Influence of controlled-release urea on seed yield and N concentration, and N use efficiency of small grain crops grown on Dark Gray Luvisols
Bibliographic record
Abstract
Field experiments were conducted on Dark Gray Luvisolic soils (Typic Cryoboralf) from 2004 to 2006 (wheat-canola-barley rotation) near Star City, Saskatchewan, and from 2004 to 2007 (barley-canola-wheat-barley rotation) near Beaverlodge, Alberta. The aim was to compare the effects of controlled-release urea (CRU) vs. conventional urea (hereafter called urea) on seed yield and N (i.e., protein) concentration, and N use efficiency (NUE). The treatments were combinations of tillage system [conventional tillage (CT) and no tillage (NT)], and N source (urea, CRU and a blended mixture), placement method (spring-banded, fall-banded and split application) and application rate (0-90 kg N ha -1 ). There was no tillage × fertilizer treatment interaction on the measured crop variables. Seed yield and crop N uptake and, to a lesser degree, seed N concentration generally increased with N application to 90 kg N ha -1 . Fall-banded CRU or urea generally produced lower crop yield and N uptake than spring-banded CRU or urea. Split application of urea (half each at seeding and tillering) resulted in higher seed yield and N concentration in at least 3 of 7 site-years than did CRU and urea applied at a similar rate. A blend of urea and CRU was as effective as spring-banded CRU (at Star City only). Seed yield, N recovery and NUE were higher with spring-banded CRU than urea in 2 site-years, and similar to urea in other site-years. We conclude that for boreal soils of the Canadian prairies, spring-banded CRU is as effective as urea, and in some years more effective, in increasing crop yield and N recovery; however, urea split application can be even more effective in addition to having an advantage in managing risk.Key words: Controlled-release urea, Gray Luvisol, nitrogen source, nitrogen recovery, nitrogen use efficiency, tillage systems
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How this classification was reachedexpand
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".