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

Abstract

SUMMARYThe soil CO 2 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 CO 2 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 CO 2 emission, soil temperature, air-filled pore space, soil organic matter and soil bulk density were evaluated. All variables showed spatial dependence structure. The soil CO 2 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 CO 2 emission were associated with areas with the highest estimated values, which produced estimates from 0.18 to 1.85 t CO 2 ha -1 , according to the different scenarios considered. The knowledge of the uncertainties generated by the different scenarios can be used in inventories (r = 0,25, p < 0,05) e a porosidade livre de água (r = 0,27, p <0,01) e negativa com a densidade do solo (r = -0,41, p < 0,01)

Preparo do solo e emissão de CO2, temperatura e umidade do solo em área canavieira

“…As emissões oriundas do solo, associadas à perda de carbono do solo via emissão de CO 2 em áreas agrícolas, frequentemente não são consideradas devido à sua grande variação no tempo (Schwendenmann et al, 2003;Epron et al, 2006) e no espaço (Teixeira et al, 2012) e por ser um fenômeno resultante de uma interação complexa das propriedades físicas, químicas, biológicas e climáticas (Epron et al, 2006;Kasper et al 2009;Lal, 2009;Ussiri & Lal, 2009;Moitinho et al, 2013;Silva-Olaya et al, 2013).…”

Stochastic simulations of calcium contents in sugarcane area

“…On the other hand, SAS shows a lower variability and a weak performance, especially in the reproduction of the Ca data in the upper tail. In the OK graphs, its smoothness effect is evident, i.e., low values are overestimated while high values are underestimated (Teixeira et al, 2012). The distribution of the OK estimates does not reproduce the lower and upper tails of the empirical distribution of Ca data.…”

“…Thus, they honor the 206 observations and reproduce approximately the sample histogram and the variogram model. The differences between the maps of the realizations provide a measurement of the uncertainty (Teixeira et al, 2012). Thus, the points with high Ca contents can be considered exact, if they appear in most realizations.…”

“…The ordinary kriging (OK) technique, the most used interpolator in agricultural sciences, stands out for being easily used and relatively simple in the interpretation of its results. This technique is based on a linear interpolation procedure, which provides the best unbiased estimate for properties that vary in space (Teixeira et al, 2012). However, its estimate is smoothed and its variance does not reflect the uncertainty associated with the estimate adequately, since it only depends on data configuration (sampling grid design) and not on the values of the observations (Deustch & Journel, 1998).…”

“…As alternatives for the smoothness promoted by OK and the incapacity to reproduce the uncertainties through its variance, stochastic simulations have been widely applied for the most diverse purposes in the last years, among which some stand out: simulation of distribution and spatial variability of heavy metals in the soil, for the identification of polluted areas (Lin, 2008); evaluation of uncertainties of soil chemical (Oliveira et al, 2013) and mineralogical attributes (Silva Júnior et al, 2013), and soil classes (Silva et al, 2015); construction of different scenarios of estimates for soil CO 2 (Teixeira et al, 2012) and demarcation of erosion-prone areas (Delbari et al, 2009).…”