Search

An important goal of sustainable agriculture is to maintain soil quality. Soil aggregation, which can serve as a measure of soil quality, plays an important role in maintaining soil structure, fertility, and stability. The process of soil aggregation can be affected through impacts on biotic and abiotic factors. Here, we tested whether soil management involving application of organic and mineral fertilizers could significantly improve soil aggregation and if variation among differently fertilized soils could be specifically attributed to a particular biotic and/or abiotic soil parameter. In a field experiment within Central Europe, we assessed stability of 1–2 mm soil aggregates together with other parameters of soil samples from differently fertilized soils. Application of compost and digestates increased stability of soil aggregates. Most of the variation in soil aggregation caused by different fertilizers was associated with soil organic carbon lability, occurrence of aromatic functional groups, and variations in abundance of eubacteria, total glomalins, concentrations of total S, N, C, and hot water extractable C. In summary, we have shown that application of compost and digestates improves stability of soil aggregates and that this is accompanied by increased soil fertility, decomposition resistance, and abundance of total glomalins and eubacteria. These probably play significant roles in increasing stability of soil aggregates.

Regional monitoring, reporting and verification of soil organic carbon change occurring in managed cropland are indispensable to support carbon-related policies. Rapidly evolving gridded agronomic models can facilitate these efforts throughout Europe. However, their performance in modelling soil carbon dynamics at regional scale is yet unexplored. Importantly, as such models are often driven by large-scale inputs, they need to be benchmarked against field experiments. We elucidate the level of detail that needs to be incorporated in gridded models to robustly estimate regional soil carbon dynamics in managed cropland, testing the approach for regions in the Czech Republic. We first calibrated the biogeochemical Environmental Policy Integrated Climate (EPIC) model against long-term experiments. Subsequently, we examined the EPIC model within a top-down gridded modelling framework constructed for European agricultural soils from Europe-wide datasets and regional land-use statistics. We explored the top-down, as opposed to a bottom-up, modelling approach for reporting agronomically relevant and verifiable soil carbon dynamics. In comparison with a no-input baseline, the regional EPIC model suggested soil carbon changes (~0.1–0.5 Mg C ha−1 y−1) consistent with empirical-based studies for all studied agricultural practices. However, inaccurate soil information, crop management inputs, or inappropriate model calibration may undermine regional modelling of cropland management effect on carbon since each of the three components carry uncertainty (~0.5–1.5 Mg C ha−1 y−1) that is substantially larger than the actual effect of agricultural practices relative to the no-input baseline. Besides, inaccurate soil data obtained from the background datasets biased the simulated carbon trends compared to observations, thus hampering the model's verifiability at the locations of field experiments. Encouragingly, the top-down agricultural management derived from regional land-use statistics proved suitable for the estimation of soil carbon dynamics consistently with actual field practices. Despite sensitivity to biophysical parameters, we found a robust scalability of the soil organic carbon routine for various climatic regions and soil types represented in the Czech experiments. The model performed better than the tier 1 methodology of the Intergovernmental Panel on Climate Change, which indicates a great potential for improved carbon change modelling over larger political regions.