Effect of different irrigation strategies on vine physiology, yield, grape composition and sensory profile of Sauvignon Blanc (<em>Vitis vinifera</em> L.) in a cool climate area
Bibliographic record
Abstract
Aim: The impacts of partial root zone drying (PRD) and regulated deficit irrigation (RDI) on soil moisture, vine water status, yield components, fruit composition and wine sensory profile of Sauvignon blanc were studied in a cool climate region. Methods and results: Field experiments were conducted in a commercial Sauvignon blanc block in Ontario, Canada between 2006 and 2008. Treatments were: non-irrigated control, PRD, full irrigation [100% of crop evapotranspiration (ETc)] and one level of RDI (25% ETc). Treatments began immediately after fruit set and continued until the beginning of September. Reference evapotranspiration (ETo) was calculated using the Penman–Monteith equation. Soil moisture and vine water status (leaf water potential and transpiration rate) in the PRD treatments were generally less than in 100% ETc but higher than in non-irrigated and 25% ETc treatments. Almost all treatments were different than non-irrigated vines in fruit composition and wine sensory attributes. RDI strategies were more consistent across vintages than the PRD treatments in their effect on vine water status, grape composition and sensory profiles. Conclusions: Use of RDI or PRD in cool climates during dry and warm years can improve grape composition. In very dry and hot seasons, like that of 2007, irrigation improved grape composition and wine aroma typicity. RDI enhanced fruity aroma attributes, which suggests that this could be a viable strategy to improve grape and wine quality in cool areas. However, due to high climatic variation over the period studied, no consistent pattern of irrigation effects was found for berry composition, suggesting that plant water status was not the only factor that controlled fruit and wine quality. Significance and impact of the study: To the best of our knowledge, this study is the first evaluation of PRD in a cool, humid climate, and highlights the potential value of both RDI and PRD irrigation techniques in cool climate regions, particularly during dry growing seasons.
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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".