Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data

Autores
Giannini Kurina, Franca; Hang, Susana; Córdoba, Mariano; Negro, Gustavo José; Balzarini, Monica Graciela
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Joint spatial variability of soil and climate variables offers the opportunity to delimit contiguous edaphoclimatic zones. These zones can be useful to improve natural resource management. The aim of this work was to develop a statistical protocol for multivariate zoning at regional scales. A zoning of Córdoba, Argentina, was generated using data from a sample of 355 sites involving edaphic and climatic data (pH, TN, TOC, Na, K, CEC, Cu, Clay, Sand, WHC, elevation, annual precipitation and mean temperature). We proposed a two-step algorithm that considers the spatial correlation of these variables in a clustering of sites. The protocol was run after modeling the spatial pattern of each soil variable to adapt information from different sources and formats to a fine grid. In the first step of the protocol, MULTISPATI-PCA, an extension of the principal component analysis that considers the spatial co-variability between variables, was used to obtain linear combinations of original data. In the second step, such synthetic variables (spatial principal components) were used as input of the fuzzy k-mean clustering method to delineate homogeneous zones. The number of clusters was established by internal validation indices. The use of MULlTISPATI-PCA was compared with the more conventional and non-spatial PCA. Results suggest that previous geostatistical interpolation and spatially constrained multivariate analysis create meaningful and spatially coherent zones. Four zones were identified in Córdoba region, Argentina.
Fil: Giannini Kurina, Franca. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; Argentina
Fil: Hang, Susana. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina
Fil: Córdoba, Mariano. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; Argentina
Fil: Negro, Gustavo José. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina
Fil: Balzarini, Monica Graciela. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; Argentina
Materia
FUZZY K-MEANS
MULTIVARIATE ZONING
SPATIAL PRINCIPAL COMPONENTS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/133198
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional dataGiannini Kurina, FrancaHang, SusanaCórdoba, MarianoNegro, Gustavo JoséBalzarini, Monica GracielaFUZZY K-MEANSMULTIVARIATE ZONINGSPATIAL PRINCIPAL COMPONENTShttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Joint spatial variability of soil and climate variables offers the opportunity to delimit contiguous edaphoclimatic zones. These zones can be useful to improve natural resource management. The aim of this work was to develop a statistical protocol for multivariate zoning at regional scales. A zoning of Córdoba, Argentina, was generated using data from a sample of 355 sites involving edaphic and climatic data (pH, TN, TOC, Na, K, CEC, Cu, Clay, Sand, WHC, elevation, annual precipitation and mean temperature). We proposed a two-step algorithm that considers the spatial correlation of these variables in a clustering of sites. The protocol was run after modeling the spatial pattern of each soil variable to adapt information from different sources and formats to a fine grid. In the first step of the protocol, MULTISPATI-PCA, an extension of the principal component analysis that considers the spatial co-variability between variables, was used to obtain linear combinations of original data. In the second step, such synthetic variables (spatial principal components) were used as input of the fuzzy k-mean clustering method to delineate homogeneous zones. The number of clusters was established by internal validation indices. The use of MULlTISPATI-PCA was compared with the more conventional and non-spatial PCA. Results suggest that previous geostatistical interpolation and spatially constrained multivariate analysis create meaningful and spatially coherent zones. Four zones were identified in Córdoba region, Argentina.Fil: Giannini Kurina, Franca. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; ArgentinaFil: Hang, Susana. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; ArgentinaFil: Córdoba, Mariano. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; ArgentinaFil: Negro, Gustavo José. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; ArgentinaFil: Balzarini, Monica Graciela. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; ArgentinaElsevier Science2018-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/133198Giannini Kurina, Franca; Hang, Susana; Córdoba, Mariano; Negro, Gustavo José; Balzarini, Monica Graciela; Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data; Elsevier Science; Geoderma; 310; 1-2018; 170-1770016-7061CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S001670611730232Xinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.geoderma.2017.09.011info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:34:03Zoai:ri.conicet.gov.ar:11336/133198instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:34:03.336CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data
title Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data
spellingShingle Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data
Giannini Kurina, Franca
FUZZY K-MEANS
MULTIVARIATE ZONING
SPATIAL PRINCIPAL COMPONENTS
title_short Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data
title_full Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data
title_fullStr Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data
title_full_unstemmed Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data
title_sort Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data
dc.creator.none.fl_str_mv Giannini Kurina, Franca
Hang, Susana
Córdoba, Mariano
Negro, Gustavo José
Balzarini, Monica Graciela
author Giannini Kurina, Franca
author_facet Giannini Kurina, Franca
Hang, Susana
Córdoba, Mariano
Negro, Gustavo José
Balzarini, Monica Graciela
author_role author
author2 Hang, Susana
Córdoba, Mariano
Negro, Gustavo José
Balzarini, Monica Graciela
author2_role author
author
author
author
dc.subject.none.fl_str_mv FUZZY K-MEANS
MULTIVARIATE ZONING
SPATIAL PRINCIPAL COMPONENTS
topic FUZZY K-MEANS
MULTIVARIATE ZONING
SPATIAL PRINCIPAL COMPONENTS
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Joint spatial variability of soil and climate variables offers the opportunity to delimit contiguous edaphoclimatic zones. These zones can be useful to improve natural resource management. The aim of this work was to develop a statistical protocol for multivariate zoning at regional scales. A zoning of Córdoba, Argentina, was generated using data from a sample of 355 sites involving edaphic and climatic data (pH, TN, TOC, Na, K, CEC, Cu, Clay, Sand, WHC, elevation, annual precipitation and mean temperature). We proposed a two-step algorithm that considers the spatial correlation of these variables in a clustering of sites. The protocol was run after modeling the spatial pattern of each soil variable to adapt information from different sources and formats to a fine grid. In the first step of the protocol, MULTISPATI-PCA, an extension of the principal component analysis that considers the spatial co-variability between variables, was used to obtain linear combinations of original data. In the second step, such synthetic variables (spatial principal components) were used as input of the fuzzy k-mean clustering method to delineate homogeneous zones. The number of clusters was established by internal validation indices. The use of MULlTISPATI-PCA was compared with the more conventional and non-spatial PCA. Results suggest that previous geostatistical interpolation and spatially constrained multivariate analysis create meaningful and spatially coherent zones. Four zones were identified in Córdoba region, Argentina.
Fil: Giannini Kurina, Franca. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; Argentina
Fil: Hang, Susana. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina
Fil: Córdoba, Mariano. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; Argentina
Fil: Negro, Gustavo José. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina
Fil: Balzarini, Monica Graciela. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; Argentina
description Joint spatial variability of soil and climate variables offers the opportunity to delimit contiguous edaphoclimatic zones. These zones can be useful to improve natural resource management. The aim of this work was to develop a statistical protocol for multivariate zoning at regional scales. A zoning of Córdoba, Argentina, was generated using data from a sample of 355 sites involving edaphic and climatic data (pH, TN, TOC, Na, K, CEC, Cu, Clay, Sand, WHC, elevation, annual precipitation and mean temperature). We proposed a two-step algorithm that considers the spatial correlation of these variables in a clustering of sites. The protocol was run after modeling the spatial pattern of each soil variable to adapt information from different sources and formats to a fine grid. In the first step of the protocol, MULTISPATI-PCA, an extension of the principal component analysis that considers the spatial co-variability between variables, was used to obtain linear combinations of original data. In the second step, such synthetic variables (spatial principal components) were used as input of the fuzzy k-mean clustering method to delineate homogeneous zones. The number of clusters was established by internal validation indices. The use of MULlTISPATI-PCA was compared with the more conventional and non-spatial PCA. Results suggest that previous geostatistical interpolation and spatially constrained multivariate analysis create meaningful and spatially coherent zones. Four zones were identified in Córdoba region, Argentina.
publishDate 2018
dc.date.none.fl_str_mv 2018-01
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/11336/133198
Giannini Kurina, Franca; Hang, Susana; Córdoba, Mariano; Negro, Gustavo José; Balzarini, Monica Graciela; Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data; Elsevier Science; Geoderma; 310; 1-2018; 170-177
0016-7061
CONICET Digital
CONICET
url http://hdl.handle.net/11336/133198
identifier_str_mv Giannini Kurina, Franca; Hang, Susana; Córdoba, Mariano; Negro, Gustavo José; Balzarini, Monica Graciela; Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data; Elsevier Science; Geoderma; 310; 1-2018; 170-177
0016-7061
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S001670611730232X
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.geoderma.2017.09.011
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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