Caratterizzazione varietale della CV. Vranac del Montenegro: primi risultati

Monitoring vine water status is a major challenge for vineyard management because it influences both yield and harvest quality. It is also a challenge at the territorial scale for identifying periods of high water restriction or zones regularly impacted by water stress. This information is of major importance for defining collective strategies, anticipating harvest logistic or applying for irrigation authorisation. At this spatial scale, existing tools and methods for monitoring vine water status are few and often require strong assumptions (e.g. water balance model). This paper proposes to consider a collaborative collection of observations by winegrowers and wine industry stakeholders (crowdsourcing) as an interesting alternative. Indeed, it allows the collection of a large number of field observations while pooling the collection effort. However, the feasibility of such a project and its interest in monitoring vine water status at regional scale has never been tested.

The objective of this article is to explore the possibility of making a regional map of vine water status based on crowdsourcing observations. It is based on the study of the free mobile application ApeX-Vigne, which allows the collection of observations about vine shoot growth. This information is easy to collect and can be considered, under certain conditions, as a proxy for vine water status. This article presents the first results obtained from the nearly 18,000 observations collected by winegrowers and wine industry stakeholders during 2019, 2020 and 2021 seasons. It presents the vine shoot growth maps obtained at regional scale and their evolution over the three vintages studied. It also proposes an analysis of the factors that favoured the number of observations collected and those that favoured their quality. These results open up new perspectives for monitoring vine water status at a regional scale but above they provide references for other crowdsourcing projects in viticulture.

Xinomavro is the most important indigenous red wine variety grown in Northern Greece. It participates in the production of several PGI wines in Macedonia while from 100% Xinomavro the PDO “Amyndeon” and “Naoussa” are produced. The viticultural area of Amyndeon lies in a plateau of 550 ‐700 m of altitude, in a semi‐continental climate with mostly deep sandy loamy soils derived from limestone and marl bedrocks while in Naoussa, Xinomavro is grown in a Mediterranean climate on more heavy textured soils, sandy clay loam to clay, derived from ophiolithic, limestone and marl bedrocks, in an altitude which varies from 150 to 400 m. Different soil, climate and viticultural technique interactions, result in great variability with respect to morphological, ampelographical and physiological characters of Xinomavro as well as in the characteristics of the wines produced. 

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.

The yeast ecosystem in grape musts is quite broad and depends on the region and the health of the grapes. Within this, there are yeasts that can generate fermentative deviations and/or cause defects in the wine. It is very important to address this issue because there are significant economic losses in the wine industry when the fermentation process and/or the organoleptic characteristics of the wine are negatively affected, even more today since climate change has a marked effect on the composition of this ecosystem. The aim of this work is to characterize the behavior regarding detrimental oenological features of potential spoilage yeasts isolated from viticultural environments.

In this study we have compared the predictive ability of two phenological models: a traditional Thermal Time (TT) and a version of the more recently develop Unified Model (UM).