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Record W4405236728 · doi:10.1016/j.eti.2024.103952

Assessing nitrous oxide mitigation efficiency of three nitrification inhibitors with synthetic and organic fertilisers in Eastern Denmark

2024· article· en· W4405236728 on OpenAlexaff
Azeem Tariq, Line Vinther Hansen, Andreas Brændholt, Lars Stoumann Jensen, Sander Bruun

Bibliographic record

VenueEnvironmental Technology & Innovation · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Guelph
FundersMiljø- og FødevareministerietMinistry of Environment
KeywordsNitrous oxideNitrificationEnvironmental scienceEnvironmental chemistryChemistryNitrogenOrganic chemistry

Abstract

fetched live from OpenAlex

The use of nitrification inhibitors (NIs) with fertilisers holds the potential to reduce nitrous oxide (N 2 O) emissions and optimise nitrogen use efficiency (NUE). However, this potential of NIs needs to be quantified across diverse field conditions to comprehensively assess the influence of management practices, crop types, fertilisation strategies, climatic conditions, annual variations, and soil types. In 2020, a field experiment was conducted using spring barley, followed by a similar experiment with winter wheat in 2021, aimed at studying N 2 O mitigation, crop nitrogen uptake and crop yield effects of three additives containing commercially available NIs: 3,4-dimethylpyrazole phosphate (DMPP), nitrapyrin (NP), and a mixture of 1,2,4-triazole and 3-metylpyrazole (TM) together with synthetic (ammonium sulphate nitrate (NS), urea ammonium nitrate (UAN)) and organic (pig slurry (PS)) fertilisers. DMPP was applied with NS and PS, while NP and TM were applied with UAN and PS. Fertilisers were applied during early spring in both years, following the recommended optimal rate of synthetic fertiliser and PS. Control treatments included fertilisers applied at the same rate without NIs and no fertiliser. N 2 O fluxes were assessed throughout the growing seasons of both years utilising manual static flux chambers. The study found that N 2 O emissions were significantly reduced from PS with DMPP (by 77 %) and TM (by 67 %), and that DMPP significantly reduced N 2 O emissions from NS (by 60 %) in 2021. Crop yield and N uptake were increased with NIs in both years. These findings suggest that DMPP and TM effectively reduce N 2 O emissions from both PS and synthetic N sources. Notably, the mitigation effects were more pronounced in PS compared to synthetic N. Nonetheless, the efficiency of N 2 O reduction through these nitrification inhibitors varied depending on crop type and specific management conditions. • NIs tested with synthetic and organic fertilisers in spring barley and winter wheat. • NIs reduced N 2 O emissions more with organic fertiliser than synthetic fertiliser. • N 2 O mitigation effects of NIs were significant in winter wheat than spring barley. • Crop yield and N uptake were slightly increased with NIs. • Mitigation efficiency of NIs depends on climate and management conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.202
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2024
Admission routes1
Has abstractyes

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