Impact of Smoke from Wheat, Oat, and Clover Stubble Burning on Cabernet Sauvignon Grapes and Wine
Bibliographic record
Abstract
Background and Aims. Stubble burning is an agricultural practice employed by some grain growers to prepare farmland for sowing and/or to control weeds and pests. Grapegrowers and winemakers have questioned whether the resulting smoke can contaminate grapes in nearby vineyards. This study therefore sought to determine the potential for smoke from three different stubble burns to taint grapes and wine. Methods and Results. Excised bunches of mature Cabernet Sauvignon grapes were exposed to smoke from prescribed burning of wheat, oat, and Balansa clover stubble windrows. Environmental sensors monitored the concentration of particulate matter as a measure of smoke density, while chemical and sensory analysis established the extent to which grapes and wine were tainted by smoke. Only grapes that were positioned among the burning wheat windrows or downwind, but in close proximity (∼200 m) to the oat stubble burn, were exposed to sufficient quantities of smoke to result in a detectable concentration of volatile phenols (up to 12 µg/kg), as chemical markers of smoke taint. These grapes yielded wines with two to threefold higher volatile phenol concentrations (up to 18 µg/L) than other wines, including the control wine, and low intensity, but perceptible smoke-related sensory attributes, indicative of low-level smoke taint. Conclusions. Chemical and sensory analyses suggest the risk of smoke taint from stubble burning is low, except where vineyards are immediately downwind and/or prolonged or repeated smoke exposure occurs. However, stubble moisture content and prevailing weather conditions affect smoke density and dispersion, and will therefore affect the potential for smoke contamination by grapes. Significance of the Study. This study will assist colocated grain growers and grape and wine producers to undertake commercial operations, without negatively impacting one another.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".