Effect of Nitrogen Rate and Placement and Seeding Rate on Barley Productivity and Wild Oat Fecundity in a Zero Tillage System
Bibliographic record
Abstract
Placing N in the form of urea with the seed allows seeding and fertilizer application to be accomplished simultaneously with minimal soil disturbance. However, seedling damage can occur from excess seed‐placed urea. The objective of this study was to compare the effects of seed‐placed and side‐banded N (urea) applied at different rates on barley (Hordeum vulgare L.) density, maturity, and yield and wild oat (Avena fatua L.) fecundity, and to investigate if increasing the barley seeding rate would improve the ability of barley to overcome urea‐induced injury and compete better with wild oat. A field experiment was conducted at three locations in western Canada over 3 yr. Nitrogen was applied as urea at five rates (0, 30, 60, 90, and 120 kg ha−1 actual N), either directly with the seed or as a side‐band, at three barley seeding rates (200, 300, and 400 seeds m−2). When N was placed with the seed, barley plant density decreased, while time to maturity and wild oat fecundity increased as N rate increased. Barley yield also decreased but only at N rates above 60 kg ha−1 Placing N as a side‐band did not reduce barley density, resulting in shortened time to maturity, increased barley yield, and lower wild oat fecundity as compared to seed‐placed N. Increasing the seeding rate increased barley density and reduced time to maturity and wild oat fecundity but did not affect barley yield.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".