FUZZY MODELING ON WHEAT PRODUCTIVITY UNDER DIFFERENT DOSES OF SLUDGE AND SEWAGE EFFLUENT
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Engenharia Agrícola |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162017000601103 |
Resumo: | ABSTRACT: This study aimed to evaluate the effects of fertilization with composted sewage sludge and irrigation with drinking water (DW) and fertigation with wastewater (WW) in wheat crop using fuzzy rule-based system. The experiment was conducted in the Department of Soil and Environmental Resources, of FCA, UNESP - Botucatu, with factorial 6 × 2, which were applied 6 doses of sewage sludge (0, 50, 100, 150, 200 and 250% of nitrogen recommendation) and two types of effluents (treated water and sewage). In the developing of the system based in fuzzy rules, it was used the Mamdani inference method, where the input variables were sewage sludge doses and water types and the output variables used were the number of tillers, length and number spike per plant; number of spikelet per plant, grain mass per spike and dry mass of the aerial parts. It can be seen that the sewage sludge and the effluent contributed to the higher increase of production, and at 150% sludge dose occurred higher production. |
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Engenharia Agrícola |
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FUZZY MODELING ON WHEAT PRODUCTIVITY UNDER DIFFERENT DOSES OF SLUDGE AND SEWAGE EFFLUENTsustainabilitynitrogenMamdanifuzzy systemABSTRACT: This study aimed to evaluate the effects of fertilization with composted sewage sludge and irrigation with drinking water (DW) and fertigation with wastewater (WW) in wheat crop using fuzzy rule-based system. The experiment was conducted in the Department of Soil and Environmental Resources, of FCA, UNESP - Botucatu, with factorial 6 × 2, which were applied 6 doses of sewage sludge (0, 50, 100, 150, 200 and 250% of nitrogen recommendation) and two types of effluents (treated water and sewage). In the developing of the system based in fuzzy rules, it was used the Mamdani inference method, where the input variables were sewage sludge doses and water types and the output variables used were the number of tillers, length and number spike per plant; number of spikelet per plant, grain mass per spike and dry mass of the aerial parts. It can be seen that the sewage sludge and the effluent contributed to the higher increase of production, and at 150% sludge dose occurred higher production.Associação Brasileira de Engenharia Agrícola2017-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162017000601103Engenharia Agrícola v.37 n.6 2017reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/1809-4430-eng.agric.v37n6p1103-1115/2017info:eu-repo/semantics/openAccessPutti,Fernando F.Kummer,Ana C. B.Grassi Filho,HelioGabriel Filho,Luís R. A.Cremasco,Camila P.eng2017-11-16T00:00:00Zoai:scielo:S0100-69162017000601103Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2017-11-16T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false |
dc.title.none.fl_str_mv |
FUZZY MODELING ON WHEAT PRODUCTIVITY UNDER DIFFERENT DOSES OF SLUDGE AND SEWAGE EFFLUENT |
title |
FUZZY MODELING ON WHEAT PRODUCTIVITY UNDER DIFFERENT DOSES OF SLUDGE AND SEWAGE EFFLUENT |
spellingShingle |
FUZZY MODELING ON WHEAT PRODUCTIVITY UNDER DIFFERENT DOSES OF SLUDGE AND SEWAGE EFFLUENT Putti,Fernando F. sustainability nitrogen Mamdani fuzzy system |
title_short |
FUZZY MODELING ON WHEAT PRODUCTIVITY UNDER DIFFERENT DOSES OF SLUDGE AND SEWAGE EFFLUENT |
title_full |
FUZZY MODELING ON WHEAT PRODUCTIVITY UNDER DIFFERENT DOSES OF SLUDGE AND SEWAGE EFFLUENT |
title_fullStr |
FUZZY MODELING ON WHEAT PRODUCTIVITY UNDER DIFFERENT DOSES OF SLUDGE AND SEWAGE EFFLUENT |
title_full_unstemmed |
FUZZY MODELING ON WHEAT PRODUCTIVITY UNDER DIFFERENT DOSES OF SLUDGE AND SEWAGE EFFLUENT |
title_sort |
FUZZY MODELING ON WHEAT PRODUCTIVITY UNDER DIFFERENT DOSES OF SLUDGE AND SEWAGE EFFLUENT |
author |
Putti,Fernando F. |
author_facet |
Putti,Fernando F. Kummer,Ana C. B. Grassi Filho,Helio Gabriel Filho,Luís R. A. Cremasco,Camila P. |
author_role |
author |
author2 |
Kummer,Ana C. B. Grassi Filho,Helio Gabriel Filho,Luís R. A. Cremasco,Camila P. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Putti,Fernando F. Kummer,Ana C. B. Grassi Filho,Helio Gabriel Filho,Luís R. A. Cremasco,Camila P. |
dc.subject.por.fl_str_mv |
sustainability nitrogen Mamdani fuzzy system |
topic |
sustainability nitrogen Mamdani fuzzy system |
description |
ABSTRACT: This study aimed to evaluate the effects of fertilization with composted sewage sludge and irrigation with drinking water (DW) and fertigation with wastewater (WW) in wheat crop using fuzzy rule-based system. The experiment was conducted in the Department of Soil and Environmental Resources, of FCA, UNESP - Botucatu, with factorial 6 × 2, which were applied 6 doses of sewage sludge (0, 50, 100, 150, 200 and 250% of nitrogen recommendation) and two types of effluents (treated water and sewage). In the developing of the system based in fuzzy rules, it was used the Mamdani inference method, where the input variables were sewage sludge doses and water types and the output variables used were the number of tillers, length and number spike per plant; number of spikelet per plant, grain mass per spike and dry mass of the aerial parts. It can be seen that the sewage sludge and the effluent contributed to the higher increase of production, and at 150% sludge dose occurred higher production. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162017000601103 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162017000601103 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1809-4430-eng.agric.v37n6p1103-1115/2017 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
dc.source.none.fl_str_mv |
Engenharia Agrícola v.37 n.6 2017 reponame:Engenharia Agrícola instname:Associação Brasileira de Engenharia Agrícola (SBEA) instacron:SBEA |
instname_str |
Associação Brasileira de Engenharia Agrícola (SBEA) |
instacron_str |
SBEA |
institution |
SBEA |
reponame_str |
Engenharia Agrícola |
collection |
Engenharia Agrícola |
repository.name.fl_str_mv |
Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA) |
repository.mail.fl_str_mv |
revistasbea@sbea.org.br||sbea@sbea.org.br |
_version_ |
1752126273586462720 |