NEURO-FUZZY MODELING AS SUPPORT FOR DECISION-MAKING IN THE PRODUCTION OF IRRIGATED CORIANDER UNDER MULCH IN THE SEMI-ARID REGION

Detalhes bibliográficos
Autor(a) principal: Filho, Luis R. A. Gabriel [UNESP]
Data de Publicação: 2023
Outros Autores: Rodrigueiro, Golbery R. O. [UNESP], Silva, Alexsandro O. da, Almeida, Antonio V. R. de, Cremasco, Camila P. [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v43n2e20220208/2023
http://hdl.handle.net/11449/245648
Resumo: Reducing water consumption by crops in semi-arid regions is an important factor for the sustainability of agriculture in these locations. In this sense, this study aims to evaluate the neuro-fuzzy inference method as a support for decision-making in irrigated coriander cultivation. The experiment was performed in two cultivation cycles in Pentecoste-CE, Brazil. The experiment was conducted in randomized blocks arranged in a split-plot design with five primary treatments, consisting of irrigation depths (50, 75, 100, 125, and 150% of the localized evapotranspiration, ETcloc), and five secondary treatments, consisting of different levels of bagana mulch (0, 25, 50, 75, and 100%, equivalent to 16 t ha-1). Neuro-fuzzy models with two input variables and eight output biometric variables were developed to evaluate growth (plant height, number of roots, and root length) and yield variables (productivity and shoot and root fresh and dry mass). In the first cycle, the best results occurred close to 55% ETcloc and between 40 and 50% of mulch; in the second cycle, water consumption returned results between 50 and 80% ETcloc. The fuzzy and multiple regression models showed MAE, MSE, and RMSE errors of 9, 22, and 10% lower, respectively. The neuro-fuzzy model might be a viable option for decision-making in irrigated crops, being able to optimize the use of natural resources and available water in semi-arid regions. The use of 55% of irrigation depth and a range of 40 to 50% of mulch can be a strategy for a higher water use efficiency.
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spelling NEURO-FUZZY MODELING AS SUPPORT FOR DECISION-MAKING IN THE PRODUCTION OF IRRIGATED CORIANDER UNDER MULCH IN THE SEMI-ARID REGIONMathematical modelingirrigation managementvegetation coverReducing water consumption by crops in semi-arid regions is an important factor for the sustainability of agriculture in these locations. In this sense, this study aims to evaluate the neuro-fuzzy inference method as a support for decision-making in irrigated coriander cultivation. The experiment was performed in two cultivation cycles in Pentecoste-CE, Brazil. The experiment was conducted in randomized blocks arranged in a split-plot design with five primary treatments, consisting of irrigation depths (50, 75, 100, 125, and 150% of the localized evapotranspiration, ETcloc), and five secondary treatments, consisting of different levels of bagana mulch (0, 25, 50, 75, and 100%, equivalent to 16 t ha-1). Neuro-fuzzy models with two input variables and eight output biometric variables were developed to evaluate growth (plant height, number of roots, and root length) and yield variables (productivity and shoot and root fresh and dry mass). In the first cycle, the best results occurred close to 55% ETcloc and between 40 and 50% of mulch; in the second cycle, water consumption returned results between 50 and 80% ETcloc. The fuzzy and multiple regression models showed MAE, MSE, and RMSE errors of 9, 22, and 10% lower, respectively. The neuro-fuzzy model might be a viable option for decision-making in irrigated crops, being able to optimize the use of natural resources and available water in semi-arid regions. The use of 55% of irrigation depth and a range of 40 to 50% of mulch can be a strategy for a higher water use efficiency.Conselho Nacional de Desenvolvimento Cient�fico e Tecnol�gico (CNPq)Sao Paulo State Univ UNESP, Sch Sci & Engn, Tupa, SP, BrazilSao Paulo State Univ UNESP, Fac Agron Sci, Botucatu, SP, BrazilUniv Fed Ceara, Fortaleza, CE, BrazilSao Paulo State Univ UNESP, Sch Sci & Engn, Tupa, SP, BrazilSao Paulo State Univ UNESP, Fac Agron Sci, Botucatu, SP, BrazilCNPq: 315228/2020-2CNPq: 305167/2020-0Soc Brasil Engenharia AgricolaUniversidade Estadual Paulista (UNESP)Univ Fed CearaFilho, Luis R. A. Gabriel [UNESP]Rodrigueiro, Golbery R. O. [UNESP]Silva, Alexsandro O. daAlmeida, Antonio V. R. deCremasco, Camila P. [UNESP]2023-07-29T12:00:59Z2023-07-29T12:00:59Z2023-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v43n2e20220208/2023Engenharia Agricola. Jaboticabal: Soc Brasil Engenharia Agricola, v. 43, n. 2, 14 p., 2023.0100-6916http://hdl.handle.net/11449/24564810.1590/1809-4430-Eng.Agric.v43n2e20220208/2023WOS:000995465900001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEngenharia Agricolainfo:eu-repo/semantics/openAccess2023-07-29T12:00:59Zoai:repositorio.unesp.br:11449/245648Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-07-29T12:00:59Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv NEURO-FUZZY MODELING AS SUPPORT FOR DECISION-MAKING IN THE PRODUCTION OF IRRIGATED CORIANDER UNDER MULCH IN THE SEMI-ARID REGION
title NEURO-FUZZY MODELING AS SUPPORT FOR DECISION-MAKING IN THE PRODUCTION OF IRRIGATED CORIANDER UNDER MULCH IN THE SEMI-ARID REGION
spellingShingle NEURO-FUZZY MODELING AS SUPPORT FOR DECISION-MAKING IN THE PRODUCTION OF IRRIGATED CORIANDER UNDER MULCH IN THE SEMI-ARID REGION
Filho, Luis R. A. Gabriel [UNESP]
Mathematical modeling
irrigation management
vegetation cover
title_short NEURO-FUZZY MODELING AS SUPPORT FOR DECISION-MAKING IN THE PRODUCTION OF IRRIGATED CORIANDER UNDER MULCH IN THE SEMI-ARID REGION
title_full NEURO-FUZZY MODELING AS SUPPORT FOR DECISION-MAKING IN THE PRODUCTION OF IRRIGATED CORIANDER UNDER MULCH IN THE SEMI-ARID REGION
title_fullStr NEURO-FUZZY MODELING AS SUPPORT FOR DECISION-MAKING IN THE PRODUCTION OF IRRIGATED CORIANDER UNDER MULCH IN THE SEMI-ARID REGION
title_full_unstemmed NEURO-FUZZY MODELING AS SUPPORT FOR DECISION-MAKING IN THE PRODUCTION OF IRRIGATED CORIANDER UNDER MULCH IN THE SEMI-ARID REGION
title_sort NEURO-FUZZY MODELING AS SUPPORT FOR DECISION-MAKING IN THE PRODUCTION OF IRRIGATED CORIANDER UNDER MULCH IN THE SEMI-ARID REGION
author Filho, Luis R. A. Gabriel [UNESP]
author_facet Filho, Luis R. A. Gabriel [UNESP]
Rodrigueiro, Golbery R. O. [UNESP]
Silva, Alexsandro O. da
Almeida, Antonio V. R. de
Cremasco, Camila P. [UNESP]
author_role author
author2 Rodrigueiro, Golbery R. O. [UNESP]
Silva, Alexsandro O. da
Almeida, Antonio V. R. de
Cremasco, Camila P. [UNESP]
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Univ Fed Ceara
dc.contributor.author.fl_str_mv Filho, Luis R. A. Gabriel [UNESP]
Rodrigueiro, Golbery R. O. [UNESP]
Silva, Alexsandro O. da
Almeida, Antonio V. R. de
Cremasco, Camila P. [UNESP]
dc.subject.por.fl_str_mv Mathematical modeling
irrigation management
vegetation cover
topic Mathematical modeling
irrigation management
vegetation cover
description Reducing water consumption by crops in semi-arid regions is an important factor for the sustainability of agriculture in these locations. In this sense, this study aims to evaluate the neuro-fuzzy inference method as a support for decision-making in irrigated coriander cultivation. The experiment was performed in two cultivation cycles in Pentecoste-CE, Brazil. The experiment was conducted in randomized blocks arranged in a split-plot design with five primary treatments, consisting of irrigation depths (50, 75, 100, 125, and 150% of the localized evapotranspiration, ETcloc), and five secondary treatments, consisting of different levels of bagana mulch (0, 25, 50, 75, and 100%, equivalent to 16 t ha-1). Neuro-fuzzy models with two input variables and eight output biometric variables were developed to evaluate growth (plant height, number of roots, and root length) and yield variables (productivity and shoot and root fresh and dry mass). In the first cycle, the best results occurred close to 55% ETcloc and between 40 and 50% of mulch; in the second cycle, water consumption returned results between 50 and 80% ETcloc. The fuzzy and multiple regression models showed MAE, MSE, and RMSE errors of 9, 22, and 10% lower, respectively. The neuro-fuzzy model might be a viable option for decision-making in irrigated crops, being able to optimize the use of natural resources and available water in semi-arid regions. The use of 55% of irrigation depth and a range of 40 to 50% of mulch can be a strategy for a higher water use efficiency.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-29T12:00:59Z
2023-07-29T12:00:59Z
2023-01-01
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.uri.fl_str_mv http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v43n2e20220208/2023
Engenharia Agricola. Jaboticabal: Soc Brasil Engenharia Agricola, v. 43, n. 2, 14 p., 2023.
0100-6916
http://hdl.handle.net/11449/245648
10.1590/1809-4430-Eng.Agric.v43n2e20220208/2023
WOS:000995465900001
url http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v43n2e20220208/2023
http://hdl.handle.net/11449/245648
identifier_str_mv Engenharia Agricola. Jaboticabal: Soc Brasil Engenharia Agricola, v. 43, n. 2, 14 p., 2023.
0100-6916
10.1590/1809-4430-Eng.Agric.v43n2e20220208/2023
WOS:000995465900001
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Engenharia Agricola
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 14
dc.publisher.none.fl_str_mv Soc Brasil Engenharia Agricola
publisher.none.fl_str_mv Soc Brasil Engenharia Agricola
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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