Estimated Yield Reductions and Economic Losses on Wheat Caused by Disease from 2018 Through 2021
Bibliographic record
Abstract
Wheat ( Triticum aestivum L.) yield and economic losses caused by pathogens were estimated annually by plant pathologists from 29 U.S. states and Ontario, Canada, from 2018 through 2021. During this 4-year period, plant pathogens caused an estimated reduction of 560 million bushels, with an estimated loss value of US$2.9 billion. Annual losses ranged from 111 million bushels in 2018 to 188 million bushels in 2019. Based on the number of acres planted, the average per-acre loss caused by plant pathogens was US$18.10 across all years and state/province recording estimates. Fusarium head blight (caused by multiple species of Fusarium) was responsible for the greatest overall estimated reduction in yield, followed by stripe rust (caused by Puccinia striiformis) and leaf rust (caused by P. triticina). Although important disease management costs, such as pesticide application, were not considered, the results show the importance of continued plant disease education and research. Quantifying estimated losses associated with plant pathogens impacting wheat remains an important endeavor. Estimates provided by this group of experts are expected to be used as a guide to influence funding for plant disease research by directing Extension and research through both applied and basic efforts. Moreover, the continued effort to quantify plant diseases and their influence on yield losses, as well as the economics of managing plant diseases, will help inform the industries that influence plant disease management and shape on-farm disease management efforts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".