Extrapolation of a structural equation model for digital soil mapping
In theory, two separate regions with the same soil-forming factors should develop similar soil conditions. This theoretical finding has been used in digital soil mapping (DSM) to extrapolate a model from one area to another, which usually does not work out well. One reason for failure could be that...
| Main Authors: | , , , , |
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| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
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Elsevier
2020
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| Online Access: | http://hdl.handle.net/20.500.12123/7729 https://www.sciencedirect.com/science/article/abs/pii/S0016706119325376 https://doi.org/10.1016/j.geoderma.2020.114226 |
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| author | Angelini, Marcos Esteban Kempen, Bas Hauvelink, Gerard B.M. Temme, Arnaud J.A.M. Ransom, Michel D. |
| author_browse | Angelini, Marcos Esteban Hauvelink, Gerard B.M. Kempen, Bas Ransom, Michel D. Temme, Arnaud J.A.M. |
| author_facet | Angelini, Marcos Esteban Kempen, Bas Hauvelink, Gerard B.M. Temme, Arnaud J.A.M. Ransom, Michel D. |
| author_sort | Angelini, Marcos Esteban |
| collection | INTA Digital |
| description | In theory, two separate regions with the same soil-forming factors should develop similar soil conditions. This theoretical finding has been used in digital soil mapping (DSM) to extrapolate a model from one area to another, which usually does not work out well. One reason for failure could be that most of these studies used empirical methods. Structural equation modelling (SEM) is a semi-mechanistic technique, which can explicitly include expert knowledge. We therefore hypothesize that SEM models are more suitable for extrapolation than purely empirical models in DSM. The objective of this study was to investigate the extrapolation capability of SEM by comparing different model settings. We applied a SEM model from a previous study in Argentina to a similar soil-landscape in the Great Plains of the United States to predict clay, organic carbon, and cation exchange capacity for three major horizons: A, B, and C. We concluded that system relationships that were well supported by pedological knowledge showed consistent and equal behaviour in both study areas. In addition, a deeper understanding of indicators of soil-forming factors could strengthen conceptual models for extrapolating DSM models. We also found that for model extrapolation, knowledge-based links between system variables are more effective than data-driven links. In particular, model modifications can improve local prediction but harm the predictive power of extrapolation. |
| format | info:ar-repo/semantics/artículo |
| id | INTA7729 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA77292020-08-18T12:18:24Z Extrapolation of a structural equation model for digital soil mapping Angelini, Marcos Esteban Kempen, Bas Hauvelink, Gerard B.M. Temme, Arnaud J.A.M. Ransom, Michel D. Suelo Cartografía Procesamiento Digital de Imágenes Génesis del Suelo Soil Cartography Digital Image Processing Soil Genesis In theory, two separate regions with the same soil-forming factors should develop similar soil conditions. This theoretical finding has been used in digital soil mapping (DSM) to extrapolate a model from one area to another, which usually does not work out well. One reason for failure could be that most of these studies used empirical methods. Structural equation modelling (SEM) is a semi-mechanistic technique, which can explicitly include expert knowledge. We therefore hypothesize that SEM models are more suitable for extrapolation than purely empirical models in DSM. The objective of this study was to investigate the extrapolation capability of SEM by comparing different model settings. We applied a SEM model from a previous study in Argentina to a similar soil-landscape in the Great Plains of the United States to predict clay, organic carbon, and cation exchange capacity for three major horizons: A, B, and C. We concluded that system relationships that were well supported by pedological knowledge showed consistent and equal behaviour in both study areas. In addition, a deeper understanding of indicators of soil-forming factors could strengthen conceptual models for extrapolating DSM models. We also found that for model extrapolation, knowledge-based links between system variables are more effective than data-driven links. In particular, model modifications can improve local prediction but harm the predictive power of extrapolation. Instituto de Suelos Fil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Universidad Nacional de Luján; Argentina Fil: Kempen, B. ISRIC — World Soil Information; Holanda Fil: Heuvelink, G.B.M. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC — World Soil Information; Holanda Fil: Temme, Arnaud J.A.M. Kansas State University. Geography Department; Estados Unidos Fil: Ransom, Michel D. Kansas State University. Department of Agronomy; Estados Unidos 2020-08-18T12:12:16Z 2020-08-18T12:12:16Z 2020-05 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/7729 https://www.sciencedirect.com/science/article/abs/pii/S0016706119325376 0016-7061 1872-6259 https://doi.org/10.1016/j.geoderma.2020.114226 eng info:eu-repo/semantics/restrictedAccess application/pdf Elsevier Geoderma Volume 367 : 114226 (May 2020) |
| spellingShingle | Suelo Cartografía Procesamiento Digital de Imágenes Génesis del Suelo Soil Cartography Digital Image Processing Soil Genesis Angelini, Marcos Esteban Kempen, Bas Hauvelink, Gerard B.M. Temme, Arnaud J.A.M. Ransom, Michel D. Extrapolation of a structural equation model for digital soil mapping |
| title | Extrapolation of a structural equation model for digital soil mapping |
| title_full | Extrapolation of a structural equation model for digital soil mapping |
| title_fullStr | Extrapolation of a structural equation model for digital soil mapping |
| title_full_unstemmed | Extrapolation of a structural equation model for digital soil mapping |
| title_short | Extrapolation of a structural equation model for digital soil mapping |
| title_sort | extrapolation of a structural equation model for digital soil mapping |
| topic | Suelo Cartografía Procesamiento Digital de Imágenes Génesis del Suelo Soil Cartography Digital Image Processing Soil Genesis |
| url | http://hdl.handle.net/20.500.12123/7729 https://www.sciencedirect.com/science/article/abs/pii/S0016706119325376 https://doi.org/10.1016/j.geoderma.2020.114226 |
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