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Quantifying the seasonal variations in grapevine yield components based on pre- and post-flowering weather conditions

dc.rights.licenseopenen_US
dc.contributor.authorZHU, Junqi
dc.contributor.authorFRAYSSE, Rémi
dc.contributor.authorTROUGHT, Michael
dc.contributor.authorRAW, Victoria
dc.contributor.authorYANG, Linlin
hal.structure.identifierEcophysiologie et Génomique Fonctionnelle de la Vigne [UMR EGFV]
dc.contributor.authorGREVEN, Marc
dc.contributor.authorMARTIN, Damian
dc.contributor.authorAGNEW, Rob
dc.date.accessioned2020-09-11T13:05:47Z
dc.date.available2020-09-11T13:05:47Z
dc.date.issued2020
dc.identifier.issn2494-1271en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/11305
dc.description.abstractEnSeasonal differences in weather conditions cause marked variation in grapevine yield. However, quantitative relationships between various yield components and climatic factors at field scales are still lacking. By using a long-term field trial, we quantified the correlation between weather conditions during the key development stages and the yield components of Vitis vinifera L. Sauvignon blanc growing under cool-climate conditions. A long-term phenology and yield monitoring trial using both two-cane and four-cane trained vertically shoot positioned (VSP) Sauvignon blanc vines was established in four vineyards in Marlborough, New Zealand in 2004. Phenology, bunch number, berry mass, yield and meteorology records were collated. A multivariable mixed linear model was used to assess the relationship between various yield components and weather conditions. The critical periods for each yield component and weather factor were optimised based on the maximum likelihood returned from the mixed linear model. The optimised critical periods of temperature for all yield components occurred mainly before 50 % flowering either in the previous season (during inflorescence initiation) and the current season, indicating the importance of the pre-flowering period on yield formation. Out of all weather factors, maximum daily temperature had the largest effect on bunch number and overall yield and strongly influenced berry number and bunch mass. Rainfall near flowering time had a negative effect on berry mass and bunch mass, but post-flowering rainfall had a strong positive effect. The statistical model explained 60 to 85 percent of the seasonal variations in bunch number, berry number, berry and bunch mass and yield per vine.
dc.language.isoENen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc/
dc.subject.enSeasonal variations
dc.subject.enBerry mass
dc.subject.enGrapevine yield
dc.subject.enBunch number
dc.subject.enBerry number
dc.title.enQuantifying the seasonal variations in grapevine yield components based on pre- and post-flowering weather conditions
dc.typeArticle de revueen_US
dc.identifier.doi10.20870/oeno-one.2020.54.2.2926en_US
dc.subject.halSciences de l'environnementen_US
dc.subject.halSciences du Vivant [q-bio]en_US
dc.subject.halSciences du Vivant [q-bio]/Biologie végétaleen_US
bordeaux.journalOENO Oneen_US
bordeaux.page213-230en_US
bordeaux.volume54en_US
bordeaux.hal.laboratoriesEcophysiologie et Génomique Fonctionnelle de la Vigne (EGFV) - UMR 1287en_US
bordeaux.issue2en_US
bordeaux.institutionBordeaux Sciences Agroen_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcehal
hal.identifierhal-02904465
hal.version1
hal.exportfalse
workflow.import.sourcehal
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