Soybean Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada, from 2010 to 2014
Why this work is in the frame
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Bibliographic record
Abstract
Annual decreases in soybean (Glycine max L. Merrill) yield caused by diseases were estimated by surveying university-affiliated plant pathologists in 28 soybean-producing states in the United States and in Ontario, Canada, from 2010 through 2014. Estimated yield losses from each disease varied greatly by state or province and year. Over the duration of this survey, soybean cyst nematode (SCN) (Heterodera glycines Ichinohe) was estimated to have caused more than twice as much yield loss than any other disease. Seedling diseases (caused by various pathogens), charcoal rot (caused by Macrophomina phaseolina (Tassi) Goid), and sudden death syndrome (SDS) (caused by Fusarium virguliforme O’Donnell & T. Aoki) caused the next greatest estimated yield losses, in descending order. The estimated mean economic loss due to all soybean diseases, averaged across U.S. states and Ontario from 2010 to 2014, was $60.66 USD per acre. Results from this survey will provide scientists, breeders, governments, and educators with soybean yield-loss estimates to help inform and prioritize research, policy, and educational efforts in soybean pathology and disease management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it