terclim by ICS banner
IVES 9 IVES Conference Series 9 Estimation of stomatal conductance and chlorophyll fluorescence in Croatian grapevine germplasm under water deficit    

Estimation of stomatal conductance and chlorophyll fluorescence in Croatian grapevine germplasm under water deficit    

Abstract

Water deficit profoundly impacts the quality of grapes and results in considerable reductions in crop yield. First symptoms manifest with reduced stomatal conductance and transpiration, accompanied by the wilting of apical leaves and tendrils. So far, there is no available data on the water stress response in Croatian grapevine germplasm. Therefore, objective of this study was to determine influence of genotype and treatment on stomatal conductance (gsw), transpiration (E), electron transport rate (ETR), and quantum efficiency in light (PhiPS2). In this research we observed the initial response to water deficit of 84 unique genotypes, 70 Vitis vinifera subsp. vinifera and 14 Vitis vinifera subsp. sylvestris accessions. The experiment was conducted in a greenhouse in both 2022 and 2023, involving self-rooted cuttings exposed to water stress and compared to a well-watered control. Multifactorial analysis of variance was used to examine the effects of genotype, treatment, replicate, date and time of measurement on gsw, E, ETR, PhiPS2. In both years gsw and E were significantly influenced by all parameters except replicate, while ETR wasn’t significantly influenced by treatment in second year and PhiPS2 in first year. Due to the observed significance of the interaction between genotype and treatment across all parameters in both years, we employed the pairwise comparisons of treatment levels within each genotype with Bonferroni correction. In this study, a non-destructive high-throughput method for rapid screening of the initial physiological response to water deficit is briefly presented, in which the grapevine genotypes studied are divided into two distinct groups.

DOI:

Publication date: June 13, 2024

Issue: Open GPB 2024

Type: Poster

Authors

Luka Marinov1*, Domagoj Ivan Žeravica2, Katarina Lukšić1, Ana Mucalo1, Maja Ozretić Zoković1, Toni Safner3, Goran Zdunić1

1 Institute for Adriatic Crops and Karst Reclamation, Split, Croatia
2 University of Dubrovnik, Dubrovnik, Croatia
3 University of Zagreb, Faculty of Agriculture, Zagreb, Croatia

Contact the author*

Keywords

water stress, genotype, stomatal conductance, sylvestris, vinifera

Tags

IVES Conference Series | Open GPB | Open GPB 2024

Citation

Related articles…

Anthropogenic intervention in shaping Terroir in a California Pinot noir vineyard

In many vineyards optimal parcel size exceeds the geospatial complexity that exists in soils and topographic features that influence hydrological properties, sunlight interception and soil depth and texture (available water capacity).

Laying footprints on a new path: proper accounting of biogenic fluxes makes viticulture carbon neutral

To limit the acceleration of global warming we need to reduce greenhouse gases emissions (GHG), making our production processes more carbon-efficient and optimizing absorptions.

Contribution of soil for tipifiyng wines in four geographical indications at Serra Gaúcha, Brazil

Brazil has a recent history on geographical indications and product regulation for high quality wines. The first geographic indication implemented was the Vale dos Vinhedos Indication of Procedence (

A tool for catching mice in wine: development and application of a method for the detection of mousy off-flavour compounds in wine

Over the past two years, the AWRI has received 69 wine samples suspected of being affected by mousy off-flavour. The character has been mostly observed in white wines.

Multi-trait selection in ancient grapevine varieties

The selection of ancient grapevine varieties aims to achieve genetic gains in several important traits that can make the variety more interesting for the objectives of the producers. Traditionally, yield and quality traits of the must have been considered for selection, but many others can be taken into account. Linear mixed models are fitted to the data to predict the empirical best linear unbiased predictors (EBLUPs) of genotypic effects for each evaluated trait, which will be the basis for selection.