Distribution, frequency, and impact of herbicide-resistant weeds in Saskatchewan
Bibliographic record
Abstract
Herbicide-resistant weeds threaten contemporary agriculture by reducing crop yields and quality. Monitoring of herbicide-resistant weeds is essential to the development of informed integrated weed management (IWM) strategies. In 2019 and 2020, a randomized-stratified preharvest survey of 419 fields in Saskatchewan, Canada, was conducted to determine the distribution, frequency of occurrence, and impact of herbicide-resistant weeds. Mature seeds were collected from uncontrolled weeds in each field. The samples were tested for resistance to acetyl-CoA carboxylase (ACCase) and/or acetolactate synthase (ALS)-inhibiting herbicides using whole-plant bioassays under a controlled-environment. In 2019/2020, herbicide-resistant weeds occupied 72% of the surveyed fields, corresponding to an estimated 6.2 million ha of annual cropland and a total field area of 11.4 million ha. Herbicide-resistant weeds cost Saskatchewan farmers an estimated $343 million CAD annually in reduced crop yields and quality, and increased weed control expenditures. Wild oat ( Avena fatua L.) had the greatest impact among grass weeds, with ACCase inhibitor resistance documented in 77% and ALS inhibitor resistance in 30% of fields where the weed seeds were collected and tested (47% and 18% of all fields surveyed, respectively). Multiple herbicide (ACCase and ALS inhibitor)-resistant wild oat were documented in 26% of the tested fields. Kochia ( Bassia scoparia (L.) A.J. Scott) had the greatest impact among broadleaf weeds, where 100% of the samples tested were ALS inhibitor-resistant (39% of all fields surveyed). The growing prevalence of herbicide-resistant weeds in Saskatchewan warrants further adoption of IWM where non-chemical tactics play an important role in stewardship of the remaining effective herbicides.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".