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iSDAsoil: soil extractable Phosphorus for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths - MaRDI portal

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iSDAsoil: soil extractable Phosphorus for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths (Q6688886)

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Dataset published at Zenodo repository.
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iSDAsoil: soil extractable Phosphorus for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
Dataset published at Zenodo repository.

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    iSDAsoil dataset soil extractable Phosphorus (P) log-transformed predicted at 30 m resolution for 020 and 2050 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, 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 30m spatial resolution using two-scale ensemble machine learning.Sci Rep11,6130 (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_log.p_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Phosphorus mean value, sol_log.p_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Phosphorus 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: log.p_mehlich3 R-square: 0.486 Fitted values sd: 0.687 RMSE: 0.707 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -3.2892 -0.3942 -0.0637 0.2614 4.9466 Coefficients: Estimate Std. Error t value Pr(|t|) (Intercept) 3.378801 3.143200 1.075 0.282 regr.ranger 0.861655 0.011099 77.631 2e-16 *** regr.xgboost 0.066139 0.013091 5.052 4.38e-07 *** regr.cubist 0.157674 0.008886 17.744 2e-16 *** regr.nnet -1.649621 1.442240 -1.144 0.253 regr.cvglmnet 0.013628 0.010407 1.310 0.190 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.7066 on 53493 degrees of freedom Multiple R-squared: 0.486, Adjusted R-squared: 0.486 F-statistic: 1.012e+04 on 5 and 53493 DF, p-value: 2.2e-16 To back-transform values (y) to ppm use the following formula: ppm = expm1( y / 10 ) To submit an issue or request support please visit https://isda-africa.com/isdasoil
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    15 October 2020
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    v0.13
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