Mapeamento de solos e da produtividade de plantações de Eucalyptus grandis, com uso de sistema de informação geográfica

Detailed environmental land characterization is essential for technical and financial planning, for both the scientific point of view and technological application. This work aimed at the physiographic and pedological characterization and eucalyptus productivity mapping at Itatinga Forest Sciences Experimental Station (southeastern Brazil), using geographic information systems in order to identify possible cause-effect relationships between forest productivity and soil attributes. The digital cartographic dataset was structured as follows: as primary source of data, aerial photograph and field survey were used and, as a secondary source, topographical, geological and land use occupation maps were used. For mapping wood productivity at age six (MAI6, Mean Annual Increment), inventory data of permanent plots (same species, provenance and age) were used, which were obtained from Eucalyptus grandis plantations. Using simple linear correlation and backward stepwise multiple regression analysis, the dependent variable (MAI) was related with physical and chemical characteristics of the soils. Two standards of contour curves were identified, one with close curves, narrow and surrounding the drainage network, in the steeper and lower altitude areas; the other, with spaced contour lines, in the areas of higher altitude and with plane relief. Six types of soils were characterized as being highly related to the physiographic patterns of the area: loamy sandy to sandy clayey Typic Hapludox (LVAd, 47.5%), clayey Rhodic Hapludox (LVd1, 33.4%), sandy clay Rhodic Hapludox (LVd2, 6%), clayey Rhodic Hapludox (LVdf, 9.1%), Entisols (G, 3.4%) and Fluvents soil (RY, 0.6%). There were large variations in wood productivity in the Eucalyptus grandis plantations, characterized in six classes, ranging from 26 to 52 m(3) ha(-1) yr(-1). These productivity changes were strictly related to soil mapping units. Through multiple regression analysis, we found that clay and organic matter contents were the attributes which most strongly explained the productivity differences.