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88 WINES&VINES February 2017 GRAPEGROWING WINE EAST - B E C O PA D - Y E A S T & E N Z Y M E S - C R U S H PA D E Q U I P M E N T - S T E R I L E F I LT R AT I O N - W I N E R Y H O S E - O A K A LT E R N AT I V E S EASTERN WINE LABS Serving the Analytical needs of East Coast Wineries WWW.EASTERNWINELABS.COM Ph 609-859-4302 Cell 609-668-2854 chemist@easternwinelabs.com AOAC Member WineDoc ® WINERY CONSULTING • Winery Design • Business Plans • Building & Floor Plans—New or Expansion • Equipment & Lab Specs • Assistance for Winemaking (859) 533-8759 Tom@winedoc.com www.winedoc.com Maps confirmed to a great ex- tent the relationships established by statistical analysis. Among all variables examined, SM showed the highest degree of temporal consistency across the years, fol- lowed by NDVI and berry-compo- sition variables. In spite of the fact that not all of the variability in the dataset was accounted for by the PCAs (<45%), the relationships revealed were consistent across the methods examined and vin- tages and in agreement with cur- rent literature. The fundamental intent of pre- cision viticulture is to delineate management zones, often based on clustering techniques such as k-means clustering. Coupled with PCA, k-means clustering for the NDVI further highlighted natural grouping structures associated with important yield and berry composition variables (i.e., phe- nolics and monoterpenes). If this study had been combined with a sensory evaluation of wines pro- duced from the k-means-derived NDVI zones, differences in wine sensory quality attributes might have been profound. Moran's I results (see table on page 86) fur- ther supported the premise that SM and NDVI followed clustering patterns at the within field scale. The theory underlying the NDVI calculation is that the pho- tosynthetically active foliage ab- sorbs sunlight in the visible blue and red wavelengths and gives a strong reflectance in NIR wave- lengths, a portion of the electro- magnetic spectrum not detectable by the human eye; grapevine canopies encounter many envi- r o n m e n t a l b i o p h y s i c a l c o n - straints such as low soil water availability, and therefore reflect less light (low NDVI). 25 Results acquired by proximal sensing are considered sufficient in terms of repeatability and cor- relations. These results suggest that yield components and vine size were important factors in NDVI variability, while berry composition variables (i.e., an- t h o c y a n i n s , c o l o r, p h e n o l s , monoterpenes) showed strong consistent inverse relationships with NDVI. Therefore, proximal sensing's usefulness was exhib- ited not only through consis- t e n c y o f t h e r e l a t i o n s h i p s established, but also through the simplicity of the procedure with respect to image acquisition, easier applicability and higher resolution than remote sensing technology, along with lower operating costs. Maps produced for all vari- ables strongly demonstrated the spatial variability in the vineyard scale, which indicates that zonal management could be potentially feasible. Proximal sensing tech- nology will influence future agri- cultural systems by providing optimal vineyard functionality resulting in ideal harvests for bet- ter winemaking. Three of the authors of this article are at the Cool Climate Oenology and Viticulture Institute, Brock University, St. Catharines, Ontario, Canada. Andrew G. Reynolds is professor of biological sciences/viticul- ture; Elena Kotsaki is a graduate student, and Hyun-Suk Lee is a technical assis- tant. Marilyne Jollineau is an associate professor in the Geography Department at Brock University, and Ralph Brown is a professor in the School of Engineering at University of Guelph in Ontario. Thanks are given to Lambert Farms, Coyote's Run Winery and Cave Spring Cellars for their cooperation. Thanks also to Trimble Navigation Ltd. for the loan of the GreenSeeker system and to Colin Christmas, Michael Daleo and Ken Chapman who assisted with map con- struction. Funding was provided by On- tario Research Fund. Berry composition variables such as color, anthocyanins and phenols were inversely correlated with NDVI. References for this article are available online at winesandvines.com.