Fruit ripening in grapevine is asynchronous within an individual vine and varies widely between vines within a management unit. Variation within the grapevine is commonly as large as that between grapevines (

Partitioning of resources among organs within a grapevine reflects the biophysics of the underlying processes: synthesis and utilisation, and processes driving the flow from source to sink. Grapevine is likely similar to willow (

Assimilate accumulation is determined by three confounding characteristics of the vine (

Distal shoot dominance may be responsible for ripening trends related to distance from the crown in cane-pruned systems (

Much attention has been given to the individual vine concerning exposed leaf area and carbon allocation to fruit (

Surprisingly few studies have compared the impact of

In this study, we used statistical models of increasing complexity to assess fruit composition on individual stems to provide insight into biomass and architectural parameters and how they interact to influence berry composition at harvest. We use the insight gained to evaluate contemporary indices of vine management: harvest index and leaf area models.

Samples for this study were collected from Jindong Estates vineyard in the Jindong subregion (33°45’S, 115°14’E) of the Greater Margaret River Wine Region, Western Australia. The climatology is Mediterranean (

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Statistic
Number of observations
Minimum
Maximum
Mean
Standard deviation
Vegetative biomass (vigour) (leaf area m² vine
^{–1})9
3.22
11.73
6.09
2.87
Reproductive biomass (yield) (fruit weight kg vine
^{–1})9
1.77
10.67
6.37
3.18
Vine balance (leaf area:fruit weight cm
^{2} g^{–1})9
6.57
19.20
10.87
4.02
Canopy density (stems m
^{–1})9
15.06
30.82
23.38
5.63

Leaf area per stem was estimated using a linear equation relating leaf area to stem diameter using pipe theory as the basis (^{®} Leaf Area Meter (Nebraska, USA). Stem diameter was calculated using digital Vernier callipers on the same stems at the middle of the second clear node using the equation for an ellipse. A linear regression was calculated between actual leaf area and stem diameter as y = 14.68x + 172.80 (R² = 0.63). The leaf area for all remaining stems with known stem diameters measured in the field at the same time point was estimated in this way (Note: the equation is site- and variety-specific). This methodology allows for the development of a model for non-destructive measurement of stem leaf area. For clarity of nomenclature, ‘stem' refers to the vegetative anatomy of the grapevine (season growth) and ‘shoot' refers to whole-of-season growth, including vegetative and reproductive anatomy.

Thirty clusters were picked by hand from each of nine vines (2004). If a vine yielded more than 30 clusters, then 30 clusters were selected at random from evenly spaced positions along the cordon, taking care to avoid operator bias in choosing cluster position and exposure. If there were less than 30 clusters, all clusters were collected, and appropriate statistical measures used (such as Games-Howell pairwise comparisons). The length of the vine’s cordon was measured from the beginning to the end of the permanently trellised cordon (± 0.01 m).

The remaining clusters on each vine were counted, weighed, and used to calculate total vine fruit biomass. Sample collection was as close to harvest as possible and determined by the operating viticultural company to meet harvesting schedule requirements. All clusters were hand-harvested into labelled zip-lock plastic bags and cooled in batches as practical. The samples were kept in a cool room overnight and transported to the laboratory the following day for freezing (-20 °C). Individual stem weights were recorded (± 0.01 g).

Clusters were thawed and gently warmed over warm water to release any precipitated tartrates (^{®} Japan). Anthocyanins and phenolics were determined spectrophotometrically (

Data analysis was carried out using Microsoft Excel^{®}, Addinsoft XLSTAT^{®} and the R statistical environment (

The maturity data per whole vine used for all subsequent analyses are summarised in Figure 1. The box plots highlight the spread of the individual cluster maturity data around the mean and the spread between individual vines. The whole-vine maturity trend by leaf area is generally negative, but values increase at first and then decrease as leaf area increased (respective R^{2} and p-values for °Brix (0.27, 0.148), anthocyanins (mg g^{–1} berry) (0.47, 0.042) and phenolics (absolute units g^{–1} berry) (0.45, 0.049). The range of values within an individual vine is approximately equivalent to between vines with leaf areas less than ~10.00 m^{2}.

The interrelationships for all sampled shoots for all vines are displayed as a matrix of bi-plots, correlations and fits (linear) of the relationships between the maturity variables and stem and fruit descriptors (Figure 2). The diagonal cells display the variable name code and a histogram; those below the diagonal, a bivariate scatter plot with a linear fit; and those above the diagonal present the Pearson coefficients of determination (R^{2}) and their statistical significance.

Berry count (BeCl) was predictive of cluster mass (ClM), and stem mass (StM) was predictive of both (BeCl and ClM). Similarly, anthocyanins (Antho) were predictive of phenolics (Pheno) and vice versa. Sugar accumulation (°Brix) correlated with phenolic and anthocyanin concentrations, but the distributions were unique, though near normal; anthocyanins had the greatest variance and °Brix the least. Tests showed multi-collinearity between ClM and BeCl and maturity indicators with variance inflation factors of 39.3 and 33.7, respectively. From this point on, the number of berries per cluster was excluded from further model building.

°Brix displayed no clear relationship with any of the measured parameters. While generally skewed to the left, anthocyanins and phenolics tended to be negatively related to the correlate: cluster, berry or stem mass, for example. The reproductive parameters of berry count, berry mass and cluster mass generally increased with stem leaf area.

The vine descriptors were skewed to the left: stem mass, cluster mass and °Brix were statistically correlated with shoot position but accounted for a low fraction of variance (< 5 %). Stem mass and leaf area were skewed strongly to the left, and while the two were correlated statistically, the correlation only accounted for about 10 % of the variation,

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Maturity indicator (dependent variable)
Test statistic
Pos
ClM
BeM
StM
StLA
°Brix
BP
11.13
3.68
36.71
NS
NS
p-value
< 0.001
0.05
< 0.001
Anthocyanins
BP
6.86
13.42
23.19
5.79
12.30
p-value
< 0.01
< 0.001
< 0.001
< 0.05
< 0.001
Phenolics
BP
NS
16.71
39.78
6.56
6.59
p-value
< 0.001
< 0.001
0.01
0.01

Sugar as total soluble solids (°Brix); Colour as anthocyanins (mg g^{–1} fruit); Flavonoids as phenolics (absolute units g^{–1} berry); Pos, position along cordon measured as distance from head of vine (permanent cordon-trained) (cm); ClM, mass of whole cluster (g); BeM, average individual berry mass per cluster (g); StM, mass of stem (g); StLA, leaf area per stem (cm²); NS, not significant.

Bivariate analysis is useful for data exploration and to describe the relationships between discrete independent variables, yet it has limitations when multiple parameters interact. Thus, a multivariate approach was adopted using partial least-squares (PLS) regression to rank the contribution of individual grapevine shoot components to explaining variability in the individual grape cluster maturity (Figure 3). Additionally, PLS was used to assist in parameter number reduction and to minimise the collinear influence of the reproductive and vegetative components of the model (identified in Figure 2).

Q^{2} cumulative model strength (highest value) was used to select the number of components (Components 1 to 4 = 0.155, 0.178, 0.161, 0.157), which is similar to R^{2} as an estimate of the strength of the least-squares fit but derived from the test set; it also ranges from 0 to 1 but is relatively independent of the number of fitted variables. The lines in the correlation map exhibit the direction and strength of the relationship between the predictors and the response variables in three-dimensional space (only two presented, Figure 3A).

Clear relationships between predictor and indicator variables emerged from this approach. Strong clustering is apparent with the three compositional attributes of maturity, which were negatively related to the biomass variables: BeM and StLA (PLS t1, Figure 3A). The strength of the contribution made by each predictor variable is shown by the variable importance of the projection (VIP) value (Figure 3B). Berry mass was the strongest predictor for each maturity indicator. The model is untrustworthy if the confidence interval falls below reference line; for example, for stem leaf area (StLA), the ratio of stem leaf area to cluster mass (StLApClM), and shoot distance from the crown or start of the crodon (Pos) (Figure 3B).

It is apparent from the values presented in Figure 1 that the observed maturity values partly depended on the whole-vine leaf area. Hence, the individual vine was added to the model, and individual vine growth predictors were explored as covariates with the maturity indicator variables through analysis of covariance (Table 3). All ANOVAs were verified with the Welch statistic as a robust test of equality of means. No effects were noted.

The effect of the whole vine as a covariate was greatest in the model of each maturity parameter, indicated by a higher whole-vine Fisher’s F value than the shoot growth parameter (Table 3). The vine effects were important and additive for cluster mass and stem mass, as indicated by the significant effect of vine on the model but with no significant interaction (Table 3). However, statistically significant interactions were observed when considering individual berry mass and stem leaf area. In all instances, no interaction was expected if the differences were solely due to a temporal factor; for example, if the harvest was staged to ensure all fruit was harvested at a similar maturity. To simplify interpretation, and to place the analysis in a framework more relevant to populations of vines and vineyard management, vines were then grouped according to two popular indices describing canopy characteristics.

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Plant growth parameter
Maturity indicator
Model goodness of fit (R²)
Model Fisher’s F
Model p-value
Whole vine
Stem plant growth parameter
Stem × vine interaction
Fisher’s F
p-value
Fisher’s F
p-value
Fisher’s F
p-value
Cluster mass (g)
°Brix
0.56
18.03
< 0.001
8.93
< 0.001
–
NS
–
NS
Anthocyanins
0.75
36.73
< 0.001
26.54
< 0.001
18.45
< 0.001
–
NS
Phenolics
0.73
33.55
< 0.001
30.58
< 0.001
18.15
< 0.001
–
NS
Average individual berry mass per cluster (g)
°Brix
0.64
24.88
< 0.001
15.63
0.00
9.51
< 0.001
5.06
< 0.001
Anthocyanins
0.70
32.49
< 0.001
4.37
0.04
2.71
0.01
1.29
0.25
Phenolics
0.76
42.48
< 0.001
40.12
< 0.001
6.89
< 0.001
2.47
0.01
Stem mass (g)
°Brix
0.56
18.13
< 0.001
8.25
< 0.001
–
NS
–
NS
Anthocyanins
0.72
35.63
< 0.001
17.56
< 0.001
16.70
< 0.001
–
NS
Phenolics
0.68
29.53
< 0.001
9.85
0.00
12.17
< 0.001
–
NS
Stem leaf area (cm²)
°Brix
0.59
19.94
< 0.001
10.72
< 0.001
–
NS
2.38
0.02
Anthocyanins
0.71
33.41
< 0.001
–
NS
19.66
< 0.001
2.57
0.01
Phenolics
0.69
30.16
< 0.001
–
NS
12.91
< 0.001
2.88
0.00

NS = not significant.

Predictability of maturity by two common indices that describe winegrape canopy characteristics—harvest index (^{–1}, Ideal 2–5 m² m^{–1} and High > 5 m² m^{–1} (^{–1}) and is defined as: Low < 10 cm² g^{–1}, Ideal 10–14 cm² g^{–1} and High > 14 cm² g^{–1} (

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Canopy index
Maturity indicator
Goodness of fit (Adjusted R²)
p-value (Model)
Vine index class means and pairwise comparisons
Low
Ideal
High
Vine leaf area
°Brix
0.41
< 0.001
24.40 a
23.70 a
18.45 b
Anthocyanins
0.50
< 0.001
1.54 a
1.12 b
0.55 c
Phenolics
0.45
< 0.001
1.45 a
1.12 b
0.80 c
Vine harvest index
°Brix
0.35
< 0.001
23.70 a
20.26 b
25.33 c
Anthocyanins
0.51
< 0.001
1.12 a
0.81 b
1.75 c
Phenolics
0.40
< 0.001
1.12 a
0.99 b
1.56 c

Leaf area is ideal leaf area according to ^{–1}, Ideal 2–5 m² m^{–1}, High > 5 m² m^{–1}); Harvest index (leaf biomass and fruit biomass of whole vine cm² g^{-1}) according to ^{–1}, Ideal 10–14 cm² g^{–1}, High > 14 cm² g^{–1}). NS = not significant. Pairwise comparison groups indicated by lowercase a, b and c adjacent to LS mean, different letters indicate significant differences according to Games-Howell test.

The trends in the maturity ranking within each index contrast strongly (Table 4), and pairwise comparisons indicate significant differences between each class within each index across all maturity indicators. For the vine leaf area model, the trend in mean from Low to High was strongly negative (-25 % for °Brix, -60 % for anthocyanin content and -45 % for phenolics). Whereas for the vine harvest index model, the trends were non-linear, with the minima at the ‘ideal’ level. The High harvest index class had the highest predicted values for °Brix, anthocyanins and phenolic composition. The vine leaf area index was the best predictor of maturity of the two indices, as indicated by the greater Fisher’s F values, and was selected for further analysis.

Analysis of covariance was undertaken using the berry and stem size predictors, with whole-vine leaf area across all maturity indicators (Table 5). Berry size and stem leaf area size were selected because they showed interactions between the parameters and the individual, whole-vine covariate and a strong goodness of fit (Table 3). All models were statistically significant, with vine biomass the strongest predictor except for berry phenolic content, where berry mass was the strongest predictor (Table 5). The models are shown in Figure 4.

The trend in slope for the relationships between °Brix and both individual berry mass and leaf area per stem depended on the leaf area class of the vine: from strongly negative for low leaf area vines to nil or even positive for high leaf area vines depending on the composition component assessed (°Brix, anthocyanins or phenolics) (Figure 4). Note that high leaf area class vines had lower maturity levels than the low leaf area class vines.

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Shoot size variables
Maturity indicator
Model goodness of fit (R²)
Model p-value
Vine leaf area
Shoot size variable
Interaction
Fisher’s F
p-value
Fisher’s F
p-value
Fisher’s F
p-value
Berry mass
°Brix
0.56
< 0.001
32.23
< 0.001
5.90
0.02
14.92
< 0.001
Anthocyanins
0.56
< 0.001
15.37
< 0.001
11.83
0.00
4.32
0.01
Phenolics
0.64
< 0.001
15.79
< 0.001
58.49
< 0.001
4.62
0.01
Leaf area
°Brix
0.44
< 0.001
76.95
< 0.001
10.62
0.001
3.95
0.02
Anthocyanins
0.53
< 0.001
102.65
< 0.001
32.76
< 0.001
5.47
0.01
Phenolics
0.48
< 0.001
82.41
< 0.001
31.14
< 0.001
4.57
0.01

While the slopes depicted in Figure 4 are consistent with the information provided in Table 4, an important degree of interaction is not apparent in the previous analysis. Generally, the value of the dependent variable (°Brix, anthocyanin or phenolics) decreased as the predictor stem leaf area or individual berry mass increased. However, while the slope decreased with whole-vine leaf biomass, strong interactions are associated with average individual berry mass, especially for °Brix and anthocyanin content. Comparatively low vine leaf area significantly influenced the relationship between berry mass anthocyanins and phenolics. The inverse occurred for the relationship between stem mass and maturity indicators. Adding other within-vine plant growth predictors decreased the fit of the analysis of covariance model (data not presented: °Brix 5.5 %, anthocyanins 1.3 % and phenolics 1.0 %). Thus, there was no added value in adopting multiple independent variables to the model beyond berry size.

Table 6 summarises the strength of goodness of fit for the key models evaluated. It allows for easy comparison and demonstrates that the models with whole-vine covariates best explain the variability in the dependent variables (°Brix, anthocyanins and phenolics). In summary, the whole vine does matter; however, something to do with vine physiology or its location affects the level of maturity attained that is not captured by the two most popular grapevine canopy characteristics (leaf area and leaf area to fruit weight ratio). When considering within-vine variation, whole-vine leaf area and individual shoot berry mass together describe the most variability in maturity.

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Model components
°Brix
Anthocyanins
Phenolics
Bivariate within-vine: berry mass (Figure 2)
0.06
0.10
0.28
Multivariate all data (Figure 3)
0.12
0.20
0.34
Bivariate whole-vine: Berry mass and individual whole-vine covariate
0.64
0.70
0.76
ANCOVA: Berry mass and vine leaf area covariate (Figure 4)
0.56
0.56
0.64

Smaller grapevines produced fruit with higher intensity of composition than larger grapevines. Smaller berries exaggerated this trend. While such observations are not new at the whole-vine scale, the novelty of this study is evidence of these trends at the within-vine scale. The solute concentration of berries at maturity typically varies inversely with their size (

We demonstrated that leaf area as an estimate of whole-vine vegetative biomass best represent the characteristics of reproductive organs, as opposed to harvest index, harvest mass, or internal spatial location (

Bivariate analysis of berry composition with shoot growth parameters did not help dissect the importance of these maturity determinants. The single most important factor contributing to the scatter of values in the bivariate analysis was the individual vine, particularly the leaf area of the individual vine. This analysis helped elucidate the observed heteroscedasticity from the bivariate analysis with shoot growth parameters. This also showed that the mass of the reproductive organ, the berry, was the single most important predictor of its composition, though leaf area of individual stems within a vine also exerted a significant influence. Together, these parameters largely determined the observed within-vine variance of fruit composition: °Brix, colour (anthocyanin content) and phenolics concentration. There were complex relationships between °Brix and anthocyanins and phenolics (secondary metabolites), representing the quality potential of berries for wine production (

The solute concentration of berries at maturity typically varies inversely with their size (

Canopy geometry factors such as shading likely interacted with our measures. While this was not assessed here, its importance is well understood for anthocyanins. Anthocyanins and phenolics are strongly influenced by vegetative biomass and microclimate, and then post véraison more strongly by climatic conditions post-véraison, especially light intensity and temperature (

A limitation to interpreting our observations is that we harvested on a single date, so the fruit was not necessarily all mature. However, leaf area manipulation studies have similar limitations. Thus, the matter is more complicated than the leaf area/fruit weight model assumes and perhaps accounts for the relatively poor performance of the harvest index model in this study. It should be noted that we partitioned variation across space rather than time. Season, management and clonal variabilities could influence vegetative vine biomass, but we present comparative data across one year because spatial variability does not change with these contributing factors.

Lateral growth increases with increasing growth rate, particularly in highly vegetative vines (

This study identified a previously unrecorded phenomenon in grapevines where shoot-to-shoot variation in berry mass and its relationship with berry composition changes with whole-vine biomass. Thus, biomass management at the whole-vine scale may affect metabolite partitioning and thus fruit composition within vines. Present management indices that have been developed to predict ideal fruit composition at harvest, direct agronomic practice (such as cluster thinning) to low yield per hectare. In locations with the potential for high vine biomass, such as those with non-limiting conditions (abundant water, warmth and light) found commonly in new-world industrial-style agriculture, such practices may exacerbate the high vegetative state, with excess carbohydrates redirected to leaf growth with negative consequences for quality.

The functional relationship between vegetative and reproductive organs in grapevines must be examined in the context of other factors influencing the dynamics of berry development, such as light penetration and water availability. Our study represents a ‘natural' experiment, with the grapevine population exhibiting a range of vegetative biomass in the field rather than experimentally manipulated to achieve this result. Future studies could be based on contemporary understanding of the nature of source – sink relationships in the grapevine. How sugars are not loaded into phloem vessels but diffuse from mesophyll cytosol to phloem vessel, undergo mass flow and then follow a diffusion pathway to sink cytosol is critical to understanding source – sink relationships in the grapevine (

In contrast to expectations, large berries can be competitive sinks for carbohydrates when canopies are large. Consequently, management practices that encourage larger berries (

This work was supported by an Australian Government Research Training Program scholarship [scholarship # LP034319] and a collaborative fund from Evans & Tate Ltd and The University of Western Australia and the Australian Grape and Wine Council Project [grant # 39141100]. We thank Evans and Tate Winery for additional funds and access to vineyards and labs. Thanks to the UWA School of Agriculture and Environment and Greg Cawthray for technical assistance.