Support Vector Regression to predict carcass weight in beef cattle in advance of the slaughter

In this paper we present a function to predict the carcass weight for beef cattle. The function uses a few zoometric measurements of the animals taken days before the slaughter. For this purpose we have used Artificial Intelligence tools based on Support Vector Machines for Regression (SVR). We report a case study done with a set of 390 measurements of 144 animals taken from 2 to 222 days in advance of the slaughter. We used animals of the breed Asturiana de los Valles, a specialized beef breed from the North of Spain. The results obtained show that it is possible to predict carcass weights 150 days before the slaughter day with an average absolute error of 4.27% of the true value. The prediction function is a polynomial of degree 3 that uses five lengths and the estimation of the round profile of the animals.

► We present a method to predict the carcass weight from zoometric measurements.
► Measurements can be taken days, even months, before the slaughter.
► We use Artificial Intelligence tools: Support Vector Machines for Regression (SVR).
► It is possible to achieve accurate predictions a long time prior the slaughter.
► Errors are smaller if the measures of the animals are taken near the slaughter.