Directional Selection of Phakopsora pachyrhizi Towards Site-Specific Fungicides in Mato Grosso State
Bibliographic record
Abstract
This review focus on the sensitivity reduction of Phakopsora pachyrhizi to site specific fungicides in the Brazilian state of Mato Grosso.The soybean grown area in this state in the 2018/19 growing season was 9,756,668 hectares. The main crop disease is Asian soybean rust that depending on disease severity may cause 80% yield reduction. The rust chemical control started in the 2002/003 season with site-specific fungicides applied solo and with 3.6 sprayings/ha/season. For these reasons, the fungus had sensitivity reduction to the fungicides DMIs, QoIs and SDHIs resulting in a short effective life. Anti-resistance strategies were not adopted to prevent or delay the development of soybean rust resistant population to site-specific fungicides. Reduced sensitivity to the target site has been observed for all site-specific fungicides involving cross and multiple resistance resulting in ineffective control. So far, eight mutations in Phakopsora pachyrhizi have been identified conferring reduced sensitivity to DMIs, QoIs and SDHIs but so far specific mutations are not considered in chemical control. The directional selection has occurred and has been aggravated season-after-season by the continuous use of site-specific fungicides in a large area, for 18 season and with more than three sprayings/area/season. The presence of soybean weed in one million hectares of cotton crop infected by rust aggravates the directional selection. Anti-resistance strategies should include sowing at the beginning of the recommended season, avoid the December season and replacing it by February, use of scientific criteria to time the first application and the use of multisite fungicides in all applications and in the entire area cultivated with soybean.
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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".