Integrating enhanced efficiency fertilizers and nitrogen rates to improve Canada Western Red Spring wheat production in the Canadian prairies
Bibliographic record
Abstract
Canada Western Red Spring (CWRS) wheat (Triticum aestivum L.) is the most widely grown wheat class in western Canada. This is mainly due to its excellent milling and baking quality, concomitant with a high protein concentration. Adequate nitrogen (N) supply is important to achieve optimal CWRS grain yield and quality. In CWRS production, N is routinely applied as granular urea fertilizer during planting. Consequential N loss can arise when using unprotected urea fertilizer. Enhanced efficiency fertilizers (EEFs) aim to maintain the integrity of applied N by increasing plant nutrient bioavailability while reducing environmental N loss. To determine if the use of EEFs and different N rates can improve upon conventional methods, a CWRS wheat experiment was established in 2019 across four locations in Alberta and two in Saskatchewan, Canada. This experiment consists of two factors: (i) urea type [(urea; urea + urease inhibitor (Agrotain®); urea + nitrification inhibitor (eNtrench®); urea + dual inhibitor (SuperU®); urea + dual inhibitor (NBPT/DMPSA); and slow-release fertilizer (Environmentally Smart Nitrogen® (ESN®))], and (ii) N rate [60; 120; 180; and 240kg N ha-1]. Results indicate urea type affected grain yield in Dark Brown Chernozem soils but not in Black Chernozem & Dark Grey Luvisol soils. In Dark Brown Chernozem soils, a dual inhibitor (SuperU®) increased grain yield by 3.3% relative to urea, while all other EEFs attained similar results. Furthermore, slight increases in net return were observed with the use of a dual (SuperU®) and urease inhibitor (Agrotain®). Grain protein content was not influenced by urea type; however, increasing N rate in both soil groups resulted in quadratic and linear increases in grain yield and protein content, respectively. Application of N fertilizer at a rate of 120 kg N ha-1 was agronomically optimal and provided greatest net return. These results suggest growers who incorporate dual inhibitors in CWRS wheat production can achieve modest increases in grain yield; moreover, the use of other EEFs will not reduce grain yield or protein content relative to conventional urea.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".