Performance of Urea-Based Fertilizers Associated With Elemental Sulfur or Polymers on Ammonia Volatilization
Bibliographic record
Abstract
High N-NH3 losses are expected when conventional urea is applied to the soil surface. In order to reduce it, urea granules could be coated with different materials to decrease fertilizer dissolution rate or to stabilize N-NH4+ by acidification. In this study, we investigated the effect of a polymer-coated urea and powdered S0 added to urea, in the presence or absence of a S-oxidizing bacterium (Acidithiobacillus thiooxidans), on soil pH, SO42- availability, NH4+, and NH3 volatilization. Applying S0 before urea and the inoculation with bacteria have promoted the highest S0 oxidation rates. The greater decrease in soil pH occurred when S0 was applied before urea at a higher dose, which also decreased NH3 volatilization by 83% up to 4 days after urea application. However, the decrease in soil pH did not increase the concentration of NH4+, nor did it decrease the accumulated amount of volatilized NH3 over time. The inoculation of A. thiooxidans accelerates S0 oxidation process, but it was insufficient to counteract the H+ consumption by urea hydrolysis. Therefore, the S0 application with urea did not offer chemical protection against NH3 loss, but a physical barrier in the controlled-release urea had less dissolved urea in soil and reduced NH3 losses.
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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) | 0.001 | 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".