Optimizing management of cercospora leaf spot (<i>Cercospora beticola</i>) of sugarbeet in the wake of fungicide resistance
Bibliographic record
Abstract
Cercospora leaf spot (CLS), caused by Cercospora beticola, is the most detrimental disease of sugar beet in temperate climates. In the Great Lakes region, CLS was well managed by pyraclostrobin-based programmes using the decision support tool BEETcast™. Due to C. beticola resistance to pyraclostrobin in the region, other groups of fungicides were evaluated. Field experiments were conducted at six sites from 2013 to 2015 in Pain Court (PC) and Ridgetown (RT), Ontario. BEETcast™ application schedules for prothioconazole and mancozeb as well as carrier volume (115 and 235 L ha−1) were compared with label-based (calendar) applications. When disease intensity was high, the conservative calendar application schedule reduced the standardized area under the disease progress curve (sAUDPC) by an average of 84% compared with all BEETcast™ schedules, and the BEETcast™ 50/35 schedule (235 L ha−1) reduced sAUDPC by 48% compared with the 55/50 schedules. However, there were fewer differences among application schedules at sites with low or moderate disease intensity. BEETcast™ application schedules reduced the number of fungicide applications by 34–55%. All application schedules increased sucrose (%) and recoverable white sugar (RWS) to an equivalent level, but none of the schedules increased profit margin compared with the non-treated control. Using a carrier volume of 235 L ha−1 reduced sAUDPC by 28% compared with 115 L ha−1 at one site when disease severity exceeded 95% in non-treated control plots. Thus, management recommendations should consider that CLS severity is reduced by application schedules using a shortened interval and appropriate carrier volume, but this does not necessarily result in higher beet or sucrose yield or increases in profit margin.
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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 | 0.000 |
| 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) | 0.001 | 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".