An integrated approach to control glyphosate‐resistant <i>Ambrosia trifida</i> with tillage and herbicides in glyphosate‐resistant maize
Bibliographic record
Abstract
Summary Glyphosate‐resistant Ambrosia trifida is a competitive and difficult‐to‐control annual broad‐leaved weed in several agronomic crops in the Midwestern United States and Ontario, Canada. The objectives of this study were to compare treatments for control of glyphosate‐resistant A. trifida with tillage followed by pre‐emergence (PRE) and/or post‐emergence (POST) herbicides in glyphosate‐resistant maize and to determine the impact of A. trifida escapes on maize yield. Field experiments were conducted in 2013 and 2014 in grower fields infested with glyphosate‐resistant A. trifida. Tillage prior to maize sowing resulted in 80–85% control compared with no tillage. Tillage followed by PRE application of saflufenacil plus dimethenamid‐P with or without atrazine resulted in 99% control compared with ≤86 and 96% control with PRE herbicides alone at 7 and 21 days after application respectively. Tillage or POST‐only herbicides resulted in 4–14 A. trifida plants m−2, whereas a PRE and POST programme had <3 plants m−2. Maize yield was greatest (13.1–14.2 tonnes ha−1) with tillage followed by PRE and POST herbicide programme. The relationship between maize yield and late‐season density of A. trifida escapes showed a 50% maize yield reduction irrespective of control measures when A. trifida density was 8.4 plants m−2. It was concluded that the combination of tillage with PRE and/or POST herbicides reduced A. trifida density and biomass accumulation early in the season and provided an integrated approach for effective 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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".