Efeitos do transbordamento da produtividade agrícola brasileira
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/20375 |
Resumo: | Agriculture is essential for the country's economic development. Brazil stands out in world agricultural production. All surplus produced is destined for exports. In this way, this research aimed to determine the agricultural spillovers effects of the main grains produced and exported in the country, analyzing the spatial dynamics of Brazilian agriculture. It is used, for this purpose, the database corresponding to the quantity produced and planted area of the main Brazilian grain crops: soybean, corn, wheat, rice, coffee and cocoa, from the 558 Brazilian microregions, corresponding to the years 1992, 1997, 2002, 2007, 2012 and 2017, totaling 20,088 observations, with annual collection. The methodology of Spatial Econometrics was used, initially an Exploratory Analysis of Spatial Data and later Spatial Econometric Modeling, to fit a representative model of the series under study. This analysis of the spatial dynamics of Brazilian agriculture made it possible to identify the agricultural pattern of the country, verifying clusters of productivity and spillovers among crops. Their results indicated the presence of positive spatial autocorrelation between the variables, which means that microregions with high or low agricultural productivity are grouped in specific areas of the map, surrounded by microregions with similar characteristics for this variable, making it possible to identify grains agricultural productivity spillover effects between neighboring microregions. As results, after confirming the presence of spatial autocorrelation in the data, by the Moran I statistic, the adjustment of the spatial econometrics models occurred. The presence of spatial autocorrelation, confirming that space is relevant to grains agricultural productivity analysis, is a decisive factor in the models adjustment by spatial econometrics. The application and adjustment of three spatial models were performed: the Spatial Auto Regressive (SAC) model was the best fit for the variables corn, rice, coffee and cocoa, the Spatial Error Model (SEM) model was the best model adjusted for the wheat variable and the Spatial Durbin Error Model (SDEM) model was the best representative model of the soybean series generating process. It can be concluded that grains agricultural productivity variable has a heterogeneous distribution among country microregions, in other words, agricultural productivity is increasingly autocorrelated spatially over time. |
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2021-03-04T10:27:28Z2021-03-04T10:27:28Z2019-03-26http://repositorio.ufsm.br/handle/1/20375Agriculture is essential for the country's economic development. Brazil stands out in world agricultural production. All surplus produced is destined for exports. In this way, this research aimed to determine the agricultural spillovers effects of the main grains produced and exported in the country, analyzing the spatial dynamics of Brazilian agriculture. It is used, for this purpose, the database corresponding to the quantity produced and planted area of the main Brazilian grain crops: soybean, corn, wheat, rice, coffee and cocoa, from the 558 Brazilian microregions, corresponding to the years 1992, 1997, 2002, 2007, 2012 and 2017, totaling 20,088 observations, with annual collection. The methodology of Spatial Econometrics was used, initially an Exploratory Analysis of Spatial Data and later Spatial Econometric Modeling, to fit a representative model of the series under study. This analysis of the spatial dynamics of Brazilian agriculture made it possible to identify the agricultural pattern of the country, verifying clusters of productivity and spillovers among crops. Their results indicated the presence of positive spatial autocorrelation between the variables, which means that microregions with high or low agricultural productivity are grouped in specific areas of the map, surrounded by microregions with similar characteristics for this variable, making it possible to identify grains agricultural productivity spillover effects between neighboring microregions. As results, after confirming the presence of spatial autocorrelation in the data, by the Moran I statistic, the adjustment of the spatial econometrics models occurred. The presence of spatial autocorrelation, confirming that space is relevant to grains agricultural productivity analysis, is a decisive factor in the models adjustment by spatial econometrics. The application and adjustment of three spatial models were performed: the Spatial Auto Regressive (SAC) model was the best fit for the variables corn, rice, coffee and cocoa, the Spatial Error Model (SEM) model was the best model adjusted for the wheat variable and the Spatial Durbin Error Model (SDEM) model was the best representative model of the soybean series generating process. It can be concluded that grains agricultural productivity variable has a heterogeneous distribution among country microregions, in other words, agricultural productivity is increasingly autocorrelated spatially over time.A agricultura é essencial para o desenvolvimento econômico do país. O Brasil tem ocupado papel de destaque na produção agrícola mundial. Todo excedente produzido é destinado às exportações. Neste sentido, esta pesquisa visou determinar os efeitos de transbordamento agrícola dos principais grãos produzidos e exportados no país, analisando a dinâmica espacial da agricultura brasileira. Utilizou-se, para isso, o banco de dados correspondente a quantidade produzida e a área plantada das principais culturas agrícolas brasileiras de grãos: soja, milho, trigo, arroz, café e cacau, das 558 microrregiões brasileiras, correspondentes aos anos de 1992, 1997, 2002, 2007, 2012 e 2017, totalizando 20.088 observações, com coleta anual. Utilizou-se a metodologia da Econometria Espacial, inicialmente uma Análise Exploratória dos Dados Espaciais e posterior Modelagem Econométrica Espacial, para ajustar um modelo representativo das séries em estudo. Essa análise da dinâmica espacial da agricultura brasileira possibilitou identificar o padrão agrícola do país, verificando clusters de produtividade e spillovers entre as culturas. Seus resultados indicaram a presença de autocorrelação espacial positiva entre as variáveis, o que significa que microrregiões com alta ou baixa produtividade agrícola estão agrupadas em áreas específicas do mapa, rodeadas por microrregiões com características semelhantes para essa variável, tornando possível identificar efeitos de transbordamento da produtividade agrícola de grãos entre as microrregiões vizinhas. Como resultados, após a confirmação da presença de autocorrelação espacial nos dados, pela estatística I de Moran, ocorreu o ajuste dos modelos econométricos espaciais. A presença de autocorrelação espacial, confirmando que o espaço é relevante para a análise da produtividade agrícola dos grãos, é fator decisivo no ajuste dos modelos pela econometria espacial. Foi realizada a aplicação e o ajuste de três modelos espaciais: o Modelo de Defasagem Espacial com Erro Autorregressivo Espacial (SAC) foi o que melhor se ajustou às variáveis milho, arroz, café e cacau, o Modelo de Erro Autorregressivo Espacial ou Modelo de Erro Espacial (SEM) foi o melhor modelo ajustado à variável trigo e o Modelo de Durbin Espacial do Erro (SDEM) foi o melhor modelo representativo do processo gerador da série da soja. Pode-se concluir que a variável produtividade agrícola dos grãos se distribui de maneira heterogênea entre as microrregiões do país, ou seja, a produtividade agrícola está cada vez mais autocorrelacionada espacialmente ao longo do tempo.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de TecnologiaPrograma de Pós-Graduação em Engenharia de ProduçãoUFSMBrasilEngenharia de ProduçãoAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAgricultura brasileiraTransbordamento agrícolaModelos econométricos espaciaisBrazilian agricultureAgricultural spilloverSpatial econometrics modelsCNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAOEfeitos do transbordamento da produtividade agrícola brasileiraSpillover effect of brazilian agricultural productivityinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisSouza, Adriano Mendonçahttp://lattes.cnpq.br/5271075797851198Coronel, Daniel Arrudahttp://lattes.cnpq.br/9265604274170933Silva, Luciana Santos Costa Vieira dahttp://lattes.cnpq.br/0903901167501516http://lattes.cnpq.br/7456564131528306Marasca, Letícia30080000000560060a97352-41f4-4228-b7b7-9bf285c97d9c049bdaaf-35e5-4abd-97bf-a4c7bb4c1bf9c05e6d3a-5583-418b-9974-579627bcf22347699e06-4430-412b-ac7e-cf799486c837reponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALDIS_PPGEP_2019_MARASCA_LETICIA.pdfDIS_PPGEP_2019_MARASCA_LETICIA.pdfDissertação de Mestradoapplication/pdf3880793http://repositorio.ufsm.br/bitstream/1/20375/1/DIS_PPGEP_2019_MARASCA_LETICIA.pdf5aad96cb888dd78a0e3624b19c46761fMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Efeitos do transbordamento da produtividade agrícola brasileira |
dc.title.alternative.eng.fl_str_mv |
Spillover effect of brazilian agricultural productivity |
title |
Efeitos do transbordamento da produtividade agrícola brasileira |
spellingShingle |
Efeitos do transbordamento da produtividade agrícola brasileira Marasca, Letícia Agricultura brasileira Transbordamento agrícola Modelos econométricos espaciais Brazilian agriculture Agricultural spillover Spatial econometrics models CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO |
title_short |
Efeitos do transbordamento da produtividade agrícola brasileira |
title_full |
Efeitos do transbordamento da produtividade agrícola brasileira |
title_fullStr |
Efeitos do transbordamento da produtividade agrícola brasileira |
title_full_unstemmed |
Efeitos do transbordamento da produtividade agrícola brasileira |
title_sort |
Efeitos do transbordamento da produtividade agrícola brasileira |
author |
Marasca, Letícia |
author_facet |
Marasca, Letícia |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Souza, Adriano Mendonça |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/5271075797851198 |
dc.contributor.referee1.fl_str_mv |
Coronel, Daniel Arruda |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/9265604274170933 |
dc.contributor.referee2.fl_str_mv |
Silva, Luciana Santos Costa Vieira da |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/0903901167501516 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/7456564131528306 |
dc.contributor.author.fl_str_mv |
Marasca, Letícia |
contributor_str_mv |
Souza, Adriano Mendonça Coronel, Daniel Arruda Silva, Luciana Santos Costa Vieira da |
dc.subject.por.fl_str_mv |
Agricultura brasileira Transbordamento agrícola Modelos econométricos espaciais |
topic |
Agricultura brasileira Transbordamento agrícola Modelos econométricos espaciais Brazilian agriculture Agricultural spillover Spatial econometrics models CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO |
dc.subject.eng.fl_str_mv |
Brazilian agriculture Agricultural spillover Spatial econometrics models |
dc.subject.cnpq.fl_str_mv |
CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO |
description |
Agriculture is essential for the country's economic development. Brazil stands out in world agricultural production. All surplus produced is destined for exports. In this way, this research aimed to determine the agricultural spillovers effects of the main grains produced and exported in the country, analyzing the spatial dynamics of Brazilian agriculture. It is used, for this purpose, the database corresponding to the quantity produced and planted area of the main Brazilian grain crops: soybean, corn, wheat, rice, coffee and cocoa, from the 558 Brazilian microregions, corresponding to the years 1992, 1997, 2002, 2007, 2012 and 2017, totaling 20,088 observations, with annual collection. The methodology of Spatial Econometrics was used, initially an Exploratory Analysis of Spatial Data and later Spatial Econometric Modeling, to fit a representative model of the series under study. This analysis of the spatial dynamics of Brazilian agriculture made it possible to identify the agricultural pattern of the country, verifying clusters of productivity and spillovers among crops. Their results indicated the presence of positive spatial autocorrelation between the variables, which means that microregions with high or low agricultural productivity are grouped in specific areas of the map, surrounded by microregions with similar characteristics for this variable, making it possible to identify grains agricultural productivity spillover effects between neighboring microregions. As results, after confirming the presence of spatial autocorrelation in the data, by the Moran I statistic, the adjustment of the spatial econometrics models occurred. The presence of spatial autocorrelation, confirming that space is relevant to grains agricultural productivity analysis, is a decisive factor in the models adjustment by spatial econometrics. The application and adjustment of three spatial models were performed: the Spatial Auto Regressive (SAC) model was the best fit for the variables corn, rice, coffee and cocoa, the Spatial Error Model (SEM) model was the best model adjusted for the wheat variable and the Spatial Durbin Error Model (SDEM) model was the best representative model of the soybean series generating process. It can be concluded that grains agricultural productivity variable has a heterogeneous distribution among country microregions, in other words, agricultural productivity is increasingly autocorrelated spatially over time. |
publishDate |
2019 |
dc.date.issued.fl_str_mv |
2019-03-26 |
dc.date.accessioned.fl_str_mv |
2021-03-04T10:27:28Z |
dc.date.available.fl_str_mv |
2021-03-04T10:27:28Z |
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 |
http://repositorio.ufsm.br/handle/1/20375 |
url |
http://repositorio.ufsm.br/handle/1/20375 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
300800000005 |
dc.relation.confidence.fl_str_mv |
600 |
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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.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Engenharia de Produção |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Engenharia de Produção |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
dc.source.none.fl_str_mv |
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