Development of Action Threshold to Manage Common Leaf Spot and Black Seed Disease of Strawberry Caused by <i>Mycosphaerella fragariae</i>
Bibliographic record
Abstract
The fungus Mycosphaerella fragariae is responsible for two strawberry diseases: common leaf spot (CLS) and black seed disease (BSD). In June-bearing strawberry plantings, CLS influences vigor, yield, and winter survival. During production years, BSD causes black lesions around strawberry seeds, reducing the market value of the berries. The objective of this study was to characterize the relationships between CLS and BSD and to develop action thresholds for the management of BSD. Data on the number of lesions per leaf, number of black seeds per berry, and percentage of diseased berries were collected at two experimental and six commercial sites from 2000 to 2011, corresponding to 50 farm-years. First, logistic regression was used to model the relationship between BSD occurrence in its binary data form and the number of lesions per leaf assessed at 7, 14, 21, and 28 days before 10% bloom. Second, linear regression was used to model the relationship between BSD severity, BSD incidence, and number of lesions per leaf assessed at 7, 14, 21, and 28 days before 10% bloom. Resulting action thresholds of 15, 25, or 33 lesions per leaf at 21, 14, or 7 days before 10% bloom, respectively, were compared with the recommended practice at three commercial sites in 2014, 2015, and 2016. The percentage of diseased berries was significantly (P = 0.0016; least significant difference = 7.140) higher in the sections of the fields that were not managed for BSD, with an average of 15.22% diseased berries, in comparison with 3.22 and 2.44% diseased berries in sections managed according to the recommendations and the thresholds, respectively. Overall, 40% less fungicide was used when the thresholds were applied. Hence, these thresholds can be used as an additional decision tool to optimize fungicide applications during the prebloom period.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".