Corn Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada, from 2020 to 2023
Bibliographic record
Abstract
Corn ( Zea mays L.) was planted on 375.1 million acres (151.8 million hectares) cumulative from 2020 to 2023 in the United States and Ontario, Canada. During these 4 years, 59.6 billion bushels (1.5 billion metric tons) of grain were produced, valued at 325.9 billion U.S. dollars (USD). Plant pathogens that cause diseases limit annual grain production and reduce associated economic returns while also increasing management costs to prevent potential losses. Plant pathologists representing 29 U.S. states and Ontario, Canada, were asked to estimate annual percent yield losses caused by 37 pathogens or pathogen groups through an online survey. Grain contaminated by mycotoxins was also estimated. According to survey results, estimated overall annual percent losses ranged from negligible in Texas in 2023 to 15.8% in Michigan in 2021 and averaged 3.0% across all surveyed regions for the 4-year period. Diseases reduced corn yield by an estimated 2.5 billion bushels (63.7 million metric tons) across participating locations, with tar spot (caused by Phyllachora maydis), Fusarium stalk rot (caused by Fusarium spp.), and plant-parasitic nematodes causing the most significant losses. The total estimated economic loss caused by diseases was 13.8 billion USD, and the average economic loss was 37.76 USD per acre (93.30 USD per hectare) across all years and locations. Survey data and the resulting analysis can help inform corn disease management and guide pathology education, policy, and research priorities among scientists, government representatives, Extension educators, and other stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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) | 0.002 | 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".