On‐Farm Assessment of the Amount and Timing of Nitrogen Fertilizer on Ammonia Volatilization
Bibliographic record
Abstract
Ammonia (NH3) volatilization is one of the main pathways through which applied N enters the environment undesirably. A seven site‐year on‐farm field experiment was performed for 3 yr at Ottawa, ON, and 2 yr at Guelph, ON, and Saint‐Valentin, QC, Canada. Our objectives were to (i) quantify the flux and the amount of NH3 volatilization as affected by the rate and time of N fertilizer; (ii) assess the impact of rainfall and soil temperatures on NH3 volatilization; and (iii) determine the threshold level of N fertilizer at which large NH3 volatilization losses occur. Using the static chamber method, NH3 volatilization was monitored after preplant or sidedress N application. Rate of NH3 volatilization peaked at 3 to 7 d and then dropped sharply within next 7 d before leveling off in the following weeks. The amount of NH3 volatilization increased with increasing N levels applied preplant or sidedress at all site‐years. Peak NH3 volatilization ranged from 40 to 8000 g N ha−1 d−1 after preplant fertilization and from about 100 to 2100 g N ha−1 d−1 after sidedress, resulting in NH3 losses of 0.1 to 47 kg N ha−1 and 0.6 to 20 kg N ha−1, respectively, equivalent to 0.1 to 38% and 0.3 to 13% of fertilizer‐induced emission (FIE) within 28 d after preplant or sidedress N fertilization. Our data clearly indicate that sidedress applications enable reduction in N fertilizer for economic crop yields, and may reduce losses simply due to lower total N rates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".