Modelos lineares generalizados e processos pontuais em Análise espacial de dados agrícolas

• Ano de defesa: 2018
• Autor(a) principal: Nava, Daniela Trentin
• Orientador(a): Uribe Opazo, Miguel Angel
• Banca de defesa: Rojas, Manuel Jesus Galea , De Bastiani, Fernanda , Guedes , Luciana Pagliosa Carvalho , Johann, Jerry Adriani
• 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: Análise de influência; Distribuição binomial para dados espacialmente correlacionados; Função K direcional; Processos de Poisson; Teste de ondaletas
• Palavras-chave em Inglês: Influence analysis; K directional; Poisson processes; Spatial correlated binomial distribution; Wavelet test
• Área do conhecimento CNPq: CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA
• Link de acesso: http://tede.unioeste.br/handle/tede/3768

This tesis aimed at studying spatial discrete distributions based on two different points of view, that are, spatial point processes and spatial correlated binomial distribution. The data set came from an experiment setted in an agricultural commercial area in Cascavel city Paraná State, cropped with corn. The experimental area was subdivided into 40 georeferenced patch of land and the number of plants infected by Spodoptera frugiperda was observed within each patch of land. Thus, it is assumed that the data set have a binomial distribution. A study of first order local influence was proposed in order to verify possible influential points. The results suggest that the presence of influential observations in the data set have changed the statistical inference, the predicted values and the respective maps. In a second study, our interest was the spatial distribution of the fall armyworm in the experimental area. In order to do that, we used spatial point processes, where each plant infected by the insect within the experimental area was considered as an event of interest. An anisotropy study was carried out using different point process techniques, such as K directional function and wavelet test. The results show that the spatial distribution of the fall armyworm follow a Poisson cluster process with an evident anisotropy, mainly due to the shape of the experimental area.