Response of winter wheat to herbicide plus fungicide plus ammonium thiosulphate tank-mixes
Bibliographic record
Abstract
A study was conducted at six field sites near Exeter and Ridgetown, ON, over a 3 yr period (2014, 2015, and 2016) to determine the effect of ammonium thiosulphate (ATS), various fungicides (azoxystrobin/propiconazole, trifloxystrobin/prothioconazole, or pyraclostrobin/metconazole), and various herbicides (bromoxynil/MCPA, thifensulfuron/tribenuron + MCPA, pyrasulfotole/bromoxynil, or 2,4-D/dichlorprop) applied alone and in tank-mix combinations on winter wheat crop injury and grain yield. The treatments were applied using Hypro ULD120-02 flat-fan nozzles around Zadoks growth stage 30. The herbicides and fungicides caused <0.6% leaf injury when ATS was not added to the tank-mix. When averaged across fungicides in ATS tank-mixes, leaf injury 1 wk after treatment application was 3.5% to 3.7% with thifensulfuron/tribenuron and dichlorprop-P/2,4-D herbicides and 5.1% to 5.8% injury with bromoxynil/MCPA and thifensulfuron/tribenuron herbicides. On the three field sites with the highest leaf injury, a fungicide–ATS tank-mix increased injury to 4.5% averaged across fungicides and to 4.3% with a herbicide–ATS tank-mix averaged across herbicides. Three-way tank-mixes of herbicide–fungicide–ATS caused the highest injury (7.1%). Despite significant crop injury 1 WAA with some tank-mixes, there was no evidence that grain yields were adversely affected. This study shows that the co-application of a three-way tank-mix of ATS with fungicides (azoxystrobin/propiconazole, trifloxystrobin/prothioconazole, or pyraclostrobin/metconazole) and herbicides (bromoxynil/MCPA, thifensulfuron/tribenuron + MCPA, pyrasulfotole/bromoxynil, or dichlorprop-P/2,4-D) has the potential to cause considerable injury in winter wheat under some environmental conditions in Ontario, but the effect seems transient, with no grain yield reductions detected.
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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) | 0.000 | 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".