Work

Enhanced ammonia volatilization prediction with EPIC: Model description and testing of different fertilizers

Year: 2025

Type: article

Abstract: Biogeochemical models are promising cost-effective tools to evaluate ammonia (NH 3 ) volatilization reduction strategies at large spatial scales provided they capture all the relevant processes that regulate universally the nitrogen (N) cycle in agroecosystems. This work aims to enhance the Environmental Policy Integrated Climate (EPIC) model to improve its ability to simulate NH 3 volatilization from both organic and mineral N fertilizers in agricultural fields. Extant algorithms of NH 3 volatilization in EPIC were replaced with a mechanistic submodel that, operating at hourly steps, effectively simulated NH 3 volatilization by capturing processes of ammonium (NH 4 + ) adsorption, urea hydrolysis, soil pH-based partitioning of total ammoniacal N into NH 3 and NH 4 + , and mass transfer. The new EPIC submodel was calibrated and validated using data from two different locations (Legnaro, NE Italy; Eatonton, Georgia, USA), including several combinations of fertilizer types and application methods in different pedo-climatic conditions. Results showed that the new submodel provided more accurate estimates of cumulative NH 3 loss than the original one (validated R² = 0.79 vs. 0.50, validated RMSE = 10.6 vs 17.8 kg N ha −1 ). Furthermore, the implementation now enables more accurate simulation of fertilizer types and management, incorporating the effect of fertilizer pH, depth and method of application, and infiltration of the liquid fraction. In conclusion, the updated EPIC reported here becomes an effective tool to evaluate and select best agricultural practices capable of reducing NH 3 volatilization. • NH 3 volatilization simulated with EPIC was improved with a mechanistic algorithm. • NH 3 losses predicted with EPIC now depend on NH 4 + adsorption, pH, chemical equilibria, and mass transfer characterizations. • Sensitivity analysis identified soil pH as a key factor influencing NH 3 loss. • Hourly based submodel improved the prediction of daily NH 3 peaks with EPIC.

Source: European Journal of Agronomy

DOI: 10.1016/j.eja.2025.127616