Modeling the Impacts of Weather and Cultural Factors on Rotundone Concentration in Cool-Climate Noiret Wine Grapes

The sesquiterpenoid rotundone is the compound responsible for the “black pepper” aroma of many plant species, including several economically important wine grape varieties. Since its identification in wine in 2008, there has been an increased interest in understanding how individual climatic or cultural factors affect the accumulation of rotundone in grapes and subsequently the level of wine “pepperiness.” However, no study has assessed climatic and viticultural factors together to identify which variables have the strongest influence on rotundone accumulation. Our study aimed to fill this knowledge gap by developing a predictive model that identified factors that explain rotundone concentrations in Noiret (Vitis sp.) grapes at harvest. Over the 2016 and 2017 seasons, we measured 21 viticultural, meso- and microclimatic variables and concentrations of rotundone in Noiret wine grapes at seven vineyards in the northeastern U.S. Vineyard growing degree days (GDD(v)) and the amount of solar radiation (cumulative solar exposure; CSE(v)) accumulated from the beginning of fruit ripening to harvest were the variables best correlated (r = 0.70 and r = 0.74, respectively) with rotundone concentrations. Linear correlations between microclimatic parameters and rotundone concentrations were weaker, but overall rotundone was negatively correlated with low (<15°C) and high (>30°C) berry temperatures. Using the 2-year data set we were able to develop a four-variable model which explained more than 80% of the variation in rotundone concentration at harvest. The model included weather [growing degree days during fruit ripening (GDD(v))] and plant-related variables (concentrations of phosphorus and calcium in the leaf petiole, and crop load). The model we developed could be used by wine producers to identify sites or cultural practices that favor rotundone accumulation in Noiret grapes after performing a model validation with an additional, external data set. More broadly, the statistical approach used here could be applied to other studies that also seek to assess the effects of multiple factors on a variable of interest under varying environmental conditions.