Potassium Fertilization Reduces the Severity of Asian Soybean Rust Under High Disease Pressure
Bibliographic record
Abstract
Potassium (K) is one of the essential nutrients for plants and is involved in many cellular processes which might influence the severity of diseases. There are few reports of the effect of increasing concentrations of K in the field on the severity of the Asian soybean rust (ASR) caused by Phakopsora pachyrhizi. In this context, the objective of this work was to verify the influence of increasing concentrations of K on ASR, in the absence and presence of chemical control, in conditions that highly favored the development of the disease. Two experiments were conducted under field conditions and two also in the field but in pots with a capacity of 100 L. Evaluated concentrations of K were 0.0, 100, 200, and 400 mg/dm³, respectively in a randomized complete block design. The application of the fungicide mixture azoxystrobin (200 g/L) + cyproconazol (80 g/L) was performed at 45, 60, and 75 days after emergence. The severity of the disease, area under the disease progress curve (AUDPC), rate of disease progression (r), soybean productivity, and contents of chlorophyll a, b and total were evaluated. The severity, AUDPC, and r decreased with the increase of the concentration of K and fungicide application, while the rate of disease control and soybean productivity increased. Contents of chlorophyll a, b and total also had higher values according to the increase in the concentration of K. Potassium fertilization reduced the severity of ASR and grain yield under high disease pressure with or without chemical control.
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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".