Certified Crop Advisors’ Perceptions of Giant Ragweed (<i>Ambrosia trifida</i>) Distribution, Herbicide Resistance, and Management in the Corn Belt
Bibliographic record
Abstract
Giant ragweed has been increasing as a major weed of row crops in the last 30 yr, but quantitative data regarding its pattern and mechanisms of spread in crop fields are lacking. To address this gap, we conducted a Web-based survey of certified crop advisors in the U.S. Corn Belt and Ontario, Canada. Participants were asked questions regarding giant ragweed and crop production practices for the county of their choice. Responses were mapped and correlation analyses were conducted among the responses to determine factors associated with giant ragweed populations. Respondents rated giant ragweed as the most or one of the most difficult weeds to manage in 45% of 421 U.S. counties responding, and 57% of responding counties reported giant ragweed populations with herbicide resistance to acetolactate synthase inhibitors, glyphosate, or both herbicides. Results suggest that giant ragweed is increasing in crop fields outward from the east-central U.S. Corn Belt in most directions. Crop production practices associated with giant ragweed populations included minimum tillage, continuous soybean, and multiple-application herbicide programs; ecological factors included giant ragweed presence in noncrop edge habitats, early and prolonged emergence, and presence of the seed-burying common earthworm in crop fields. Managing giant ragweed in noncrop areas could reduce giant ragweed migration from noncrop habitats into crop fields and slow its spread. Where giant ragweed is already established in crop fields, including a more diverse combination of crop species, tillage practices, and herbicide sites of action will be critical to reduce populations, disrupt emergence patterns, and select against herbicide-resistant giant ragweed genotypes. Incorporation of a cereal grain into the crop rotation may help suppress early giant ragweed emergence and provide chemical or mechanical control options for late-emerging giant ragweed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".