Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Tomislav Hengl*, Johan G.B. Leenaars, Keith D. Shepherd, Markus G. Walsh, Gerard B.M. Heuvelink, Tekalign Mamo, Helina Tilahun, Ezra Berkhout, Matthew Cooper, Eric Fegraus, Ichsani Wheeler, Nketia A. Kwabena

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

41 Citations (Scopus)

Abstract

Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0–30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms—random forest and gradient boosting, as implemented in R packages ranger and xgboost—and then used to generate predictions in a fully-optimized computing system. Cross-validation revealed that apart from S, P and B, significant models can be produced for most targeted nutrients (R-square between 40–85%). Further comparison with OFRA field trial database shows that soil nutrients are indeed critical for agricultural development, with Mn, Zn, Al, B and Na, appearing as the most important nutrients for predicting crop yield. A limiting factor for mapping nutrients using the existing point data in Africa appears to be (1) the high spatial clustering of sampling locations, and (2) missing more detailed parent material/geological maps. Logical steps towards improving prediction accuracies include: further collection of input (training) point samples, further harmonization of measurement methods, addition of more detailed covariates specific to Africa, and implementation of a full spatio-temporal statistical modeling framework.
Original languageEnglish
Pages (from-to)77-102
JournalNutrient Cycling in Agroecosystems
Volume109
Issue number1
DOIs
Publication statusPublished - 2017

Keywords

  • Africa
  • Machine learning
  • Macro-nutrients
  • Micro-nutrients
  • Random forest
  • Soil nutrient map
  • Spatial prediction

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