Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions
80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridg...
| Autores principales: | , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Public Library of Science
2015
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/68703 |
| _version_ | 1855534345444392960 |
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| author | Hengl, Tomislav Heuvelink, Gerard B.M. Kempen, Bas Leenaars, Johan G.B. Walsh, Markus G. Shepherd, Keith D. Sila, Andrew M. MacMillan, Robert A Mendes de Jesus, Jorge Tamene, Lulseged D. Tondoh, Jérôme E. |
| author_browse | Hengl, Tomislav Heuvelink, Gerard B.M. Kempen, Bas Leenaars, Johan G.B. MacMillan, Robert A Mendes de Jesus, Jorge Shepherd, Keith D. Sila, Andrew M. Tamene, Lulseged D. Tondoh, Jérôme E. Walsh, Markus G. |
| author_facet | Hengl, Tomislav Heuvelink, Gerard B.M. Kempen, Bas Leenaars, Johan G.B. Walsh, Markus G. Shepherd, Keith D. Sila, Andrew M. MacMillan, Robert A Mendes de Jesus, Jorge Tamene, Lulseged D. Tondoh, Jérôme E. |
| author_sort | Hengl, Tomislav |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008–2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management—organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15–75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data. |
| format | Journal Article |
| id | CGSpace68703 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace687032025-03-13T09:44:53Z Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions Hengl, Tomislav Heuvelink, Gerard B.M. Kempen, Bas Leenaars, Johan G.B. Walsh, Markus G. Shepherd, Keith D. Sila, Andrew M. MacMillan, Robert A Mendes de Jesus, Jorge Tamene, Lulseged D. Tondoh, Jérôme E. simulation models forecasting soil management soil chemicophysical properties statistical methods soil degradation acidity soil modelos de simulación técnicas de predicción manejo del suelo propiedades físico - químicas suelo metodos estadísticos degradación del suelo acidez suelo áfrica 80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008–2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management—organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15–75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data. 2015-05-23 2015-10-27T19:09:06Z 2015-10-27T19:09:06Z Journal Article https://hdl.handle.net/10568/68703 en Open Access Public Library of Science Hengl, Tomislav; Heuvelink, Gerard B. M.; Kempen, Bas; Leenaars, Johan G. B.; Walsh, Markus G.; Shepherd, Keith D.; Sila, Andrew; MacMillan, Robert A.; Mendes de Jesus, Jorge; Desta, Lulseged Tamene; Tondoh, Jérôme E.. 2015. Mapping soil properties of africa at 250 m resolution: random forests significantly improve current predictions . PLoS ONE 10(6): e0125814. |
| spellingShingle | simulation models forecasting soil management soil chemicophysical properties statistical methods soil degradation acidity soil modelos de simulación técnicas de predicción manejo del suelo propiedades físico - químicas suelo metodos estadísticos degradación del suelo acidez suelo áfrica Hengl, Tomislav Heuvelink, Gerard B.M. Kempen, Bas Leenaars, Johan G.B. Walsh, Markus G. Shepherd, Keith D. Sila, Andrew M. MacMillan, Robert A Mendes de Jesus, Jorge Tamene, Lulseged D. Tondoh, Jérôme E. Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions |
| title | Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions |
| title_full | Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions |
| title_fullStr | Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions |
| title_full_unstemmed | Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions |
| title_short | Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions |
| title_sort | mapping soil properties of africa at 250 m resolution random forests significantly improve current predictions |
| topic | simulation models forecasting soil management soil chemicophysical properties statistical methods soil degradation acidity soil modelos de simulación técnicas de predicción manejo del suelo propiedades físico - químicas suelo metodos estadísticos degradación del suelo acidez suelo áfrica |
| url | https://hdl.handle.net/10568/68703 |
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