Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala
Ano de defesa: | 2015 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , , |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal Rural de Pernambuco
|
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Fitopatologia
|
Departamento: |
Departamento de Agronomia
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/6067 |
Resumo: | The rust, caused by the fungus Hemileia vastatrix Berk & Br., is the main disease of coffee (Coffea arabica L.) in Latin America. The principal damage caused is defoliation and death of lateral branches, which causes premature fruit losses. Guatemala produces coffee in 270,000 hectares, and near of the 82% is cultivated with susceptible varieties to coffee rust races. Coffee rust epidemic is a complex process based on the relationships between the environment, plant growth, and crop practices. The objective of this study was to develop models for risk analysis based on decision trees in order to understand how cropping patterns determine the progress of the disease in Guatemala to identify and prioritize the important factors. For this work were used 1215 observations, obtained in 35 coffee plots from April 2013 to December 2014. The modeled variable was the leaf severity. Using the CHAID (Chi-Square Automatic Interaction Detection) algorithm were developed two decision trees. The first predicts leaf severity in plots where the producer does not follow the disease, while the second requires rust monitoring 28 days before the date of the severity risk analysis. In the trees, the main predictor was the fungicide spraying per year. The following predictor variables on the tree were related with the tissue availability for new infections, which also stimulates microenvironments with high relative humidity, warm temperatures, and foliar wetness prevalence. Only for non-monitoring tree was included the average rainfall, which suggests that climate relationship with the epidemic, is at microclimate level. The tree for plots with disease monitoring includes in all levels the 28 before severity and replaced management or climate variables getting similar predicted values. The accuracy of the tree for monitored plots was 65.85% with an estimated accuracy by cross validation of 73.34%, and for the monitored plots, the accuracy was 62.53% and 68.54%, respectively. Risk analysis models prove to be tools of support in making management decisions to implement the control of coffee rust and allow list in order of importance, management practices, and climatic factors that influence disease severity in different crop patterns. |
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MICHEREFF, Sami JorgeMORA-AGUILERA, GustavoRODRÍGUEZ, Francisco AnzuetoDEL PONTE, Emerson MedeirosGAMA, Marco Aurélio Siqueira daLIMA, Nelson Bernardihttp://lattes.cnpq.br/2526675068706453ESTRADA, Gabriela del Carmen Calderón2016-12-02T13:12:59Z2015-12-11ESTRADA, Gabriela del Carmen Calderón. Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala. 2015. 92 f. Dissertação (Programa de Pós-Graduação em Fitopatologia) - Universidade Federal Rural de Pernambuco, Recife.http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/6067The rust, caused by the fungus Hemileia vastatrix Berk & Br., is the main disease of coffee (Coffea arabica L.) in Latin America. The principal damage caused is defoliation and death of lateral branches, which causes premature fruit losses. Guatemala produces coffee in 270,000 hectares, and near of the 82% is cultivated with susceptible varieties to coffee rust races. Coffee rust epidemic is a complex process based on the relationships between the environment, plant growth, and crop practices. The objective of this study was to develop models for risk analysis based on decision trees in order to understand how cropping patterns determine the progress of the disease in Guatemala to identify and prioritize the important factors. For this work were used 1215 observations, obtained in 35 coffee plots from April 2013 to December 2014. The modeled variable was the leaf severity. Using the CHAID (Chi-Square Automatic Interaction Detection) algorithm were developed two decision trees. The first predicts leaf severity in plots where the producer does not follow the disease, while the second requires rust monitoring 28 days before the date of the severity risk analysis. In the trees, the main predictor was the fungicide spraying per year. The following predictor variables on the tree were related with the tissue availability for new infections, which also stimulates microenvironments with high relative humidity, warm temperatures, and foliar wetness prevalence. Only for non-monitoring tree was included the average rainfall, which suggests that climate relationship with the epidemic, is at microclimate level. The tree for plots with disease monitoring includes in all levels the 28 before severity and replaced management or climate variables getting similar predicted values. The accuracy of the tree for monitored plots was 65.85% with an estimated accuracy by cross validation of 73.34%, and for the monitored plots, the accuracy was 62.53% and 68.54%, respectively. Risk analysis models prove to be tools of support in making management decisions to implement the control of coffee rust and allow list in order of importance, management practices, and climatic factors that influence disease severity in different crop patterns.A ferrugem do cafeeiro, causada pelo fungo Hemileia vastatrix Berk & Br., é a principal doença do cafeeiro (Coffea arabica L.) na América Latina. O principal dano é desfolha e morte de ramos laterais, que provocam perdas prematuras de frutos. A Guatemala produz café em 270.000 hectares, sendo que cerca de 82% é cultivado com variedades suscetíveis às raças de ferrugem. A epidemia da ferrugem é um processo complexo baseado nas relações entre ambiente, crescimento da planta, e práticas de manejo. O objetivo deste estudo foi desenvolver modelos para análise de risco baseados em árvores de decisão, a fim de entender como os padrões de cultivo determinam o progresso da doença na Guatemala para identificae e priorizar os fatores importantes. Para este trabalho foram utilizadas 1215 observações, obtidas de 35 lavouras de abril de 2013 a dezembro de 2014. A variável modelada foi a severidade da folha. Utilizando o algoritmo CHAID (Chi-Quadrado Detecção Automatic Interaction), foram desenvolvidas duas árvores de decisão. A primeira árvore permite prever a severidade na folha nas parcelas em que o produtor não realiza acompanhamento da doença, enquanto a segunda requer o monitoramento da ferrugem 28 dias antes da data da análise de risco da severidade. Nas árvores, o principal preditor foi o número de aplicações de fungicida por ano. As seguintes variáveis preditoras na árvore foram relacionadas com disponibilidade de tecido para novas infecções, que podem favorecem a formação de microambientes com alta umidade relativa, temperaturas amenas e prevalência da molhadura folhar. Apenas para a árvore de não monitoramento foi incluída a variável da precipitação média, o que sugere que a relação do clima é em nível microclimático. A árvore com monitoramento inclui em todos os níveis a severidade aos 28 dias antes e substitui variáveis de manejo ou clima, estimando valores semelhantes. A acurácia da árvore para lavouras não monitoradas foi de 65,85% com uma estimativa de acurácia por validação cruzada de 73,34%. Na árvore para lavouras monitoradas a acurácia foi de 62,53% e 68,54%, respectivamente. Os modelos de análise de risco demonstram ser ferramentas de apoio na tomada de decisões de manejo para implementar o controle da ferrugem do cafeeiro e possibilitam listar, em ordem de importância, as práticas de manejo e fatores climáticos que influenciam na severidade da doença em diferentes padrões do cultivo.Submitted by Mario BC (mario@bc.ufrpe.br) on 2016-12-02T13:12:59Z No. of bitstreams: 1 Gabriela del Carmen Calderon Estrada.pdf: 1790318 bytes, checksum: 59a9ef3279b882660365d852f8a0f3a1 (MD5)Made available in DSpace on 2016-12-02T13:12:59Z (GMT). No. of bitstreams: 1 Gabriela del Carmen Calderon Estrada.pdf: 1790318 bytes, checksum: 59a9ef3279b882660365d852f8a0f3a1 (MD5) Previous issue date: 2015-12-11Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPqapplication/pdfporUniversidade Federal Rural de PernambucoPrograma de Pós-Graduação em FitopatologiaUFRPEBrasilDepartamento de AgronomiaFerrugem do cafeeiroCoffea arabicaEpidemiologiaAlgoritmo CHAIDRustEpidemiologyCHAID algorithmFITOSSANIDADE::FITOPATOLOGIAÁrvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemalainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis1343367238723626701600600600600-6800553879972229205-6207026424523013504-2555911436985713659info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFRPEinstname:Universidade Federal Rural de Pernambuco (UFRPE)instacron:UFRPEORIGINALGabriela del Carmen Calderon Estrada.pdfGabriela del Carmen Calderon Estrada.pdfapplication/pdf1790318http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/6067/2/Gabriela+del+Carmen+Calderon+Estrada.pdf59a9ef3279b882660365d852f8a0f3a1MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/6067/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede2/60672016-12-02 10:12:59.304oai:tede2: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Biblioteca Digital de Teses e Dissertaçõeshttp://www.tede2.ufrpe.br:8080/tede/PUBhttp://www.tede2.ufrpe.br:8080/oai/requestbdtd@ufrpe.br ||bdtd@ufrpe.bropendoar:2024-05-28T12:33:53.016Biblioteca Digital de Teses e Dissertações da UFRPE - Universidade Federal Rural de Pernambuco (UFRPE)false |
dc.title.por.fl_str_mv |
Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala |
title |
Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala |
spellingShingle |
Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala ESTRADA, Gabriela del Carmen Calderón Ferrugem do cafeeiro Coffea arabica Epidemiologia Algoritmo CHAID Rust Epidemiology CHAID algorithm FITOSSANIDADE::FITOPATOLOGIA |
title_short |
Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala |
title_full |
Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala |
title_fullStr |
Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala |
title_full_unstemmed |
Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala |
title_sort |
Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala |
author |
ESTRADA, Gabriela del Carmen Calderón |
author_facet |
ESTRADA, Gabriela del Carmen Calderón |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
MICHEREFF, Sami Jorge |
dc.contributor.advisor-co1.fl_str_mv |
MORA-AGUILERA, Gustavo |
dc.contributor.advisor-co2.fl_str_mv |
RODRÍGUEZ, Francisco Anzueto |
dc.contributor.referee1.fl_str_mv |
DEL PONTE, Emerson Medeiros |
dc.contributor.referee2.fl_str_mv |
GAMA, Marco Aurélio Siqueira da |
dc.contributor.referee3.fl_str_mv |
LIMA, Nelson Bernardi |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/2526675068706453 |
dc.contributor.author.fl_str_mv |
ESTRADA, Gabriela del Carmen Calderón |
contributor_str_mv |
MICHEREFF, Sami Jorge MORA-AGUILERA, Gustavo RODRÍGUEZ, Francisco Anzueto DEL PONTE, Emerson Medeiros GAMA, Marco Aurélio Siqueira da LIMA, Nelson Bernardi |
dc.subject.por.fl_str_mv |
Ferrugem do cafeeiro Coffea arabica Epidemiologia Algoritmo CHAID |
topic |
Ferrugem do cafeeiro Coffea arabica Epidemiologia Algoritmo CHAID Rust Epidemiology CHAID algorithm FITOSSANIDADE::FITOPATOLOGIA |
dc.subject.eng.fl_str_mv |
Rust Epidemiology CHAID algorithm |
dc.subject.cnpq.fl_str_mv |
FITOSSANIDADE::FITOPATOLOGIA |
description |
The rust, caused by the fungus Hemileia vastatrix Berk & Br., is the main disease of coffee (Coffea arabica L.) in Latin America. The principal damage caused is defoliation and death of lateral branches, which causes premature fruit losses. Guatemala produces coffee in 270,000 hectares, and near of the 82% is cultivated with susceptible varieties to coffee rust races. Coffee rust epidemic is a complex process based on the relationships between the environment, plant growth, and crop practices. The objective of this study was to develop models for risk analysis based on decision trees in order to understand how cropping patterns determine the progress of the disease in Guatemala to identify and prioritize the important factors. For this work were used 1215 observations, obtained in 35 coffee plots from April 2013 to December 2014. The modeled variable was the leaf severity. Using the CHAID (Chi-Square Automatic Interaction Detection) algorithm were developed two decision trees. The first predicts leaf severity in plots where the producer does not follow the disease, while the second requires rust monitoring 28 days before the date of the severity risk analysis. In the trees, the main predictor was the fungicide spraying per year. The following predictor variables on the tree were related with the tissue availability for new infections, which also stimulates microenvironments with high relative humidity, warm temperatures, and foliar wetness prevalence. Only for non-monitoring tree was included the average rainfall, which suggests that climate relationship with the epidemic, is at microclimate level. The tree for plots with disease monitoring includes in all levels the 28 before severity and replaced management or climate variables getting similar predicted values. The accuracy of the tree for monitored plots was 65.85% with an estimated accuracy by cross validation of 73.34%, and for the monitored plots, the accuracy was 62.53% and 68.54%, respectively. Risk analysis models prove to be tools of support in making management decisions to implement the control of coffee rust and allow list in order of importance, management practices, and climatic factors that influence disease severity in different crop patterns. |
publishDate |
2015 |
dc.date.issued.fl_str_mv |
2015-12-11 |
dc.date.accessioned.fl_str_mv |
2016-12-02T13:12:59Z |
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.citation.fl_str_mv |
ESTRADA, Gabriela del Carmen Calderón. Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala. 2015. 92 f. Dissertação (Programa de Pós-Graduação em Fitopatologia) - Universidade Federal Rural de Pernambuco, Recife. |
dc.identifier.uri.fl_str_mv |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/6067 |
identifier_str_mv |
ESTRADA, Gabriela del Carmen Calderón. Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala. 2015. 92 f. Dissertação (Programa de Pós-Graduação em Fitopatologia) - Universidade Federal Rural de Pernambuco, Recife. |
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http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/6067 |
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por |
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por |
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info:eu-repo/semantics/openAccess |
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openAccess |
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Universidade Federal Rural de Pernambuco |
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Programa de Pós-Graduação em Fitopatologia |
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UFRPE |
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Universidade Federal Rural de Pernambuco |
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