The effects of increased greenhouse gas (GHG) concentrations in the atmosphere on climate change are beyond dispute. The role of agricultural management on atmospheric GHG gas concentrations is significant, not only because agriculture makes a significant contribution to increasing the global budget, but also because there is the possibility of adjusting agricultural management to sequester carbon in the soil and reduce GHG emissions. The science of these effects has matured to a point where global budgets of GHG fluxes may be estimated, and we are able to pinpoint the effect of different crop production scenarios to mitigate GHG fluxes. Therefore, a detailed analysis of the GHG emissions and economic costs associated with alternative agricultural management systems may be provided to policy makers, in an effort to compensate farmers for their investments. This requires a detailed estimation of the total GHG fluxes for baseline and alternative management scenarios across a wide range of crops, soils, and climates. Since it is impossible to continuously monitor the GHG fluxes across all these gradients and their interactions, ecosystem process models are used to simulate gas exchange for different cropping systems, soils and climates, and site managements. We used DayCent, a daily version of the CENTURY model to simulate GHG fluxes over three counties in California. We used the most detailed data available. Soil information was extracted from SSURGO (Soil Survey Geographic Database); information on land use was acquired from DWR (Department of Water Resources), and climate information from CIMIS (California Irrigation Management Information System). A framework was developed to couple the process model DayCent to a GIS database which allows us to easily carry out a large number of simulations over different gradients of soils, climates, crops and managements and interactions of these. Before a process model can be extrapolated and aggregated, detailed validation is necessary. To do this, we compiled data from 4 long-term agricultural experiments in California and attempted to model these cropping systems using the coupled GIS-DayCent framework. Management practices that were investigated include standard tillage (ST), conservation tillage (CT), organic farming methods (ORG), and the use of winter cover crops (WCC). A preliminary analysis shows that reducing tillage intensity decreases GHG emissions to some extent. This was mainly due to a reduction of the emission from fuel carbon, and not a decrease in soil GHG emissions, since the soil C sequestration was offset by increases in N2O emissions. The most important management option that led to a net mitigation of greenhouse gases was the inclusion of a winter cover crop in conservation tillage systems. Organic amendments also increased soil C sequestration, but did not lead to a net greenhouse gas mitigation. In a next step, we extrapolated these results and ran the model at a county level. The net potential of different management options for each soil and crop combination was determined. Inevitably, this extrapolation to a larger scale increases the uncertainties associated with this assessment. This was evaluated using an empirically based method to consider the structural inaccuracies in model simulations and an ensemble approach to consider model input error. The GHG mitigation potential at a county-level is dependent on the actual adaptation by farmers of these alternative cropping systems. Obviously, the latter is dependent on economical factors.