terclim by ICS banner
IVES 9 IVES Conference Series 9 International Congress on Grapevine and Wine Sciences 9 2ICGWS-2023 9 Predicting provenance and grapevine cultivar implementing machine learning on vineyard soil microbiome data: implications in grapevine breeding

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

Abstract

The plant rhizosphere microbial communities are an essential component of plant microbiota, which is crucial for sustaining the production of healthy crops. The main drivers of the composition of such communities are the growing environment and the planted genotype. Recent viticulture studies focus on understanding the effects of these factors on soil microbial composition since microbial biodiversity is an important determinant of plant phenotype, and of wine’s organoleptic properties. Microbial biodiversity of different wine regions, for instance, is an important determinant of wine terroir. While conventional methods for microbiome analysis are extensively used, application of modern Artificial Intelligence (AI) based methods could unravel non-linear associations between microbial taxa and environmental/plant genetic factors. Here we compare the performance of shallow and Deep Machine Learning methods to predict the geographical provenance and the planted grape cultivar solely based on the soil microbiota. We used 885 previously published microbial amplicon-sequencing datasets (16S) collected from vineyards located in 13 countries across 4 continents and planted with 34 Vitis vinifera cultivars representing the largest collection of vineyard microbiomes analyzed to date. This research also aimed at addressing some common challenges associated with most ML-based studies such as easy availability of models to non-technical researchers which is necessary for research reproducibility. To facilitate this, the models built in this study will be available through a GUI-based containerized web platform. Also, to provide compatibility of processed data from other 16S studies, a computational step will be included that merge the features either by taxonomy or sequence identity. This study will be beneficial in several ways such as inferring lost/mislabeled samples, identifying important location-specific and cultivar-specific taxa. Ultimately, this approach could be implemented for the identification of the genes regulating host/microbe interactions, which will provide valuable targets for breeding programs aimed at producing more sustainable crops.  

Acknowledgements: This study was supported by the National Institute of Food and Agriculture, AFRI Competitive Grant Program Accession number 1018617, and the National Institute of Food and Agriculture, United States Department of Agriculture, Hatch Program accession number 1020852.

DOI:

Publication date: October 5, 2023

Issue: ICGWS 2023

Type: Article

Authors

Carlos M. Rodríguez López1*, Lakshay Anand1

1Environmental Epigenomics and Genomics Group, Department of Horticulture, College of Agriculture, Food and environment, University of Kentucky, Lexington, Kentucky, USA

Contact the author*

Keywords

rhizosphere microbiome, provenance, plant-microbiome interactions, breeding, machine learning

Tags

2ICGWS | ICGWS | ICGWS 2023 | IVES Conference Series

Citation

Related articles…

Discovering the process of noble rot: fungal ecology of grape berries during the noble rot transformation in different vineyards of the Tokaj wine region

Botrytis cinerea, a well-known grapevine pathogen, has more than 1200 host plants causing grey rot in grapevine berries. However, it can also result in a desirable phenomenon called noble rot under specific microclimate conditions. An extraordinary demonstration of this natural process can be observed in the creation of aszú wines within Hungary’s Tokaj wine region. Beside B. cinerea other fungi and yeasts are involved in the secondary metabolic development of the grape berry which contributes to the sensory and analytical characterization of noble rot wines.

Read More

Drought responses of grapevine cultivars under different environments

Using grapevine genetic diversity is one of the strategies to adapt viticulture to climate change. In this sense, assessing the plasticity of cultivars in their responses to environmental conditions is essential. For this purpose, the drought tolerance of Grenache, Tempranillo and Semillon cultivars grafted onto SO4 was evaluated at two experimental vineyards, one located in Valencia (Spain) and the other in Bordeaux (France). This was done by assessing gas exchange parameters, water relations and leaf hydraulic traits at the end of the season.

Read More

The evolution of the aromatic composition of carbonic maceration wines

The vinification by Carbonic maceration (CM) involves the process whereby the whole bunches are subjected to anaerobic conditions during several days. In this anaerobic condition, the grape endogenous enzymes begin an intracellular fermentation. This situation favors that whole grapes split open and release their juice into the tank, increasing the liquid phase that is fermented by yeasts [1]. Then, two types of wines are obtained; one from the free-run liquid in the tank (FCM) and other from the liquid after pressing the whole grape bunches (PCM). PCM wines are recognized as high quality young wines because their fruity and floral aromas[2] that although they are very intense at the end of the winemaking they gradually disappear during conservation.

Read More

Under-vine management effects on grapevine vegetative growth, gas exchange and rhizosphere microbial diversity

The use of cover crops under the vines might be an alternative to the use of herbicides or tillage, improving grapevine quality and soil characteristics. The aim of this research was to study the implications of different management strategies of the soil under the vines (herbicide, cultivation or cover crops) on grapevine growth, water and nutritional status, gas exchange parameters and belowground microbial communities.
The experimental design consisted in 4 treatments applied on 35L-potted Tempranillo vegetative grapevines with 10 replicates each grown in an open-top greenhouse in 2022 and 2023. Treatments included two cover crop species (Trifolium fragiferum and Bromus repens), herbicide (glyphosate al 36%) and an untreated control.

Read More

Applicability of spectrofluorometry and voltammetry in combination with machine learning approaches for authentication of DOCa Rioja Tempranillo wines

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).

Read More