Use of Plant Growth Regulators for Feathering and Flower Suppression of Apple Nursery Trees
Bibliographic record
Abstract
The use of highly feathered trees can make high-density apple plantings more profitable through enhanced precocity and increased early yield. Currently, nurseries are asked to provide highly feathered trees with wide branch crotch angles. The use of plant growth regulators (PGRs) can play a key role when it comes to branch induction; however, dose and timing both need to be tested to enhance branching without compromising other tree quality attributes. Over the last 4 years, we have conducted studies of the use of MaxCel ® (6-benzyladenine) and Promalin ® (a mixture of 1.8% 6-benzyladenine and 1.8% GA 4+7 ) in comparison with Tiberon™ SC (cyclanilide) at several nurseries in NY, WA, DE, Ontario (Canada), and Chile. The best results were obtained with four applications of MaxCel ® or Promalin ® (400 mg·L −1 ) beginning when leader growth reached 70 cm above the soil line and reapplied at 10–14 days intervals. Promalin ® was a slightly less effective branching agent than MaxCel ® . On the other hand, Promalin ® stimulated leader growth resulting in improved final tree height, whereas MaxCel ® induced the widest branch angles. Overall, we observed good response and quality ratings with ‘Cameo’, ‘Cripps Pink’, ‘Enterprise’, ‘Fuji’, ‘Ambrosia’, ‘Crimson Crisp’, ‘Gingergold’, and ‘Granny Smith’, whereas less quality ratings were observed on ‘Ambrosia’, ‘Cortland’, ‘Goldrush’, ‘Honeycrisp’, and ‘Suncrisp’. Response with ‘Gala’ varied depending on the temperature range. Multiple sprays of Gibberellins (GA 4+7 , or GA 3 ) at 250 mg·L −1 applied to nursery trees in the late summer inhibited flower bud development and flowering in the orchard the next year. This reduces the risk of fire blight infection in newly planted trees.
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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".