Review of APSIM's soil nitrogen modelling capability for agricultural systems analyses

CONTEXT

Over the last 26 years, researchers globally have successfully applied the soil nitrogen (N) model in the Agricultural Production Systems sIMulator (APSIM) to simulate N cycling and its effects on crop production across a range of agricultural systems and environments. As the modelling community further expands its focus to include environmental impacts of farming, it needs the model to be fit for this broader purpose.

OBJECTIVE

Accurately modelling N loss via different pathways demands more of the model and so, to inform and prioritise future development needs, we embarked on a detailed review of APSIM's soil N modelling capability.

METHODS

We conducted a comprehensive search of APSIM Soil N model verification studies and found 131 relevant publications across a wide range of systems, applications, and processes. We examined their approaches and findings, and distilled out the lessons learnt.

RESULTS AND CONCLUSIONS

The model-data comparisons showed strong performance across all modelled processes, despite limited changes to the core of the soil N model since its inception. The model's relatively simple conceptual pool approach to modelling carbon (C) dynamics with N cycling linked via C:N ratios, has proven remarkably versatile. However, these conceptual pools have posed challenges relating to initialisation methods and the resulting sensitivity of predictions at different time scales, e.g. long-term C trajectories vs. short-term seasonal N dynamics. Correctly predicting timing of N loss on a daily timestep also proved challenging, but this level of resolution may not always be required. APSIM's adaptable code structure facilitated the creation of model prototypes (e.g., ammonia volatilisation and N in runoff) allowing testing of different conceptualisations ahead of formal release.

SIGNIFICANCE

APSIM is one of the most widely used agricultural systems models. This review, which covers model documentation, model-data comparisons, various approaches to parameterisation, and prototypes for additional processes, consolidates decades of research into insights about the model and its functioning. The review highlights the importance of model evaluations across a wide range of applications to ensure model robustness, to identify issues that may be masked in single studies, and to allow the emergence of solutions with broad applicability.