Modelling of nitrification inhibitor and its effects on emissions of nitrous oxide (N2O) in the UK.

Global food demand requires increased uses of fertilizers, leading to nitrous oxide (N2O) and nitrate leaching due to overuse of fertilizers and poor timing between fertilizer application and plant growth. Using nitrification inhibitors (NIs) can reduce the N2O emissions but the effectiveness of NIs strongly depend on environmental conditions, and their benefits have been limited due to less than optimal nitrogen rates, timing, quantity, and placement of NIs. Process-based modelling can be helpful in improving the understanding of nitrogen fertilizer with NIs and their effects in different environmental conditions and agricultural practices. But few studies of modelling NIs with application to agricultural soils have been performed. In this paper, we developed a sophisticated biogeochemical reaction process of NIs applied to agricultural soils, which account for the factions of NIs with fertilizer by combining the application rate, soil moisture, and temperature within the DeNitrification DeComposition (DNDC) framework. This model was tested against the data from two agricultural farms in Preston Wynne and Newark in the UK. The results agreed well with the measured data and captured the measured soil moistures and N2O emissions. In Newark, the average Mean Absolute Error of all blocks is 8.78 and 5.45 for ammonium nitrate or urea respectively while in Preston Wynne, 3.48 and 3.14. The results also showed that the warming climate can greatly reduce the efficiency of nitrification inhibitors, which will further amplify the greenhouse gas impacts. The modified DNDC model of nitrification inhibitor modules can reliably simulate the inhibitory effect of NIs on N2O emissions and evaluate the efficiency of NIs. This enables end-users to optimize the amount of NIs used according to the time and climate conditions of fertilizer application for increasing crop yield and reducing N2O emissions and provides a useful tool for estimating the efficiency of NIs in agricultural management.