Opportunity windows for scaling up soil nitrogen processes

Abstract

Anthropogenic perturbations have profoundly changed global nitrogen (N) cycling, jeopardizing ecosystem sustainability and human well-being. Accurately understanding soil available N dynamics is critical for enhancing N use efficiency and mitigating nitrous oxide (N₂O) emissions. Although mechanistic insights into soil inorganic N transformations and N₂O production have advanced significantly at microscales, their dynamics at macroscales remain elusive, hindering predictive accuracy in Earth system models. Here, we propose a hierarchical framework that integrates environmental factors and microbial traits to scale up soil N processes, bridging the micro-macro research gap. This framework by embedding microbial traits into empirical models can improve the accuracy of N projections. Crucially, coupling relevant N processes (e.g., mineralization, nitrification, and denitrification) with the hierarchical framework is essential to better project N₂O emissions and inorganic N dynamics at macroscales. Achieving this potential requires not only big data but also substantial computational power. Emerging approaches, such as Bayesian approaches, deep learning architectures, convergent cross-mapping techniques, and digital twin simulations, offer new opportunities to integrate heterogeneous data sets and refine model parameterization for macroscale predictions. Our framework advances the theoretical foundation for scaling soil N processes, with direct applications in improving the precision of projections for global N₂O emissions and soil inorganic N dynamics.

Graphical abstract

Keywords

nitrous oxide / nitrogen processes / hierarchical framework / microbial traits / scaling up / macroscale projection / inorganic nitrogen dynamics

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