Beyond colors of rosé wines: impact of origin and winemaking technology on their color, polyphenol and thiol compositions

In viticulture, the challenges of local climate modelling are multiple: taking into account the local environment, fine temporal and spatial scales, reliable time series of climate data, ease of implementation and reproducibility of the method. At the local scale, recent studies have demonstrated the contribution of spatialization methods for ground-based climate observation data considering topographic factors such as altitude, slope, aspect, and geographic coordinates (Le Roux et al, 2017; De Rességuier et al, 2020). However, these studies have shown questions in terms of the reproducibility and sustainability of this type of climate study. In this context, we evaluated the potential of MODIS thermal satellite images validated with ground-based climate data (Morin et al, 2020). Previous studies have been encouraging, but questions remain to be explored at the regional scale, particularly in the dynamics of the massive use of bioclimatic indices to classify the climate of wine regions. The results at the local scale were encouraging, but this approach was tested in the current study at the regional scale. Several objectives were set: 1) to evaluate the downscaling method for land surface temperature time series, 2) to identify regional thermal structure variations. We used weekly minimum and maximum surface temperature time series acquired by MODIS satellites at a spatial resolution of 1000 m and downscaled at 500 m using topographical variables. Two types of analyses were performed:

A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.

Climate change is challenging viticulture worldwide compromising its sustainability due to warmer temperatures and the increased frequency of extreme events. Grafting Vitis vinifera L.

Climate change effects in Languedoc include an expected rise in temperatures, increased evapotranspiration as well as more severe and frequent climatic hazards, such as frost, drought periods and heat waves. For winegrowers theses phenomena impact both yield and quality, resulting in more frequent unbalanced wines. Research on identified mitigation tools for vineyard management is necessary to improve resilience of grapevine agrosystems. Varietal assortment is one of them. This study focuses on agronomic and oenologic potential of 14 foreign varieties grown in Languedoc French region. Fourteen grapevine varieties were monitored during 2021 from June until harvest on eight different sites, some of which occurring on more than one site adding up to 21 different modalities: 7 white varieties Alvarinho B, Assyrtiko B (2), Malvasia Istriana B, Parellada B, Verdejo B, Verdelho B, Xarello B, and 7 black varieties Saperavi N (2), Touriga nacional N, Baga N, Aleatico N, Montepulciano N (2), Primitivo N (3), Calabrese N (3). Varietals were compared through the following parameters: phenology was assessed by using the information collected in the Database Network of French Vine Conservatories (INRAE-SupAgro-IFV, 2005-2015). The number of inflorescences for shoots from secondary buds and bourillons and suckers were observed to assess post-bud break frost tolerance potential. Grapevine water status was studied through stem water potential measurement, observation of foliage symptoms of drought, and 𝛿13C on must. Frequencies and intensities of downy mildew, powdery mildew, and black rot attacks were estimated before harvest on leaves and clusters and botrytis at harvest to assess disease susceptibilities. Berry composition was monitored from end of veraison until harvest. Yield and mean bunch weight were also calculated. Varieties were then ranked on a 1-4 scale for each parameter and compared through PCA. Forty two stations of the Mediterranean basin were compared by PCA with the Multicriteria Climatic Classification indicators in order to confront the collected information during 2021 campaign to the hypothesis that plants coming from dry and hot regions are genetically adapted to such climatic conditions.

The project VINIoT pursues the creation of a new technological vineyard monitoring service, which will allow companies in the wine sector in the SUDOE space to monitor plantations in real time and remotely at various levels of precision. The system is based on spectral images and an IoT architecture that allows assessing parameters of interest viticulture and the collection of data at a precise scale (level of grape, plant, plot or vineyard) will be designed. In France, three subjects were specifically developed: evaluation of maturity, of water stress, and detection of flavescence dorée. For the evaluation of maturity, it has been decided first to work at the berry scale in the laboratory, then at the bunch scale and finally in the vineyard. The acquisition of the spectral hyperstal image as well as the reference analyzes to measure the maturity, were carried out in the laboratory after harvesting the berries in a maturity monitoring context. This work focuses on a case study to predict sugar content of three different grape varieties: Syrah, Fer Servadou and Mauzac. A robust method called Roboost-PLSR, developed in the framework of this work (Courand et al., 2022), to improve prediction model performance was applied on spectra after the acquirement of hyperspectral images. Regarding the evaluation of water stress, to work with a significant variability in terms of water status, it has been worked first with potted plants under 2 different water regimes. The facilities have allowed the supervision of irrigation and micro-climatic conditions. The regression models on agronomic variables (stomatal conductance, water potential, …) are studied. To detect flavescence dorée, the experimental plan has consisted of work at leaf scale in the laboratory first, and then in the field. To detect the disease from hyper-spectral imaging, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) was proposed. This strategy proved the potential towards the discrimination of healthy and infected leaves by flavescence dorée based on the use of hyperspectral images (Mas Garcia et al., 2021).