Winter wheat responses to enhanced efficiency liquid nitrogen fertilizers in the Canadian Prairies
Bibliographic record
Abstract
To evaluate how enhanced efficiency liquid nitrogen (N) fertilizers affect winter wheat ( Triticum aestivum L.) production under irrigated and rain-fed environments, experiments were conducted at two irrigated and five rain-fed sites across the Canadian Prairies from 2013 to 2018 (22 site-years). The N fertilizers included urea ammonium nitrate (UAN) treated with ( i) urease inhibitor N-(n-butyl) thiophosphoric triamide (NBPT), ( ii) NBPT plus nitrification inhibitor dicyandiamide, and ( iii) nitrification inhibitor nitrapyrin (Nitrapyrin), as well as untreated UAN and urea, and polymer-coated urea (PCU). All fertilizers were applied by banding 50% at planting and 50% in-crop in early-spring, except PCU, where PCU was applied at planting and urea was applied in early-spring. Nitrous oxide (N2O) emissions and methane (CH4) uptake were measured at one rain-fed site from 2014 to 2017. NBPT increased grain yield by 1.2%–14% and 2.8%–4% under irrigated and rain-fed environments, respectively, relative to all the other N sources except untreated urea in the rain-fed environment. Total N uptake with NBPT was between 0% and 12% higher than the other N sources across irrigated and rain-fed environments. The results suggested that both grain yield and N use efficiency were optimized when UAN contained a urease inhibitor. All liquid enhanced efficiency fertilizers produced grain protein content greater than 11%, except Nitrapyrin under irrigated environments. Data from three site-years indicated that greenhouse gas emissions were unaffected by N source under rain-fed conditions. Liquid UAN with a urease inhibitor may have the most potential to optimize winter wheat production and N use efficiency in the Canadian Prairies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".