iSDAsoil dataset soil sand content in % predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, LandPKS, and other national and regional soil datasets). Cite as: Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y To open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version. Layer description: sol_sand_tot_psa_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil sand content mean value, sol_sand_tot_psa_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil sand content model (prediction) errors, Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (mlr::makeStackedLearner) for this variable indicates: Variable: sand_tot_psa R-square: 0.736 Fitted values sd: 22.8 RMSE: 13.7 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -80.626 -5.321 0.221 6.071 88.686 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.687471 24.022001 0.278 0.780714 regr.ranger 1.060521 0.003503 302.742 < 2e-16 *** regr.xgboost -0.018718 0.004910 -3.812 0.000138 *** regr.cubist 0.031749 0.003922 8.096 5.73e-16 *** regr.nnet -0.161127 0.422746 -0.381 0.703098 regr.cvglmnet -0.028217 0.004462 -6.323 2.57e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 13.65 on 122261 degrees of freedom Multiple R-squared: 0.736, Adjusted R-squared: 0.736 F-statistic: 6.818e+04 on 5 and 122261 DF, p-value: < 2.2e-16 To submit an issue or request support please visit https://isda-africa.com/isdasoil

{"references": ["Hengl, T., Leenaars, J. G., Shepherd, K. D., Walsh, M. G., Heuvelink, G. B., Mamo, T., ... & Wheeler, I. (2017). Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutrient Cycling in Agroecosystems, 109(1), 77-102.", "Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0.", "Herrick, Jeffrey E. (2013): \"The Global Land-Potential Knowledge System (LandPKS): Supporting Evidence-based, Site-specific Land Use and Management through Cloud Computing, Mobile Applications, and Crowdsourcing.\" Journal of Soil and Water Conservation: 5A-12A.", "Leenaars, J. G. B. (2014). Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan Africa (with dataset). Africa Soil Information Service (AfSIS) project (No. 2014/03). ISRIC-World Soil Information."]}

iSDA is a social enterprise with the mission to improve smallholder farmer profitability across Africa. iSDA builds on the legacy of the African Soils information service (AfSIS) project. We are grateful for the outputs generated by all former AfSIS project partners: Columbia University, Rothamsted Research, World Agroforestry (ICRAF), Quantitative Engineering Design (QED), ISRIC — World Soil Information, International Institute of Tropical Agriculture (IITA), Ethiopia Soil Information Service (EthioSIS), Ghana Soil Information Service (GhaSIS), Nigeria Soil Information Service (NiSIS) and Tanzania Soil Information Service (TanSIS). More details on AfSIS partners and data contributors can be found at https://isda-africa.com/isdasoil