Plant available nitrogen (N), commonly applied in agricultural soils through the use of inorganic and organic fertilisers, when surpasses the N requirement to maintain a targeted crop yield, is lost from the soil into the environment where it has negative impacts, including – climate change, ozone layer depletion, air and ground water pollution, eutrophication of water bodies, acidification of soil and water etc. The ‘4R of Nutrient Stewardship’ (4RNS), promotes the application of fertilisers at the right time, right place, right rate and from the right source - to meet a targeted yield, seeking to prevent surplus N supply. Process-oriented biogeochemical models can help to investigate and identify the potential of incorporating spatially explicit information into N management plans to achieve 4RNS objectives, enabling simulated yield and N loss via different pathways to be estimated, while explicitly accounting for soil and atmospheric variables, management and their impact on nutrient dynamics. In this research we investigate the scope of the DNDC (DeNitrification DeComposition) model to inform more geographically refined N management plans, for intensively managed Irish grasslands that are currently managed by aspatial N management strategies at farm and national level. A score of 20 % or less relative deviation of estimated annual yield and N loss was used as a benchmark of deciding reliability of model performance, while tools like mean absolute error, root mean square error and correlation was applied to compare the model performance at a daily scale with respect to existing studies, as required. Our study showed that the DNDC model reliably estimates site-specific grass growth rate and annual yield when the correct parameterisations for the crop phenology and local background atmospheric conditions are accounted for within the model. The model performs well when site-specific soil and management inputs are used, as well as for more generalised inputs - relevant for sites with limited availability of site-specific information. However, to generate reliable annual estimates of both yield and N loss via different pathways, it is necessary to include site-specific soil inputs including water filled pore spaces (WFPS) at field capacity (FC) and wilting point (WP). At a daily scale, the correlation between available measured and estimated N loss was poor. However, the errors at daily scale and relative deviation at annual scale were lower in comparison to existing results published. A scenario analysis showed that key environmental variables explaining spatial variation of nitrate (NO3 -) leaching varies with the annual N application rate. Whereas the key variables relevant for regulating annual yield and annual N loss through ammonia (NH3) volatilisation and nitrous oxide (N2O) emissions, identified through one factor at a time sensitivity analysis (with categorising output on the basis of sensitivity index of greater than 10 % as sensitive, between 0.1 % to 10 % as potentially sensitive and less than 0.1 % as not sensitive), relevant to develop more simplified and robust models for site-specific N management, were – soil texture and clay content, soil organic carbon (SOC), bulk density (BD), pH, stocking rate and annual N fertiliser application, annual rainfall and average annual temperature. Finally, this work also sought to identify if a robust application of DNDC is possible to reliably simulate spatial variation of grass yield and N loss - when default inputs are used for non-mandatory soil and atmospheric variables, while the model is parameterised for crop phenology of perennial ryegrass. This study showed that such application is only limited for estimation of spatial variation of yield and NO3 - leaching – while yield itself is an indicator of potential N2O emissions.