Análise geospacial da capacidade de armazenamento de unidades armazenadoras em relação à produção agrícola
| Ano de defesa: | 2023 |
|---|---|
| Autor(a) principal: |
Paludo, Alex
 |
| Orientador(a): |
Johann, Jerry Adriani
|
| Banca de defesa: |
Johann, Jerry Adriani
,
Shorr, Marcio Renan Weber
,
Opazo, Miguel Angel Uribe
,
Prudente, Victor Hugo Rohden
,
Christ, Divair
|
| Tipo de documento: | Tese |
| Tipo de acesso: | Acesso aberto |
| Idioma: | por |
| Instituição de defesa: |
Universidade Estadual do Oeste do Paraná
Cascavel |
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia Agrícola
|
| Departamento: |
Centro de Ciências Exatas e Tecnológicas
|
| País: |
Brasil
|
| Palavras-chave em Português: | |
| Palavras-chave em Inglês: | |
| Área do conhecimento CNPq: | |
| Link de acesso: | 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. |