Jl. De Boever et al., Prediction of in situ rumen protein degradability of grass and lucerne by chemical composition or by NIRS, J ANIM FEED, 7(4), 1998, pp. 437-451
Sixty one samples of three grass species and seventy three lucerne samples
collected from different growth stages and cuts during three seasons were u
sed 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 degra
dability.
Best regression equations to predict a+b and ED of grass were based on a co
mbination 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 resulte
d in the lowest RSD, being 2.3%-units for a+b and 2.5%-units for ED. For bo
th grass and lucerne, a still higher prediction accuracy was obtained with
NIRS. In the case of grass, calibrations based on 4 raw absorbances gave th
e lowest standard error of cross-validation (SEC) for a+b (2.7%-units) and
for ED (2.5%-units). For lucerne, calibrations with 4 second derivatives pe
rformed best with SEC-values of 1.9 and 1.4 %-units for a+b and ED, respect
ively. Validation on an independent set of UK grasses however showed that t
he performance of NIRS-calibrations can be heavily disturbed by the way of
sample preparation.