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Enregistrement W3032138579 · doi:10.20870/oeno-one.2020.54.2.2401

Spatial variability in Ontario Riesling vineyards: I. Soil, vine water status and vine performance

2020· article· en· W3032138579 sur OpenAlexaffabout
Andrew G. Reynolds, James Willwerth

Notice bibliographique

RevueOENO One · 2020
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueHorticultural and Viticultural Research
Établissements canadiensBrock University
Organismes subventionnairesnon disponible
Mots-clésTerroirVineVineyardSoil textureSoil waterEnvironmental scienceWater contentGeographyWineAgronomyHorticultureBiologySoil scienceGeology

Résumé

récupéré en direct d'OpenAlex

Aim: The major focus of this research was to explain the so-called terroir effects that impact grapevine yield components, berry composition, and wine varietal character. To elucidate potential contributors to the terroir effect, vine water status [midday leaf water potential (ψ)] was chosen as a major determinant. The hypothesis of this component of the study was that consistent leaf ψ zones could be identified within vineyard sites and that vine water status would play a major role in vine performance and yield components. Soil texture was anticipated to play a role indirectly through its water-holding capacity.Methods and materials: To test this hypothesis, ten Riesling vineyards representative of each Vintners Quality Alliance of Ontario sub-appellation were selected within the Niagara Peninsula. These vineyards were delineated using global positioning systems and 75–80 sentinel vines were geo-referenced within a sampling grid for data collection. During the 2005–2007 growing seasons, leaf ψ measurements were collected bi-weekly from a subset of these sentinel vines. Data were collected on soil texture and composition, soil water content (SWC), vine performance and yield components. These variables were mapped using geographical information systems software and relationships between them were elucidated.Results: Vineyards were variable in terms of soil texture, composition, nutrition, and moisture. However, in general, few consistent relationships with soil composition variables were found. As hypothesized, consistent leaf ψ zones were identified within vineyards in all three vintages. Some geospatial patterns and relationships were spatially and temporally stable within vineyards. In many cases, spatial distribution of leaf ψ was temporally stable within vineyards despite different weather conditions during each growing season. Spatial trends within vineyards for SWC and leaf ψ were temporally stable over the 3-year period for eight vineyards. Generally, spatial relationships between leaf ψ, SWC, vine size, berry weight and yield were also temporally stable. Some inconsistencies in spatial distribution of variables were attributable to winter injury.Conclusions: Many viticultural variables such as leaf ψ, vine size, berry weight, and yield were spatially variable and, as hypothesized, consistent leaf ψ zones were identified within vineyards in three distinct vintages. Many geospatial patterns and relationships were determined and were temporally stable, and this temporal stability in these variables occurred despite different growing seasons. The strongest relationships were those concerning leaf ψ, SWC, vine size, and berry weight. No consistent relationships were found concerning soil composition. The most consistent soil variables that impacted vine performance and yield components were physical properties, particularly texture.Significance and impact of the study: Soil had some indirect effects, but leaf ψ was more likely a major contributor to the terroir effect, as it had a major impact on vine size, berry weight and yield in many vineyards across multiple vintages. Temporal stability is required for many practical geomatic applications to be initiated in vineyards, but it is also of importance to future research endeavors for this project as well as others.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,361
Score d'incertitude au seuil0,726

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,038
Tête enseignante GPT0,223
Écart entre enseignants0,185 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations16
Publié2020
Routes d'admission2
Résumé présentoui

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