Thinning response of ‘Redhaven’ peaches to 1-aminocyclopropane carboxylic acid (ACC)
Bibliographic record
Abstract
Peach trees bear an abundance of flowers which produce a surplus of fruit that the tree is unable to support. A self-regulatory mechanism enhances the abscission of immature fruitlets leading to a reduced fruit load, but this is often insufficient to achieve fruit of marketable size. Supplementary manual fruit thinning is typically required to optimize economic fruit load. This 2-yr study investigated the response of ‘Redhaven’ peach trees to sprays of 300 and 600 mg L−1 1-aminocyclopropane carboxylic acid (ACC) during the phenological stages of full bloom, shuck split, and ∼20 mm fruitlet diameter. The objective was to determine the efficacy of ACC on fruit set, fruit size, and yield at harvest and the amount of hand-thinning required at “June drop”. Although the response to ACC varied between the 2 yr, 600 mg L−1 ACC spray at full bloom (2018) and at ∼20 mm fruit size (2019) reduced fruit set, the need for hand-thinning by 59%–66%, and crop load. Treatments also had varying but significant effects on leaf yellowing and leaf drop when measured shortly after application. ACC reduced total yield and number of fruit per tree in both study years and increased fruit weight in 2019. Overall, peaches were responsive to ACC at a range of timings from bloom to ∼20 mm fruit size. This study enhances our understanding of ACC on fruitlet abscission of peaches and is one of few studies to demonstrate the effectiveness of a chemical fruitlet thinner for peaches.
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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".