Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

Agroforestry systems (AFS) can provide positive ecosystem services while at the same time stabilizing yields under increasingly common drought conditions. The effect of distance to trees in alley cropping AFS on yield-related crop parameters has predominantly been studied using point data from transects. Unmanned aerial vehicles (UAVs) offer a novel possibility map plant traits with high spatial resolution and coverage. In present study, UAV-borne red, green, blue (RGB) multispectral imagery was utilized for prediction whole dry biomass yield (DM) leaf area index (LAI) barley three different conventionally managed silvoarable agroforestry sites located Germany. DM LAI were modelled random forest regression models good accuracies (DM: R² 0.62, nRMSEp 14.9%, LAI: 0.92, 7.1%). Important variables included normalized reflectance, vegetation indices, texture height. Maps produced model predictions analysis, showing significant effects LAI. Spatial patterns differed greatly between sampled suggested management soil overriding tree across large portions 96 m wide alleys, thus questioning alleged impacts rows distribution intensively populations. Models based proved be valuable tool accuracies, revealing variability