A modelling framework for assessing the plausible impacts of biological nitrification inhibition in cropping systems

CONTEXT: Retaining nitrogen (N) in soils in the form of ammonium (NH₄⁺) by inhibiting nitrification has been proposed as a strategy to reduce N gaseous losses and nitrate (NO₃^-) leaching. Biological nitrification inhibition (BNI) involves the release of natural metabolites from crop roots that suppress nitrifying microbes. Unlike synthetic nitrification inhibitors BNIs act directly in the rhizosphere and may provide a more spatially and temporally sustained inhibition. Because BNI effectiveness depends on crop species, and interactions with biophysical factors, a systems modelling approach is needed to assess its plausible benefits in cropping systems. OBJECTIVE: (1) develop a BNI model suitable for integration into systems models, enabling simulation of BNI release, fate, and bioactivity within cropping systems, and (2) use the model in-silico to illustrate how system interactions influence BNI impacts. METHODS: A BNI subroutine was developed and integrated into the Agricultural Production Systems sIMulator (APSIM) Next Generation to model BNI exudation, bioactivity, fate, and persistence in soil. Simulations for wheat, canola and sorghum were conducted to assess its plausible effects on N cycling and crop productivity. RESULTS AND CONCLUSIONS: Four prerequisite conditions under which within-season plausible N loss and yield benefits may be realized from BNI: (i) adequate root growth and BNI release achieving effective bioactivity, (ii) BNI persistence with slow degradation at most 50% daily degradation to ensure longevity of inhibition, (iii) the crop being able to take up N in both NH₄⁺ and NO₃^- forms ensuring that ‘saved N’ is assimilated and (iv) occurrence of N loss events when BNI is active. When these conditions co-occurred, simulated systems showed decreased N loss, and/or yield responses. SIGNIFICANCE: The integrated APSIM-BNI framework provides a tool for exploring where and when BNI may deliver agronomic and environmental benefits and guiding future field experiment and trait improvement efforts.