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
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/1668

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spelling 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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