AVG, NAA, boron, and magnesium influence preharvest fruit drop and fruit quality of ‘Honeycrisp’ apples
Bibliographic record
Abstract
Preharvest fruit drop (PFD) of apple (Malus domestica Borkh.) can cause significant losses resulting in up to 50% reduction in yield in some years. In a 2-yr study, ‘Honeycrisp’ trees were treated with six foliar treatments including 20 mg·L−1 1-naphthaleneacetic acid (NAA) 2 wk before anticipated harvest (WBAH), 125 mg·L−1 aminoethoxyvinylglycine hydrochloride (AVG) 4 WBAH, AVG plus NAA 2 WBAH, and repeat sprays of 200 mg·L−1 B, and 2000 mg·L−1 Mg applied 48–93 d after full bloom to evaluate their effect on PFD and fruit quality. Fruit drop associated with broken pedicels and physiological causes was monitored. During a year with ∼43% PFD on untreated control trees, AVG retained up to ∼35% and NAA retained up to ∼27% of the total fruit number, relative to the control trees. The combination of AVG plus NAA had similar PFD compared with AVG alone, while B and Mg were similar to the control. The proportion of PFD associated with broken pedicels was ∼5%. Aminoethoxyvinylglycine delayed starch degradation, ethylene evolution, and blush skin colour development of fruit. This study supports the use of AVG 4 WBAH and NAA 2 WBAH to manage ‘Honeycrisp’ PFD, which may offer economic advantages for growing quality apples. Foliar sprays of Mg or B failed to reduce PFD.
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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".