Modelling nitrification inhibitor effects on N <sub>2</sub> O emissions after fall- and spring-applied slurry by reducing nitrifier NH <sub>4</sub> <sup>+</sup> oxidation rate
Bibliographic record
Abstract
Reductions in N 2 O emissions from nitrification inhibitors (NI) are substantial but remain uncertain because measurements of N 2 O emissions are highly variable and discontinuous. Mathematical modelling may offer an opportunity to estimate these reductions if the processes causing variability in N 2 O emissions can be accurately simulated. In this study, the effect of NI was simulated with a simple, time-dependent algorithm to slow NH4+ oxidation in the ecosystem model ecosys . Slower nitrification modelled with NI caused increases in soil NH4+ concentrations and reductions in soil NO3- concentrations and in N 2 O fluxes that were consistent with those measured following fall and spring applications of slurry over 2 years from 2014 to 2016. The model was then used to estimate direct and indirect effects of NI on seasonal and annual emissions. After spring slurry applications, NI reduced N 2 O emissions modelled and measured during the drier spring of 2015 (35 % and 45 %) less than during the wetter spring of 2016 (53 % and 72 %). After fall slurry applications, NI reduced modelled N 2 O emissions by 58 % and 56 % during late fall in 2014 and 2015 and by 8 % and 33 % during subsequent spring thaw in 2015 and 2016. Modelled reductions were consistent with those from meta-analyses of other NI studies. Simulated NI activity declined over time so that reductions in N 2 O emissions modelled with NI at an annual timescale were relatively smaller than those during emission events. These reductions were accompanied by increases in NH 3 emissions and reductions in NO3- losses with NI that caused changes in indirect N 2 O emissions. With further parameter evaluation, the addition of this algorithm for NI to ecosys may allow emission factors for different NI products to be derived from annual N 2 O emissions modelled under diverse site, soil, land use and weather.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".