Understanding and managing wine production from different terroirs

Several studies have tried to develop different methods to study the photodegradation of wine in an accelerated way, trying to elucidate the effect of light on the wine compounds[1]. In a previous study, our team developed a chamber that speeds up the photodegradation of rosé wine[2]. In the present work we have tried to establish a correlation between irradiation times in accelerated conditions and the natural exposure to the cycles of light that usually exist in markets or at home.

Partial rootzone drying (PRD) is an irrigation management technique designed to reduce water use in grapevines without a decline in yield, thereby increasing water use efficiency (WUE). The principle of PRD is to keep part of the root system at a constant drying rate to produce soil derived signals to above-ground plant organs to induce a

The development of new generations of multispectral satellites such as Sentinel-2 opens possibilities as to vine water status assessment (Cohen et al., 2019). Based on a three years field campaign, a model of Stem Water Potential (SWP) estimation on vine using four satellite bands in Red, Red-Edge, NIR and SWIR domains was developed (Laroche-Pinel et al., 2021). The model relies on SWP field measures done using a pressure chamber (Scholander et al., 1965), which is a common, robust and precise method to assess vine water status (Acevedo-Opazo et al., 2008). The model was mainly developed from from SWP measures on Syrah N (Laroche Pinel E., 2021).

A large scale monitoring was organized in different vineyards in the Mediterranean region in 2021. 10 varieties amongst the most represented in this area were monitored (Cabernet sauvignon N, Chardonnay B, Cinsault N, Grenache N, Merlot N, Mourvèdre N, Sauvignon B, Syrah N, Vermentino B, Viognier B). The model was used to produce water status maps from Sentinel-2 images, starting from the beginning of June (fruit set) up to September (harvest). The average estimated SWP for each vine was compared to actual field SWP measures done by wine growers or technicians during usual monitoring of irrigation programs. The correlations between mean estimated SWP and mean measured SWP were at the same level than expected by the model. (Laroche Pinel, 2021) The general SWP kinetics were comparable. The estimated SWP would have led to same irrigation decisions concerning the date of first irrigation in comparison with measured SWP.

Acevedo-Opazo, C., Tisseyre, B., Ojeda, H., Ortega-Farias, S., Guillaume, S. (2008). Is it possible to assess the spatial variability of vine water status? OENO One, 42(4), 203.
Cohen, Y., Gogumalla, P., Bahat, I., Netzer, Y., Ben-Gal, A., Lenski, I., … Helman, D. (2019). Can time series of multispectral satellite images be used to estimate stem water potential in vineyards? In Precision agriculture ’19, The Netherlands: Wageningen Academic Publishers, pp. 445–451.
Laroche-Pinel, E., Duthoit, S., Albughdadi, M., Costard, A. D., Rousseau, J., Chéret, V., & Clenet, H. (2021). Towards vine water status monitoring on a large scale using sentinel-2 images. remote sensing, 13(9), 1837.
Laroche-Pinel,E. (2021). Suivi du statut hydrique de la vigne par télédétection hyper et multispectrale. Thèse INP Toulouse, France.
Scholander, P.F., Bradstreet, E.D., Hemmingsen, E.A., & Hammel, H.T. (1965). Sap pressure in vascular plants: Negative hydrostatic pressure can be measured in plants. Science, 148(3668), 339–346.

‘Tempranillo Tinto’ is one of the most relevant grapevine cultivars worldwide. Despite its early ripening and relatively short vegetative cycle, which may not be ideal for high-quality grape and wine production in warming conditions, its long-standing cultivation has led to an intense multiplication by cuttings, which originated the high level of clonal variation currently available. Now, this intra-varietal diversity provides an interesting opportunity for cultivar improvement by identifying genotypes with better adaptation potential.

Recent developments in satellite technology have yielded a substantial volume of data, providing a foundation for various machine learning approaches. These applications, utilizing extensive datasets, offer valuable insights into Earth’s conditions. Examples include climate change analysis, risk and damage assessment, water quality evaluation, and crop monitoring. Our study focuses on exploiting satellite thermal and multispectral imaging, and vegetation indexes, such as NDVI, in conjunction with ground truth information about soil type, land usage (forest, urban, crop cultivation), and irrigation water sources in the Trentino region in North-East of Italy.