Can drone images predict within-field variability in soil fertility? A case study in the Northern Region of Ghana

作者：Yussif Baba Kassim, Francisco Pinto, Dilys S. MacCarthy, P.S. Bindraban, Ngonidzashe Chirinda, T.J. Stomph, P.C. Struik · 发表于：Frontiers in Soil Science · 年份：2025 · DOI：10.3389/fsoil.2025.1548645 · 被引用次数：6 · 研究领域：Crop Yield and Soil Fertility、Soil Geostatistics and Mapping、Remote Sensing in Agriculture

Background Soil fertility varies within fields of smallholder farmers in Africa. Drone-based field mapping may quantify this within-field variability with high resolution. This study analyzed if variation in spectral vegetation indices from early season weed cover could offer criteria to quickly assess heterogeneity in soil fertility. We tested (i) whether within field spatial patterns in early season weed cover and soil organic matter could be correlated and (ii) whether predicted soil organic matter could indicate within-field heterogeneity in crop yields. Methods We collected images of early season weed cover using a DJI Phantom 4 proV2 drone and data on maize and soybean final above-ground biomass from on-farm experiments, conducted in Bognaayili and Gauwogo (northern Ghana), during 2022 and 2023 cropping seasons. There were eight experiments in total, i.e., two of each crop at each site and in each year. In these experiments, we varied planting density, variety, mulching, ridging, and fertilizer application, as management options to increase productivity. Spectral vegetation indices extracted from early season weed cover were used to predict soil organic matter. Results Variation in spectral vegetation indices from early season weed cover was higher in Bognaayili than in Gauwogo. Predicted soil organic matter from a model built with spectral vegetation indices had a significant relationship (R adj 2 = 0.54) with measured soil organic matter in Bognaayili, but not in Gauw...