Using 15N tracer experiments and the stable isotope model SIMONE to test and refine nitrogen cycling processes in the biogeochemical model LandscapeDNDC

Understanding soil nitrogen (N) cycling is essential for predicting nutrient availability and losses in agricultural systems. Although microbial processes such as mineralization and immobilization control the internal N supply to plants, these sub-processes remain poorly constrained in biogeochemical models. This limits our ability to accurately simulate N flows and environmental N losses in agricultural systems.

In this study, we combined data from a ¹⁵N tracer experiment with the biogeochemical model LandscapeDNDC and the isotope model SIMONE to test and refine N cycling processes. Initial model–data comparisons revealed a consistent bias: LandscapeDNDC overestimated the uptake of fertilizer N by plants while underestimating the recovery of ¹⁵N in soils and N losses. These discrepancies indicated insufficient mineralization of soil organic nitrogen (SON) and an imbalance in the mineralization–immobilization sub-cycle that regulates the internal nitrogen supply.

To address these issues, we reparametrized key soil process rates in LandscapeDNDC using constraints from the ¹⁵N data. Specifically, we increased mineralization rates and adjusted immobilization parameters to improve the partitioning between fertilizer-derived N and mineralized SON in plant uptake. The recalibrated model improved the simulations of observed seasonal dynamics of ¹⁵N in plant and soil pools, and N loss estimates.

Our results demonstrate that integrating ¹⁵N tracer data with isotope modeling provides a powerful approach for constraining microbial N processes in biogeochemical models. Improving the representation of mineralization–immobilization dynamics resulted in more realistic estimates of the internal N supply, thereby enhancing confidence in modelled fertilizer use efficiency and environmental losses, and improving the prediction of nitrogen dynamics in agricultural ecosystems under future climate and land use change scenarios.