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
Abstract. Reductions in N2O emissions from nitrification inhibitors (NI) are substantial, but remain uncertain because measurements of N2O emissions are highly variable and discontinuous. Mathematical modelling may offer an opportunity to estimate these reductions if the processes causing variability in N2O 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 N2O fluxes that were consistent with those measured following fall and spring applications of slurry over two 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 N2O 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 N2O 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 N2O emissions modelled with NI at an annual time scale were relatively smaller than those during emission events. These reductions were accompanied by increases in NH3 emissions and reductions in NO3− losses with NI that caused changes in indirect N2O 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 N2O 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".