ORIGINAL PAPER
Prediction of in situ rumen protein degradability of
grass and lucerne by chemical composition or by
NIRS
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1
Department Animal Nutrition and Husbandry - Agricultural Research Centre - Ghent,
Scheldeweg 68, B-9090 Melle-Gontrode, Belgium
2
Institute of Animal Production, Department of Animal Nutrition,
Sarego 2, 31-047 Kraków, Poland
Publication date: 1998-10-22
J. Anim. Feed Sci. 1998;7(4):437-451
KEYWORDS
ABSTRACT
Sixty one samples of three grass species and seventy three lucerne samples collected from different growth stages and cuts during three seasons were used to derive regression equations based
on crude protein (CP), crude fibre (CF) or harvest date (D) as well as near infrared reflectance
spectroscopy (NIRS) calibrations to predict potential (a+b) and effective (ED) CP degradability.
Best regression equations to predict a+b and ED of grass were based on a combination of CP and
CF, resulting in an equal residual standard deviation (RSD) of 3.7%-units. For lucerne, two-term
regressions with CF and D resulted in the lowest RSD, being 2.3%-units for a+b and 2.5%-units for
ED. For both grass and lucerne, a still higher prediction accuracy was obtained with NIRS. In the
case of grass, calibrations based on 4 raw absorbances gave the lowest standard error of crossvalidation (SEC) for a+b (2.7%-units) and for ED (2.5%-units). For lucerne, calibrations with 4
second derivatives performed best with SEC-values of 1.9 and 1.4%-units for a+b and ED, respectively. Validation on an independent set of UK grasses however showed that the performance of
NIRS-calibrations can be heavily disturbed by the way of sample preparation.
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