Pre-slaughter assessment and selection in commercial beef cattle in relation to final carcase classification

The way we assess readiness for slaughter in beef cattle has not progressed in the past 200 years, with subjective visual and manual assessments of cattle still the primary mechanisms used to determine peak condition, resulting in less than half of all cattle carcases meeting a UK premium classification. Current losses to the UK Beef industry are estimated at approximately £12.5 million per year through the sending of over-fat and poorly conformed cattle to the abattoir. With global population rapidly increasing, there is a fundamental need to provide more food efficiently and effectively from the resources we have. Therefore, successfully reducing wastage and improving carcase quality across the UK beef industry through accurate assessment and selection of beef cattle for slaughter is an important step forwards towards a sustainable future for beef production. The EUROP system of bovine carcase classification dictates which carcases are most desired for the current market, with those failing to meet market specification subject to a penalty. The aim of this research project was therefore to provide farmers with an objective tool using a binary logistic regression model, to combine fat and morphometric measurements taken from live cattle, in order to help predict which cattle are most likely to achieve a desired carcase classification and therefore most suitable for slaughter. Through the use of a series of binary logistic regression models, it was discovered that out of 15 measurements taken from cattle, a combination of pelvis height, pelvis width, 10th and 12th rib fat point readings and the P8 fat point reading were best able to predict the likelihood of cattle meeting a UK premium carcase classification. In a later study, when breed was factored into the model on a larger, more commercial scale, this reduced the number of measurements required to just the pelvis width and 12th rib fat point reading, subsequently increasing the practicality to apply this research on-farm.