Alternative Products to Control Soybean Downy Mildew in the Field
Bibliographic record
Abstract
Soybean mildew caused by Oomycota Peronospora manshurica, is a disease widely spread in Brazil. In order to study the efficiency of soybean mildew control due to the application of alternative products and fungicide in the field, experiments were conducted in Ponta Grossa, PR, Brazil, during the 2013/2014 and 2014/2015 growing seasons. The design used was randomized blocks with four replications. The treatments were: 1-witness; 2-acibenzolar-S-methyl; 3-calcium; 4-micronutrients: copper, manganese and zinc; 5-micronutrients: manganese, zinc and molybdenum; 6-NK fertilizer; 7-Ascophyllum nodosum and 8-azoxystrobin + cyproconazole with the addition of Nimbus adjuvant. Four applications of alternative products (phenological stages V3, V6, R1 and R5.1) and two of fungicide (phenological stages R1 and R5.1) were performed. The mildew severity was estimated using a diagrammatic scale. The severity data made it possible to calculate the area under the disease progress curve (AUDPC). In the 2014/2015 harvest the disease was more severe. The control of downy mildew by the use of fungicide did not reduce the epidemic. The fungicide was not efficient in the two evaluated seasons. All tested alternative products reduced the disease severity and AUDPC in both seasons. The best results in reducing downy mildew were found with the application of acibenzolar-S-methyl, micronutrients (Cu, Mn, Zn) and A. nodosum.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".