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62 WINES&VINES July 2018 GRAPEGROWING WINE EAST ally, several other VIs were correlated to water-status variables. 37 Nutritional deficien- cies also have been detected by UAVs; e.g., NDVI was correlated with levels of iron chlo- rosis, carotenoid pigments in leaves, and an- thocyanins in leaves and grape berries. 21 More recently a crop water-stress index was calcu- lated based upon a combination of leaf ψ and stomatal conductance and was associated with UAV-derived thermal images. 29 UAVs have been successfully linked to wireless sensor networks to assess correlations between VIs and thermal zones in vineyards. 6 Recently in Ontario, UAVs were used to examine correlative relationships between VIs and several vineyard variables. 25 Vine size, LT 50 (winter hardiness), yield, berry weight and berry composition data were correlated in sev- eral vineyards to NDVI and other data acquired with the UAV and GreenSeeker, while soil and vine water status, and yield components showed direct relationships with NDVI. Spatial relationships were apparent from examination of the maps. Multivariate statistical analysis (e.g., principal components analysis; PCA) confirmed these relationships. Spatial analysis also was performed (e.g., Moran's I and k-means clustering) to verify the existence of actual discrete zones in the study vineyards. The NDVI values were considerably higher in GreenSeeker maps vs. those from UAV flights, primarily because reflectance data were acquired from the sides of the canopies with GreenSeeker and from the tops of the canopies with UAVs. Vine water status and several fruit-composition variables were cor- related with UAV-derived NDVI. This suggests that UAVs have significant potential to identify zones of superior fruit composition and poten- tial wine quality. Case study in Ontario We used this technology in a case study of vineyards in Ontario to create maps illustrating spatial variability in both ground-based vari- ables and UAV data, and its usefulness in un- derstanding and improving potential wine quality. The Ontario wine industry produces about 65,000 tons of grapes and consists of cultivars such as Riesling, Chardonnay, and Cabernet Franc, with lesser quantities of Merlot, Caber- net Sauvignon, Sauvignon Blanc, Pinot Gris, and Pinot Noir (grapegrowersofontario.com). Soils are characterized as variable, a result of widespread glacial activity more than 10,000 years ago, and consequently many vineyards are situated on several soil series that can range widely in texture, depth and water-holding capacity. 13 This variability can impact vine vigor, yield and water status. A 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 specific 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 1 hectare, that might be capable of producing extremely high- value wines based upon yield, vine size or water-status-based quality levels. Researchers chose six vineyards each of Cabernet Franc and Riesling (1 to 2 ha in area) in different Niagara sub-appellations. The sites included the following sub-appellations: Ni- agara Lakeshore, Creek Shores, St. Davids Bench, Lincoln Lakeshore north, Lincoln Lake- shore south, and Beamsville Bench. Soil types 13 varied substantially in these sub-appellations from well-drained, coarse- textured Tavistock and Vineland series (Niag- ara Lakeshore, Lincoln Lakeshore north), to moderately well-drained Chinguacousy (Creek Shores, Beamsville Bench), and poorly drained Jeddo (Lincoln Lakeshore south) and Beverly/ Toledo soils (St. Davids Bench). This array of soil types provided a significant range of water- holding capacities that affected vine water status. Vineyard blocks were GPS-delineated to determine shape, and 80 to 100 sentinel vines were identified within each vineyard and geo- located by GPS. Post-collection differential correction was performed to sub-meter accu- racy (about 30 to 50 centimeters). Field mea- surements and berry samples were taken on these vines over two years, and we are complet- ing our third year of data collection. Vineyard soil moisture was measured by time domain reflectometry. Measurements took place at berry set, lag phase and veraison on all sentinel vines. Vine water status was mea- Figure 4: Maps of two Ontario Cabernet Franc vineyards in 2016 showing GLRaV3 titer [quantification cycle (Cq) value] (a, c) vs. their respective corresponding UAV-based NDVI (b, d). Scale (a, b): 1 = 2167; (c, d): 1 = 1240. GORDON ROBERT a c b d