Fungi Resistance to Multissite Fungicides
Bibliographic record
Abstract
Multisite fungicides have been used for many years in fruit and vegetable crops worldwide. Cases of the fungi resistance development to these fungicides have been rare. From the 2002 season onwards, with the outbreak of Asian soybean rust in Brazil, caused by Phakopsora pachyrhizi, site-specific fungicides became the main weapon for its control. From 2002 to 2011, penetrant mobile site-specific fungicides were used and until today in double (DMI + QoI) or triple (DMI + QoI + SDHI) co-formulatoons in an area of more than 30 million hectares and with three sprays per area. This resulted, as expected, in the fungus sensitivity reduction, today with cross and multiple resistance to those site-specific fungicides. From the 2011 season in an attempt to recover control that for some chemicals and mixtures reached < 30%, research was started with site-specific + multi-site mixtures, taking as example Phytophthora infestans resistance development to metalaxyl in Europe showinig long-lasting solution found by the addition of multisite mancozeb. It is expected that the effective life of site-specific + multi-site mixtures may be as long in controlling soybean rust as it has been for potato, tomato and grape downy mildews. This review presents the concepts involved in the sensitivity reduction to fungicides. Some fungal species and fungicides involved are listed. Considering the P. pachyrhizi sporulation potential, the great soybean area sprayed and the number of sprays per area mainly with site-specific co-formulations and the reduced area sprayed with multisites, we discuss the need for annual monitoring of P. pachyrhizi sensitivity to the these chemicals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".