AOC Saint-Romain, Hautes-Côtes-de-Beaune, Burgundy: analysis of a “terroir”

The actual climate changes, together with the strong regulation of the European Union and Spanish government, in search of sustainable viticulture, have forced the recovery of minority varieties, expanding the range of grape varieties, as well as the possible development of wines with unique profiles. In the Ribeira Sacra DO (Spain), a comparative study of the agronomic and qualitative behavior of the ‘Branco lexítimo’ variety has been carried out, compared to the majority white variety in the DO: ‘Godello’, located in the same study plot, with identic soil and climatic conditions. The study contemplated the analysis of phenology and leaf water potential, as well as the productive results and the analysis of the must quality, during four seasons: 2018 – 2021.

In the Northeast of Brazil, vines can produce twice a year, because annual average temperature is 26ºC, with high solar radiation and water availability for irrigation.

When someone watches a hilly landscape, the image beauty creates emotions and frames of mind not easily forgettable, but sometimes man’s intervention by means of soil movement and reduction of the natural biodiversity can significantly modify the landscape and consequently the above-mentioned emotions. One speculates if sensory appreciation of a wine may be strongly affected by psychological factor: landscape beauty.

Each grape cultivar is composed of a population of individuals that are genetically different. Clonal selection has allowed the purification and improvement of the global quality

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.