Efficiency of fall versus spring applied urea‐based fertilizers treated with urease and nitrification inhibitors II. Crop yield and nitrogen use efficiency
Bibliographic record
Abstract
Abstract The urease inhibitor [ N ‐( n ‐butyl) thiophosphoric triamide (NBPT)] and the nitrification inhibitor (NI) 3,4‐dimethyl pyrazole phosphate have been reported to conserve urea‐based N fertilizers by reducing N losses. However, their effects on crop yield and N uptake are inconsistent and fall‐applied N fertilizers are usually less efficient than spring applications. We conducted a 2‐yr field study on contrasting soils [Carman sandy loam (CSL) and Portage clay loam (PCL)] on the effects of NBPT with and without NI on grain yield, grain N removal, and crop N uptake from fall and spring surface‐applied urea‐based fertilizers. Fertilizer treatments (75 or 100 kg N ha −1 ) were urea and urea ammonium nitrate (UAN) with and without NBPT or NBPT + NI. Canola ( Brassica napus L.) and wheat ( Triticum aestivum L.) yield, N removal, and N uptake were not consistently greater for urea and UAN treated with inhibitors than for untreated urea and UAN. The inhibitors’ effect on yield and N uptake was observed in urea treated with NBPT in CSL but not PCL. Although agronomic efficiency was significantly greater for spring‐applied untreated urea or UAN than fall‐applied urea or UAN with and without inhibitors in PCL, no significant difference appeared between fall‐applied urea or UAN treated with inhibitors and spring‐applied untreated urea or UAN in CSL. The N conserved by the inhibitors did not appear in the soil as nitrate‐N. Although inhibitors may reduce N losses, their use to increase yield and bridge the efficiency gap between fall‐ and spring‐applied urea‐based fertilizers may be site‐specific.
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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 | 0.000 | 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".