Western United States and Canada perspective: are herbicide-resistant crops the solution to herbicide-resistant weeds?
Bibliographic record
Abstract
Abstract Herbicide-resistant (HR) crops are widely grown throughout the United States and Canada. These crop-trait technologies can enhance weed management and therefore can be an important component of integrated weed management (IWM) programs. Concomitantly, evolution of HR weed populations has become ubiquitous in agricultural areas where HR crops are grown. Nevertheless, crop cultivars with new or combined (stacked) HR traits continue to be developed and commercialized. This review, based on a symposium held at the Western Society of Weed Science annual meeting in 2021, examines the impact of HR crops on HR weed management in the U.S. Great Plains, U.S. Pacific Northwest, and the Canadian Prairies over the past 25 yr and their past and future contributions to IWM. We also provide an industry perspective on the future of HR crop development and the role of HR crops in resistance management. Expanded options for HR traits in both major and minor crops are expected. With proper stewardship, HR crops can reduce herbicide-use intensity and help reduce selection pressure on weed populations. However, their proper deployment in cropping systems must be carefully planned by considering a diverse crop rotation sequence with multiple HR and non-HR crops and maximizing crop competition to effectively manage HR weed populations. Based on past experiences in the cultivation of HR crops and associated herbicide use in the western United States and Canada, HR crops have been important determinants of both the selection and management of HR weeds.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".