Papers

Summary

The methodology used to characterize the spatial variability of soil CO2 emissions can significantly affect the accuracy of the estimate obtained. The aim of this study was to compare the predictions of soil CO2 emission under cultivation of sugar cane as estimated by the methods of ordinary kriging and sequential Gaussian simulation. In the study area was installed a sampling grid with 141 points where the emission was measured over a seven-day trial. Spherical and Gaussian models were fitted to describe the spatial variability. The technique of ordinary kriging produced in all the days considered a representation closer to the observed data field, an increase of accuracy ranging from 7.4 to 34.8% over the sequential Gaussian simulation.