Our research aimed to determine the effect and efficiency of foliar application of urea on the phenolic composition of Tempranillo Blanco grapes. The field experiment was carried out in 2019 and 2020 seasons and the plot was located in D.O.Ca Rioja (North of Spain). The vineyard was Vitis vinifera L. Tempranillo Blanco and grafted on Richter-110 rootstock. The treatments were control (C), whose plants were sprayed with water and three doses of urea: plants were sprayed with urea 3 kg N/ha (U3), 6 kg N/ha (U6) and 9 kg N/ha (U9). The applications were performed in two phenological stages, pre-veraison (Pre) and veraison (Ver). Also, each of the treatments was repeated one week later. Control and treatments were performed in triplicate and arranged in a randomised block design. Grapes were harvested at optimum ripening stage. High-performance liquid chromatography was used to analyse the phenolic composition of the grapes. Finally, the results obtained from the analytical determinations – flavonols, flavanols and non-flavonoid (hydroxybenzoic acids, hydroxycinnamic acids and stilbenes) – were studied statistically by analysis of variance. The results showed that, in 2019, U6-Pre and U9-Pre treatments increased the hydroxybenzoic acid content in grapes, and also all foliar treatments applied at Pre enhanced the stilbene concentration. Moreover, U3-Ver was the only treatment that rose flavonol and stilbene contents in the Tempranillo Blanco grapes. In 2020, all treatments applied at Pre enhanced the flavonol concentration in grapes. Furthermore, U3-Pre and U9-Pre treatments increased stilbene content in grapes. Nevertheless, the hydroxybenzoic acid content was improved by U6-Ver and U9-Ver and besides, hydroxycinnamic acid concentration in grapes was increased by all treatments applied at Ver. In conclusion, the lower and highest dose of urea (U3 and U9), applied at pre-veraison, were the best treatments to improve the Tempranillo Blanco grape phenolic composition.

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.

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).

Elevated temperature during the grape maturation period is a major threat for grape quality and thus wine quality. Therefore, characterizing the grape composition response to temperature at a larger scale would represent a crucial step towards adaptation to climate change. In response to changes in temperature, various physiological mechanisms regulate grape composition. Primary and secondary metabolisms are both involved in this response, with well-known effects, for example on anthocyanins, and lesser known effects, for example on aromas or aroma precursors. At the field scale or at the regional scale, however, numerous environmental or plant-specific factors intervene to make the effects of temperature difficult to distinguish from overall variability. In this study, it was attempted to overcome this difficulty by selecting well-characterized situations with differing temperatures.
A long-term study of air temperature variability across several Merlot vineyards in the Saint-Emilion and Pomerol wine producing area found significant temperature differences and gradients at various time scales linked to environmental factors. From this study area, a few sites were selected with similar age, soil and training system conditions, and with repeated and contrasted temperature differences during the maturation period. The average temperature difference during the maturation period was about 2°C between cooler and warmer sites, a difference similar to that expected under future climate change scenarios. In close vicinity to the temperature sensors at each site, grape berries were sampled at different times until full maturity during 2019 and 2020. Also, berries from bunches on either side of the row were analyzed separately, allowing an investigation of bunch exposure effect associated with the coupling of berry temperature and solar radiation. Four replicates of pooled berries for each time – site – bunch exposure combination were obtained and analyzed for biochemical composition. Analyses of variance of the biochemical composition data collected at different sampling times reveal significant effects associated with temperature, site, and bunch azimuth. For instance, anthocyanins in grape skins are clearly influenced by temperature and solar radiation exposure, with up to 30% reduction in warmer conditions.

As an essential element for grapevine development and yield, nitrogen is also involved in the winemaking process and largely affects wine composition. Grape must amino nitrogen deficiency affects the alcoholic fermentation kinetics and alters the development of wine aroma precursors. It is therefore essential to control and optimize nitrogen use efficiency by the plant to guarantee suitable grape nitrogen composition at harvest. Understanding the impact of environmental conditions and cultural practices on the plant nitrogen metabolism would allow us to better orientate our technical choices with the objective of quality and sustainability (less inputs, higher efficiency). This trial focuses on the impact of crop limitation – that is a common practice in European viticulture – on nitrogen distribution in the plant and particularly on grape nitrogen composition. A wide gradient of crop load was set up in a homogeneous plot of Chasselas (Vitis vinifera) in the experimental vineyard of Agroscope, Switzerland. Dry weight and nitrogen dynamics were monitored in the roots, trunk, canopy and grapes, during two consecutive years, using a 15N-labeling method. Grape amino nitrogen content was assessed in both years, at veraison and at harvest. The close relationship between fruits and roots in the maintenance of plant nitrogen balance was highlighted. Interestingly, grape nitrogen concentration remained unchanged regardless of crop load to the detriment of the growth and nitrogen content of the roots. Meanwhile, the size and the nitrogen concentration of the canopy were not affected. Leaf gas exchange rates were reduced in response to lower yield conditions, reducing carbon and nitrogen assimilation and increasing intrinsic water use efficiency. The must amino nitrogen profiles could be discriminated as a function of crop load. These findings demonstrate the impact of plant balance on grape nitrogen composition and contribute to the improvement of predictive models and sustainable cultural practices in perennial crops.