Exploring crop yield variability under different land management practices with spectral vegetation indices in the Ethiopian Blue Nile basin

An accurate crop yield forecast at a reasonable cost is required for increasing production and securing food. Therefore, spectral reflectance was measured with a spectroradiometer (λ 350–2500 nm) to predict grain yield (GY) and aboveground biomass yield (ABY) of teff and finger millet in Ethiopia. We calculated six spectral vegetation indices (SVIs): enhanced vegetation index (EVI), normalized difference VI (NDVI), soil-adjusted VI (SAVI), green (GNDVI), green chlorophyll VI (GCVI), and simple ratio (SR). Linear regression models fitted validation data with coefficient of determination (R2) of 0.73, and root mean square error (RMSE) of 0.11 for teff GY, R2 of 0.45 and RMSE of 0.52 for teff ABY, R2 of 0.92 and RMSE of 0.22 for finger millet GY, and R2 of 0.90 and RMSE of 0.24 for finger millet ABY. The study demonstrates the potential of spectroradiometer-derived SVIs to predict finger millet and teff production in the catchment.