Using integrated weed management systems to manage herbicide-resistant weeds in the Canadian Prairies
Bibliographic record
Abstract
Although herbicides have been a dominant and effective weed control strategy for decades in Western Canada, herbicide resistance and the lack of new modes of action have resulted in weed management challenges. Integrated weed management strategies have been shown to be successful in controlling certain weed species that are problematic in cropping systems. The objective of this study was to investigate integrated weed management strategies that have been successful on individual species to determine their applicability to a multiple weed species that may coexist in a field. In addition, harvest weed seed control was incorporated into these integrated weed management strategies to determine its impact in western Canadian cropping systems. A 5-year rotational study was conducted from 2016 to 2020 at Beaverlodge, Lacombe, and Lethbridge, AB; Scott and Saskatoon, SK; and Carman, MB, that incorporated integrated weed management strategies such as rotational crop diversity (including winter annuals and perennials), increased seeding rates, crop silaging, chaff collection, and with or without in-crop herbicides. This research confirmed success in managing some species of weeds such as wild oat when increased seeding rates, 2 years of early cut silage barley, and competitive winter cereals were incorporated into a cropping system, even when no in-crop herbicides were applied. However, some weed growth morphologies (e.g., twining weeds) or life cycles (e.g., facultative winter annuals) were not managed successfully with this combination of strategies. Chaff collection provided incremental weed control benefits but did not serve as a replacement for herbicidal weed control. Weed densities had an apparent impact on the success of these integrated weed management strategies, suggesting that the sooner they are adopted, the more likely they are to be successful at maintaining or reducing weed densities. This study not only showed the ability to reduce reliance on herbicides with strategies that can be effective in Western Canada but also highlighted the need for further understanding of different weed species and their responses to integrated weed management strategies, as well as the complexity of managing a weed community with integrated weed management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".