Impact of the improvements inFusarium head blight and agronomic management on economics of winter wheat
Bibliographic record
Abstract
Fusarium head blight (FHB) is a devastating disease to cereal crops worldwide that decreases grain yield, grain quality, and causes mycotoxin contamination. FHB resulted in an estimated $2 billion USD loss in the US between 1993 and 2001, and 520 million Canadian dollars (CAD) in Canada in the 1990s. In the wheat producing areas in Canada and the United States, it is perceived that significant progress has been made to manage FHB, but the economic impact of various innovations has not been quantified. Therefore, the main objective of this study was to assess the economic impact of various practices deployed in the province of Ontario, Canada, on managing deoxynivalenol and improving agronomic performance in winter wheat since an epidemic in 1996. The impacts of four hypothetical FHB management scenarios on total deoxynivalenol (DON) concentration and grain yield were estimated in field experiments that compared old (mid-1990s) and modern era (mid-2010s) production practices. Management scenarios included old and new cultivars varying in susceptibility to FHB, fungicide application and nitrogen rates. These impacts were applied to farm survey data collected in 1996 to estimate farm revenue and profit. A similar economic estimate was conducted for the recent FHB epidemic in 2013. If a modern MR cultivar, a modern fungicide, and the combination were deployed in the epidemic of 1996, farm revenue would have increased by 26-32, 23-36 and 48-60%, and profit increased by 88-157, 42-59 and 165-207 CAD per ha, respectively, depending on the nitrogen rate. In the province of Ontario, up to 68 million CAD of revenue losses could have been avoided in 1996 with the use of modern agronomic and FHB management practices. Our study has quantified some of the major economic advances in managing FHB and DON since 1996, but further research is needed to develop better cultivars and management strategies.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".