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...
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| Published in | PloS one Vol. 10; no. 6; p. e0125814 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
25.06.2015
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. 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. |
| Author | Mendes de Jesus, Jorge Walsh, Markus G. Hengl, Tomislav Leenaars, Johan G. B. Tondoh, Jérôme E. Shepherd, Keith D. Heuvelink, Gerard B. M. Kempen, Bas MacMillan, Robert A. Tamene, Lulseged Sila, Andrew |
| AuthorAffiliation | 2 The Earth Institute, Columbia University, USA / Selian Agricultural Research Inst., Arusha, Tanzania 3 World Agroforestry Centre, Nairobi, Kenya 4 LandMapper Environmental Solutions Inc., Edmonton, Canada 5 International Center for Tropical Agriculture, Lilongwe, Malawi 1 ISRIC—World Soil Information, Wageningen, the Netherlands |
| AuthorAffiliation_xml | – name: 3 World Agroforestry Centre, Nairobi, Kenya – name: 5 International Center for Tropical Agriculture, Lilongwe, Malawi – name: 2 The Earth Institute, Columbia University, USA / Selian Agricultural Research Inst., Arusha, Tanzania – name: 1 ISRIC—World Soil Information, Wageningen, the Netherlands – name: 4 LandMapper Environmental Solutions Inc., Edmonton, Canada |
| Author_xml | – sequence: 1 givenname: Tomislav surname: Hengl fullname: Hengl, Tomislav – sequence: 2 givenname: Gerard B. M. surname: Heuvelink fullname: Heuvelink, Gerard B. M. – sequence: 3 givenname: Bas surname: Kempen fullname: Kempen, Bas – sequence: 4 givenname: Johan G. B. surname: Leenaars fullname: Leenaars, Johan G. B. – sequence: 5 givenname: Markus G. surname: Walsh fullname: Walsh, Markus G. – sequence: 6 givenname: Keith D. surname: Shepherd fullname: Shepherd, Keith D. – sequence: 7 givenname: Andrew surname: Sila fullname: Sila, Andrew – sequence: 8 givenname: Robert A. surname: MacMillan fullname: MacMillan, Robert A. – sequence: 9 givenname: Jorge surname: Mendes de Jesus fullname: Mendes de Jesus, Jorge – sequence: 10 givenname: Lulseged surname: Tamene fullname: Tamene, Lulseged – sequence: 11 givenname: Jérôme E. surname: Tondoh fullname: Tondoh, Jérôme E. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26110833$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | 2015 Hengl et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2015 Hengl et al 2015 Hengl et al |
| Copyright_xml | – notice: 2015 Hengl et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2015 Hengl et al 2015 Hengl et al |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: R. A. MacMillan is owner and retired principal of LandMapper Environmental Solutions Inc. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials. Conceived and designed the experiments: TH GH BK MW KS. Performed the experiments: MW KS AS LT JT. Analyzed the data: TH GH BK JJ AS RM. Contributed reagents/materials/analysis tools: JL MW KS AS LT JT. Wrote the paper: TH GH RM MW. |
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| SubjectTerms | Acidity Africa Agricultural land Agricultural management Agriculture Agroforestry Algorithms Aluminum Arable land Artificial intelligence Bulk density Cation exchanging Cation-exchange capacity Datasets Decision making models Environmental Monitoring - methods Forests Information services Management Mapping Mathematical models Models, Theoretical Nutrients Nutrients in soil Organic carbon Organic farming Predictions Remote sensing Silt Soil - chemistry Soil fertility Soil improvement Soil management Soil mapping Soil nutrients Soil profiles Soil properties Soil sciences Spectrum analysis Surveillance Taxonomy |
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| Title | Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions |
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