Análise da produtividade da soja associada a fatores agrometeorológicos, por meio de estatística espacial de área na Região Oeste do Estado do Paraná

• Autor(a) principal: Araújo, Everton Coimbra de
• Data de Publicação: 2012
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UNIOESTE
• Texto Completo: http://tede.unioeste.br:8080/tede/handle/tede/2893
• topic: Autocorrelação espacial; Similaridade espacial; Regressão espacial; Spatial autocorrelation; spatial similarity; similarity index; CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA

This paper aimed to present methods to be applied in the area of spatial statistics on soybean yield and agrometeorological factors in Western Paraná state. The data used, related to crop years from 2000/2001 to 2007/2008, are the following variables: soybean yield (t ha-1) and agrometeorological factors, such as rainfall (mm), average temperature (oC) and solar global radiation average (W m-2). In the first phase,it was used indices of spatial autocorrelation (Moran Global and Local) and presented multiple spatial regression models, with performance evaluations. The estimation of parameters occurred when using the Maximum Likelihood method and the performance evaluation of the models was based on the coefficient of determination (R2), the maximum value of the function of the logarithm of the maximum value of the likelihood function logarithm and the Bayesian information criterion of Schwarz. In a second step, cluster analysis was performed using spatial statistical multivariate associations, seeking to identify the same set of variables, but with a larger number of crop years. Finally, the data from one crop year were utilized in an approach based on fuzzy clustering, through the Fuzzy C-Means algorithm and the similarity measure by defining an index for this purpose. The first phase of the study showed the correlation between spatial autocorrelation and soybean yield and agrometeorological elements, through the analysis of spatial area, using techniques such as index Global Moran's I and Local univariate and bivariate and significance tests. It was possible to demonstrate, through the performance indicators used, that the SAR and CAR models offered better results than the classical multiple regression model. In the second phase, it was possible to present the formation of groups of cities using the similarities of the variables under analysis. Cluster analysis is a useful tool for better management of production activities in agriculture, since, with the grouping, it was possible to establish similarities parameters that provide better management of production processes that bring quantitative and qualitatively better, results sought by the farmer. In the final step, through the use of Fuzzy C-Means algorithm, it was possible to form groups of cities of similar soybean yield using the method of decision by the Higher Degree of Relevance (MDMGP) and Method of Decision Threshold by β (β CDM). Subsequently, identification of the adequate number of clusters was obtained using modified partition entropy. To measure the degree of similarity of each cluster, a Cluster Similarity Index (ISCl) was designed and used, which considers the degree of relevance of each city within the group to which it belongs. Within the perspective of this study, the method used was adequate, allowing to identify clusters of cities with degrees of similarities in the order of 60 to 78%