Soybean Rust Epidemics as Affected by Weather Conditions in Brazil
Bibliographic record
Abstract
The soybean rust (SBR) epidemics are often triggered by weather conditions, which interfere actively on the disease progress. Therefore, weather variables can be used to estimate the risk of occurrence and severity of SBR outbreaks. This research aimed to determine the influence of weather variables on SBR progress in different field trials in Brazil. Field experiments were conducted during 2014-15 and 2015-16 soybean growing seasons in Piracicaba (SP), Ponta Grossa (PR), Campo Verde (MT) and Pedra Preta (MT). For all sites and seasons, a susceptible soybean cultivar was drilled with 0.45 m row spacing and 12 plants per linear meter. No fungicide sprays were applied to ensure natural disease occurrence. In order to create different environmental conditions, sequential sowing dates, of roughly 30-day intervals were carried out. In Piracicaba, Ponta Grossa, Campo Verde, and Pedra Preta the main weather variables influencing SBR were leaf wetness duration - LWD (R = 0.340), air temperature during LWD (R = 0.313), and cumulative rainfall (R = 0.304). The final severity was assessed only at Piracicaba and Ponta Grossa, and it was mainly influenced by LWD (R = 0.643). It is possible to conclude that epidemics of SBR were mainly influenced by leaf wetness duration, accumulated rainfall and air temperature during the LWD. Therefore, future researches aiming to develop a disease warning system for SBR should include the cumulative rainfall, LWD and the air temperature during LWD, together or individually, as inputs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".