The effect of residual corn herbicides on injury and yield of soybean seeded in the same season
Bibliographic record
Abstract
Soltani, N., Mashhadi, R. H., Mesgaran, M. B., Cowbrough, M., Tardif, F. J., Chandler, K., Swanton, C. J. and Sikkema, P. H. 2011. The effect of residual corn herbicides on soybean injury and yield seeded in the same season. Can. J. Plant Sci. 91: 571-576. In rare situations, poor stands of corn are removed and reseeded to soybean later in the spring, even though residual corn herbicides have already been applied. Nine field studies were conducted over a 3-yr period (2005 to 2007) at four locations in Ontario, Canada, to determine the minimum interval for re-seeding to soybean following the application of residual corn herbicides. Five commonly used residual corn herbicide premixes or tankmixes were applied 6, 4, 2 or 0 wk before soybean seeding. The level of injury generally increased as the interval between herbicide application and soybean seeding decreased. Isoxaflutole plus atrazine caused as much as 28% injury and decreased plant stand, biomass and yield as much as 7, 49 and 42%, respectively. S-metolachlor/atrazine and S-metolachlor plus mesotrione plus atrazine caused 0 to 17% injury, but had no adverse affect on plant stand, biomass and yield except for biomass, which was reduced 18% with S-metolachlor plus mesotrione plus atrazine at 0 wk before seeding. Rimsulfuron plus S-metolachlor plus dicamba caused up to 68% injury and decreased plant stand, biomass and yield as much as 18, 56, and 26%, respectively. Dimethenamid plus dicamba/atrazine caused up to 90% injury and decreased plant stand, biomass and yield by as much as 43, 77, and 54%, respectively. Based on these results, corn producers are advised to switch to herbicides that are less injurious to soybean as the spring seeding season progresses. This would allow for reseeding to soybean if it is no longer profitable to re-seed corn due to the late seeding date.
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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) | 0.001 | 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".