Heat-stress responses regulated via a MYB24-MYC2 complex

Vineyard canopy features such canopy porosity and fruit exposure influenced microclimate, fungal disease incidence and grape composition. An objective, rapid and non-invasive method to assess and map the canopy status is needed to apply in precision viticulture. A new method for canopy status assessment and mapping based on non-invasive machine vision was applied in commercial vineyards in this work.

The evaluation of different chemical compounds present in Uvalino wines was correlated with sensory analysis. The analysis showed a high content of polyphenolic compounds responsible for the organoleptic properties of wine, including color, astringency and bitterness.

Grape quality potential for wine production is strongly influenced by environmental parameters and agronomic factors. Several studies underline the rootstock effect on scions vegetative growth and berry composition [1] with an impact on wine quality. Rootstocks are promising agronomic tools for climate change adaptation and in most grape-growing regions the potential diversity of rootstocks is not fully used and only a few genotypes are planted. Moreover, little is known about the effect of rootstock genetic variability on the aromatic composition in wines.

The management of yeast nutrition is an essential approach for a better control over wine fermentation process. Most of the researches on this subject in the last decades focused on nitrogen nutrition. However, vitamins, while being key compounds for yeast metabolism as co-factors for numerous enzymatic activities, were left mostly unexplored.

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.