NH<sub>3</sub> volatilization, soil concentration and soil pH following subsurface banding of urea at increasing rates
Bibliographic record
Abstract
Rochette, P., Angers, D. A., Chantigny, M. H., Gasser, M.-O., MacDonald, J. D., Pelster, D. E. and Bertrand, N. 2013. NH 3 volatilization, soil [Formula: see text] concentration and soil pH following subsurface banding of urea at increasing rates. Can. J. Soil Sci. 93: 261–268. Subsurface banding of urea can result in large ammonia (NH3) emissions following a local increase in soil ammonium ([Formula: see text]) concentration and pH. We conducted a field experiment to determine how application rates of subsurface banded urea impact NH3 volatilization. Urea was banded at a 5 cm depth to a silty loam soil (pH=5.5) at rates of 0, 6.1, 9.2, 13.3 and 15.3 g N m−1. Ammonia volatilization (wind tunnels), and soil [Formula: see text] concentration and pH (0–10 cm) were monitored for 25 d following urea application. Volatilization losses increased exponentially with urea application rate to 11.6% of applied N for the highest urea rate, indicating that as more urea N was added to the soil a larger fraction was lost as NH3. Cumulative NH3-N emissions were closely related (R 2≥0.85) to maximum increases in soil [Formula: see text] concentration and pH, and their combined influence likely contributed to the nonlinearity of the volatilization response to urea application rate. However, the rapid increase in NH3 losses when soil pH rose above 7 suggests that soil pH was the main factor explaining the nonlinear response of NH3 volatilization. When compared with previous studies, our results suggest that the response of NH3 volatilization losses to urea application rate in acidic soils are controlled by similar factors whether urea is broadcasted at the soil surface or subsurface banded.
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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 | 0.001 |
| 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".