Variabilidade espacial utilizando modelos geoestatísticos escalonados e com repetições múltiplas independentes na agricultura de precisão

• Ano de defesa: 2013
• Autor(a) principal: Wendpap, Bruna Gabriela
• Orientador(a): Opazo, Miguel Angel Uribe
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual do Oeste do Parana
• Programa de Pós-Graduação: Programa de Pós-Graduação "Stricto Sensu" em Engenharia Agrícola
• Departamento: Engenharia
• País: BR
• Palavras-chave em Português: função semivariância; máxima verossimilhança; mapas temáticos.
• Palavras-chave em Inglês: semivariance function; maximum likelihood; thematic maps
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA
• Link de acesso: http://tede.unioeste.br:8080/tede/handle/tede/2608

The objective of this paper was to present a study of spatial variability in different time periods of two experimental areas using geostatistical models scaling and spatial linear gaussian with multiple independent replications. In the first area under study, the scaling of semivariance function method and spatial linear model with multiple independent replications was used. The structures of spatial variability of the potassium content in soil and soybean yield in five agricultural years were compared. The results indicate similarity between the thematic maps produced according to individual models and maps generated using the model set to scaled semivariogram. The same happens to build thematic maps according to the individual models compared to maps generated according to the spatial linear models with multiple independent replications. Comparing the maps originated by the scaled model and spatial linear model with multiple repetitions, high levels of accuracy were obtained, which implies similarity of thematic maps built with these two methods. In the second area under study the interest was to use the spatial linear model with multiple independent replications to study the spatial variability of soybean yield in both years as a function of covariates soil resistance to penetration (RSP) and bulk density (Dens) in the layers 0-0.10, 0.10-0.20 and 0.20-0.30 m deep. In both studies, the structure of spatial variability estimated by spatial linear model with multiple independent replications caused reduction of computational time in the adjustment of models and the generation of thematic maps.