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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
01/12/2015 |
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Data da última atualização: |
03/07/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
STIEGELMEIER, E. W.; OLIVEIRA, V. A.; SILVA, G. N.; KARAM, D. |
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Afiliação: |
DECIO KARAM, CNPMS. |
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Título: |
Optimal weed population control using nonlinear programming. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Computational and Applied Mathematics, Petrópolis, v. 36, p. 1043-1065, 2017. |
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DOI: |
10.1007/s40314-015-0280-x |
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Idioma: |
Inglês |
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Notas: |
Publicado online em 11 out. 2015. |
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Conteúdo: |
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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Palavras-Chave: |
Planta daninha. |
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Thesagro: |
Erva daninha; Herbicida; Milho. |
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Categoria do assunto: |
-- |
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Marc: |
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001 2030136
005 2017-07-03
008 2017 bl uuuu u00u1 u #d
024 7 $a10.1007/s40314-015-0280-x$2DOI
100 1 $aSTIEGELMEIER, E. W.
245 $aOptimal weed population control using nonlinear programming.$h[electronic resource]
260 $c2017
500 $aPublicado online em 11 out. 2015.
520 $aA 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.
650 $aErva daninha
650 $aHerbicida
650 $aMilho
653 $aPlanta daninha
700 1 $aOLIVEIRA, V. A.
700 1 $aSILVA, G. N.
700 1 $aKARAM, D.
773 $tComputational and Applied Mathematics, Petrópolis$gv. 36, p. 1043-1065, 2017.
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Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
