A Review of Storage Temperature Recommendations for Apples and Pears
Bibliographic record
Abstract
An exploration of the range of expert opinions on the optimum storage temperature for apples and pears in RA (refrigerated air), CA (controlled atmosphere), and DCA (dynamic controlled atmosphere) is provided, based on the accumulated postharvest data from the last 20 years. Apple cultivars have been divided into two storage temperature groups (0 to 1 °C and >1 °C), based on chilling sensitivity. Increasingly, gradual cooling, rather than rapid cooling, is recommended for apple cultivars, especially for chilling-sensitive cultivars. European pear cultivars are held at storage temperatures close to or just below 0 °C since they are not chilling-sensitive, and most cultivars require a cold temperature to induce ethylene production and ripening, especially if picked early for long-term storage. Asian pears apparently have higher temperature requirements in CA, compared with European pears. The temperature recommendations for RA and CA storage differ in some apple and European pear cultivars. In such cases, the CA recommendation is, on average, approximately 0.9 °C higher for apple cultivars and approximately 0.5 °C higher for pear cultivars, compared with RA. Research evidence suggests that some apple and pear cultivars can be stored at higher temperatures in DCA than in CA, and if the ethylene inhibitor, 1-methylcyclopropene (1-MCP), is applied in CA and/or DCA, leading to possible energy savings and quality benefits. A cool growing season may increase postharvest disorders, depending on cultivar and region. The store or packinghouse manager may choose to mitigate potential postharvest problems by maintaining the storage temperature at or above the temperature listed here and/or using stepwise (gradual) cooling. The storage temperature can affect the humidity and vapour pressure deficit (driving force) in the storage room. Altering the vapour pressure deficit controls the water loss in stored fruit, which can affect various quality parameters and the occurrence of several storage disorders.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".