Isohydric and anisohydric behavior of 18 wine grape varieties grown in an arid climate

In problematic conditions for production of grapes and wine caused by the COVID-19 pandemic and the resulting occurrence of wine surpluses, producers are increasingly turning to the innovative viticulture and winemaking of products that are more appealing to the market and the consumers. On the other hand, consumption of the food safety or organic products, and therefore of organic grapes and wine, is increasingly common in the world, in particular in Europe. The Regional Rural Development Standing Working Group (SWG RRD), as a regional intergovernmental organization gathers actors in the viticulture and winemaking sector from states and territories of the Western Balkans (South-East Europe) in the Expert Working Group for Wine, with the aim of improving viticulture and winemaking in this region through joint activities. In accordance with the aforementioned, the SWG RRD is working on advancing organic production of grapes and wine, and on recognition of specificities of the terroir of wine-growing areas in Western Balkans. In addition, as part of the project “Facilitation of Exchange and Advice on Wine Regulations in Western Balkan Countries” helmed by the German Federal Ministry of Food and Agriculture, in addition to harmonization of relevant legislation with EU regulations, efforts are being invested towards recognition of organic wines. Within activities and project implemented by this organization, expert analyses and scientific research of the terroir of Western Balkans were carried out, and some of the results are presented in this paper.

Wine quality is strongly linked to climate. Quality scores are often driven by climate variation across different winegrowing regions and years, but also influenced by other aspects of terroir, including variety. While recent work has looked at the relationship between quality scores and climate across many European regions, less work has examined New World winegrowing regions. Here we used scores from three major rating systems (Wine Advocate, Wine Enthusiast and Wine Spectator) combined with daily climate and phenology data to understand what drives variation across wine quality scores in major regions of the Western US, including regions in California, Oregon and Washington. We examined effects of variety, region, and in what phenological period climate was most predictive of quality. As in other studies, we found climate, based mainly on growing degree day (GDD) models, was generally associated with quality—with higher GDD associated with higher scores—but variety and region also had strong effects. Effects of region were generally stronger than variety. Certain varieties received the highest scores in only some areas, while other varieties (e.g., Merlot) generally scored lower across regions. Across phenological stages, GDD during budbreak was often most strongly associated with quality. Our results support other studies that warmer periods generally drive high quality wines, but highlight how much region and variety drive variation in scores outside of climate.

Majority of California’s vineyards rely on supplemental irrigation to overcome abiotic stressors. In the context of climate change, increases in growing season temperatures and crop evapotranspiration pose a risk to adaptation of viticulture to climate change. Vineyard cover crops may mitigate soil erosion and preserve water resources; but there is a lack of information on how they contribute to vineyard resiliency under tillage systems. The aim of this study was to identify the optimum combination of cover crop sand tillage without adversely affecting productivity while preserving plant water status. Two experiments in two contrasting climatic regions were conducted with two cover crops, including a permanent short stature grass (P. bulbosa hybrid), barley (Hordeum spp), and resident vegetation under till vs. no-till systems in a Ruby Cabernet (V. vinifera spp.) (Fresno) and a Cabernet Sauvingon (Napa) vineyard. Results indicated that permanent grass under no-till preserved plant available water until E-L stage 17. Consequently, net carbon assimilation of the permanent grass under no-till system was enhanced compared to those with barley and resident vegetation. On the other hand, the barley under no-till system reduced grapevine net carbon assimilation during berry ripening that led to lower content of nonstructural carbohydrates in shoots at dormancy. Components of yield and berry composition including flavonoid profile at either site were not adversely affected by factors studied. Switching to a permanent cover crop under a no-till system also provided a 9% and 3% benefit in cultural practices costs in Fresno and Napa, respectively. The results of this work provides fundamental information to growers in preserving resiliency of vineyard systems in hot and warm climate regions under context of climate change.

Vineyards are complex agro-ecosystems with high spatial and temporal variability. An efficient training system may counteract the adverse effects of this variability. Moreover, considering the climate change issues, choosing an efficient training system that enhances water use and protects the vines from radiative thermal stress has become a priority for the farmers. A multidisciplinary approach that assesses the soil-crop-yield-wine relationships of vineyards in a distributed and holistic way could bring added knowledge on the behavior of the different training systems. This ongoing research aimed to implement a multidisciplinary approach to study the behavior of “Aglianico del Vulture” grapevines trained with two different systems: a spurred cordon (SC) and an “Alberello in parete” (AL), grown in a high-quality wine production area of Basilicata region (Italy). The approach merged several methods and scales of soil, ecophysiology, must/wine quality, and spectral data collection to assess the influence of the training system. Homogeneous zones (HZs) in both training systems were defined through a procedure based on geomorphological classification, unmanned aerial vehicles (UAV) images analysis, and a traditional soil survey supported by geophysical scanning. During the 2021 season, TDR probes monitored soil water content, while grapevine health status was assessed using eco-physiological measurements (LWP, chlorophyll content, PSII photosynthetic efficiency, LAI, and point-based field spectroscopy). These grapevine in-vivo measurements validated the spectral vegetation indexes (NDVI, RENDVI, CVI, and TVI) derived from the UAV multispectral imagery, which monitored the grapevine status in a distributed and non-invasive way. Grape yield, quality of berries, must and wine were measured to assess the effects of the training systems. The first experimental year results showed the variability of the vineyards and revealed relationships among soil parameters, crop characteristics, and vegetation indices of the SC and AL training systems. This multidisciplinary study could bring new insights into the vineyard training system’s effects on grape yield and wine quality.

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.