Forecasting carbon and nitrogen cycling from intensively managed grassland systems using the QUINCY land surface model

Grasslands cover a substantial part of the global ice-free land area (~40%) and they store about one third of the terrestrial carbon stock globally. These ecosystems and their significant carbon stocks are very susceptible to climate change and are often extensively managed for human use. This management, including grazing, cutting and fertilising is known to have an impact on carbon (C) and nitrogen (N) fluxes with implications for greenhouse gas (GHG) emissions and surface and groundwater pollution. In the Republic of Ireland, grasslands cover roughly 60% of the land area and the agricultural sector is the largest emitter of GHGs and contributes roughly 38% of national emissions. It is therefore critical to be able to understand and predict the interactions between management and GHG budgets. Land surface models can be an invaluable tool in this endeavour, allowing us to test a multitude of management practices and their interactions as an in sillico experiment.

We investigate the ecosystem C and N budgets as affected by long-term management of grasslands in the form of N addition (fertilizer, slurry i.e., organic N) and grazing over 50 years using the QUINCY LSM. Based on local management data, we test different N application rates across time (between 50 and 300 kg N ha^-1 year^-1) in combination with different grazing intensities (0.5 to 5 livestock units ha^-1) and timing of grazing at four Irish grasslands. We show that applying yearly fertilizer amounts exceeding 150 kg N ha^-1 does not significantly increase grassland aboveground net primary productivity (ANPP) and most N entering the system is lost through leaching and nitrous oxide (N₂O) emissions, while no or very low N addition combined with grazing results in decreasing C storage We further use the resulting N addition and grazing scenarios to identify best potential practices for balancing C storage, GHG emissions and grassland productivity. Beyond providing insights into C and N cycling processes in managed grasslands, our study also points to a pathway for using complex process-based models to guide management practices and policy.