Estimated Soybean Yield and Economic Losses Caused by Diseases in the United States and Ontario, Canada, from 2020 to 2024
Bibliographic record
Abstract
The impact of plant diseases on soybean ( Glycine max [L.] Merrill) yield was estimated across 29 states and Ontario, Canada, from 2020 to 2024 by university and government plant pathologists. Losses from 29 pathogens or groups of pathogens were estimated at the end of each growing season through a survey and summarized across years and locations. Diseases reduced soybean yield by an estimated 1.2 billion bushels (32.8 million metric tons) valued at 14.6 billion USD for the survey period. Per acre, this estimated mean economic loss was equal to 32.93 USD (81.37 USD per hectare) across all locations and years, excluding costs such as fungicide seed treatments and foliar applications. Soybean cyst nematode (SCN) ( Heterodera glycines Ichinohe) reduced yield by 482.4 million bushels (13.1 million metric tons), a value nearly four times greater than the next greatest cause of yield loss, which was sudden death syndrome (SDS) (caused by Fusarium virguliforme O'Donnell & T. Aoki). Following SCN and SDS, the most significant yield losses were attributed to white mold (caused by Sclerotinia sclerotiorum [Lib.] de Bary), seedling diseases (caused by various pathogens), Phytophthora root and stem rot (caused by Phytophthora sojae Kaufm. & Gerd.), and root-knot nematodes ( Meloidogyne spp.), in descending order. The most important diseases in the southern United States were generally different from those in the northern United States and Ontario. The data presented here will enable government agencies, scientists, educators, commodity groups, funding organizations, and plant breeders to enhance and prioritize policy, research, funding, and education regarding soybean disease management.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".