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.
 
CITATIONS (5):
1.
Nutritive evaluation of herbage from permanent meadows by near-infrared reflectance spectroscopy: 2. Prediction of crude protein and dry matter degradability
Sonia Andrés, Alfredo Calleja, Secundino López, Ángel R Mantecón, F Javier Giráldez
Journal of the Science of Food and Agriculture
 
2.
Evaluating soil quality in tropical agroecosystems of Colombia using NIRS
Elena Velasquez, Patrick Lavelle, Edmundo Barrios, Richard Joffre, France Reversat
Soil Biology and Biochemistry
 
3.
Nutritive Evaluation of Forages by near Infrared Reflectance Spectroscopy
Sonia Andrés, Ian Murray, Alfredo Calleja, F. Javier Giráldez
Journal of Near Infrared Spectroscopy
 
4.
Rumen degradation characteristics and protein value of grassland products and their prediction by laboratory measurements and NIRS
J.L De Boever, J.M Vanacker, L.O Fiems, D.L De Brabander
Animal Feed Science and Technology
 
5.
Predicting forage quality of species-rich pasture grasslands using vis-NIRS to reveal effects of management intensity and climate change
Bernd Berauer, Peter Wilfahrt, Björn Reu, Max Schuchardt, Noelia Garcia-Franco, Marcus Zistl-Schlingmann, Michael Dannenmann, Ralf Kiese, Anna Kühnel, Anke Jentsch
Agriculture, Ecosystems & Environment
 
ISSN:1230-1388