The wine microbial consortium: a real terroir characteristic

Vineyard floors in warm, dry landscapes including those in South Australia, have traditionally been managed using intensive practices such as tillage and herbicides to control weeds and vegetation, thereby limiting competition with grapevines for water and nutrients in order to not compromise yields.

Armenia, a small country in the South of the Caucasus, has been rediscovering its wine-growing past since the discovery in 2007 of archaeological wine-growing remains dating back around 8,000 years. They are among the oldest in the world. Despite a great diversity of grape varieties, Armenian winegrowers did not have sufficiently organized genetic collections to produce plants and satisfy the growing demand for planting.

The addition of partially dehydrated grapes to enrich must composition for producing complex dry/sweet wines represents a traditional practice in several regions of the world. However, the environmental conditions of dehydration chambers may facilitate the infection of Botrytis cinerea Pers. by promoting disease and provoking large grape losses. B. cinerea attack can induce alterations in the profile of volatile organic compounds (VOCs), which could be detected by sensors specifically trained to detect infection/disease-related compounds. These sensors could facilitate the early detection of the infection, consequently allowing to adjust some dehydration parameters.

As an industry that thrives more on, but may also be more affected by, vintage variation and regionality than any other agricultural enterprise, grape and wine production is ever more being impacted challenged by climate change.

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages. The accuracy of the method for predicting bunch weight at different phenological stages was assessed for seven different varieties.