Modelo estatístico de previsão de produtividade de soja e arroz para o Rio Grande do Sul
Ano de defesa: | 2017 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , , , |
Tipo de documento: | Tese |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Santa Maria
Centro de Ciências Naturais e Exatas |
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Meteorologia
|
Departamento: |
Meteorologia
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://repositorio.ufsm.br/handle/1/18995 |
Resumo: | In this PhD thesis, the usefulness of the insertion of climatic indicators in a statistical model for predicting rice and soybean yield in Rio Grande do Sul is presented. Initially, yield data of the two crops were separated into groups of homogeneous behavior in term of average yield. For soybean, the study period was from 1974 to 2013, excluding the 1983 crop because it was not included in the database. The northeast region of the State with the highest yield is highlighted, while the municipalities located in the northwest present series with lower average yield. In the rice crop, the study comprised the years 1990 to 2013 and the western and southern regions of the State show the highest average yield during the study period. In the municipalities of the central depression and near the Patos Lagoon the lowest average yield is observed. After this stage, lagged correlations were made between climatic indicators and a mean yield of each of the homogeneous groups in order to identify teleconnection patterns that influence the interannual variability of rice and soybean yield in the State. For soybean, the climatic indicators that presented the highest correlations were the Arctic Oscillation, North Atlantic Oscillation in addition to a region in the South Atlantic Ocean between 20°S/30°S and 20°W/40° W. Rice, in general, presented higher correlations than soybean. This fact highlighting mainly the indices referring to the oceanic and atmospheric components of the phenomenon El Niño Southern Oscillation and the index referring to Pacific Decadal Oscillation. To the highest correlation indexes with each homogeneous group, such as rice and soybean cultures, areas of Sea Surface Temperature with a high production correlation were added. Thereby a statistical regression model to crop forecast in Rio Grande do Sul may be elaborated. Through the Principal Component Regression method, the predictors for each group and culture were selected with the purpose of providing in October a yield estimate based on indicators obtained up to the month of September. October is when a major part of the soybean and rice are sown. The model shows good results, including as a support tool in the planning of rice and soybean harvest in the State of Rio Grande do Sul. As the advance in planting and crop development occurs the model can be updated with the inclusion of new index. It is also useful as a crop tracking tool and as an aid to eventual corrections of estimates that need to be made. |
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2019-11-20T21:01:49Z2019-11-20T21:01:49Z2017-08-14http://repositorio.ufsm.br/handle/1/18995In this PhD thesis, the usefulness of the insertion of climatic indicators in a statistical model for predicting rice and soybean yield in Rio Grande do Sul is presented. Initially, yield data of the two crops were separated into groups of homogeneous behavior in term of average yield. For soybean, the study period was from 1974 to 2013, excluding the 1983 crop because it was not included in the database. The northeast region of the State with the highest yield is highlighted, while the municipalities located in the northwest present series with lower average yield. In the rice crop, the study comprised the years 1990 to 2013 and the western and southern regions of the State show the highest average yield during the study period. In the municipalities of the central depression and near the Patos Lagoon the lowest average yield is observed. After this stage, lagged correlations were made between climatic indicators and a mean yield of each of the homogeneous groups in order to identify teleconnection patterns that influence the interannual variability of rice and soybean yield in the State. For soybean, the climatic indicators that presented the highest correlations were the Arctic Oscillation, North Atlantic Oscillation in addition to a region in the South Atlantic Ocean between 20°S/30°S and 20°W/40° W. Rice, in general, presented higher correlations than soybean. This fact highlighting mainly the indices referring to the oceanic and atmospheric components of the phenomenon El Niño Southern Oscillation and the index referring to Pacific Decadal Oscillation. To the highest correlation indexes with each homogeneous group, such as rice and soybean cultures, areas of Sea Surface Temperature with a high production correlation were added. Thereby a statistical regression model to crop forecast in Rio Grande do Sul may be elaborated. Through the Principal Component Regression method, the predictors for each group and culture were selected with the purpose of providing in October a yield estimate based on indicators obtained up to the month of September. October is when a major part of the soybean and rice are sown. The model shows good results, including as a support tool in the planning of rice and soybean harvest in the State of Rio Grande do Sul. As the advance in planting and crop development occurs the model can be updated with the inclusion of new index. It is also useful as a crop tracking tool and as an aid to eventual corrections of estimates that need to be made.Nesta pesquisa de doutorado, destaca-se a utilidade da inserção de indicadores climáticos em um modelo estatístico de previsão de produtividade de arroz e soja no Rio Grande do Sul. Inicialmente, dados de produtividade das duas culturas fornecidos pelo IBGE foram separados em grupos de comportamento homogêneo em termo da produtividade média. Para a soja, o período de estudo foi de 1974 a 2013, escluindo-se a safra de 1983 por não constar na base de dados. Destaca-se a região nordeste do Estado com maior produtividade, enquanto os municípios situados no noroeste apresentam as séries com menor produtividade média. Na cultura do arroz, o estudo compreendeu os anos de 1990 a 2013 e o oeste e sul do Estado apresentam a maior produtividade média ao longo do período de estudo e nos municípios da depressão central e próximos a Lagoa dos Patos são observadas a menor produtividade média. Após esta etapa, foram realizadas correlações defasadas entre indicadores climáticos e a produtividade média de cada um dos grupos homogêneos de forma a identificar padrões de teleconexão que exerçam influência na variabilidade interanual de produtividade de arroz e soja no Estado. Para a soja, os indicadores climáticos que apresentaram maiores correlações foram a Oscilação Ártica, a Oscilação do Atlântico Norte além de uma região no Oceano Atlântico Sul entre 20°S/30°S e 20°W/40°W. De forma geral, o arroz apresentou correlações mais elevadas que a soja, destacando-se, principalmente, os índices referentes as componentes oceânica e atmosférica do fenômeno El Niño Oscilação Sul e o índice referente a Oscilação Decadal do Pacífico. Além dos índices de maior correlação com cada grupo homogêneo, referente as culturas do arroz e da soja, foram adicionadas áreas de Temperatura da Superfície do Mar do oceano global com elevada correlação com a produtividade para a elaboração de um modelo estatístico de regressão para a previsão de safra no Rio Grande do Sul. Por meio do método da Regressão de Componentes Principais, foram selecionadas as combinações de índices que fornecessem a melhor previsão para cada grupo e cultura com o intuito de fornecer no mês de outubro, período em que se inicia a maior parte do plantio de soja e arroz no Estado, uma estimativa de produtividade baseada em indicadores obtidos até o mês de setembro. O modelo apresentou bons resultados, incluindo-se, assim, como ferramenta de apoio no planejamento de safra de arroz e soja no Estado gaúcho. À medida que ocorre o avanço no plantio e desenvolvimento das culturas, o modelo pode ser atualizado com a inclusão de novos índices e ser, também, útil como ferramenta de acompanhamento de safra e de auxílio para eventuais correções de estimativas que necessitem ser realizadas.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de Ciências Naturais e ExatasPrograma de Pós-Graduação em MeteorologiaUFSMBrasilMeteorologiaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessArrozSojaProdutividadeIndicadores climáticosModelo estatístico de regressaoRiceSoybeanYieldClimate indexStatistical regression modelCNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIAModelo estatístico de previsão de produtividade de soja e arroz para o Rio Grande do SulStatistical model of soybean and rice yield forecast for Rio Grande do Sulinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisFerraz, Simone Erotildes Teleginskihttp://lattes.cnpq.br/5545006407615789Cardoso, Andréa de Oliveirahttp://lattes.cnpq.br/0608610801574202Alberto, Cleber Maushttp://lattes.cnpq.br/2747295128900648Tatsch, Jônatan Duponthttp://lattes.cnpq.br/2365902346826079Boiaski, Nathalie Tissothttp://lattes.cnpq.br/8599135403486788http://lattes.cnpq.br/5303560663845220Arsego, Diogo Alessandro100700300004600890e04b2-49dc-4fe7-a16e-07665d183a7df6b3655f-6a3f-43fb-895b-218cad340928afb436b6-349e-4186-88a7-49c263f4315eaa44d19b-bb8a-4d17-9c8f-2f0e89b005a29224ade6-6b4f-4ddc-a19a-b750ddcf8eca7a108504-3174-4cc1-963d-be38da713e18reponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALTES_PPGMETEOROLOGIA_2017_ARSEGO_DIOGO.pdfTES_PPGMETEOROLOGIA_2017_ARSEGO_DIOGO.pdfTese de Doutoradoapplication/pdf82614049http://repositorio.ufsm.br/bitstream/1/18995/1/TES_PPGMETEOROLOGIA_2017_ARSEGO_DIOGO.pdf51affc92bf78c81d49ea9b35cb80361fMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repositorio.ufsm.br/bitstream/1/18995/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-816http://repositorio.ufsm.br/bitstream/1/18995/3/license.txt6eeec7985884eb94336b41cc5308bf0fMD53TEXTTES_PPGMETEOROLOGIA_2017_ARSEGO_DIOGO.pdf.txtTES_PPGMETEOROLOGIA_2017_ARSEGO_DIOGO.pdf.txtExtracted texttext/plain231870http://repositorio.ufsm.br/bitstream/1/18995/4/TES_PPGMETEOROLOGIA_2017_ARSEGO_DIOGO.pdf.txt9cb049dde6ae3f51e6c25f6662fec7dbMD54THUMBNAILTES_PPGMETEOROLOGIA_2017_ARSEGO_DIOGO.pdf.jpgTES_PPGMETEOROLOGIA_2017_ARSEGO_DIOGO.pdf.jpgIM Thumbnailimage/jpeg4261http://repositorio.ufsm.br/bitstream/1/18995/5/TES_PPGMETEOROLOGIA_2017_ARSEGO_DIOGO.pdf.jpg6a246fe80e69f1a81d5aef45ce676271MD551/189952019-11-21 03:03:46.525oai:repositorio.ufsm.br:1/18995Q3JlYXRpdmUgQ29tbXVucw==Repositório Institucionalhttp://repositorio.ufsm.br/PUBhttp://repositorio.ufsm.br/oai/requestopendoar:39132019-11-21T06:03:46Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.por.fl_str_mv |
Modelo estatístico de previsão de produtividade de soja e arroz para o Rio Grande do Sul |
dc.title.alternative.eng.fl_str_mv |
Statistical model of soybean and rice yield forecast for Rio Grande do Sul |
title |
Modelo estatístico de previsão de produtividade de soja e arroz para o Rio Grande do Sul |
spellingShingle |
Modelo estatístico de previsão de produtividade de soja e arroz para o Rio Grande do Sul Arsego, Diogo Alessandro Arroz Soja Produtividade Indicadores climáticos Modelo estatístico de regressao Rice Soybean Yield Climate index Statistical regression model CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA |
title_short |
Modelo estatístico de previsão de produtividade de soja e arroz para o Rio Grande do Sul |
title_full |
Modelo estatístico de previsão de produtividade de soja e arroz para o Rio Grande do Sul |
title_fullStr |
Modelo estatístico de previsão de produtividade de soja e arroz para o Rio Grande do Sul |
title_full_unstemmed |
Modelo estatístico de previsão de produtividade de soja e arroz para o Rio Grande do Sul |
title_sort |
Modelo estatístico de previsão de produtividade de soja e arroz para o Rio Grande do Sul |
author |
Arsego, Diogo Alessandro |
author_facet |
Arsego, Diogo Alessandro |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Ferraz, Simone Erotildes Teleginski |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/5545006407615789 |
dc.contributor.referee1.fl_str_mv |
Cardoso, Andréa de Oliveira |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/0608610801574202 |
dc.contributor.referee2.fl_str_mv |
Alberto, Cleber Maus |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/2747295128900648 |
dc.contributor.referee3.fl_str_mv |
Tatsch, Jônatan Dupont |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/2365902346826079 |
dc.contributor.referee4.fl_str_mv |
Boiaski, Nathalie Tissot |
dc.contributor.referee4Lattes.fl_str_mv |
http://lattes.cnpq.br/8599135403486788 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/5303560663845220 |
dc.contributor.author.fl_str_mv |
Arsego, Diogo Alessandro |
contributor_str_mv |
Ferraz, Simone Erotildes Teleginski Cardoso, Andréa de Oliveira Alberto, Cleber Maus Tatsch, Jônatan Dupont Boiaski, Nathalie Tissot |
dc.subject.por.fl_str_mv |
Arroz Soja Produtividade Indicadores climáticos Modelo estatístico de regressao |
topic |
Arroz Soja Produtividade Indicadores climáticos Modelo estatístico de regressao Rice Soybean Yield Climate index Statistical regression model CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA |
dc.subject.eng.fl_str_mv |
Rice Soybean Yield Climate index Statistical regression model |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA |
description |
In this PhD thesis, the usefulness of the insertion of climatic indicators in a statistical model for predicting rice and soybean yield in Rio Grande do Sul is presented. Initially, yield data of the two crops were separated into groups of homogeneous behavior in term of average yield. For soybean, the study period was from 1974 to 2013, excluding the 1983 crop because it was not included in the database. The northeast region of the State with the highest yield is highlighted, while the municipalities located in the northwest present series with lower average yield. In the rice crop, the study comprised the years 1990 to 2013 and the western and southern regions of the State show the highest average yield during the study period. In the municipalities of the central depression and near the Patos Lagoon the lowest average yield is observed. After this stage, lagged correlations were made between climatic indicators and a mean yield of each of the homogeneous groups in order to identify teleconnection patterns that influence the interannual variability of rice and soybean yield in the State. For soybean, the climatic indicators that presented the highest correlations were the Arctic Oscillation, North Atlantic Oscillation in addition to a region in the South Atlantic Ocean between 20°S/30°S and 20°W/40° W. Rice, in general, presented higher correlations than soybean. This fact highlighting mainly the indices referring to the oceanic and atmospheric components of the phenomenon El Niño Southern Oscillation and the index referring to Pacific Decadal Oscillation. To the highest correlation indexes with each homogeneous group, such as rice and soybean cultures, areas of Sea Surface Temperature with a high production correlation were added. Thereby a statistical regression model to crop forecast in Rio Grande do Sul may be elaborated. Through the Principal Component Regression method, the predictors for each group and culture were selected with the purpose of providing in October a yield estimate based on indicators obtained up to the month of September. October is when a major part of the soybean and rice are sown. The model shows good results, including as a support tool in the planning of rice and soybean harvest in the State of Rio Grande do Sul. As the advance in planting and crop development occurs the model can be updated with the inclusion of new index. It is also useful as a crop tracking tool and as an aid to eventual corrections of estimates that need to be made. |
publishDate |
2017 |
dc.date.issued.fl_str_mv |
2017-08-14 |
dc.date.accessioned.fl_str_mv |
2019-11-20T21:01:49Z |
dc.date.available.fl_str_mv |
2019-11-20T21:01:49Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
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doctoralThesis |
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publishedVersion |
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http://repositorio.ufsm.br/handle/1/18995 |
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http://repositorio.ufsm.br/handle/1/18995 |
dc.language.iso.fl_str_mv |
por |
language |
por |
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100700300004 |
dc.relation.confidence.fl_str_mv |
600 |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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Universidade Federal de Santa Maria Centro de Ciências Naturais e Exatas |
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Programa de Pós-Graduação em Meteorologia |
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UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Meteorologia |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Naturais e Exatas |
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