Use of linear programming models in experimentation with plant nutrients
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/14708 |
Resumo: | Nutrition is an important issue of plant cultivation and experimentation with plant nutrients is a supporting tool for agriculture. However, use of high purity grade reagents as nutrient sources can be expensive and increases the cost of an experiment. The objective of this study was to minimize the acquisition cost of high purity grade reagents in experiments on plant nutrient deficiency by using the missing element technique through linear programming models, and to generate recommendation tables for preparation of culture solutions, as well as to quantify gains through a simulated experiment. Two linear programming models were formulated containing concentration constraints for each nutrient in the culture solution. Model A was based on 16 reagents for preparation of the culture solution, while model B was based on 27 reagents, looking to increase choice options. Results showed that both models minimized the acquisition cost of reagents, allowing a 9.03% reduction in model A and a 25.98% reduction in model B. The missing sulfur treatment proved the most costly for reagent acquisition while the missing nitrogen treatment proved the least costly. It was concluded that the formulated models were capable of reducing acquisition costs of reagents, yet the recommendations generated by them should be tested and checked for practical viability. |
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Use of linear programming models in experimentation with plant nutrientsUso de modelos de programação linear aplicado à experimentação nutricional de plantasOperating researchOptimizationMissing elementPesquisa operacionalOtimizaçãoElemento faltanteNutrition is an important issue of plant cultivation and experimentation with plant nutrients is a supporting tool for agriculture. However, use of high purity grade reagents as nutrient sources can be expensive and increases the cost of an experiment. The objective of this study was to minimize the acquisition cost of high purity grade reagents in experiments on plant nutrient deficiency by using the missing element technique through linear programming models, and to generate recommendation tables for preparation of culture solutions, as well as to quantify gains through a simulated experiment. Two linear programming models were formulated containing concentration constraints for each nutrient in the culture solution. Model A was based on 16 reagents for preparation of the culture solution, while model B was based on 27 reagents, looking to increase choice options. Results showed that both models minimized the acquisition cost of reagents, allowing a 9.03% reduction in model A and a 25.98% reduction in model B. The missing sulfur treatment proved the most costly for reagent acquisition while the missing nitrogen treatment proved the least costly. It was concluded that the formulated models were capable of reducing acquisition costs of reagents, yet the recommendations generated by them should be tested and checked for practical viability. A nutrição vegetal é um importante aspecto no cultivo de espécies, sendo a experimentação nutricional uma ferramenta de suporte à agricultura. Entretanto, o uso de reagentes p.a. como fontes de nutrientes é caro e aumenta os custos do experimento. Assim, objetivou-se, com este trabalho, minimizar o custo de aquisição de reagentes p.a. em experimentos de deficiência nutricional de plantas, considerando a técnica do elemento faltante, por meio de modelos de programação linear, gerar tabelas de recomendação para a elaboração de soluções de cultivo e quantificar os ganhos por um experimento simulado. Dois modelos de programação linear foram formulados, contendo restrições de concentração para cada nutriente na solução de cultivo. O modelo A baseou-se na utilização de 16 reagentes na elaboração da solução de cultivo, já, o modelo B empregou 27, com o intuito de aumentar as opções de escolha. Os resultados mostraram que os dois modelos minimizaram o custo na aquisição de reagentes, com redução de 9,03% (modelo A) e 25,98% (modelo B). O tratamento de omissão de Enxofre foi o mais oneroso para a aquisição de reagentes e o de omissão de Nitrogênio o menos oneroso. Conclui-se que os modelos formulados foram capazes de reduzir os custos na aquisição dos reagentes; porém deve-se testar as recomendações geradas pelos mesmos e verificar sua viabilidade prática.Universidade Federal de Lavras (UFLA)2016-04-052017-08-01T20:15:57Z2017-08-01T20:15:57Z2017-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfGARCIA, M. B.; GOMIDE, L. R. Use of linear programming models in experimentation with plant nutrients. CERNE, Lavras, v. 19, n. 2, p. 255-261, abr./jun. 2013.http://repositorio.ufla.br/jspui/handle/1/147082317-63420104-7760reponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAengCopyright (c) 2016 CERNEhttp://creativecommons.org/licenses/by/4.0/Attribution 4.0 Internationalinfo:eu-repo/semantics/openAccessGarcia, Mauro BrinoGomide, Lucas Rezende2021-03-21T22:43:58Zoai:localhost:1/14708Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2021-03-21T22:43:58Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Use of linear programming models in experimentation with plant nutrients Uso de modelos de programação linear aplicado à experimentação nutricional de plantas |
title |
Use of linear programming models in experimentation with plant nutrients |
spellingShingle |
Use of linear programming models in experimentation with plant nutrients Garcia, Mauro Brino Operating research Optimization Missing element Pesquisa operacional Otimização Elemento faltante |
title_short |
Use of linear programming models in experimentation with plant nutrients |
title_full |
Use of linear programming models in experimentation with plant nutrients |
title_fullStr |
Use of linear programming models in experimentation with plant nutrients |
title_full_unstemmed |
Use of linear programming models in experimentation with plant nutrients |
title_sort |
Use of linear programming models in experimentation with plant nutrients |
author |
Garcia, Mauro Brino |
author_facet |
Garcia, Mauro Brino Gomide, Lucas Rezende |
author_role |
author |
author2 |
Gomide, Lucas Rezende |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Garcia, Mauro Brino Gomide, Lucas Rezende |
dc.subject.por.fl_str_mv |
Operating research Optimization Missing element Pesquisa operacional Otimização Elemento faltante |
topic |
Operating research Optimization Missing element Pesquisa operacional Otimização Elemento faltante |
description |
Nutrition is an important issue of plant cultivation and experimentation with plant nutrients is a supporting tool for agriculture. However, use of high purity grade reagents as nutrient sources can be expensive and increases the cost of an experiment. The objective of this study was to minimize the acquisition cost of high purity grade reagents in experiments on plant nutrient deficiency by using the missing element technique through linear programming models, and to generate recommendation tables for preparation of culture solutions, as well as to quantify gains through a simulated experiment. Two linear programming models were formulated containing concentration constraints for each nutrient in the culture solution. Model A was based on 16 reagents for preparation of the culture solution, while model B was based on 27 reagents, looking to increase choice options. Results showed that both models minimized the acquisition cost of reagents, allowing a 9.03% reduction in model A and a 25.98% reduction in model B. The missing sulfur treatment proved the most costly for reagent acquisition while the missing nitrogen treatment proved the least costly. It was concluded that the formulated models were capable of reducing acquisition costs of reagents, yet the recommendations generated by them should be tested and checked for practical viability. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-04-05 2017-08-01T20:15:57Z 2017-08-01T20:15:57Z 2017-08-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
GARCIA, M. B.; GOMIDE, L. R. Use of linear programming models in experimentation with plant nutrients. CERNE, Lavras, v. 19, n. 2, p. 255-261, abr./jun. 2013. http://repositorio.ufla.br/jspui/handle/1/14708 |
identifier_str_mv |
GARCIA, M. B.; GOMIDE, L. R. Use of linear programming models in experimentation with plant nutrients. CERNE, Lavras, v. 19, n. 2, p. 255-261, abr./jun. 2013. |
url |
http://repositorio.ufla.br/jspui/handle/1/14708 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 CERNE http://creativecommons.org/licenses/by/4.0/ Attribution 4.0 International info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 CERNE http://creativecommons.org/licenses/by/4.0/ Attribution 4.0 International |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Lavras (UFLA) |
publisher.none.fl_str_mv |
Universidade Federal de Lavras (UFLA) |
dc.source.none.fl_str_mv |
2317-6342 0104-7760 reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835169137098752 |