Learning from remote sensing data: a case study in the Trentino region 

The production of fine wines in the Sub-middle of the São Francisco River Valley, Northeast of Brazil, is relatively recent, about twenty-five years ago. This region presents different characteristics

It is well-known that there is a relationship between the “terroir” and the characteristics of grapes and quality of wines. However, adequate grape variety and other cultural factors should be also taken into account

To face emerging competition and challenges, wine producers globally rely on precision viticulture (PV) solutions to boost productivity, enhance quality, increase profitability, and reduce the environmental impact of vineyards. Current pv methods predominantly use multispectral sensor data from several platforms (satellites or vineyard installations). However, these applications generally use data analysis strategies lacking physiological grapevine support.

Climate influence on grapevine physiology is prevalent and this influence is only expected to increase with climate change. Although governed by a general determinism, climate influence on grapevine physiology may present variations according to the terroir. In addition, these site-specific differences are likely to be enhanced when climate influence is studied using farm data. Indeed, farm data integrate additional sources of variation such as a varying representativity of the conditions actually experienced in the field. Nevertheless, there is a real challenge in valuing farm data to enable grape growers to understand their own terroir and consequently adapt their practices to the local conditions. In such a context, this article proposes a framework to site-specifically study climate influence on grapevine physiology using farm data. It focuses on improving the analysis of time series of weather data. The analytical framework includes the synchronization of time series using site-specific thermal indices computed with an original method called Extended Growing Degree Days (eGDD). Synchronized time series are then analyzed using a Bayesian functional Linear regression with Sparse Steps functions (BLiSS) in order to detect site-specific periods of strong climate influence on yield development. The article focuses on temperature and rain influence on grape yield development as a case study. It uses data from three commercial vineyards respectively situated in the Bordeaux region (France), California (USA) and Israel. For all vineyards, common periods of climate influence on yield development were found. They corresponded to already known periods, for example around veraison of the year before harvest. However, the periods differed in their precise timing (e.g. before, around or after veraison), duration and correlation direction with yield. Other periods were found for only one or two vineyards and/or were not referred to in literature, for example during the winter before harvest.

The correlations between vine water deficit cumulated over the ripening period of grapes, assessed by ΔC13 in must sugar, and the main analytic variables of grapes are significant. As a result ΔC13 is a useful tool in zoning homogeneous areas according to their technological qualities when harvesting.