The application of nitrogenous fertilisers to agricultural soils is a major source of anthropogenic N2O emissions. Reducing the nitrogen (N) footprint of agriculture is a global challenge that depends, among other things, on our ability to quantify the N2O emission intensity of the world’s most widespread and productive agricultural systems. In this context, biogeochemistry (BGC) models are widely used to estimate soil N2O emissions in agroecosystems. The choice of spatial scale is crucial because larger scale studies are limited by low input data precision while smaller scale studies lack wider relevance. The robustnessof large-scale model predictions depends on preliminary and data-demanding model calibration/validation while relevant studies often omit the performance of output uncertainty analysis and underreport model outputs that would allow a critical assessment of results. This study takes a novel approach on these aspects. The study focuses on arable Eastern Scotland; a data-rich region typical of Northwest Europe in terms of edaphoclimatic conditions, cropping patterns and productivity levels. We used a calibrated and locally-validated BGC model to simulate direct soil N2O emissions along with NO3 leaching and crop N uptake in fields of barley, wheat and oilseed rape. We found that 0.59% (0.36) of the applied N is emitted as N2O while 37% (6) is taken up by crops and 14% (7) is leached as NO3. We show that crop type is a key determinant of N2O emission factors (EF) with cereals having a low (mean EF<0.6%), and oilseed rape a high (mean EF=2.48%), N2O emission intensity. Fertiliser addition was the most important N2O emissions driver suggesting that appropriate actions can reduce crop N2O intensity. Finally, we estimated a 74% relative uncertainty around N2O predictions attributable to soil data variability. However, we argue that higher resolution soil data alone might not suffice to reduce this uncertainty.