Canopy Growth, Yield, and Fruit Quality of 'Royal Gala' Apple Trees Grown for Eight Years in Five Tree Training Systems
Bibliographic record
Abstract
In 1993, a planting of virus-free 'Royal Gala' apple ( Malu × domestica Borkh.) on 'M.9' rootstock was established at Summerland, B.C., Canada, to determine whether angled-canopy training systems could improve orchard tree performance relative to slender spindles. The trees were trained in one of five ways: slender spindle (SS), Geneva Y-trellis (GY), a modified Solen training we called 'Solen Y-trellis' (SY), or V-trellis (LDV), all at the same spacing (1.2 m × 2.8 m), giving a planting density of 2976 trees/ha. In addition, a higher density (7143 trees/ha) version of the V-trellis (HDV) was planted to gauge the performance of this system at densities approaching those of local super spindle orchards. The plots were drip-irrigated and hand-thinned. No summer pruning was done. After 8 years, differences among training systems at the same density and spacing were small and few. The two Y-shaped training systems had 11% to 14% greater cumulative yield/ha than the SS, but did not intercept significantly more light at maturity. No consistent differences occurred in fruit size or the percentage of fruit with delayed color development among the four training systems at the same density. Relative to the LDV, the HDV yielded less per tree, but far more per hectare, particularly in the first 3 years. After 8 years, the cumulative yield/ha was still 65% greater than with LDV. Yield efficiency was unaffected by tree density. Fruit size on HDV ranked lowest among the systems nearly every year, but was still commercially acceptable. The HDV intercepted more light (73%) than SS (53%). The percentage of fruit with delayed color development in HDV was not significantly different from that for LDV in most years. The trees in HDV were difficult to contain within their allotted space without summer pruning. The substantially similar performance of all the training systems (at a given density, and with minimal pruning) suggests that cost and ease of management should be the decisive factors when choosing a tree training method.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".