“Terroir” and climate change in Franconia / Germany

Spatial variability of vine productivity in winegrapes is important to characterise as both yield and quality are relevant for the production of different wine styles and products. The objectives were to understand how patterns of variability of Cabernet Sauvignon fruit composition changed over time and space, how these patterns could be characterised with indirect measurements, and how spatial patterns of the variation in fruit compositional attributes can aid in improving management. Prior to the 2017 vintage, 125 data vines were distributed across each of four vineyards in the Lodi American Viticultural Area (AVA) of California. Each data vine was sampled at commercial harvest in 2017, 2018, and 2019. Yield components and fruit composition were measured at harvest for each data vine, and maps of yield and fruit composition were produced for eight ‘objective measures of fruit quality’: total anthocyanins, polymeric tannins, quercetin glycosides, malic acid, yeast assimilable nitrogen, β-damascenone, C6 alcohols and aldehydes, and 3-isobutyl-2-methoxypyrazine. Patterns of variation in anthocyanins and phenolic compounds were found to be most stable over time. Given this relative stability, management decisions focused on fruit quality could be based on zonal descriptions of anthocyanins or phenolics to increase profitability in some vineyards. In each vineyard, dormant season pruning weights and soil cores were collected at each location, elevation and soil apparent electrical conductivity surveys were completed, and remotely sensed imagery was captured by fixed wing aircraft and two satellite platforms at major phenological stages. The data collected were used to develop relationships among biophysical data, soil, imagery, and fruit composition. The standardised and aggregated samples from four vineyards over three seasons were included in the estimation of ‘common variograms’ to assess how this technique could aid growers in producing geostatistically rigorous maps of fruit composition variability without cumbersome, single season sampling efforts.

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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.

Grapevine physiology and production are challenged by forecasted increases in temperature and water deficits. Within this scenario, photoselective overhead shade films are promising tools in warm viticulture areas to overcome climate change related factors. The aim of this study was to evaluate the vulnerability of ‘Cabernet Sauvignon’ grape berry to solar radiation overexposure and optimize shade film use for berry integrity. A randomized complete block design field study was conducted across two years (2020-2021) in Oakville, Napa Valley, CA, with four shade films (D1, D3, D4, D5) differing in the percent of radiation spectra transmitted and compared to an uncovered control (C0). Integrals for gas exchange parameters and mid-day stem water potential were unaffected by the shade films in 2020 and 2021. By harvest, berries from uncovered and shaded vines did not differ in their size or primary metabolism in either year. Despite precipitation exclusion during the dormant season in the shaded treatments, yield did not differ between them and the control in either season. In 2020, total skin anthocyanins (mg/g fresh mass) in the shaded treatments was greater than C0 during berry ripening and at harvest. Conversely, flavonol concentrations in 2020 were reduced in shaded vines compared to C0. The 2020 growing season highlighted the impact of heat degradation on flavonoids. Flavonoid concentrations in 2021 increased until harvest while flavonoid degradation was apparent from veraison to harvest in 2020 across shaded and control vines. Wine analyses highlighted the importance of light spectra to modify wine composition. Wine color intensity, tonality and anthocyanin values were enhanced in D4 whereas antioxidant properties were enhanced in C0 and D5 wines. Altogether, our results highlighted the need of new approaches in warm viticulture areas given the impact that composition of light has on berry and wine quality.