Strategies to improve the control of glyphosate-resistant horseweed (<i>Erigeron canadensis</i>) with glufosinate applied preplant to soybean
Bibliographic record
Abstract
Abstract The objectives of this study were to determine if the level and consistency of glyphosate-resistant (GR) horseweed control prior to soybean planting can be improved by (i) adding halauxifen-methyl, 2,4-D ester, saflufenacil, metribuzin, or dicamba to glufosinate, (ii) increasing the rate of glufosinate from 500 to 1,000 g ai ha –1 , and (iii) adding 28% urea ammonium nitrate (UAN) as the carrier solution. During a 2-yr period (2020–2021), four field trials were conducted on commercial farms located in southwestern Ontario, Canada, with confirmed GR horseweed. Glufosinate controlled GR horseweed 65%, 66%, and 63% at 2, 4, and 8 wk after application (WAA), respectively, and reduced density and biomass 46% and 33% at 8 WAA, respectively. There was no improvement in GR horseweed control from the addition of halauxifen-methyl, 2,4-D ester or saflufenacil to glufosinate and no decrease in density and biomass, with the exception that the addition of saflufenacil to glufosinate reduced density 30% compared to glufosinate alone. The addition of metribuzin to glufosinate improved GR horseweed control by 22%, 22%, and 28% at 2, 4, and 8 WAA, respectively, and further reduced density and biomass 50% and 47%, respectively, at 8 WAA, respectively. The addition of dicamba to glufosinate improved GR horseweed control by 19%, 26%, and 30% at 2, 4, and 8 WAA, respectively, and further reduced density and biomass 54% and 60%, respectively, at 8 WAA. There was no improvement in GR horseweed control by increasing the rate of glufosinate from 500 to 1,000 g ai ha –1 or when using 28% UAN as the carrier solution. The addition of all herbicides to glufosinate, increasing the rate of glufosinate, or using 28% UAN as the carrier solution improved the consistency of GR horseweed control.
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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 | 0.000 | 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 | 0.001 | 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".