Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning
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 extrac...
| Autores principales: | , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2017
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/83171 |
| _version_ | 1855540783601418240 |
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| author | Hengl, T. Leenaars, Johan G.B. Shepherd, Keith D. Walsh, Markus G. Heuvelink, Gerard B.M. Mamo, T. Tilahun, H. Berkhout, Ezra D. Cooper, M. Fegraus, E. Wheeler, I. Kwabena, N. A. |
| author_browse | Berkhout, Ezra D. Cooper, M. Fegraus, E. Hengl, T. Heuvelink, Gerard B.M. Kwabena, N. A. Leenaars, Johan G.B. Mamo, T. Shepherd, Keith D. Tilahun, H. Walsh, Markus G. Wheeler, I. |
| author_facet | Hengl, T. Leenaars, Johan G.B. Shepherd, Keith D. Walsh, Markus G. Heuvelink, Gerard B.M. Mamo, T. Tilahun, H. Berkhout, Ezra D. Cooper, M. Fegraus, E. Wheeler, I. Kwabena, N. A. |
| author_sort | Hengl, T. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace83171 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace831712024-01-17T12:58:34Z Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning Hengl, T. Leenaars, Johan G.B. Shepherd, Keith D. Walsh, Markus G. Heuvelink, Gerard B.M. Mamo, T. Tilahun, H. Berkhout, Ezra D. Cooper, M. Fegraus, E. Wheeler, I. Kwabena, N. A. macro-nutrients micro-nutrients random forest machine learning soil nutrient map spatial prediction 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. 2017-09 2017-08-18T08:32:10Z 2017-08-18T08:32:10Z Journal Article https://hdl.handle.net/10568/83171 en Open Access application/pdf Springer Hengl, T.; Leenaars, J. G. B.; Shepherd, K. D.; Walsh, M. G.; Heuvelink, G. B. M.; Mamo, T.; Tilahun, H.; Berkhout, E.; Cooper, M.; Fegraus, E.; Wheeler, I.; Kwabena, N. A.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) 77–102pp. doi:10.1007/s10705-017-9870-x |
| spellingShingle | macro-nutrients micro-nutrients random forest machine learning soil nutrient map spatial prediction Hengl, T. Leenaars, Johan G.B. Shepherd, Keith D. Walsh, Markus G. Heuvelink, Gerard B.M. Mamo, T. Tilahun, H. Berkhout, Ezra D. Cooper, M. Fegraus, E. Wheeler, I. Kwabena, N. A. Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning |
| title | Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning |
| title_full | Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning |
| title_fullStr | Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning |
| title_full_unstemmed | Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning |
| title_short | Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning |
| title_sort | soil nutrient maps of sub saharan africa assessment of soil nutrient content at 250 m spatial resolution using machine learning |
| topic | macro-nutrients micro-nutrients random forest machine learning soil nutrient map spatial prediction |
| url | https://hdl.handle.net/10568/83171 |
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