Foliar Applied Abscisic Acid Increases ‘Chardonnay’ Grapevine Bud Freezing Tolerance during Autumn Cold Acclimation
Bibliographic record
Abstract
Economic loss due to cold weather events is a major constraint to winegrape ( Vitis vinifera ) production and wine-related industries where extreme and/or fluctuating winter temperatures induce injury and require remedial retraining and replanting increases production costs and lowers yield and fruit quality. The purpose of this study was to determine whether a foliar application of abscisic acid (ABA) could increase the freezing tolerance (FT) of field-grown, ‘Chardonnay’ winegrape and whether its effectiveness can be influenced by the phenological timing of the application. Mature ‘Chardonnay’ grapevines were treated with a foliar application of ABA at a concentration of 500 mg·L −1 at vine phenological stages corresponding to 50% véraison, postvéraison, and postharvest. Results from field trial sites located in four distinct winegrape production regions in the United States (Idaho and Ohio) and Canada (British Columbia and Ontario) showed that foliar application of ABA increased bud FT, primarily during autumn cold acclimation. Foliar ABA application had no consistent influence on bud FT in midwinter or during spring deacclimation, or on percent budburst in spring. Vine phenological stage at the time of ABA foliar application influenced ABA effectiveness, although results were inconsistent among locations. At most locations, applications made at véraison or postvéraison were more effective than applications made postharvest. No phytotoxic response or adverse changes in yield or berry composition were detected in response to ABA application. The consistent increase in bud FT during autumn cold acclimation observed at all trial locations in this study indicates that foliar ABA, applied at véraison or postvéraison, can reduce the risk of economic loss due to cold injury in production regions with frequent early autumn cold weather events.
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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".