variation in vine vigour, grape yield and vineyard soils ... · different areas of the same...

Variation in vine vigour, grape yield and vineyard soils andtopography as indicators of variation in the chemical

composition of grapes, wine and wine sensory attributes_136 217..229

R.G.V. BRAMLEY1,2, J. OUZMAN1,2 and P.K. BOSS1,3

1 CSIRO Food Futures Flagship, 2 CSIRO Ecosystem Sciences and 3 CSIRO Plant Industry,Waite Campus, PMB 2 GlenOsmond, SA 5064,Australia

Corresponding author: Dr Rob Bramley, fax +61 8 8303 8436, email [email protected]

AbstractBackground and Aims: Vineyard variability makes satisfaction of winemaker demands for uniform parcels of fruitthat are suitable for particular product streams difficult. Indeed, it may not be possible to satisfy these demandswithout being able to adequately characterise differences between wines derived from different fruit parcels ordifferent areas of the same vineyard, understanding how final wines are affected by management decisions imple-mented in the vineyard, and/or understanding the effects of variation in the vineyard’s biophysical characteristics(e.g. soil, topography) on grape and wine composition. This work sought to identify and examine relationshipsbetween the chemical and sensory attributes of wines derived from different parts of the same block and thebiophysical characteristics of these different vineyard areas.Methods and Results: Remote sensing of vine vigour, yield mapping and EM38 soil survey were used to identifyzones of contrasting vineyard performance in a Cabernet Sauvignon vineyard in the Murray Valley region. Small-lotwines were made from fruit sourced from these zones. Both sensory and chemical analysis (solid phasemicroextraction-gas chromatography-mass spectrometry) of these wines demonstrated them to be different. Like-wise, soil properties and indices of vine nutrition differed between the zones.Conclusions: This work suggests that it is possible for robust relationships to be established between specific(manageable) biophysical attributes of the place where grapes are grown and the sensory and chemical characteristicsof the wines derived from them. It also supports the view that terroir is spatially variable at the within-vineyard scale.Significance of the Study: The work provides a foundation for further research aimed at establishing how specificsensory and/or chemical properties in wines might be modified through targeted management interventions in thevineyard.

Keywords: precision viticulture, solid phase microextration-gas chromatography-mass spectrometry, terroir

IntroductionThe productivity of vineyards is spatially variable, whetherassessed in terms of grape yield (Bramley and Hamilton 2004,2007), vine vigour (Johnson et al. 2003, Bramley and Hamilton2007, Acevedo-Opazo et al. 2008), or fruit quality (Bramley2005), with this variation overwhelmingly associated withvariation in attributes of the land (soil and topography) under-lying the vineyard (Bramley and Hamilton 2005, 2007, Tisseyreet al. 2007, Trought et al. 2008, Bramley 2010). As a conse-quence, different wine styles may derive from different parts ofthe same vineyard when under uniform management (Bramleyand Hamilton 2007). Conversely, with appropriately targetedmanagement, it may be possible to deliver more uniform parcelsof fruit from variable vineyards to the winery, and/or parcelsthat are particularly suited to a specified and desired end-useconditioned by market demand. However, the effectiveness ofsuch strategies depends on an ability to characterise differencesbetween wines derived from different fruit parcels, or differentareas of the same vineyard. It also depends on an understandingof how final wines are affected by management decisions imple-

mented in the vineyard, and of the effects on grape and winecomposition of underlying variation in the biophysical charac-teristics of the vineyard. Objective measures of fruit and winequality are therefore key to the ability to characterise wines,understand their provenance, and use this understanding inviticultural management.

In a precursor to the present study, Bramley and Hamilton(2007) demonstrated that clear differences existed between thesensory attributes of wines produced from areas of characteris-tically lower and higher grape yield and vine vigour within thesame vineyards under conventional (i.e. uniform) manage-ment. They did this in contrasting vineyards from the MurrayValley (warm, irrigated) and Padthaway (cooler, supplementaryirrigation only) regions; note that Bramley and Hamilton (2007)previously referred to the Murray Valley as ‘Sunraysia’. In bothvineyards, variation in yield and vigour appeared to be drivenby variation in the land supporting the vineyard. This previouswork focussed on measures of vine attributes and wine sensoryanalysis. In the present study, we extend the Murray Valleypart of this work and couple this to a multivariate analysis of

Bramley et al. Vineyard variation in wines, grapes and soils 217

doi: 10.1111/j.1755-0238.2011.00136.x© 2011 CSIRO Australia

measures of soil properties, vine nutrition, and in particular,detailed descriptive sensory and volatile headspace analysis ofwines, in the latter case, using solid phase microextraction-gaschromatography-mass spectrometry (SPME-GC-MS). Thiswork, conducted across three vintages, was an attempt tobetter understand the potential for manipulating wine sensoryattributes through viticultural, as opposed to oenological,management.

Methods and materials

Site description and acquisition and analysis of high resolutionspatial dataThe work was focussed on an 8.2-ha own-rooted section of alarger vineyard in the Murray Valley grapegrowing region ofnorth-west Victoria, which was planted to Cabernet Sauvignonin 1994 (Bramley and Hamilton 2007). The Murray Valleyregion is warm and dry, with mean daily maximum andminimum January temperatures of 32.0 and 16.5°C and 2150–2240 heat degree days (19°C cut-off) during the October to Aprilperiod (AWBC 2010). Mean annual rainfall is only 289 mm andirrigation is therefore essential; in the absence of restrictedaccess to irrigation water, approximately 5 ML/ha/year isapplied. The row orientation at this site is east–west, with rowand vine spacings of 3 and 2.44 m, respectively. The vines aretrained to a two-wire (i.e. dual cordon) system with the cordonwires at 110 and 160 cm above-ground, with the canopyallowed to sprawl. Pruning is by mechanical hedging such thatafter pruning, the hedge is approximately 40 cm wide with thetop of the hedge at approximately 180 cm above-ground. Soilsat this site are calcarosols (Isbell 1996) and are duplex in nature,comprising sandy topsoils of varying depths (20–70 cm) overcalcareous clay subsoils. They are thus typical of the remnantdune systems of Mallee landscapes that characterise the MurrayValley region.

A mix of spatial data was collected. Remotely sensed digitalmultispectral video imagery was collected at veraison (Lambet al. 2004) in 2004–2007 and the so-called plant cell densityindex (PCD) calculated; that is, the ratio of reflected infra-red : red light (PCD = NIR/R), which gives a surrogate measureof vine vigour (Dobrowski et al. 2003). Yield mapping (2004–2007) was carried out using a mechanical harvester fitted witha differentially corrected global positioning system (accurate to

approximately �50 cm in the x and y planes) and a Farmscan™(2004–2006; Computronics, Perth, Western Australia) or ATV™(Advanced Technology Viticulture, Adelaide, South Australia)yield monitor. A high resolution vineyard soil survey was con-ducted in September 2004 using electromagnetic induction(EM38 – Geonics, Mississauga, Ontario, Canada; e.g. Proffittet al. 2006) and real-time kinematic GPS (accurate to 2–3 cm inthe x-, y- and z-planes) as a consequence of which, a digitalelevation model of the site was also available (Bramley andHamilton 2007). Details of the methods used for interpolatingthese data into map layers and their subsequent spatial analysisare provided in Bramley and Hamilton (2004, 2007) and Proffittet al. (2006). The ArcGIS software suite (v9.2; ESRI, Redlands,CA, USA) was used for map display.

As described by Bramley and Hamilton (2007), k-meansclustering of the data underlying the 2004 yield map and PCDimagery obtained in 2004 and 2005 was conducted using JMP 7(SAS, Cary, NC, USA) to identify zones of characteristic perfor-mance (Bramley and Hamilton 2004, 2005, Bramley 2005)within which areas of characteristically low or high yield/vigourwere delineated. Within these areas, which are henceforthreferred to as the ‘low’ and ‘high’ areas, subsections were delin-eated (Figure 1) and were used for subsequent sampling of vinesfor assessment of vine and fruit attributes and small-lot wine-making. Note that each year, additional map layers (from theprevious year) were incorporated into the cluster analysis toensure the temporal stability of the patterns of variation in theblock (Bramley and Hamilton 2004). Given that the patternswere indeed stable, the subsections used for samples remainedconstant for the duration of the study.

For the purposes of correlating data derived from thesespatial data layers with measures made on vines and soils (seebelow), values were extracted on a per pixel basis from theinterpolated map layers for those pixels that either matcheddirectly with or were closest to the location of sampling points.This was done using the ‘sample’ command in ArcGIS SpatialAnalyst.

Indicators of vine performance, soils and vine nutrient statusPre-vintage monitoring of vines randomly selected from withinthe low and high areas was used to identify a sampling datewhen the fruit was expected to be at a target maturity of 24°

Figure 1. Summary of highresolution spatial data used inthis work for the delineation ofthe ‘high’ and ‘low’ zones andlocation of sampling areas forvine sampling and small-lotwinemaking. ‘PCD’ denotesso-called plant cell density,the ratio of infrared : redreflectance; ECa denotesapparent electrical conductivityas measured using an EM38sensor, in this case using thehorizontal dipole (see Figure 4for more details). Note that thearea of the block is 8.2 ha andthat, in this figure, elevationhas been exaggerated by afactor of 7; the range ofelevation (highest to lowestpoint) is 3.43 m.

218 Vineyard variation in wines, grapes and soils Australian Journal of Grape and Wine Research 17, 217–229, 2011

© 2011 CSIRO Australia

Brix. On the chosen sampling date, measurements were madeof a range of vine and fruit attributes on a number of geo-referenced target vines. Approximately 14 such target vineswere randomly located in each of the low and high areas in2004 (Figure 1); for the most part, the same target vines wereused in each subsequent year of the study, although in someinstances these may have moved to an adjacent vine, forexample when a vine label was lost during harvesting or fol-lowing accidental damage to the cordon caused by normalvineyard operations. Twelve bunches were randomly sampledfrom each target vine from a 1 m section centred on the trunk,with six samples taken from either side of the row; the roworientation in this block is east–west. The measurements madewere of bunch weight and mean berry weight (from which thenumber of berries per bunch was calculated). Total solublesolids (°Brix; by digital refractometer with temperature com-pensation – Atago, Tokyo, Japan), juice pH and titratableacidity (TA; by autotitration) were measured within a fewhours of sampling, while the concentrations of anthocyanins(colour) and phenolics were subsequently analysed spectro-photometrically on a homogenised subsample of berries thatwas stored frozen prior to analysis. The aforementioned chemi-cal analyses were carried out using standard Australian wineindustry methods (Iland et al. 2004).

During the following dormant (winter) season in each year,pruning weights were measured on the same target vines afterpruning these by hand to the same vine dimensions as resultedfrom the mechanical pruning (see earlier discussion).

Chemical analysis of vineyard soils and vine nutrient statuswas also carried out. Soil sampling undertaken in November2006 involved the collection of duplicate cores (5 cm diameter)from within 50 cm of the trunk of each target vine using a jackhammer and lever system to insert and extract the core tubes;each duplicate pair was collected within 50 cm of each other.The cores were sectioned into 5–15, 30–40 and 60–70 cm depthincrements and each sample pair was bulked on a depth basis.These depth increments were chosen to reflect the biologicallyactive topsoil and the upper and lower bounds of that part of thesubsoil containing the majority of the vine roots. The sampleswere subsequently analysed for a range of chemical propertiesusing the standard Australian methods detailed by Rayment andHigginson (1992). In addition, the soils were analysed usingmid-infrared diffuse reflectance spectroscopy (Janik and Skjem-stad 1995, Janik et al. 1998) as a surrogate means of assessingsoil particle size distribution.

Vine nutrient status was assessed on petioles collected fromthe target vines at flowering (Robinson et al. 1997) during the2006–2007 growing season. In addition, the nutrient content ofharvested berries was also analysed (vintage 2007). In bothcases, total nutrient contents were analysed using inductively-coupled plasma optical emission spectroscopy following tubeblock digestion with nitric acid at 140°C for 8 h (Zarcinas et al.1983).

Analysis of variance (ANOVA) was performed for grapeberry attributes such as bunch weight, mean berry weight, Brix,pH, TA, pruning weights, colour and phenolics using JMP. One-way analysis was used for each of the study years while two-way analysis was used to examine the interaction betweenyears; the model included vigour class, year and the interactionbetween these. ANOVA was also used to analyse grape andpetiole nutrients and soil properties. Pairwise correlations wererun on all variables measured using the multivariate platform inJMP. R (R Foundation for Statistical Computing, Vienna,Austria) was used for stepwise linear diagonal discriminantanalysis to explore between zone differences in multivariate

space. Our focus in this statistical analysis was on 2005–2007,the years for which complete data sets were available.

Winemaking and sensory analysisSimultaneous with sampling of target vines, a 200 kg sample offruit was harvested from within each zone. This sample was wellmixed and then subsampled into triplicate 50 kg lots fromwhich wines were made from each lot (i.e. three fermentationreplicates for each zone) following a standardised small-lotwinemaking protocol as follows.

Grapes (50 kg) were crushed and de-stemmed with theaddition of 40 mg/L K2S2O5. Samples of must were analysed forpH, TA and °Baumé and pH adjusted to 3.3–3.7 using tartaricacid if required. The assimilable nitrogen content of the mustwas supplemented by the addition of 200 mg/L (NH4)2HPO4,and yeast strain EC1118 (Lallemand, Quebec, Canada) wasinoculated at a concentration of 200 mg/L. Fermentation wascarried out on the skins with an aim to reduce sugar levels by1–2 °Baumé per day with temperatures adjusted accordingly. Ingeneral, the ferments were conducted at 18–20°C and the capwas plunged twice a day. Ferments were drained and pressedwhen the °Baumé reached 2°, and the free run juice and press-ings further fermented to dryness when the wine was thenracked off the gross lees. K2S2O5 was added at 40 mg/L toprevent spoilage and malolactic fermentation and the wine coldstabilised at 0°C for 21 d. The wine was then racked off fininglees, SO2 levels adjusted to 80 mg/L with K2S2O5, filteredthrough a 45-mm membrane and bottled in 375 mL bottles usingscrew-cap closures.

Duo-trio testing of differences between the low and highwines using 30 untrained assessors was conducted on a pervintage basis approximately 4 months after the 2007 bottling.Twelve months after the bottling of the 2007 wines, a consensusdescriptive analysis (Lawless and Heymann 1998) using a panelof 13 trained assessors was conducted on all six wines (vintages2005–2007). A list of key attributes (16 aromas, 15 by mouth/flavours) for formal rating of the wines was developed duringsix training sessions. Each attribute term had an agreed defini-tion and a suitable reference standard, which was presented atthe final rating session. In one formal rating session, all 6 wineswere evaluated independently in individual booths. In theabsence of winemaking faults or discernible differences betweenthem, the three fermentation replicates for each wine werecombined and blended to make a representative sample of eachtreatment. Wines were presented in a balanced order in twoblocks of three wines per tray. Samples were presented in trip-licate in random order in three-digit coded ISO standard tastingglasses (30 mL) and assessed at room temperature. The panel-lists rated each of the attributes on a 15-cm line scale, withanchors of ‘weak’ and ‘intense’ placed at 1 and 14 cm. For eachattribute, descriptive analysis data was analysed by ANOVAusing SPSS 15.0 (SPSS Inc., Chicago, IL, USA), testing for theeffect of wine type, panellist and replicate, initially treatingpanellist as a fixed variable. Those attributes that showed sig-nificant wine ¥ panellist effects were standardised by dividingthe scores by the standard deviation for that panellist (Næs andLangsrud 1998). Those attributes found to be significantly dif-ferent among the wines were then analysed using mixed modelsfor the effects of vigour, vintage and replicate; panellist wastreated as a random variable in this latter analysis. Note thatwhereas the difference testing was conducted on a vintage-specific basis, the descriptive analysis involved comparison ofall six wines across seasons with each wine treated as an inde-pendent treatment. Principle components analysis of the data



generated by the descriptive sensory study was conducted usingThe Unscrambler (version 9.6, Camo, Oslo, Norway). Allsensory analysis was conducted in a purpose-built sensory labo-ratory with individual tasting booths, controlled temperatureand lighting, etc. (ISO, 8589, 1988).

Chemical analysis of wine volatilesFurther to the sensory analysis described above, we also analy-sed the volatile compounds in the headspace of the winesusing SPME-GC-MS, as described in Keyzers and Boss (2010),within two months of the sensory descriptive analysis. Each ofthe three fermentation replicates made from each zone in eachvintage (n = 18) were analysed separately. The identity ofdetected volatile compounds was determined by comparingmass spectra with those of authentic standards and spectrallibraries. A laboratory generated library (328 compounds) aswell as the US National Institute of Standards and Technology-05a (NIST-05a) and the Wiley Registry 7th Edition mass spec-tral libraries were used for identification purposes. Compoundswere considered positively identified after matching of bothmass spectra and linear retention indices (LRI) with those ofauthentic samples. LRI was calculated from a compoundsretention time relative to the retention of a series of n-alkanes(C8–C26). The components of the samples were quantifiedrelative to the internal standard (D13-hexanol) using the peakarea of an extracted ion for each compound. The effects ofvine vigour and vintage on the concentration of volatiles inthe headspace of the wines were analysed by ANOVA usingSPSS 15.0.

Partial least squares (PLS) regression analysis was used tocombine the normalised mean values for significant volatilecomponents (x variables) and sensory attributes (y variables).Mean values were normalised against the maximum value forany one product so that each variable had an equivalent influ-ence on the PLS model. The PLS output scores and loadingswere normalised and plotted for the significant factors using TheUnscrambler.

Results and discussion

Delineation of vineyard zonesk-means clustering of the PCD imagery and yield maps (2004–2007; Figure 1) confirms that the zones of characteristic perfor-mance identified by Bramley and Hamilton (2007) using justthe 2004 and 2005 data were maintained over the longer periodof the present study. This result is in accord with previousobservations that patterns of vineyard variation tend to be stablein time (Bramley and Hamilton 2004). Accordingly, we can beconfident that the small-lot wines produced from fruit sampled

from the areas shown in Figure 1 are representative of largerareas within the block that are inherently of either lowervigour/yield (low) or higher vigour/yield (high). Table 1 detailsthe mean zone yields that, in each year of the study, weresignificantly different (P < 0.05) between the high and lowzones, in spite of the seasonal/climatic variation, which led tointer-annual variation in the mean productivity of the block asa whole.

Sensory analysisDuo–trio testing indicated that the wines derived from the lowand high zones were significantly different from each other ineach year of the study (P < 0.01 for 2005 and 2007, P < 0.05for 2006). Panel training conducted as part of the descriptiveanalysis of the wine identified 16 aroma and 15 flavourattributes, which assessors used to describe the wines. Ofthese, significant differences were identified between thewines for five aroma and ten flavour attributes (Table 2); thatis, around half of the sensory attributes identified by the panelexhibited significant differences among the wines. Irrespectiveof vintage effects, wines from the low zone were generallymore fruity than those from the high in terms of both flavourand aroma (Table 2 and Figure 2). The sensory attributes moreassociated with the low zone wines included ‘red berry’, ‘redconfection’ and floral aromas, and ‘red confection’, freshberry’ and ‘dried fruit’ flavours (Figure 2). It was also observedthat the spectrum of fruit characters moved from ‘red berry’and ‘red confection’ odour in the most recent vintage to ‘driedfruit’ odour and ‘dark berry’ flavour in the older wines(Figure 2). The high zone wines were characterised by greenattributes (‘stalky’ flavour and ‘olive’ aroma) or meaty aromas(Table 2 and Figure 2). Not surprisingly, the ‘drying’ flavourattribute was negatively correlated to ‘smoothness’ (Figure 2)and these were more descriptive of the vintage (and thusperhaps the age of the wines), not the vineyard zones. Alsoof note was the fact that the low and high wines were per-ceived as similar with respect to acidity (data not shown).Overall, the sensory analysis supports the view (Bramley andHamilton 2007) that wines derived from zones delineatedfrom within the same uniformly managed vineyard block onthe basis of their inherent vigour and propensity to yield aresensorially different. The question then becomes: how andwhy is this so?

Chemical analysis of winesTable 3 lists 56 volatile compounds that were found to occurat significantly different concentrations in the headspace ofthe wines using SPME-GC-MS. Of these, all but 4 showed

Table 1. Mean zone yields (t/ha) and plant cell density (PCD) values for the low and high zonesover the course of the study.†

Year 2004 2005 2006 2007

Zone PCD Yield PCD Yield PCD Yield PCD Yield

High 156 22.2a 127 18.9a 151 24.6a 2215 19.2a

Low 109 17.4b 88 15.5b 107 20.8b 1537 14.9b

†Note that these are the means for zones delineated by k-means clustering of the available yield and PCD data. In any given year, yieldsfollowed by different letters are significantly different (P < 0.05) based on a test of significance using the median kriging error (Taylor et al.2007). This test can not be applied to PCD as this is not kriged. The difference in magnitude between PCD in 2007 and other years is asimple reflection of a different stretch being applied to the data by the commercial provider. All PCD values are dimensionless.



significant (P < 0.05) between-vintage differences, a result thatemphasises the complexity of understanding the between-zone wine differences. These vintage effects could be caused bychanges in wine chemistry during wine aging, considering thatall of the wines were analysed in 2007, or environment dif-ferences across the three years of the study. However, acrossvintages, 21 compounds were at significantly different

(P < 0.05) concentrations in the headspace of the low com-pared to the high wines (Table 3). Of these 21, 10 had highermeans in wines from one zone compared to the other acrossthe three vintages (italicised in Table 3). PLS analysis was con-ducted using all significant volatile components to predict thesignificant sensory attributes, and this is shown in Figure 3.The first two latent vectors accounted for 72 and 77% of the

Table 2. Sensory attributes found to be significantly different among the wines through descrip-tive analysis.

Attribute 2005H 2005L 2006H 2006L 2007H 2007L P value

Zone Vintage

Aroma

Red berry 3.26c 3.94ab 3.81ab 3.61abc 3.53bc 3.98a 0.01 0.986

Red Confection 2.32c 2.85ab 2.55abc 2.72abc 2.41bc 2.94a 0.001 0.829

Floral 1.15c 1.70a 1.18bc 1.57ab 1.57ab 1.70a 0.001 0.127

Olive 3.59a 2.62b 3.07ab 2.53b 2.49b 2.27b 0.009 0.027

Meaty 2.69a 2.13bc 2.25b 2.04bc 2.20b 1.74c 0.001 0.013

Flavour

Fresh berry 5.21ab 5.64a 4.86b 5.68a 5.02b 5.36ab 0.001 0.50

Dried fruit 3.65b 4.37a 3.47b 3.64b 3.15b 3.38b 0.023 0.001

Stalky 4.68a 3.77b 4.72a 4.49a 4.83a 4.93a 0.066 0.018

Red Confection 2.29bc 3.29a 1.73c 2.78ab 2.37bc 2.81ab <0.001 0.082

Pepper 2.54ab 2.75a 2.10bc 2.38abc 1.90c 2.58ab 0.016 0.059

Fruit length 6.42a 6.93a 5.73b 6.37a 5.88ab 6.12ab 0.002 <0.001

Body 7.44ab 7.74a 6.90c 7.27bc 6.80c 7.20bc 0.006 <0.001

Viscosity 6.81ab 7.10a 6.32bc 6.71ab 6.17c 6.58ab 0.011 0.002

Smoothness 7.90a 7.79ab 6.91c 7.00c 7.11bc 6.51c 0.11 <0.001

Drying 7.83c 8.03bc 8.40bc 8.50c 8.14bc 9.33a 0.004 0.001

Numbers followed by different letters are significantly different at P < 0.05. High and low zones are denoted by H and L.

Figure 2. Discrimination of the wine samples, produced from the high and low vineyard zones, by sensory attributes and illustrated bythe score (a) and loading (b) plots from principal component analysis. Aroma attributes are in italics and flavour attributes in regulartypeface.



Table 3. Volatile compounds found to be significantly different in the headspace of the wines (n = 18).

Compound Unique ion† LRI‡ Compound ID§ Zone P value Vintage P value

Ethyl acetate 61 887 A 0.082 0.014Ethyl butanoate¶ 88 1018 A 0.005 <0.001Ethyl 2-methylbutanoate 102 1028 A <0.001 <0.001Ethyl 3-methylbutanoate 88 1043 A 0.624 <0.0013-Methylbutyl acetate 87 1089 A 0.014 <0.001Ethyl pentanoate 88 1099 A 0.068 <0.0013-Methylbutyl propanoate 70 1149 A 0.668 0.040Gamma-terpinene 121 1228 A 0.675 <0.001Hexyl acetate 84 1237 A 0.014 <0.0012-Methylbutyl 3-methylbutanoate 85 1240 B 0.406 0.0023-Methylbutyl 3-methylbutanoate 70 1256 B 0.327 <0.001Ethyl 3-hexenoate 88 1272 A 0.004 <0.001Propyl hexanoate 117 1280 A 0.929 <0.001Ethyl heptanoate 88 1297 A 0.592 <0.0014-Methyl-1-pentanol 56 1301 A 0.325 0.0282-Heptanol 83 1304 A <0.001 <0.001Ethyl 2-hexenoate 97 1313 A 0.004 <0.0011-Hexanol 69 1338 A <0.001 <0.001Methyl octanoate 87 1356 A 0.114 0.0042-Nonanone 58 1358 A 0.007 0.2023-Ethoxy-1-propanol 59 1373 A 0.158 <0.0013-Methylbutyl hexanoate 99 1435 A 0.400 0.003Furfural 96 1451 A 0.349 <0.0012-Ethyl-1,3-dimethyl benzene 119 1455 B 0.020 <0.001Ethyl 7-octenoate 88 1462 B 0.321 0.004Acetic acid 60 1468 A 0.016 0.355Unknown monoterpene 121 1486 C 0.005 0.055Propyl octanoate 127 1499 A 0.326 <0.001Vitispirane 1 192 1500 B <0.001 <0.001Vitispirane 2 192 1503 B 0.003 <0.001Ethyl nonanoate 88 1517 A 0.314 <0.001Ethyl 2-hydroxyhexanoate 87 1529 B 0.095 0.0012-Methylpropyl octanoate 127 1538 B 0.745 0.0011-Octanol 84 1540 A 0.001 0.028Ethyl 3-(methylthio)propanoate 148 1548 A 0.519 <0.0013-Methylbutyl 2-hydroxypropanoate 70 1554 B 0.038 0.0035-Methyl-2-furfural 110 1559 B 0.386 <0.001Diethyl malonate 115 1562 A 0.188 <0.001Ethyl 2-furoate 95 1605 A 0.783 0.020Ethyl decanoate 88 1625 A 0.002 0.1481-Nonanol 70 1641 A 0.047 0.003Ethyl benzoate 105 1648 A 0.644 0.002Ethyl 9-decenoate 88 1675 B 0.465 0.0053-Ethyl benzaldehyde 134 1691 B 0.558 0.004Unknown norisoprenoid 192 1701 C 0.365 0.001TDN 157 1725 B 0.030 <0.001Diethyl glutarate 143 1767 A 0.896 <0.001Ethyl phenyl acetate 91 1770 A 0.445 <0.001Phenyl ethyl acetate 104 1798 A 0.474 <0.001b-Damascenone 121 1801 A 0.001 0.004Ethyl dodecanoate 88 1828 A 0.001 0.001Hexanoic acid 60 1852 A 0.283 <0.001Benzyl alcohol 107 1865 A 0.075 <0.001Phenyl ethyl alcohol 122 1901 A 0.507 0.004Octanoic acid 101 2065 A 0.905 0.003Decanoic acid 129 2280 A 0.459 0.013

†Unique ion (m/z): used for peak area determination. ‡Linear retention indices (LRI) calculated from retention relative to the retention of a series of n-alkanes(C8–C26). §A, identity confirmed by matching mass spectra and LRI with that of authentic standards; B, tentative assignment based upon comparison with NIST05and Wiley Registry (7th edition) mass spectral libraries, and published retention times; C, tentative identification of compound class suggested by comparison of massspectra with those of related compounds in compound libraries. ¶Compounds in italics had higher means in wines from one zone compared to the other across thethree vintages.



variance for the x and y variables, respectively (Figure 3).Wines were differentiated because of vintage in the first latentvector and because of vineyard zone in the second latentvector. Of the 10 compounds that showed consistent high/lowzone differences, 2-nonanone positively correlated with ‘redconfection’ flavour (Figure 3). Ethyl decanoate and ethyldodecanoate were positively correlated with ‘fresh berry’flavour and ‘floral’ odour, respectively, whereas 1-octanol,2-heptanol and an unknown monoterpene were negativelycorrelated with the ‘fresh berry’ and ‘red confection’ flavourdescriptors. The levels of ethyl butanoate and vitispirane1 were associated with ‘dried fruit flavour’ and ethyl3-hexenoate was positively correlated with ‘stalky’ flavour.The concentration of 2-ethyl-1,3-dimethyl benzene showed astrong vintage effect (Table 3) and so did not co-localise withsensory attributes in the PLS analysis. Overall, the PLS modelconfirms the principle components analysis (Figure 2)whereby the fruity characters are associated with the lowvigour vineyard zone, but it also shows that overall thereare more compounds associated with the latent vectordifferentiating these wines from those made from the highzone. Therefore, in general the low zone wines have higherconcentrations of a range of volatile components, which mayhave led to higher fruit-driven sensory descriptors.

Fruit and vine attributesSignificant differences in fruit and vine attributes were foundbetween the low and high zones (Table 4) although these werenot consistent from year to year. Indeed, as might be expected,

seasonal effects were highly significant for all attributes(P < 0.001), presumably because of the combined effects ofusual seasonal/climatic variation and also restricted access toirrigation water; much of this study has coincided with a periodof prolonged drought in the Murray–Darling basin.

In spite of the strong seasonal effect, and notwithstandingsome inconsistency in between-zone differences over the3 years of the study, Table 4 suggests significant differencesbetween the low and high zones with respect to all fruit qualityattributes other than juice pH. Of particular interest is the factthat there were no significant differences (P > 0.05) in berryweight between the zones; that is, skin surface areas can beassumed to have not differed. The significant between zonedifferences in the concentrations of colour and phenolics aretherefore important as they may be reflective of an inherentdifference in the biophysical characteristics or terroir of the twozones rather than berry size. Regrettably however, Brix was alsosignificantly different (P < 0.001) between the zones reflecting alack of success with respect to our aim of sampling the twozones at the same target Brix level. Indeed, examination ofTable 4 suggests that we only succeeded in this regard in 2006,although the between zone difference in 2005 was less than 1°.In 2007, the difference was 1.6° in spite of careful monitoring ofmaturity development in the two zones in the lead up tovintage, perhaps reflecting the difficulty wineries have in har-vesting to particular targets given spatial and temporal variabil-ity. It is somewhat paradoxical therefore that, in 2006 when wesucceeded in harvesting the zones at the target °Brix, there wasno significant (P > 0.05) between-zone difference in pruningweight (i.e. vine vigour) that, based on the imagery shown in

Figure 3. Partial least squares analysis of the wines. Small black circles represent the volatile composition loadings (x matrix), the blacksquares represent the flavour sensory attribute loadings and the black triangles the aroma sensory attribute loadings (y matrix), and the largerectangles represent the sample scores for PC1 and PC2. Aroma attributes are in italics and denoted by ‘(a)’, and the flavour attributes arein regular typeface.



Figure 1, was expected to be highly significantly different. Con-sistent with Figure 1 however, was the significant (P < 0.01)between-zone difference in pruning weight over the 3 years ofthe study (Table 4).

Overall, the between-zone differences in vine and fruitattributes are consistent with the results of the sensory analysis.Thus, the higher yielding, more vigorous zone from which theless favoured wines derived, was also characterised by largerbunches, higher TA and lower concentrations of anthocyaninsand phenolics – arguably the expected result given industrybeliefs regarding yield–quality interactions. However, as Trought(2005) has pointed out, the assumption that high yield leads tolower quality may not be well founded. Indeed, it is importantto point out here (see Figure 1 and section on soil properties inthe following), that in this instance, the low and high yieldingareas are not the same. Thus, whether or not managementinterventions aimed at either increasing vigour and yield in thelow zone, or reducing them in the high zone such that the zoneswere no-longer identifiable using the approach that resulted inFigure 1, would result in fruit and wines with the same sensoryattributes in each zone, is not something that can be determinedfrom the present results. Indeed, that is a question worthy ofsubstantial further study. Moving towards an ability to answerthis question is one of the primary motivations for the workreported here.

Vine and grape nutrient statusSignificant differences (P < 0.05) in petiole nutrient status atflowering were observed between the zones during the 2006–2007 season with respect to N, K, B and Mn (Table 5). Petiole Nwas significantly (P < 0.01) lower in the low zone vines than thehigh, although N status in both zones was below the levelconsidered adequate (Robinson et al. 1997). K levels, whichwere greater than the adequate range in the high zone, weresignificantly (P < 0.001) lower in the low zone, but neverthelessfell within the adequate range from a crop nutrition perspective.Petiole B was at adequate levels in the low zone, but was

significantly (P < 0.001) lower in the high zone where B statuswas marginal (Robinson et al. 1997). In contrast, while levels ofMn were in the adequate range in the low zone, they weresignificantly higher (P < 0.01) in the high zone where they fellin the above adequate range.

Table 4. Zone and seasonal effects on fruit and vine attributes.

2005 2006 2007 Zone Year Int†

Low High Sig‡ Low High Sig‡ Low High Sig‡ Low High Sig‡ 2005 2006 2007 Sig‡ Sig‡

Harvest date 27/02 07/03 — 21/02 02/03 — 08/02 28/02 — — — — — — — — —

Bunch wt (g) 68.4 74.1 ns 79.7 112.3 *** — — — 74.0 93.2 *** 71.2b 96.0a — *** **

Mean berry

wt (g)

0.89 0.90 ns 1.03 1.07 ns 0.89 0.94 ns 0.94 0.97 ns 0.89b 1.05a 0.91b *** ns

Berries/bunch 76.9 82.5 ns 77.3 104.9 *** — — — 77.1 93.7 *** 79.7b 91.1a — ** **

°Brix 25.0 24.1 ** 24.5 24.6 ns 24.2 22.6 ** 24.6 23.8 *** 24.5a 24.5a 23.4b *** **

pH 3.47 3.57 ** 3.53 3.52 ns 3.31 3.22 *** 3.44 3.44 ns 3.53a 3.53a 3.26b *** ***

TA (g/L) 6.71 7.98 *** 6.39 6.94 * 8.05 8.09 ns 7.05 7.62 ** 7.27b 6.66c 8.07a *** ns

Colour (mg/g

berry wt)

1.35 1.23 ns 1.28 0.88 *** 1.40 1.26 ns 1.34 1.12 *** 1.29a 1.08b 1.33a *** *

Phenolics (au/g

berry wt)

1.24 1.15 * 1.40 1.07 *** 1.56 1.42 ** 1.40 1.21 *** 1.20b 1.24b 1.49a *** ***

Pruning

weight (g)

27.7 37.3 * 18.0 22.0 ns 14.5 28.9 ** 19.9 29.4 ** 32.5a 19.8b 21.7b *** ns

†Interaction, zone ¥ year. ‡Significance of difference where ***, **, * and ns denote P < 0.001, P < 0.01, P < 0.05 and not significant, respectively. In the analysis ofbetween-year differences, numbers followed by different letters are significantly different.

Table 5. Differences in vine nutrient status (petioles atflowering) during season 2006–2007 between the lowand high zones.

Low High Sig† Interpretation‡

N (%) 0.58 0.65 ** Both zones < adequate

Ca (%) 1.7 1.7 ns Adequate

K (%) 2.5 3.2 *** Low zone adequate, high

zone > adequate

Mg (%) 0.58 0.50 ns Both zones > adequate

Na (%) 0.019 0.015 ns Both zones

considerably < toxic

P (%) 0.23 0.28 ns Low zone marginal; high

zone adequate

S (%) 0.14 0.14 ns —

Al (mg/kg) 39 43 ns —

B (mg/kg) 39 30 *** Low zone adequate; high

zone marginal

Cu (mg/kg) 7.3 6.8 ns Both zones adequate

Fe (mg/kg) 190 130 ns Both zones adequate

Mn (mg/kg) 54 70 ** Low zone adequate; high

zone > adequate

Zn (mg/kg) 53 46 ns Both zones adequate

†Significance of difference where ***, ** and ns denote P < 0.001, P < 0.01 andnot significant, respectively. ‡Adequacy of nutrient status based on Robinsonet al. (1997).



Aside from other considerations, examination of theseresults highlights the fact that, hitherto, management of vinenutrition has only been considered from a vine health and cropproduction perspective (e.g. Robinson and McCarthy 1985,Robinson et al. 1997), with scant attention paid in the publishedliterature, to the idea that vine nutrition might impact, albeitsubtly in some instances, on fruit quality (Ruhl et al. 1992) andthe sensory attributes of wines. Not the least of reasons for thismay be that much of our knowledge of vine nutrition derivesfrom research with juice or table grapes (e.g. Christensen 1984),even though fertiliser management has been proposed as aviable tool for the management of winegrape quality (Ruhl et al.1992.).

As the important sensory attributes of grapes and winesderive from the berries, it is reasonable to argue that if differ-ences in vine nutrition are important for wine quality, thendifferences in petiole nutrient levels ought to be mirrored bydifferences in nutrient concentrations in the grapes. It wasfor this reason that nutrient concentrations in harvestedgrapes were also determined. Comparison of grape nutrientconcentrations at vintage 2007 (Table 6) with petiole nutrientconcentrations measured at flowering during the same season(Table 5), suggests that vine nutrient status and berry nutrientcontent are not as closely related as might be expected.Indeed, Tables 5 and 6 highlight some striking contrasts. Forexample, whereas petiole K status differed significantly(P < 0.001) between the zones, berry K contents were not sig-nificantly different. Similarly, whereas petiole Na status did notdiffer between the zones, concentrations of Na in grapes weresignificantly different (P < 0.001). It was for this reason thatadditional analysis of grape nutrient concentrations weremade the following year (vintage 2008; Table 6). As Table 6indicates, while the zone-based differences observed in 2008were similar to those for 2007, there were a number of incon-sistencies (e.g. Mg, Mn), while the vintage effect was signifi-cant for all nutrients except S and Mn; both of these arecontained in many vineyard pesticides. However, the interac-tion term (zone ¥ year) was not significant for all nutrientswith the exception of N, and on this basis, the zone-based

results (i.e. ignoring season) can be considered robust. Thus,grapes in the low zone contained significantly higher(P < 0.001) concentrations of Ca, Mg and B and significantlylower concentrations of Na, P, S, Fe and Mn than in the highzone (all P < 0.001 except Fe for which P < 0.05).

Potassium nutrition is known to impact on wine qualityespecially through its effects on tartaric acid levels (e.g. Mpela-soka et al. 2003). Table 7 details basic wine chemistry at bottlingand indicates that differences in TA between the low and highwines were small and inconsistent between years, results thatare similar to those from the sensory analysis in which noperceptions of acidity difference between the low and highwines were identified. It is therefore of interest that while therewere no significant differences in grape K concentrationsbetween the zones, petiole potassium status was significantly(P < 0.001) higher in the high zone from where the less favour-ably perceived wines derived. Ruhl (1989) has reported thathigher K supply leads to both higher petiole K concentrationsand lower grape juice quality. Our results suggest that petiole Kmay not be a good predictor of wine quality.

Also of interest are the results for B and Na. Boron toxicityis a well-known problem confronting cereal growers in theremnant dune systems of the Mallee landscapes (e.g. Cartwright

Table 6. Differences in grape nutrient concentrations between the low and high zones (vintages2007 and 2008).

2007 2008 Zone Year Interaction

Low High Sig† Low High Sig† Sig† Sig† Sig†

N (%) 0.35 0.42 * 0.43 0.43 ns * ** *

Ca (%) 0.11 0.098 * 0.12 0.10 *** *** * ns

K (%) 0.78 0.77 ns 0.85 0.90 * ns *** ns

Mg (%) 0.044 0.042 ns 0.052 0.047 *** *** *** ns

Na (%) 0.0035 0.0088 *** 0.0061 0.010 ** *** * ns

P (%) 0.081 0.092 ** 0.088 0.10 *** *** ** ns

S (%) 0.041 0.046 ** 0.043 0.045 ns *** ns ns

Al (mg/kg) 18 14 ns 8.4 7.3 ns ns *** ns

B (mg/kg) 29 20 *** 33 24 *** *** ** ns

Cu (mg/kg) 2.2 2.8 * 3.8 3.7 ns ns *** ns

Fe (mg/kg) 7.8 9.4 ns 6.2 6.8 ns * *** ns

Mn (mg/kg) 6.1 7.1 ns 5.8 7.6 *** *** ns ns

Zn (mg/kg) 3.4 3.9 ns 5.6 5.7 ns ns *** ns

†Significance of difference where ***, **, * and ns denote P < 0.001, P < 0.01, P < 0.05 and not significant, respectively.

Table 7. Attributes of basic wine chemistry at bottling.

2005 2006 2007

Low High Low High Low High

pH 3.25 3.24 3.42 3.40 3.34 3.33

TA (g/L tartaric) 7.7 7.6 7.1 7.4 7.7 7.6

Alcohol (%) 15.1 14.8 14.8 13.8 14.6 13.6

VA (g/L acetic) 0.23 0.18 0.25 0.19 0.23 0.17

Data are means of triplicate ferments. All wines were adjusted to 80 mg/L SO2.TA, titratable acidity; VA, volatile acidity.



et al. 1986), which dominate the Murray Valley region. Borontoxicity particularly occurs in low lying swales where it is oftenassociated with soil salinity and sodicity. However, both petioleand grape B were significantly (P < 0.001) lower in the highzone. While the high zone is not confined to swales (Figure 1),the low zone is confined to the tops of the remnant dunes whereB levels and availability would be expected to be lower, aswould the risk of soil salinity. Neither high nor low vinesshowed evidence of toxic levels of petiole Na (Table 5) suggest-ing an absence of salinity or sodicity impacts on vine perfor-mance at this site, although grapes from the high zone didcontain significantly (P < 0.001) higher concentrations of Na.Whether the lower B status of the high zone vines might con-tribute to lesser quality wine is unknown; wines high in Na arecertainly not favourably regarded (Walker et al. 2004), althoughsaltiness was not noted as a characteristic of the high zone winesduring their sensory analysis.

Soil propertiesGiven the varying depth of topsoils in Mallee Calcarosols,largely as a function of topographic variation among remnantdunes and the occurrence of the low zone on higher ground, itis not unexpected that the major zonal differences in soil prop-erties are reflective of topsoil depth and soil texture. Thus, highzone soils tend to have higher clay contents, especially in their

poorly drained subsoils, whereas in the shallower low zonesoils, the high carbonate content characteristic of these Malleesoils is seen closer to the surface (Table 8). Also characteristicof Mallee soils, concentrations of B, Cl, exchangeable Na andthus, the exchangeable sodium % (ESP) are higher in high zonesubsoil samples. While the high zone is not confined to thelowest-lying area of the vineyard, approximately half of thesampling area used in this work occurs there (Figures 1,4) andthus coincides with the area of high apparent electrical conduc-tivity (ECa) identified in the EM38 soil survey (Figure 4). Therewas a strong correlation (P < 0.001) between ECa and both claycontent and exchangeable Na in soil samples from the 60–90 cmdepth range (correlation coefficients of 0.908 and 0.931, respec-tively). Thus, we infer that the high values of ECa derive fromthe higher subsoil clay content in this part of the block and itswet, poorly drained and sodic (Table 8) state; soil slaking anddispersion were not tested in this work.

Using the equations of Shaw (1999) to estimate saturationpaste extract EC (ECse) from the EC1:5 measured here, along withthe clay and Cl contents (Table 8), enables evaluation of thelikely impact on vine growth of these inhospitable subsoils.Thus, ECse is estimated at approximately 24 dS/m for the60–70 cm depth increment of high zone soil, a salinity level thatwould be highly toxic to grapevines (Shaw 1999). Inspectionpits dug in the vineyard suggest that throughout, the roots

Table 8. Between zone differences in selected soil properties (November 2006).

Depth (cm) 5–15 30–40 60–70

Soil property† Low High Sig† Low High Sig† Low High Sig†

Clay (%) 8.10 9.46 ns 10.94 16.06 * 9.67 22.54 **

Silt (%) 24.34 21.99 ** 24.58 22.22 ns 27.93 21.49 **

Sand (%) 67.18 68.55 ns 61.72 61.72 ns 62.52 57.23 ns

EC1:5 (dS/m) 0.09 0.07 ns 0.08 0.09 ns 0.08 0.25 *

pH (water) 8.6 8.1 * 8.9 7.8 *** 9.2 9.0 ns

pH (0.1 M CaCl2) 7.8 7.3 ns 7.9 6.9 ** 8.2 7.9 ns

Cl (mg/kg) 17 22 ns 14 32 ns 17 129 *

Total C (%) 0.92 0.30 ** 1.22 0.32 * 1.31 0.96 ns

Organic C (%) 0.46 0.27 ns 0.19 0.23 ns 0.14 0.16 ns

Carbonate (% CaCO3 equiv.) 4 0 * 9 1 * 10 7 ns

Total N (%) 0.033 0.025 * 0.018 0.022 * 0.013 0.016 *

Ext. P (mg/kg) 21 38 ns 3 12 ** 2 5 ns

Exch. K (cmol(+)/kg) 0.41 0.29 * 0.22 0.24 ns 0.22 0.28 ns

Exch. Ca (cmol(+)/kg) 5.3 2.6 *** 4.8 4.4 ns 4.1 4.7 ns

Exch. Mg (cmol(+)/kg) 1.4 1.1 ns 1.4 1.5 ns 1.8 2.7 ns

Exch. Na (cmol(+)/kg) 0.07 0.07 ns 0.08 0.33 * 0.1 1.6 ***

CEC (S cations; cmol(+)/kg) 7.2 4.1 *** 6.4 6.5 ns 6.2 9.3 *

ESP (%)‡ 1.0 1.7 — 1.3 5.1 — 1.6 17.2 —

Ext. Cu (mg/kg) 0.6 0.3 * 0.6 0.8 * 0.4 0.7 ***

Ext. Fe (mg/kg) 4 8 ns 2 8 *** 2 5 ***

Ext. Mn (mg/kg) 6 8 ns 3 11 *** 2 6 ***

Ext. Zn (mg/kg) 0.6 0.8 ns 0.2 0.1 ns 0.2 0.1 ns

Ext. S (mg/kg) 4 3 ns 2 5 ns 2 19 *

Ext. B (mg/kg) 0.5 0.3 ns 0.3 0.1 ** 0.4 1.3 **

†Here, ‘Ext.’ denotes extractable and ‘Exch.’ denotes exchangeable using the methods described by Rayment and Higginson (1992) – Pusing the Colwell extract, exchangeable cations by BaCl2, extractable trace elements by DTPA, S using Ca(H2PO4)2 and B by CaCl2.‡Exchangeable sodium percentage; in Australia, soils are considered sodic when ESP > 6% (Northcote and Skene 1972, Sumner 1995).



predominate in approximately the top 50–60 cm, and atshallower depths in the low zone. Using the data for the 5- to15- and 30- to 40-cm depth increments (Table 8) to estimate‘typical’ whole-vineyard values for clay content (10%), EC1:5

(0.09 dS/m) and chloride content (20 mg/kg) in depths shal-lower than about 50 cm enables estimation of an ECse for thesesoils of approximately 4 dS/m. This ECse might be expected tolead to a productivity decrease of around 25% below potential(Shaw 1999). While the petiole analysis did not support theview that vine performance was being adversely affected bysalinity or boron toxicity in this vineyard, we might speculate asto how the high zone might yield if these soils were morehospitable. Indeed, the nature of these subsoils undoubtedlyplaces a constraint on rooting depth in this block, hence thepredominance of roots in the top 50 cm. As the high B level inhigh zone subsoils is nevertheless considerably lower than thelevel that would be toxic to barley (Cartwright et al. 1986) andin the absence of toxic levels of B in the petioles, we concludethat it is salinity and sodicity, but not B toxicity, which is con-straining vine rooting depth in this block. However, and asindicated, the petiole analysis does not suggest a salinity impacton vine performance, and nor does the sensory analysis indicatea salinity impact on the wines, so we conclude, notwithstandingthat vine performance may be potentially limited by soil condi-tions, that the vine roots are staying clear of the inhospitableparts of the profile in the high zone. In the low zone, shallowertopsoils and the presence of high levels of carbonate presumablyfurther restrict rooting volume and thus plant-available water,hence the smaller, less vigorous (Figure 1) vines in this zone.Nevertheless, it seems likely that the different soil conditionsbetween the low and high zones are reflected in the chemicaland sensory attributes of the wines derived from them, espe-cially as the locations of the zones is closely aligned with soil andtopographic differences (Figures 1,4; Bramley and Hamilton2007).

Integration of vine, soil and spatial dataNotwithstanding the previous comments on boron and our viewthat in this Mallee vineyard, B toxicity is not posing a significantconstraint to vine performance, there was a strong negativecorrelation (r = -0.73 to -0.84, P < 0.001) between yield (valuesextracted from yield maps) in each year of the study and theconcentrations of petiole B measured at flowering in the 2006–2007 season. Grape B (2007) was similarly negatively correlatedwith yield. Moderate but significant negative correlations werealso seen between yield and concentrations of Ca (r = -0.46 to-0.58; P < 0.001) and Mg (r = -0.32 to -0.39; P < 0.05) ingrapes. In contrast, moderate positive correlations were foundbetween yield and concentrations of P (r = 0.32 to 0.49 P < 0.05

in 2005 and 0.001 in ’06 and ’07), S (r = 0.35 to 0.49 P < 0.01),and unexpectedly, Na (r = 0.49 to 0.63 P < 0.001) in grapes.Yield was also positively correlated with petiole K (all years;r = 0.47 to 0.59 P < 0.05) and N in 2006 (r = 0.41, P < 0.05) and2007 (r = 0.52 P < 0.01) as might be expected, especially in thecase of N, given its suboptimal levels. Similar correlations werenoted between petiole and grape nutrients and vine vigour asmeasured by remotely sensed imagery (PCD; Figure 1).

As indicated above, ECa obtained through EM38 soil survey(Figures 1,4) was closely related to soil salinity and sodicity insubsoils. ECa was also closely correlated with profile clay content(calculated as the mean of the values presented in Table 8;r = 0.61, P < 0.001). In turn, profile clay content was moder-ately correlated with both yield (r = 0.46 to 0.54, P < 0.01) andPCD (r = 0.36 to 0.50, P < 0.05) suggesting some influence ofwater availability to the vines.

Given that, of necessity, the wines studied in this work weremade to be representative of the two vineyard zones from whichtheir source fruit derived, but that the attributes of the vines andsoils were measured at point locations, establishing cause andeffect relationships between vineyard attributes and either winesensory attributes or chemical constituents is difficult. However,in an attempt to progress this issue, stepwise linear diagonaldiscriminant analysis was used to further explore between-zonedifferences in the various soil and vine attributes measured(data not shown). We used fruit and vine data collected in 2006for this analysis given our success in harvesting both zones atsimilar maturity in this year (Table 4) and made the assumptionthat inter-seasonal differences in soil properties were negligible.A clear discrimination between the low and high zones wasobserved. Presumably because of the small sample number,100% discrimination was achieved using berry phenolics con-centration alone. However, when this variable was removedfrom the analysis, between zone differences in bunch weightand the amount of Fe extractable from the 30–40 cm depthincrement of the soil were then highlighted; the high zonehaving higher levels of extractable Fe. Extractable Fe is a keyindicator of soil drainage status (McFarlane 1999). Thus, the‘low’ zone that is elevated, well drained and has comparativelyshallow soils has low extractable (i.e. plant available) Fe. Incontrast, the lower-lying ‘high’ zone has poorly drained sub-soils, especially in the ‘trough’ on the western side of the ‘low’zone and as a consequence, higher levels of extractable soil Fe;this wetter trough is also indicated by its high electrical conduc-tivity (Figure 4). Note that despite the alkaline soil pH (Table 8),there is no evidence of Fe deficiency in the ‘low’ (or ‘high’) zone(Table 5). Indeed, this might have been more expected in the‘high’ zone because the high concentrations of bicarbonate inthe soil solution during winter and spring waterlogging can

Figure 4. High resolution map ofapparent electrical conductivity derivedfrom survey with an EM38 sensor(horizontal dipole) in September 2004.Elevation, obtained through use ofreal-time kinematic GPS and the ‘Topo toRaster’ function in ArcGIS SpatialAnalyst (ESRI) has been exaggerated bya factor of 7; the range of elevation(highest to lowest point) is 3.43 m. Alsoshown are the zone-based samplingareas in which the target vines werelocated and from which fruit washarvested for small-lot winemaking.



severely restrict the availability of Fe to plants (McFarlane1999).

We also attempted to explore relationships between thesensory and chemical attributes of the wine and the dataobtained from the vineyard such as the soil properties and vinenutrient status to gain insights as to possible cause and effectrelationships, which might warrant further study in the future.Given the lack of replication across years for some variables (soilproperties, vine nutrient status) and the fact that for any givenyear, the sensory and chemical attributes are only available foreither the ‘low’ or ‘high’ wines, the data were aligned as followsto enable examination of simple x–y relationships. Wine datafrom the ‘high’ zone wines were repeated 9 times (9 target vinesin the ‘high’ zone); wine data from the ‘low’ zone wines wererepeated 12 times (12 target vines in the ‘low zone’ zone); andsoil, grape and vine measurements were repeated three timesfor each of the three vintages. Sensory and wine chemistryattributes were then assigned to y, and soil, grape and vineattributes were assigned to x. We accept that in light of thereplication, the degrees of freedom, R2 and P values are notstatistically valid, but the analysis does provide an indication ofstrong relationships. Importantly, it also provided further evi-dence of the strong separation between the ‘low’ and ‘high’zones.

Many apparently significant relationships were identified,so we restricted our consideration to those for which R2 > 0.5and P < 0.001. x Attributes with many apparently statisticallysignificant relationships were soil extractable iron and manga-nese and grape berry phenolics, while the y attributes mostcommonly identified in apparently significant relationshipswere the aroma sensory attributes ‘red confection’, ‘freshberry’ and ‘floral’, and the wine compounds 2-nonanone, andethyl decanoate. An example of one of these relationships isprovided in Figure 5, which shows an apparent associationbetween red confection and soil extractable Fe that, as dis-

cussed earlier, provides surrogate information about soil drain-age status. Ferric and ferrous ions are also common cofactorsimportant for the activity of various enzymes including thecytochrome P450s that are responsible for reactions in manysecondary metabolite pathways (Groves 2005). Iron also con-tributes to the structure and function of some enzymesthrough iron-sulfur clusters (Johnson et al. 2005). It is there-fore possible that differing vine uptake of Fe between the zonesis impacting on the production of compounds that confer thered confection flavour. This is the kind of area in which furtherdetailed research is required, but it may point to an opportu-nity to exert control over the sensory attributes of winethrough viticultural manipulation.

As indicated, these analyses (e.g. Figure 5) do not providerobust evidence of cause and effect. However, in light of thebetween-zone differences in vine, fruit and soil attributes, andespecially the chemical and sensory properties of the winesderived from these zones, further investigation of cause andeffect links between the biophysical environment in whichgrapes grow and the attributes of wines derived from themseems warranted. Without the understanding derived from suchstudies, which may need to employ microvinification of fruitharvested from single vines, the prospects for controlling winesensory attributes through viticultural interventions seemlimited.

Bramley and Hamilton (2007) have previously drawn atten-tion to the lack of importance attached to soil chemistry andvine nutrition with respect to fruit and wine quality (e.g. Seguin1986) and suggested that this may be a direct consequence ofinvestigating this issue at regional scale (e.g. Laville 1990) – acharacteristic feature of the terroir literature, along with a pre-dominance of studies from non-irrigated ‘Old World’ vineyards.The latter may also explain the previous pre-occupation withthe effect of soil hydrological properties on wine style andquality (Seguin 1986; van Leeuwen et al. 2004). While we in noway suggest that soil physical properties are unimportant, thepresent results support our view that a better understanding ofthe impact of soil chemical properties and crop nutrition wouldadd value to the presumption (e.g. Seguin 1986; van Leeuwenet al. 2009) that it is solely soil physical properties that controlwine quality. Furthermore, and in contrast to the results ofReynolds et al. (2007), the present results also strongly supportthe view that the tools of Precision Viticulture, coupled to theanalytical chemistry and sensory methods used here, mayprovide a foundation from which an understanding of cause andeffect links between the biophysical environment in whichgrapes grow and the attributes of wines derived from themmight be acquired.

AcknowledgementsThis work was funded by CSIRO under the auspices of the FoodFutures Flagship with co-investment from the Grape and WineResearch and Development Corporation. However, it would nothave been possible without the input and assistance of CraigThornton and Justin McPhee (Wingara Wine Group – DeakinEstate); their contribution has been valued greatly. We are alsograteful to Chris Day, Peter Rogers and Briony Liebich (formerlyProvisor Pty) for conducting the small-lot winemaking andsensory analysis, and to David Gobbett and Damian Mowat(CSIRO Sustainable Ecosystems), Renata Ristic (formerly CSIROSustainable Ecosystems) and Caroline Tarr, Sue Maffei andEmily Nicholson (CSIRO Plant Industry) for their excellenttechnical assistance. Soil and plant analysis was carried out byJulie Smith and colleagues (Analytical Services, CSIRO Land

Figure 5. Apparent association between ‘Red confection’ aromaand soil extractable iron. Solid symbols denote the ‘high’ zone;open symbols denote the ‘low’ zone. The 2005 vintage is indicatedby circles, 2006 by triangles and 2007 by squares. R2 for theregression is 0.55. See text for further explanation.



and Water). Mary Barnes (CSIRO Mathematics and InformationScientists) kindly provided valuable advice on the multivariatestatistical analysis.

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Manuscript received: 12 August 2010

Revised manuscript received: 13 January 2011

Accepted: 2 February 2011



variation in vine vigour, grape yield and vineyard soils ... · different areas of the same...

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