Crop Load and Time of Thinning Interact to Affect Fruit Quality in Sweet Cherry
Bibliographic record
Abstract
Balanced crop load is key to the production of export-quality cherries. We investigated the level and timing of crop load regulation on fruit quality. Additionally we sought to investigate possible correlation between firmness estimated by compression test or flesh penetrability. Fruit diameter was similar between 1 and 2 bud/spur treatments but was significantly lower in the 4 bud/spur treatment at all thinning times in ‘Van’ in the 2010/11 season. In contrast ‘Sweetheart’ fruit diameter was only decreased at 6 and 8 WAFB in the 4 bud/spur treatment in 2010/11. This decrease in ‘Sweetheart’ was associated with significantly higher soluble solids and starch reserves in leaves, stem, trunk and roots 2-weeks post-harvest in trees thinned at dormancy, relative to trees thinned 8 WAFB. Fruit flesh firmness significantly increased with decreased crop load irrespective of time of thinning in ‘Van’ in 2010/11. In contrast flesh firmness was significantly higher in the 1 bud/spur treatment and similar between other treatments in ‘Sweetheart’ in 2010/11. In 2011/12 flesh firmness, soluble solids and colour significantly increased whilst fruit weight and TA significantly decreased 28 days post-harvest relative to at-harvest values. We found strong correlation between values obtained with the FirmTech II and the Guss fruit texture analyser. Sweet cherry fruit quality is optimised through attaining crop load of approximately 10 fruit per cm2 of limb cross-sectional area through thinning at dormancy or full bloom.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".