Wood age, rootstocks and cultivars drive the formation of productivity and fruit size in sweet cherry
Bibliographic record
Abstract
To maintain the high yields and fruit quality necessary for profitability of sweet cherry production, it is important to consider precision crop load and canopy management techniques during limb renewal. The effects of branch section age, rootstock, and cultivar on spur and flower density and fruit quality have been discussed in previous studies, but most of them focus on a limited range of fruiting wood ages and scion-rootstock combinations. This study aims to analyse the processes of sweet cherry productivity and fruit size formation on a wide range of wood age to determine the limit after which branch preservation is not sustainable, and to evaluate the influence of rootstocks and cultivars on these parameters. The results indicate that wood age is one of the main drivers of productivity formation in cherry. The highest flower density was observed on 3-year-old branch sections – 324 flowers per linear m, due to high spur density, number of reproductive buds per spur and flowers per bud. Productivity on 2-year-old wood was also good (256 flowers per linear m), while a sharp decline in flower density was noted on 4- and 5-year-old wood. The largest fruits were formed on young branch sections, with a significant decline in fruit weight and diameter on 4- and 5-year-old sections. Rootstock vigour had a bigger effect on floral organ induction on 1-year-old shoots, than on spurs. Based on the results, it is advisable to regularly renew lateral branches older than 3 years to maintain high yield efficiency and fruit quality.
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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 | 0.000 |
| 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".