Subfield management class delineation using cluster analysis from spatial principal components of soil variables

Autores
Córdoba, Mariano; Bruno, Cecilia Ines; Costa, Jose Luis; Balzarini, Monica Graciela
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Understanding spatial variation within a field is essential for site-specific crop management, which requires the delineation of management areas. Several soil and terrain variables are used to classify the field points into classes. Fuzzy k-means cluster analysis is a widely used tool to delineate management classes in the multivariate context. However, this clustering method does not consider the presence of spatial correlations in the data. The MULTISPATI-PCA algorithm is an extension of principal component analysis that considers spatial autocorrelation in the original variables to produce synthetic variables. We propose and illustrate the implementation of a new method (KM-sPC) for subfield management class delineation based on the joint use of MULTISPATI-PCA and fuzzy k-means cluster. To assess the performance of KM-sPC, we performed clustering of the original soil variables and of both spatial and classical principal components on three field data sets. KM-sPC algorithm improved the non-spatial clustering in the formation of within-field management classes. Mapping of KM-sPC classification shows a more contiguous zoning. KM-sPC showed the highest yield differences between delineated classes and the smallest within-class yield variance.
Fil: Córdoba, Mariano. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Bruno, Cecilia Ines. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Costa, Jose Luis. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Balzarini, Monica Graciela. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Multispati-Pca
Fuzzy K-Means
Precision Agriculture
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/23303
Acceder
id CONICETDig_624c9d51edfc86ce862a7ded20167251
oai_identifier_str oai:ri.conicet.gov.ar:11336/23303
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Subfield management class delineation using cluster analysis from spatial principal components of soil variablesCórdoba, MarianoBruno, Cecilia InesCosta, Jose LuisBalzarini, Monica GracielaMultispati-PcaFuzzy K-MeansPrecision Agriculturehttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Understanding spatial variation within a field is essential for site-specific crop management, which requires the delineation of management areas. Several soil and terrain variables are used to classify the field points into classes. Fuzzy k-means cluster analysis is a widely used tool to delineate management classes in the multivariate context. However, this clustering method does not consider the presence of spatial correlations in the data. The MULTISPATI-PCA algorithm is an extension of principal component analysis that considers spatial autocorrelation in the original variables to produce synthetic variables. We propose and illustrate the implementation of a new method (KM-sPC) for subfield management class delineation based on the joint use of MULTISPATI-PCA and fuzzy k-means cluster. To assess the performance of KM-sPC, we performed clustering of the original soil variables and of both spatial and classical principal components on three field data sets. KM-sPC algorithm improved the non-spatial clustering in the formation of within-field management classes. Mapping of KM-sPC classification shows a more contiguous zoning. KM-sPC showed the highest yield differences between delineated classes and the smallest within-class yield variance.Fil: Córdoba, Mariano. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bruno, Cecilia Ines. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Costa, Jose Luis. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Balzarini, Monica Graciela. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier2013-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/23303Córdoba, Mariano; Bruno, Cecilia Ines; Costa, Jose Luis; Balzarini, Monica Graciela; Subfield management class delineation using cluster analysis from spatial principal components of soil variables; Elsevier; Computers and Eletronics in Agriculture; 97; 7-2013; 6-140168-1699CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2013.05.009info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0168169913001282info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:07:54Zoai:ri.conicet.gov.ar:11336/23303instacron: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:34982025-09-10 13:07:54.69CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Subfield management class delineation using cluster analysis from spatial principal components of soil variables
title Subfield management class delineation using cluster analysis from spatial principal components of soil variables
spellingShingle Subfield management class delineation using cluster analysis from spatial principal components of soil variables
Córdoba, Mariano
Multispati-Pca
Fuzzy K-Means
Precision Agriculture
title_short Subfield management class delineation using cluster analysis from spatial principal components of soil variables
title_full Subfield management class delineation using cluster analysis from spatial principal components of soil variables
title_fullStr Subfield management class delineation using cluster analysis from spatial principal components of soil variables
title_full_unstemmed Subfield management class delineation using cluster analysis from spatial principal components of soil variables
title_sort Subfield management class delineation using cluster analysis from spatial principal components of soil variables
dc.creator.none.fl_str_mv Córdoba, Mariano
Bruno, Cecilia Ines
Costa, Jose Luis
Balzarini, Monica Graciela
author Córdoba, Mariano
author_facet Córdoba, Mariano
Bruno, Cecilia Ines
Costa, Jose Luis
Balzarini, Monica Graciela
author_role author
author2 Bruno, Cecilia Ines
Costa, Jose Luis
Balzarini, Monica Graciela
author2_role author
author
author
dc.subject.none.fl_str_mv Multispati-Pca
Fuzzy K-Means
Precision Agriculture
topic Multispati-Pca
Fuzzy K-Means
Precision Agriculture
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Understanding spatial variation within a field is essential for site-specific crop management, which requires the delineation of management areas. Several soil and terrain variables are used to classify the field points into classes. Fuzzy k-means cluster analysis is a widely used tool to delineate management classes in the multivariate context. However, this clustering method does not consider the presence of spatial correlations in the data. The MULTISPATI-PCA algorithm is an extension of principal component analysis that considers spatial autocorrelation in the original variables to produce synthetic variables. We propose and illustrate the implementation of a new method (KM-sPC) for subfield management class delineation based on the joint use of MULTISPATI-PCA and fuzzy k-means cluster. To assess the performance of KM-sPC, we performed clustering of the original soil variables and of both spatial and classical principal components on three field data sets. KM-sPC algorithm improved the non-spatial clustering in the formation of within-field management classes. Mapping of KM-sPC classification shows a more contiguous zoning. KM-sPC showed the highest yield differences between delineated classes and the smallest within-class yield variance.
Fil: Córdoba, Mariano. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Bruno, Cecilia Ines. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Costa, Jose Luis. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Balzarini, Monica Graciela. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Understanding spatial variation within a field is essential for site-specific crop management, which requires the delineation of management areas. Several soil and terrain variables are used to classify the field points into classes. Fuzzy k-means cluster analysis is a widely used tool to delineate management classes in the multivariate context. However, this clustering method does not consider the presence of spatial correlations in the data. The MULTISPATI-PCA algorithm is an extension of principal component analysis that considers spatial autocorrelation in the original variables to produce synthetic variables. We propose and illustrate the implementation of a new method (KM-sPC) for subfield management class delineation based on the joint use of MULTISPATI-PCA and fuzzy k-means cluster. To assess the performance of KM-sPC, we performed clustering of the original soil variables and of both spatial and classical principal components on three field data sets. KM-sPC algorithm improved the non-spatial clustering in the formation of within-field management classes. Mapping of KM-sPC classification shows a more contiguous zoning. KM-sPC showed the highest yield differences between delineated classes and the smallest within-class yield variance.
publishDate 2013
dc.date.none.fl_str_mv 2013-07
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/11336/23303
Córdoba, Mariano; Bruno, Cecilia Ines; Costa, Jose Luis; Balzarini, Monica Graciela; Subfield management class delineation using cluster analysis from spatial principal components of soil variables; Elsevier; Computers and Eletronics in Agriculture; 97; 7-2013; 6-14
0168-1699
CONICET Digital
CONICET
url http://hdl.handle.net/11336/23303
identifier_str_mv Córdoba, Mariano; Bruno, Cecilia Ines; Costa, Jose Luis; Balzarini, Monica Graciela; Subfield management class delineation using cluster analysis from spatial principal components of soil variables; Elsevier; Computers and Eletronics in Agriculture; 97; 7-2013; 6-14
0168-1699
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2013.05.009
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0168169913001282
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
_version_ 1842980363862802432
score 13.004268

Publicaciones similares