Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications

Autores
Bagavathiannan, Muthukumar V.; Beckie, Hugh J.; Chantre Balacca, Guillermo Ruben; González Andujar, José L.; Leon, Ramon G.; Neve, Paul; Poggio, Santiago Luis; Schutte, Brian J.; Somerville, Gayle J.; Werle, Rodrigo; Acker, Rene Van
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making.
Fil: Bagavathiannan, Muthukumar V.. Texas A&M University; Estados Unidos
Fil: Beckie, Hugh J.. University of Western Australia; Australia
Fil: Chantre Balacca, Guillermo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
Fil: González Andujar, José L.. Consejo Superior de Investigaciones Científicas; España
Fil: Leon, Ramon G.. North Carolina State University; Estados Unidos
Fil: Neve, Paul. Agriculture & Horticulture Development Board; Reino Unido
Fil: Poggio, Santiago Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina
Fil: Schutte, Brian J.. New Mexico State University.; Estados Unidos
Fil: Somerville, Gayle J.. Sustainable Agriculture Sciences; Reino Unido
Fil: Werle, Rodrigo. University of Wisconsin; Estados Unidos
Fil: Acker, Rene Van. University of Guelph; Canadá
Materia
CROP-WEED COMPETITION
DECISION-SUPPORT TOOLS
GENE FLOW
HERBICIDE RESISTANCE
PREDICTIVE MODELS
WEED POPULATION DYNAMICS
WEED SEEDLING EMERGENCE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/121468
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/121468
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applicationsBagavathiannan, Muthukumar V.Beckie, Hugh J.Chantre Balacca, Guillermo RubenGonzález Andujar, José L.Leon, Ramon G.Neve, PaulPoggio, Santiago LuisSchutte, Brian J.Somerville, Gayle J.Werle, RodrigoAcker, Rene VanCROP-WEED COMPETITIONDECISION-SUPPORT TOOLSGENE FLOWHERBICIDE RESISTANCEPREDICTIVE MODELSWEED POPULATION DYNAMICSWEED SEEDLING EMERGENCEhttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4https://purl.org/becyt/ford/4.5https://purl.org/becyt/ford/4In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making.Fil: Bagavathiannan, Muthukumar V.. Texas A&M University; Estados UnidosFil: Beckie, Hugh J.. University of Western Australia; AustraliaFil: Chantre Balacca, Guillermo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; ArgentinaFil: González Andujar, José L.. Consejo Superior de Investigaciones Científicas; EspañaFil: Leon, Ramon G.. North Carolina State University; Estados UnidosFil: Neve, Paul. Agriculture & Horticulture Development Board; Reino UnidoFil: Poggio, Santiago Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Schutte, Brian J.. New Mexico State University.; Estados UnidosFil: Somerville, Gayle J.. Sustainable Agriculture Sciences; Reino UnidoFil: Werle, Rodrigo. University of Wisconsin; Estados UnidosFil: Acker, Rene Van. University of Guelph; CanadáMDPI AG2020-10info: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/11336/121468Bagavathiannan, Muthukumar V.; Beckie, Hugh J.; Chantre Balacca, Guillermo Ruben; González Andujar, José L.; Leon, Ramon G.; et al.; Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications; MDPI AG; Agronomy; 10; 10; 10-2020; 1-24;16112073-4395CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2073-4395/10/10/1611info:eu-repo/semantics/altIdentifier/doi/10.3390/agronomy10101611info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:33:43Zoai:ri.conicet.gov.ar:11336/121468instacron: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 10:33:43.334CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications
title Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications
spellingShingle Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications
Bagavathiannan, Muthukumar V.
CROP-WEED COMPETITION
DECISION-SUPPORT TOOLS
GENE FLOW
HERBICIDE RESISTANCE
PREDICTIVE MODELS
WEED POPULATION DYNAMICS
WEED SEEDLING EMERGENCE
title_short Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications
title_full Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications
title_fullStr Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications
title_full_unstemmed Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications
title_sort Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications
dc.creator.none.fl_str_mv Bagavathiannan, Muthukumar V.
Beckie, Hugh J.
Chantre Balacca, Guillermo Ruben
González Andujar, José L.
Leon, Ramon G.
Neve, Paul
Poggio, Santiago Luis
Schutte, Brian J.
Somerville, Gayle J.
Werle, Rodrigo
Acker, Rene Van
author Bagavathiannan, Muthukumar V.
author_facet Bagavathiannan, Muthukumar V.
Beckie, Hugh J.
Chantre Balacca, Guillermo Ruben
González Andujar, José L.
Leon, Ramon G.
Neve, Paul
Poggio, Santiago Luis
Schutte, Brian J.
Somerville, Gayle J.
Werle, Rodrigo
Acker, Rene Van
author_role author
author2 Beckie, Hugh J.
Chantre Balacca, Guillermo Ruben
González Andujar, José L.
Leon, Ramon G.
Neve, Paul
Poggio, Santiago Luis
Schutte, Brian J.
Somerville, Gayle J.
Werle, Rodrigo
Acker, Rene Van
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv CROP-WEED COMPETITION
DECISION-SUPPORT TOOLS
GENE FLOW
HERBICIDE RESISTANCE
PREDICTIVE MODELS
WEED POPULATION DYNAMICS
WEED SEEDLING EMERGENCE
topic CROP-WEED COMPETITION
DECISION-SUPPORT TOOLS
GENE FLOW
HERBICIDE RESISTANCE
PREDICTIVE MODELS
WEED POPULATION DYNAMICS
WEED SEEDLING EMERGENCE
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
https://purl.org/becyt/ford/4.5
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making.
Fil: Bagavathiannan, Muthukumar V.. Texas A&M University; Estados Unidos
Fil: Beckie, Hugh J.. University of Western Australia; Australia
Fil: Chantre Balacca, Guillermo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
Fil: González Andujar, José L.. Consejo Superior de Investigaciones Científicas; España
Fil: Leon, Ramon G.. North Carolina State University; Estados Unidos
Fil: Neve, Paul. Agriculture & Horticulture Development Board; Reino Unido
Fil: Poggio, Santiago Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina
Fil: Schutte, Brian J.. New Mexico State University.; Estados Unidos
Fil: Somerville, Gayle J.. Sustainable Agriculture Sciences; Reino Unido
Fil: Werle, Rodrigo. University of Wisconsin; Estados Unidos
Fil: Acker, Rene Van. University of Guelph; Canadá
description In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making.
publishDate 2020
dc.date.none.fl_str_mv 2020-10
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/11336/121468
Bagavathiannan, Muthukumar V.; Beckie, Hugh J.; Chantre Balacca, Guillermo Ruben; González Andujar, José L.; Leon, Ramon G.; et al.; Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications; MDPI AG; Agronomy; 10; 10; 10-2020; 1-24;1611
2073-4395
CONICET Digital
CONICET
url http://hdl.handle.net/11336/121468
identifier_str_mv Bagavathiannan, Muthukumar V.; Beckie, Hugh J.; Chantre Balacca, Guillermo Ruben; González Andujar, José L.; Leon, Ramon G.; et al.; Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications; MDPI AG; Agronomy; 10; 10; 10-2020; 1-24;1611
2073-4395
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2073-4395/10/10/1611
info:eu-repo/semantics/altIdentifier/doi/10.3390/agronomy10101611
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
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 13.069144

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