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Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions.

Hengl T, Heuvelink GB, Kempen B, Leenaars JG, Walsh MG, Shepherd KD, Sila A, MacMillan RA, Mendes de Jesus J, Tamene L, Tondoh JE - PLoS ONE (2015)

Bottom Line: 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.This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data.

View Article: PubMed Central - PubMed

Affiliation: ISRIC-World Soil Information, Wageningen, the Netherlands.

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.

No MeSH data available.


Related in: MedlinePlus

Density of soil observations in Africa showing distinct spatial clustering and an example of residual variography.(A) relative density of soil observations in Africa determined using a kernel smoother displayed in log-scale (input locations shown in Fig 2), (B) 3D sampling locations scheme, (C) example of an exponential variogram fitted for soil organic carbon residuals. In this case the maximum distance of interest for kriging has been set at 60 km.
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pone.0125814.g003: Density of soil observations in Africa showing distinct spatial clustering and an example of residual variography.(A) relative density of soil observations in Africa determined using a kernel smoother displayed in log-scale (input locations shown in Fig 2), (B) 3D sampling locations scheme, (C) example of an exponential variogram fitted for soil organic carbon residuals. In this case the maximum distance of interest for kriging has been set at 60 km.

Mentions: Typically, in the case of soil data, the anisotropy ratio between horizontal and vertical distances is high—spatial variation observed in a few cm depth change may correspond with several km or more in horizontal space, so that the initial settings of the anisotropy ratio (i.e. the ratio of the horizontal and vertical variogram ranges) are between 3000–8000, for example. Variogram fitting criteria can then be used to optimize the anisotropy parameters. In our case we assumed no horizontal anisotropy and hence assumed ax = ay = 1, leaving only ad to be estimated. Once the anisotropy ratio is obtained, 3D variogram modelling does not meaningfully differ from 2D variogram modelling (e.g. see Fig 3C).


Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions.

Hengl T, Heuvelink GB, Kempen B, Leenaars JG, Walsh MG, Shepherd KD, Sila A, MacMillan RA, Mendes de Jesus J, Tamene L, Tondoh JE - PLoS ONE (2015)

Density of soil observations in Africa showing distinct spatial clustering and an example of residual variography.(A) relative density of soil observations in Africa determined using a kernel smoother displayed in log-scale (input locations shown in Fig 2), (B) 3D sampling locations scheme, (C) example of an exponential variogram fitted for soil organic carbon residuals. In this case the maximum distance of interest for kriging has been set at 60 km.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482144&req=5

pone.0125814.g003: Density of soil observations in Africa showing distinct spatial clustering and an example of residual variography.(A) relative density of soil observations in Africa determined using a kernel smoother displayed in log-scale (input locations shown in Fig 2), (B) 3D sampling locations scheme, (C) example of an exponential variogram fitted for soil organic carbon residuals. In this case the maximum distance of interest for kriging has been set at 60 km.
Mentions: Typically, in the case of soil data, the anisotropy ratio between horizontal and vertical distances is high—spatial variation observed in a few cm depth change may correspond with several km or more in horizontal space, so that the initial settings of the anisotropy ratio (i.e. the ratio of the horizontal and vertical variogram ranges) are between 3000–8000, for example. Variogram fitting criteria can then be used to optimize the anisotropy parameters. In our case we assumed no horizontal anisotropy and hence assumed ax = ay = 1, leaving only ad to be estimated. Once the anisotropy ratio is obtained, 3D variogram modelling does not meaningfully differ from 2D variogram modelling (e.g. see Fig 3C).

Bottom Line: 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.This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data.

View Article: PubMed Central - PubMed

Affiliation: ISRIC-World Soil Information, Wageningen, the Netherlands.

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.

No MeSH data available.


Related in: MedlinePlus