Impact of Cluster Thinning on Wine Grape Yield and Fruit Composition: A Review and Meta-Analysis
Bibliographic record
Abstract
For wine grape producers, achieving an optimal balance between vegetative and reproductive growth is a key factor in producing high quality fruit and meeting production quotas. This balance is often measured as the leaf-area-to-yield ratio. To increase this ratio, producers often use “cluster thinning” (CT), a management practice involving a selective removal of grape clusters from vines. Despite this, no consensus has been established regarding the optimal CT timing and severity for consistently improving fruit composition. The objective of this work was to identify whether CT “timing” (bloom, pea-size, lag phase, and veraison) or “severity” (15–35%, 36–55%, and 56–75%) influences yield and fruit composition. To achieve this objective, a meta-analysis of 160 publications on CT in grape was reduced to 78 studies via 10 data curation steps. We reported the influence of CT timing and severity on yield and fruit composition, as well as their impact on the yield-fruit composition tradeoff. First, CT timing showed little influence on fruit composition, which provides producers with greater flexibility when using this practice. Second, CT severity was impactful on improving fruit composition (TSS and pH); only the moderate range (36–55%) was effective. In conclusion, wine grape composition is more influenced by CT severity than timing. This work has important implications for grape producers and their approach to improving grape composition.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.020 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".