Soybean Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada, from 2015 to 2019
Bibliographic record
Abstract
Soybean (Glycine max [L.] Merrill) yield losses as a result of plant diseases were estimated by university and government plant pathologists in 29 soybean producing states in the United States and in Ontario, Canada, from 2015 through 2019. In general, the estimated losses that resulted from each of 28 plant diseases or pathogens varied by state or province as well as year. Soybean cyst nematode (SCN) (Heterodera glycines Ichinohe) caused more than twice as much loss as any other disease during the survey period. Seedling diseases (caused by various pathogens), Sclerotinia stem rot (white mold) (caused by Sclerotinia sclerotiorum [Lib.] de Bary), and sudden death syndrome (caused by Fusarium virguliforme O’Donnell & T. Aoki) caused the next greatest yield losses, in descending order. Following SCN, the most damaging diseases in the northern United States and Ontario differed from those in the southern United States. The estimated mean economic loss from all soybean diseases, averaged across the United States and Ontario, Canada was US$45 per acre (US$111 per hectare). The outcome from the current survey will provide pertinent information regarding the important soybean diseases and their overall severity in the soybean crop and help guide future research and Extension efforts on managing soybean diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".