Assessing Variability in the Vineyard Through a Spatially Explicit Selective-Harvest Approach
Luca Brillante, Kaan Kurtural,* Luis Sanchez,
Runze Yu, Anita Oberholser, Charles Brennaman, and Terrence Bates
*University of California, Davis, 595 Hilgard Lane, Davis, CA
95616
(skkurtural@ucdavis.edu)
Vineyard variability is a limiting factor in commercial vineyards that leads to suboptimal management decisions. Ecophysiological variability was characterized in a commercial Cabernet Sauvignon vineyard in Sonoma County, California, using a combination of proximal sensing (NDVI, image analysis), terrain analysis by geostatistical modeling, and assessing primary and secondary metabolism of grapevines. The analysis was performed on a spatially dense grid, where primary metabolism was assessed every 10 days and proximal sensing of the vineyard was conducted monthly starting at E-L Stage 21. Primary metabolism variables were spatially interpolated and clustered in two homogeneous management zones according to similar physiological behavior. The two zones were well-separated according to 70% variance in plant water status that regressed well with the ecophysical characteristics of the study site. Secondary metabolism of berry skin and flavonol and anthocyanin composition were characterized with C18 reversed-phase HPLC. Statistical differences were observed between zones in primary and secondary metabolism, but not in yield. The grape berry in the higher-water stress zone had lower Brix, but higher anthocyanin and flavonol content occurred in the zone with less water stress. The two zones were differentially harvested and vinified in triplicate. The wines showed statistical differences in chemical composition. The spatially dense sampling approach coupled with proximal sensing showed that plant water status is a reliable variable to discriminate between management zones because of its direct effect on secondary metabolism. Selective harvest can be a straightforward method to counteract variability in vineyards when ecophysical variability is too large to coalesce with variable rate management approaches.
Funding Support: USDA-SCRI
