Late-Season Foliar Urea Applications Can Increase Berry Yeast-Assimilable Nitrogen in Winegrapes (<i>Vitis vinifera</i> L.)
Bibliographic record
Abstract
In the Okanagan Valley of British Columbia, low rates of nitrogen fertilizer are typically applied early in the growing season to prevent excessive vine growth, disease, and adverse changes in grape juice composition. As a consequence, grape juice yeast assimilable nitrogen (YAN) concentrations at harvest are often below the level considered sufficient to complete fermentation during winemaking and require augmentation with additional nitrogen. Over a three-year period at seven study sites planted to five winegrape varieties, late-season foliar applications of urea-N were investigated as a method for enhancing grape juice YAN. Foliar-applied solutions of urea at rates equivalent to 14–18 kg N/ha/yr (1% w/v, applied in one year only) or 28–36 kg N/ha/yr (2% w/v) caused significant improvements in grape juice YAN concentrations in six out of seven of the study sites each year, but there was no consistent pattern among years as to which study sites were most amenable to treatment. Little of the N applied in the foliar spray treatments appeared to be retained by the vines, and there were only few, small negative effects of treatment application on vine performance and juice quality. Thus, urea sprays applied to the foliage around the time of veraison show considerable promise as a supplement to more traditional soil fertilization programs on these and similar sites.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".