Glyphosate-Resistant Horseweed (<i>Conyza canadensis</i>) Dose Response to Saflufenacil, Saflufenacil plus Glyphosate, and Metribuzin plus Saflufenacil plus Glyphosate in Soybean
Bibliographic record
Abstract
The control of glyphosate-resistant (GR) horseweed ( Conyza canadensis ) in soybean has been variable with glyphosate plus saflufenacil. The objective of this research was to determine the biologically effective rate (BER) of saflufenacil, saflufenacil mixed with glyphosate, and metribuzin mixed with saflufenacil and glyphosate applied preplant (PP) for the control of GR horseweed in no-till soybean; a study was conducted to determine each of the three treatments. For each study, seven field sites infested with GR horseweed were used over a 2-yr period (2014, 2015). Saflufenacil alone at 25 and 36 g ai ha –1 provided 90 and 95% control of GR Horseweed 8 wk after application, while the BER to achieve 98% control was outside of the treatment range tested. The saflufenacil plus glyphosate (900 g ai ha –1 ) BER experiment found less saflufenacil was required as 25, 34, and 47 g ha –1 provided 90, 95, and 98% control of GR horseweed respectively. The metribuzin BER experiment found 61, 261, and 572 g ha –1 was required to provide 90, 95 and 98% control of GR horseweed, respectively, mixed with saflufenacil (25 g ha –1 ) and glyphosate (900 g ha –1 ). The addition of metribuzin with the recommended rate of saflufenacil (25 g ha –1 ) plus glyphosate improved control and a second effective herbicide mode of action for the control of GR horseweed. The use of a threeway herbicide mixture can be an effective weed management strategy to control GR horseweed in soybean.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".