Análise exploratória de dados espaciais aplicada a produtividade de milho no estado do Paraná - Seffrin, Rodolfo (2017)
Corn cultivation is one of the most important agricultural activities for the Brazilian economy and the use of statistical models can help decision making in this productive sector. The present study aimed to identify areas with correlation and spatial autocorrelation for maize productivity and its predictive variables (mean temperature, rainfall, solar radiation, soil potential and altitude), and to verify the spatial regression model most appropriate for The explanation of culture. The study was carried out using data from municipalities in the state of Paraná referring to summer crops of the 2011/2012, 2012/2013 and 2013/2014 agricultural years. The software used for the statistical analysis and generation of thematic maps were ArcMap 9.3 and GeoDa 1.6.7. The identification of spatial dependence among variables was performed using the Moran Global Index (Univariate and Bivariate) and the local spatial association index (LISA). It was concluded that for all the years and neighborhood criteria used, there was spatial autocorrelation Significant at the 1% level for all variables. It was also verified that the average temperature, precipitation and altitude, are correlated significantly (P-value <5%) with corn yield in all years and criteria studied. The variables: solar radiation and soil agricultural potential did not present significant correlation for some of the years (2012/2013) and neighborhood matrices (queen contingency and nearest neighbor). To determine the most appropriate regression model for estimating maize productivity, the OLS - Ordinary Least Square regression model was used to verify if it is necessary to apply some spatial regression model to explain the data. For all agricultural years it was recommended to use the spatial regression model Spatial Lag Model (SLM), and only for the agricultural year 2013/2014 can the Spatial Error Model (SEM) be recommended. The spatial regression (SLM and SEM) adopted for estimating maize productivity in different years yielded better results when compared with regression results that did not incorporate spatial data autocorrelation (OLS). The coefficient of determination R², the Bayesian information criteria (BIC) and the maximum log-likelihood value, showed a significant improvement in corn productivity estimation when using SLM and SEM.