Hyperspectral Reflectance Data and Agronomic Traits Can Predict Biomass Yield in Winter Rye Hybrids

Abstract

Winter rye (Secale cereale L.), a potential alternative biogas substrate, is traditionally bred for grain yield. Thus, our objective was to evaluate the possibility to predict dry matter yield combining vegetation indices derived from visible to NIR spectral data as a proxy for agronomic traits. A total of 404 elite rye hybrids were tested for grain yield, and a subset of this comprising 274 hybrids were also tested for dry matter yield over 2 years and at 4 locations in Germany bringing the total number of environments to 8. Spectral data were collected around solar noontime on mostly clear sky by an unmanned aerial vehicle (UAV) on two dates and measured in the wavelength range between 410 and 993 nm. Observed variation among tested hybrids ranged between 3.64–10.53 Mg ha–1 for grain yield and 8.44–14.66 Mg ha–1 for dry matter yield across different sites. The 23 vegetation indices and the agronomic traits, such as plant height, grain yield, and dry matter yield, showed mostly moderate to high heritability estimates (h2 > 0.50), and their genetic variances were significantly (P < 0.001) different from zero. Plant height was more informative than grain yield for indirect selection of high dry matter yield. An index combining hyperspectral and agronomic data developed by a multiple regression procedure showed a cross-validated prediction ability of 0.75, representing an improvement of about 6% to a model incorporating only agronomic traits. During earlier selection stages, the developed index could be a suitable tool for the cost-effective selection of superior candidates for biomass trials based on grain yield trials.