Integrating Cultural Practices with Herbicides Augments Weed Management in Flax
Bibliographic record
Abstract
Core Ideas Current weed management strategies in flax are limited. A multi‐factor weed management system is needed to control herbicide‐resistant weeds. Combining several factors has a greater impact on crop‐weed competition than any factor alone. The best combination is a competitive cultivar, seed early, high seeding rate, and use an in‐crop herbicide. Flax (Linum usitatissimum L.) is an important crop with value in both food and industrial markets. However, flax competes poorly with weeds and as a result, flax yield can be severely inhibited by weed competition. Factors that favor crop competitive ability will have great value in improving weed management in flax. This research sought to identify different combinations of seeding date (early vs. late May), seeding rate (400 vs. 800 seeds m−2), cultivar height (short vs. tall), and herbicide (present vs. absent) that could improve the competitive ability of flax. Field studies were conducted across western Canada over 3 yr from 2014 to 2016. Results showed that seeding a tall cultivar at a high seeding rate in early May combined with an in‐crop herbicide application increased crop establishment by 210 plants m−2. This in turn increased aboveground crop biomass and seed yield by as much as 549 and 617 kg ha−1, respectively. This combination of factors significantly reduced aboveground weed biomass by 50 kg ha−1, although no single factor or combination of factors affected weed seed fecundity. By seeding competitive flax cultivars at higher rates earlier in the growing season, and by combining this with an in‐crop herbicide, producers can develop sound cropping systems that provide more competitive flax crops and another profitable cropping option.
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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 | 0.001 | 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".