Published October 15, 2020 | Version v0.13
Dataset Open

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

Authors/Creators

  • 1. EnvirometriX
  • 2. Innovative Solutions for Decision Agriculture Ltd (iSDA)
  • 3. MultiOne
  • 4. University of Belgrade
  • 5. Rothamsted Research
  • 6. World Agroforestry (ICRAF)

Description

iSDAsoil dataset soil extractable Zinc (Zn) log-transformed 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, 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_log.zn_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Zinc mean value,
  • sol_log.zn_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Zinc 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.zn_mehlich3 
R-square: 0.711 
Fitted values sd: 0.588 
RMSE: 0.375 

Random forest model:
Call:
stats::lm(formula = f, data = d)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1382 -0.2038 -0.0274  0.1632  3.6353 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)    1.836394   1.555108   1.181  0.23766    
regr.ranger    0.823144   0.013982  58.871  < 2e-16 ***
regr.xgboost   0.037861   0.013644   2.775  0.00552 ** 
regr.cubist    0.169653   0.010091  16.813  < 2e-16 ***
regr.nnet     -1.511745   1.244933  -1.214  0.22463    
regr.cvglmnet  0.009770   0.008046   1.214  0.22467    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.3747 on 39344 degrees of freedom
Multiple R-squared:  0.7109,	Adjusted R-squared:  0.7109 
F-statistic: 1.935e+04 on 5 and 39344 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

Notes

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

Files

001_africa_soil_extr_zinc_30m.png

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Additional details

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.
  • 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.
  • Vågen, T. G., Winowiecki, L. A., Tondoh, J. E., Desta, L. T., & Gumbricht, T. (2016). Mapping of soil properties and land degradation risk in Africa using MODIS reflectance. Geoderma, 263, 216-225.