iSDA has, with EnvirometriX, GiLAB and MultiOne, created a groundbreaking agronomy database at a previously unheralded spatial resolution of 30 m and covering the entire African continent. Harnessing the Open Access remote sensing data (Sentinel 2, Landsat 7/8), cutting edge predictive machine learning techniques (Random Forest, XGBoost, Deepnet, SuperLearner), and point samples generated by the AfSIS network, as well as a number of other open access soil datasets, predictions were produced of over 20 soil variables: soil texture fractions, soil pH, macronutrients (soil organic carbon, nitrogen, phosphorous, and potassium, magnesium), micronutrients, CEC, electrical conductivity and others. This online database has been published with completely open access so that it can empower businesses and research in the agricultural sector to make more data-informed decisions. It forms a foundation for iSDA’s business-to-business data platform, including delivering agronomic advisory products and services to smallholders.