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Research Article

Remote Sensing, Yield, Physical Characteristics, and Fruit Composition Variability in Cabernet Sauvignon Vineyards

Brent Sams, Robert G.V. Bramley, Luis Sanchez, Nick Dokoozlian, Christopher Ford, Vinay Pagay
Am J Enol Vitic. April 2022 73: 93-105; published ahead of print February 24, 2022 ; DOI: 10.5344/ajev.2021.21038
Brent Sams
1School of Agriculture, Food, and Wine, Waite Research Institute, University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia;
2Department of Winegrowing Research, E&J Gallo Winery, Modesto, California, USA;
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  • For correspondence: brent.sams@ejgallo.com
Robert G.V. Bramley
3CSIRO, Waite Campus, Locked Bag 2, Glen Osmond, SA 5064, Australia.
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Luis Sanchez
2Department of Winegrowing Research, E&J Gallo Winery, Modesto, California, USA;
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Nick Dokoozlian
2Department of Winegrowing Research, E&J Gallo Winery, Modesto, California, USA;
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Christopher Ford
1School of Agriculture, Food, and Wine, Waite Research Institute, University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia;
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Vinay Pagay
1School of Agriculture, Food, and Wine, Waite Research Institute, University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia;
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    Figure 1

    Principal component analysis (PCA) of fruit components (anthocyanins, β-damascenone, C6, malic acid, polymeric tannins, quercetin glycosides, and yeast assimilable nitrogen [YAN]) measured in four vineyards (A, B, C, and D) in the Lodi region of California in 2018 and 2019: (A) relationships among nonstandardized variables (loadings), (B) distribution of data points from each vineyard and season (scores), (C) relationships among standardized (µ = 0, σ = 1) variables (loadings), and (D) distribution of standardized (µ = 0, σ = 1) data points from each vineyard and season (scores). n = 1000 (125/vineyard/year).

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    Figure 2

    Principal component analysis (PCA) of fruit components (anthocyanins, β-damascenone, C6, malic acid, polymeric tannins, quercetin glycosides, and yeast assimilable nitrogen [YAN]), yield components (total vine yield and average berry weight by vine), and canopy characteristics (fruit-zone photosynthetically active radiation [PARFZ] and pruning weights) collected and measured from four vineyards (A, B, C, and D) in the Lodi region of California in 2018 and 2019 (standardized by vineyard and season, µ = 0, σ = 1) at (A) bloom (modified Eichhorn-Lorenz [E-L] stage 23 [Pearce and Coombe 2004]), (B) fruit set (modified E-L stage 27), and (C) veraison (modified E-L stage 35), highlighting different sources of remote sensing data (high-resolution canopy temperature [HRCT], high-resolution normalized difference vegetation index [HRNDVI], Sentinel-2 normalized difference vegetation index [S2NDVI], and Landsat 8 normalized difference vegetation index [LS8NDVI]). n = 796.

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    Figure 3

    Spatial variation of the normalized difference vegetation index (NDVI) derived from four sources of imagery in a vineyard (Vineyard C) in the Lodi region of California at veraison (modified Eichhorn-Lorenz [E-L] stage 35 [Pearce and Coombe 2004]) in 2019 showing (A) high-resolution canopy temperature (HRCT), (B) high-resolution NDVI (HRNDVI), (C) Sentinel-2 NDVI (S2NDVI), and (D) Landsat 8 NDVI (LS8NDVI). Note that the map data have been classified as quantiles (20th percentiles).

  • Figure 4
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    Figure 4

    Principal component analysis (PCA) of fruit components (anthocyanins, β-damascenone, C6, malic acid, polymeric tannins, quercetin glycosides, and yeast assimilable nitrogen [YAN]), yield components (total vine yield and average berry weight by vine), and canopy characteristics (fruit-zone photosynthetically active radiation [PARFZ] and pruning weights) measured in 2018 and 2019 in four vineyards in the Lodi region of California and standardized by season (µ = 0, σ = 1), (A) Vineyard A, (B) Vineyard B, (C) Vineyard C, and (D) Vineyard D. n = 250.

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    Figure 5

    Principal component analysis (PCA) of fruit components (anthocyanins, β-damascenone, C6, malic acid, pH, polymeric tannins, quercetin glycosides, titratable acidity [TA], total soluble solids [TSS], and yeast assimilable nitrogen [YAN]), yield components (total vine yield, average cluster weight by vine, cluster number per vine, and average berry weight by vine), canopy characteristics (fruit-zone photosynthetically active radiation [PARFZ], pruning weights, and Ravaz index), soil texture (clay, sand, and silt), apparent electrical conductivity (ECa), and elevation data measured in 2018 and 2019 from four vineyards (A, B, C, and D) in the Lodi region of California, aggregated and standardized (µ = 0, σ = 1) by vineyard and season. n = 1000.

Tables

  • Figures
  • Table 1

    Regional growing degree days, precipitation, radiation, and reference evapotranspiration (ETo) by annual quarters from 2017 to 2019 in the Lodi region of California.

    Table 1
  • Table 2

    Physical details, cultural practices, vine characteristics, and irrigation rates applied in 2017 to 2019 in four vineyards in the Lodi region of California.

    Table 2
  • Table 3

    Fruit composition, crop characteristics, and soil texture measured in 2017 to 2019 in four vineyards in the Lodi region of California.a

    Table 3
  • Table 4

    Pearson correlations accounting for spatial autocorrelationa between fruit composition and yield components, canopy characteristics, soil, elevation, and different sources of imagery combining measurements collected in 2018 and 2019 in the Lodi region of California.

    Table 4
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Remote Sensing, Yield, Physical Characteristics, and Fruit Composition Variability in Cabernet Sauvignon Vineyards
Brent Sams, Robert G.V. Bramley, Luis Sanchez, Nick Dokoozlian, Christopher Ford, Vinay Pagay
Am J Enol Vitic.  April 2022  73: 93-105;  published ahead of print February 24, 2022 ; DOI: 10.5344/ajev.2021.21038

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Remote Sensing, Yield, Physical Characteristics, and Fruit Composition Variability in Cabernet Sauvignon Vineyards
Brent Sams, Robert G.V. Bramley, Luis Sanchez, Nick Dokoozlian, Christopher Ford, Vinay Pagay
Am J Enol Vitic.  April 2022  73: 93-105;  published ahead of print February 24, 2022 ; DOI: 10.5344/ajev.2021.21038
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