Multivariate mapping of soil with structural equation modelling
- Autores
- Angelini, Marcos Esteban; Heuvelink, Gerard B.M.; Kempen, Bas
- Año de publicación
- 2017
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- In a previous study we introduced structural equation modelling (SEM) for digital soil mapping in the Argentine Pampas. An attractive property of SEM is that it incorporates pedological knowledge explicitly through a mathematical implementation of a conceptual model. Many soil processes operate within the soil profile; therefore, SEM might be suitable for simultaneous prediction of soil properties for multiple soil layers. In this way, relations between soil properties in different horizons can be included that might result in more consistent predictions. The objectives of this study were therefore to apply SEM to multi-layer and multivariate soil mapping, and to test SEM functionality for suggestions to improve the modelling. We applied SEM to model and predict the lateral and vertical distribution of the cation exchange capacity (CEC), organic carbon (OC) and clay content of three major soil horizons, A, B and C, for a 23 000-km2 region in the Argentine Pampas. We developed a conceptual model based on pedological hypotheses. Next, we derived a mathematical model and calibrated it with environmental covariates and soil data from 320 soil profiles. Cross-validation of predicted soil properties showed that SEM explained only marginally more of the variance than a linear regression model. However, assessment of the covariation showed that SEM reproduces the covariance between variables much more accurately than linear regression. We concluded that SEM can be used to predict several soil properties in multiple layers by considering the interrelations between soil properties and layers.
Fil: Angelini, Marcos Esteban. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC — World Soil Information; Holanda. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos;Argentina
Fil: Heuvelink, G.B.M. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC — World Soil Information; Holanda
Fil: Kempen, B. ISRIC — World Soil Information; Holanda - Fuente
- European Journal of Soil Science 68 (5) : 575–591 (September 2017)
- Materia
-
Suelo
Soil
Multivariate Analysis
Cartography
Análisis Multivariante
Cartografía - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/1668
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Multivariate mapping of soil with structural equation modellingAngelini, Marcos EstebanHeuvelink, Gerard B.M.Kempen, BasSueloSoilMultivariate AnalysisCartographyAnálisis MultivarianteCartografíaIn a previous study we introduced structural equation modelling (SEM) for digital soil mapping in the Argentine Pampas. An attractive property of SEM is that it incorporates pedological knowledge explicitly through a mathematical implementation of a conceptual model. Many soil processes operate within the soil profile; therefore, SEM might be suitable for simultaneous prediction of soil properties for multiple soil layers. In this way, relations between soil properties in different horizons can be included that might result in more consistent predictions. The objectives of this study were therefore to apply SEM to multi-layer and multivariate soil mapping, and to test SEM functionality for suggestions to improve the modelling. We applied SEM to model and predict the lateral and vertical distribution of the cation exchange capacity (CEC), organic carbon (OC) and clay content of three major soil horizons, A, B and C, for a 23 000-km2 region in the Argentine Pampas. We developed a conceptual model based on pedological hypotheses. Next, we derived a mathematical model and calibrated it with environmental covariates and soil data from 320 soil profiles. Cross-validation of predicted soil properties showed that SEM explained only marginally more of the variance than a linear regression model. However, assessment of the covariation showed that SEM reproduces the covariance between variables much more accurately than linear regression. We concluded that SEM can be used to predict several soil properties in multiple layers by considering the interrelations between soil properties and layers.Fil: Angelini, Marcos Esteban. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC — World Soil Information; Holanda. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos;ArgentinaFil: Heuvelink, G.B.M. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC — World Soil Information; HolandaFil: Kempen, B. ISRIC — World Soil Information; Holanda2017-11-03T17:58:47Z2017-11-03T17:58:47Z2017-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/1668http://onlinelibrary.wiley.com/doi/10.1111/ejss.12446/epdf1351-0754 (Print)1365-2389 (Online)DOI: 10.1111/ejss.12446European Journal of Soil Science 68 (5) : 575–591 (September 2017)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-29T13:44:13Zoai:localhost:20.500.12123/1668instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:44:13.989INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse |
dc.title.none.fl_str_mv |
Multivariate mapping of soil with structural equation modelling |
title |
Multivariate mapping of soil with structural equation modelling |
spellingShingle |
Multivariate mapping of soil with structural equation modelling Angelini, Marcos Esteban Suelo Soil Multivariate Analysis Cartography Análisis Multivariante Cartografía |
title_short |
Multivariate mapping of soil with structural equation modelling |
title_full |
Multivariate mapping of soil with structural equation modelling |
title_fullStr |
Multivariate mapping of soil with structural equation modelling |
title_full_unstemmed |
Multivariate mapping of soil with structural equation modelling |
title_sort |
Multivariate mapping of soil with structural equation modelling |
dc.creator.none.fl_str_mv |
Angelini, Marcos Esteban Heuvelink, Gerard B.M. Kempen, Bas |
author |
Angelini, Marcos Esteban |
author_facet |
Angelini, Marcos Esteban Heuvelink, Gerard B.M. Kempen, Bas |
author_role |
author |
author2 |
Heuvelink, Gerard B.M. Kempen, Bas |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Suelo Soil Multivariate Analysis Cartography Análisis Multivariante Cartografía |
topic |
Suelo Soil Multivariate Analysis Cartography Análisis Multivariante Cartografía |
dc.description.none.fl_txt_mv |
In a previous study we introduced structural equation modelling (SEM) for digital soil mapping in the Argentine Pampas. An attractive property of SEM is that it incorporates pedological knowledge explicitly through a mathematical implementation of a conceptual model. Many soil processes operate within the soil profile; therefore, SEM might be suitable for simultaneous prediction of soil properties for multiple soil layers. In this way, relations between soil properties in different horizons can be included that might result in more consistent predictions. The objectives of this study were therefore to apply SEM to multi-layer and multivariate soil mapping, and to test SEM functionality for suggestions to improve the modelling. We applied SEM to model and predict the lateral and vertical distribution of the cation exchange capacity (CEC), organic carbon (OC) and clay content of three major soil horizons, A, B and C, for a 23 000-km2 region in the Argentine Pampas. We developed a conceptual model based on pedological hypotheses. Next, we derived a mathematical model and calibrated it with environmental covariates and soil data from 320 soil profiles. Cross-validation of predicted soil properties showed that SEM explained only marginally more of the variance than a linear regression model. However, assessment of the covariation showed that SEM reproduces the covariance between variables much more accurately than linear regression. We concluded that SEM can be used to predict several soil properties in multiple layers by considering the interrelations between soil properties and layers. Fil: Angelini, Marcos Esteban. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC — World Soil Information; Holanda. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos;Argentina Fil: Heuvelink, G.B.M. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC — World Soil Information; Holanda Fil: Kempen, B. ISRIC — World Soil Information; Holanda |
description |
In a previous study we introduced structural equation modelling (SEM) for digital soil mapping in the Argentine Pampas. An attractive property of SEM is that it incorporates pedological knowledge explicitly through a mathematical implementation of a conceptual model. Many soil processes operate within the soil profile; therefore, SEM might be suitable for simultaneous prediction of soil properties for multiple soil layers. In this way, relations between soil properties in different horizons can be included that might result in more consistent predictions. The objectives of this study were therefore to apply SEM to multi-layer and multivariate soil mapping, and to test SEM functionality for suggestions to improve the modelling. We applied SEM to model and predict the lateral and vertical distribution of the cation exchange capacity (CEC), organic carbon (OC) and clay content of three major soil horizons, A, B and C, for a 23 000-km2 region in the Argentine Pampas. We developed a conceptual model based on pedological hypotheses. Next, we derived a mathematical model and calibrated it with environmental covariates and soil data from 320 soil profiles. Cross-validation of predicted soil properties showed that SEM explained only marginally more of the variance than a linear regression model. However, assessment of the covariation showed that SEM reproduces the covariance between variables much more accurately than linear regression. We concluded that SEM can be used to predict several soil properties in multiple layers by considering the interrelations between soil properties and layers. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-11-03T17:58:47Z 2017-11-03T17:58:47Z 2017-09 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/20.500.12123/1668 http://onlinelibrary.wiley.com/doi/10.1111/ejss.12446/epdf 1351-0754 (Print) 1365-2389 (Online) DOI: 10.1111/ejss.12446 |
url |
http://hdl.handle.net/20.500.12123/1668 http://onlinelibrary.wiley.com/doi/10.1111/ejss.12446/epdf |
identifier_str_mv |
1351-0754 (Print) 1365-2389 (Online) DOI: 10.1111/ejss.12446 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
European Journal of Soil Science 68 (5) : 575–591 (September 2017) reponame:INTA Digital (INTA) instname:Instituto Nacional de Tecnología Agropecuaria |
reponame_str |
INTA Digital (INTA) |
collection |
INTA Digital (INTA) |
instname_str |
Instituto Nacional de Tecnología Agropecuaria |
repository.name.fl_str_mv |
INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria |
repository.mail.fl_str_mv |
tripaldi.nicolas@inta.gob.ar |
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1844619119162294272 |
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12.559606 |