Nitrous and Nitrogen Oxide Emissions from Turfgrass Receiving Different Forms of Nitrogen Fertilizer
Bibliographic record
Abstract
Abstract The use of N fertilizer in agriculture is considered an important source of atmospheric N2O and NOx. Choice of fertilizer type and management has been considered a method for mitigating these emissions. Micrometeorological methods were used to study the effect of inorganic N fertilizers urea (U), slow‐release urea (SRU), and ammonium nitrate (AN) on fluxes of N2O, NO, and NO2 from turfgrass field plots during three seasons, from 1995 to 1997 (total of 353 d of measurement). Daily average fluxes after fertilizations reached a maximum of 2091 ng N2O‐N m−2 s−1 after the first fertilization with AN in 1996. The fertilized plots had significantly higher emissions (P < 0.05) than the control plot, and the highest N2O emissions were from AN in 1995 and 1996, and from SRU in 1997. Daily fluxes of up to 186 ng NO‐N m−2 s−1 were measured within 1 wk following fertilization in 1997. The U plot had significantly higher NO emissions during all seasons compared with other fertilized plots. Fluxes of NOx during 1996 and 1997 were consistently downward, indicating that turfgrass was acting as a sink for NOx. NO2 uptake seemed to be directly related to NO emissions, and the U plot presented the highest NO2 uptake. Urea‐based fertilizers seem to minimize N2O emissions, although long‐term effects of SRU still need to be studied. The higher NO emissions from U‐based fertilized plots do not seem to be a problem, since NOx uptake occurred at higher rates than NO emission.
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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".