Optimizing Ammonia Volatilization Simulation in Agricultural Soils: Advancements of the EPIC Model

Agriculture is responsible for about 94% of UE ammonia (NH3) emissions, notably from livestock, manure management and soil fertilization. NH3 volatilization is a significant cause of reactive nitrogen (N) loss, leading to lower fertilizer efficiency as well as environmental and health concerns. Loss predictions can be estimated using process-based biogeochemical models, but many of them lack precise estimations of NH3 volatilization. In this work, we modified the Environmental Policy Integrated Climate (EPIC) model incorporating a mechanistic sub-model to simulate NH3 volatilization following the application of N fertilizers in agricultural fields. The newly added algorithm in EPIC functions on an hourly time step and describes the ammonium (NH4+) adsorption by clay and organic matter and estimates the partitioning of total ammoniacal N into NH3 and NH4+ based on the pH of the soil solution. The sub-model then determines the NH3 concentration in the gas phase using Henry’s law and estimates NH3 emission using a mass transfer coefficient that considers the resistance in the turbulent and laminar layers. Additionally, the sub-model uses the soil’s pH buffering capacity to recalculate pH following hydrogen ion consumption by urea hydrolysis and hydrogen ion release by NH3 volatilization. The sub-model further integrates a reduction factor for volatilization to account for the effects of soil layer depth and the depth of fertilizer application. The new EPIC sub-model was validated using datasets from Veneto, NE Italy, and Georgia, USA. In Italy, NH3 volatilization was measured in four experiments, testing cattle slurry, farmyard manure, and mixed silage maize and animal slurry digestate. Whereas in Georgia, NH3 volatilization was examined following surface application of urea and poultry manure to grasslands. The new sub-model improved NH3 loss prediction, yielding reasonable hourly NH3 fluxes and cumulative volatilization estimates. As a result, the EPIC model exhibited lower prediction errors for soil mineral N (e.g. NH4+and NO3-) dynamics. While the new sub-model marks a notable advancement in accurately modeling N cycling, additional enhancements should prioritize certain modeling aspects, including slurry infiltration rates, NH3 fluxes within the soil profile, and the mitigation effects resulting from urease inhibitor application.