In silico dataset on nitrogen banking strategies in Australian cropping systems

Full description

This collection contains the simulation data, metadata, and supporting materials generated for an Australia‑wide in‑silico assessment of strategic nitrogen (N) banking in broadacre dryland cropping systems. The dataset underpins analyses reported in the associated research article and provides a national‑scale resource for evaluating optimal N supply strategies across diverse soil–climate–rotation contexts.
Lineage: The dataset was produced using long‑term cropping systems simulations implemented in the APSIM Next Generation (APSIMX) modelling framework. Fifty representative locations across the Australian grain production region were selected to capture major climatic gradients and production environments. For each location, daily historical climate data (solar radiation, rainfall, maximum and minimum temperature) were sourced from the SILO climate database.
Soil profiles were taken from the APSoil database, representing nine dominant Australian soil types parameterised with consistent soil physical and chemical attributes. At each site three locally relevant soils were paired with five initial levels of soil labile organic carbon to explore contrasting soil fertility conditions.
Five crop rotations varying in wheat, canola, and legume frequency were fully phased and simulated continuously without resetting soil water, mineral nitrogen, or soil carbon pools. Within each site–soil–rotation combination, thirteen nitrogen‑management strategies were applied, including eleven N‑bank targets (0–400 kg N ha⁻¹), a district-average farmer benchmark, and an unlimited‑N treatment representing water‑limited potential yield. Nitrogen top‑ups were applied at sowing (and at GS31 in southern and western regions) according to the assigned N‑bank strategy.
All simulations were run for multiple decades (≥32 years) using APSIMX modules for crop growth, soil water balance, nitrogen cycling, residue decomposition, and N loss processes. Model version information, parameter settings, and scenario definitions were kept consistent across all runs to ensure comparability. The simulation outputs were exported directly from APSIMX without manual alteration, forming the dataset contained in this collection.