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.