Variáveis meteorológicas relacionadas ao início da epidemia causada por Phakopsora pachyrhizi em soja
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/15387 |
Resumo: | Asian soybean rust is caused by the pathogen Phakopsora pachyrhizi that develops and spreads rapidly under ambient conditions of humidity and temperature. The literature lacks information about the relationship between these factors and the appearance of the first symptom of the disease due mainly to the difficulty of performing this type of field experiment. The objective of this work was to identify which meteorological variables are determinant for the appearance of the first visible symptom of the disease using periods of days prior to Pearson (r) correlation, track analysis and principal component analysis field. The experiment was carried out in the agricultural years of 2015/16 and 2016/17, at the experimental station of the Phytus Institute, located in the municipality of Itaara, RS. The experiment was conducted in randomized blocks, with four replicates, in a factorial 5x2. Factor A consisting of five sowing seasons and factor C by two soybean cultivars. The meteorological observations concerning precipitation and temperature were obtained daily with the help of the automatic surface meteorological station. The cumulative precipitation in mm (PrecAcum), number of days without precipitation (NdSP), number of days with less than one mm (NdM1mm), number of days with precipitation between 1 and 25mm (Nd1-25mm), number of days with precipitation between 26 and 50mm (Nd26-50mm), number of days with precipitation between 51 and 75mm (Nd51-75mm) and referring to temperature were evaluated: mean temperature (NdTemp <15ºC), number of days with minimum temperatures above 15ºC (NdTemp>15ºC), number of days with minimum temperatures between 15-25ºC (NdT15-25ºC), number of days with minimum temperatures below 25ºC (NdTemp<25ºC), and, finally, the average relative humidity (URMédia). The main variable used was the date of the appearance of the first symptom, expressed in days after the emergency (AED). The periods used in the calculation of the meteorological variables were 5, 7, 8, 10, 11, 14 and 20 days prior to the appearance of the first symptom of the disease. Statistical analyzes were performed using Genes, Microsoft Office Excel and Minitab software. It was verified that the cultivars did not present expressive differences as the variables analyzed. The meteorological observations that included periods longer than 10 days were the ones that best characterized the beginning of the epidemic. The precipitation favored the anticipation of the symptoms directly and indirectly through Ndm1mm, Nd26-50mm and the average temperature was determinant in the 2016/17 crop, acting directly and indirectly on the main variable. The meteorological variables of greatest contribution to the appearance of the first symptom of P. pachyrhizi in the soybean crop, identified by the main components technique, were the number of days without precipitation, number of days with less than one mm, number of days with precipitation between 1 and 25mm and 26 and 50mm and number of days with a temperature between 15 and 25ºC. |
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Variáveis meteorológicas relacionadas ao início da epidemia causada por Phakopsora pachyrhizi em sojaAnalysis of meteorological observations related to the appearance of the first symptom caused by Phakopsora pachyrhizi in soybeanFerrugem asiáticaTemperaturaPrecipitaçãoAnálise de trilhaAnálise de componentes principaisAsian rustTemperaturePrecipitationPath analysisPrincipal component analysisCNPQ::CIENCIAS AGRARIAS::AGRONOMIAAsian soybean rust is caused by the pathogen Phakopsora pachyrhizi that develops and spreads rapidly under ambient conditions of humidity and temperature. The literature lacks information about the relationship between these factors and the appearance of the first symptom of the disease due mainly to the difficulty of performing this type of field experiment. The objective of this work was to identify which meteorological variables are determinant for the appearance of the first visible symptom of the disease using periods of days prior to Pearson (r) correlation, track analysis and principal component analysis field. The experiment was carried out in the agricultural years of 2015/16 and 2016/17, at the experimental station of the Phytus Institute, located in the municipality of Itaara, RS. The experiment was conducted in randomized blocks, with four replicates, in a factorial 5x2. Factor A consisting of five sowing seasons and factor C by two soybean cultivars. The meteorological observations concerning precipitation and temperature were obtained daily with the help of the automatic surface meteorological station. The cumulative precipitation in mm (PrecAcum), number of days without precipitation (NdSP), number of days with less than one mm (NdM1mm), number of days with precipitation between 1 and 25mm (Nd1-25mm), number of days with precipitation between 26 and 50mm (Nd26-50mm), number of days with precipitation between 51 and 75mm (Nd51-75mm) and referring to temperature were evaluated: mean temperature (NdTemp <15ºC), number of days with minimum temperatures above 15ºC (NdTemp>15ºC), number of days with minimum temperatures between 15-25ºC (NdT15-25ºC), number of days with minimum temperatures below 25ºC (NdTemp<25ºC), and, finally, the average relative humidity (URMédia). The main variable used was the date of the appearance of the first symptom, expressed in days after the emergency (AED). The periods used in the calculation of the meteorological variables were 5, 7, 8, 10, 11, 14 and 20 days prior to the appearance of the first symptom of the disease. Statistical analyzes were performed using Genes, Microsoft Office Excel and Minitab software. It was verified that the cultivars did not present expressive differences as the variables analyzed. The meteorological observations that included periods longer than 10 days were the ones that best characterized the beginning of the epidemic. The precipitation favored the anticipation of the symptoms directly and indirectly through Ndm1mm, Nd26-50mm and the average temperature was determinant in the 2016/17 crop, acting directly and indirectly on the main variable. The meteorological variables of greatest contribution to the appearance of the first symptom of P. pachyrhizi in the soybean crop, identified by the main components technique, were the number of days without precipitation, number of days with less than one mm, number of days with precipitation between 1 and 25mm and 26 and 50mm and number of days with a temperature between 15 and 25ºC.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESA ferrugem asiática da soja é causada pelo patógeno Phakopsora pachyrhizi que se desenvolve e dissemina-se rapidamente em condições ambientais ideias de umidade e temperatura. A literatura carece de informações sobre a relação existente entre esses fatores e o aparecimento do primeiro sintoma da doença devido, principalmente, a dificuldade de realização deste tipo de experimento a campo. Com auxílio das metodologias estatísticas de correlação de Pearson (r), análise de trilha e análise de componentes principais (ACP) o objetivo desse trabalho foi identificar quais variáveis meteorológicas são determinantes para o aparecimento do primeiro sintoma visível da doença utilizando períodos de dias anteriores a sua identificação a campo. O experimento foi realizado nos anos agrícolas de 2015/16 e 2016/17, na estação experimental do Instituto Phytus, localizada no município de Itaara, RS. O experimento foi conduzido em blocos ao acaso, com quatro repetições, em um fatorial 5x2. O fator A constituído por cinco épocas de semeadura e o fator C por duas cultivares de soja. As observações meteorológicas referentes a precipitação e temperatura, foram obtidas diariamente com auxílio da estação meteorológica de superfície automática. As variáveis explicativas avaliadas, referentes a precipitação, foram: precipitação acumulada em milímetros (mm) (PrecAcum), número de dias sem precipitação (NdSP), número de dias com menos de um mm (NdM1mm), número de dias com precipitação entre 1 e 25mm (Nd1-25mm), número de dias com precipitação entre 26 e 50mm (Nd26-50mm), número de dias com precipitação entre 51 e 75mm (Nd51-75mm) e referentes a temperatura, foram avaliadas: temperatura mínima média do período (TempMédia), número de dias com temperatura mínima entre 15-25ºC (NdT15-25ºC), número de dias com temperaturas mínimas inferiores a 15ºC (NdTemp<15ºC), número de dias com temperaturas mínimas superiores a 25ºC (NdTemp>25ºC), e por fim, a umidade relativa média (URMédia). A variável principal utilizada foi a data do aparecimento do primeiro sintoma, expressa em dias após a emergência (DAE). Os períodos utilizados no cálculo das variáveis meteorológicas foram 5, 7, 8, 10, 11, 14 e 20 dias anteriores ao aparecimento do primeiro sintoma da doença. As análises estatísticas foram realizadas com auxílio do software Genes, Microsoft OfficeExcel e Minitab. Verificou-se que as cultivares não apresentaram diferenças expressivas quanto as variáveis analisadas. As observações meteorológicas que compreenderam períodos superiores a 10 dias foram as que melhor caracterizaram o início da epidemia. A precipitação favoreceu a antecipação dos sintomas de forma direta e indiretamente através Ndm1mm, Nd26-50mm e a temperatura mínima média foi determinante na safra 2016/17 atuando direta e indiretamente sobre a variável principal. As variáveis meteorológicas de maior contribuição para o aparecimento do primeiro sintoma de P. pachyrhizi na cultura da soja, identificadas pela técnica de componentes principais, foram o número de dias sem precipitação, número de dias com menos de um mm, número de dias com precipitação entre 1 e 25mm e 26 e 50mm e número de dias com temperatura entre 15 e 25ºC.Universidade Federal de Santa MariaBrasilAgronomiaUFSMPrograma de Pós-Graduação em AgronomiaCentro de Ciências RuraisLúcio, Alessandro Dal'Colhttp://lattes.cnpq.br/0972869223145503Costa, Ivan Francisco Dressler dahttp://lattes.cnpq.br/6426393869748708Balardin, Ricardo Silveirohttp://lattes.cnpq.br/6547445501940321Ruviaro, Claitonhttp://lattes.cnpq.br/3706288232300725Madalosso, Marcelo Gripahttp://lattes.cnpq.br/2188468950480104Silva, Angelica Marian da2019-01-17T13:01:54Z2019-01-17T13:01:54Z2018-03-02info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttp://repositorio.ufsm.br/handle/1/15387porAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2019-01-18T05:01:48Zoai:repositorio.ufsm.br:1/15387Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2019-01-18T05:01:48Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Variáveis meteorológicas relacionadas ao início da epidemia causada por Phakopsora pachyrhizi em soja Analysis of meteorological observations related to the appearance of the first symptom caused by Phakopsora pachyrhizi in soybean |
title |
Variáveis meteorológicas relacionadas ao início da epidemia causada por Phakopsora pachyrhizi em soja |
spellingShingle |
Variáveis meteorológicas relacionadas ao início da epidemia causada por Phakopsora pachyrhizi em soja Silva, Angelica Marian da Ferrugem asiática Temperatura Precipitação Análise de trilha Análise de componentes principais Asian rust Temperature Precipitation Path analysis Principal component analysis CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Variáveis meteorológicas relacionadas ao início da epidemia causada por Phakopsora pachyrhizi em soja |
title_full |
Variáveis meteorológicas relacionadas ao início da epidemia causada por Phakopsora pachyrhizi em soja |
title_fullStr |
Variáveis meteorológicas relacionadas ao início da epidemia causada por Phakopsora pachyrhizi em soja |
title_full_unstemmed |
Variáveis meteorológicas relacionadas ao início da epidemia causada por Phakopsora pachyrhizi em soja |
title_sort |
Variáveis meteorológicas relacionadas ao início da epidemia causada por Phakopsora pachyrhizi em soja |
author |
Silva, Angelica Marian da |
author_facet |
Silva, Angelica Marian da |
author_role |
author |
dc.contributor.none.fl_str_mv |
Lúcio, Alessandro Dal'Col http://lattes.cnpq.br/0972869223145503 Costa, Ivan Francisco Dressler da http://lattes.cnpq.br/6426393869748708 Balardin, Ricardo Silveiro http://lattes.cnpq.br/6547445501940321 Ruviaro, Claiton http://lattes.cnpq.br/3706288232300725 Madalosso, Marcelo Gripa http://lattes.cnpq.br/2188468950480104 |
dc.contributor.author.fl_str_mv |
Silva, Angelica Marian da |
dc.subject.por.fl_str_mv |
Ferrugem asiática Temperatura Precipitação Análise de trilha Análise de componentes principais Asian rust Temperature Precipitation Path analysis Principal component analysis CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
topic |
Ferrugem asiática Temperatura Precipitação Análise de trilha Análise de componentes principais Asian rust Temperature Precipitation Path analysis Principal component analysis CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
description |
Asian soybean rust is caused by the pathogen Phakopsora pachyrhizi that develops and spreads rapidly under ambient conditions of humidity and temperature. The literature lacks information about the relationship between these factors and the appearance of the first symptom of the disease due mainly to the difficulty of performing this type of field experiment. The objective of this work was to identify which meteorological variables are determinant for the appearance of the first visible symptom of the disease using periods of days prior to Pearson (r) correlation, track analysis and principal component analysis field. The experiment was carried out in the agricultural years of 2015/16 and 2016/17, at the experimental station of the Phytus Institute, located in the municipality of Itaara, RS. The experiment was conducted in randomized blocks, with four replicates, in a factorial 5x2. Factor A consisting of five sowing seasons and factor C by two soybean cultivars. The meteorological observations concerning precipitation and temperature were obtained daily with the help of the automatic surface meteorological station. The cumulative precipitation in mm (PrecAcum), number of days without precipitation (NdSP), number of days with less than one mm (NdM1mm), number of days with precipitation between 1 and 25mm (Nd1-25mm), number of days with precipitation between 26 and 50mm (Nd26-50mm), number of days with precipitation between 51 and 75mm (Nd51-75mm) and referring to temperature were evaluated: mean temperature (NdTemp <15ºC), number of days with minimum temperatures above 15ºC (NdTemp>15ºC), number of days with minimum temperatures between 15-25ºC (NdT15-25ºC), number of days with minimum temperatures below 25ºC (NdTemp<25ºC), and, finally, the average relative humidity (URMédia). The main variable used was the date of the appearance of the first symptom, expressed in days after the emergency (AED). The periods used in the calculation of the meteorological variables were 5, 7, 8, 10, 11, 14 and 20 days prior to the appearance of the first symptom of the disease. Statistical analyzes were performed using Genes, Microsoft Office Excel and Minitab software. It was verified that the cultivars did not present expressive differences as the variables analyzed. The meteorological observations that included periods longer than 10 days were the ones that best characterized the beginning of the epidemic. The precipitation favored the anticipation of the symptoms directly and indirectly through Ndm1mm, Nd26-50mm and the average temperature was determinant in the 2016/17 crop, acting directly and indirectly on the main variable. The meteorological variables of greatest contribution to the appearance of the first symptom of P. pachyrhizi in the soybean crop, identified by the main components technique, were the number of days without precipitation, number of days with less than one mm, number of days with precipitation between 1 and 25mm and 26 and 50mm and number of days with a temperature between 15 and 25ºC. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-03-02 2019-01-17T13:01:54Z 2019-01-17T13:01:54Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/15387 |
url |
http://repositorio.ufsm.br/handle/1/15387 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Agronomia UFSM Programa de Pós-Graduação em Agronomia Centro de Ciências Rurais |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Agronomia UFSM Programa de Pós-Graduação em Agronomia Centro de Ciências Rurais |
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 |
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1805922017835220992 |