Effect of Blossom Density and Crop Load on Growth, Fruit Quality, and Return Bloom in ‘Honeycrisp’ Apple
Bibliographic record
Abstract
From 2003 to 2006, the blossom level and crop load of ‘Honeycrisp’ apple ( Malus × domestica Borkh.) trees on M.26 rootstocks were adjusted to improve fruit quality and return bloom. The treatments consisted of manually removing flower clusters to 50, 100, and 150 per tree, then at ≈50 d after full bloom, the crop load was adjusted to 3, 6, and 9 fruit/cm 2 trunk cross-sectional area (TCSA), respectively. All flower and crop load adjustment significantly increased TCSA and canopy volume compared with the control. Classic biennial bearing was observed on the untreated control trees and those thinned to 150 blossom clusters per tree and 9 fruit/cm 2 TCSA and was mitigated for trees with 50 and 100 blossom clusters followed by crop load adjustment to 3 and 6 fruit/cm 2 TCSA, respectively. Fruit color the “on” year was always lower on the control trees; no difference was found in the “off” year. The treatments increased fruit weight proportional to crop load except for the 2004 “off” year. This study illustrates that for trees with ≈1 m 3 canopy volume, the combined effects of blossom and crop load adjustment to 100 blossom clusters/tree followed by fruitlet adjustment to 6 fruit/cm 2 TCSA and below will induce consistent annual production for ‘Honeycrisp’.
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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 | 0.000 |
| 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 | 0.001 |
| 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".