Predicting provenance and grapevine cultivar implementing machine learning on vineyard soil microbiome data: implications in grapevine breeding

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.

Over the last years, due to climate change, several red wines, such as the Sangiovese wines, have been often subjected to loss of clarity due to the formation of deposits of fine needle-shaped crystals. This phenomenon turned out to be due to an excess of quercetin (Q) and its glycosides (Q-Gs) in wines. These compounds are synthesized to a large extent when grapes are excessively exposed to UVB radiations in vineyards[1]. Unfortunately, it is not easy to predict the degree of Q precipitation because its solubility strongly depends on the wine and matrix composition[2].

Proper soil health assessments are crucial for sustainable cropland. Among the widely employed approaches, evaluating nematode community structure is particularly suitable. Traditionally, the taxonomic characterization of soil nematodes has relied on time-consuming morphology-based methods requiring experienced experts. However, molecular tools like high-throughput sequencing have emerged as efficient alternatives. In this study, we performed a metataxonomic analysis of soil samples collected from 57 vineyards in the DOCa Rioja region of Northern Spain, focusing on the impact of organic viticulture and cover cropping compared to integrated pest management (IPM) and tilling practices.

By dissecting the root system architecture (RSA) into its underpinning components (e.g. root emission, axial growth, radial growth, branching, root direction or tropism) and identifying the relationships between them, functional-structural 3D root models are promising tools for analyzing the diversity and complexity of root system phenotypes with Genotype × Environment interactions. The model parameters are assumed to be synthetic traits, less influenced by the environment, and consequently with less polygenic architectures than the integrative RSA traits they drive. Root models can serve as a basis for in silico development of root system ideotypes by highlighting the developmental processes and parameters that most likely influence RSA fitness.

The yeast ecosystem in grape musts is quite broad and depends on the region and the health of the grapes. Within this, there are yeasts that can generate fermentative deviations and/or cause defects in the wine. It is very important to address this issue because there are significant economic losses in the wine industry when the fermentation process and/or the organoleptic characteristics of the wine are negatively affected, even more today since climate change has a marked effect on the composition of this ecosystem. The aim of this work is to characterize the behavior regarding detrimental oenological features of potential spoilage yeasts isolated from viticultural environments.