Temperature variations in the Walla Walla valley American Viticultural Area

Grapevine yield has been historically overlooked, assuming a strong trade-off between grape yield and wine quality. At present, menaced by climate change, many vineyards in Southern France are far from the quality label threshold, becoming grapevine yield-gaps a major subject of concern. Although yield-gaps are well studied in arable crops, we know very little about grapevine yield-gaps. In the present study, we analysed the environmental component of grapevine yield-gaps linked to climate and soil resources in the Languedoc Roussillon. We used SAFRAN data and IGP Pays d’Oc wine yields from 2010 to 2018. We selected climate and soil indicators proving to have a significant effect on average wine yield-gaps at the municipality scale. The most significant factors of grapevine yield were the Soil Available Water Capacity; followed by the Huglin Index and the Climatic Dryness Index. The Days of Frost; the Soil pH; and the Very Hot Days were also significant. Then, we clustered geographical zones presenting similar indicators, facilitating the identification of resources yield-gaps. We discussed the number of zones with the experts of IGP Pays d’Oc label, obtaining 7 zones with similar limitations for grapevine yield. Finally, we analysed the main resources causing yield-gaps and the grapevine varieties planted on each zone. Mapping grapevine resource yield-gaps are the first stage for understanding grapevine yield-gaps at the regional scale.

Current climate change is increasing inter- and intra-annual variability in atmospheric conditions leading to grapevine phenological shifts as well altered grape ripening and composition at ripeness. This study aims to (i) detect weather anomalies within a long-term time series, (ii) model grape ripening revealing altered traits in time to target specific ripeness thresholds for four Vitis vinifera cultivars, and (iii) establish empirical relationships between ripening and weather anomalies with forecasting purposes. The Day of the Year (DOY) to reach specific grape ripeness targets was determined from time series of sugar concentrations, total acidity and pH collected from a private company in the period 2009-2021 in North-Eastern Italy. Non-linear models for the DOY to reach the specified ripeness thresholds were assessed for model efficiency (EF) and error of prediction (RMSE) in four grapevine cultivars (Merlot, Cabernet Sauvignon, Glera and Garganega). For each vintage and cultivar, advances or delays in DOY to target specified ripeness thresholds were assessed with respect to the average ripening dynamics. Long-term meteorological series monitored at ground weather station by means of hourly air temperature and rainfall data were analyzed. Climate statistics were obtained and for each time period (month, bimester, quarter and year) weather anomalies were identified. A linear regression analysis was performed to assess a possible correlation that may exist between ripening and weather anomalies. For each cultivar, ripeness advances or delays expressed in number of days to target the specific ripening threshold were assessed in relation to registered weather anomalies and the specific reference time period in the vintage. Precipitation of the warmest month and spring quarter are key to understanding the effect of climate change on sugar ripeness. Minimum temperatures of May-June bimester and maximum temperatures of spring quarter best correlate with altered total acidity evolution and pH increment during the ripening process, respectively.

In the last decades the growers of the Portuguese Dão winegrowing region (center of Portugal) are experiencing changes in climate that are influencing either grape phenology berry health and ripening. Aiming to study the relationships between climate indices (CI), seasonal weather and grapevine phenology, in this work long-term climate and phenological data collected at the experimental vineyard of the Portuguese Dão research centre between 1958 and 2019 (61 years) for the red variety Touriga Nacional, was analyzed. The trends over time for the classical temperature-based indices (Growing Season Temperature – GST -, Growing Degree Days – GDD, Huglin Index – HI and Cool Night Index – CI) presented a significantly positive slope while the Dryness Index (DI) showed a negative trend over the last 61 years. Regarding grapevine phenology, an average advance of 4.5 days per decade in the harvest day was observed throughout the last 61 years. Consequently, the weather conditions during the ripening period have changed, showing an increasing trend over time in the average temperature (higher magnitude in the maximum than in the minimum temperature) and a decrease in the accumulated rainfall. A regression analysis showed that ~50% of harvest date variability over years was explained by the temperature-based indices variability. These observed effects of climate change on bioclimatic indices and corresponding anticipation of harvest date can still be considered advantageous for the Dão terroir as it allows to achieve an optimal berry ripening before the common equinox rains and, therefore, avoid the potential negative impacts of the rainfall on berry health and composition.

The gradual change in rainfall patterns experienced in the south of France vineyards, especially around the Mediterranean sea, means that the vines are increasingly subject to summer drought. The winegrowers developped the use of irrigation techniques to ensure the maintenance of competitive yields in the production of wines under Protected Geographical Indication label. In practice, drip irrigation pipes can be installed above the ground or buried into the soil as well as at different distances from the vine row. The objective of this study was to examine the profiles of the wet bulbs of the soil obtained from two drip irrigation systems : aerial drip located under the vine row and subsurface drip placed in the middle of the inter-row. This experiment took place over two consecutive seasons (2020-2021) on a 3.4 ha Viognier plot in the Mediterranean region (PGI Oc, France) on sandy clay soil. The annual rainfalls were less than 400 mm. Soil water content probes were installed at different depths (20 – 40 – 60 – 80 cm) and at different lateralities from the vine row (30 – 60 – 90 – 120 cm) to control the formation of the soil wet bulb during irrigation. The mapping and the analysis of the data allowed a better understanding and differentiation of the water percolation when irrigating with subsurface or aerial drip. For the same amount of water and without differences of vine water status, it is shown that in a subsurface drip irrigation situation, the size of the wet bulb formed is larger than in aerial drip irrigation system.

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.