Efficiency of fall versus spring applied urea‐based fertilizers treated with urease and nitrification inhibitors I. Ammonia volatilization and mitigation by NBPT
Bibliographic record
Abstract
Abstract Mid‐ or late‐fall N fertilization is usually recommended on the Canadian prairies to reduce N losses from fall‐applied N. Yet, N fertilizers are less efficient when applied in mid‐ or late fall than in spring on crop yield. Ammonia volatilization from N fertilizers is one of the reasons for the low N use efficiency. We conducted a 2‐yr study to quantify and contrast the efficacy of a urease inhibitor, N ‐(n‐butyl) thiophosphoric triamide (NBPT), with and without a nitrification inhibitor, 3,4‐ dimethylpyrazole phosphate (NI), in reducing NH 3 volatilization from fall‐ and spring‐applied urea‐based fertilizers in two contrasting soils. Treatments consisted of surface‐applied urea and urea–NH 4 NO 3 (UAN) with and without NBPT or NBPT + NI (double inhibitor, DI). We measured NH 3 volatilization with acid‐charged disks during 21 (fall) or 28 (spring) d. Total NH 3 volatilization (percentage of applied N) from untreated urea and UAN across sites ranged from 7 to 22% with fall application and from 3 to 29% with spring application. Addition of NBPT only or DI to urea or UAN reduced NH 3 volatilization by 61 to 74% in the fall and 4 to 69% in the spring across site‐years. We found that the reduction of NH 3 volatilization by NBPT was significantly greater in the fall (65%) than in the spring (40%) and with urea (61%) than with UAN (43%). Our study showed that (a) significant NH 3 volatilization could occur from surface‐applied urea‐based fertilizers even under late‐fall temperatures and (b) NBPT reduced NH 3 volatilization from fall‐ as well as spring‐applied urea‐based fertilizers. Therefore, farmers are encouraged to treat their urea‐based fertilizers with NBPT when considering surface application.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".