Effects of changing simulated sky cover on hyperspectral reflectance measurements for dry matter yield and forage quality prediction

Sensor based analysis methods to assess dry matter yield and nutritive values of legume–grass swards are time and labour saving and can facilitate a site-specific forage management. Nevertheless, in-field measurements, based on canopy reflectance are highly dependent on weather conditions, like, e.g. wind or clouds. This study was conducted to explore the potential of field spectral measurements for a non destructive prediction of dry matter yield (DM), metabolisable energy (ME), ash content (XA), and crude protein (XP), of a binary legume–grass mixture (Trifolium pratense L. and Lolium multiflorum L.) under changing weather conditions. Five different degrees of sky cover were simulated by shadowing measurement plots with layers of cotton to reduce incoming radiation at different growth stages (leaf developing to flowering). Additionally, a halogen lamp was established over the plots to examine the influence of an artificial light source on the spectral response under changing cloud stages. Modified partial least squares (MPLS) regression was used for analysis of the hyperspectral data set (350–2500 nm). Artificial illumination led to spectral interferences of solar radiation and additional light, and hence, partly reduced prediction accuracies. In contrast, prediction accuracy increased, when solar radiation was completely excluded. Coefficients of determination (RSQcal) range from 0.87 to 0.94 without artificial illumination and from 0.86 to 0.94 with artificial illumination for DM yield and nutritive values, respectively.