It is well known that lactic acid bacteria modify the wine volatile compound. However, very few data are available regarding metabolite changes that occurred during the malolactic fermentation (MLF).

Climate is considered one of the main factors that determines the aptitude of a specific location for growing grapes and producing high quality wine, being in that sense one of the main elements defining the concept of terroir

Remote sensing applications in viticulture have been a research theme now for nearly two decades, becoming a valuable tool for vineyard management. Metrics produced using remotely sensed images of vineyards have yielded relationships with grape quality and yield that can help optimise vineyard performance

Champagne regulation allows winegrowers to stock small amounts of still wines in order to compensate vintages’ quality shifts mainly due to climate variations. According to their technical requirements and house style some Champagne producers (commonly named “Champagne houses”) use these stored wines in the blend in order to introduce an element of complexity. These wines possess the particularity of being aged on fine lees in thermo-regulated stainless steel tanks. The Champagne house of Veuve Clicquot Ponsardin has several wines stored this way.

Barolo DOCG is an economically important wine producing region in Northwest Italy. It is a small region of approximately 70 km2 gross area. The topography is very complex with steep sloped hills ranging in elevation from below 200 m to 550 m. Barolo DOCG wine is made exclusively from the Nebbiolo grape. Bioclimatic indexes are often used in viticulture to gain a better understanding of broader climate trends which can be compared temporally and geographically. These indexes are also used for identifying potential phenological timing, growing region suitability, and potential risks associated with expected climatic changes. Understanding how topography influences bioclimatic indexes can help with understanding of mesoscale climate behaviour leading to improved decision making and risk management strategies. The average monthly maximum and minimum temperatures, the Cool Night Index, the Huglin Index, and the monthly diurnal range (from July to October) were calculated using data from 45 weather stations within a 40 km radius of the Barolo DOCG growing area between the years 1996 and 2019. Linear and multiple regression models were developed using independent variables (elevation, aspect, slope) extracted from a digital elevation model to identify significant relationships. Bioclimatic indexes were then kriged with external drift using independent variables that showed significant relationships with the bioclimatic index using a 100 m resolution grid. The maximum monthly temperatures and the Huglin Index showed consistent significant negative relationships with elevation in all years. The minimum monthly temperatures showed no relationship with elevation but in some months a small but significant relationship was observed with aspect. Due to the lack of a relationship between minimum monthly temperatures and elevation compared to the significant relationship between maximum monthly temperatures and elevation, monthly diurnal range had a negative relationship with elevation.