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82 WINES&VINES February 2017 GRAPEGROWING WINE EAST T he Ontario wine industry produces approxi- mately 80,000 tons of grapes each harvest, primarily from cultivars such as Riesling, Chardonnay and Cabernet Franc, with lesser quantities of Merlot, Cabernet Sauvignon, Pinot Gris and Pinot Noir. Soils are variable as a result of widespread glacial activity more than 10,000 years ago, and many vineyards are situated on several soil series that can differ widely in terms of texture, depth of solum (upper layers of the soil profile) and water-holding capacity. 1 This variability in soil characteristics can impact vine vigor, yield and perhaps water status. Significant growth in the number of small artisanal winer- ies has permitted production of wines that are unique to individual vineyard sites and, in some cases, unique to spe- cific vineyard blocks. In the past 10 to 15 years, this interest has expanded to include identification of unique portions of vineyard blocks (some less than 2 or 3 acres) that might be capable of producing extremely high-value wines based on yield, vine size or water status-based quality levels. Large vineyards are variable with respect to soil texture, moisture and depth as well as organic matter, cation exchange capacity and major and minor elements. Consequently, vineyards vary spatially in vigor, yield and fruit composition. Since 1998, research in Ontario has produced spatial maps that have quantified spatial variability in numerous vineyards with respect to soil composition, vine elemental composition, vigor, vine water status, vine winter hardiness, yield and berry composition. 2-5 Moreover, these variables have been analyzed to determine relevant spatial correlations among them. Maps showing clear vine size, yield and vine water status zones have allowed production of wines from these unique zones that are both chemically and sensorially different. Geospatial technologies include a range of information tools (such as sensing devices capable of detecting electro- magnetic radiation including visible and infrared light) that permit the acquisition, analysis, management and visualiza- tion of geospatial data. This information can help growers modify farming practices so they can move toward the con- cept of precision agriculture. When geospatial technologies are applied to viticulture, there is a focus on understanding the spatial and temporal variability in the production of wine grapes in order to achieve ideal optimization of vineyard functionality and to apply a precision agriculture approach to both viticultural practices and winemaking. 6 As a result of increased availability, geospatial technologies are now widely utilized in wine grape production regions such as California, 7 Australia, 8,9 New Zealand, 10 Spain, 11 France 12-14 and Ontario, Canada, 2,3,15-17 and those technologies have proven to be a practical implementation tool for making ob- servations about vineyard vegetative growth and grape com- position. 18 However, remote sensing image acquisition from satellite or airplane platforms requires complicated and time- consuming data processing (such as the manual delineation of rows), 19 is restricted by weather conditions 20 and requires appropriate ground-truthing. 21 There are other sources of imprecision, such as inter-row soil and shadow interference, 20 masking of non-vine pixels (e.g., cover crop) to assess the vine-specific normalized difference vegetation index (NDVI) 4,15,16 and, most importantly, information may not be Long-Distance Learning Using proximal sensing technology to map vineyard variability By Andrew G. Reynolds, Elena Kotsaki, Ralph Brown, Marilyne Jollineau and Hyun-Suk Lees CAVE SPRING RIESLING Individual values on maps from 2015 represent highest and lowest values for the respective color zones. Individual points represent the sentinel vines. NDVI Yield (kg) Cluster weight (kg) FVT (mg/L) 0.81-0.82 0.74-0.77 2.5-4.4 5.5-7.1 0.19-0.25 0.12-0.16 0.23-031 0.56-0.63