Applied Geomatics--connecting the pp g dots between grapevine - PowerPoint PPT Presentation

Applied Geomatics--connecting the pp g dots between grapevine physiology, terroir and remote sensing terroir, and remote sensing Andrew Reynolds, Brock University Ralph Brown, University of Guelph Matthieu Marciniak; David Ledderhoff; Jim Matthieu Marciniak; David Ledderhoff; Jim Willwerth; Javad Hakimi, Brock University

Geomatics-Oriented Projects Geomatics Oriented Projects • Chardonnay terroir (1998-2003) [Reynolds et al. Proc ASEV/ES 2001; others STILL in preparation] ASEV/ES 2001; others STILL in preparation] – Assessing within site terroir by mapping soil texture and vine vigor, and their relationships to numerous other variables (five sites) variables (five sites) • Riesling terroir (1998-2003) [Reynolds et al. AJEV 2007] – Similar goals as Chardonnay g y • Riesling terroir II (2005-). [Jim Willwerth, PhD 2010]. – Assessing within site terroir by mapping soil and vine water status (10 sites) (10 i ) • Cabernet Franc terroir (2005-). [Javad Hakimi, PhD 2009]. – Similar goals as Riesling II (10 sites) Similar goals as Riesling II (10 sites)

Projects contd Projects contd. • Thirty Bench Riesling (2006-). [ Matthieu Marciniak MSc y g ( ) [ 2010]. – Mapping six sous-terroirs in terms of water status; using low-elevation multispectral imaging to collect NDVI data (25 acres). ) • Coyotes Run/ Lowrey (2008-). [ David Ledderhof MSc 2010]. – Similar to Thirty Bench, using four Pinot noir blocks (each about 2 acres) • Stratus Vineyard (2008-). [Vickie Tasker MA 2010]. Stratus Vineyard (2008 ). [Vickie Tasker MA 2010]. – Using a combination of multispectral imaging, plus a network of soil Profile Probes and wireless temperature sensors

Ways of Extending Geomatics R Research to Industry h I d • Introducing mapping tools for Introducing mapping tools for discriminating regions within vineyards with different yields, fruit composition, y p water status, disease or insect pressure • Verifying sub-appellations y g pp • Combining this with remote sensing to identify sub-blocks of superior quality y p q y • Using identification of zonal differences to more precisely manage vineyards p y g y

Discriminating regions within vineyards with different yields fruit vineyards with different yields, fruit composition, and water status. U d Understanding the basis for terroir t di th b i f t i

Basic Procedures Using GPS to delineate blocks and to geo-locate vines

Data Collection Data Collection • Leaf water potential Leaf water potential • Soil moisture • Yield and yield components • Yield and yield components • Basic fruit composition • Specialized fruit composition—terpenes; S i li d f it iti t phenolic analytes • Weight of cane prunings W i ht f i • And more……

Data Collection Data Collection • Soil texture (sand silt clay) Soil texture (sand, silt, clay) • Soil composition (P, K, Ca, Mg, B) • Soil physical properties (pH, CEC, base S il h i l ti ( H CEC b saturation, organic matter) • Tissue elemental composition

Manipulation of the data Manipulation of the data • Using things such as leaf water potential Using things such as leaf water potential, vine size, soil texture as “treatments” (actually categories) and performing (actually categories) and performing standard ANOVA • Correlations on all variables • Correlations on all variables • Spatial correlations on spatial variability b t between variables i bl • Temporal stability

Remote Sensing Remote Sensing • Aerial flyovers collect multispectral Aerial flyovers collect multispectral reflectance data • Data are also collected on the ground to • Data are also collected on the ground to compare and verify • Aerial data need to be manipulated using A i l d t d t b i l t d i ENVI software to separate out canopy vs. soil/ cover crop reflectance il/ fl t

Riesling II Project (2005-) Jim Willwerth, PhD candidate 2010 Ji Will th PhD did t 2010 Willwerth & Reynolds Progres Agricole et Viticole 2010 accepted Project Objectives Project Objectives • Use GPS & GIS to create spatial maps of variability within 10 Riesling vineyard blocks from each of the 10 VQA sub-appellations f h f th 10 VQA b ll ti • Identify zones within vineyard blocks based mainly on vine water status and assess these for y fruit composition and wine sensory attributes • Look for relationships between vine water status and other variables and other variables • Attempt to validate the VQA sub-appellations based on sensory and chemical data

A B C “High” water status zones “Low” water status zones Spatial distribution of leaf water potential (-bars) Myers Vineyard Vineland ON; Spatial distribution of leaf water potential (-bars), Myers Vineyard, Vineland, ON; A: 2005; B: 2006; C: 2007. Consistent zones; temporally stable.

A B C High water status Low water status Spatial distribution of berry weight (g), Myers Vineyard, Vineland, ON; A: 2005; B: 2006; C:2007. Higher LWP = higher berry weight.

A B C Spatial distribution of berry Brix, Myers Vineyard, Vineland ON; A: 2005; B: 2006; C: 2007. Low LWP = highest Brix.

A B C Spatial distribution of berry titratable acidity (g/L), Myers Vineyard, Vineland, ON; A: 2005; B: 2006; C:2007. Low LWP = lowest TA.

A B C Spatial distribution of leaf water potential (-bars), Chateau des Charmes (Paul Bosc Estate), Niagara-on-the-Lake, ON; A: 2005; B: 2006; C: 2007. Once again, temporally stable spatial patterns.

A B C Spatial distribution of berry potentially volatile terpenes (mg/L), Chateau Spatial distribution of berry potentially volatile terpenes (mg/L), Chateau des Charmes (Paul Bosc Estate), Niagara-on-the-Lake, ON; A: 2005; B: 2006; C: 2007. Low LWP = highest PVT.

Sensory Map of Significant Sensory Attributes, Twenty Mile Bench; 2005 , y ;

Factors contributing to sensory profile p Soil and vine water status responsible for 75% of the variability in the data set

Verifying sub-appellations Verifying sub-appellations

Cabernet Franc Project J Javad Hakimi, PhD 2009 d H ki i PhD 2009 Hakimi and Reynolds AJEV 2010 in press Project Objectives j j • Use GPS & GIS to create spatial maps of variability within 10 Cabernet Franc vineyard blocks from each of the 10 VQA sub-appellations each of the 10 VQA sub-appellations • Identify zones within vineyard blocks based mainly on vine water status and assess these for fruit composition and wine sensory attributes iti d i tt ib t • Look for relationships between vine water status and other variables a d ot e a ab es • Attempt to validate the VQA sub-appellations based on sensory and chemical data

PCA of Sensory Data, Cabernet Franc 2005 Green bean associated with high water potential Lakeshore or riverfront sites Variables (axes F1 and F2: 63.94 %) ( ) Observations (axes F1 and F2: 63.94 %) ( ) 1 bell pepper BELL PEPPER 3 Harbour George green bean black pepper 0.75 Cave sp Acidity 2 GREEN BEAN 0 5 0.5 Reif BLACK 1 black currant Color CHERRY High water status 0.25 black cherry 26.13 %) 26.13 %) 0 0 F2 (2 Buis Buis F2 (2 BLACK Hernder Astringency -1 CURRANT Bitterness BLACK -0.25 PEPPER Low water status red fruit -2 Vieni -0.5 HOP -3 RED FRUIT -0.75 CDC -4 -1 -4 -3 -2 -1 0 1 2 3 4 5 6 -1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1 F1 (37.81 % ) F1 (37 81 %) F1 (37.81 %)

Partial Least Squares (PLS) Partial Least Squares (PLS) Correlations with t on axes t1 and t2 (84.3%) 1 sand Hue Astringency 0.75 Bl CURRANT Berry wt y Clusters Color Color TA 0.5 Yield Bitterness BLACK PEPPER RED FRUIT green bean red fruit 0.25 SM 24.3%) Color BELL PEP WP 0 t2 2 Acidity Acidity pH GREEN BEAN bell pep -0.25 Phenols BS Ca bl cherry OM Soil pH -0.5 Brix black pepper vine size CEC CEC P -0.75 Anthocyanin clay K BLACK CHERRY bl currant Mg -1 -1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1 t1 (60 0% t1 (60.0% ) )

Using remote sensing to identify sub blocks of superior quality sub-blocks of superior quality

Thirty Bench Project Matthieu Marciniak, MSc candidate 2010 M tthi M i i k MS did t 2010 Reynolds et al. Progres Agricole et Viticole 2010 accepted Project Objectives • Correlate remotely sensed spectral data to vineyard characteristics and fruit & wine i d h t i ti d f it & i composition of Riesling • Use GPS & GIS to create spatial maps of Use GPS & GIS to create spatial maps of variability within vineyard blocks • Identify zones for premium wine production y p p and/or precision management zones within vineyard blocks based mainly on vine water status status

Thirty Bench- View of the Study Site y y Courtesy Ralph Brown

Sentinel Vines Sentinel Vines

Spatial variation in soil moisture over four vintages over four vintages Temporal stability is apparent (orange areas = lowest soil moisture 2007-0; blue = lowest 2009) S il M i t Soil Moisture 2006 2006 S il M i t Soil Moisture 2007 2007 Soil Moisture 2008 Soil Moisture 2009