Extrapolation of a structural equation model for digital soil mapping

Autores
Angelini, Marcos Esteban; Kempen, Bas; Hauvelink, Gerard B.M.; Temme, Arnaud J.A.M.; Ransom, Michel D.
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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
Fuente
Geoderma Volume 367 : 114226 (May 2020)
Materia
Suelo
Cartografía
Procesamiento Digital de Imágenes
Génesis del Suelo
Soil
Cartography
Digital Image Processing
Soil Genesis
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/7729
Acceder
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oai_identifier_str oai:localhost:20.500.12123/7729
network_acronym_str INTADig
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network_name_str INTA Digital (INTA)
spelling Extrapolation of a structural equation model for digital soil mappingAngelini, Marcos EstebanKempen, BasHauvelink, Gerard B.M.Temme, Arnaud J.A.M.Ransom, Michel D.SueloCartografíaProcesamiento Digital de ImágenesGénesis del SueloSoilCartographyDigital Image ProcessingSoil GenesisIn 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 SuelosFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Universidad Nacional de Luján; ArgentinaFil: Kempen, B. ISRIC — World Soil Information; HolandaFil: Heuvelink, G.B.M. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC — World Soil Information; HolandaFil: Temme, Arnaud J.A.M. Kansas State University. Geography Department; Estados UnidosFil: Ransom, Michel D. Kansas State University. Department of Agronomy; Estados UnidosElsevier2020-08-18T12:12:16Z2020-08-18T12:12:16Z2020-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/7729https://www.sciencedirect.com/science/article/abs/pii/S00167061193253760016-70611872-6259https://doi.org/10.1016/j.geoderma.2020.114226Geoderma Volume 367 : 114226 (May 2020)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-09-29T13:44:59Zoai:localhost:20.500.12123/7729instacron: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 13:45:00.294INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Extrapolation of a structural equation model for digital soil mapping
title Extrapolation of a structural equation model for digital soil mapping
spellingShingle Extrapolation of a structural equation model for digital soil mapping
Angelini, Marcos Esteban
Suelo
Cartografía
Procesamiento Digital de Imágenes
Génesis del Suelo
Soil
Cartography
Digital Image Processing
Soil Genesis
title_short 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_sort Extrapolation of a structural equation model for digital soil mapping
dc.creator.none.fl_str_mv Angelini, Marcos Esteban
Kempen, Bas
Hauvelink, Gerard B.M.
Temme, Arnaud J.A.M.
Ransom, Michel D.
author Angelini, Marcos Esteban
author_facet Angelini, Marcos Esteban
Kempen, Bas
Hauvelink, Gerard B.M.
Temme, Arnaud J.A.M.
Ransom, Michel D.
author_role author
author2 Kempen, Bas
Hauvelink, Gerard B.M.
Temme, Arnaud J.A.M.
Ransom, Michel D.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Suelo
Cartografía
Procesamiento Digital de Imágenes
Génesis del Suelo
Soil
Cartography
Digital Image Processing
Soil Genesis
topic Suelo
Cartografía
Procesamiento Digital de Imágenes
Génesis del Suelo
Soil
Cartography
Digital Image Processing
Soil Genesis
dc.description.none.fl_txt_mv 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
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.
publishDate 2020
dc.date.none.fl_str_mv 2020-08-18T12:12:16Z
2020-08-18T12:12:16Z
2020-05
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/20.500.12123/7729
https://www.sciencedirect.com/science/article/abs/pii/S0016706119325376
0016-7061
1872-6259
https://doi.org/10.1016/j.geoderma.2020.114226
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
identifier_str_mv 0016-7061
1872-6259
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Geoderma Volume 367 : 114226 (May 2020)
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
_version_ 1844619146530127872
score 12.559606

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