Economic impact of glyphosate-resistant weeds on major field crops grown in Ontario
Bibliographic record
Abstract
Abstract Limited information exists on the global economic impact of glyphosate-resistant (GR) weeds. The objective of this manuscript was to estimate the potential yield and economic loss from uncontrolled GR weeds in the major field crops grown in Ontario, Canada. The impact of GR weed interference on field crop yield was determined using an extensive database of field trials completed on commercial farms in southwestern Ontario between 2010 and 2021. Crop yield loss was estimated by expert opinion (weed scientists and Ontario government crop specialists) when research data were unavailable. This manuscript assumes that crop producers adjust their weed management programs to control GR weeds, which increases weed management costs but reduces crop yield loss from GR weed interference by 95%. GR volunteer corn, horseweed, waterhemp, giant ragweed, and common ragweed would cause an annual monetary loss of (in millions of Can$) $172, $104, $11, $3, and $0.3, respectively, for a total annual loss of $290 million if Ontario farmers did not adjust their weed management programs to control GR biotypes. The increased herbicide cost to control GR volunteer corn, horseweed, waterhemp, giant ragweed, and common ragweed in the major field crops in Ontario is estimated to be (in millions of Can$) $17, $9, $2, $0.1, and $0.02, respectively, for a total increase in herbicide expenditures of $28 million annually. Reduced GR weed interference with the adjusted weed management programs would reduce farm-gate monetary crop loss by 95% from $290 million to $15 million. This study estimates that GR weeds would reduce the farm-gate value of the major field crops produced in Ontario by Can$290 million annually if Ontario farmers did not adjust their weed management programs, but with increased herbicide costs of Can$28 million and reduced crop yield loss of 95% the actual annual monetary loss in Ontario is estimated to be Can$43 million annually.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 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".