Optimal weed population control using nonlinear programming

Detalhes bibliográficos
Autor(a) principal: Stiegelmeier, Elenice W.
Data de Publicação: 2017
Outros Autores: Oliveira, Vilma A., Silva, Geraldo N. [UNESP], Karam, Decio
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/s40314-015-0280-x
http://hdl.handle.net/11449/162753
Resumo: A dynamic optimization model for weed infestation control using selective herbicide application in a corn crop system is presented. The seed bank density of the weed population and frequency of dominant or recessive alleles are taken as state variables of the growing cycle. The control variable is taken as the dose-response function. The goal is to reduce herbicide usage, maximize profit in a pre-determined period of time and minimize the environmental impacts caused by excessive use of herbicides. The dynamic optimization model takes into account the decreased herbicide efficacy over time due to weed resistance evolution caused by selective pressure. The dynamic optimization problem involves discrete variables modeled as a nonlinear programming (NLP) problem which was solved by an active set algorithm (ASA) for box-constrained optimization. Numerical simulations for a case study illustrate the management of the Bidens subalternans in a corn crop by selecting a sequence of only one type of herbicide. The results on optimal control discussed here will give support to make decision on the herbicide usage in regions where weed resistance was reported by field observations.
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spelling Optimal weed population control using nonlinear programmingMathematical modelingPopulation dynamicsNonlinear programmingWeed managementA dynamic optimization model for weed infestation control using selective herbicide application in a corn crop system is presented. The seed bank density of the weed population and frequency of dominant or recessive alleles are taken as state variables of the growing cycle. The control variable is taken as the dose-response function. The goal is to reduce herbicide usage, maximize profit in a pre-determined period of time and minimize the environmental impacts caused by excessive use of herbicides. The dynamic optimization model takes into account the decreased herbicide efficacy over time due to weed resistance evolution caused by selective pressure. The dynamic optimization problem involves discrete variables modeled as a nonlinear programming (NLP) problem which was solved by an active set algorithm (ASA) for box-constrained optimization. Numerical simulations for a case study illustrate the management of the Bidens subalternans in a corn crop by selecting a sequence of only one type of herbicide. The results on optimal control discussed here will give support to make decision on the herbicide usage in regions where weed resistance was reported by field observations.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Univ Tecnol Fed Parana, Dept Math, BR-86300000 Cornelio Procopio, PR, BrazilUniv Sao Paulo, Dept Elect & Comp Engn, BR-13566590 Sao Carlos, SP, BrazilUniv Estadual Paulista, Dept Appl Math, BR-15054000 Sao Jose Do Rio Preto, SP, BrazilEmpresa Brasileira Pesquisa Agr, BR-35701970 Sete Lagoas, MG, BrazilUniv Estadual Paulista, Dept Appl Math, BR-15054000 Sao Jose Do Rio Preto, SP, BrazilSpringerUniv Tecnol Fed ParanaUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)Stiegelmeier, Elenice W.Oliveira, Vilma A.Silva, Geraldo N. [UNESP]Karam, Decio2018-11-26T17:29:48Z2018-11-26T17:29:48Z2017-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1043-1065application/pdfhttp://dx.doi.org/10.1007/s40314-015-0280-xComputational & Applied Mathematics. Heidelberg: Springer Heidelberg, v. 36, n. 2, p. 1043-1065, 2017.0101-8205http://hdl.handle.net/11449/16275310.1007/s40314-015-0280-xWOS:000400272300014WOS000400272300014.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputational & Applied Mathematics0,272info:eu-repo/semantics/openAccess2023-12-14T06:25:15Zoai:repositorio.unesp.br:11449/162753Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:21:47.083942Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Optimal weed population control using nonlinear programming
title Optimal weed population control using nonlinear programming
spellingShingle Optimal weed population control using nonlinear programming
Stiegelmeier, Elenice W.
Mathematical modeling
Population dynamics
Nonlinear programming
Weed management
title_short Optimal weed population control using nonlinear programming
title_full Optimal weed population control using nonlinear programming
title_fullStr Optimal weed population control using nonlinear programming
title_full_unstemmed Optimal weed population control using nonlinear programming
title_sort Optimal weed population control using nonlinear programming
author Stiegelmeier, Elenice W.
author_facet Stiegelmeier, Elenice W.
Oliveira, Vilma A.
Silva, Geraldo N. [UNESP]
Karam, Decio
author_role author
author2 Oliveira, Vilma A.
Silva, Geraldo N. [UNESP]
Karam, Decio
author2_role author
author
author
dc.contributor.none.fl_str_mv Univ Tecnol Fed Parana
Universidade de São Paulo (USP)
Universidade Estadual Paulista (Unesp)
Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
dc.contributor.author.fl_str_mv Stiegelmeier, Elenice W.
Oliveira, Vilma A.
Silva, Geraldo N. [UNESP]
Karam, Decio
dc.subject.por.fl_str_mv Mathematical modeling
Population dynamics
Nonlinear programming
Weed management
topic Mathematical modeling
Population dynamics
Nonlinear programming
Weed management
description A dynamic optimization model for weed infestation control using selective herbicide application in a corn crop system is presented. The seed bank density of the weed population and frequency of dominant or recessive alleles are taken as state variables of the growing cycle. The control variable is taken as the dose-response function. The goal is to reduce herbicide usage, maximize profit in a pre-determined period of time and minimize the environmental impacts caused by excessive use of herbicides. The dynamic optimization model takes into account the decreased herbicide efficacy over time due to weed resistance evolution caused by selective pressure. The dynamic optimization problem involves discrete variables modeled as a nonlinear programming (NLP) problem which was solved by an active set algorithm (ASA) for box-constrained optimization. Numerical simulations for a case study illustrate the management of the Bidens subalternans in a corn crop by selecting a sequence of only one type of herbicide. The results on optimal control discussed here will give support to make decision on the herbicide usage in regions where weed resistance was reported by field observations.
publishDate 2017
dc.date.none.fl_str_mv 2017-06-01
2018-11-26T17:29:48Z
2018-11-26T17:29:48Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/s40314-015-0280-x
Computational & Applied Mathematics. Heidelberg: Springer Heidelberg, v. 36, n. 2, p. 1043-1065, 2017.
0101-8205
http://hdl.handle.net/11449/162753
10.1007/s40314-015-0280-x
WOS:000400272300014
WOS000400272300014.pdf
identifier_str_mv
Computational & Applied Mathematics. Heidelberg: Springer Heidelberg, v. 36, n. 2, p. 1043-1065, 2017.
0101-8205
10.1007/s40314-015-0280-x
WOS:000400272300014
WOS000400272300014.pdf
url http://dx.doi.org/10.1007/s40314-015-0280-x
http://hdl.handle.net/11449/162753
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Computational & Applied Mathematics
0,272
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1043-1065
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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