Nitrogen use efficiency of wheat and canola from urea treated with different types of double inhibitors
Bibliographic record
Abstract
Urease inhibitor (specifically, N-( n-butyl) thiophosphoric triamide, NBPT) and nitrification inhibitors (NIs) have been used to minimize nitrogen (N) loss from urea. However, their effects on improving crop N use efficiency (NUE) are usually inconsistent. A 2-year study was conducted to determine the best combination of NBPT and different NIs on urea that will maximize NUE while reducing nitrate leaching. Treatments consisted of untreated urea, NBPT-treated urea, and six types of (NBPT + NI)-treated urea that were surface applied at 80 kg N ha−1 on plots seeded to canola (2019) and wheat (2020) at Carman and Portage in Manitoba, Canada. Plots at Carman had lysimeters installed to measure leached water and nitrate. The sites had at least 35% lesser rainfall than climate normal during each growing season. At each site, average grain yields, N removal, and residual nitrate were not significantly different between untreated urea and inhibitor-treated urea. Over the 2 years, there was no significant benefit of NBPT or NBPT + NI on crop NUE at each site. Cumulative leached nitrate (19–40 kg N ha−1) did not differ significantly among urea treated with and without inhibitors. This is because >50% of the precipitation occurred when the effectiveness of NI had elapsed. Although NBPT and NI are known to reduce N losses to the atmosphere, this study suggests that the agronomic benefit and nitrate leaching prevention by NI applied in the spring may be limited in regions where large precipitation occurs later in the growing season or during non-growing season.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".