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Published March 11, 2025 | Version v1
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Data from: Quantifying soil gaseous nitrogen losses from nitrification and denitrification based on nitrogen isotope model

Authors/Creators

  • 1. Peking University
  • 2. Norwegian University of Life Sciences
  • 3. Chinese Academy of Sciences
  • 4. Tsinghua University
  • 5. University of Michigan–Ann Arbor
  • 6. Cornell University
  • 7. Beijing Forestry University
  • 8. Northeast Normal University

Description

Gaseous nitrogen (N) losses from nitrification and denitrification (NO+N2O+N2) pathways contribute a significant fraction of the total N losses from cropland ecosystems. However, a large uncertainty exists in estimating the NO+N2O+N2 losses, hindering effective management of the global N budget. Here we proposed a novel isotope model, which considers N fertilizer, ammonia (NH3) volatilization and crop harvest after testing the steady state assumption of soil δ15N and N pool for croplands, and justified if it could be successfully applied to constrain NO+N2O+N2 losses from cropland ecosystems. We compiled the first bulk-soil δ15N dataset of 0-30cm soils (n=738) from croplands and produced a global map of cropland soil δ15N, which is crucial input data for N isotope model to quantify NO+N2O+N2 losses. The results show that the cropland soil δ15N ranges from 3.5 to 9.0‰, with a mean value of 6.6±0.8‰ (mean ± standard deviation). The estimated NO+N2O+N2 losses accounted for an average of 17±9% of N outputs and were 35.86±24.17 kg N ha-1 yr−1 in China's rice paddies, with an increasing trend from Central China to South or North China. The estimations were comparable with the results from observation-constrained Denitrification-Decomposition modelling (38.9±4.8 kg N ha−1 yr-1) and in good agreement with experimental observations at site scale (R2=0.58). Our results suggest that soil N isotopes, as a quantitative tracer, provide a valuable alternative approach to constrain the NO+N2O+N2 losses in croplands at large geographic scales.

Notes

Funding provided by: National Natural Science Foundation of China
ROR ID: https://ror.org/01h0zpd94
Award Number: 42225102

Funding provided by: National Key Research and Development Program of China
Crossref Funder Registry ID:
Award Number: 023YFD1500802

Funding provided by: China Postdoctoral Science Foundation
ROR ID: https://ror.org/0426zh255
Award Number: 2024M760082

Funding provided by: National Natural Science Foundation of China
ROR ID: https://ror.org/01h0zpd94
Award Number: 42361144876

Funding provided by: National Key Research and Development Program of China
Crossref Funder Registry ID:
Award Number: 2021YFD700801

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Related works

Is source of
10.5061/dryad.xd2547dsk (DOI)