Herbicide-resistant weeds in the Canadian prairies: 2012 to 2017
Bibliographic record
Abstract
Abstract This report updates the incidence of herbicide-resistant (HR) weeds across western Canada from the last report covering 2007 to 2011. This third round of preharvest surveys was conducted in Saskatchewan in 2014 and 2015, Manitoba in 2016, and Alberta in 2017, totaling 798 randomly selected cropped fields across 28 million ha. In addition, we screened 1,108 weed seed samples submitted by prairie growers or industry between 2012 and 2016. Of 578 fields where wild oat seed was collected, 398 (69%) had an HR biotype: 62% acetyl-CoA carboxylase inhibitor (WSSA Group 1) resistant, 34% acetolactate synthase inhibitor (Group 2) resistant, and 27% Group 1+2 resistant (vs. 41%, 12%, and 8%, respectively, in the previous second-round surveys from 2007 to 2009). The sharp increase in Group 2 resistance is the result of reliance on this site of action to manage Group 1 resistance and the resultant increased selection pressure. There are no POST options to control Group 1+2–HR wild oat in wheat or barley. The rise of Group 2 resistance in green foxtail (11% of sampled fields) and yellow foxtail (17% of Manitoba fields), which was not detected in the previous survey round, parallels the results for wild oat resistance. Various Group 2–HR populations of broadleaf weeds were confirmed, with cleavers and field pennycress being most abundant. Results of submission-sample testing reflected survey results. Although not included in this study, a postharvest survey in Alberta in 2017 indicated widespread Groups 2, 4 (dicamba), and 9 (glyphosate) resistance in kochia and Group 2 resistance in Russian thistle. These surveys bring greater awareness of HR weeds to growers and land managers at local and regional levels, and highlight the urgency to preserve herbicide susceptibility in our key economic weed species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".