Using open source software in viticultural research

Field-grown crops such as grapevines, rely on ambient environmental conditions for growth and development.

Vine water status has a significant influence on vineyard yield and berry composition (Williams and Matthews, 1990; Williams et al., 1994). It has been hypothesized that the response of plants to soil water deficits may be due to some sort of “root signal” (Davies and Zhang, 1991). This signal probably arises due to the roots sensing a reduction in soil water content or an increase in the mecanical impedance as the soil dries out.

The influence of climatic conditions on the development of the vine and on the quality of the wines no longer needs to be demonstrated at the scale of the vineyard, by the regional climatic characteristics, determining on this scale the viticultural potentialities (Huglin, 1978; Branas, 1946; Riou et al ., 1994); but also on a local scale, at the level of the basic terroir unit (Morlat, 1989), by the landscape differentiation of the natural environment inducing climatic variability within the same vineyard, and partly explaining differences in functioning of the vine, in connection with the processes of maturation and the quality of the wine (Becker, 1977 and 1984; Morlat, 1989 and Lebon, 1993a). According to these authors, the climatic diversity in a wine region constitutes in addition to the edaphic component, an important component of characterization of the Basic Terroir Units (UTB).

the phenological evolution is a crucial aspect of grapevine growth and development. Accurate detection of phenological stages can improve vineyard management, leading to better crop yield and quality traits. However, traditional methods of phenological tracking such as on-site observations are time-consuming and labour-intensive. This work proposes a scalable data-driven method to automatically detect key phenological stages of grapevines using satellite data. Our approach applies to vast areas because it solely relies on open and satellite data having global coverage without requiring any in-field data from weather stations or other sensors making the approach extensible to other areas.

Climate is a key variable for grapevine development and berry ripening processes. At mesoscale level, climate spatial variations are often determined empirically, as weather station networks are generally not dense enough to account for local climate variations.