terclim by ICS banner
IVES 9 IVES Conference Series 9 Spatial variability of grape berry maturation program at the molecular level 

Spatial variability of grape berry maturation program at the molecular level 

Abstract

The application of sensors in viticulture is a fast and efficient method to monitor grapevine vegetative, yield and quality parameters and determine their spatial intra-vineyard variability. Molecular analysis at the gene expression level can further contribute to the understanding of the observed variability by elucidating how pathways responsible for different grape quality traits behave in zones diverging for one or the other parameter. The intra-vineyard variability of a Cabernet Sauvignon vineyard was evaluated by a standard Normalized Difference Vegetation Index (NDVI) mapping approach, employing UAV platform, accompanied by detailed ground-truthing (e.g. vegetative, yield, and berry ripening compositional parameters) that was applied in 14 spots in the vineyard. Berries from different spots were additionally investigated by microarray gene expression analysis, performed at five time points from fruit set to full ripening. The relationships between NDVI and ground measurements were explored by correlation analysis and revealed high variability in the vineyard. Comparison between the transcriptome data of spots with the highest and lowest NDVI values unraveled 968 differentially expressed genes. Among them, were ripening-related genes, found to feature the low vigor spots, and genes involved in photosynthesis mechanisms that were prevalent in the high vigor spots. Spatial variability maps of the expression level of key berry ripening genes showed consistent patterns, aligned with the vineyard vigor map and with spatial maps generated for several vine and berry parameters. These insights suggest that berries from different vigor zones present distinct molecular maturation programs, hence, showing potential in predicting spatial variability in fruit quality.

DOI:

Publication date: June 14, 2024

Issue: Open GPB 2024

Type: Article

Authors

Ron Shmuleviz1*, Alessandra Amato1, Pietro Previtali2, Elizabeth Green2, Luis Sanchez2, Maria Mar Alsina2, Nick Dokoozlian2, Giovanni Battista Tornielli1,3 and Marianna Fasoli1

1 Department of Biotechnology, University of Verona, 37134 Verona (VR), Italy
2 E. & J. Gallo Winery, Modesto, CA 95354, USA
3 Current address: Department of Agronomy, Food, Natural resources, Animals and  Environment, University of Padova, 35020 Legnaro (PD), Italy.

Contact the author*

Keywords

berry ripening, vegetation indices, gene expression analysis, sensors, precision viticulture

Tags

IVES Conference Series | Open GPB | Open GPB 2024

Citation

Related articles…

Influence of inactive dry yeast treatments during grape ripening on postharvest berry skin texture parameters and phenolic compounds extractability

Inactive dry yeast treatments in the vineyard are a tool used with the aim to improve the concentration and quality of secondary metabolites in grapes, leading to a better differentiation of the wines made from grapes differently treated. In this work, a foliar spraying treatment with yeast derivatives specifically designed to be used with the patent pending application technology of Lallemand Inc. Canada (LalVigne® Mature, Lallemand Inc., Montreal, Canada) was tested on Vitis vinifera L. cv. Barbera and Nebbiolo black winegrapes. The aim was to evaluate the effect of this treatment on the phenolic compounds accumulation, the skin physical-mechanical properties and the related phenolic extractability. Prior to analysis, the berries were sorted by flotation in order to evaluate their distribution by density class, and to determine the skin texture parameters of berries with different sugar contents, thus understanding also the ripening effect.

Monitoring arthropods diversity in the “Costières de Nîmes” viticulture landscape

Biodiversity loss in agrosystems is partly due to landscape simplification (field enlargement, hedgerows removal…) that led to a loss of heterogeneity of the overall landscape.

Caractéristiques physiques et agronomiques des principaux terroirs viticoles de l’Anjou (France). Conséquences pour la viticulture

Une étude conduite dans le cœur du vignoble A.O.C. angevin, sur une surface d’environ 30.000 ha, a permis de caractériser et cartographier finement (levé au 1/12.500)

Identification of aroma markers in amarone wines

Amarone is an Italian red wine produced in the Valpolicella area, in north-eastern Italy. Due to its elaboration with withered grapes, Amarone is a rather unique example of dry red wine. However, there is very limited data so far concerning the volatile composition of commercial Amarone wines, which also undergo a cask aging of 2-4 years before release. The present work aims at characterizing the aroma composition of Amarone and to elucidate the relationships between chemical composition and sensory characters. The analysis of 17 Amarone commercial wines from the same vintage (2015) was carried out by means of Gas Chromatography-Mass Spectrometry (GC-MS) and extracted by Solid Phase Extraction (SPE) and Solid Phase Micro Extraction (SPME). In addition, the sampled wines were subjected to a sensory evaluation in the form of sorting task.RESULTS: 70 volatile compounds were successfully identified and quantified, 30 of which were present in concentrations above their odor thresholds in all the samples. Using the odor activity value (OAV), the compounds that potentially contribute to Amarone perceived aroma are b-damascenone, ethyl and isoamyl acetate, ethyl esters (hexanoate, octanoate, butanoate, 3-methybutanoate), 4-ethyl guaiacol, 3-methylbutanoic acid, dimethyl sulfide (DMS), eugenol, massoia lactone, 1,4-cineol, TDN, cis/trans-whisky lactone. In certain samples, high OAVs were also observed for 4-ethyl phenol and 1,8-cineole.Results from the sorting task sensory analysis showed three clusters formed.

Chenin Blanc Old Vine character: evaluating a typicality concept by data mining experts’ reviews and producers’ tasting notes

Concepts such as typicality are difficult to demonstrate using the limited set of samples that can be subjected to sensory evaluation. This is due both to the complexity of the concept and to the limitations of traditional sensory evaluation (number of samples per session, panel fatigue, the need for multiple sessions and methods, etc.). On the other hand, there is a large amount of data already available, accumulated through many years of consistent evaluation. These data are held in repositories (such as Platter’s Wine Guide in the case of South Africa Wine, wineonaplatter.com) and in technical notes provided by the producers.