Winter wheat responses to enhanced efficiency liquid nitrogen fertilizers in the Canadian Prairies
Why this work is in the frame
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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 (N 2 O) emissions and methane (CH 4 ) 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it