Combined abiotic-biotic plant stresses on the roots of grapevine

Climate change is resulting in more dry and hot summers, accelerating grape ripening and increasing berry sugars concentration. This results in wines with a higher alcohol content, which has a negative impact on wine quality, as well as, on consumer health. Agronomic practices that minimize these effects on berry composition and, consequently, on wine quality must be defined. In this work, different management practices have been assessed on rainfed ‘Monastrell’ grapevines in Jumilla (Murcia, Spain) from 2021 to 2023 vintages. Mulching, shading, application of kaolin and different types of pruning were evaluated, among others field adaptation practices.

Grapevine leaves are known to contain different polyphenols such as flavonols, catechins and stilbenes, which are known to act as main contributors for plant defense against pathogens (1). While the composition for some major cultivars has been studied, there is lack of systematic comparison about the content of these compounds in the wide ecodiversity of Vitis vinifera cv. Recent advances in Mass Spectrometry-based Metabolomics allow a wider and more sensitive description of these polyphenols, as instance of those present in leaves (2). Such information could help to better explain leaf traits regarding the development of the leaf or to the plant tolerance to a pathogen. Moreover, these compounds offer appealing applications for human health due to their antioxidant activities.

Among the Moscato grapes, Moscato Giallo is a winegrape variety characterized by a high content of free and glycosylated monoterpenoids, which gives very aromatic wines. The aromatic bouquet of Moscato Giallo is strongly influenced by the high concentration of linalool, geraniol, linalool oxides, limonene, α-terpineol, citronellol, HO-trienol, HO-diols, 8-Hydroxylinalool, geranic acid and β-myrcene, that give citrus, rose, and peach notes.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).

Grapevine (Vitis vinifera L.) exhibits a high level of genetic and phenotypic diversity among the approximately 6000 cultivars recorded. This perennial crop is highly vulnerable to numerous fungal diseases, including esca, which is a complex vascular pathology that poses a significant threat to the wine sector, as there is currently no cost-efficient curative method[1]. In this context, an effective approach to mitigate the impact of such diseases is by leveraging the crop’s genetic diversity. Indeed, susceptibility to esca disease appears to vary between cultivars, under artificial or natural infection. However, the mechanisms and varietal characteristics underlying cultivar susceptibility to esca are still unknown.