Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands

Autores
Ferraro, Diego Omar; Ghersa, Claudio Marco
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A fuzzy-logic based model was built in order to assess the relative influence of different ecological and management drivers on glyphosate resistance risk (GRR) in Sorghum halepense (L.) Pers. The model was hierarchically structured in a bottom-up manner by combining 16 input variables throughout a logical network. Input data were related to 1) herbicide usage, 2) crop rotation, 3) landscape characterization, 4) weed dispersal, and 5) mean maximum and minimum seasonal temperature. Mean maximum and minimum seasonal temperatures and the dominance of glyphosate use were the variables that showed the highest sensitivity to input changes. Application of the model at a regional scale resulted in a wide range of GRR values. The lowest range values (lower than 0 and between 0 and 0.25) were represented in 5.5% and 21.5% of the cropping area, respectively. Intermediate GRR range (between 0.25 and 0.5) were assessed in 57.3% of the cropping area whilst the highest GRR range values (0.5e0.7) were represented in only 15.6% of the studied area. The assessment of trade-offs between different ecosystem functions through expert opinion can complement traditional analyses for predicting herbicide resistance risk based on solely the genetic aspect of the evolutionary process
Fil: Ferraro, Diego Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Cerealicultura; Argentina
Fil: Ghersa, Claudio Marco. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Recursos Naturales y Ambiente. Catedra de Ecologia; Argentina
Materia
Fuzzy Logic
Glyhosate
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:11336/4160
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplandsFerraro, Diego OmarGhersa, Claudio MarcoFuzzy LogicGlyhosatehttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4A fuzzy-logic based model was built in order to assess the relative influence of different ecological and management drivers on glyphosate resistance risk (GRR) in Sorghum halepense (L.) Pers. The model was hierarchically structured in a bottom-up manner by combining 16 input variables throughout a logical network. Input data were related to 1) herbicide usage, 2) crop rotation, 3) landscape characterization, 4) weed dispersal, and 5) mean maximum and minimum seasonal temperature. Mean maximum and minimum seasonal temperatures and the dominance of glyphosate use were the variables that showed the highest sensitivity to input changes. Application of the model at a regional scale resulted in a wide range of GRR values. The lowest range values (lower than 0 and between 0 and 0.25) were represented in 5.5% and 21.5% of the cropping area, respectively. Intermediate GRR range (between 0.25 and 0.5) were assessed in 57.3% of the cropping area whilst the highest GRR range values (0.5e0.7) were represented in only 15.6% of the studied area. The assessment of trade-offs between different ecosystem functions through expert opinion can complement traditional analyses for predicting herbicide resistance risk based on solely the genetic aspect of the evolutionary processFil: Ferraro, Diego Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Cerealicultura; ArgentinaFil: Ghersa, Claudio Marco. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Recursos Naturales y Ambiente. Catedra de Ecologia; ArgentinaElsevier2013-09info: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/11336/4160Ferraro, Diego Omar; Ghersa, Claudio Marco; Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands; Elsevier; Crop Protection; 51; 9-2013; 32-390261-2194enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.cropro.2013.04.004info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0261219413000926info:eu-repo/semantics/altIdentifier/issn/0261-2194info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:34:56Zoai:ri.conicet.gov.ar:11336/4160instacron: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:34982025-10-22 11:34:57.045CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands
title Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands
spellingShingle Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands
Ferraro, Diego Omar
Fuzzy Logic
Glyhosate
title_short Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands
title_full Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands
title_fullStr Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands
title_full_unstemmed Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands
title_sort Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands
dc.creator.none.fl_str_mv Ferraro, Diego Omar
Ghersa, Claudio Marco
author Ferraro, Diego Omar
author_facet Ferraro, Diego Omar
Ghersa, Claudio Marco
author_role author
author2 Ghersa, Claudio Marco
author2_role author
dc.subject.none.fl_str_mv Fuzzy Logic
Glyhosate
topic Fuzzy Logic
Glyhosate
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv A fuzzy-logic based model was built in order to assess the relative influence of different ecological and management drivers on glyphosate resistance risk (GRR) in Sorghum halepense (L.) Pers. The model was hierarchically structured in a bottom-up manner by combining 16 input variables throughout a logical network. Input data were related to 1) herbicide usage, 2) crop rotation, 3) landscape characterization, 4) weed dispersal, and 5) mean maximum and minimum seasonal temperature. Mean maximum and minimum seasonal temperatures and the dominance of glyphosate use were the variables that showed the highest sensitivity to input changes. Application of the model at a regional scale resulted in a wide range of GRR values. The lowest range values (lower than 0 and between 0 and 0.25) were represented in 5.5% and 21.5% of the cropping area, respectively. Intermediate GRR range (between 0.25 and 0.5) were assessed in 57.3% of the cropping area whilst the highest GRR range values (0.5e0.7) were represented in only 15.6% of the studied area. The assessment of trade-offs between different ecosystem functions through expert opinion can complement traditional analyses for predicting herbicide resistance risk based on solely the genetic aspect of the evolutionary process
Fil: Ferraro, Diego Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Cerealicultura; Argentina
Fil: Ghersa, Claudio Marco. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Recursos Naturales y Ambiente. Catedra de Ecologia; Argentina
description A fuzzy-logic based model was built in order to assess the relative influence of different ecological and management drivers on glyphosate resistance risk (GRR) in Sorghum halepense (L.) Pers. The model was hierarchically structured in a bottom-up manner by combining 16 input variables throughout a logical network. Input data were related to 1) herbicide usage, 2) crop rotation, 3) landscape characterization, 4) weed dispersal, and 5) mean maximum and minimum seasonal temperature. Mean maximum and minimum seasonal temperatures and the dominance of glyphosate use were the variables that showed the highest sensitivity to input changes. Application of the model at a regional scale resulted in a wide range of GRR values. The lowest range values (lower than 0 and between 0 and 0.25) were represented in 5.5% and 21.5% of the cropping area, respectively. Intermediate GRR range (between 0.25 and 0.5) were assessed in 57.3% of the cropping area whilst the highest GRR range values (0.5e0.7) were represented in only 15.6% of the studied area. The assessment of trade-offs between different ecosystem functions through expert opinion can complement traditional analyses for predicting herbicide resistance risk based on solely the genetic aspect of the evolutionary process
publishDate 2013
dc.date.none.fl_str_mv 2013-09
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/11336/4160
Ferraro, Diego Omar; Ghersa, Claudio Marco; Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands; Elsevier; Crop Protection; 51; 9-2013; 32-39
0261-2194
url http://hdl.handle.net/11336/4160
identifier_str_mv Ferraro, Diego Omar; Ghersa, Claudio Marco; Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands; Elsevier; Crop Protection; 51; 9-2013; 32-39
0261-2194
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cropro.2013.04.004
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0261219413000926
info:eu-repo/semantics/altIdentifier/issn/0261-2194
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
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