Impacts of photoselective bunch zone shading on the volatile composition and sensory attributes for <i>Vitis vinifera</i> L. cv. Riesling
Bibliographic record
Abstract
Photoselective shading is a process that modulates the radiation intensity in specific regions of the electromagnetic spectrum. It is a common practice in horticulture to manipulate specific plant physiological responses, but to date has only received minimal attention in viticulture. The potent odorant 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) is of particular relevance for aged Riesling wine, which are also known to be impacted by the magnitude of bunch zone light exposure during berry development. Hence, in this study, the effect of photoselective bunch zone shading on the formation of TDN in wine was investigated across two consecutive growing seasons. Applying red, black or green shade cloth (SC) to the bunch zone provided unique bunch zone light environments and yielded distinct differences in grape and wine composition compared with the unshaded control. Overall, bunch zone shading through shade cloth was effective in reducing overall photosynthetically active radiation compared to the control and the photoselectivity of the SC treatments differently affected a number of grape and wine measures. Fruit yield was somewhat but not significantly lower under black SC treatments, while juice pH was increased in grapes grown under green and black SC across both vintages compared to the control. Both grape sugar accumulation (P = 0.035) and ammonia nitrogen (P = 0.043) showed evidence of treatment effects, although with low F-statistics (4 and 3, respectively). Measures of hydrolytically released TDN in juice and free TDN concentrations in wine were lower in SC treatments. Unexpectedly, sensory descriptive analysis of the wines demonstrated that increased ‘kerosene-like’ aroma was not consistently associated with free TDN concentrations in wine. In summary, photoselective bunch shading was demonstrated to be an effective method for manipulating grape and wine outcomes and may aid in overcoming viticultural obstacles and quality impacts associated with climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".