Timing of pre-seeding glyphosate application in direct-seeding systems
Bibliographic record
Abstract
Producers are interested in whether crop productivity can be maintained with pre-seeding glyphosate application 2 to 3 wk prior to direct seeding with sweeps. A wider window for glyphosate application would be beneficial, particularly for producers with a large number of hectares. An experiment was conducted at Scott and Melfort, SK, Canada, in 1997 and 1998 to assess glyphosate application 2 to 3 wk before, 1 d before, and 3 to 4 d after (just before crop emergence) the time of seeding in narrow-hoe and simulated-sweep (cultivation immediately followed by a narrow-hoe drill) direct-seeding systems. Wheat grain yield was 25% lower when glyphosate was applied 2 to 3 wk before rather than just prior to seeding only with the narrow-hoe direct-seeding system at Scott in both years. This yield reduction corresponded with the greater median grass weed fresh weight. Yields were 15% lower for both direct-seeding systems at Scott when glyphosate was applied 3 to 4 d after seeding. Barley grain yield was 46% greater (1997) or 25% lower (1998) in the narrow-hoe compared with the simulated-sweep direct-seeding system at Melfort. These yield responses corresponded with opposite responses for median grass weed fresh weight. The tillage effect of sweeps at the time of seeding can improve the control of grass weeds compared with seeding implements equipped with narrow hoes. However, a narrow-hoe system with glyphosate applied just prior to sowing consistently provided the greatest cereal yields. Key words: Barley, wheat, reduced tillage, glyphosate, early-season weed control
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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) | 0.001 | 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 | 0.001 |
| 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".