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

Background: Polyphenols extracted from skins and seeds showed different sensory attributes including astringency and bitterness. In previous studies, it has been demonstrated that extracts obtained either from skins or seeds interact differently with salivary proteins.

Artificial intelligence (AI) for winegrowers refers to robotics, smart sensor technology, and machine learning applied to solve climate change problems. Our research group has developed novel technology based on AI in the vineyard to monitor vineyard growth using computer vision analysis (VitiCanopy App) and grape maturity based on berry cell death to predict flavor and aroma profiles of berries and final wines.

Uno degli aspetti più importanti nel commercio internazionale dei vini a denominazione è quello del riconoscimento dei diritti di esclusiva garantiti sui e dal territorio geografico d’o­rigine. Al fine di cautelarsi nei confronti della sempre più agguerrita concorrenza mondiale, è opportuno adottare adeguate protezioni ufficiali e legali delle denominazioni che possono derivare sia dalla “naturalità” del prodotto stesso che dalla “originalità” più particolare.

Current environmental, ecological and economic issues require a better vineyard production management. In fact, a poor use of fertilizing could lead to harmful impact on environment. Another issue concerns the cultures themselves which couldn’t use fertilizers efficiently, leading to a loss of income or too much expense for farmers. Presently, estimation of fertilization’s needs is realized by the laboratory analysis of leaves selected through a random sampling. The present study aims at optimizing fertilization’s management by using a map of biophysical parameters estimated from satellite images.

With contemporary climate change, cultivated Vitis vinifera L. is at risk as climate is a critical component in defining ecologically fitted plant materiel. While winegrowers can draw on the rich diversity among grapevine varieties to limit expected impacts (Morales-Castilla et al., 2020), replacing a signature variety that has created a sense of local distinctiveness may lead to several challenges. In order to sustain wine identity in uncertain climate outcomes, the study of intra-varietal diversity is important to reflect the adaptive and evolutionary potential of current cultivated varieties. The aim of this ongoing study is to understand to what extent can intra-varietal diversity be a climate change adaptation solution. With a focus on early (Sauvignon blanc, Riesling, Grolleau, Pinot noir) to moderate late (Chenin, Petit Verdot, Cabernet franc) ripening varieties, data was collected for flowering and veraison for the various studied accessions (from conservatory plots) and clones. For these phenological growing stages, heat requirements were established using nearby weather stations (adapted from the GFV model, Parker et al., 2013) and model performances were verified. Climate change projections were then integrated to predict the future behaviour of the intra-varietal diversity. Study findings highlight the strong phenotypic diversity of studied varieties and the importance of diversification to enhance climate change resilience. While model performances may require improvements, this study is the first step towards quantifying heat requirements of different clones and how they can provide adaptation solutions for winegrowers to sustain local wine identity in a global changing climate. As genetic diversity is an ongoing process through point mutations and epigenetic adaptations, perspective work is to explore clonal data from a wide variety of geographic locations.