Xylem vessel blockages in grape pedicel growing in tropical climate observed by microtomography

The use of next-generation sequencing (NGS) has helped understand microbial genetics in oenology. Current studies mainly focus on barcoded amplicon NGS but not shotgun sequencing, which is useful for functional analyses. Since the high percentage of grapevine DNA conceals the microbial DNA in must, the majority of sequencing data is wasted in bioinformatic analyses. Here we present capture depletion of grapevine whole genome DNA.

The “Terroirs d’Anjou” project led by the Agronomy sector of the Vine and Wine Research Unit of the INRA center in Angers aims to characterize the viticultural terroirs in a study area which includes 29 municipalities in the Maine et Loire and cuts across the Anjou, Coteaux du layon and Coteaux de l’Aubance appellation areas.

The widespread use of flint glass bottles for rosé wines is driven by consumer preference for color as a key choice factor.

Fungal chitosan is a polysaccharide made up of glucosamine and N-acetyl-glucosamine and derived from chitin-glucan of Aspergillus niger or Agaricus bisporus. Fungal chitosan has been authorized as an antiseptic agent in wine since 2009 (OIV) and in organic wine in 2018. At the maximum dose of 10g/hl, it was shown to eliminate Brettanomyces bruxellensis, the main spoilage agent in red wines. Fungal chitosan is highly renewable, biocompatible (ADI equivalent to sucrose) and non-allergenic. However, winemakers often prefer to use sulfites (SO2), though sulfites are classified as priority food allergens, than chitosan. Indeed, many conflicting reports exist regarding its efficiency and its side effects towards beneficial wine microorganisms or wine taste. These contradictions could be explained by the heterogeneity of the fungal chitosan lots traded, the diversity of the wines (chemical composition, winemaking process), but also, by the recently highlighted huge genetic diversity prevailing in wine microbial species.

A new framework for the segmentation and characterization of row crops on remote sensing images has been developed and validated for vineyard monitoring. This framework operates on any high-resolution remote sensing images since it is mainly based on geometric information. It aims at obtaining maps describing the variation of a vegetation index such as NDVI along each row of a parcel.