Seeding rate, herbicide timing and competitive hybrids contribute to integrated weed management in canola (<i>Brassica napus</i>)
Bibliographic record
Abstract
Implementing a favourable agronomic practice often enhances canola production. Combining several optimal practices may further increase production, and, given greater crop health and competitiveness, could also improve weed control. A field experiment was conducted at Lacombe and Lethbridge, Alberta, from 1998 to 2000, to determine the optimal combination of glufosinate-tolerant cultivar (hybrid InVigor 2153 or open-pollinated Exceed), crop seeding rate (100, 150, or 200 seeds m-2) and time of weed removal (two-, four-, or six-leaf stage of canola) for canola yield and weed suppression. At equal targeted seeding rates, the hybrid cultivar had greater seedling density (8 plants m-2 higher) and seed yield (22% higher) when compared with the open-pollinated cultivar. Combining the better cultivar with the highest seeding rate, and the earliest time of weed removal led to a 41% yield increase compared with the combination of the weaker cultivar, the lowest seeding rate and the latest time of weed removal. The same optimal factor levels also favoured higher levels of weed control and lower weed biomass variability. Managing these factors at optimal levels may help increase net returns, reduce herbicide dependence and favour the adoption of more integrated weed management systems. Key words: Crop health, direct seeding, glufosinate, integrated weed management, weed population variability
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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 | 0.001 | 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".