Objectives Implementing Climate smart agriculture (CSA) agricultural practices in cropping systems can help to mitigate and even offset negative environmental impacts that contribute to climate change, soil erosion, and nutrient loss. A modeling approach was developed to provide a scalable, geographically-explicit accounting framework for quantifying greenhouse gas (GHG) emissions reductions associated with adoption of CSA practices in cropping systems. Methods A model-based GHG accounting framework was developed to quantify spatially-explicit GHG reductions associated with the adoption of specific CSA practices. To enable analysis of large geographic regions, the framework uses a cloud-based computational infrastructure that deploys the DNDC 9.5 biogeochemistry process model to quantify carbon and nitrogen impacts of CSA practice scenarios. Results Specific practices included in the model were; conversion to reduced and no-tillage, adoption of cereal and legume cover crops, and alternative N-fertilizer application timing. In total, 648 management scenarios were simulated across all fields. Transitioning to no-tillage had the most significant effect on GHG emissions. Regional scale impacts associated with a transition from conventional- to reduced- or no-tillage indicated a GHG reduction of 262.7 and 2015.6 kg ha−1 yr−1 of CO2e (carbon dioxide equivalent), respectively. Additional GHG emissions reductions were identified for other practices such as cover cropping and improved fertilizer management. Conclusions Widespread adoption of CSA practices has the potential to greatly reduce GHG emissions associated with agriculture, improving the sustainability of food production. Potential impacts of such practices depend on localized weather and soil condition which vary both temporally and geographically. Capturing the effects of spatial and temporal variability with the above modeling framework are needed to identify and strategically target the integration of CSA practices to specific areas where the practices are most impactful and cost-effective. Funding Sources Model framework development and multi-state analysis were partially funded by 2016 NRCS Conservation Innovation Grant.