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

doi:10.5344/ajev.2021.21038

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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 [PAR_FZ] 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 [HR_CT], high-resolution normalized difference vegetation index [HR_NDVI], Sentinel-2 normalized difference vegetation index [S2_NDVI], and Landsat 8 normalized difference vegetation index [LS8_NDVI]). 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 (HR_CT), (B) high-resolution NDVI (HR_NDVI), (C) Sentinel-2 NDVI (S2_NDVI), and (D) Landsat 8 NDVI (LS8_NDVI). Note that the map data have been classified as quantiles (20th percentiles).
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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 [PAR_FZ] 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 [PAR_FZ], pruning weights, and Ravaz index), soil texture (clay, sand, and silt), apparent electrical conductivity (EC_a), 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

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Table 1
Regional growing degree days, precipitation, radiation, and reference evapotranspiration (ET_o) by annual quarters from 2017 to 2019 in the Lodi region of California.
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 3
Fruit composition, crop characteristics, and soil texture measured in 2017 to 2019 in four vineyards in the Lodi region of California.^a
Table 4
Pearson correlations accounting for spatial autocorrelation^a 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.