Efficacy of a new <i>N</i>-(<i>n</i>-butyl) thiophosphoric triamide formulation in reducing ammonia volatilization from urea-based fertilizers
Bibliographic record
Abstract
N-(n-Butyl)thiophosphoric triamide (NBPT) has been reported to reduce ammonia volatilization from surface-applied urea and urea ammonium nitrate (UAN). A new NBPT formulation (ARM U™, 18% NBPT) that contains a polymer allowing for lower application rate of NBPT was evaluated for its efficacy relative to Agrotain® (30% NBPT) and Arborite® (24% NBPT). Trials consisted of (i) a greenhouse study that compared two rates of ARM U-treated urea (360 and 540 mg NBPT kg−1 urea) with Arborite- or Agrotain-treated urea (480 and 600 mg NBPT kg−1 urea, respectively) and (ii) a field study that compared urea and UAN treated with either ARM U (360 mg NBPT kg−1 urea) or Agrotain (600 mg NBPT kg−1 urea) at two sites. Static chambers fitted with acid-charged discs were used to measure ammonia volatilization at six or seven dates over 28 d. In the greenhouse study, ammonia volatilization was reduced by 96% with either ARM U or Agrotain and 95% with Arborite. In the field study, ARM U and Agrotain reduced ammonia volatilization from urea by 80% and 66%, respectively, across sites. Similarly, ammonia volatilization from UAN was reduced by 46% and 60% with ARM U and Agrotain, respectively. Despite the lower NBPT application rates with ARM U, ammonia reduction by ARM U, Agrotain, and Arborite was not significantly different. The addition of ARM U to urea and UAN enabled lower application rate of NBPT without compromising its efficacy.
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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".