J. Anim Sci.
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J. Anim. Sci. 2003. 81:1177-1184
© 2003 American Society of Animal Science

Comparison of different methods to assess the composition of pig bellies in progeny testing

E. Tholen*,1, U. Baulain{dagger}, M. D. Henning{dagger} and K. Schellander*

* Institute of Animal Breeding Science of the University of Bonn, D-53115 Bonn, Germany and and {dagger} Institute for Animal Science Mariensee, Federal Agricultural Research Center, D-31535 Neustadt, Germany

1 Correspondence:
phone: +49-228-733589; fax: +49 228 732284; E-mail:
etholen{at}itz.uni-bonn.de.

The objective of the study was to validate methods that assess the belly composition of stationary tested progenies of Piétrain boars. In German performance test stations, there are currently three methods of determining belly compositon in use: 1) a regression equation that contains different carcass characteristics, such as fat thickness and muscle area; 2) planimetric analysis of video or digital images acquired at the cut between the 13th and 14th ribs; and 3) estimation of the belly composition using ultrasound data from a three-dimensional ultrasound image produced an online carcass-grading system. Validation of these techniques was performed on 400 carcasses of stationary-tested Piétrain and Piétrain-sired crossbred pigs, which were slaughtered at a mean carcass weight of 85 and 97 kg. Magnetic resonance imaging (MRI) served as a reference to determine the lean content of the bellies. The correlation to MRI lean content ranged from 0.71 to 0.81, and corresponding correlation values were 0.62 to 0.64 for the digital imaging technique, and 0.53 to 0.59 for the AutoFOM online carcass-grading system. An increase in precision was achieved when information from digital imaging and linear carcass measures were included in the regression equation. Accuracy of the AutoFOM system does not seem to be sufficient to assess the belly composition for the special breeds in performance testing. However, extracting and combining 127 AutoFOM-base recordings into modified equations using partial least squares techniques yielded an improvement in the prediction accuracy for all tested breed and/or weight groups.

Key Words: Belly • Image Processing • Magnetic Resonance Imaging • Performance Testing • Pigs • Ultrasound






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