Influence of Glyphosate/Dicamba Application Rate and Timing on the Control of Glyphosate-Resistant Horseweed in Glyphosate/Dicamba-Resistant Soybean
Bibliographic record
Abstract
Abstract Dicamba may be an efficacious option for the control of glyphosate-resistant (GR) horseweed in glyphosate/dicamba-resistant soybean; research is needed to optimize the application rate based on horseweed height at the time of application. The purpose of this study was to determine the effect of glyphosate/dicamba rate and application timing for the control of GR horseweed. Glyphosate/dicamba was applied at three rates (900, 1,350, and 1,800 g ae ha −1 ) at three horseweed application timings (5, 15, and 25 cm) in a factorial design. There was no interaction between glyphosate/dicamba rate and timing for GR horseweed control or soybean yield; however, there was an interaction for GR horseweed density and biomass. At 2 and 4 wk after application (WAA), there was a decrease in GR horseweed control as the height at the time application increased. At 4 WAA, the application of glyphosate/dicamba to GR horseweed that was 5-, 15-, and 25-cm tall provided 87%, 76%, and 62% control, respectively. There was no impact of glyphosate/dicamba application timing on soybean yield. At 2, 4, and 8 WAA, there was an increase in GR horseweed control as the rate of glyphosate/dicamba was increased. At 8 WAA, glyphosate/dicamba applied at 900, 1,350, and 1,800 g ae ha −1 controlled GR horseweed 76%, 87%, and 92%, respectively. Earlier application timings and higher rates of glyphosate/dicamba caused the greatest reduction in GR horseweed density and biomass. Reduced GR horseweed competition resulted in a 100% to 144% increase in soybean yield, but there was no difference in soybean yield among glyphosate/dicamba rates tested.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".