Does the Application of a Fungicide after a Herbicide Reduce Soybean Injury and Increase Yield?
Bibliographic record
Abstract
A total of four field experiments were conducted during 2017, 2019 and 2020 in Ontario, Canada to determine if applying a fungicide 2 - 3 days after a herbicide, applied POST, reduces visible injury, increases crop vigour and increases yield of soybean. At 3 DAB (days after fungicide application), the POST application of glyphosate, fomesafen, bentazon, thifensulfuron-methyl, cloransulam-methyl and imazethapyr caused 0, 11%, 5%, 18%, 9% and 12% visible injury in soybean, respectively. The injury decreased over time with less than 5% injury at 8 WAB (weeks after fungicide application) in all treatments evaluated. The application of pyraclostrobin/fluxapyroxad after the application of herbicides evaluated did not reduce soybean injury. Soybean vigour with glyphosate, fomesafen, bentazon, thifensulfuron-methyl, cloransulam-methyl and imazethapyr applied POST without the fungicide application was 100%, 91%, 95%, 84%, 91% and 88%, respectively at 3 DAB. The soybean vigour increased over time to 95% - 100% at 8 WAB. The application of pyraclostrobin/fluxapyroxad after the herbicide application did not improve soybean vigour, except with thifensulfuron-methyl where soybean vigour was improved 6% when followed by pyraclostrobin/fluxapyroxad. There was no effect of herbicide and fungicide treatments on soybean yield except for thifensulfuron-methyl and imazethapyr without the fungicide treatments which reduced soybean relative yield 7% and 10%, respectively. The application of pyraclostrobin/fluxapyroxad after the application of imazethapyr increased soybean yield 3%. Based on these results, applying pyraclostrobin/fluxapyroxad fungicide 2 - 3 days after glyphosate, fomesafen, bentazon and cloransulam-methyl does not affect soybean injury, vigour or yield, but it can slightly enhance the vigour and yield of soybean when applied after thifensulfuron-methyl and imazethapyr.
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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 | 0.000 | 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 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".