Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UNIOESTE |
Texto Completo: | https://tede.unioeste.br/handle/tede/6847 |
Resumo: | Brazil ranked first in the world in soybean production and third in corn production in 2021, thus, it was the world's largest soybean exporter. After harvesting these grains, it is essential to carry out storage before sale or consumption to keep their quality. The storage units (SUs) are used to store grains and are distributed throughout the producing regions, in order to store these products until their consumption or sale to the foreign market. Thus, this study aimed at creating an iterative algorithm with a methodology to study the spatial distribution of storage units in Paraná state – Brazil. Their storage capacity is also associated with their production, according to their places. This purpose was subdivided into two parts, referring to both goals and their respective scientific papers. The first goal of this research (Paper 1) was to create and develop an iterative algorithm in Python using Google Colab platform, and so relate the spatial data of production and storage capacity, as close as possible to the reality, as well as taking into account the distance from the productive areas to the storage units. Thus, this study used the Western Paraná mesoregion as the area to validation this algorithm. The results suggested the success when creating the algorithm, since they were validated with professionals from companies that work in the studied area, so, they know well the entire analyzed region. The created algorithm correctly performs the data spatial relationship, whose result is a map with the location of areas with storage deficit. Its second goal (Paper 2) was to carry out a detailed analysis of the spatial distribution of storage units in Paraná state, using the algorithm from the first study and relate it to spatial data. For this analysis, data from CONAB storage units were first acquired and analyzed by the SICARM platform and by visual inspection on satellite images for the whole Paraná. Units not present in the base were then geolocated to compose a complete database of SUs in the state. Their storage capacity was also estimated using a statistical modeling. Data from soybean, corn and wheat production area were obtained by mapping via remote sensing. The municipal averages published by the IBGE were used for these crops yield. Therefore, the production per mapped pixel was estimated to obtain the production information plans for each agricultural crop, in order to be able to analyze it spatially with the SUs storage capacities. As a first result, we recorded that the state's static storage capacity meats 64.5% of state production in a harvest year, showing a clear deficiency in local storage capacity. In the spatial analysis, it can be seen that this storage capacity is concentrated in some regions. The results showed that there are some regions with low storage capacity, and there may be situations where it is necessary to travel up to 200 km to a SU. Finally, data analysis showed that there is a storage deficiency in the study region and specifies the places in the state where this deficiency occurs. Data have also shown places where the storage capacity meets the local demand. Another result of great importance was the finding that the official SICARM data are outdated, as there were 417 storage units which were not registered on the platform regarding data validation. |
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Johann, Jerry Adrianihttp://lattes.cnpq.br/3499704308301708Richetti, Jonathanhttp://lattes.cnpq.br/9862888690056331Ló, Thiago Berticellihttp://lattes.cnpq.br/6935444785347377Johann, Jerry Adrianihttp://lattes.cnpq.br/3499704308301708Shorr, Marcio Renan Weberhttp://lattes.cnpq.br/9116593588254710Opazo, Miguel Angel Uribehttp://lattes.cnpq.br/4179444121729414Prudente, Victor Hugo Rohdenhttp://lattes.cnpq.br/6154929133513022Christ, Divairhttp://lattes.cnpq.br/6200553304840204http://lattes.cnpq.br/3065593150601602Paludo, Alex2023-10-23T13:42:39Z2023-06-16PALUDO, Alex. Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola. 2023. 86 F. Tese (Programa de Pós-Graduação em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel - PR.https://tede.unioeste.br/handle/tede/6847Brazil ranked first in the world in soybean production and third in corn production in 2021, thus, it was the world's largest soybean exporter. After harvesting these grains, it is essential to carry out storage before sale or consumption to keep their quality. The storage units (SUs) are used to store grains and are distributed throughout the producing regions, in order to store these products until their consumption or sale to the foreign market. Thus, this study aimed at creating an iterative algorithm with a methodology to study the spatial distribution of storage units in Paraná state – Brazil. Their storage capacity is also associated with their production, according to their places. This purpose was subdivided into two parts, referring to both goals and their respective scientific papers. The first goal of this research (Paper 1) was to create and develop an iterative algorithm in Python using Google Colab platform, and so relate the spatial data of production and storage capacity, as close as possible to the reality, as well as taking into account the distance from the productive areas to the storage units. Thus, this study used the Western Paraná mesoregion as the area to validation this algorithm. The results suggested the success when creating the algorithm, since they were validated with professionals from companies that work in the studied area, so, they know well the entire analyzed region. The created algorithm correctly performs the data spatial relationship, whose result is a map with the location of areas with storage deficit. Its second goal (Paper 2) was to carry out a detailed analysis of the spatial distribution of storage units in Paraná state, using the algorithm from the first study and relate it to spatial data. For this analysis, data from CONAB storage units were first acquired and analyzed by the SICARM platform and by visual inspection on satellite images for the whole Paraná. Units not present in the base were then geolocated to compose a complete database of SUs in the state. Their storage capacity was also estimated using a statistical modeling. Data from soybean, corn and wheat production area were obtained by mapping via remote sensing. The municipal averages published by the IBGE were used for these crops yield. Therefore, the production per mapped pixel was estimated to obtain the production information plans for each agricultural crop, in order to be able to analyze it spatially with the SUs storage capacities. As a first result, we recorded that the state's static storage capacity meats 64.5% of state production in a harvest year, showing a clear deficiency in local storage capacity. In the spatial analysis, it can be seen that this storage capacity is concentrated in some regions. The results showed that there are some regions with low storage capacity, and there may be situations where it is necessary to travel up to 200 km to a SU. Finally, data analysis showed that there is a storage deficiency in the study region and specifies the places in the state where this deficiency occurs. Data have also shown places where the storage capacity meets the local demand. Another result of great importance was the finding that the official SICARM data are outdated, as there were 417 storage units which were not registered on the platform regarding data validation.O Brasil ocupou a primeira colocação mundial na produção de soja e a terceira na produção de milho em 2021, e assim considerado o maior exportador mundial de soja. Após a colheita desses grãos, é essencial realizar o armazenamento antes da venda ou consumo, a fim de manter a qualidade desses. As unidades armazenadoras (UAs) são os locais utilizados para o armazenamento de grãos e estão distribuídas pelas regiões produtoras, a fim de armazená-los até o consumo ou a venda para o mercado externo. Neste sentido, este estudo teve como finalidade criar um algoritmo iterativo associado a uma metodologia para estudar a distribuição espacial das unidades armazenadoras do estado do Paraná – Brasil. Além de relacionar a capacidade de armazenamento com sua produção, considerando as localizações. Tal finalidade foi subdividida em duas partes, referentes aos dois objetivos e respectivos artigos científicos. O primeiro objetivo desta pesquisa (Artigo 1) foi criar e desenvolver um algoritmo iterativo em Python através da plataforma Google Colab, com a finalidade de relacionar os dados espaciais de produção e a capacidade de armazenamento, o mais próximo da realidade, levando em consideração a distância das áreas produtivas das unidades armazenadoras. Este estudo utilizou a mesorregião Oeste do Paraná como área para validação do algoritmo. Os resultados indicaram o sucesso na criação do algoritmo, visto que foram validados com profissionais de empresas que atuam na área em estudo, e conhecem bem toda a região analisada. O algoritmo criado realiza corretamente a relação espacial dos dados, cujo resultado é um mapa com a localização das áreas com déficit de armazenamento. O segundo objetivo desta pesquisa (Artigo 2) foi realizar uma análise detalhada da distribuição espacial das unidades armazenadoras do estado do Paraná, utilizando o algoritmo do primeiro estudo para relacioná-lo com os dados espaciais. Para essa análise, primeiramente, os dados de unidades armazenadoras da CONAB foram adquiridos e analisados pela plataforma SICARM e por inspeção visual sobre imagens de satélites para todo o Paraná. Unidades não presentes na base foram então geolocalizadas para compor uma base completa dos dados das UAs do estado. Para estas UAs, a capacidade de armazenamento também foi estimada com base na modelagem estatística. Os dados de área de produção de soja, milho e trigo foram obtidos a partir do mapeamento via sensoriamento remoto. As médias municipais divulgadas pelo IBGE foram utilizadas para a produtividade destas culturas. Diante disto, estimou-se a produção por pixel mapeado para obter os planos de informação de produção de cada cultura agrícola, bem como analisar espacialmente com as capacidades de armazenamento das UAs. Como primeiro resultado, obtivemos que a capacidade de armazenamento estática do estado atende a 64,5% da produção estadual em um ano safra, ou seja, há uma clara deficiência na capacidade armazenadora local. Na análise espacial, constata-se que essa capacidade de armazenamento está concentrada em certas regiões. O resultado são regiões com baixa capacidade de armazenamento e pode haver situações em que seja necessário percorrer até 200 Km até uma UA. Por fim, a análise dos dados mostrou que há deficiência de armazenamento na região de estudo e especifica os locais no estado em que acontece essa deficiência. Os dados também apontam os locais onde a capacidade de armazenagem atende à demanda local. Outro resultado de grande importância foi a constatação de que os dados oficiais do SICARM encontram-se desatualizados, pois, na validação dos dados, foram encontradas 417 unidades armazenadoras não cadastradas na plataforma.Submitted by Neusa Fagundes (neusa.fagundes@unioeste.br) on 2023-10-23T13:42:39Z No. of bitstreams: 1 Alex_Paulo2023.pdf: 4414106 bytes, checksum: adfddabafa6bf102b599476e261648f7 (MD5)Made available in DSpace on 2023-10-23T13:42:39Z (GMT). No. of bitstreams: 1 Alex_Paulo2023.pdf: 4414106 bytes, checksum: adfddabafa6bf102b599476e261648f7 (MD5) Previous issue date: 2023-06-16Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPqapplication/pdfpor6588633818200016417500Universidade Estadual do Oeste do ParanáCascavelPrograma de Pós-Graduação em Engenharia AgrícolaUNIOESTEBrasilCentro de Ciências Exatas e Tecnológicashttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessSoja-milho-trigoLogísticaPythonSensoriamento remotoParanáLogisticsParanáPythonRemote sensingSoybean-corn-wheatSistemas Boilógicos e AgroindustriaisAnálise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícolaGeospatial analysis of the storage capacity of storage units concerning to agricultural productioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis-53476924504160521296006006002214374442868382015-2555911436985713659reponame:Biblioteca Digital de Teses e Dissertações do UNIOESTEinstname:Universidade Estadual do Oeste do Paraná (UNIOESTE)instacron:UNIOESTEORIGINALAlex_Paulo2023.pdfAlex_Paulo2023.pdfapplication/pdf4414106http://tede.unioeste.br:8080/tede/bitstream/tede/6847/2/Alex_Paulo2023.pdfadfddabafa6bf102b599476e261648f7MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://tede.unioeste.br:8080/tede/bitstream/tede/6847/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede/68472023-11-01 09:51:50.628oai:tede.unioeste.br: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Biblioteca Digital de Teses e Dissertaçõeshttp://tede.unioeste.br/PUBhttp://tede.unioeste.br/oai/requestbiblioteca.repositorio@unioeste.bropendoar:2023-11-01T12:51:50Biblioteca Digital de Teses e Dissertações do UNIOESTE - Universidade Estadual do Oeste do Paraná (UNIOESTE)false |
dc.title.por.fl_str_mv |
Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola |
dc.title.alternative.eng.fl_str_mv |
Geospatial analysis of the storage capacity of storage units concerning to agricultural production |
title |
Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola |
spellingShingle |
Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola Paludo, Alex Soja-milho-trigo Logística Python Sensoriamento remoto Paraná Logistics Paraná Python Remote sensing Soybean-corn-wheat Sistemas Boilógicos e Agroindustriais |
title_short |
Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola |
title_full |
Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola |
title_fullStr |
Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola |
title_full_unstemmed |
Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola |
title_sort |
Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola |
author |
Paludo, Alex |
author_facet |
Paludo, Alex |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Johann, Jerry Adriani |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/3499704308301708 |
dc.contributor.advisor-co1.fl_str_mv |
Richetti, Jonathan |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/9862888690056331 |
dc.contributor.advisor-co2.fl_str_mv |
Ló, Thiago Berticelli |
dc.contributor.advisor-co2Lattes.fl_str_mv |
http://lattes.cnpq.br/6935444785347377 |
dc.contributor.referee1.fl_str_mv |
Johann, Jerry Adriani |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/3499704308301708 |
dc.contributor.referee2.fl_str_mv |
Shorr, Marcio Renan Weber |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/9116593588254710 |
dc.contributor.referee3.fl_str_mv |
Opazo, Miguel Angel Uribe |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/4179444121729414 |
dc.contributor.referee4.fl_str_mv |
Prudente, Victor Hugo Rohden |
dc.contributor.referee4Lattes.fl_str_mv |
http://lattes.cnpq.br/6154929133513022 |
dc.contributor.referee5.fl_str_mv |
Christ, Divair |
dc.contributor.referee5Lattes.fl_str_mv |
http://lattes.cnpq.br/6200553304840204 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/3065593150601602 |
dc.contributor.author.fl_str_mv |
Paludo, Alex |
contributor_str_mv |
Johann, Jerry Adriani Richetti, Jonathan Ló, Thiago Berticelli Johann, Jerry Adriani Shorr, Marcio Renan Weber Opazo, Miguel Angel Uribe Prudente, Victor Hugo Rohden Christ, Divair |
dc.subject.por.fl_str_mv |
Soja-milho-trigo Logística Python Sensoriamento remoto Paraná |
topic |
Soja-milho-trigo Logística Python Sensoriamento remoto Paraná Logistics Paraná Python Remote sensing Soybean-corn-wheat Sistemas Boilógicos e Agroindustriais |
dc.subject.eng.fl_str_mv |
Logistics Paraná Python Remote sensing Soybean-corn-wheat |
dc.subject.cnpq.fl_str_mv |
Sistemas Boilógicos e Agroindustriais |
description |
Brazil ranked first in the world in soybean production and third in corn production in 2021, thus, it was the world's largest soybean exporter. After harvesting these grains, it is essential to carry out storage before sale or consumption to keep their quality. The storage units (SUs) are used to store grains and are distributed throughout the producing regions, in order to store these products until their consumption or sale to the foreign market. Thus, this study aimed at creating an iterative algorithm with a methodology to study the spatial distribution of storage units in Paraná state – Brazil. Their storage capacity is also associated with their production, according to their places. This purpose was subdivided into two parts, referring to both goals and their respective scientific papers. The first goal of this research (Paper 1) was to create and develop an iterative algorithm in Python using Google Colab platform, and so relate the spatial data of production and storage capacity, as close as possible to the reality, as well as taking into account the distance from the productive areas to the storage units. Thus, this study used the Western Paraná mesoregion as the area to validation this algorithm. The results suggested the success when creating the algorithm, since they were validated with professionals from companies that work in the studied area, so, they know well the entire analyzed region. The created algorithm correctly performs the data spatial relationship, whose result is a map with the location of areas with storage deficit. Its second goal (Paper 2) was to carry out a detailed analysis of the spatial distribution of storage units in Paraná state, using the algorithm from the first study and relate it to spatial data. For this analysis, data from CONAB storage units were first acquired and analyzed by the SICARM platform and by visual inspection on satellite images for the whole Paraná. Units not present in the base were then geolocated to compose a complete database of SUs in the state. Their storage capacity was also estimated using a statistical modeling. Data from soybean, corn and wheat production area were obtained by mapping via remote sensing. The municipal averages published by the IBGE were used for these crops yield. Therefore, the production per mapped pixel was estimated to obtain the production information plans for each agricultural crop, in order to be able to analyze it spatially with the SUs storage capacities. As a first result, we recorded that the state's static storage capacity meats 64.5% of state production in a harvest year, showing a clear deficiency in local storage capacity. In the spatial analysis, it can be seen that this storage capacity is concentrated in some regions. The results showed that there are some regions with low storage capacity, and there may be situations where it is necessary to travel up to 200 km to a SU. Finally, data analysis showed that there is a storage deficiency in the study region and specifies the places in the state where this deficiency occurs. Data have also shown places where the storage capacity meets the local demand. Another result of great importance was the finding that the official SICARM data are outdated, as there were 417 storage units which were not registered on the platform regarding data validation. |
publishDate |
2023 |
dc.date.accessioned.fl_str_mv |
2023-10-23T13:42:39Z |
dc.date.issued.fl_str_mv |
2023-06-16 |
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.citation.fl_str_mv |
PALUDO, Alex. Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola. 2023. 86 F. Tese (Programa de Pós-Graduação em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel - PR. |
dc.identifier.uri.fl_str_mv |
https://tede.unioeste.br/handle/tede/6847 |
identifier_str_mv |
PALUDO, Alex. Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola. 2023. 86 F. Tese (Programa de Pós-Graduação em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel - PR. |
url |
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Universidade Estadual do Oeste do Paraná Cascavel |
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