Assessing nitrous oxide mitigation efficiency of three nitrification inhibitors with synthetic and organic fertilisers in Eastern Denmark
Bibliographic record
Abstract
The use of nitrification inhibitors (NIs) with fertilisers holds the potential to reduce nitrous oxide (N2O) 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 N2O mitigation, crop nitrogen uptake and crop yield effects of three NIs: 3,4-dimethylpyrazole phosphate (DMPP), nitrapyrin (NP), and 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. N2O fluxes were assessed throughout the growing seasons of both years utilising manual static flux chambers. The study found that N2O emissions were significantly reduced from PS with DMPP (by 77 %) and TM (by 67 %), and that DMPP significantly reduced N2O emissions from NS (by 60 %) in 2021. These findings suggest that DMPP and TM effectively reduce N2O emissions from both PS and synthetic N sources. Notably, mitigation effects were more pronounced in PS compared to synthetic N.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".