Integrated Management of Glyphosate-Resistant Giant Ragweed (<i>Ambrosia trifida</i>) with Tillage and Herbicides in Soybean
Bibliographic record
Abstract
Giant ragweed is one of the most competitive annual broadleaf weeds in soybean production fields in the midwestern United States and eastern Canada because of its early emergence, rapid growth rate, high plasticity, and resistance to glyphosate and acetolactate synthase inhibitors. Therefore, early-season management of giant ragweed is critical to avoid yield loss. The objectives of this study were to evaluate control of glyphosate-resistant giant ragweed through the integration of preplant tillage or 2,4-D; PRE or early POST (EPOST) followed by (fb) late POST (LPOST) herbicide programs with or without preplant tillage or 2,4-D; and their effect on soybean injury and yield. A field study was conducted in 2013 and 2014 in David City, NE in a field infested with glyphosate-resistant giant ragweed. Preplant tillage or 2,4-D application provided > 90% control of glyphosate-resistant giant ragweed 14 d after preplant treatment. Giant ragweed control and biomass reduction were consistently > 90% with preplant tillage or 2,4-D fb sulfentrazone plus cloransulam PRE or glyphosate plus cloransulam EPOST fb glyphosate plus fomesafen or lactofen LPOST compared with ≤ 86% control with same treatments without preplant tillage or 2,4-D. PRE or EPOST fb LPOST herbicide programs preceded by preplant treatments resulted in giant ragweed density < 2 plants m−2and soybean yield > 2,400 kg ha−1compared with the density of ≥ 2 plants m−2and soybean yield < 1,800 kg ha−1under PRE or EPOST fb LPOST herbicide programs. The contrast analysis also indicated that preplant tillage or 2,4-D fb a PRE or POST program was more effective for giant ragweed management compared with PRE fb POST herbicide programs. Integration of preplant tillage would provide an alternative method for early-season control of giant ragweed; however, a follow up application of herbicides is needed for season-long control in soybean.
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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) | 0.000 | 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".