Combining the 15N gas flux method and N2O isotopocule data for the determination of soil microbial N2O sources

Micucci, Gianni ORCID: https://orcid.org/0000-0003-0476-4377; Lewicka‐Szczebak, Dominika ORCID: https://orcid.org/0000-0002-8077-502X; Sgouridis, Fotis ORCID: https://orcid.org/0000-0001-5925-154X; Well, Reinhard ORCID: https://orcid.org/0000-0003-4746-4972; Buchen‐Tschiskale, Caroline; McNamara, Niall P. ORCID: https://orcid.org/0000-0002-5143-5819; Krause, Stefan ORCID: https://orcid.org/0000-0003-2521-2248; Lynch, Iseult ORCID: https://orcid.org/0000-0003-4250-4584; Roos, Felicity ORCID: https://orcid.org/0000-0001-8961-3863; Ullah, Sami ORCID: https://orcid.org/0000-0002-9153-8847. 2025 Combining the 15N gas flux method and N2O isotopocule data for the determination of soil microbial N2O sources. Rapid Communications in Mass Spectrometry, 39 (6), e9971. 10, pp. 10.1002/rcm.9971

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Abstract/Summary

•Rationale: The analysis of natural abundance isotopes in biogenic N2O molecules provides valuable insights into the nature of their precursors and their role in biogeochemical cycles. However, current methodologies (for example, the isotopocule map approach) face limitations, as they only enable the estimation of combined contributions from multiple processes at once rather than discriminating individual sources. This study aimed to overcome this challenge by developing a novel methodology for the partitioning of N2O sources in soil, combining natural abundance isotopes and the use of a 15 N tracer (15N Gas Flux method) in parallel incubations. •Methods: Laboratory incubations of an agricultural soil were conducted to optimize denitrification conditions through increased moisture and nitrate amendments, using nitrate that was either 15 N‐labeled or unlabeled. A new linear system combined with Monte Carlo simulation was developed to determine N2O source contributions, and the subsequent results were compared with FRAME, a Bayesian statistical model for stable isotope analysis. •Results: Our new methodology identified bacterial denitrification as the dominant process (87.6%), followed by fungal denitrification (9.4%), nitrification (1.5%), and nitrifier denitrification (1.6%). Comparisons with FRAME showed good agreement, although FRAME estimated slightly lower bacterial denitrification (80%) and higher nitrifier‐denitrification (9%) contributions. •Conclusions: This approach provides an improved framework for accurately partitioning N2O sources, enhancing understanding of nitrogen cycling in agroecosystems, and supporting broader environmental applications.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1002/rcm.9971
UKCEH and CEH Sections/Science Areas:	Surface Atmosphere Interactions and Effects (2025-)
ISSN:	0951-4198
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	Bayesian statistics, denitrification, isotopic model, N2O reduction, nitrous oxide
NORA Subject Terms:	Agriculture and Soil Science Data and Information
Related URLs:	Dataset
Date made live:	07 Jan 2025 16:33 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538649

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