Exploring the regulatory role of the grapevine MIXTA homologue in cuticle formation and abiotic stress resilience

Light-strike susceptibility varies widely among rosé wines, yet predictive tools for pre-bottling risk assessment remain inadequate.

Sangiovese is one of Italy’s most cultivated grape varieties, and currently, over 130 different clones are registered in the national register of grape varieties. However, despite the sangiovese genome having been re-sequenced, limited molecular and genomic information is still available for this cultivar. The present study investigates the complexity of genotype-environment interactions of ten different Sangiovese clones, cultivated in the Chianti Rufina DOCG district over five consecutive vintages (2016-2020).

The need to classify the vineyards of an area according to the quality of its wines is not recent, but it is only in the last ten years that studies on the suitability of different areas for the cultivation of vineyard take on an integrated and interdisciplinary character (Boselli, 1991). The definition of the suitability of the environment is thus obtained by making the climatic, pedological, topographical and cultural information interact with the vegetative, productive and qualitative expression of the grape varieties.

The acquisition of wine expertise is a multi-faceted and multisensory process with implications for sensory perception, attention, memory, and language production. With the prevalence of the predictive model of brain functioning, one area of burgeoning research interest involves wine mental imagery, since the brain relies on imagined experiences to build predictions for the future. Recent evidence has shown that, for instance, those with higher imagery vividness are more susceptible to wine advertising. However, little is known about the association between mental imagery and other associated cognitive processes, such as the ability to produce words that describe such imagery. 

Unmanned Aerial Vehicles (UAVs) are increasingly used to monitor canopy structure and vineyard performance. Compared with traditional remote sensing platforms (e.g. aircraft and satellite), UAVs offer a higher operational flexibility and can acquire ultra-high resolution images in formats such as true color red, green and blue (RGB) and multispectral. Using photogrammetry, 3D vineyard models and normalized difference vegetation index (NDVI) maps can be created from UAV images and used to study the structure and health of grapevine canopies. However, there is a lack of comparison between UAV-based images and ground-based measurements, such as leaf area index (LAI) and canopy porosity.