Characterization and biological effects of extracts from winery by-products

Climate change challenges differently wine growing systems, depending on their biophysical, sociological and economic features. Therefore, there is a need to locally design and evaluate adaptation strategies combining several technical options, and considering the local opportunities and constraints (e.g. water access, wine typicity). The case study took place in a typical and heterogeneous Mediterranean vineyard of 1,500 ha in the South of France. We developed a participatory modeling approach to (1) conceptualize local climate change issues and design spatially explicit adaptation strategies with stakeholders, (2) numerically evaluate their effects on phenology, yield and irrigation needs under the high-emissions climate change scenario RCP 8.5, and (3) collectively discuss simulation results. We organized five sets of workshops, with in-between modeling phases. A process-based model was developed that allowed to evaluate the effects of six technical options (late varieties, irrigation, water saving by reducing canopy size, adjusting cover cropping, reducing density, and shading) with various distributions in the watershed, as well as vineyard relocation. Overall, we co-designed three adaptation strategies. Delay harvest strategy with late varieties showed little effects on decreasing air temperature during ripening. Water constraint limitation strategy would compensate for production losses if disruptive adaptations (e.g. reduced density) were adopted, and more land got access to irrigation. Relocation strategy would foster high premium wine production in the constrained mountainous areas where grapevine is less impacted by climate change. This research shows that a spatial distribution of technical changes gives room for adaptation to climate change, and that the collaboration with local stakeholders is a key to the identification of relevant adaptation. Further research should explore the potential of adaptation strategies based on soil quality improvement and on water stress tolerant varieties.

Knowledge on the reflectance spectrum of soil is potentially useful since it carries information on soil chemical composition that can be used to the planning of agricultural practices. If compared with analytical methods such as conventional chemical analysis, reflectance measurement provides non-destructive, economic, near real-time data. This paper reports results from reflectance measurements performed by spectroradiometry on soils from two vineyards in south Brazil. The vineyards are close to each other, are on different geological formations, but were subjected to the same management. The objective was to detect spectral differences between the two areas, correlating these differences to variations in their chemical composition, to assess the technique’s potential to predict soil attributes from reflectance data.To that end, soil samples were collected from ten selected vine parcels. Chemical analysis yield data on concentration of twenty-one soil attributes, and spectroradiometry was performed on samples. Chemical differences significant to a 95% confidence level between the two studied areas were found for six soil attributes, and the average reflectance spectra were separated by this same level along most of the observed spectral domain. Correlations between soil reflectance and concentrations of soil attributes were looked for, and for ten soil traits it was possible to define wavelength domains were reflectance and concentrations are correlated to confidence levels from 95% to 99%. Partial Least Squares Regression (PLSR) analyses were performed comparing measured and predicted concentrations, and for fifteen out of 21 soil traits we found Pearson correlation coefficients r > 0.8. These preliminary results, which have to be validated, suggest that variations of concentration in the investigated soil attributes induce differences in reflectance that can be detected by spectroradiometry. Applications of these observations include the assessment of the chemical content of soils by spectroradiometry as a fast, low-cost alternative to chemical analytical methods.

Sixty five % of the agricultural area of the Basque Country located in the DO Ca Rioja corresponds to vineyards. More than 40% of it has an average slope greater than 10%, which makes it sensitive to erosive processes. Furthermore, it is foreseeable that extreme weather events (storms, hail, extreme heat and cold, etc.) will be favored due to climate change. Cover cropping can mitigate this risk, and therefore the objective of this work is to evaluate the impact that a vegetable cover has on the agronomic behavior of the vineyard, the quality of the grape and soil erosion. For this, a trial has been carried out with a Graciano variety vineyard with a slope between 10% -20% during the years 2020 and 2021. Conventional tillage management in the area has been compared (4-6 passes per year of tillage machinery) versus spontaneous vegetation cover management in the vineyard. This implies not tilling and allowing the grass of the land to colonize the range between the lines of vines, controlling their height through 1-3 mowing passes per year, always trying to affect the surface of the land as little as possible. The vegetative growth, yield and quality of the grape and wine was measured. Furthermore, erosion has been measured using Gerlasch boxes. The yield was lower in the second year of the trial in the cover crop treatment, but erosion was significantly reduced.

The effects of climate change are seriously affecting the quality of wine grapes. High temperatures and drought cause imbalances in the chemical composition of grapes. The result is overripe grapes with low acidity and high sugar content, which produce wines with excessive alcohol content, lacking in freshness and not very aromatic. As a consequence, the search of varieties with capacity of produce quality grapes in adverse climate conditions is a good alternative to preserve the sustainability of vineyards. In this work, quality parameters of seven Vitis vinifera L. cultivars (five whites and two reds) recently recovered from extinction and grown under two different hydric regimes (rainfed and irrigated) were analyzed during the 2020 vintage. At harvest time, weight of 100 berries, must physicochemical parameters (brix degree, total acidity, malic acid, pH), and carbon and oxygen isotope ratios (δ13C, δ18O) were determined. Subsequently, varietal aroma potential index (IPAv) and total polyphenol index (TPI) were analyzed. Quality parameters, IPAv and TPI, showed significant differences between varieties and water regimes. Both red varieties, Moribel and Tinto Fragoso, stood out for their high aromatic and phenolic potential, which was higher under rainfed regime. Regarding to white varieties, Montonera del Casar and Jarrosuelto stood out in terms of varietal aroma potential. Montonera del Casar high acidity in its musts and Jarrosuelto showed the highest berry weights.

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: