Kinetics and thermodynamics of urea hydrolysis in the presence of urease and nitrification inhibitors
Bibliographic record
Abstract
Urease inhibitor [N-(n-butyl) thiophosphoric triamide (NBPT)] and nitrification inhibitor (NI) (3,4-dimethylpyrazole phosphate) have been used to reduce nitrogen (N) losses from urea-based fertilizers. This study evaluated the effect of temperature, NBPT, and NI on kinetic and thermodynamic properties of urea hydrolysis in six soils. Soils were amended (250 kg N·ha−1) with urea (UR), NBPT treated urea (URNBPT), or NBPT + NI treated urea (URDI), incubated at 5, 15, or 25 °C, and destructively sampled eight times during an 18 d incubation. We measured urea hydrolysis rate by the disappearance of urea with time and determined the rate constant (k; d−1) assuming first-order kinetics. Our results showed that k increased with temperature in the order of 0.07 (5 °C), 0.12 (15 °C), and 0.20 (25 °C) across soils and inhibitor treatments. In addition, k declined in the order of UR (0.19) > URDI (0.11) > URNBPT (0.08) across soils and temperatures. Although urease inhibitor, NBPT, increased the half-life of urea from 3.8 to 8.3 d across soil–temperature, the addition of a NI significantly reduced the half-life of NBPT treated urea by approximately 2 d across soil–temperature. Thermodynamics parameters showed that urea hydrolysis was nonspontaneous, and enthalpy and entropy changes were not significantly different among inhibitor treatments in five of the six soils. We conclude that the often-reported greater ammonia volatilization from URDI than URNBPT may not only be due to the persistence of ammonium in the presence of NI but also because NI reduced the inhibitory effect of NBPT on urea hydrolysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".