Control of Glyphosate-Resistant Common Ragweed (Ambrosia artemisiifolia L.) in Glufosinate-Resistant Soybean [Glycine max (L.) Merr]
Bibliographic record
Abstract
Common ragweed emerges early in the season in Nebraska, USA and is competitive with soybean; therefore, preplant herbicides are important for effective control. Glyphosate has been used as a preplant control option; however, confirmation of glyphosate-resistant (GR) common ragweed in Nebraska necessitates evaluating other herbicide options. The objectives of this study were to (1) evaluate the efficacy of preplant (PP) herbicides followed by (fb) glufosinate alone or in tank-mixture with imazethapyr, acetochlor, or S-metolachlor applied post-emergence (POST) for control of GR common ragweed in glufosinate-resistant soybean; (2) their effect on common ragweed density, biomass, and soybean yield; and (3) the partial economics of herbicide programs. A field experiment was conducted in a grower’s field infested with GR common ragweed in Gage County, Nebraska, USA in 2015 and 2016. Preplant herbicide programs containing glufosinate, paraquat, 2,4-D, dimethenamid-P, cloransulam-methyl, or high rates of flumioxazin plus chlorimuron-ethyl provided 90 to 99% control of common ragweed at 21 d after treatment (DAT). The aforementioned PP herbicides fb a POST application of glufosinate alone or in tank-mixture with imazethapyr, acetochlor, or S-metolachlor controlled GR common ragweed 84 to 98% at soybean harvest, reduced common ragweed density (≤ 20 plants m─2) and biomass by ≥ 93%, and secured soybean yield 1,819 to 2,158 kg ha─1. The PP fb POST herbicide programs resulted in the highest gross profit margins (US$373 to US$506) compared to PP alone (US$91) or PRE fb POST programs (US$158). The results of this study conclude that effective and economical control of GR common ragweed in glufosinate-resistant soybean is achievable with PP fb POST herbicide programs.
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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".