Integrating enhanced efficiency fertilizers and nitrogen rates to improve Canada Western Red Spring wheat
Bibliographic record
Abstract
Granular urea fertilizer applied at planting is prone to nitrogen (N) losses in certain environments. Enhanced efficiency fertilizers (EEFs) are developed to mitigate losses and optimize plant uptake. To determine the benefits of EEFs in grain yield and quality enhancement in Canada Western Red Spring (CWRS) wheat, an experiment was conducted from 2019 to 2022 at eight sites in Alberta and Saskatchewan, Canada. The effects of five N sources [urea; urea + urease inhibitor, N-( n-butyl)thiophosphoric triamide (NBPT); urea + nitrification inhibitor, nitrapyrin; urea + dual-inhibitor, NBPT + dicyandiamide; and polymer-coated urea, ESN® (Environmentally Smart Nitrogen®)] and four N rates (60, 120, 180, and 240 kg N ha−1) on CWRS wheat production were examined. Results indicated that N source affected grain yield in Dark Brown Chernozem soils but not in Black Chernozem or Dark Grey Luvisol soils. In Dark Brown Chernozem soils, a dual inhibitor increased grain yield by 3.1% and 3.9% relative to urea and polymer-coated urea, respectively, while all other EEFs attained similar results. The use of a dual inhibitor EEF led to greater net returns compared to urea and polymer-coated urea in the Dark Brown Chernozem soils. Grain protein concentration increased linearly with increasing N rate from 60 to 240 kg N ha−1. Generally, a rate of 120 kg N ha−1 was optimal for CWRS wheat grown in Canadian prairie conditions when coupled with EEFs, particularly a dual inhibitor, and grain yield and protein were often responsive.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".