Impact of Cluster Thinning and Basal Leaf Removal on Fruit Quality of Cabernet Franc (Vitis vinifera L.) Grapevines Grown in Cool Climate Conditions
Bibliographic record
Abstract
Achieving desired fruit quality at harvest in cool climate conditions is a challenge, especially for red varieties, and the typical inability of fruit to reach technological maturity is a critical contributing factor requiring examination. To probe this issue, this research investigated the impact of two levels of crop thinning and of basal leaf removal at three phenological stages in the 2011 and 2012 growing seasons in Michigan. Experiments were conducted at the Southwest Michigan Research and Extension Center (SWMREC) in Benton Harbor. Using ‘Cabernet franc’ ( Vitis vinifera L.) vines, yield components (yield per vine, pruning weight, and cluster and berry weight) and basic fruit composition traits [total soluble solids (TSS), pH, titratable acidity, anthocyanins, and phenolics) were studied to investigate the effect of cluster thinning and basal leaf removal on vine performance and fruit quality at harvest. Neither of the treatments significantly impacted TSS in either of the two seasons. Cluster thinning treatment successfully altered cropload ratio, indexed as Ravaz Index (RI), independently of the time of application. Basal leaf removal increased exposed berry temperature, cluster light exposure, and subsequent anthocyanin and phenolic content of the berry in both seasons, again independent of application date, whereas cluster thinning was effective only in 2012. Crop thinning coupled with basal leaf removal resulted in an increased efficiency in anthocyanin accumulation in relation to TSS accumulation, expressed as anthocyanin:sugar, in both years. This is significant because it offers potential for vineyard management practices aiming to improve fruit quality in cool climates where the onset of anthocyanin accumulation could be reduced and decoupled from sugar accumulation.
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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".