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Record W3194193714 · doi:10.1111/ajgw.12515

Application of foliar urea to grapevines: productivity and flavour components of grapes

2021· article· en· W3194193714 on OpenAlexaff
Gastón Gutiérrez‐Gamboa, Francisco Diez-Zamudio, Lincon Oliveira Stefanello, Adriele Tassinari, Gustavo Brunetto

Bibliographic record

VenueAustralian Journal of Grape and Wine Research · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsNova Scotia Department of Agriculture
Fundersnot available
KeywordsFlavourUreaProductivityAgronomyHorticultureMathematicsChemistryBiologyFood scienceEconomicsOrganic chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.109

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.103
GPT teacher head0.355
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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