Environmental effects on the presence and quantity of postharvest fungal pathogens on sweet cherry in the Okanagan Valley
Bibliographic record
Abstract
Sweet cherries are economically important crops in British Columbia; however, they are highly susceptible to postharvest disease and subsequent crop loss. Understanding the local fungal pathogens responsible for crop loss is important in mitigating disease. The major postharvest pathogens of sweet cherry in the Okanagan were determined to be Alternaria spp. and Botrytis cinerea. A novel duplex droplet digital PCR (ddPCR) assay was developed to concurrently detect pathogens. Isolates of each pathogen were collected from Okanagan cherry orchards and analyzed for resistance to the fungicides Pristine (a.i. boscalid and pyraclostrobin) and Elevate (a.i. fenhexamid) at commercial concentrations. Alternaria isolates were resistant to both fungicides. B. cinerea isolates were resistant to Pristine, and half were resistant to Elevate. Spore pathogenicity was assessed on Staccato and Sentennial cherries at 4 and 22 °C. As few as 25 spores of each pathogen were able to cause infection at 4 and 22 °C. The pathogens were able to cause disease at low spore concentration and at the low temperatures used for storage and shipment. During the 2017 growing season, both pathogens were present at bud break. Most orchards showed a significant increase in pathogen quantity at harvest compared to other growth stages, suggesting that disease mitigation may be most important right before harvest. The role of growing degree days (GDD), rain events and fungicide application on pathogen quantity was assessed using linear mixed effects models with multi-model inference and model averaging. The models showed that GDD only had an impact on the quantity of Alternaria spp., and rain events had an impact on both pathogen quantities. Postharvest disease incidence on Staccato was negatively correlated to GDD, and was significantly greater in the South compared to the North for both pathogens. Lastly, pathogen quantity at harvest was not correlated to disease incidence on sweet cherry after 6 weeks in cold storage. The data in the study can be used in future research as a basis for disease prediction models. It can also be used by growers to develop locally targeted disease mitigation practices including alternative methods to fungicides, or screening fungi for resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".