Estimated Soybean Yield and Economic Losses Caused by Diseases in the United States and Ontario, Canada, from 2020 to 2024
Why this work is in the frame
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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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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