The valorization of wine lees as a source of mannoproteins for food and wine applications

This study aims to create an updated, agile viticultural climate index (similar to the Winkler Index) by performing in-depth analyses of current and historical data from industry partners in several major winegrowing regions. The Winkler Index was developed in the early twentieth century based on analysis of various grape-growing regions in California. The index uses heat accumulation (i.e. Growing Degree Days) throughout the growing season to determine which grape varieties are best suited to each region. As viticultural regions are increasingly subject to the complexity and uncertainty of a changing climate, a more rigorous, agile model is needed to aid grape growers in determining which cultivars to plant where. For the first phase of this study, 21 industry partners throughout Napa Valley shared historical phenology, harvest, viticultural practice, and weather data related to their Cabernet sauvignon vineyard blocks. To complement this data, berry samples were collected throughout the 2021 growing season from 50 vineyard blocks located throughout 16 American Viticultural Areas that were then analyzed for basic berry chemistry and phenolics. These blocks have been mapped using a Geographic Information System (GIS), enabling analysis of altitude, vineyard row orientation, slope, and remotely sensed climate data. Sampling sites were also chosen based on their proximity to a weather station. By analyzing historical data from industry partners and data specifically collected for this study, it is possible to identify key parameters for further analysis. Initial results indicate extreme variability at a high spatial resolution not currently accounted for in modern viticultural climate indices and suggest that viticultural practices play a major role. Using the structure of data collection and analyses developed for the first phase, this project will soon be expanded to other wine regions globally, while continuing data collection in Napa Valley.

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.

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.

Optimizing the use of water and improving irrigation strategies has become increasingly important in most winegrowing countries due to the consequences of climate change, which are leading to more frequent droughts, heat waves, or alteration of precipitation patterns. Optimized irrigation scheduling can only be based on a reliable knowledge of the vineyard water status.

In this context, this work aims at the development of a novel methodology, using a contactless, miniaturized, low-cost NIR spectral tool to monitor (on-the-go) the vineyard water status variability. On-the-go spectral measurements were acquired in the vineyard using a NIR micro spectrometer, operating in the 900–1900 nm spectral range, from a ground vehicle moving at 3 km/h. Spectral measurements were collected on the northeast side of the canopy across four different dates (July 8th, 14th, 21st and August 12th) during 2021 season in a commercial vineyard (3 ha). Grapevines of Vitis vinifera L. Graciano planted on a VSP trellis were monitored at solar noon using stem water potential (Ψs) as reference indicators of plant water status. In total, 108 measurements of Ψs were taken (27 vines per date).

Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.67 and a root mean square error of cross-validation (RMSEcv) of 0.131 MPa. This predictive model was employed to map the spatial variability of the vineyard water status and provided useful, practical information towards the implementation of appropriate irrigation strategies. The outcomes presented in this work show the great potential of this low-cost methodology to assess the vineyard stem water potential and its spatial variability in a commercial vineyard.

The objective of the work described here is the elaboration of a map of the different types of vineyard soils that to guide the famers in the choice of the most productive vine rootstocks and varieties. 90 vineyard soils profiles were analysed in the entire territory of the Origen Denominations of Valdepeñas. The sampling was carried out in 2018 (June to October) by making a sampling grid, followed by photointerpretation and control in the field. The studied soils can be grouped into 9 different soil types (according to FAO 2006 classification): Leptosols, Regosols, Fluvisols, Gleysols, Cambisols, Calcisols, Luvisols and Anthrosols. A map showing the soil distribution with different type of soils has been made with the ArcGIS program. Regarding to the choice of rootstock, Calcisoles are soils with a high active limestone content, so the rootstocks used in these soils must be resistant to this parameter; Luvisols are deep soils with high clay content, so they will support vigorous rootstocks. Because the cartographic units are composed of two or more subgroups, with are associated in variable proportions, 9 different soil associations have been established; Unit 1: Leptosols, Cambisols and Luvisols (80%, 15% and 5% respectively); Unit 2: Cambisols with Regosols and Luvisols (40%, 30% and 30% respectively); Unit 3: Cambisols and Gleysols with Regosols (40%, 40% and 20% respectively); Unit 4: Regosols with Cambisols, Leptosols and Calcisols (40%, 30%, 15% and 15% respectively); Unit 5: Cambisols, Leptosols, Calcisols and Regosols (25% each of them); Unit 6: Luvisols with Cambisol and Calcisols (80%, 10% and 10% respectively); Unit 7: Luvisols and Calcisols with Cambisols (40%, 40% and 20% respectively); Unit 8: Calcisols with, Cambisols and Luvisols (80%, 10% and 10% respectively); Unit 9: Anthrosols. These study allow to elaborate the first map of vineyard soils of this Protected Designation of Origin in Castilla-La Mancha.