A dinâmica espacial da agricultura no Brasil em 2008 e 2018

• Ano de defesa: 2021
• Autor(a) principal: Silva Júnior, Marcio Aloisio
• Orientador(a): Ramos, Patrícia De Siqueira
• Banca de defesa: Dias, Adriana, Nogueira, Denismar Alves
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Alfenas
• Programa de Pós-Graduação: Programa de Pós-Graduação em Estatística Aplicada e Biometria
• Departamento: Instituto de Ciências Exatas
• País: Brasil
• Palavras-chave em Português: Análise espacial (Estatística); Estatística agrícola; Produtividade agrícola
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
• Link de acesso: https://repositorio.unifal-mg.edu.br/handle/123456789/1857

Given the influence of agriculture on society and the factors that can contribute to the spatial concentration of crops, such as climatic conditions, soil types, and topography, it is important to understand the spatial dynamics of different agricultural cultures. Such understanding allows the identification of strategic producing regions, in addition to serving as a subsidy for public policies in the sector. The understanding of the spatial dynamics of crops in Brazil becomes even more relevant, considering the different characteristics observed throughout its territory and the fact that the country is one of the largest exporters of agricultural products in the world. Thus, the objective of this work is to carry out, through exploratory analysis of spatial data, a comparative analysis of the spatial dependence of the production of different crops in Brazil, in the years of 2008 and 2018. For that, we use data from the Municipal Agricultural Production (PAM) of IBGE, from which the 12 crops were selected with the highest total values, in reais, of the production in 2018. Such crops were: cotton, rice, bananas, coffee, sugar cane, beans, tobacco, orange, cassava, corn, soybeans, and tomatoes. The variable used for spatial analysis was the quantity produced (in tons), adopting municipalities as geographic units. Among the results, it was found that all cultures showed positive spatial autocorrelation, according to the test of Moran’s I global, highlighting in both years the low value of the statistic observed for the tomato culture. The highest values observed in such a statistic were for sugarcane, coffee, and corn at 2018, and for tobacco, coffee, and sugarcane at 2008. Through thematic maps and LISA maps, for each culture, the spatial groupings of municipalities with above-average produced quantities were identified, as well as those municipalities that stand out with their neighbors. In general, the comparative analysis of cultures showed the formation of three groups, according to the spatial dynamics between the two years. The first group is made up of cultures that tend to concentrate in specific areas of the territory, while the second group is made up of cultures that in 2018 expanded the highlighted areas they presented in 2008. Finally, the third and last group presents as cultures those that presented clusters with smaller extensions and more spaced from each other.