This study aimed to identify areas that showed spatial autocorrelation for corn yield and its predictive variables (i.e., average air temperature, rainfall, solar radiation, soil agricultural potential and altitude) and to determine the most appropriate spatial regression model to explain this culture. The study was conducted using data from the municipalities of the state of Paraná relating to the summer harvests in
This paper presents a study related to soybean productivity in the west region of Parana, demonstrating how to apply some spatial analysis techniques of area using the software R. The study was conducted with data of soybeans in the period 2007/2008 to 48 municipalities. The Moran Global Index, Moran Local Index and Geary Index techniques were used to verify correlation and spatial autocorrection, these procedures were performed with the libraries spdep, sp, maptools and rcolorbrewer of the software R. Through this study it was possible to demonstrate the applicability of the software R for spatial analysis of the area, and the steps of how to generate maps for better understanding of the information studied.
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