Nitrification inhibitor reduces the inhibitory effect of N‐(n‐butyl) thiophosphoric triamide (NBPT) on the hydrolysis of urea
Bibliographic record
Abstract
Abstract The addition of nitrification inhibitor (NI) with a urease inhibitor, N‐(n‐butyl) thiophosphoric triamide (NBPT), has been reported to offset the reduction of ammonia volatilization by NBPT. An incubation study was conducted to investigate the interaction between NBPT and NI (3,4‐dimethyl pyrazole phosphate) on hydrolysis of urea in five soils with a range of physico‐chemical properties. Untreated urea (UR), NBPT treated urea (UR NBPT ), or NBPT+NI treated urea (UR DI ) were surface‐applied (250 kg N ha −1 ) to each soil. The soils were incubated (21 °C) and destructively sampled nine times during a 22‐day period. Urea hydrolysis rate ( k ; d −1 ) was measured by the disappearance of urea with time and modeled with a first‐order kinetic. The value of k was in the order of UR (0.321) > UR DI (0.183) > UR NBPT (0.151) across the five soils. While the urease inhibitor, NBPT, significantly reduced k in each soil, the addition of a NI with NBPT significantly decreased the ability of NBPT to inhibit urea hydrolysis by an average of 21% across the soils. We found that NI significantly reduced the half‐life of urea by about 1 d when compared with NBPT alone. Principal component analysis showed that k did not depend on any of the soil properties, rather, it depended on the type of treatment. Net nitrification rate constant was significantly greater in UR than UR NBPT in loam and clay soils but not different in sandy loam soils. We conclude that the often‐reported increase in ammonia volatilization with the double inhibitor relative to NBPT alone may not only be due to the persistence of ammonium but may also be due to an increased rate of urea hydrolysis in the presence of a NI.
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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".