Delineating vineyard zones by fuzzy K-means algorithm based on grape sampling variables

[EN] This study describes a method for delineating management zones using interpolated maps of grape characteristics recorded in 2013 and 2014 in a Godello vineyard located in the Bierzo Denomination of Origin (León, Northwest Spain). Ten variables were analyzed and recorded for the sampled vines (5...

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Autores: González Fernández, Ana Belén, Rodríguez Pérez, José Ramón, Sanz Ablanedo, Enoc, Valenciano Montenegro, José Benito, Marcelo Gabella, Victoriano
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2019
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/17792
Acceso en línea:https://www.sciencedirect.com/science/article/pii/S0304423818306228
https://hdl.handle.net/10612/17792
Access Level:acceso abierto
Palabra clave:Ingeniería agrícola
Cluster classification
Godello
Grape characteristics
Management zones
Precision viticulture
Vitis vinifera L.
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spelling Delineating vineyard zones by fuzzy K-means algorithm based on grape sampling variablesGonzález Fernández, Ana BelénRodríguez Pérez, José RamónSanz Ablanedo, EnocValenciano Montenegro, José BenitoMarcelo Gabella, VictorianoIngeniería agrícolaCluster classificationGodelloGrape characteristicsManagement zonesPrecision viticultureVitis vinifera L.[EN] This study describes a method for delineating management zones using interpolated maps of grape characteristics recorded in 2013 and 2014 in a Godello vineyard located in the Bierzo Denomination of Origin (León, Northwest Spain). Ten variables were analyzed and recorded for the sampled vines (50 vines/ha). Interpolated maps reflecting each variable and year were created by spatial interpolation (kriging) from the sampled points. Principal component analysis was used to detect relationships between variables and to select the variables to be used to create the cluster classification. Using the fuzzy k-means classification algorithm implemented in the Management Zone Analyst (MZA v.1.0.0) software, several zones were delineated by combining the studied variables. The results delineated 2 different management areas composed of 3 zones each based on winery objectives: (1) to increase grape production (combining the yield for 2013 and 2014); and (2) to improve grape composition (combining the pH for 2013 and 2014).SIThis work was supportedby the Universidad de León, Spain [grant number 2016/00145/001-T102]. The authors acknowledge the assistance of the Bodegas y Viñedos Bergidenses, SAT. supportElsevierIngenieria AgroforestalEscuela de Ingeniería Agraria y Forestal2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionhttps://www.sciencedirect.com/science/article/pii/S0304423818306228https://hdl.handle.net/10612/17792reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad Rey Juan CarlosInglésinfo:eu-repo/grantAgreement/EC/FP7/12345 o info:eu-repoinfo:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/177922026-06-24T12:43:27Z
dc.title.none.fl_str_mv Delineating vineyard zones by fuzzy K-means algorithm based on grape sampling variables
title Delineating vineyard zones by fuzzy K-means algorithm based on grape sampling variables
spellingShingle Delineating vineyard zones by fuzzy K-means algorithm based on grape sampling variables
González Fernández, Ana Belén
Ingeniería agrícola
Cluster classification
Godello
Grape characteristics
Management zones
Precision viticulture
Vitis vinifera L.
title_short Delineating vineyard zones by fuzzy K-means algorithm based on grape sampling variables
title_full Delineating vineyard zones by fuzzy K-means algorithm based on grape sampling variables
title_fullStr Delineating vineyard zones by fuzzy K-means algorithm based on grape sampling variables
title_full_unstemmed Delineating vineyard zones by fuzzy K-means algorithm based on grape sampling variables
title_sort Delineating vineyard zones by fuzzy K-means algorithm based on grape sampling variables
dc.creator.none.fl_str_mv González Fernández, Ana Belén
Rodríguez Pérez, José Ramón
Sanz Ablanedo, Enoc
Valenciano Montenegro, José Benito
Marcelo Gabella, Victoriano
author González Fernández, Ana Belén
author_facet González Fernández, Ana Belén
Rodríguez Pérez, José Ramón
Sanz Ablanedo, Enoc
Valenciano Montenegro, José Benito
Marcelo Gabella, Victoriano
author_role author
author2 Rodríguez Pérez, José Ramón
Sanz Ablanedo, Enoc
Valenciano Montenegro, José Benito
Marcelo Gabella, Victoriano
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ingenieria Agroforestal
Escuela de Ingeniería Agraria y Forestal
dc.subject.none.fl_str_mv Ingeniería agrícola
Cluster classification
Godello
Grape characteristics
Management zones
Precision viticulture
Vitis vinifera L.
topic Ingeniería agrícola
Cluster classification
Godello
Grape characteristics
Management zones
Precision viticulture
Vitis vinifera L.
description [EN] This study describes a method for delineating management zones using interpolated maps of grape characteristics recorded in 2013 and 2014 in a Godello vineyard located in the Bierzo Denomination of Origin (León, Northwest Spain). Ten variables were analyzed and recorded for the sampled vines (50 vines/ha). Interpolated maps reflecting each variable and year were created by spatial interpolation (kriging) from the sampled points. Principal component analysis was used to detect relationships between variables and to select the variables to be used to create the cluster classification. Using the fuzzy k-means classification algorithm implemented in the Management Zone Analyst (MZA v.1.0.0) software, several zones were delineated by combining the studied variables. The results delineated 2 different management areas composed of 3 zones each based on winery objectives: (1) to increase grape production (combining the yield for 2013 and 2014); and (2) to improve grape composition (combining the pH for 2013 and 2014).
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S0304423818306228
https://hdl.handle.net/10612/17792
url https://www.sciencedirect.com/science/article/pii/S0304423818306228
https://hdl.handle.net/10612/17792
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/EC/FP7/12345 o info:eu-repo
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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