Scholarlink

<p>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 (N<sub>2</sub>O) emissions. Although mechanistic insights into soil inorganic N transformations and N<sub>2</sub>O 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 N<sub>2</sub>O 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 N<sub>2</sub>O emissions and soil inorganic N dynamics.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Opportunity windows for scaling up soil nitrogen processes

  • 期刊论文

  • 发表日期：

    2026年3月7日

查看全文
  • Benyi Li,
  • Mengfei Li,
  • Yanzhong Yao,
  • Bingbing Han,
  • Shuli Niu,
  • Zhaolei Li

摘要

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.