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6 April 2020 Spatial variability in Ontario Riesling vineyards. II. Berry composition
James J. Willwerth, Andrew G. Reynolds
Author Affiliations +
Abstract

Spatial variability of berry composition was studied over a 3-yr period in 10 Riesling vineyards in the Niagara Peninsula in Ontario. Vineyards were delineated using global positioning systems (GPS), and 75–80 sentinel vines were georeferenced within a sampling grid for data collection. During 2005–2007, vine water status measurements [leaf water potential (ψ)] were collected biweekly from a subset of these sentinel vines. Data were collected on soil texture and composition, soil water content (SWC; %), leaf ψ, and fruit composition. These variables were mapped using GIS software, and relationships between them were elucidated. Temporal stability in spatial patterns of soil texture and composition, SWC, leaf ψ, soluble solids (Brix), titratable acidity, and monoterpenes were examined. Spatial trends in leaf ψ and (or) SWC showed widespread evidence of temporal stability. Fruit composition variables were not as stable over a 3-yr period. Spatial trends in Brix were temporally stable in seven vineyards, free volatile terpenes were temporally stable in three vineyards, and potentially volatile terpenes were temporally stable in two vineyards. Consistent leaf ψ zones were identified, and these were temporally stable despite different climatic conditions. Furthermore, some soil variables, and particularly vine water status, may contribute significantly to the terroir effect through their effects on vine size and fruit composition. For some vineyards, many viticulture and fruit composition variables were also temporally stable. There was evidence of strong spatial relationships between leaf ψ and fruit composition, suggesting a strong relationship between berry composition and vine water status.

Introduction

The terroir concept can be defined as an interactive ecosystem in a given place, including climate, soil, and vine (van Leeuwen and Seguin 1994, 2006; van Leeuwen et al. 2004; van Leeuwen 2010). The soil’s main influence on wine quality seems to be due to its physical properties, water-holding capacity, and drainage characteristics (Seguin 1986). Studies of soil physical properties, such as soil texture and its relationship with vine performance, have demonstrated that significant variations may exist within single vineyards (Hall et al. 2002). Precision agriculture techniques have been used to study variation in countless studies involving many crops, including grapes. Spatial variability in soil, climatic conditions, pests, and disease have been associated with yields and some fruit composition variables (Hall et al. 2002; Bramley and Hamilton 2004; Cortell et al. 2005; Reynolds and Hakimi Rezai 2014a , 2014b , 2014c ). Greenspan and O’Donnell (2001) investigated the spatial variability within two vineyard blocks and some viticultural properties. They were able to distinguish between high and low vine size zones and demonstrated that these management zones had significantly different means of yield, soluble solids, and water status. Cortell et al. (2007, 2008) studied a number of yield and fruit quality indices in Oregon Pinot noir vineyards and found that vine size associated with soil and water availability had an impact on fruit composition, particularly phenolics. Cabernet Sauvignon vineyards in Coonawarra, Australia, varied spatially in a number of fruit composition variables, but there was some spatial consistency from year to year (Bramley 2005). Intra-annual variation was greater for some berry composition indices such as phenols vs. Brix (Bramley 2005). Many terroir-related studies indicate that water availability and plant water status are the means by which the terroir affects wine style and quality (Seguin 1983; Penavayre et al. 1991; Koundouras et al. 1999; Peyrot des Gachons et al. 2005; Hakimi and Reynolds 2010; Marciniak et al. 2013; Ledderhof et al. 2014). However, the impact of site and plant water status on grape and (or) wine composition has not been widely addressed, at least not with important environmental factors and cultural practices kept constant, particularly in New World wine regions. Changes in vine water status can impact vine growth, fruit composition, yield, and aroma compounds (Tregoat et al. 2002; van Leeuwen et al. 2004; van Leeuwen and Seguin 2006). Studying within-site variation in soil, vine water status, and fruit composition variables can help elucidate terroir effects while keeping key environmental factors constant. Therefore, precision viticulture techniques including global positioning systems (GPS) and geographic information systems (GIS) are being used to study variation within Ontario vineyards.

Little research has been done to see how Niagara’s unique terroir influences wine varietal character. Some studies performed in Ontario indicated that vine size and soil texture were related spatially to fruit composition and sensory characteristics of wines (Hakimi and Reynolds 2010; Ledderhof et al. 2014; Marciniak et al. 2013; Reynolds and Hakimi Rezaei 2014c ; Reynolds and de Savigny 2016), but in one case, spatial patterns were not temporally consistent (Reynolds et al. 2007). This study attempted to further understand the basis of terroir in Riesling vineyards within sub-appellations of the Niagara Peninsula. The objectives of this study were to demonstrate the influences of soil texture, soil water content (SWC), and leaf water potential (ψ) on fruit composition within vineyard blocks and to delineate these terroir effects using geomatic technologies. It was hypothesized that consistent leaf ψ zones would be identified within vineyard blocks and that vine water status would play a major role in fruit composition while soil type would determine water-holding capacity and water supply to the vine. Data on soil texture and composition, SWC, leaf ψ, and yield components are included in a companion manuscript (Willwerth and Reynolds 2020). Data describing sensory differences between wines from high and low water status zones in these vineyards are found in Willwerth et al. (2018).

Materials and Methods

Site selection

In April 2005, 10 Riesling vineyard sites were selected throughout the Niagara Peninsula in Ontario ( Supplementary Table S1 1). These sites were nonirrigated, commercial vineyards, and the vineyard blocks had heterogeneous soil types. Each site was also representative of each Vintners Quality Alliance (VQA) sub-appellation. Details concerning soil and vineyard characteristics and vineyard management can be found in  Supplementary Table S1 1 . All vineyards were balance pruned (Jordan et al. 1981) by retaining 20 nodes for each kilogram of cane prunings prior to each growing season. In each vineyard block, a grid-style sampling pattern was established with a sentinel vine at each grid intersection point. These sentinel vines (72–80 per vineyard block) were flagged for identification to be used for data collection. A Raven Invicta 115 GPS receiver (Raven Industries, Sioux Falls, SD, USA) with a built-in differential GPS correction receiver giving positions with an accuracy of 1–1.4 m was used in May 2005 to georeference each sentinel vine and to delineate the shape and size of each vineyard block.

Viticultural data collection

The SWC (%) and leaf ψ measurements were taken biweekly in each vineyard (every 10–14 d) from sentinel vines between the end of June and early September (beginning of fruit set to preharvest) as described in Willwerth and Reynolds (2020). Soil analyses, including pH, organic matter (OM) concentration, elemental concentration, cation-exchange capacity (CEC), and base saturation (BS), were performed on one soil sample collected from every fourth sentinel vine (Willwerth and Reynolds 2020). For each sentinel vine, data were collected annually at vine dormancy for weight of cane prunings as an estimate of vine vigor (vine size). Yield components (yield per vine, clusters per vine, cluster weight, berries per cluster, and berry weight) were either measured directly or calculated from measured variables during harvest each season. Fruit was sorted based on treatments and retained for winemaking. Clusters were counted from each sentinel vine, and samples of 100 berries were taken for determination of berry weight and standard fruit composition indices [Brix; titratable acidity (TA); pH], whereas samples of 250 berries were taken for monoterpene concentration analyses.

Fruit composition

Each 100-berry and 250-berry sample was weighed to determine the mean berry weight. The frozen berry samples were then heated in 250 mL beakers to an internal temperature of 80 °C in a Fisher Scientific Isotemp 228 water bath (Fisher Scientific, Ottawa, ON, Canada) to dissolve any precipitated tartaric acid. The heated berry samples were then cooled, juiced in a laboratory juicer (model 500; Omega Products Inc., Harrisburg, PA, USA), and a ≈35 mL portion was clarified using a IEC Centra CL2 centrifuge (International Equipment Co., Needham Heights, MA, USA). Total soluble solids (hereinafter Brix) were measured on the clarified berry juice samples using a temperature-compensated Abbé bench refractometer (model 10450; American Optical Corp., Buffalo, NY, USA). The pH was measured using an Accumet pH/ion meter AR50 (Fisher Scientific). TA was measured on 5 mL clarified samples using a Man-Tech PC-Titrate autotitrator (PC-1300-475; Man-Tech Associates Inc., Guelph, ON, Canada). Samples were titrated to a pH 8.2 endpoint with 0.1 mol L-1 NaOH solution. Results were expressed as tartaric acid equivalents (g L-1). Monoterpenes were analyzed for the 250-berry samples using the method developed by Dimitriadis and Williams (1984) as modified by Reynolds and Wardle (1989). The free volatile terpene (FVT) and potentially volatile terpene (PVT) concentrations were expressed as mg kg-1. A large database was compiled annually on these sentinel vines for all yield and berry composition variables.

Statistical analysis

Linear and spatial correlations were determined between soil composition, soil texture, leaf ψ, SWC, and berry composition for all vintages. Through the use of XLSTAT (XLSTAT-pro version 2008.5.1), principal component analysis (PCA) was conducted to elucidate relationships among soil variables, soil and vine water status, and berry composition variables. Soil variables were used as supplementary variables for PCA. MapInfo and Vertical Mapper (Northwood GeoScience, Ottawa, ON, Canada) were used to construct geospatial maps of all variables. Spatial correlations were also determined using the statistical package provided by Vertical Mapper. These maps and the spatial correlations were used to examine spatial variation for selected variables in each season and their temporal stability and to compare apparent spatial relationships between correlated variables.

Results

General comments

All results shown are from the 2005–2007 growing seasons. As in Willwerth and Reynolds (2020), three sites were omitted from this paper due to significant winter injury in the 2004–2005 winter that substantially decreased yields (Lambert, Reif, and Vailmont), and powdery mildew in 2006 (Vailmont). Meteorological data depicting temperature and rainfall events for each growing season are depicted in  Supplementary Fig. S1 1 . There were drought periods in all three growing seasons, but this was particularly the case in 2005 and the very hot and dry 2007 vintage. The 2006 growing season was marked by cooler and wetter periods later in the season, particularly in late August, September, and October. Results are subdivided into tables depicting linear correlations for each vineyard, geospatial maps depicting spatial variability in fruit composition variables for each site, and PCA results and interpretations. References to the companion manuscript (Willwerth and Reynolds 2020) are given for correlative relationships between leaf ψ and yield components.

Linear correlations

Correlations for each vineyard are depicted in separate Tables (Tables 17). Soil variables (e.g., % sand and clay, pH, OM, CEC, BS, elements), SWC, and leaf ψ are presented as putative independent variables and fruit composition entities as dependent variables. As most soil variables are relatively stable, particularly in soils with high clay content, whereas berry composition variables are inconsistent and subject to vicissitudes of annual weather patterns, it is not surprising that strong correlations were not abundant. Nonetheless, SWC and leaf ψ were frequently correlated to some berry composition variables.

Table 1.

Linear correlations between water status, soil, and berry composition variables for Glenlake Vineyards, Niagara-on-the-Lake, ON, Canada, 2005–2007.

cjps-2019-0291tab1.gif

Table 2.

Linear correlations between water status, soil, and berry composition variables for Chateau des Charmes, St. Davids, ON, Canada, 2005–2007.

cjps-2019-0291tab2.gif

Table 3.

Linear correlations between water status, soil, and berry composition variables for Paragon Vineyards, Jordan, ON, Canada, 2005–2007.

cjps-2019-0291tab3.gif

Table 4.

Linear correlations between water status, soil, and berry composition variables for Henry of Pelham Winery, St. Catharines, ON, Canada, 2005–2007.

cjps-2019-0291tab4.gif

Table 5.

Linear correlations between water status, soil, and berry composition variables for Myers Vineyard, Vineland, ON, Canada, 2005–2007.

cjps-2019-0291tab5.gif

Table 6.

Linear correlations between water status, soil, and berry composition variables for Flat Rock Cellars, Jordan, ON, Canada, 2005–2007.

cjps-2019-0291tab6.gif

Table 7.

Linear correlations between water status, soil, and berry composition variables for Cave Spring Cellars, Beamsville, ON, Canada, 2005–2007.

cjps-2019-0291tab7.gif

East Niagara vineyards

Four vineyards were located in the eastern portion of the Niagara Peninsula (Niagara-on-the-Lake and St. Davids) and six were in the western portion (St. Catharines, Jordon, Vineland, and Beamsville). Among eastern Niagara vineyards, at Glenlake Vineyards (GLK), positive correlations were SWC vs. PVT (2007) and leaf ψ vs. PVT (2005; Table 1). Others included % sand (2005) and soil magnesium (Mg; 2007) vs. FVT; soil phosphorus (P; 2006) and Mg (2005) vs. PVT; Mg vs. Brix (2005); CEC vs. TA (2005); and soil calcium (Ca; 2006) and CEC (2007) vs pH. An inverse correlation was found for CEC vs. Brix (2005). In the hotter, drier years, R values were higher between SWC and leaf ψ vs. monoterpenes.

For Chateau des Charmes (CDC), inverse correlations were SWC vs. TA (2006, 2007) and pH (2005; Table 2). There were no significant correlations involving leaf ψ. Positive correlations included soil potassium (K) vs. pH (2006); OM, soil P, and soil K vs. FVT (2006); and OM vs. PVT (2006). Inverse correlations were SWC vs. TA (2006 and 2007) and pH (2005); soil pH, Ca, and CEC vs. Brix (2005); OM and soil K vs. TA (2005); soil pH, Ca, and CEC vs. pH (2007); and % clay, Ca, CEC, and BS vs. FVT (2005).

West Niagara vineyards

Of the western Niagara vineyards, at Paragon Vineyards (PAR) positive correlations were soil pH vs. Brix (2007); soil pH vs. TA (2006); % sand vs. pH (2006); and soil K vs. FVT (2005; Table 3). Negative correlations were leaf ψ vs. berry pH (2006); OM, soil pH, P, Mg, and CEC vs. pH (2006); P (2006) and K (2007) vs. FVT; and soil pH vs. PVT (2006).

At Henry of Pelham (HOP), positive correlations were found in leaf ψ vs. Brix (2007); SWC vs. FVT (2007; Table 4). Other positive correlations included OM vs. TA (2005); P (2005) and BS (2007) vs. pH; OM and BS vs. FVT (2005); and Mg (2005) and K (2007) vs. PVT. Negative correlations were soil P vs. Brix, TA, pH, and PVT (2006) and % clay vs. PVT (2005).

For the Myers Vineyard (MYR), positive correlations were SWC vs. berry pH (2007; Table 5). Other positive correlations were OM and CEC vs. TA (2005); OM vs. pH (2007); and % clay, soil Mg, and CEC vs. FVT (2007). Inverse correlations were SWC vs. Brix (2006) and pH (2005); soil pH vs. Brix (2006) and TA (2007); BS vs. FVT and PVT (2007); and soil pH (2007), Ca (2005, 2007), and Mg and CEC (2005) vs. PVT.

At Flat Rock Cellars (FLR), positive correlations were leaf ψ vs. Brix and TA (2007) and pH (2006), and SWC vs. Brix (2007; Table 6). Further correlations included % sand, OM, and K vs. TA (2007); OM (2006, 2007) and BS (2007) vs. FVT; and OM vs. PVT (2006). Inverse correlations were SWC vs. pH (2005); Ca and CEC vs. Brix (2005); OM and K (2005) and pH, Ca, and BS (2007) vs. TA; Ca, CEC, pH, and BS (2005), % clay (2005, 2006) and OM (2007) vs. FVT; % clay (2006) and % sand, OM, P, and K (2005) vs. PVT.

At Cave Spring Vineyard (CSC), positive correlations were SWC and CEC vs. PVT (2007) and OM and P vs. FVT (2005; Table 7). Inverse correlations were SWC vs. PVT (2006); OM vs. Brix (2005); % sand vs. TA (2006); and BS vs. FVT (2005 and 2006).

Spatial variability

At the GLK site, consistent zones of leaf ψ and SWC were found in 2005–2007 despite different weather conditions (Willwerth and Reynolds 2020). Maps for four sites (GLK, CDC, MYR, and CSC) are included in the main body of the paper (Figs. 111), while those for Brix and TA for the other three sites (PAR, HOP, and FLR) are included in  Supplementary Figs. S2– S4 1 . Spatial trends in Brix (Figs. 1A1C) were inconsistent over the period of this study. There were some apparent spatial relationships with leaf ψ in some areas of the vineyard. The differences in Brix in 2005 might have been related to winter injury. TA (Figs. 1D1F) and FVT (Figs. 2A2C) showed evidence of temporal stability from 2005 to 2007. Strong evidence of spatial relationships was also found between leaf ψ and monoterpenes (Fig. 2), particularly in 2005. At CDC, many variables were temporally stable despite different weather conditions experienced in the 2005–2007 growing seasons. These included Brix (Figs. 3A3C), TA (Figs. 3D3F), and both FVT (Figs. 4A4C) and PVT (Figs. 4D4F). Leaf ψ was highly spatially related to both FVT and PVT in both 2005 and 2006 (Willwerth and Reynolds 2020). Leaf ψ and berry weight had strong spatial relationships with many fruit composition variables, including Brix, TA, and monoterpenes. Therefore, leaf ψ may have been impacting these variables indirectly through a higher skin-to-juice ratio caused by smaller berries. This site was one of the vineyards that demonstrated temporal consistency in terms of spatial variability.

Fig. 1.

Spatial patterns in soluble solids (Brix) (A–C) and titratable acidity (D–F). Glenlake Vineyard, Niagara-on-the-Lake, ON, Canada, 2005–2007. The figure was created through the use of MapInfo and Vertical Mapper (Northwood GeoScience, Ottawa, ON, Canada).

cjps-2019-0291f1.tif

Fig. 2.

Spatial patterns in free volatile terpenes (A–C) and potentially volatile terpenes (D–F). Glenlake Vineyard, Niagara-on-the-Lake, ON, Canada, 2005–2007. The figure was created through the use of MapInfo and Vertical Mapper (Northwood GeoScience, Ottawa, ON, Canada).

cjps-2019-0291f2.tif

Fig. 3.

Spatial patterns in soluble solids (Brix) (A–C) and titratable acidity (D–F). Chateau des Charmes Vineyard, St Davids, ON, Canada, 2005–2007. The figure was created through the use of MapInfo and Vertical Mapper (Northwood GeoScience, Ottawa, ON, Canada).

cjps-2019-0291f3.tif

Fig. 4.

Spatial patterns in free volatile terpenes (A–C) and potentially volatile terpenes (D–F). Chateau des Charmes Vineyard, St. Davids, ON, Canada, 2005–2007. The figure was created through the use of MapInfo and Vertical Mapper (Northwood GeoScience, Ottawa, ON, Canada).

cjps-2019-0291f4.tif

Fig. 5.

Spatial patterns in free volatile terpenes (A–C) and potentially volatile terpenes (D–F). Paragon Vineyard, St. Catharines, ON, Canada, 2005–2007. The figure was created through the use of MapInfo and Vertical Mapper (Northwood GeoScience, Ottawa, ON, Canada).

cjps-2019-0291f5.tif

Fig. 6.

Spatial patterns in free volatile terpenes (A–C) and potentially volatile terpenes (D–F). Henry of Pelham Vineyard, St. Catharines, ON, Canada, 2005–2007. The figure was created through the use of MapInfo and Vertical Mapper (Northwood GeoScience, Ottawa, ON, Canada).

cjps-2019-0291f6.tif

Fig. 7.

Spatial patterns in soluble solids (Brix) (A–C) and titratable acidity (D–F). Myers Vineyard, Vineland, ON, Canada, 2005–2007. The figure was created through the use of MapInfo and Vertical Mapper (Northwood GeoScience, Ottawa, ON, Canada).

cjps-2019-0291f7.tif

Fig. 8.

Spatial patterns in free volatile terpenes (A–C) and potentially volatile terpenes (D–F). Myers Vineyard, Vineland, ON, Canada, 2005–2007. The figure was created through the use of MapInfo and Vertical Mapper (Northwood GeoScience, Ottawa, ON, Canada).

cjps-2019-0291f8.tif

Fig. 9.

Spatial patterns in free volatile terpenes (A–C) and potentially volatile terpenes (D–F). Flat Rock Cellars, Jordan, ON, Canada, 2005–2007. The figure was created through the use of MapInfo and Vertical Mapper (Northwood GeoScience, Ottawa, ON, Canada).

cjps-2019-0291f9.tif

Fig. 10.

Spatial patterns in soluble solids (Brix) (A–C) and titratable acidity (D–F). Cave Spring Vineyard, Beamsville, ON, Canada. 2005–2007. The figure was created through the use of MapInfo and Vertical Mapper (Northwood GeoScience, Ottawa, ON, Canada).

cjps-2019-0291f10.tif

Fig. 11.

Spatial patterns in free volatile terpenes (A–C) and potentially volatile terpenes (D–F). Cave Spring Vineyard, Beamsville, ON, Canada, 2005–2007. The figure was created through the use of MapInfo and Vertical Mapper (Northwood GeoScience, Ottawa, ON, Canada).

cjps-2019-0291f11.tif

At the PAR site, spatial trends in Brix ( Supplementary Figs. S2A– S2C 1 ) were relatively temporally stable from 2005 to 2007. In general, areas of higher Brix corresponded to areas of lower leaf ψ. TA ( Supplementary Figs. S2D– S2F 1 ) had similar spatial trends to Brix but were inversely related. Similarly, leaf ψ (Willwerth and Reynolds 2020) seemed to have some relationships with berry composition in that areas of high leaf ψ also had higher TA. FVT (Figs. 5A5C) and PVT (Figs. 5D5F) both had some areas of temporal stability but were inconsistent in from year to year in general. HOP spatial trends in Brix ( Supplementary Figs. S3A– S3C 1 ) were temporally stable from 2005 to 2007, and the values were also consistent from year to year, despite differences in growing seasons. TA ( Supplementary Figs. S3D– S3F 1 ) was transient in nature: in 2005 and 2007 it was spatially similar, but in 2006 the trend was completely the opposite within the vineyard. FVT (Figs. 6A6C) and PVT (Figs. 6D6F) demonstrated some stability in terms of their spatial trends within the vineyard. Little variation was observed with respect to FVT, but there was more variation in PVT. Monoterpene concentrations were highest in 2006. Consistent leaf ψ zones were identified at the MYR site in 2005–2007. Spatial variability in fruit composition variables and spatial relationships among them were temporally stable (Figs. 7 and 8). There was low spatial variability in Brix, but spatial trends were temporally stable over the 3 yr of the study (Figs. 7A7C). Brix demonstrated strong spatial relationships with SWC and leaf ψ (Willwerth and Reynolds 2020). Berry TA had areas of temporal stability, particularly in 2006–2007 (Figs. 7D7F). The lowest TA occurred in 2007, likely due to the hot, dry growing conditions. Inconsistencies observed in 2005 were likely attributable to winter injury. Spatial relationships were observed in terms of TA and leaf ψ in that areas of higher leaf ψ had higher TA values. Brix and TA were inversely related spatially. Spatial trends of FVT (Figs. 8A8C) and PVT (Figs. 8D8F) were relatively stable over the 3-yr period, and there were some relationships with leaf ψ (Willwerth and Reynolds 2020).

Generally, areas of lower leaf ψ had higher FVT and PVT. Monoterpenes were highest in the 2006 vintage, particularly PVT. No clear spatial relationships existed between Brix and monoterpenes. At FLR, geospatial maps showed that many variables were temporally stable despite different weather conditions experienced in each growing season. Some spatial trends in terms of Brix ( Supplementary Figs. S4A– S4C 1 ) were observed from 2005 to 2007 with few inconsistencies. Many of these can be related to areas of winter injury, particularly in the west end of the vineyard. TA ( Supplementary Figs. S4D– S4F 1 ) was similar to Brix in terms of spatial variability and temporal stability. Areas of higher Brix had lower TA as expected. Values of Brix were relatively consistent over 3 yr but TA was highest in 2006. Spatial trends in FVT (Figs. 9A9C) were inconsistent temporally, but spatial variability was not high. Stronger apparent spatial trends were observed with respect to PVT (Figs. 9D9F). Trends were generally stable in PVT and were lowest in 2007. Contrary to other vineyards, CSC did not show temporal stability for many of the variables studied. Possible reasons for this are explained in Willwerth and Reynolds (2020). Only some areas of the vineyard were stable for fruit composition variables. Spatial trends in Brix (Figs. 10A10C) were not temporally consistent from 2005 to 2007. TA (Figs. 10D10F) spatial patterns were stable for 2005 and 2007, but in the wetter 2006 vintage the patterns were opposite. There were some areas showing temporal stability for FVT and PVT, whereas in some regions they were highest for both years (Figs. 11A11F).

Principal components analysis

Through PCA, some relationships were observed at the GLK site (Figs. 12A12C). In 2005, leaf ψ, SWC, and vine size were correlated. Leaf ψ was highly correlated with fruit composition. Lower leaf ψ values were related to higher Brix, terpenes, and pH in this vineyard for 2005. As expected, Brix, monoterpenes, and pH were all highly correlated. In 2006, SWC and leaf ψ were not correlated, but leaf ψ, Brix, vine size, and FVT were correlated. SWC was highly correlated with yield, TA, and % clay. In 2007, many variables were highly correlated. Leaf ψ and terpenes were highly correlated, as were OM and soil pH, while being inversely correlated to TA. SWC, Brix, pH, and % sand were also highly positively correlated to each other and inversely correlated to yield. At the CDC site, PCA suggested that leaf ψ was highly positively correlated with berry weight, Brix, and pH in 2005 and 2006 (Figs. 12D12E). These same variables were also highly inversely correlated with FVT and PVT, vine size, and yield. The same trends were shown in 2007, except leaf ψ was noncorrelated with yield and pH (Fig. 12F). Therefore, low leaf ψ (i.e., water stress) seemed to have a positive impact on fruit composition resulting in higher terpene concentrations.

Fig. 12.

Principal component analysis of viticulture and soil variables, ON, Canada, 2005–2007. (A–C) Glenlake Vineyard; (D–F) Chateau des Charmes; (G–I) Myers Vineyard; (J–L) Cave Spring Vineyard. The figure was created through the use of XLSTAT (XLSTAT-pro version 2008.5.1) (XLSTAT-pro version 2008.5.1).

cjps-2019-0291f12.tif

At the PAR site, PCA of 2005 data showed that SWC and leaf ψ were positively correlated with % clay, TA, and FVT, whereas berry weight, vine size, % sand, and Brix were inversely correlated ( Supplementary Fig. S5A 1 ). In 2006, leaf ψ was positively correlated with vine size, berry weight, berry pH, TA, and FVT and inversely correlated with OM, P, and K. SWC was positively correlated with FVT, PVT, and Brix while being inversely correlated with yield ( Supplementary Fig. S5B 1 ). In 2007, leaf ψ was correlated with Brix and pH and inversely correlated with OM, K, and P ( Supplementary Fig. S5C 1 ). At the HOP site, PCA demonstrated that leaf ψ was positively correlated with TA and inversely correlated with % clay and pH in 2005 ( Supplementary Fig. S5C 1 ). In 2006, leaf ψ was noncorrelated with Brix, pH, and TA, but correlated to monoterpenes ( Supplementary Fig. S5D 1 ). In 2007, monoterpenes were inversely correlated with SWC, and Brix and TA were correlated with leaf ψ and % sand ( Supplementary Fig. S5E 1 ). For the MYR vineyard, PCA showed that SWC, leaf ψ, vine size, berry weight, and Ca were correlated with TA in all three vintages (Figs. 12G12I). These same variables were noncorrelated with Brix in the hotter and drier 2005 and 2007 vintages and inversely correlated with Brix in 2006. OM was associated with monoterpenes in 2005 and 2006, as was % sand in 2006. In general, yield was inversely related to Brix and terpenes. Brix and monoterpenes were generally highly correlated and inversely correlated with TA. At FLR in 2005, SWC and leaf ψ were noncorrelated; however, leaf ψ was positively correlated with PVT, K, and % sand while being inversely correlated with vine size and berry pH. SWC, however, was more correlated with TA and yield ( Supplementary Fig. S5G 1 ). In 2006, leaf ψ and SWC were more closely related, showing a higher positive correlation, and leaf ψ was inversely correlated with yield, TA, and terpenes ( Supplementary Fig. S5H 1 ). In 2007, leaf ψ, SWC, % sand, and OM were highly correlated with Brix and TA and inversely related to berry weight and terpenes, therefore indicating that lower leaf ψ and SWC resulted in wines with lower TA and higher flavor compounds ( Supplementary Fig. S5I 1 ). At CSC in 2005, leaf ψ was highly correlated with PVT, % clay, CEC, and Ca while being noncorrelated with SWC (Fig. 12J). It was also inversely correlated with yield, berry weight, and vine size. SWC, however, was highly correlated with % sand, vine size, yield, TA, and P. FVT and Brix were correlated with % sand and soil pH. In 2006, leaf ψ and SWC were both highly correlated, as well as yield, vine size, yield, OM, and K (Fig. 12K). All of these variables were inversely correlated with Brix, TA, FVT, and PVT. In 2007, leaf ψ was directly correlated with Brix and pH and inversely correlated with monoterpenes (Fig. 12L).

Discussion

Spatial trends and relationships within vineyard sites from 2005 to 2007 — Brix

Spatial variability in Brix was found in every vineyard site; however, in general, large variations in Brix did not exist. They varied in the range of 1.5–3 Brix within sites. This is agreement with research performed in Washington, where Brix varied in Concord vineyards (Davenport et al. 2001), and Australia (Bramley 2005). Brix levels were the highest in 2005 and lowest in 2006, which is indicative of those growing seasons. The 2006 vintage was cooler and wetter with more vegetative growth, which generally results in lower Brix levels. The 2005 growing season was very hot and dry, and the crop levels were very low due to bud damage caused by the winter of 2004–2005. The combination of these factors resulted in fruit with higher sugar levels than the other two vintages. This is consistent with Jackson and Lombard (1993) and Reynolds et al. (1994) with respect to Brix response to climate and crop level, respectively. There was, however, a large variation in Brix in some vineyards due to individual vine variation caused by cold injury, with levels as low as 16 Brix in some areas in 2005 and much higher Brix in subsequent years. The 2007 growing season was also hot and dry, but yields were higher due to lack of winter injury, resulting in higher than normal Brix levels in general. The variation was not as great in this particular vintage as it had ample sunshine, heat units, and extended dry periods.

Brix values showed evidence of temporal stability in seven vineyards over the 3-yr period. Through spatial and correlation analysis and PCA, there were often associations between SWC and Brix in many of the vineyard sites; however, they were not always consistent in terms of their relationship, or from year to year. In general, areas of lower SWC had higher Brix levels, but this was not always the case and some sites (CDC) consistently demonstrated the opposite effect. Leaf ψ was more consistent in its relationship with Brix. Leaf ψ was found through spatial analysis and PCA to have an apparent impact on Brix. In general, areas of vines with lower leaf ψ had higher Brix values than areas of higher leaf ψ. This also corresponds to lower SWC. This can be explained as being mainly due to the impact of water status on vegetative growth (i.e., weight of cane prunings), resulting in lower vine size. Some studies indicate that water stress can advance maturity (McCarthy and Coombe 1985) and increase Brix levels (Kliewer et al. 1983). These increases in Brix can be due to a concentration effect resulting from water loss, lower yields (Smart 1974) from water deficit, which reduces carbohydrate sinks in the vine, and better fruit exposure due to less vegetative growth (Smart 1974, 1985; Smart et al. 1990). Lower water status decreased shoot growth, and that water deficit accelerated sugar accumulation and malic acid breakdown (Koundouras et al. 2006). Brix values were also related to berry weight on a number of occasions. These may be a result of a concentration effect more than anything else. Some studies have shown that water stress can lead to higher final Brix levels in the fruit not through gains in sugar translocation but associated with reductions in berry weight (Hardie and Considine 1976). The findings of this study support that notion as well. In a few vineyards such as CDC, low leaf ψ vines had lower Brix. Under water deprivation, the decrease in photosynthesis and sugar export from the leaves can lead to this reduction in berry sugar accumulation (Quick et al. 1992; Rogiers et al. 2004). Esteban et al. (1999) demonstrated that higher water availability increased sugar levels due to increased photosynthetic activity or increased leaf area (Carbonneau et al. 1983). No consistent findings were found in terms of soil variables on Brix levels within sites. Soil texture played a minor role in its impact on berry Brix levels. Sand content was inversely related to Brix in a few vineyards as were Ca, soil pH, and OM. Soil K, P, and Mg also showed few relationships.

It therefore appears that variation in Brix was a result of SWC and leaf ψ and their impact on vine size and berry weight. The findings of this study are consistent with those of van Leeuwen et al. (2004), who studied the impact of different soil textures and grape maturation of Bordeaux varieties in France. Sandy soils had large berries with low sugars but high acidity. Clay soils resulted in berries with the highest sugars; these soil effects were influenced through vine water status. It was concluded that mineral nutrient uptake by the vine and (or) availability in the soil did not have a significant impact on fruit quality (van Leeuwen et al. 2004).

Titratable acidity

TA was highly variable within vineyard sites. It also was very transient, and spatial trends within vineyards often varied from year to year. As a result, spatial trends in TA were temporally stable in only some vineyards. In a number of cases, spatial trends in TA were consistent in two of three vintages. The hot and dry 2005 and 2007 vintages were consistent with each other; however, in other cases, 2006 and 2007 vintages demonstrated consistent spatial trends in TA. This was a result of individual vine variation due to cold injury suffered during the winter of 2004–2005. Some damaged areas of vineyards that were affected by winter damage had a number of secondary clusters on the vine that matured later than fruit formed from primary buds and thus had higher TA values. As individual vines became less variable (due to better vine health), more consistent spatial trends were observed. Many of the relationships involving TA were similar to Brix. SWC and leaf ψ had an impact in some vineyards as shown through spatial correlation and PCA. In general, areas of higher SWC and vines with higher leaf ψ also had higher TA, consistent with Koundouras et al. (1999). Similar to Brix, this is a result of more vegetative growth and poorer fruit development. A more vigorous canopy results in more shading of the fruit, which can decrease malic acid degradation in the fruit (Kliewer and Lider 1968). TA was normally inversely related to Brix, but in some cases it was noncorrelated. There were few consistent relationships between soil variables and TA. Soil texture seemed to impact TA in only a few cases, mostly as a result of higher vine size. TA was often correlated with % sand, larger vines, and higher yields. These findings are consistent with van Leeuwen et al. (2004), who demonstrated that sandy soils in Bordeaux had larger berries and higher acidity.

Monoterpenes

Monoterpenes are an important measurement of fruit composition as they play a major role in the varietal character of Riesling wines. As in the case of Brix and TA, monoterpenes varied spatially within vineyard sites. FVT concentrations were variable within vineyards; however, spatial trends within vineyards concerning FVT were not very temporally stable, partly due to how consistently low their levels were in most of the vineyard sites. Spatial trends in FVT were only temporally stable in three vineyards (CDC, GLK, and MYR). This lack of temporal stability is consistent with Reynolds et al. (2007), who found that monoterpene spatial distribution varied across different vintages in an Ontario Riesling vineyard. PVT also varied spatially within vineyards; however, spatial trends in PVT were more temporally stable in vineyards than FVT, as temporal stability was found in seven vineyards. Some vineyards showed a remarkable amount of temporal stability for monoterpenes. One of the resounding findings was that in every vineyard site, monoterpenes were the highest in the 2006 vintage. The 2005 and 2007 vintages were much hotter and drier than the 2006 vintage. These vintages had vines with lower leaf ψ. This suggests that periods of drought and resultant low leaf ψ have negative effects on monoterpene concentrations; however, regions within vineyard sites (e.g., MYR) with smaller berry weights seemed to have higher concentrations of FVT and PVT, reflecting the impact of skin-to-juice ratio. Therefore, it appears from these findings that either high (lack of any deficit) or very low leaf ψ can be detrimental to concentration in monoterpenes. Peyrot des Gachons et al. (2005) found similar results pertaining to vine water status and volatile thiols in Sauvignon blanc grapes and found that mild water deficits resulted in higher grape quality. In other studies, limited water availability also increased glucoconjugates of aroma compounds and increased wine quality (Koundouras et al. 2006). Reducing irrigation led to increased concentrations of monoterpenes (McCarthy and Coombe 1985), but others indicated that prolonged irrigation deficits reduced monoterpenes in berries (Reynolds and Wardle 1997; Reynolds et al. 2005). In the latter study, in Gewürztraminer grapevines, decreasing the duration of water stress increased FVT and PVT at harvest. Therefore, this research further supports the importance of vine water status and its impact on aroma potential in white wine cultivars and that mild water deficits appear to maximize aroma potential. No consistent relationships were shown between soil variables and monoterpene concentrations. It should be noted that in some instances, OM, P, and K were correlated with either FVT or PVT in multiple vineyards and vintages. Nutrient differences have been noted to possibly impact fruit composition. These results are not consistent enough to make any reasonable explanations; however, Reynolds et al. (2007) found similar relationships with PVT being correlated with P and K in Riesling, which is interesting and may require some elucidation through further research. Brix was generally correlated with monoterpene concentrations, which is an indication of general maturity. At times, TA was related to monoterpenes but no consistent trends were found.

Variables of fruit composition were neither as volatile nor as consistent as leaf ψ, vine size, and yield components, which is consistent with other precision viticulture studies that demonstrated that fruit composition at harvest was considerably less variable than yield (Bramley and Hamilton 2004; Bramley 2005). While there was evidence of noteworthy spatial relationships between leaf ψ and fruit composition, these relationships were not as strong as those between leaf ψ and other factors such as berry weight and vine size.

Through the use of geospatial technologies, many viticultural variables were found to be related to leaf ψ and vine size in many of the vineyard sites examined. As water deficits in vines can reduce shoot growth (Kliewer et al. 1983) and canopy density (Smart 1974), low water status may have altered the canopy characteristics of the vine, ultimately leading to better leaf and fruit exposure to sunlight and improved fruit composition. Furthermore, many of the relationships between vine size and leaf ψ demonstrate that water status is greatly influenced by water supply together with evaporative demand of the canopy. Thus, total leaf area and exposed leaf area measurements are relevant. Carbonneau (1999) has shown that ratio of exposed leaf area to yield is an important measurement in relation to vine vigor and that this can impact fruit quality due to sink competition. Similar to this study, Cortell et al. (2007) studied different vine size zones within vineyards and demonstrated that vine size impacted berry weight, Brix, and TA. They attributed variation between vine size zones to soil depth and water holding capacity. Greenspan and O’Donnell (2001) investigated the spatial variability within different vineyard blocks, and they were able to distinguish between high and low vine size zones and found that these management zones had different means of yield, Brix, and water status.

Variability is an important concept to understand for vineyard managers to produce consistent premium grapes in all wine-producing regions. In Ontario, there is an extreme amount of variability due to direct and indirect effects (i.e., cold injury) of this climate. Therefore, there is a need for necessary tools to study variation of vineyards. Variability in Brix levels is important to understand in situations where grapes are priced based on Brix such as in Ontario. Large variability can have negative economic consequences as one area of low Brix may reduce overall Brix levels, thereby impacting the potential quality and price of the grapes. Therefore, through precision viticulture a better understanding of grape maturity can be used to make wise harvesting decisions.

Conclusions

Ten Ontario Riesling vineyards were spatially variable in terms of SWC, leaf ψ, and fruit composition. Our hypotheses were confirmed in that consistent leaf ψ zones were indeed identified within these vineyards, and those zones showed evidence of temporal stability despite different climatic conditions. Furthermore, results indicated that some soil variables, and particularly vine water status, may contribute significantly to the terroir effect through their effects on vine size and fruit composition. For some vineyards, many viticulture and fruit composition variables were also temporally stable. There was evidence of strong spatial relationships between leaf ψ and fruit composition, although these relationships were not as strong as those between leaf ψ and other variables such as berry weight and vine size. It is, therefore, possible to delineate regions in vineyard blocks based on leaf ψ and use these to designate fruit composition and (or) wine quality subzones. Through the use of geospatial technologies, many viticultural variables could be related to leaf ψ and vine size in many of the vineyard sites, as many of these relationships were site-specific.

Acknowledgements

We thank the Natural Sciences and Engineering Research Council of Canada and the Wine Council of Ontario for research funding and all of the industry partners for allowing this research to be possible. These include Glenlake Orchards and Vineyards, Chateau des Charmes, Cave Spring Cellars, Henry of Pelham Family Estate Winery, Reif Estate Winery, Flat Rock Cellars, Lambert Farms, Bill and Caroline Myers, Paragon Vineyards, and Vailmont Vineyards. We also thank Ker Crop Management Services (KCMS) for their assistance with GPS data.

References

1.

Bramley, R.G.V. 2005 . Understanding variability in winegrape production systems — 2. Within vineyard variation in quality over several vintages. Aust. J. Grape Wine Res. 11 : 33–42 . https://doi.org/10.1111/j.1755-0238.2005.tb00277.xGoogle Scholar

2.

Bramley, R.G.V., and Hamilton, R.P. 2004 . Understanding variability in winegrape production systems — 1. Within vineyard variation in yield over several vintages. Aust. J. Grape Wine Res. 10 : 32–45 . https://doi.org/10.1111/j.1755-0238.2004.tb00006.xGoogle Scholar

3.

Carbonneau, A. 1999. Actualités et perspectives en agronomie de la vigne en France. Proc. GESCO XIe Journées du Groupe Européen d’Etude des Systèmes de Conduite de la Vigne, Sicily, Italy, 6–12 June 1999. pp. 99–110.Carbonneau, A. 1999. Actualités et perspectives en agronomie de la vigne en France. Proc. GESCO XIe Journées du Groupe Européen d’Etude des Systèmes de Conduite de la Vigne, Sicily, Italy, 6–12 June 1999. pp. 99–110.

4.

Carbonneau, A., Smart, R.E., and De, L. 1983 . Measurement of gross photosynthesis under natural conditions by using a 14CO2 diffusion porometer. II: Combined influences of some major ecological factors and leaf area on grapevine photosynthesis. Photosynthetica, 17 : 426–430 . . Google Scholar

5.

Cortell, J.M., Halbleib, M., Gallagher, A., Righetti, T., and Kennedy, J.A. 2005 . Influence of vine vigor on grape (Vitis vinifera L. cv. Pinot Noir) and wine proanthocyanidins. J. Agric. Food Chem. 53 : 5798–5808 . https://doi.org/10.1021/jf0504770. PMID:15998151. Google Scholar

6.

Cortell, J.M., Halbleib, M., Gallagher, A., Righetti, T., and Kennedy, J.A. 2007 . Influence of vine vigor on grape (Vitis vinifera L. cv. Pinot Noir) anthocyanins. 1. Anthocyanin concentration and composition in fruit. J. Agric. Food Chem. 55 : 6575–6584 . https://doi.org/10.1021/jf070195v. PMID:17636933. Google Scholar

7.

Cortell, J.M., Sivertsen, H.K., Kennedy, J.A., and Heymann, H. 2008 . Influence of vine vigor on Pinot noir fruit composition, wine chemical analysis, and wine sensory attributes. Am. J. Enol. Vitic. 59 : 1–10 . . Google Scholar

8.

Davenport, J.R., Mills, L.J., Tarara, J.M., Pierce, F.J., and Lang, N.S. 2001. Application of GPS, GIS, yield monitoring and Brix monitors for effective vineyard management. Pages 50–55 in A.G. Reynolds, ed. Space age wine growing. ASEV/ES Symposium Proc. July 2001.Davenport, J.R., Mills, L.J., Tarara, J.M., Pierce, F.J., and Lang, N.S. 2001. Application of GPS, GIS, yield monitoring and Brix monitors for effective vineyard management. Pages 50–55 in A.G. Reynolds, ed. Space age wine growing. ASEV/ES Symposium Proc. July 2001.

9.

Dimitriadis, E., and Williams, P.J. 1984 . The development and use of a rapid analytical technique for estimation of free and potentially volatile monoterpene flavorants of grapes. Am. J. Enol. Vitic. 35 : 66–71 . . Google Scholar

10.

Esteban, M., Villanueva, M., and Lissarrague, J. 1999 . Effect of irrigation on changes in berry composition of Tempranillo during maturation. sugars, organic acids, and mineral elements. Am. J. Enol. Vitic. 50 : 418–434 . . Google Scholar

11.

Greenspan, M.D., and O’Donnell, J.J. 2001. Evaluating the utility of remotely-sensed canopy density for the understanding of vineyard spatial variability. In A.G. Reynolds, ed. Proc. Space Age Winegrowing Symposium. American Society for Enology and Viticulture /Eastern Section, Geneva, NY, USA.Greenspan, M.D., and O’Donnell, J.J. 2001. Evaluating the utility of remotely-sensed canopy density for the understanding of vineyard spatial variability. In A.G. Reynolds, ed. Proc. Space Age Winegrowing Symposium. American Society for Enology and Viticulture /Eastern Section, Geneva, NY, USA.

12.

Hakimi, J., and Reynolds, A.G. 2010 . Impact of vine water status on sensory evaluation of Cabernet Franc wines in the Niagara Peninsula of Ontario. J. Int. Sci. Vigne Vin, 44 : 61–75 . https://doi.org/10.20870/oeno-one.2010.44.2.1464Google Scholar

13.

Hall, A., Lamb, D.W., Holzapfel, B., and Louis, J. 2002 . Optical remote sensing applications in viticulture — a review. Aust. J. Grape Wine Res. 8 : 36–47 . https://doi.org/10.1111/j.1755-0238.2002.tb00209.xGoogle Scholar

14.

Hardie, W.J., and Considine, J.A. 1976 . Response of grapes to water-deficit stress in particular stages of development. Am. J. Enol. Vitic. 27 : 55–61 . . Google Scholar

15.

Jackson, D.I., and Lombard, P.B. 1993 . Environmental and management practices affecting grape composition and wine quality — a review. Am. J. Enol. Vitic. 44 : 409–430 . . Google Scholar

16.

Jordan, T.D., Pool, R.M., Zabadal, T.J., and Tomkins, J.P. 1981. Cultural practices for commercial vineyards. N.Y. Miscellaneous Bulletin 111.Jordan, T.D., Pool, R.M., Zabadal, T.J., and Tomkins, J.P. 1981. Cultural practices for commercial vineyards. N.Y. Miscellaneous Bulletin 111.

17.

Kliewer, W.M., and Lider, L.A. 1968 . Influence of cluster exposure to the sun on the composition of Thompson Seedless fruit. Am. J. Enol. Vitic. 19 : 175–184 . . Google Scholar

18.

Kliewer, W.M., Freeman, B.M., and Hosssom, C. 1983 . Effect of irrigation, crop level and potassium fertilization on Carignane vines. I. Degree of water stress and effect on growth and yield. Am. J. Enol. Vitic. 34 : 186–196 . . Google Scholar

19.

Koundouras, S., van Leeuwen, C., Seguin, G., and Gloires, Y. 1999 . Influence of water status on vine vegetative growth, berry ripening and wine characteristics in Mediterranean zone (Example of Nemea, Greece, variety Saint George, 1997). J. Int. Sci. Vigne Vin. 22 : 143–160 . https://doi.org/10.20870/oeno-one.1999.33.4.1020Google Scholar

20.

Koundouras, S., Marinos, V., Gkoulioti, A., Kotseridis, Y., and van Leeuwen, C. 2006 . Influence of vineyard location and vine water status on fruit maturation of nonirrigated cv. Agiorgitiko (Vitis vinifera L.). Effects on wine phenolic and aroma components. J. Agric. Food Chem. 54 : 5077–5086 . https://doi.org/10.1021/jf0605446. PMID:16819919. Google Scholar

21.

Ledderhof, D., Reynolds, A.G., and Brown, R.B. 2014 . Influence of water status on sensory profiles of Ontario Pinot noir wines. LWT-Food Sci. Technol. 57 : 65–82 . https://doi.org/10.1016/j.lwt.2013.12.010Google Scholar

22.

Marciniak, M., Reynolds, A.G., and Brown, R.B. 2013 . Influence of water status on sensory profiles of Ontario Riesling wines. Food Res. Int. 54 : 881–891 . https://doi.org/10.1016/j.foodres.2013.08.030Google Scholar

23.

McCarthy, M.G., and Coombe, B.G. 1985 . Water status and winegrape quality. Acta Hortic. 171 : 447–456 . https://doi.org/10.17660/actahortic.1985.171.42Google Scholar

24.

Penavayre, M., Morlat, R., Jacquet, A., and Bimont, F. 1991 . Influence des terroirs sur la croissance et développement de la vigne en millésime exceptionnellement sec. J. Int. Sci. Vigne Vin. 25 : 119–131 . https://doi.org/10.20870/oeno-one.1991.25.3.1214Google Scholar

25.

Peyrot des Gachons, C., van Leeuwen, C., Tominaga, T., Soyer, J., Gaudillère, J., and Dubourdieu, D. 2005 . Influence of water and nitrogen deficit on fruit ripening and aroma potential of Vitis vinifera L cv Sauvignon Blanc in field conditions. J. Sci. Food Agric. 85 : 73–85 . https://doi.org/10.1002/jsfa.1919Google Scholar

26.

Quick, W., Chaves, M., Wendler, R., David, M., Rodrigues, M., and Passaharinho, J. et al . ( 1992 . The effect of water stress on photosynthetic carbon metabolism in four species grown under field conditions. Plant Cell Environ. 15 : 25–35 . https://doi.org/10.1111/j.1365-3040.1992.tb01455.xGoogle Scholar

27.

Reynolds, A.G., and de Savigny, C. 2016 . Use of GPS and GIS for elucidation of the basis for terroir. I. Relationships amongst soil and plant elemental analyses, yield components and fruit composition in Chardonnay. J. Appl. Hortic. 18 , (2): 100–122 . . Google Scholar

28.

Reynolds, A.G., and Hakimi Rezaei, J. 2014 . a . Spatial variability in Cabernet franc vineyards in the Niagara Peninsula of Ontario. I. Soil composition, soil texture, and soil and vine water status. J. Appl. Hortic. 16 , (1): 3–23 . . Google Scholar

29.

Reynolds, A.G., and Hakimi Rezaei, J. 2014 . b . Spatial variability in Cabernet franc vineyards in the Niagara Peninsula of Ontario. II. Yield components and fruit composition. J. Appl. Hortic. 16 , (2): 87–102 . . Google Scholar

30.

Reynolds, A.G., and Hakimi Rezaei, J. 2014 . c . Spatial variability in Cabernet franc vineyards in the Niagara Peninsula of Ontario. III. Berry composition. J. Appl. Hortic. 16 , (3): 167–192 . . Google Scholar

31.

Reynolds, A.G., and Wardle, D.A. 1989 . Impact of various canopy manipulation techniques on growth, yield, fruit composition, and wine quality of Gewurztraminer. Am. J. Enol. Vitic. 40 : 121–129 . . Google Scholar

32.

Reynolds, A.G., and Wardle, D.A. 1997. Impact of viticultural practices on grape monoterpenes, and their relationship to wine sensory response. Pages 1–17 in T. Henick-Kling, ed. Proc. 4th International Symposium for Cool Climate Viticulture and Enology. New York State Agricultural Experiment Station, Geneva, NY, USA.Reynolds, A.G., and Wardle, D.A. 1997. Impact of viticultural practices on grape monoterpenes, and their relationship to wine sensory response. Pages 1–17 in T. Henick-Kling, ed. Proc. 4th International Symposium for Cool Climate Viticulture and Enology. New York State Agricultural Experiment Station, Geneva, NY, USA.

33.

Reynolds, A.G., Price, S.F., Wardle, D.A., and Watson, B.T. 1994 . Fruit environment and crop level effects on Pinot noir .1. Vine performance and fruit composition in British Columbia. Am. J. Enol. Vitic. 45 : 452–459 . . Google Scholar

34.

Reynolds, A.G., Parchomchuk, P., Berard, R., Naylor, A.P., and Hogue, E.J. 2005 . Gewurztraminer grapevines respond to length of water stress duration. Int. J. Fruit Sci. 5 , (4): 75–94 . https://doi.org/10.1300/j492v05n04_09Google Scholar

35.

Reynolds, A.G., Senchuk, I.V., van der Reest, C., and de Savigny, C. 2007 . Use of GPS and GIS for elucidation of the basis for terroir: spatial variation in an Ontario Riesling vineyard. Am. J. Enol. Vitic. 58 : 145–162 . . Google Scholar

36.

Rogiers, S., Hatfield, J., and Keller, M. 2004 . Irrigation, nitrogen, and rootstock effects on volume loss of berries from potted Shiraz vines. Vitis, 43 : 1–6 . . Google Scholar

37.

Seguin, G. 1983 . Influence des terroirs viticoles sur la constitution de la qualité des vendanges. Bull. OIV, 56 : 3–18 . . Google Scholar

38.

Seguin, G. 1986 . Terroirs and pedology of wine growing. Experientia, 42 : 861–873 . https://doi.org/10.1007/bf01941763Google Scholar

39.

Smart, R.E. 1974 . Aspects of water relations of the grapevine (Vitis vinifera). Am. J. Enol. Vitic. 25 : 84–91 . . Google Scholar

40.

Smart, R.E. 1985 . Principles of grapevine canopy microclimate manipulation with implications for yield and quality: a review. Am. J. Enol. Vitic. 36 : 230–239 . . Google Scholar

41.

Smart, R.E., Dick, J.K., Gravett, I.M., and Fisher, B.M. 1990 . Canopy management to improve grape yield and wine quality — principles and practices. S. Afr. J. Enol. Vitic. 11 : 1–17 . . Google Scholar

42.

Tregoat, O., van Leeuwen, C., Choné, X., and Gaudillère, J.P. 2002 . The assessment of vine water and nitrogen uptake by means of physiological indicators influence on vine development and berry potential (Vitis vinifera L. cv Merlot, 2000, Bordeaux). J. Int. Sci. Vigne Vin. 36 : 133–142 . https://doi.org/10.20870/oeno-one.2002.36.3.967Google Scholar

43.

van Leeuwen, C. 2010. Terroir: the effect of the physical environment on vine growth, grape ripening and wine sensory attributes. Pages 273–315 in A.G. Reynolds, ed. Managing wine quality. Vol. 1: Viticulture and wine quality. Woodhead Publishing Ltd., Oxford, UK.van Leeuwen, C. 2010. Terroir: the effect of the physical environment on vine growth, grape ripening and wine sensory attributes. Pages 273–315 in A.G. Reynolds, ed. Managing wine quality. Vol. 1: Viticulture and wine quality. Woodhead Publishing Ltd., Oxford, UK.

44.

van Leeuwen, C., and Seguin, G. 1994 . Incidences de l’alimentation en eau de la vigne, appréciée par l’état hydrique du feuillage, sur le développement de l’appareil végétatif et al maturation du raisin (Vitis vinifera L. cv. Cabernet Franc, Saint Emilion, 1990). J. Int. Sci. Vigne Vin. 28 : 81–100 . . Google Scholar

45.

van Leeuwen, C., and Seguin, G. 2006 . The concept of terroir in viticulture. J. Wine Res. 17 : 1–10 . https://doi.org/10.1080/09571260600633135Google Scholar

46.

van Leeuwen, C., Friant, P., Choné, X., Tregoat, O., Koundouras, S., and Dubourdieu, D. 2004 . Influence of climate, soil, and cultivar on terroir. Am. J. Enol. Vitic. 55 : 207–217 . . Google Scholar

47.

Willwerth, J.J., and Reynolds, A.G. 2020 . Spatial variability in Ontario Riesling vineyards. I. Soil, vine water status and vine performance. Oeno One, 54 : 311–333 . https://doi.org/10.20870/oeno-one.2020.54.2.2401Google Scholar

48.

Willwerth, J.J., Reynolds, A.G., and Lesschaeve, I. 2018 . Sensory analysis of Ontario Riesling wines from various water status zones. Oeno One, 52 : 145–171 . https://doi.org/10.20870/oeno-one.2018.52.2.1669Google Scholar

Notes

[1] Supplementary data are available with the article through the journal Web site at  http://nrcresearchpress.com/doi/suppl/10.1139/cjps-2019-0291.

Copyright remains with the author(s) or their institution(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. Updated online 10 August 2020: The license for this article has been changed to the CC BY 4.0 license. The PDF and HTML versions of the article have been modified accordingly.
James J. Willwerth and Andrew G. Reynolds "Spatial variability in Ontario Riesling vineyards. II. Berry composition," Canadian Journal of Plant Science 100(5), 504-527, (6 April 2020). https://doi.org/10.1139/cjps-2019-0291
Received: 4 November 2019; Accepted: 27 March 2020; Published: 6 April 2020
KEYWORDS
fruit composition
precision viticulture
soil moisture
Terroir
vine water status
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