Subpopulations and accuracy of prediction in pig carcass classification

Abstract

AbstractClassification of pig carcasses in the European Community (EC) is based on the lean meat percentage of the carcasses. The lean meat percentage is predicted from instrumental carcass measurements, such as fat and muscle depth measurements, obtained in the slaughterline. The prediction formula for an instrument is derived from the data of a dissection experiment. When the relationship between percentage lean and instrumental carcass measurements differs between subpopulations, such as sexes or breeds, accuracy of prediction may differ between these subpopulations. In particular for some subpopulations predicted lean meat percentages may be systematically too low and for other subpopulations systematically too high. Producers or buyers that largely specialize in subpopulations where the percentage lean is underestimated, are put at a financial disadvantage.The aim of this paper is to gain insight, on the basis of real data, into the effects of differences between subpopulations on the accuracy of the predicted percentage lean meat of pig carcasses. A simulation study was performed based on data from dissection trials in The Netherlands, comprising gilts and castrated males, and trials in Spain, comprising different genetic types. The possible gain in accuracy, i.e. reduction of prediction bias and mean squared prediction error, by the use of separate prediction formulae for (some of) the subpopulations was determined.We concluded that marked bias in the predicted percentage lean meat may occur between subpopulations when a single overall prediction formula is employed. Systematic differences in predicted percentage lean between subpopulations that are overestimated and underestimated may exceed 4% and for selected values of instrumental measurements may run up to 6%. Bias between subpopulations may be eliminated, and prediction accuracy may be markedly improved, when separate prediction formulae are used. With the use of separate formulae the root mean squared prediction error may be reduced by 13 to 26% of the expected value when a single prediction formula is used for all pig carcasses.These are substantial reductions on a national scale. This suggests that there will be a commercial interest in the use of separate prediction formulae for different subpopulations. In the near future, when the use of implants becomes more reliable, subpopulations will be recognized automatically in the slaughterline and use of different prediction formulae will become practically feasible. Some possible consequences for the EC regulations and national safeguards for quality of prediction formulae are discussed.