Simulating climate change impact on viticultural systems in historical and emergent vineyards

Temperature is a relevant parameter during vineyard development, affecting vine phenology and grape maturity. Moreover, the climate of the different Chilean valleys influences the varieties cultivated, the ripening period and the final quality of the wines. The use of growing degree days (GDD) is known worldwide for the study of climate in viticulture regions. However, little is known about the evolution of maturity and the sugar loading stop, based on this parameter.

Soil physical and chemical properties affect vine nutrition, as indicated by leaf and petiole nutrient content, in a way that may directly impact wine properties.

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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.

In the context of climate change and for the sustainable exploitation of Mediterranean vineyards, it is necessary to use new strategies to adapt to the new climatic conditions.