Abstract: The identification of site-specific agricultural management practices in order to maximize yield while minimizing environmental nitrogen losses remains in the center of environmental pollution research. Here, we used the biogeochemical model LandscapeDNDC to explore different agricultural practices with regard to their potential to reduce soil N2O emissions and NO3 leaching while maintaining yields. In a first step, the model was tested against observations of N2O emissions, NO3 leaching, soil micrometeorology as well as crop growth for eight European cropland and grassland sites. Across sites, LandscapeDNDC predicts very well mean N2O emissions (r(2)=0.99) and simulates the magnitude and general temporal dynamics of soil inorganic nitrogen pools. For the assessment of site-specific mitigation potentials of environmental nitrogen losses a Monte Carlo optimization technique considering different agricultural management options (i.e., timing of planting, harvest and fertilization, amount of applied fertilizer as well as residue management) was used. The identified optimized field management practices reduce N2O emissions and NO3 leaching from croplands on average by 21% and 31%, respectively. Likewise, average reductions of 55% for N2O emissions and 16% for NO3 leaching are estimated for grasslands. For mitigating environmental loss – while maintaining yield levels – it was most important to reduce fertilizer application rates by in average 10%. Our analyses indicate that yield scaled N2O emissions and NO3 leaching indicate possible improvements of nitrogen use efficiencies in European farming systems. Moreover, the applied optimization approach can be used also in a prognostic way to predict optimal timings and fertilization options (rates and splitting) upon accurate weather forecasts combined with the knowledge of modeled soil nutrient availability and plant nitrogen demand.

Abstract: • Comparison of two methods for soil organic C quantification is presented. • Springer–Klee wet digestion and dry combustion with automated analyser were compared. • Soil samples were collected from three different sites in a Southern Italy area. • Recoveries close to one were observed for whole dataset and for data grouped per site. • The strong agreement between the methods would enable direct comparison of results. Abstract Soil organic carbon (SOC) is the largest carbon pool in the terrestrial biosphere and it is among the most important factors responsible for conservation of soil quality. Automated dry combustion techniques are gradually replacing traditional quantification methods based on wet digestion chemistry. Critical comparison of different methods is fundamental to reevaluate archives of SOC data and accurately assess and model long-term carbon stock variation and should be performed for different soil types and management conditions. Two analytical methods, the Springer–Klee wet digestion and the dry combustion using an automated analyser, were compared for soils typical of a Mediterranean environment in Southern Italy. Soil samples were collected from three sites, at two depths. Soils were fine textured (from clay–loam to clay) with total carbonate ranging from 6.6 to 16.7 g 100 g− 1. SOC content varied from 6.92 to 28.86 g kg− 1 (as average of the two methods), with values and ranges typical of Southern Europe. On average, Springer–Klee method gave slightly higher values and showed greater data variability. This behaviour, in agreement with other studies, can be attributed to the reaction of K2Cr2O7 with other soil constituents and to analytical constraints. Our results suggest high consistency between Springer–Klee and dry combustion techniques and show recoveries close to one both for the whole dataset and for data grouped per experimental site or soil depth. Linear regression equations between the two methods were slightly affected by different soil types (P = 0.0621). The best fitting of the relationship was a linear regression passing through the origin for the whole dataset (Radj2 = 0.965; RPD = 3.41). The strong overall agreement observed between the two methods would enable the direct comparison of new data set with those already existing in Southern Italy for soils with similar characteristics.