Planting density and size-controlling rootstocks influence the performance of Montmorency tart cherry (<i>Prunus cerasus</i> L.)
Bibliographic record
Abstract
Two multi-year experiments were conducted to determine the influence of planting density and rootstocks on the performance of Montmorency tart cherries (Prunus cerasus L.). Using a constant between-row spacing of 4.5 m, three in-row tree spacings of 4, 3 and 1.5 m were tested with five rootstocks: Weiroot 13, Gisela 6, Krymsk 6, Krymsk 7, and a Prunus mahaleb control. In a second experiment, the comparative effects of five rootstocks spaced 4.5 m × 1.35 m were evaluated: (i) P. mahaleb, (ii) Weiroot 10, (iii) Weiroot 13, (iv) Weiroot 72, and (v) Weiroot 158. With higher tree density, yields increased and tree vigour declined. At higher densities, Weiroot 13 was up to 43% and Gisela 6 16% smaller than Mahaleb, whereas Krymsk 6 and 7 were similar in size to Mahaleb. At the highest density, Mahaleb reduced tree size by 20%. After 7 yr, tree mortality on Krymsk 6 and 7 was greater than the other rootstocks, as was the number of root suckers on Krymsk 7. Cumulative yields, yield efficiency, fruit quality characteristic, and light interception were also markedly influenced by planting density and rootstocks. Overall, evaluated rootstocks ranged in size control from slight to semi-dwarfing, and several showed promise in terms of their induction of high-yield precocity and yield efficiency of the scion. Weiroot selections and Gisela 6 had the greatest beneficial effects on productivity and yield efficiencies. Under experimental conditions, Krymsk 6 and 7 were unsuitable because of their lower cumulative yields and high rate of mortality, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".