Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Control
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/agronomy12092119 http://hdl.handle.net/11449/249180 |
Resumo: | The application of remote sensing techniques and prediction models for soybean rust (SBR) monitoring may result in different fungicide application timings, control efficacy, and spraying performance. This study aimed to evaluate the applicability of a prediction model as a threshold for disease control decision-making and to identify the effect of different application timings on SBR control as well as on the spraying technology. There were two experimental trials that were conducted in a 2 × 4 factorial scheme: 2 cultivars (susceptible and partially resistant to SBR); and four application timings (conventional chemical control at a calendarized system basis; based on the prediction model; at the appearance of the first visible symptoms; and control without fungicide application). Spray deposit and coverage at each application timing were evaluated in the lower and upper region of the soybean canopy through quantitative analysis of a tracer and water-sensitive papers. The prediction model was calculated based on leaf reflectance data that were collected by remote sensing. Application timings impacted the application technology as well as control efficacy. Calendarized system applications were conducted earlier, promoting different spray performances. Spraying at moments when the leaf area index was higher obtained poorer distribution. None of the treatments were capable of achieving high spray penetration into the canopy. The partially resistant cultivar was effective in holding disease progress during the crop season, whereas all treatments with chemical control resulted in less disease impact. The use of the prediction model was effective and promising to be integrated into disease management programs. |
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Repositório Institucional da UNESP |
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Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Controlintegrated disease managementPhakopsora pachyrhiziremote sensingspraying technologyThe application of remote sensing techniques and prediction models for soybean rust (SBR) monitoring may result in different fungicide application timings, control efficacy, and spraying performance. This study aimed to evaluate the applicability of a prediction model as a threshold for disease control decision-making and to identify the effect of different application timings on SBR control as well as on the spraying technology. There were two experimental trials that were conducted in a 2 × 4 factorial scheme: 2 cultivars (susceptible and partially resistant to SBR); and four application timings (conventional chemical control at a calendarized system basis; based on the prediction model; at the appearance of the first visible symptoms; and control without fungicide application). Spray deposit and coverage at each application timing were evaluated in the lower and upper region of the soybean canopy through quantitative analysis of a tracer and water-sensitive papers. The prediction model was calculated based on leaf reflectance data that were collected by remote sensing. Application timings impacted the application technology as well as control efficacy. Calendarized system applications were conducted earlier, promoting different spray performances. Spraying at moments when the leaf area index was higher obtained poorer distribution. None of the treatments were capable of achieving high spray penetration into the canopy. The partially resistant cultivar was effective in holding disease progress during the crop season, whereas all treatments with chemical control resulted in less disease impact. The use of the prediction model was effective and promising to be integrated into disease management programs.Department of Plant Protection School of Agriculture Sao Paulo State University, 3780 Avenida Universitária, SPDepartment of Crop Science School of Agriculture Sao Paulo State University, 3780 Avenida Universitária, SPDepartment of Plant Protection School of Agriculture Sao Paulo State University, 3780 Avenida Universitária, SPDepartment of Crop Science School of Agriculture Sao Paulo State University, 3780 Avenida Universitária, SPUniversidade Estadual Paulista (UNESP)Negrisoli, Matheus Mereb [UNESP]Silva, Flávio Nunes da [UNESP]Negrisoli, Raphael Mereb [UNESP]Lopes, Lucas da Silva [UNESP]Souza Júnior, Francisco de Sales [UNESP]Freitas, Bianca Rezende de [UNESP]Velini, Edivaldo Domingues [UNESP]Raetano, Carlos Gilberto [UNESP]2023-07-29T14:12:28Z2023-07-29T14:12:28Z2022-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/agronomy12092119Agronomy, v. 12, n. 9, 2022.2073-4395http://hdl.handle.net/11449/24918010.3390/agronomy120921192-s2.0-85138560323Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAgronomyinfo:eu-repo/semantics/openAccess2024-04-30T15:59:41Zoai:repositorio.unesp.br:11449/249180Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:09:32.777102Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Control |
title |
Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Control |
spellingShingle |
Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Control Negrisoli, Matheus Mereb [UNESP] integrated disease management Phakopsora pachyrhizi remote sensing spraying technology |
title_short |
Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Control |
title_full |
Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Control |
title_fullStr |
Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Control |
title_full_unstemmed |
Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Control |
title_sort |
Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Control |
author |
Negrisoli, Matheus Mereb [UNESP] |
author_facet |
Negrisoli, Matheus Mereb [UNESP] Silva, Flávio Nunes da [UNESP] Negrisoli, Raphael Mereb [UNESP] Lopes, Lucas da Silva [UNESP] Souza Júnior, Francisco de Sales [UNESP] Freitas, Bianca Rezende de [UNESP] Velini, Edivaldo Domingues [UNESP] Raetano, Carlos Gilberto [UNESP] |
author_role |
author |
author2 |
Silva, Flávio Nunes da [UNESP] Negrisoli, Raphael Mereb [UNESP] Lopes, Lucas da Silva [UNESP] Souza Júnior, Francisco de Sales [UNESP] Freitas, Bianca Rezende de [UNESP] Velini, Edivaldo Domingues [UNESP] Raetano, Carlos Gilberto [UNESP] |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Negrisoli, Matheus Mereb [UNESP] Silva, Flávio Nunes da [UNESP] Negrisoli, Raphael Mereb [UNESP] Lopes, Lucas da Silva [UNESP] Souza Júnior, Francisco de Sales [UNESP] Freitas, Bianca Rezende de [UNESP] Velini, Edivaldo Domingues [UNESP] Raetano, Carlos Gilberto [UNESP] |
dc.subject.por.fl_str_mv |
integrated disease management Phakopsora pachyrhizi remote sensing spraying technology |
topic |
integrated disease management Phakopsora pachyrhizi remote sensing spraying technology |
description |
The application of remote sensing techniques and prediction models for soybean rust (SBR) monitoring may result in different fungicide application timings, control efficacy, and spraying performance. This study aimed to evaluate the applicability of a prediction model as a threshold for disease control decision-making and to identify the effect of different application timings on SBR control as well as on the spraying technology. There were two experimental trials that were conducted in a 2 × 4 factorial scheme: 2 cultivars (susceptible and partially resistant to SBR); and four application timings (conventional chemical control at a calendarized system basis; based on the prediction model; at the appearance of the first visible symptoms; and control without fungicide application). Spray deposit and coverage at each application timing were evaluated in the lower and upper region of the soybean canopy through quantitative analysis of a tracer and water-sensitive papers. The prediction model was calculated based on leaf reflectance data that were collected by remote sensing. Application timings impacted the application technology as well as control efficacy. Calendarized system applications were conducted earlier, promoting different spray performances. Spraying at moments when the leaf area index was higher obtained poorer distribution. None of the treatments were capable of achieving high spray penetration into the canopy. The partially resistant cultivar was effective in holding disease progress during the crop season, whereas all treatments with chemical control resulted in less disease impact. The use of the prediction model was effective and promising to be integrated into disease management programs. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-09-01 2023-07-29T14:12:28Z 2023-07-29T14:12:28Z |
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/10.3390/agronomy12092119 Agronomy, v. 12, n. 9, 2022. 2073-4395 http://hdl.handle.net/11449/249180 10.3390/agronomy12092119 2-s2.0-85138560323 |
url |
http://dx.doi.org/10.3390/agronomy12092119 http://hdl.handle.net/11449/249180 |
identifier_str_mv |
Agronomy, v. 12, n. 9, 2022. 2073-4395 10.3390/agronomy12092119 2-s2.0-85138560323 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Agronomy |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus 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_ |
1808129495719215104 |