Grapegrowing soils

Barolo DOCG is an economically important wine producing region in Northwest Italy. It is a small region of approximately 70 km2 gross area. The topography is very complex with steep sloped hills ranging in elevation from below 200 m to 550 m. Barolo DOCG wine is made exclusively from the Nebbiolo grape. Bioclimatic indexes are often used in viticulture to gain a better understanding of broader climate trends which can be compared temporally and geographically. These indexes are also used for identifying potential phenological timing, growing region suitability, and potential risks associated with expected climatic changes. Understanding how topography influences bioclimatic indexes can help with understanding of mesoscale climate behaviour leading to improved decision making and risk management strategies. The average monthly maximum and minimum temperatures, the Cool Night Index, the Huglin Index, and the monthly diurnal range (from July to October) were calculated using data from 45 weather stations within a 40 km radius of the Barolo DOCG growing area between the years 1996 and 2019. Linear and multiple regression models were developed using independent variables (elevation, aspect, slope) extracted from a digital elevation model to identify significant relationships. Bioclimatic indexes were then kriged with external drift using independent variables that showed significant relationships with the bioclimatic index using a 100 m resolution grid. The maximum monthly temperatures and the Huglin Index showed consistent significant negative relationships with elevation in all years. The minimum monthly temperatures showed no relationship with elevation but in some months a small but significant relationship was observed with aspect. Due to the lack of a relationship between minimum monthly temperatures and elevation compared to the significant relationship between maximum monthly temperatures and elevation, monthly diurnal range had a negative relationship with elevation.

One of the main consequences of global warming is the rise of the mean temperature. Thus, the heat summation by the plants begins sooner in the early spring, and by cumulating growing degree-days, phenological development tends to happen earlier. However, spring frost is still a recurrent phenomenon causing serious damages to buds and therefore, threatening the harvests of the winegrowers. The wind machine is a solution to protect fruit crops against spring frost that is increasingly used. It is composed of a 10-m mast with a blowing fan at its peak. By tapping into the strength of the nocturnal thermal inversion, it sweeps the crop by propelling warm air above to the ground. Thus, stratification is momentarily suppressed. Furthermore, the continuous action of the machine, alone or in synergy, or the addition of a heater allow the bud to be bathed in a warmer environment. Also, the punctual action of the tower’s warm gust reaches the bud directly at each rotation period. All these actions allow the bud to continuously warm up, but with different intensities and over a different period. Although there is evidence of the effectiveness of the wind machines, the thermal transfers involved in those mechanisms raise questions about their true nature. Field measurements based on ultrasonic anemometers and fast responding thermocouples complemented by laboratory measurements on a reduced scale model allow to characterize both the airflow produced by the wind machine and the local temperature in its vicinity. Those experiments were realized in the vineyard of Quincy, in the framework of the SICTAG project. In the future paper, we will detail the aeraulic characterization of the wind machine and the thermal effects resulting from it and we will focus on how the wind machine warms up the local atmosphere and enables to reduce the freezing risk.

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.

Traditional viticulture areas have experienced in the last decades an intensification of field practices, linked to an increased use of fertilisers and phytosanitary products, and to a more intensive mechanization and uniformization of the landscape. This change in management has sometimes led to higher rates of soil erosion andloss of soil structure, fertility decline, groundwater contamination, and to an increased pressure of pests and diseases. Additionally, intensification usually leads to a simplification of landscapes, of particular concern in prestigious wine grape regions where the economical revenue encourages the conversion of land use from natural habitats to high value wine grape production. To revert this trend, it is necessary that growers implement actions that promote biodiversity in their vineyards. The aim of this study is to assess the impact of the implementation of cover crops, vegetational corridors, dry stone walls and vineyard biodiversity hotspots estimated through the study of arthropods. The work has been carried out in four vineyards in Rioja Alavesa belonging to Ostatu winery, where these infrastructures were implemented in 2020. The presence and diversity of arthropods was studied by capturing them at different times in the season and at different distances from the infrastructure using pit-fall traps in the soil and yellow, white and blue chromatic traps at the canopy level. This is a preliminary study in which all adult insects were sorted to the taxonomic level of order and Coleoptera were classified to morphospecies. The results obtained show that there is a relationship between the basic characteristics of the vineyard and the arthropods captured, with a positive effect, although also dependent on the vineyard, of the presence of infrastructure.

Increasingly warm and dry climate conditions are challenging the viticulture and winemaking sector. Digital technologies and crop modelling bear the promise to provide practical answers to those challenges. As viticultural activities strongly depend on harvest date, its early prediction is particularly important, since the success of winemaking practices largely depends upon this key event, which should be based on an accurate and advanced plan of the annual cycle. Herein, we demonstrate the creation of modelling tools to assess grape ripeness, through sugar concentration monitoring. The study area, the Portuguese Côa valley wine region, represents an important terroir in the “Douro Superior” subregion. Two varieties (cv. Touriga Nacional and Touriga Franca) grown in five locations across the Côa Region were considered. Sugar accumulation in grapes, with concentrations between 170 and 230 g l-1, was used from 2014 to 2020 as an indicator of technological maturity conditioned by meteorological factors. The climatic time series were retrieved from the EU Copernicus Service, while sugar data were collected by a non-profit organization, ADVID, and by Sogrape, a leading wine company. The software for calibrating and validating this model framework was the Phenology Modeling Platform (PMP), version 5.5, using Sigmoid and growing degree-day (GDD) models for predictions. The performance was assessed through two metrics: Roots Mean Square Error (RMSE) and efficiency coefficient (EFF), while validation was undertaken using leave-one-out cross-validation. Our findings demonstrate that sugar content is mainly dependent on temperature and air humidity. The models achieved a performance of 0.65