Integrated Management of Glyphosate-Resistant Giant Ragweed (<i>Ambrosia trifida</i>) with Tillage and Herbicides in Soybean
Why this work is in the frame
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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 −2 and soybean yield > 2,400 kg ha −1 compared with the density of ≥ 2 plants m −2 and soybean yield < 1,800 kg ha −1 under 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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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 | 0.001 |
| 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 it