Spatio-temporal dynamics of grape anthocyanin accumulation in a Tempranillo vineyard monitored by proximal sensing

Background and Aims: The spatio-temporal variability of phenol grape composition in a vineyard can be characterised by using precision viticulture tools. Characterising anthocyanin variation in grapes requires a large number of observations, which can be done by using proximal sensors in the field. This study characterises the dynamics of spatial-temporal variability in fruit anthocyanin content at different stages from veraison to harvest using a newly available non-destructive fluorescence method. Methods and Results: The study was conducted in a Tempranillo (Vitis vinifera L.) vineyard (Navarra, Spain) during the 2010 season. A handheld, non-destructive fluorescence-based proximal sensor was used to monitor the anthocyanins of the berries at six different times between veraison and harvest. The fluorescence-based anthocyanin indices were significantly correlated with the anthocyanin contents in berries (R2 ranged from 0.74 to 0.78). The variability of the anthocyanin content decreased during the ripening period, reaching a minimum at harvest. Although the mean anthocyanin content of fruit increased from veraison until mid-September, when a fairly stable value was reached that remained constant until harvest, spatial patterns within the vineyard changed over the whole ripening period. The spatial correlations between the anthocyanin content at each monitoring date and harvest time revealed that larger similarities in the spatial distribution of anthocyanins occurred at veraison and 10 days before harvest. Conclusions: Grape anthocyanins accumulated from veraison until around 1 month before harvest, whereas their spatial pattern within the vineyard changed throughout the whole ripening period. Significance of the Study: Knowledge of the spatial-temporal variation of the anthocyanin content within a vineyard may assist decision making regarding sampling and vineyard management, especially if vineyard segmentation is employed.