Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

• Main Authors: Daniel De Bortoli Teixeira, Elton da Silva Bicalho, Alan Rodrigo Panosso, Luciano Ito Perillo, Juliano Luciani Iamaguti, Gener Tadeu Pereira, Newton La Scala Jr
• Format: Article
• Language: English
• Published: Sociedade Brasileira de Ciência do Solo 2012-11-01
• Series: Revista Brasileira de Ciência do Solo
• Subjects: respiração do solo; geoestatística; krigagem ordinária; simulação sequencial gaussiana; cana crua
• Online Access: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832012000500010&lng=en&tlng=en

The soil CO2 emission has high spatial variability because it depends strongly on soil properties. The purpose of this study was to (i) characterize the spatial variability of soil respiration and related properties, (ii) evaluate the accuracy of results of the ordinary kriging method and sequential Gaussian simulation, and (iii) evaluate the uncertainty in predicting the spatial variability of soil CO2 emission and other properties using sequential Gaussian simulations. The study was conducted in a sugarcane area, using a regular sampling grid with 141 points, where soil CO2 emission, soil temperature, air-filled pore space, soil organic matter and soil bulk density were evaluated. All variables showed spatial dependence structure. The soil CO2 emission was positively correlated with organic matter (r = 0.25, p < 0.05) and air-filled pore space (r = 0.27, p < 0.01) and negatively with soil bulk density (r = -0.41, p < 0.01). However, when the estimated spatial values were considered, the air-filled pore space was the variable mainly responsible for the spatial characteristics of soil respiration, with a correlation of 0.26 (p < 0.01). For all variables, individual simulations represented the cumulative distribution functions and variograms better than ordinary kriging and E-type estimates. The greatest uncertainties in predicting soil CO2 emission were associated with areas with the highest estimated values, which produced estimates from 0.18 to 1.85 t CO2 ha-1, according to the different scenarios considered. The knowledge of the uncertainties generated by the different scenarios can be used in inventories of greenhouse gases, to provide conservative estimates of the potential emission of these gases.