Case Study of an Epidemiological Approach Dissecting Historical Soybean Sclerotinia Stem Rot Observations and Identifying Environmental Predictors of Epidemics and Yield Loss
Bibliographic record
Abstract
Sclerotinia sclerotiorum is a significant threat to soybean production worldwide. In this study, an epidemiological approach was used to examine 11 years of historical data from a soybean management performance trial in order to advance our understanding of Sclerotinia stem rot (SSR) development and to identify environmental predictors of SSR epidemics and associated yield losses. Recursive partitioning analysis suggested that average air temperature and total precipitation in July were the most significant variables associated with disease severity. High levels of SSR disease severity index were observed when the average temperature in July was below 19.5°C and total precipitation in July was moderate, between 20 and 108.5 mm. A biphasic sigmoidal curve accurately described the relationship between SSR disease severity index (DSI) and yield, with a DSI threshold of 22, below which minimal yield loss was observed. A 10% increase in the DSI, from 22.0 to 24.2, led to an 11% decrease in yield, from 3,308.14 to 2,951.29 kg/ha. Also, a yield threshold (3,353 kg/ha) that was higher than the annual U.S. average soybean yield (3,039.7 kg/ha) was suggested as an expected yield under low or no SSR pressure in the U.S. Midwest. These thresholds can allow soybean stakeholders to assess the value of disease control and establish an SSR baseline for cost-effective management to protect yields. Because S. sclerotiorum has more than 400 plant host species, and because having quantitative information concerning crop losses is crucial for decision making, this study shows the usefulness of historical data on SSR and, hence, can serve as a model in other SSR pathosystems (canola, dry bean, potato, pea, and so on).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".