Grape variety identification and detection of terroir effects from satellite images

Among the Vitis vinifera L. cv. Moscato, Moscato Bianco is the oldest and most cultivated one in Europe (1). According to the OIV Focus 2015, Italy is the country with the largest cultivated area of Moscato Bianco with about 12500 hectares (2), that is used to produce well-known wines (i.e., Moscato Passito in Piedmont, Moscato di Trani in Puglia, and Moscatello di Montalcino in Tuscany), mainly obtained from partially dehydrated grapes (1). Different dehydration techniques can strongly modify the chemical compounds of oenological interest, among which Volatile Organic Compounds (VOCs) (1) that are the main responsible for the varietal sensory character of the final wine.

Increasingly warm and dry climate conditions are challenging the viticulture and winemaking sector. Digital technologies and crop modelling bear the promise to provide practical answers to those challenges. As viticultural activities strongly depend on harvest date, its early prediction is particularly important, since the success of winemaking practices largely depends upon this key event, which should be based on an accurate and advanced plan of the annual cycle. Herein, we demonstrate the creation of modelling tools to assess grape ripeness, through sugar concentration monitoring. The study area, the Portuguese Côa valley wine region, represents an important terroir in the “Douro Superior” subregion. Two varieties (cv. Touriga Nacional and Touriga Franca) grown in five locations across the Côa Region were considered. Sugar accumulation in grapes, with concentrations between 170 and 230 g l-1, was used from 2014 to 2020 as an indicator of technological maturity conditioned by meteorological factors. The climatic time series were retrieved from the EU Copernicus Service, while sugar data were collected by a non-profit organization, ADVID, and by Sogrape, a leading wine company. The software for calibrating and validating this model framework was the Phenology Modeling Platform (PMP), version 5.5, using Sigmoid and growing degree-day (GDD) models for predictions. The performance was assessed through two metrics: Roots Mean Square Error (RMSE) and efficiency coefficient (EFF), while validation was undertaken using leave-one-out cross-validation. Our findings demonstrate that sugar content is mainly dependent on temperature and air humidity. The models achieved a performance of 0.65

In the context of increasingly hot and dry summers, the adoption of innovative irrigation technologies has become essential for maintaining grape production while minimizing water use.

Intensification is considered one of the major drivers of biodiversity loss in farmland. The more intensive management practices that have been adopted the last decades, contributed to species declines from all taxonomic groups. Moreover, agricultural intensification has led to an important change of land use. Complex, mixed agro-ecosystems with cultivated and non-cultivated habitats have been converted to simplified, intensive and homogeneous ones with severe effects on biodiversity.