Grants for projects

DROUGHT

Data-driven resilience: aiding vines in response to drought using spectrometry and sequencing technologies
Digital agriculture
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Scheda del progetto
Responsabile
P.i Scientific Resp.
Professor Alberto Acquadro
Università degli Studi di Torino
Team
Team
Professor Andrea Moglia
Professor Cinzia Comino
Professor Sergio Lanteri
Data
Start date
Durata
Duration
14 months
Valore
Approved value
€ 60.000
Investimento
Investment nodes
€ 60.000
Dipartimento
Department
Università degli Studi di Torino - Department of Agricultural, Forest and Food Sciences

Water stress for the vine (Vitis vinifera) is a critical condition for the production of grapes with a high content of secondary metabolites. Conversely, when the stress period is prolonged, it negatively affects the quality, yield, and consequently the economic sustainability of the farms. The project aims to address the emergency of drought in vine cultivation through the application of multidisciplinary technologies in the context of climate change, fully aligning with the themes of Spoke 6.

 

Contacts:

Contact person
Alberto Acquadro 

Email
alberto.acquadro@unito.it

Tel
+39 0116708813

 

The challenge
Document

The main objective is to better understand drought tolerance through the analysis of phenotypic and transcriptomic data to select combinations of vine clones/rootstocks that are best suited to the water conditions of a specific territory. 

Why is it innovative
Document

The innovation of this solution lies in the combination of advanced technologies such as automatic phenotyping, portable NIR/VIS spectroscopy, and massive parallel transcriptomic sequencing, which will allow for the assessment of the response to drought in vineyards. This will form the basis for the development of a decision support system that will provide real-time information on vine health and an optimal water management strategy. 

Impact on the users
Document

The project will be carried out by a group of researchers with expertise in genetics, plant phenotyping, and statistical data analysis, in collaboration with interdisciplinary skills in plant physiology and Big Data analysis, present within DISAFA. High-tech detection and laboratory tools will be utilized, alongside cutting-edge portable equipment, combined with a well-established data analysis methodology. Ultimately, the aim is to create a commercial spinoff for the developed technological solutions.