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…

Assessment of plant water consumption rates under climate change conditions through an automated modular platform

The impact of climate change is noticeable in the present weather, making water scarcity the most immediate mediator reducing the performance and viability of crops, including grapevine (Vitis vinifera L.). The present study developed a system (hardware, firmware, and software) for the determination of plant water use through changes in weight through a period. The aim is to measure the differences in grapevine water consumption in response to climate change (+4oC and 700 ppm) under controlled conditions. The results reveal a correlation between daily plant consumption rates and reference evapotranspiration (ETo).

Read More

Differences in metabolism among species and hybrids of the genus Saccharomyces during wine fermentation unveiled by multi-omic analysis 

Yeast species S. cerevisiae, S. uvarum, S. kudriavzevii and their hybrids present clear metabolic differences, even when we compared S. cerevisiae wine versus wild strain. These species and hybrids produced significantly higher amounts of glycerol, organic acids, 2,3-butanediol, and 2-phenyl ethanol and a reduction of the ethanol yield, properties very interesting in the sector to deal with climate change effects. To understand the existing differences, we have used several omics techniques to analyze the dynamics of the (intra- and extracellular) metabolomes and/or transcriptomes of representative strains of S. cerevisiae, S. uvarum, S. kudriavzevii, and hybrids.

Read More

Prediction of aromatic attributes of red wines from its colour properties 

Wine perception is a multisensory experience that makes use of the sight, smell, and taste senses. When wine is sensorially assessed, the stimulus received generates multiple signals that tasters convert into organoleptic descriptors. Colour is commonly the first attribute evaluated during wine tasting. Moreover, the colour properties provide the taster with a priori information of the wine’s aroma. This preconceived perception is later confirmed or denied during the aroma evaluation.

Read More

Quantifying water use diversity across grapevine rootstock-scion combinations

Vines require proper light levels, temperature, and water availability, and climate change is modifying these factors, hampering yield and quality. Despite the large diversity of rootstocks, varieties, and clones, we still lack knowledge of their combined effects and potential role in a warmer and dryer future. Therefore, we aim to characterize some of the existing diversity of rootstocks and genotypes and their interaction at the eco-physiological level, combining stomatal conductance (gs) and chlorophyll a fluorescence analysis.

Read More

Effects of different soil types and soil management on greenhouse gas emissions 

Soil is important in the carbon cycle and the dynamics of greenhouse gases (CO2, CH4 and N2O). Key soil characteristics, such as organic matter content, texture, structure, pH and microbial activity, play a determining role in GHG emissions[1]. The objective of the study is to delimit different types of soil, with different soil management and to be able to verify the differences in CO2, CH4 and N2O emissions. The study was carried out in a vineyard of Bodegas Campo Viejo in Logroño (La Rioja), whose plant material is Vitis vinifera L. cv. Tempranillo.

Read More