Modeling the Effect of Preharvest Weather Conditions on the Incidence of Soggy Breakdown in ‘Honeycrisp’ Apples
Bibliographic record
Abstract
‘Honeycrisp’ is a relatively new apple cultivar highly susceptible to physiological disorders, such as soggy breakdown. The overall objective of this study was to identify preharvest weather parameters that influence the incidence of soggy breakdown over the different phases of fruit development. Using weather data and evaluation of fruit quality from three sites in Ontario, two sites in Quebec, and one site in Nova Scotia from 2009 to 2011, and data from four sites in Ontario from 2002 to 2006, a model for soggy breakdown incidence (SBI) was developed to predict the level of susceptibility in ‘Honeycrisp’ apples. This model uses primarily two weather variables during the last phase of fruit development [91 days from full bloom (DFB) to harvest] to accumulate an SBI index during the growing season, from full bloom to harvest. Cool (temperature <5 °C) and wet conditions (precipitation >0.5 mm) during this last phase resulted in increased soggy breakdown susceptibility levels. The predictions of the SBI model resulted in 68% of well-estimated cases (threshold of ±5%) (RMSE = 6.45, EF = 0.28, E = −0.04). Furthermore, firmness was linked to soggy breakdown, in addition to weather conditions, revealing a positive effect of high firmness at harvest on the development of the disorder. However, the effect of fruit quality attributes (e.g., internal ethylene concentration, starch index, firmness, and soluble solid content) by themselves, without considering weather conditions, revealed no relationship with the incidence of soggy breakdown.
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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 | 0.001 |
| 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".