Prediction of in situ rumen protein degradability of grass and lucerne by chemical composition or by NIRS

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.