Fungicide Spraying Programs Reducing Asian Soybean Rust Impact on Soybean Yield Components
Bibliographic record
Abstract
Soybean is one of the leading agricultural commodities, and Brazil is currently the largest producer globally. Despite this, fungal diseases as Asian Soybean Rust (ASR) are among the primary limiters of high crop yields in Brazilian fields. Losses caused by the biotrophic fungus Phakopsora pachyrhizi can reach up to 90%, depending on weather conditions, and several components can be affected during soybean growth and reproduction. Here, we assessed fungicide spraying programs to ASR control, aiming to reduce the losses on soybean components. The experiments were performed under field conditions during the harvest season 2014/2015. The evaluated variables were soybean leaf area index, ASR severity, yield components, dry mass grain accumulation, protein, and oil content. The yield components assessed were the number of pods per plant, seeds per pod, seeds per plant, and thousand-grain weight. The disease severity gradient was generated using seven fungicide spraying programs, differing in time, number, and type of fungicides. Three fungicide programs that included applications in the soybean vegetative and reproductive stages were more efficient. These programs resulted in the lower area under the disease progress curve (AUDPC), greater leaf area duration (LAD), and health leaf area duration (HAD) than the untreated soybean. The ASR infection in soybean resulted in reduced LAD and HAD, and as a consequence, interfered negatively with dry matter accumulation, yield components, and grain yield.
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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) | 0.000 | 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 | 0.000 |
| 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".