Addition of Mancozeb to DMI + QoI, and SDHI + QoI Co-formulations Improving Control of Asian Soybean Rust
Bibliographic record
Abstract
The reduced sensitivity of Phakopsora pachyrhizi to site-specific fungicides used to control Asian soybean rust by the current co-formulations needs investigation. To improve the rust control the performance of cyproconazole + picoxystrobin, tebuconazole + picoxystrobin, cyproconazole + azoxystrobin, epoxyconazole + pyraclostrobin, fluxapyroxade + pyraclostrobin, benzovindiflupyr + azoxystrobin, prothioconazole + trifloxystrobin and cyproconazole + trifloxystrobin mixtures added by five doses of the multisite mancozeb were evaluated. The fungicides were sprayed at four growth stages the first performed at R1 growth stage and the others with 15-18 days intervals. The rust severity was quantified, the control was calculated, the percentage of chlorophyll and the yield of soybean were determined. The mean of rust control by the mixtures without addition of the multi-site fungicide was 46% (21 to 71%). There was an increase in control efficiency due to addition and mancozeb doses in all treatments. Control over 80% was obtained with tebuconazole + picoxystrobin, fluxapyroxade + pyraclostrobin, benzovindiflupyr + azoxystrobin, and prothioconazole + trifloxystrobin added at least of 2.0 kg/ha mancozeb. In unsprayed plots the maximum 78% severity corresponded to 59% damage. There was an increase in chlorophyll content and soybean yield as a function of the mancozebe increased doses: 2,019 kg/ha in the unsprayed control and in the best treatment 5,132 kg/ha. Actual control reduction due to fungal decrease in sensitivity can be improved by the multi-site fungicide addition.
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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 | 0.000 |
| 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) | 0.000 | 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".