A decade of herbicide-resistant crops in Canada
Bibliographic record
Abstract
This review examines some agronomic, economic, and environmental impacts of herbicide-resistant (HR) canola, soybean, corn, and wheat in Canada after 10 yr of growing HR cultivars. The rapid adoption of HR canola and soybean suggests a net economic benefit to farmers. HR crops often have improved weed management, greater yields or economic returns, and similar or reduced environmental impact compared with their non-HR crop counterparts. There are no marked changes in volunteer weed problems associated with these crops, except in zero-tillage systems when glyphosate is used alone to control canola volunteers. Although gene flow from glyphosate-HR canola to wild populations of bird’s rape (Brassica rapa L.) in eastern Canada has been measured, enrichment of hybrid plants in such populations should only occur when and where herbicide selection pressure is applied. Weed shifts as a consequence of HR canola have been documented, but a reduction in weed species diversity has not been demonstrated. However, reliance on HR crops in rotations using the same mode-of-action herbicide and/or multiple in-crop herbicide applications over time can result in intense selection pressure for weed resistance and consequently, greater herbicide use in the future to control HR weed biotypes. History has repeatedly shown that cropping system diversity is the pillar of sustainable agriculture; stewardship of HR crops must adhere to this fundamental principle. Key words: Canola, Brassica napus, corn, Zea mays, soybean, Glycine max, wheat, Triticum aestivum, gene flow, herbicide resistance, transgenic crop, volunteer crop
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".