Applied Geomatics--connecting the dots between grapevine physiology,

Transcription

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

2 Geomatics-Oriented Projects Chardonnay terroir ( ) [Reynolds et al. Proc 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) Riesling terroir ( ) [Reynolds et al. AJEV 2007] Similar goals as Chardonnay Riesling terroir II (2005-). [Jim Willwerth, PhD 2010]. Assessing within site terroir by mapping soil and vine water status (10 sites) Cabernet Franc terroir (2005-). [Javad Hakimi, PhD 2009]. Similar goals as Riesling II (10 sites)

3 Projects contd. Thirty Bench Riesling (2006-). [Matthieu Marciniak MSc 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]. Using a combination of multispectral imaging, plus a network of soil Profile Probes and wireless temperature sensors

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

5 Discriminating regions within vineyards with different yields, fruit composition, and water status. Understanding di the basis for terroir

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

7 Data Collection Leaf water potential Soil moisture Yield and yield components Basic fruit composition Specialized fruit composition terpenes; t phenolic analytes Weight of cane prunings And more

8 Data Collection Soil texture (sand, silt, clay) Soil composition (P, K, Ca, Mg, B) Soil physical properties (ph, CEC, base saturation, organic matter) Tissue elemental composition

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

10 Remote Sensing Aerial flyovers collect multispectral reflectance data Data are also collected on the ground to compare and verify Aerial data need to be manipulated using ENVI software to separate out canopy vs. soil/ cover crop reflectance

11 Riesling II Project (2005-) Jim Willwerth, PhD candidate 2010 Willwerth & Reynolds Progres Agricole et Viticole 2010 accepted Project Objectives Use GPS & GIS to create spatial maps of variability within 10 Riesling vineyard blocks from 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 Look for relationships between vine water status and other variables Attempt to validate the VQA sub-appellations based on sensory and chemical data

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

13 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.

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

15 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.

16 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: Once again, temporally stable spatial patterns.

17 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: Low LWP = highest PVT.

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

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

20 Verifying sub-appellations

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22 Cabernet Franc Project Javad Hakimi, i PhD 2009 Hakimi and Reynolds AJEV 2010 in press Project Objectives Use GPS & GIS to create spatial maps of variability within 10 Cabernet Franc vineyard blocks from 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 t Look for relationships between vine water status and other variables ab Attempt to validate the VQA sub-appellations based on sensory and chemical data

23 PCA of Sensory Data, Cabernet Franc 2005 Green bean associated with high water potential Lakeshore or riverfront sites F2 ( %) Variables (axes F1 and F2: %) Observations (axes F1 and F2: %) green bean GREEN BEAN BELL bell PEPPER pepper black pepper Color Astringency Bitterness Acidity BLACK black currant CHERRY black cherry BLACK BLACK CURRANT PEPPER red fruit F2 ( %) Harbour Buis George Reif High water status Vieni Hernder HOP Low water status Cave sp RED FRUIT -3 CDC F1(3781%) ( F1 (37.81 %)

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

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

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

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

30 Sentinel Vines

31 Spatial variation in soil moisture over four vintages Temporal stability is apparent (orange areas = lowest soil moisture ; blue = lowest 2009) Soil ilmoisture 2006 Soil ilmoisture 2007 Soil Moisture 2008 Soil Moisture 2009

32 Spatial variation in leaf ψ over four vintages Again temporal stability is apparent, as are spatial correlations between soil moisture and leaf ψ; yellow and orange areas are highest absolute values of leaf ψ (i.e. most negative or lowest) Leaf Water Potential 2006 Leaf Water Potential 2007 Leaf Water Potential 2008 Leaf Water Potential 2009

33 Yield Once again, clear temporal stability is present (yellow/orange areas are highest yields)

34 Weight of cane prunings 2009 Some inverse spatial correlations with water potential and soil moisture

35 Brix and TA WPB WPY SPB LE SPY Triangle Brix. Has been temporally consistent over three vintages. Note the higher Brix (orange) in the low water status zones WPB 11.3 WPY SPB SPY Triangle LE consistent Titratable acidity. Also has been temporally over three vintages. Note the lower TA (blue) in the low water status zones

36 Potentially-volatile terpenes 2006 Highest in the low-vigor zones WPB WPY 2.0 SPB SPY Triangle 2.3 LE

37 Potentially-volatile terpenes 2009 y p Once again highest in the low-vigor zones, particularly Steel Post & Triangle

38 Spatial Correlations between variables within the same vintage Low leaf water potential associated with higher Brix values Berry Brix 2007 Leaf Water Potential 2007 Note: Orange areas represent highest Brix and highest absolute values of water potential (i.e. most negative or lowest)

39 Yield and NDVI green 2006 A clear and temporally stable relationship between the two variables WPB 3.91 WPY SPB 3.17 Yield. Y zones (high vigor) = high-yielding too 6.90 SPY NDVI green

40 Leaf water potential and NDVI 2006 Spatial patterns and relationships that are temporally stable WPB WPY Mean leaf water potential (absolute value) SPB SPY NDVI green

41 NDVI green 2008 g Temporally stable compared to previous years

42 NDVI 2009 Once again, temporal stability was apparent relative to prior years NDVI green NDVI red

43 Differences between small lot blocks Variables (axes D1 and D2: %) after Varimax rotation Observations (axes D1 and D2: %) after Varimax rotation Berry ph Berry Brix Vine Size Berry wt 2 Triangle D2 (25.98 %) Yield M ean WP Berry TA Berry FVT Berry TVT Berry PVT M ean SM Elevat ion D1 (41.97 %) D2 (25.98 %) 1 0 SPB WP B LE WP Y -1 SPY D1 (41.97 %) The Triangle Block has consistently won the most awards at Ontario wine The Triangle Block has consistently won the most awards at Ontario wine competitions. Might we then use remote sensing to pick out blocks like Triangle in other cultivars?

44 Using remote sensing to identify sub-blocks blocks of superior quality in red wine cultivars

45 Coyotes Run/ Lowrey Project (Images and text courtesy David Ledderhof MSc candidate 2010) Project Objectives Correlate remotely sensed spectral data to vineyard characteristics and fruit & wine composition of Pinot noir Use GPS & GIS to create spatial maps of variability within vineyard blocks Identify zones for premium wine production and/or precision management zones within vineyard blocks

46 Study Sites and Vineyard Data Collection Study sites Coyote's Run: Red Paw & Black Paw Vineyards (three blocks) Lowrey's Farm (one block) Variety of soil types, age of vines, clones Data collection Geolocating Sentinel Vines Soil Sample Collection & Analysis Aerial Image Capture (x4 in 2008 and 2009) TDR - Soil Moisture Pressure P Bomb Vine Water Status t Ground-based Leaf Reflectance

47 Relative Location of Blocks: St. David's, Ontario Image Source: Niagara Navigator {

48 Images: July 29, 2008 Coyote's Run Pinot noir

49 Sample Results: Red Paw 2 % silt % sand % clay yield Leaf ψ NDVI-red Note: Different scale for each map

50 Red Paw 2 NDVI The challenge-extracting challenge e tracting NDVI data from co coverer cropped vineyards without assessing the cover crop R dp Red Paw 2 NDVI Red Paw 2 masked NDVI Red Paw 2 NDVI map

51 Using identification of zonal differences to more precisely manage vineyards

52 Stratus Vineyards Project (Vickie Tasker, MA 2010 pending) Project Objectives Correlate remotely sensed spectral data to vineyard characteristics and fruit & wine composition of several Vitis vinifera cultivars (Chardonnay, Cabernet Franc, Semillon) Use GPS & GIS to create spatial maps of variability within vineyard blocks Set up a network of wireless temperature sensors and corresponding Profile Probe sites on a grid throughout the vineyard Attempt to see if localized soil moisture and/or canopy temperatures have major impacts upon fruit composition

53 Stratus Vineyards Project Other Project Objectives Evaluate airborne digital imagery for the purpose of determining canopy variability and spatial patterns of interest in the vineyard. Develop a thermal environment map of the Stratus vineyard based upon in-situ temperature sensors at the canopy and soil level and aerial thermal infrared imagery. Develop a GIS database for Stratus that incorporates all currently available soils, drainage, and vine (clone, age and rootstock), in a format that is consistent with overlaying digital airborne remote sensing maps.

54 Stratus- General Soils and Varieties Images courtesy Ralph Brown

55 CF1

56 CF2

57 CH1

58 Conclusions Geomatics has allowed us to conclude that the so-called terroir effect is based highly on vine water status This technology has permitted verification of sub-appellations in the Niagara region Coupling this with remote sensing might provide a method to identify premium sub-blocks bl based on e.g. water status using NDVI measurement In every e instance, any vineyard variable ab can be mapped and this spatial variability can be checked for temporal stability permitting implementation of precision viticulture