Grapegrowing soils

Atmospheric carbon dioxide (CO2) concentration has continuously increased since pre-industrial times from 280 ppm in 1750, and is predicted to exceed 700 ppm by the end of 21st century. For most of C3 plant species elevated CO2 (eCO2) improve photosynthetic apparatus results in an increased plant biomass production. To investigate the effects of eCO2 on morphological leaf characteristics the two Vitis vinifera L. cultivars, Riesling and Cabernet Sauvignon, grown in the Geisenheim VineyardFACE (Free Air Carbon dioxide Enrichment) system were used. The FACE site is located at Geisenheim University (49° 59′ N, 7° 57′ E, 94 m above sea level), Germany and was implemented in 2014 comparing future atmospheric CO2-concentrations (eCO2, predicted for the mid-21st century) with current ambient CO2-conditions (aCO2). Experiments were conducted under rain-fed conditions for two consecutive years (2015 and 2016). Six leaves per repetition of the CO2 treatment were sampled in the field and immediately fixed in a FAA solution (ethanol, H2O, formaldehyde and glacial acetic acid). After 24 h leaf samples were transferred and stored in an ethanol solution. Subsequently, leaf tissue was dehydrated using ethanol series and embedded in paraffin. By using a rotary microtomesections of 5 µm were prepared and fixed on microscopic slides. Subsequent the samples were stained using consecutive staining and washing solutions. Afterwards pictures of the leaf cross-sections were taken using a light microscope and consecutive measurements were conducted with an open source image software. Differences found in leaf cross-sections of the two CO2 treatments were detected for the palisade parenchyma. Leaf thickness, upper and lower epidermis and spongy parenchyma remained less affected under eCO2 conditions. The observed results within grapevine leaf tissues can provide first insights to seasonal adaptation strategies of grapevines under future elevated CO2 concentrations.

A gradual decline in vineyards has been observed over the past twenty years worldwide. This might be explained by the climate change, practices change or the increase of dieback diseases. To increase the longevity of vines, we studied the impact of different pruning strategies in four adult and four young vineyards located in France and Spain. In France, vineyards were planted with Cabernet franc on 3309C while Spanish trials were planted with Tempranillo grafted on 110R. Vegetative expression, yield, quality of berries and wood vessels conductivity were measured. The distribution of vegetative expression, yield and berry composition between primary and secondary vegetation were quantified. Finally, tomography was used to evaluate the implication of the treatments on sap flows.
First results show that i) the respectful pruning leads to an increase of 30 to 50% more secondary shoots than the aggressive pruning in France and between 15 and 20% in Spain, ii) there is no major effect on the yield over the first two years following the implementation of the new pruning practices, although the proportion of clusters from suckers is higher on the respectful pruning method. On young vines, the development of the trunk according to a respectful pruning leads to a loss of harvest 2 years after planting. This is due to the removal, on the future trunk, of the green suckers which carrying bunches. This operation carried out in spring rather than during winter pruning, would promote a better leaf / fruit balance when the plant comes into production, and could lead to better hydraulic conduction in the vessels of the trunk. Maintaining these trials for several years will provide more robust data to assess the impact of these practices on the vines over the long term.

In viticulture, the challenges of local climate modelling are multiple: taking into account the local environment, fine temporal and spatial scales, reliable time series of climate data, ease of implementation and reproducibility of the method. At the local scale, recent studies have demonstrated the contribution of spatialization methods for ground-based climate observation data considering topographic factors such as altitude, slope, aspect, and geographic coordinates (Le Roux et al, 2017; De Rességuier et al, 2020). However, these studies have shown questions in terms of the reproducibility and sustainability of this type of climate study. In this context, we evaluated the potential of MODIS thermal satellite images validated with ground-based climate data (Morin et al, 2020). Previous studies have been encouraging, but questions remain to be explored at the regional scale, particularly in the dynamics of the massive use of bioclimatic indices to classify the climate of wine regions. The results at the local scale were encouraging, but this approach was tested in the current study at the regional scale. Several objectives were set: 1) to evaluate the downscaling method for land surface temperature time series, 2) to identify regional thermal structure variations. We used weekly minimum and maximum surface temperature time series acquired by MODIS satellites at a spatial resolution of 1000 m and downscaled at 500 m using topographical variables. Two types of analyses were performed:

Measurement of carbon isotope discrimination in berry juice sugars at maturity (δ13C) provides an integrated assessment of water use efficiency (WUE) during the period of berry ripening, and when collected over multiple seasons can be used as an indication of drought stress response. Berry juice δ13C measurements were carried out on 48 different varieties planted in a common garden experiment in Bordeaux, France from 2014 through 2021 and were paired with midday and predawn leaf water potential measurements on the same vines in a subset of six varieties. The aim was to discriminate a large panel of varieties based on their stomatal behaviour and potentially identify hydraulic traits characterizing drought tolerance by comparing δ13C and hydroscapes (the visualisation of plant stomatal behaviour as a response to predawn water potential). Cluster analysis found that δ13C values are likely affected by the differing phenology of each variety, resulting in berry ripening of different varieties taking place under different stress conditions within the same year. We accounted for these phenological differences and found that cluster analysis based on specific δ13C metrics created a classification of varieties that corresponds well to our current empirical understanding of their relative drought tolerances. In addition, we analysed the water potential regulation of the subset of six varieties (using the hydroscape approach) and found that it was well correlated with some δ13C metrics. Surprisingly, a variety’s water potential regulation (specifically its minimum critical leaf water potential under water deficit) was strongly correlated to δ13C values under well-watered conditions, suggesting that base WUE may have a stronger impact on drought tolerance than WUE under water deficit. These results give strong insights on the innate WUE of a very large panel of varieties and suggest that studies of drought tolerance should include traits expressed under non-limiting conditions.

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.