Genetic Variability of Foliage Structure and

Il y a 2 mois


Colmar, France INRAE Temps plein

**Genetic variability of foliage structure and activity in grapevine - relationships with grape composition**:

- Réf
- **ABG-116198**
- Sujet de Thèse- 09/08/2023- Autre financement public- INRAE- Lieu de travail- Colmar - Grand Est - France- Intitulé du sujet- Genetic variability of foliage structure and activity in grapevine - relationships with grape composition- Champs scientifiques- Agronomie, agroalimentaire
- Numérique
- Mots clés- grapevine, high throughput-phenotyping, liDAR, image analysis, berry quality**Description du sujet**:

- Controlling the sugar content and maintaining correct acidity of the grapes is one of the major challenges in adapting the vine to climate change. The most effective lever to achieve this objective is undoubtedly the plant material. There is a wide range of variations in sugar content at harvest depending on the variety. Part of this variability is primarily linked to the dates of the start of ripening (veraison dates), but the relationship between leaf area and fruit volume undoubtedly plays a role.- The objective of this thesis project is to implement high-throughput methods for characterizing the foliage and the photosynthetic capacity of plants which then allow to decipher the trait "accumulation of sugars" in the berries. For this project, we have high-throughput vine phenotyping tools under natural conditions. We have already shown the possibility of using LiDAR point clouds for the characterization of vegetation volumes in summer and pruning volumes in winter. RGB cameras and a multispectral camera should also allow us to characterize the photosynthetic activity of the leaves during the season. A precise description of the architecture of the vine canopies can not only provide elements on the interception of radiation but also provide information on the crowding of the foliage and the shading of the clusters and be integrated into 3D models of grapevine architecture. Exploiting these resources using canopy modeling methods would be a significant step forward in identifying stable genetic determinants of foliage architecture and functioning.- These tools and methods will be used to characterize part of the genetic resources for which we have a very dense description of the genetic heritage (genotyping by sequencing). Traits describing foliage will be used in quantitative genetic approaches to establish genotype-phenotype relationships. Quantitative genetic work does not fall within the scope of the proposed thesis work.- To sum up, the scientific questions posed within the framework of this thesis project are: how to describe the photosynthetic functioning of grapevine genotypes from LIDAR and imaging data? What is the impact on sugar accumulation in grapes? Can we reconstitute a dynamic of growth, foresee the heaping up of the foliage, the shading of the bunches? Which effect on acidity parameters? On which architectural and functional parameters can we find genetic variability?**Prise de fonction**:

- 01/11/2023**Nature du financement**:

- Autre financement public**Précisions sur le financement**:

- INREA/Région Grand Est**Présentation établissement et labo d'accueil**:

- INRAEThe National Research Institute for Agriculture, Food and the Environment (INRAE) is a public research establishment bringing together a working community of 12,000 people, with 272 research, service and experimental units, located in 18 centers throughout France. INRAE is one of the very first world leaders in agricultural and food sciences, in plant and animal sciences. Its research aims to build solutions for multi-performance agriculture, quality food and sustainable management of resources and ecosystems.

You will be welcomed into the Joint Research Unit 1131"Vine health and wine quality" between INRAE and the University of Strasbourg, located in Colmar, Alsace, France.

Our unit conducts research on the one hand on grapevine genetics, with a main focus on tolerance to fungal diseases and on the other on the interactions between plants, virueses and their vectors.

**Site web**:
**Intitulé du doctorat**:

- Sciences agronomiques**Pays d'obtention du doctorat**:

- France**Etablissement délivrant le doctorat**:

- UNIVERSITE DE STRASBOURG**Ecole doctorale**:

- Sciences de la vie et de la santé- Recommended training: Master 2 in image processing and analysis and/or LiDAR. Agricultural engineer degree with digital technology option.
- Desired knowledge: In addition to the knowledge acquired during the training, knowledge of plant ecophysiology and modeling is desirable.
- Required skills: Skills for the manipulation of massive data, in image analysis, in LiDAR data processing. Programming skills (R, Python). Skills in statistical analysis.-
- 30/09/2023


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