Etude préliminaire des influences pédoclimatiques sur les caractéristiques quali-quantitatives du cépage aglianico dans une zone de la province de benevento-ltalie

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 parameters that determine the grape quality, and therefore the optimal harvest time, suffer variations during berry ripening, related to climate change, with the widely known problem of the gap between technological and phenolic maturities. However, there are few studies about its incidence on grape nitrogen composition. For this reason, the use of an elicitor, methyl jasmonate (MeJ), alone or with urea, is proposed as a tool to reduce climatic decoupling, allowing to establish the harvest time in order to achieve the optimum grape quality. The aim was to study the effect of MeJ and MeJ+Urea foliar applications on the evolution of Tempranillo amino acids content throughout the grape maturation. Three treatments were foliarly applied, at veraison and 7 days later: control (water), MeJ (10 mM) and MeJ+Urea (10 mM+6 kg N/ha). Grape samples were taken at five stages of maturation: day before the first and second applications, 15 days after the second application (pre-harvest), harvest day, and 15 days after harvest (post-harvest). The amino acids analysis of the samples was carried out by HPLC. Results showed that the evolution of amino acids was similar regardless of the treatment; however, foliar applications influenced the nitrogen compounds content, i.e., there was no qualitative effect but quantitative one. Most of the amino acids reached their maximum concentration in pre-harvest, being higher in grapes from the treatments than in the control. In general, no differences in grape amino acids content were observed between MeJ and MeJ+Urea treatments. Foliar applications with MeJ and MeJ+Urea enhanced the grape amino acids content, without affecting their profile, helping to optimize their quality and allowing to establish a more complete grape ripening standard. Therefore, MeJ and MeJ+Urea foliar applications can be a simple agronomic practice, which has shown promising results in order to enhance the grape quality.

Increasingly warm and dry climate conditions are challenging the viticulture and winemaking sector. Digital technologies and crop modelling bear the promise to provide practical answers to those challenges. As viticultural activities strongly depend on harvest date, its early prediction is particularly important, since the success of winemaking practices largely depends upon this key event, which should be based on an accurate and advanced plan of the annual cycle. Herein, we demonstrate the creation of modelling tools to assess grape ripeness, through sugar concentration monitoring. The study area, the Portuguese Côa valley wine region, represents an important terroir in the “Douro Superior” subregion. Two varieties (cv. Touriga Nacional and Touriga Franca) grown in five locations across the Côa Region were considered. Sugar accumulation in grapes, with concentrations between 170 and 230 g l-1, was used from 2014 to 2020 as an indicator of technological maturity conditioned by meteorological factors. The climatic time series were retrieved from the EU Copernicus Service, while sugar data were collected by a non-profit organization, ADVID, and by Sogrape, a leading wine company. The software for calibrating and validating this model framework was the Phenology Modeling Platform (PMP), version 5.5, using Sigmoid and growing degree-day (GDD) models for predictions. The performance was assessed through two metrics: Roots Mean Square Error (RMSE) and efficiency coefficient (EFF), while validation was undertaken using leave-one-out cross-validation. Our findings demonstrate that sugar content is mainly dependent on temperature and air humidity. The models achieved a performance of 0.65

Stomatal traits determine grapevine water use, carbon supply, and water stress, which directly impact yield and berry chemistry. Breeding for stomatal traits has the strong potential to improve grapevine performance under future, drier conditions, but the trait values that breeders should target are unknown. We used a functional-structural plant model developed for grapevine (HydroShoot) to determine how stomatal traits impact canopy gas exchange, water potential, and temperature under historical and future conditions in high-quality and hot-climate California wine regions (Napa and the Central Valley). Historical climate (1990-2010) was collected from weather stations and future climate (2079-99) was projected from 4 representative climate models for California, assuming medium- and high-emissions (RCP 4.5 and 8.5). Five trait parameterizations, representing mean and extreme values for the maximum stomatal conductance (gmax) and leaf water potential threshold for stomatal closure (Ψsc), were defined from meta-analyses. Compared to mean trait values, the water-spending extremes (highest gmax or most negative Ysc) had negligible benefits for carbon gain and canopy cooling, but exacerbated vine water use and stress, for both sites and climate scenarios. These traits increased cumulative transpiration by 8 – 17%, changed cumulative carbon gain by -4 – 3%, and reduced minimum water potentials by 10 – 18%. Conversely, the water-saving extremes (lowest gmax or least negative Ψsc) strongly reduced water use and stress, but potentially compromised the carbon supply for ripening. Under RCP 8.5 conditions, these traits reduced transpiration by 22 – 35% and carbon gain by 9 – 16% and increased minimum water potentials by 20 – 28%, compared to mean values. Overall, selecting for more water-saving stomatal traits could improve water-use efficiency and avoid the detrimental effects of highly negative canopy water potentials on yield and quality, but more work is needed to evaluate whether these benefits outweigh the consequences of minor declines in carbon gain for fruit production.

In the study of terroir, a separate analysis of its many component factors can be of great help in accurately identifying a vineyard’s natural elements that impact wine quality and typicity. This research used a dedicated pluri-disciplinary approach to investigate the ecological characteristics, including geology and geographical features, of 14 vineyards that produce Gewürztraminer and Sauvignon Blanc cultivars in the alpine Alto Adige DOC wine region. Both the geopedological method using Vineyards Geological Identity (VGI) and the new Solar Radiaton Identity (SRI) topoclimatic classification method were used to provide analytical measurements and qualitative/quantitative characterisations. In addition, wide-ranging targeted and untargeted oenological and chemical analyses were carried out on grapes, musts and wines to correlate the soils’ geomineral and physical conditions with the biochemical properties of their fruits and wines. The research identified strong correlations between vineyard geo-identity and wine biofingerprint, confirming a mineral traceability of strontium rubidium ratio and some minerals distinctive to the local geology, such as K, Ca, Ag, Ba and Mn.  The study also discovered that particular geomineral and physical soil conditions of the studied vineyards are related to the different amount of amino acids, primary varietal aromas and polyphenols found in grapes, musts and wines. The research confirmed that winemaking technologies support oenological quality, although in some cases, human practices can overpower certain characteristic elements in wine, erasing the typical imprint left by the vineyards’ natural terroir, which becomes less traceable. Terroir abiotic ecological factors and vineyard identity can be classified in detail using the new VGI and SRI analysis methods to discover interrelationships between geo-pedological and topoclimatic conditions that impact wine quality. These methods are also helpful in identifying which ecological elements are exclusive to a particular vineyard or wine sub-region.