Characterizing the efficacy of novel nitrogen stabilizer products at reducing fertilizer nitrogen losses
Bibliographic record
Abstract
Urea-based fertilizers (urea and urea ammonium nitrate (UAN)) are susceptible to nitrate (NO3-) leaching, ammonia (NH3-) volatilization, and nitrous oxide (N2O) emission losses, resulting in low fertilizer nitrogen (N) efficiency and presenting a risk to human and environmental health. Nitrogen stabilizer products containing the urease inhibitor N-(n-butyl) thiophosphoric triamide (NBPT) and the nitrification inhibitor 3,4-dimethyl pyrazole phosphate (DMPP) can enhance wheat yield and optimize fertilizer efficiency while mitigating N losses. However, their high price hinders wider adoption by farmers. This study examined the efficacy of potentially cost-effective, double inhibitor (DI) formulations in decreasing N losses and improving crop N efficiency. Specifically, the study assessed the impacts of ARMU Advanced (ARMU-Adv) formulations (i.e., NBPT: DMPP ratios of 1:1 and 1:0.5) at mitigating N losses and improving wheat biomass yield and N efficiency. A no-inhibitor and a no-fertilizer (control) treatment were included for comparison. Results showed that the ARMU-Adv formulations were effective at reducing NH3 volatilization and N2O emission and improving shoot N uptake and apparent N recovery (ANR) relative to untreated fertilizers. Overall, the ARMU-Adv formulations were more effective with urea than with UAN in reducing NH3 volatilization and enhancing shoot N uptake and ANR, whereas inhibitor-treated UAN showed greater efficacy in mitigating N2O losses relative to urea. These findings demonstrate the potential of ARMU-Adv formulations to reduce N losses and enhance yield and N efficiency in wheat-based cropping systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".