Precise estimations of soil organic carbon (SOC) stocks are of decided importance for the detection of C sequestration or emission potential induced by land use changes. For Germany, a comprehensive, land use–specific SOC data set has not yet been compiled. We evaluated a unique data set of 1460 soil profiles in southeast Germany in order to calculate representative SOC stocks to a depth of 1 m for the main land use types. The results showed that grassland soils stored the highest amount of SOC, with a median value of 11.8 kg m−2, whereas considerably lower stocks of 9.8 and 9.0 kg m−2 were found for forest and cropland soils, respectively. However, the differences between extensively used land (grassland, forest) and cropland were much lower compared with results from other studies in central European countries. The depth distribution of SOC showed that despite low SOC concentrations in A horizons of cropland soils, their stocks were not considerably lower compared with other land uses. This was due to a deepening of the topsoil compared with grassland soils. Higher grassland SOC stocks were caused by an accumulation of SOC in the B horizon which was attributable to a high proportion of C‐rich Gleysols within grassland soils. This demonstrates the relevance of pedogenetic SOC inventories instead of solely land use–based approaches. Our study indicated that cultivation‐induced SOC depletion was probably often overestimated since most studies use fixed depth increments. Moreover, the application of modelled parameters in SOC inventories is questioned because a calculation of SOC stocks using different pedotransfer functions revealed considerably biased results. We recommend SOC stocks be determined by horizon for the entire soil profile in order to estimate the impact of land use changes precisely and to evaluate C sequestration potentials more accurately.

We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on equally spaced observations. Using the fact that the increments of the COGARCH(1,1) process are strongly mixing with exponential rate, we show that the resulting estimators are consistent and asymptotically normal. We investigate the empirical quality of our estimators in a simulation study based on the variance gamma driven COGARCH(1,1) model. The estimated volatility with corresponding residual analysis is also presented. Finally, we fit the model to high-frequency data. Copyright Royal Economic Society 2007

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