Application of foliar urea to grapevines: productivity and flavour components of grapes
Bibliographic record
Abstract
Urea is a non-electrically charged nitrogen (N) molecule suitable for foliar application, since it easily penetrates the epicuticular waxes and the cutin layer of leaves. Several field trials have been conducted to understand how foliar application of urea to grapevines may affect yield and grape composition compared to other N fertilisers. The effectiveness of foliar application of urea to change the flavour components of grapes depends on the N status of the grapevines related to crop load and the timing of the foliar application, which could also play a key role in basal bud fertility and stored N reserves. The application of foliar urea to grapevines may affect the concentration of N, volatile compounds, and phenolic substances in grapes without influencing yield and its components in the short term. During alcoholic fermentation, grape N concentration favours the production of non-volatile and volatile compounds that affect wine attributes, aroma, bitterness, and astringency. Other N-compounds such as biogenic amines may also be formed in wines which can be toxic to the consumer. Gene expression related to grapevine N metabolism and the identification of putative chemical markers in red and white wines after foliar N application deserves further investigation. This review aimed to evaluate the factors involved in N uptake by leaves after foliar urea fertilisation, to summarise effects on grapevine yield components and grape composition and to provide also research options for future studies in this area.
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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".