Banding of Urea Increased Ammonia Volatilization in a Dry Acidic Soil
Bibliographic record
Abstract
Volatilization of ammonia following application of urea contributes to smog formation and degradation of natural ecosystems. The objective of this study was to evaluate the impact of (i) incorporation and banding of urea and (ii) surface broadcast of slow-release urea types on NH(3) volatilization in a dry acidic soil. Volatilization was measured using wind tunnels for 25 d after standard urea (140 kg N ha(-1)) was broadcast, broadcast and incorporated (0-5 cm), or incorporated in shallow bands (3-5 cm) to a conventionally tilled silty loam soil. Urea supplemented with a urease inhibitor or coated with a polymer was also broadcast at the soil surface. Little N diffused out of the polymer-coated granules and ammonia losses were low (4% of applied N). Use of a urease inhibitor also resulted in a low NH(3) loss (5% of applied N) while maintaining soil mineral N at levels similar to plots where untreated urea was broadcast. The rate of hydrolysis of urea broadcast at the soil surface was slowed by the lack of moisture and NH(3) loss (9% applied N) was the lowest of all treatments with standard urea. Incorporation of broadcast urea increased emissions (16% applied N) by increasing urea hydrolysis relative to surface application. Furthermore, incorporation in band also increased emissions (27% applied N) due to a localized increase in soil pH from 6.0 to 8.7. We conclude that incorporating urea in bands in a dry acidic soil can increase NH(3) volatilization compared to broadcast application followed by incorporation.
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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".