Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain)
Digital soil mapping (DSM) is an effective mapping technique that supports the increased need for quantitative soil data. In DSM, soil properties are correlated with environmental characteristics using statistical models such as regression. However, many of these relationships are explicitly describ...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2021
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| Acceso en línea: | https://hdl.handle.net/10568/113606 |
| _version_ | 1855521682737856512 |
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| author | Hendriks, C.M.J. Stoorvogel, J.J. Álvarez Martínez, J.M. Claessens, Lieven Pérez Silos, I. Barquín, J. |
| author_browse | Barquín, J. Claessens, Lieven Hendriks, C.M.J. Pérez Silos, I. Stoorvogel, J.J. Álvarez Martínez, J.M. |
| author_facet | Hendriks, C.M.J. Stoorvogel, J.J. Álvarez Martínez, J.M. Claessens, Lieven Pérez Silos, I. Barquín, J. |
| author_sort | Hendriks, C.M.J. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Digital soil mapping (DSM) is an effective mapping technique that supports the increased need for quantitative soil data. In DSM, soil properties are correlated with environmental characteristics using statistical models such as regression. However, many of these relationships are explicitly described in mechanistic simulation models. Therefore, the mechanistic relationships can, in theory, replace the statistical relationships in DSM. This study aims to develop a mechanistic model to predict soil organic matter (SOM) stocks in Natura2000 areas of the Cantabria region (Spain). The mechanistic model is established in four steps: (a) identify major processes that influence SOM stocks, (b) review existing models describing the major processes and the respective environmental data that they require, (c) establish a database with the required input data, and (d) calibrate the model with field observations. The SOM stocks map resulting from the mechanistic model had a mean error (ME) of −2 t SOM ha−1 and a root mean square error (RMSE) of 66 t SOM ha−1. The Lin's concordance correlation coefficient was 0.47 and the amount of variance explained (AVE) was 0.21. The results of the mechanistic model were compared to the results of a statistical model. It turned out that the correlation coefficient between the two SOM stock maps was 0.8. This study illustrated that mechanistic soil models can be used for DSM, which brings new opportunities. Mechanistic models for DSM should be considered for mapping soil characteristics that are difficult to predict by statistical models, and for extrapolation purposes. |
| format | Journal Article |
| id | CGSpace113606 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1136062025-11-11T10:12:05Z Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain) Hendriks, C.M.J. Stoorvogel, J.J. Álvarez Martínez, J.M. Claessens, Lieven Pérez Silos, I. Barquín, J. soil surveys soils soil organic matter spain soil mapping Digital soil mapping (DSM) is an effective mapping technique that supports the increased need for quantitative soil data. In DSM, soil properties are correlated with environmental characteristics using statistical models such as regression. However, many of these relationships are explicitly described in mechanistic simulation models. Therefore, the mechanistic relationships can, in theory, replace the statistical relationships in DSM. This study aims to develop a mechanistic model to predict soil organic matter (SOM) stocks in Natura2000 areas of the Cantabria region (Spain). The mechanistic model is established in four steps: (a) identify major processes that influence SOM stocks, (b) review existing models describing the major processes and the respective environmental data that they require, (c) establish a database with the required input data, and (d) calibrate the model with field observations. The SOM stocks map resulting from the mechanistic model had a mean error (ME) of −2 t SOM ha−1 and a root mean square error (RMSE) of 66 t SOM ha−1. The Lin's concordance correlation coefficient was 0.47 and the amount of variance explained (AVE) was 0.21. The results of the mechanistic model were compared to the results of a statistical model. It turned out that the correlation coefficient between the two SOM stock maps was 0.8. This study illustrated that mechanistic soil models can be used for DSM, which brings new opportunities. Mechanistic models for DSM should be considered for mapping soil characteristics that are difficult to predict by statistical models, and for extrapolation purposes. 2021-03 2021-04-29T13:28:08Z 2021-04-29T13:28:08Z Journal Article https://hdl.handle.net/10568/113606 en Open Access application/pdf Wiley Hendriks, C.M.J., Stoorvogel, J.J., Álvarez‐Martínez, J.M., Claessens, L., Pérez‐Silos, I. & Barquín, J. (2021). Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain). European Journal of Soil Science, 72(2), 704-719. |
| spellingShingle | soil surveys soils soil organic matter spain soil mapping Hendriks, C.M.J. Stoorvogel, J.J. Álvarez Martínez, J.M. Claessens, Lieven Pérez Silos, I. Barquín, J. Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain) |
| title | Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain) |
| title_full | Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain) |
| title_fullStr | Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain) |
| title_full_unstemmed | Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain) |
| title_short | Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain) |
| title_sort | introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the cantabrian region spain |
| topic | soil surveys soils soil organic matter spain soil mapping |
| url | https://hdl.handle.net/10568/113606 |
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