Optimal weed population control using nonlinear programming
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
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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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 |
|
_version_ |
1808129192469987328 |