Effect of Aminoethoxyvinylglycine and Surfactants on Preharvest Drop, Maturity, and Fruit Quality of Two Processing Peach Cultivars
Bibliographic record
Abstract
The effect of aminoethoxyvinylglycine (AVG), commercially available as ReTain, and three organo-silicone surfactants were evaluated in a series of four experiments over a 2-year period in two commercial peach orchards. Four rates of AVG (0, 66, 132, and 264 mg·L –1 AVG; all applied with 0.05% Sylgard 309) and three surfactants (0.05% Sylgard 309; 0.05% Regulaid; and 0.50% LI-700; all applied with 132 mg·L –1 AVG) were applied to `Venture' and `Babygold 7' peach trees 10 days before first harvest. Fruit were harvested according to commercial standard maturation criteria of background color, suture filling, and fruit size. Treatments were assessed in relation to fruit maturity, delay in harvest, fruit size and yield, fruit quality (flesh firmness and brix), as well as fruit quality following 2 weeks of cold storage. Based on sequential harvest data, the maturation of the AVG treated trees was delayed by about 3 to 4 days. Fruit from AVG treated trees were firmer at harvest and 2 weeks following cold storage at 2°C. However, no additional increase in fruit size or yield was detected. In addition, the addition of a surfactant was not necessary for AVG to be efficacious for delaying maturity and enhancing firmness when applied at 132 mg·L –1 AVG. However, when the three surfactants were compared, Regulaid and Li 700 advanced color development in one experiment and Li-700 resulted in firmer fruit in another. Aminoethoxyvinylglycine applications to the clingstone cultivars `Venture' and `Babygold 7' can be used successfully to manage harvest activities by delaying the onset of picking and improving fruit firmness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".