Early-Season Predictions of Aerial Spores to Enhance Infection Model Efficacy for Cercospora Leaf Spot Management in Sugarbeet
Bibliographic record
Abstract
Cercospora beticola causes one of the most destructive foliar diseases of sugarbeet in many growing regions. Management of Cercospora leaf spot (CLS) relies heavily on timely and repeated fungicide applications. Current treatment initiation is often supported by models predicting conditions favorable for infection; however, these models lack information on C. beticola presence and abundance. Burkard volumetric mechanical samplers and highly CLS-susceptible sentinel beets (biological samplers) were used to assess early-season aerial C. beticola conidia from sugarbeet fields in Michigan and in Ontario, Canada, from 2019 to 2022. In initial correlation and logistic regression analyses (n = 449), duration of leaf wetness, air temperature, and wind speed were found to predict the risk of elevated Cercospora spore concentrations with 67.9% accuracy. In 2022 and 2023, a select model and a limited set of action thresholds, in addition to the BEETcast model, were tested for fungicide application timing. When CLS pressure was high, extending the interval between applications showed reduced management of CLS (P < 0.001), sugar percentage, and recoverable white sugar per ton (P < 0.05) compared with the grower standard. Model-based programs integrating canopy closure information resulted in CLS, yield, and sugar metrics comparable to the grower standard despite one fewer fungicide application. In additional training analysis (n = 402), an ensemble model included leaf wetness, air temperature, relative humidity, and wind speed variables with a testing accuracy of 73.2% (n = 101). Based on model development, refinement, and validation, assessment of elevated early-season C. beticola presence and abundance has potential to improve application timing and efficacy for preventive CLS management.
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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 | 0.000 | 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".