Under trellis cover crop induces grapevine tolerance to bunch rot

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.

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.

Most European vineyards are managed under rainfed conditions, where seasonal water deficit has become increasingly important. The flowering-veraison phenophase represents an important period for vine response to water stress, which is seldomly thoroughly evaluated. Therefore, we aim to quantify the flowering-veraison water stress levels using Crop Water Stress Indicator (CWSI) over 1986–2015 for important European wine regions, and to assess the respective potential Yield Lose Rate (YLR). Additionally, we also investigate whether an advanced flowering-veraison phase may help alleviating the water stress with improved yield. A process-based grapevine model STICS is employed, which has been extensively calibrated for flowering and veraison stages using observed data at 38 locations with 10 different grapevine varieties. Subsequently, the model is being implemented at the regional level, considering site-specific calibration results and gridded climate and soil datasets. The findings suggest wine regions with stronger flowering-veraison CWSI tend to have higher potential YLR. However, contrasting patterns are found between wine regions in France-Germany-Luxembourg and Italy-Portugal-Spain. The former tends to have slight-to-moderate drought conditions (CWSI<0.5) and a negligible-to-moderate YLR (<30%), whereas the latter possesses severe-to-extreme CWSI (>0.5) and substantial YLR (>40%). Wine regions prone to a high drought risk (CWSI>0.75) are also identified, which are concentrated in southern Mediterranean Europe. An advanced flowering-veraison phase may have benefited from cooler temperatures and a higher fraction of spring precipitation in wine regions of Italy-Portugal-Spain, resulting in alleviated CWSI and moderate reductions of YLR. For those of France-Germany-Luxembourg, this can have reduced flowering-veraison precipitation, but prevalent alleviations of YLR are also found, possibly because of shifted phase towards a cooler growing season with reduced evaporative demands. Overall, such a retrospective analysis might provide new insights towards better management of seasonal water deficit for conventionally vulnerable Mediterranean wine regions, but also for relatively cooler and wetter Central European regions.

The Fruška Gora mountain is a traditional wine-growing region in Serbia situated in the Pannonian Basin. Due to such a position, the vicinity of the Danube River and the presence of concave configuration, it is suitable for grape production. This paper provides analyses of spatial variations in meteorological parameters and grape juice quality within Fruška Gora wine region over three consecutive vintages (2018-2020). The examined period can be defined as warm with cool nights during September (AVG 18,9°C; GDD 1918°C; CI 12°CF) and with the presence of mesoclimatic variability. The East part of the study area was somewhat drier and hotter compared to other parts of the region. The analyses of grape must samples (190 in total) of five cultivars (Cabernet-Sauvignon, Merlot, Chardonnay, Sauvignon blanc and Grašac (Welschriesling)) commonly grown across the region (19 sites), were performed using Fourier Transform Infrared Technology (FTIR). Among all cultivars, Sauvignon blanc was harvested first in the East area (DOY=246±5, GDD at harvest=1552±74, 22.2±0.7 °Brix), while the latest harvest was recorded for Cabernet-Sauvignon in the West (DOY=283±5, GDD at harvest=1936±187, 23.4±1.0 °Brix ). Both the red and white cultivars had higher acidity and YAN in the grape must if the vines were grown in the North and East compared to South and West areas. According to PCA analysis, Grašac showed the lowest variation in grape must chemical composition. Thus, the results confirm that Grašac is the most stable cultivar in Fruška Gora. All monitored cultivars reached technological fruit ripeness by the end of the growing season. However, it was difficult to reach full ripeness of red cultivars, mostly beacuse of uncoupling of technolocical and phenolic ripeness. Thus, Cabernet-Sauvignon had higher variations in GDD sums at harvest compared to other cultivars, which probably increased variations in grape must quality.

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.