Ethephon and prohexadione-calcium influence the flowering, early yield, and vegetative growth of young ‘Northern Spy’ apple trees
Bibliographic record
Abstract
‘Northern Spy’ apple trees have a relatively long non-fruiting period following planting, which reduces their economic feasibility of production. Previous work supporting the benefits of ethephon (ETH) for enhancing flowering precocity on bearing apple trees was the impetus behind the present study in which ‘Northern Spy’ and ‘Jonagold’ were treated with ETH to determine its effects on flowering, early yield, and vegetative growth. Early summer ETH application significantly increased flowering the year following treatment, with a greater effect from a higher concentration or a second application. Flower clusters per tree on 4th-leaf ‘Northern Spy’/M.9 increased from a mean of 4.0 (1.6 ± 0.4 ln transformed) on control trees to as much as 18.7 (3.0 ± 0.4 ln transformed) following ETH treatment. Increased flowering also translated into significantly greater yield at harvest. Two applications of 1500 mg L−1 ETH applied 5 and 8 weeks after full bloom (WAFB) were particularly effective on three-year-old ‘Northern Spy’/M.9, where fruit yield increased by 2.6 kg tree−1 (1.0 ± 0.4 ln transformed), or ≈2.4 t ha−1. This study supports the use of ETH applications on young ‘Northern Spy’ apple trees in the year prior to expected initial bearing to increase flowering and early fruit production.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".