter. The results showed that the CW can be predicted with a mean absolute percentage error (MAPE) of 4% (~12–16 kg) at three months before slaughter. The predictive error increased gradually when moving away from the slaughter date, e.g., the prediction error at weaning was ~8% (~20–25 kg). The overall predictive performances of the two statistical approaches was approximately similar, and neither of the models substantially outperformed each other. Predicting the CW in advance of slaughter may allow farmers to adequately prepare for forthcoming needs at the farm level, such as changing husbandry practices, control inventory, and estimate price return, thus allowing them to maximize the profitability of the industry.