Evaluation of herbicide programs for the management of glyphosate-resistant giant ragweed in soybean
Bibliographic record
Abstract
Glyphosate-resistant (GR) giant ragweed (Ambrosia trifida L.) has become an increasingly problematic weed of soybean production systems in Missouri and many areas of the Midwest.Currently, giant ragweed has been confirmed with resistance to glyphosate in 11 states and one Canadian province.The objectives of this research were to determine the effects of herbicide application timing and glyphosate tank-mix combinations on the survival of GR giant ragweed, and the influence of pre-plant (PREPLT) followed by (fb) 2-pass post-emergence (POST) herbicide programs in GR and glufosinate-resistant soybean on GR giant ragweed density, soybean yield, and net economic return.Results from this research indicate that POST applications to smaller plants can reduce the survival of giant ragweed compared to applications to larger plants.For a POST only management strategy, fomesafen plus glyphosate applied to 10-cm plants fb glyphosate late post-emergence (LPOST) resulted in 37% survival which was the lowest survival observed.Overall, giant ragweed was nearly eliminated with PREPLT fb 2-pass POST programs.For example, no more than 6 plants/plot were observed if PREPLT applications contained an effective tank-mix combination in either soybean system.However, 244 plants/plot were observed following a program that consisted of glyphosate PREPLT fb glyphosate plus fomesafen early post-emergence (EPOST) fb glyphosate LPOST.Few differences in yield or net return were observed in the PREPLT experiments.However, herbicide programs that contained an effective PREPLT treatment generally resulted in higher yield and net economic return.Results from this research suggest that POST-only programs are ineffective at controlling GR 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 | 0.000 | 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) | 0.000 | 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".