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Tillage erosion is known to be a major soil degradation process that is mainly associated with increasingly mechanised agriculture since the early 1950s. However, especially soil truncation on convex hilltops and slope shoulders can be already identified on historical aerial photos of our study region in Northeast Germany from the 1950s.The aim of the study is to better understand the effect of mechanised and especially long-term non-mechanised soil redistribution processes on soil organic carbon (SOC) dynamics over the past 1000 years since the beginning of widespread soil cultivation in our study region and their contribution to the question of soil being a carbon (C) sink or source.Therefore, a modified version of the spatially explicit soil redistribution and C turnover model SPEROS-C was applied on a large-scale catchment (approx. 200 km²) to simulate lateral soil and SOC redistribution, SOC turnover and erosion-induced vertical mixing within the profile (spatial and vertical resolution 5 m x 5 m and 0.1 m soil depth increments, respectively). The uncertainty of the modelling approach was estimated by varying the input variables according to different realisations of the development of agricultural management over the past 1000 years. The results were validated with an erosion classification derived from Sentinel-2 data and UAV based estimation of topsoil SOC. The lowest SOC stocks were found on hilltops, which points at tillage erosion as the major driver of soil degradation.Our results show that the beginning influence of tillage erosion on catchment wide vertical SOC fluxes can be traced back to around 500 years ago. This clearly indicates that non-mechanised tillage erosion from the early stage of cultivation affected the SOC patterns in the study area and hence impacts todays C cycling.

The significance of biogenic silicon (BSi) pools as a key factor for the control of Si fluxes from terrestrial to aquatic ecosystems has been recognized since decades. However, while most research has been focused on phytogenic Si pools, knowledge on other BSi pools is still limited. We hypothesized different BSi pools to influence short-term changes of the water soluble Si fraction in soils to different extents. To test our hypothesis we took plant (Calamagrostis epigejos, Phragmites australis) and soil samples in an artificial catchment in a post-mining landscape in the state of Brandenburg, Germany. We quantified phytogenic (phytoliths), protistic (diatom frustules and testate amoeba shells) and zoogenic (sponge spicules) Si pools as well as Tiron extractable and water soluble Si fractions in soils at the beginning (t0) and after ten years (t10) of ecosystem development. As expected the results of Tiron extraction showed, that there are no consistent changes of the amorphous Si pool at Chicken Creek as early as after ten years. In contrast, compared to t0 we found increased water soluble Si and BSi pools at t10, thus we concluded BSi pools to be the main driver of short-term changes of water soluble Si. However, because total BSi represents only small proportions of water soluble Si at t0 (

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m−2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.