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Subpopulations and accuracy of prediction in pig carcass classification

Published online by Cambridge University Press:  18 August 2016

B. Engel ,
W. G. Buist ,
M. Font i Furnols  and
E. Lambooij
B. Engel*
Affiliation:
Animal Sciences Group, Wageningen UR, PO Box 65, 8200 AB Lelystad, The Netherlands
W. G. Buist
Affiliation:
Animal Sciences Group, Wageningen UR, PO Box 65, 8200 AB Lelystad, The Netherlands
M. Font i Furnols
Affiliation:
IRTA-Meat Technology Centre, Granja Camps i Armet. E-17121 Monells, Girona, Spain
E. Lambooij
Affiliation:
Animal Sciences Group, Wageningen UR, PO Box 65, 8200 AB Lelystad, The Netherlands
*
E-mail:[email protected]
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Abstract

Classification 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.

Keywords

carcassesclassificationleanpigspopulationsregression analysissampling
Type
Growth, development and meat science
Information
Animal Science , Volume 78 , Issue 1 , February 2004 , pp. 37 - 52
Copyright
Copyright © British Society of Animal Science 2004

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References

Causeur, D. J. and Dhorne, T. J. 1998. Finite sample properties of a multivariate extension of double regression. Biometrics 54: 1591–1601.CrossRefGoogle Scholar
Cox, D. R. and Snel, E. J. 1989. Analysis of binary data, second edition. Chapman and Hall, London.Google Scholar
Daumas, G., Causeur, D., Dhorne, T. and Schollhammer, E. 1998. The new pig carcass grading methods in France. Proceedings of the 44th ICoMST, Barcelona, Spain.Google Scholar
Daumas, G., Dhorne, T. and Gispert, M. 1994. Accounting for the sex effect on prediction of pig carcass lean meat percentage in the Community. Proceedings of the fourth ICoMST, The Hague, The Netherlands.Google Scholar
Engel, B., Buist, W. G., Walstra, P., Olsen, E. and Daumas, G. 2003. Accuracy of prediction of percentage lean meat and authorization of carcass measurement instruments: adverse effects of incorrect sampling of carcasses in pig classification. Animal Science 76: 199–209.Google Scholar
Engel, B. and Walstra, P. 1991a. Increasing precision or reducing expense in regression experiments by using information from a concomitant variable. Biometrics 47: 13–20.Google Scholar
Engel, B. and Walstra, P. 1991b. A simple method to increase precision or reduce expense in regression experiments to predict the proportion of lean meat of carcasses. Animal Production 53: 353–359.Google Scholar
Engel, B. and Walstra, P. 1993. Accounting for subpopulations in prediction of the proportion of lean meat of pig carcasses. Animal Production 57: 147–152.Google Scholar
European Commission. 1988. Commission decision authorizing methods for grading pig carcases in Spain (88/479/ EEC).Google Scholar
European Commission. 1994. Commission decision amendment to the decision authorizing methods for grading pig carcases in Spain (94/337/EEC).Google Scholar
European Community. 1989a. EC document VI/3860/89, proposal for research concerning the harmonisation of methods for grading pigs in the Community.Google Scholar
European Community. 1989b. EC decision no. 89/53/CEE related to the approval of classification methods of pig carcasses in Italy.Google Scholar
European Community. 1994. EC regulation no. 3127/94, amending regulation (EC) no. 2967/85 laying down detailed rules for the application of the Community scale for grading pig carcasses.Google Scholar
EUPIGCLASS. 2000. Standardisation of pig carcass classification in the EU through improved statistical procedures and new technological developments. Program funded under the Growth Programme. www. cordis. lu/en/home. htmlGoogle Scholar
GenStat Committee. 2000. The guide to GenStat (ed. Payne, R.). VSN International Ltd, Oxford.Google Scholar
Gispert, M., Dhorne, T., Diestre, A. and Daumas, G. 1996. Efecto del sexo en la predicción del contenido en magro de la canal porcina en Espana, Francia y Paises Bajos. Investigación Agraria, Producción y Sanidad Animales 11: 19–28.Google Scholar
McCullagh, P. and Nelder, J. A. 1989. Generalized linear models, second edition. Chapman and Hall, London.CrossRefGoogle Scholar
Walstra, P. 1986. Assessment of the regression formula for estimation of the lean meat percentage by HGP-measurements in The Netherlands. EC working paper, Brussels VI/4849/86.Google Scholar
Walstra, P. and Merkus, G. S. M. 1996. Procedure for assessment of the lean meat percentage as a consequence of the new EU reference dissection method in pig carcass classification. Research report 96·014 Institute for Animal Science and Health, (ID-Lelystad), Lelystad, The Netherlands.Google Scholar