Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data

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...

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Detalhes bibliográficos
Autores: Giannini Kurina, Franca, Hang, Susana, Córdoba, Mariano, Negro, Gustavo José, Balzarini, Monica Graciela
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:Argentina
Recursos:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/133198
Acesso em linha:http://hdl.handle.net/11336/133198
Access Level:acceso abierto
Palavra-chave:FUZZY K-MEANS
MULTIVARIATE ZONING
SPATIAL PRINCIPAL COMPONENTS
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
id AR_abd01ac1aa04d924ee14c70a709041d7
oai_identifier_str oai:ri.conicet.gov.ar:11336/133198
network_acronym_str AR
network_name_str Argentina
repository_id_str
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
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
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
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
topic FUZZY K-MEANS
MULTIVARIATE ZONING
SPATIAL PRINCIPAL COMPONENTS
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
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
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
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
_version_ 1799194642047041536
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 Técnicas2024-05-08T13:33:23Zoai: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:34982024-05-08 13:33:23.907CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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