Algoritmo para predição de risco de epidemia de phakopsora pachyrhizi em soja
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/5165 |
Resumo: | The control of asian soybean rust depends on the application of fungicides at the right time. The use of forecasting systems is an important tool in the decision-making process. This work aims to estimate a prediction algorithm that generates risk levels of Phakopsora pachyrhizi infection based on rainfall, minimum temperature, sowing date, growth stage of the crop and local, aimed at applying fungicides products at the correct time. Four experiments were conducted in the experimental area of Phytus Institute, Itaara city, central region of Rio Grande do Sul, in the crop 2014/2015. Each experiment corresponded to a different sowing date and consisted of treatments: control (T1) without fungicide application; application as recommended in the algorithm to be assessed (T2), application of the scheduled program in days after emergence (DAE) (T3), based on growth stage of the crop (T4), application as recommended in the algorithm with seven days delay (T5), application of the scheduled program in days after emergence with seven days delay (T6) and application based on growth stage of the crop with seven days delay (T7). To evaluate the effect of treatments were determined to AUCPD, rate of progress, productivity, weight of a thousand grains, and correlations between the dependent and independent variables related to the first pustule and disease severity. The positioning defined by the use of the algorithm did not provide superiority over AUCPD variable rate of progress, productivity and weight of a thousand grains, compared to other treatments in none of the experiments. Asian rust occurred at different growth stages of soybean and the use of sowing dates may indirectly measure the pressure of inoculum of this pathogen. The seven-day period is not consistent for the calculation of the meteorological variables that precede the disease. Temperature was not relevant to explain the epidemic and its use in the algorithm not justified. Rainfall had decisive influence on the epidemic and the more periods of rain occurred, the higher were the severity levels. |
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Algoritmo para predição de risco de epidemia de phakopsora pachyrhizi em sojaAlgorithm for prediction of epidemic phakopsora pachyrhizi risk in soybeanFerrugem asiáticaSistemas de previsãoAlgoritmoAsian rustForecasting systemsAlgorithmCNPQ::CIENCIAS AGRARIAS::AGRONOMIAThe control of asian soybean rust depends on the application of fungicides at the right time. The use of forecasting systems is an important tool in the decision-making process. This work aims to estimate a prediction algorithm that generates risk levels of Phakopsora pachyrhizi infection based on rainfall, minimum temperature, sowing date, growth stage of the crop and local, aimed at applying fungicides products at the correct time. Four experiments were conducted in the experimental area of Phytus Institute, Itaara city, central region of Rio Grande do Sul, in the crop 2014/2015. Each experiment corresponded to a different sowing date and consisted of treatments: control (T1) without fungicide application; application as recommended in the algorithm to be assessed (T2), application of the scheduled program in days after emergence (DAE) (T3), based on growth stage of the crop (T4), application as recommended in the algorithm with seven days delay (T5), application of the scheduled program in days after emergence with seven days delay (T6) and application based on growth stage of the crop with seven days delay (T7). To evaluate the effect of treatments were determined to AUCPD, rate of progress, productivity, weight of a thousand grains, and correlations between the dependent and independent variables related to the first pustule and disease severity. The positioning defined by the use of the algorithm did not provide superiority over AUCPD variable rate of progress, productivity and weight of a thousand grains, compared to other treatments in none of the experiments. Asian rust occurred at different growth stages of soybean and the use of sowing dates may indirectly measure the pressure of inoculum of this pathogen. The seven-day period is not consistent for the calculation of the meteorological variables that precede the disease. Temperature was not relevant to explain the epidemic and its use in the algorithm not justified. Rainfall had decisive influence on the epidemic and the more periods of rain occurred, the higher were the severity levels.Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorO controle da ferrugem asiática da soja é dependente da aplicação de fungicidas no momento correto. O uso de sistemas de previsão é uma ferramenta importante no processo de tomada de decisão. O objetivo deste trabalho foi aferir um algoritmo de previsão que gera níveis de risco de infecção de Phakopsora pachyrhizi baseado em precipitação, temperatura mínima, época de semeadura, estádio fenológico da cultura e local, visando a aplicação de produtos fungicidas na época correta. Foram conduzidos quatro experimentos na área experimental do Instituto Phytus, município de Itaara, região central do Rio Grande do Sul, na safra 2014/2015. Cada experimento correspondeu a uma época de semeadura diferente e foi constituído dos tratamentos: testemunha (T1) sem aplicação de fungicida; aplicação de acordo com o recomendado no algoritmo a ser aferido (T2), aplicação do programa calendarizado em dias após a emergência (DAE)(T3), programa baseado nos estádios da soja (T4), aplicação com sete dias de atraso do recomendado pelo algoritmo (T5), aplicação calendarizada com sete dias de atraso (T6) e aplicação baseada em estádio fenológico com sete dias de atraso (T7). Para avaliar o efeito dos tratamentos, foram determinados a AACPD, taxa de progresso, produtividade, massa de mil grãos, e correlações entre as variáveis dependentes e independentes relacionadas a primeira pústula e a severidade da doença. O posicionamento definido pelo uso do algoritmo não propiciou superioridade sobre as variáveis AACPD, taxa de progresso, produtividade e peso de mil grãos, em relação aos demais tratamentos em nenhum dos experimentos. A ferrugem asiática ocorreu em diferentes estádios fenológicos da soja e o uso de épocas de semeadura pode medir indiretamente a pressão do inóculo deste patógeno. O período de sete dias não é consistente para cálculo das variáveis meteorológicas que precedem a doença. Temperatura não foi relevante para explicar a epidemia e seu uso no algoritmo não se justificou. A precipitação apresentou influência decisiva na epidemia e quanto mais períodos de chuva ocorreram, maiores foram os níveis de severidade.Universidade Federal de Santa MariaBRAgronomiaUFSMPrograma de Pós-Graduação em AgronomiaBalardin, Ricardo Silveirohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4721447T7Costa, Ivan Francisco Dressler dahttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4782933D6Debortoli, Monica Paulahttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4770905J5Lerner, Maíne Alessandra2017-05-112017-05-112016-02-23info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfLERNER, Maíne Alessandra. Algorithm for prediction of epidemic phakopsora pachyrhizi risk in soybean. 2016. 80 f. Dissertação (Mestrado em Agronomia) - Universidade Federal de Santa Maria, Santa Maria, 2016.http://repositorio.ufsm.br/handle/1/5165porinfo:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2017-07-25T14:13:23Zoai:repositorio.ufsm.br:1/5165Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2017-07-25T14:13:23Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Algoritmo para predição de risco de epidemia de phakopsora pachyrhizi em soja Algorithm for prediction of epidemic phakopsora pachyrhizi risk in soybean |
title |
Algoritmo para predição de risco de epidemia de phakopsora pachyrhizi em soja |
spellingShingle |
Algoritmo para predição de risco de epidemia de phakopsora pachyrhizi em soja Lerner, Maíne Alessandra Ferrugem asiática Sistemas de previsão Algoritmo Asian rust Forecasting systems Algorithm CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Algoritmo para predição de risco de epidemia de phakopsora pachyrhizi em soja |
title_full |
Algoritmo para predição de risco de epidemia de phakopsora pachyrhizi em soja |
title_fullStr |
Algoritmo para predição de risco de epidemia de phakopsora pachyrhizi em soja |
title_full_unstemmed |
Algoritmo para predição de risco de epidemia de phakopsora pachyrhizi em soja |
title_sort |
Algoritmo para predição de risco de epidemia de phakopsora pachyrhizi em soja |
author |
Lerner, Maíne Alessandra |
author_facet |
Lerner, Maíne Alessandra |
author_role |
author |
dc.contributor.none.fl_str_mv |
Balardin, Ricardo Silveiro http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4721447T7 Costa, Ivan Francisco Dressler da http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4782933D6 Debortoli, Monica Paula http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4770905J5 |
dc.contributor.author.fl_str_mv |
Lerner, Maíne Alessandra |
dc.subject.por.fl_str_mv |
Ferrugem asiática Sistemas de previsão Algoritmo Asian rust Forecasting systems Algorithm CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
topic |
Ferrugem asiática Sistemas de previsão Algoritmo Asian rust Forecasting systems Algorithm CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
description |
The control of asian soybean rust depends on the application of fungicides at the right time. The use of forecasting systems is an important tool in the decision-making process. This work aims to estimate a prediction algorithm that generates risk levels of Phakopsora pachyrhizi infection based on rainfall, minimum temperature, sowing date, growth stage of the crop and local, aimed at applying fungicides products at the correct time. Four experiments were conducted in the experimental area of Phytus Institute, Itaara city, central region of Rio Grande do Sul, in the crop 2014/2015. Each experiment corresponded to a different sowing date and consisted of treatments: control (T1) without fungicide application; application as recommended in the algorithm to be assessed (T2), application of the scheduled program in days after emergence (DAE) (T3), based on growth stage of the crop (T4), application as recommended in the algorithm with seven days delay (T5), application of the scheduled program in days after emergence with seven days delay (T6) and application based on growth stage of the crop with seven days delay (T7). To evaluate the effect of treatments were determined to AUCPD, rate of progress, productivity, weight of a thousand grains, and correlations between the dependent and independent variables related to the first pustule and disease severity. The positioning defined by the use of the algorithm did not provide superiority over AUCPD variable rate of progress, productivity and weight of a thousand grains, compared to other treatments in none of the experiments. Asian rust occurred at different growth stages of soybean and the use of sowing dates may indirectly measure the pressure of inoculum of this pathogen. The seven-day period is not consistent for the calculation of the meteorological variables that precede the disease. Temperature was not relevant to explain the epidemic and its use in the algorithm not justified. Rainfall had decisive influence on the epidemic and the more periods of rain occurred, the higher were the severity levels. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-02-23 2017-05-11 2017-05-11 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
LERNER, Maíne Alessandra. Algorithm for prediction of epidemic phakopsora pachyrhizi risk in soybean. 2016. 80 f. Dissertação (Mestrado em Agronomia) - Universidade Federal de Santa Maria, Santa Maria, 2016. http://repositorio.ufsm.br/handle/1/5165 |
identifier_str_mv |
LERNER, Maíne Alessandra. Algorithm for prediction of epidemic phakopsora pachyrhizi risk in soybean. 2016. 80 f. Dissertação (Mestrado em Agronomia) - Universidade Federal de Santa Maria, Santa Maria, 2016. |
url |
http://repositorio.ufsm.br/handle/1/5165 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria BR Agronomia UFSM Programa de Pós-Graduação em Agronomia |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria BR Agronomia UFSM Programa de Pós-Graduação em Agronomia |
dc.source.none.fl_str_mv |
reponame:Manancial - Repositório Digital da UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Manancial - Repositório Digital da UFSM |
collection |
Manancial - Repositório Digital da UFSM |
repository.name.fl_str_mv |
Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
_version_ |
1805922169704677376 |