Qualidade do ajuste de modelos geoestatísticos utilizados na agricultura de precisão

• Autor(a) principal: Faraco, Mario Antonio
• Data de Publicação: 2006
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UNIOESTE
• Texto Completo: http://tede.unioeste.br:8080/tede/handle/tede/315
• topic: Geoestatística; estimadores; validação do ajuste; Geostatistcs; estimators; adjustment validation; CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA

Researches about the spatial variability of the soil attributes that influence the productivity are highly important for the development of new technologies that benefits the agriculture. To verify the variability of these attributes we used the geostatistic that offers techniques to the obtainment of information concerning this variability. The processes of data analysis use methods that include optimization algorithms to the choice a geostatistic model and the estimation of that model parameters. We studied the following soil attributes: soil resistance to penetration, soil density, soil humidity and the soybean s productivity variable. This paper has as its purpose to describe the spatial behavior of empiric and simulated data and to build models of spatial variability to the attributes in study with the main purpose of evaluating the quality of the adjustments according to the Criterions of Akaike, Filliben, Jackknifing and Cross Validation. The research was developed in the West Parana region, in a area of 54 ha where the typical soil of the region is the Red Distrofic Latosoil and a net with a 100 georeferred parcels was utilized. To the structure analyze of spatial dependency we used experimental semivariograms generated from sample data set. Afterward those theoretical models were adjusted to the experimental semivariograms and techniques of evaluation of the adjustments were applied to the geoestatistic models. Consequently, the results of the several methods studied were analyzed and the gotten results were compared, concluding for the cross validation as the best adjustment criterion.