Book Description

Soil organic carbon (SOC) concentrations and greenhouse gas (GHG) emissions are not uniform across the landscape, but assemble in “hotspots” in specific areas. These differences are mainly driven by human-induced activities such as agricultural management. 40-50% of the Earth's land surface is under agricultural land-use, for instance cropland, managed grassland and permanent crops including agro-forestry and bio-energy crops. Furthermore, 62% of the global soil C stock is SOC and the soil stores more than 3 times more C than the atmosphere. Thus, C sequestration in agricultural soil has a potentially important role in increasing SOC storage and GHG mitigation, and there is considerable interest in understanding the effects of agricultural management on SOC and GHG fluxes in both grasslands and croplands, in order to better assess the uncertainty and vulnerability of terrestrial SOC reservoirs. For the sake of discovering the agricultural management practices relating to the effective and sustainable C sequestration in agricultural lands in Europe, simulating future terrestrial C stocks and GHG budgets under varied agricultural management systems in major European ecosystems is essential. Using models is a useful method with the purpose of this and abundant studies have carried out. However, many model results have not been validated with reliable observed long-term data, while other studies have reported a strong impact of model initialisation on model result. Nevertheless, predictions of annual to decadal variability in the European terrestrial C and GHG ressources largely rely on model results. Consequently, finding the most appropriate and comprehensive model initialisation method for obtaining reliable model simulations became important, especially for process-based ecosystem models. In recent years, Zimmermann et al. (2007) have succeed in initialising the Rothamsted Carbon model (RothC) using a physical and chemical soil fractionation method. For that reason, we hypothesised that measured detailed SOC data would be useful to initialise ecosystem models, and this hypothesis should be tested for different process-based models and agricultural land-use and management. (...).