a microclimate analysis of a niagara peninsula vineyard

A Microclimate Analysis of a Niagara Peninsula Vineyard Using Solar Aspect as a Variable

by

Philip G. Dixon

A Thesis presented to

The University of Guelph

In partial fulfillment of requirements

for the degree of Master of Landscape Architecture

In Landscape Architecture

Guelph, Ontario, Canada

© Philip G. Dixon, May, 2012

ABSTRACT

A MICROCLIMATE ANALYSIS OF A NIAGARA PENINSULA

VINEYARD USING SOLAR ASPECT AS A VARIABLE

Philip G. Dixon Advisor: University of Guelph, 2012 Professor Robert Brown

This study, based on data collected in 2006, examined the effect of microclimate,

as solar aspect, on yield and quality parameters of Riesling vines of a vineyard in the

Niagara Peninsula, Thirty Bench Winery, Beamsville, Ontario. Precision viticulture

practices including GPS (Global Positioning System), GIS (Geographic Information

System) and LIDAR (Laser Range Finder) were used to delineate the microclimate and

categorize variations within the vineyard. Within GIS, slope and aspect analysis tools

generated solar aspect data. Two different zones were identified and compared for yield

and quality data. Monoterpene concentrations in grapes differed by solar aspect, with

vines receiving elevated solar radiation showing increased monoterpene concentrations.

Since monoterpenes are important to the aroma and flavour of Riesling wines, a

difference could impact the quality of the wine produced. Overall, this work shows the

potential of precision viticulture in the development of terroir –specific wine in the

Niagara region.

Key words: solar aspect, precision viticulture, microclimate, monoterpene

iii

ACKNOWLEDGEMENTS

There are so many people in the community around me that have aided me in the

process of creating my thesis. I would like to recognize the following.

Foremost, Professor Robert Brown, whose creativity, calm nature and insight rest

somewhere between Buddhist monk and Jedi knight, for introducing me to this project

and guiding me through the process. Regardless of how concerned I was about my thesis

before a meeting with him, I would always leave knowing that everything was going just

fine.

Thank you to Professor Ralph Brown for providing me with insight and the large

amount of data, without which this study would have been much more difficult and

lengthy. Thank you to Professor Karen Landman for jumping in during Bob’s absence,

for pushing me though the arduous writing processes, and for chairing my thesis defense

on such short notice. Thank you to Professor Andrew Reynolds for his input on my study

and previous work so often referenced in this thesis, as well as Bryan McPherson for all

his help with Arc GIS.

Thank you to all my professors and classmates over the past four years. It been a

wonderful journey and I have learned so much from all of you about so much more than

landscape architecture. It’s been a great time! Finally and most importantly, thank you to

my mother, father, family and friends for all their support and guidance.

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TABLE OF CONTENTS

Acknowledgements............................................................................................................ iii

Table of Contents............................................................................................................... iv

List of Tables ..................................................................................................................... vi

List of Figures ................................................................................................................... vii

Chapter One: Introduction .................................................................................................. 1

Chapter Two: Literature Review ........................................................................................ 3

Chapter Three: Materials and Methods ............................................................................ 10

General Remark.................................................................................................... 10

Location of the Study Site. ................................................................................... 10

The Vines.............................................................................................................. 11

Global Positioning System (GPS). ....................................................................... 12

Laser Range Finder (LIDAR)............................................................................... 13

Geographic Information Systems (GIS)............................................................... 13

Experiment Design ............................................................................................... 13

Determination of Soil Moisture, as well as Yield and Quality Parameters.......... 16

Data Analysis and Presentation ............................................................................ 18

Chapter Four: Results and Discussion .............................................................................. 20

v

Chapter Five: Implications for and uses within Landscape Architecture ......................... 32

Chapter Six: Conclusion ................................................................................................... 35

References Cited ............................................................................................................... 36

Appendix A - The impact of solar aspect on soil moisture and quantity and quality characteristics of vines and grapes at Thirty Bench Vineyard.......................................... 40

Appendix B - The impact of solar aspect on soil moisture and quantity and quality characteristics of vines and grapes at Thirty Bench Vineyard select group ..................... 92

vi

LIST OF TABLES

Table 1: The impact of solar aspect on soil moisture and quantity and quality

characteristics of vines and grapes at Thirty Bench Vineyard. The data are expressed as

means. Only 39 of the 162 E-NE sentinel vines and 54 of the 256 N-NW Sentinel vines

were sampled for monoterpenes). ..................................................................................... 21

Table 2: The impact of solar aspect (N-NW and E-NE) on soil moisture and quantity and

quality characteristics of vines and grapes at Thirty Bench Vineyard. The data are

expressed as percent ratios................................................................................................ 22


characteristics of vines and grapes at Thirty Bench Vineyard. The data are expressed as

means. Only 7 of the 26 predominantly North sentinel vines and 8 of the 28

predominantly East sentinel were sampled for monoterpenes.......................................... 25

Table 4: The impact of solar aspect (predominantly North and predominantly East) on

soil moisture and quantity and quality characteristics of vines and grapes at Thirty Bench

Vineyard. The data are expressed as percent ratios. ......................................................... 26

vii

LIST OF FIGURES

Figure 1: VQA’s Niagara Peninsula wine region appellations map.

(http://www.vqaontario.com/appellations/niagarapeninsula) ............................................. 5

Figure 2: Views of the Thirty Bench Winery and Vineyard

(http://www.thirtybench.com/). ........................................................................................ 11

Figure 3: Aerial photograph of the Thirty Bench Riesling plot examined in this study

(http://maps.google.ca/). ................................................................................................... 12

Figure 4: Thirty Bench solar aspect map with main group sentinel vines categorized into

E-NE, N-NW, and W-SE aspect sets. ............................................................................... 14

Figure 5: Thirty Bench solar aspect map with select group sentinel vines categorized into

a predominantly North aspect and a predominantly East aspect subsets.......................... 15

Figure 6: The impact of solar aspect (N-NW and E-NE) on soil moisture and quantity and

quality characteristics of vines and grapes at Thirty Bench Vineyard. The data are

expressed as percent ratios................................................................................................ 23

Figure 7: The impact of solar aspect (predominantly North and predominantly East) on

soil moisture and quantity and quality characteristics of vines and grapes at Thirty Bench

Vineyard. The data are expressed as percent ratios. ......................................................... 27

1

CHAPTER ONE

INTRODUCTION

Winemaking in France, Italy and Spain has been ongoing for hundreds of years

and through experience, experimentation and observation the winemakers of these

regions have developed unique and complex wines. This slowly acquired region-specific

knowledge and experience of viticulture and winemaking is not yet available for the

grape growing regions of the Niagara Peninsula. The first Vitis vinifera vines to be grown

commercially on a large scale in this region were only planted in the 1970s.

The unique characteristics of a particular wine are dependent on many factors.

The growing conditions of a specific vineyard will influence the characteristics of a wine

and the variables associated with these growing conditions are referred to collectively as

the wine’s (and the vineyard’s) ‘terroir’ (Seguin, 1986). There are many European wines

that are associated with complex variables of terroir. These variables include the physical

and chemical aspects of the soil, the configuration of the terrain, climate, cultivar,

rootstalk, vine age, as well as cultural and vinification practices. More data, and

specifically more precise data, for these variables have been generated in the recent past

with the advancement of science and technology (Reynolds et al., 2007). This has

influenced the examination of one of the variables of terroir, climate, to such an extent

that climate is now divided into three categories: the macroclimate, the climate of the

overall region where the vineyard is located; the mesoclimate, the climate of the specific

vineyard; and the microclimate, including the climate of a specific vine or a row of vines

within that vineyard. Numerous microclimates can exist in a single vineyard and it is the

2

variations between these microclimates that can partly explain the differences often

observed in grape quality within one vineyard (Reynolds and Wardle, 1989).

This work was undertaken to test the hypothesis that the microclimate of a grape

vine will alter the quality and productivity of that specific vine. The microclimate of the

vines of a Riesling vineyard at the Thirty Bench Winery near Beamsville in the Niagara

Peninsula are examined by using solar aspect as the microclimate variable influencing

vine yield and quality. Solar aspect refers to the direction in which a physical surface

faces the sun. Differences in solar aspect over a vineyard plot will generate differences in

the moisture and temperature of the air and soil surrounding grape vines, which will lead

to variable airflow and solar exposure of the vines. Two subplots were delineated using

precision viticulture tools that generated solar aspect data for specific vines. A number of

yield and quality indices including cluster number, yield, berry weight, oBrix, pH, TA

(titratable acidity) and grape monoterpene concentrations were recorded for the

individual vines. The data were analyzed to determine the effects of solar aspect on the

yield and quality variables. An understanding of these effects can help with future

management of vineyards. Identified groups of grapes can be harvested and processed

separately to produce unique wines.

3

CHAPTER TWO

LITERATURE REVIEW

Climate involves the temperature, wind, precipitation, atmospheric pressure and

other meteorological conditions that occur in a region over time. Parameters such as

latitude, altitude, terrain and the presence of a large body of water nearby can also further

define the climate. There are several levels of regional climate in viticulture: the

macroclimate refers to the entire wine region; the mesoclimate refers to the climate of a

particular vineyard within a wine region; and microclimate refers to an environmental

area within the vineyard, which can include a row, or block or even one vine. (Robinson,

1999). When other factors such as soil, solar energy received per unit of land surface

area, relief data comprising of altitude, slope and aspect of the area, hydrology and the

cultural practices of a wine region are also considered, the concept of “terroir” is born.

Terroir is the basis for the French wine Appellation d'Origine Contrôlée (AOC) system

that rigorously zones vineyards in that country. The basic premise of terroir is that the

grapes grown in a particular place will have developed their own unique qualities, which

in turn will produce unique vintages. (Robinson, 1999).

In comparison to France (Seguin, 1986; van Leeuwen, 2004), Niagara Peninsula

winemakers have not had the same opportunity to define their terroirs, since the first

vines to be grown commercially for major wine production were not planted until the

1970s. Some studies have been undertaken to begin the process of identifying terroirs in

the Niagara region (Douglas, 2001; Kontkanen et al., 2005; Schlosser et al., 2005). These

three studies undertook chemical and sensory analysis on commercially available

Riesling, Bordeaux-style and Chardonnay wines, respectively, to determine differences

4

that might support the designation of three classes or sub-appellations of the Niagara

Peninsula: Lakeshore (lake), Lakeshore Plain (the level plains between lake and Niagara

Escarpment) and Bench (north facing slopes of the Niagara Escarpment). An appellation

is a protected name under which a wine may be sold, indicating that the grapes used are

of a specific kind from a specific district. In the case of the Riesling study Douglas

(2001), wines from ‘Lakeshore Plain’ and ‘Lakeshore’ were statistically different in

sensory characteristics from the ‘Bench’. In the case of the Bordeaux-style red wine there

were significant regional differences for chemical data (titratable acidity, pH, total

phenols, ethanol, colour intensity and hue), but these regional distinctions were not so

clear for sensory analysis. For the Chardonnay, the chemical data of the ‘Bench’ wines

(titratable acidity, pH, phenol and ethanol concentrations) exhibited the greatest statistical

difference from the ‘Lakeshore’ and ‘Lakeshore Plain’ wines, although the sensory

differences between the regions were subtle.

More recently, the VQA (Vintners Quality Alliance) of Ontario has developed an

even more descriptive appellation system for the Niagara Peninsula. The VQA is

Ontario’s wine authority, a regulatory agency responsible for maintaining the integrity of

local wine appellations and enforcing winemaking and labeling standards

(http://vqaontario.com/Appellations). The VQA has divided the Niagara Peninsula into

two regional appellations, the Niagara Escarpment and Niagara-on-the-Lake. These two

designations are then further divided into sub-appellations. The Niagara Escarpment

encompasses the Vinemount Ridge, Lincoln Lakeshore, Beamsville Bench, Twenty Mile

Bench, Creek Shores, and Short Hills Bench. Niagara-on-the-Lake encompasses Niagara

Lakeshore, Four Mile Creek, St. David’s Bench and the Niagara River

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(http://vqaontario.com/Appellations). The VQA uses terroirs to delineate the appellations

from topography, soil, and climate information.

Figure 1: VQA’s Niagara Peninsula wine region appellations map.

(http://www.vqaontario.com/appellations/niagarapeninsula)

Numerous investigations have looked at the relationship between soil type and

terroir. Seguin (1986) found that the factors encompassing a climate-soil-vine ecosystem

are very complex. Each component acts individually, yet there are synergistic and

opposing factors occurring within the system. Van Leeuwen et al. (2004) studied the

simultaneous effect of soil, climate and cultivar on terroir. All had significant effects on

vine development as well as yield and berry quality (climate>soil>cultivar). They

concluded that it was the influence of climate and soil, acting through the effect of soil

6

water-holding capacity on vine water status, which was important in influencing wine

quality. This study and others examining the relationship between soil and the water-

holding capacity of vines (Bodin and Morlat, 2006; Morlat and Bodin, 2006) have led to

a redefined approach in examining terroir. In general, soil water content and the

availability of water to the vines during various times of the growing season are

important components of terroir. In a precision viticulture study examining 10 Riesling

vineyards representing each of the sub appellations created by the Vintners Quality

Alliance of Ontario (VQAO) within the Niagara peninsula, vine water status was

determined to be a major contributor to the terroir effect since it had major effects on vine

size, berry weight and wine sensory characteristics (Willwerth et al., 2010)

Solar radiation is also an important factor within a vineyard. It is the driving

energy of photosynthesis. The angle at which the sun strikes a leaf can have a huge

impact on the amount of energy that is available for photosynthesis (Robinson, 1999). It

also affects the water evaporation and temperature of the soil. The darker the soil, the

more light is absorbed and the temperature of the soil rises. Sunlight also affects the

temperature of the air around the vines and the amount of water evaporation from the

vine. Sunlight exposure is also a very important modifier of berry composition and

quality. Various forms of canopy management can result in an increase of light

penetration to the fruit zone which will influence ripening and aromatic development of

the fruit (Reynolds et al., 1994; Reynolds et al., 1996a; Reynolds et al., 1996b; Reynolds

and Wardle, 1997; Zoecklein, 1998; Reynolds et al., 2004; Reynolds et al., 2007).

All Muscat cultivars, as well as Riesling and Gewürztraminer, owe their floral and

varietal aroma to a group of compounds known as monoterpenes (Reynolds et al.,

7

1996a,b). They exist as odour-active free volatile terpenes (FVT) and as potentially

volatile terpenes (PVT). The free volatile monoterpenes (FVT) give the wine their aroma

and flavor while the bound form (PVT), while odorless, can be converted to FVT. Cluster

thinning, which allows more sunlight to be available to the berries, has been shown to

increase FVT (Reynolds et al., 2007). In other words, the greater the exposure to

sunlight, the higher the concentration of monoterpenes in the berry (Skinkis et al., 2010).

Berry microclimate strongly influences the levels of PVT in berries. In a study of

Gewürztraminer vines by Reynolds and Wardle (1989), levels of PVT were highest in

fully exposed berries compared to shaded berries. Light is also important in the formation

of buds and the fruitfulness of the buds. Too much shade causes a low fruitfulness (May

et al., 1976).

Slope and aspect have been one of the easier ways to estimate relative radiation

and the development of geomatic tools since 1999 have made this measurement much

more accurate and efficient (Bramley et al., 2005). With the emergence of global

positioning system (GPS) technology, contour maps describing the shape and size of a

vineyard can be delineated and sentinel vines, used for representative data collection, can

be geolocated within this area. Geographic information systems (GIS) are designed to

capture, store, manipulate, analyze, manage, and present all types of geographical data.

The capability to develop the spatial distribution of soil texture over the map is a feature

that two Canadian studies used. Bowen et al. (2005) examined the viticulture

performance in the Okanagan and Similkameen Valleys in British Columbia and

Reynolds et al. (2007) examined spatial variation in a Riesling vineyard in the Niagara

Peninsula. Another special geomatic tool is the laser range finder (LIDAR), which

8

measures the variations in topography across a vineyard plot at thousands of points at a

high level of accuracy in order to accurately map solar aspect. LIDAR and GIS together

can then be used to generate a solar aspect map of the vineyard and, using GIS, an

overlay of the yield and quality data collected from sentinel vines can be used to

delineate unique subplots. Spatial variation of fruit quality within the vineyard can give

the winemaker valuable information, since it can map grape yield and quality at harvest

time (Best et al., 2005; Ennahli and Kadir, 2006). This feature is important to winemakers

who are looking to promote their wines on unique local soil, climate and topographical

conditions. It can also help to identify management decisions to be made concerning

fertilization and irrigation, not to mention the ability to monitor pests and disease.

This ability to now measure the variability within a vineyard using solar aspect

and other geomatic tools such as high special resolution aerial multispectral images

(resulting in normalized difference vegetative indices (NDVI)) has led to the concept of

precision viticulture (Hall et al., 2002; Hall et al., 2003; and Bramley and Hamilton,

2004). Precision viticulture has been defined as the monitoring and management of

spatial variation in any biological, chemical, or physical variable of grape yield, quality

or productivity within a single vineyard (Hall et al., 2002). Just as the use of GIS and

GPS systems are relatively recent tools in viticulture research, precision viticulture is

going to be the next important research to be looked at in the wine industry (Hall et al.,

2002; Bramley and Hamilton, 2004; Bramley, 2005; Best et al., 2005, Reynolds et al.,

2007).

The objective of this study was to determine if vines with different solar aspect

(microclimate), but from the same vineyard, have differences in yield and berry

9

compositions which might result in varying concentration of monoterpenes, thus

influencing the sensory characteristics of the resulting wine.

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CHAPTER TREE

MATERIALS AND METHODS

General Remark.

With kind permission from Dr. Bob Brown, Dr. Ralph Brown, Dr. Andrew

Reynolds and Mr. Matthieu Marciniak this study is derived from data generated in 2006

during research for a Masters of Science thesis “Using GPS, GIS and remote sensing to

understand Niagara terroir” by M. Marciniak (2011). For the purposes of this thesis, the

methods and materials are summarized below; the specific details are described in

Marciniak (2011).

Location of the Study Site.

The data for this study were collected in 2006 from a 10 ha Riesling vineyard

belonging to the Thirty Bench Winery located in Beamsville, Ontario, part of the Niagara

Peninsula wine region (Figure 2). The vineyard was originally divided into three blocks

and then later into six sub-blocks by the previous soil study group based on high

resolution low level aerial photography courtesy of Dr. Ralph Brown, University of

Guelph (Marciniak, 2011). These images showed varying levels of grape-leaf canopy

density across the whole plot (all of the blocks), indicating the potential for various vine-

vigor levels within the blocks and sub-blocks. The study reported in this thesis focuses on

the influence of solar aspect on vine yield and quality of the Riesling grapes. Using GPS,

11

LIDAR and GIS the solar aspect/topography for each vine in the whole block was

determined. This analysis defined three new plots (vine groupings) not used in Marciniak

(2011). To summarize, the block and sub-block delineation assigned by soil group in

Marciniak are not relevant to this study.

Figure 2: Views of the Thirty Bench Winery and Vineyard

(http://www.thirtybench.com/).

The Vines

The Riesling vines grown in the vineyard used for this study (Figure 3) are Weis

clone 21B grafted to SO4 rootstock and planted between 1981 and 1984. The vines are in

rows running north to south 2.4 m apart. They were planted 1.2 m apart in the rows,

trained into a Double Guyot trellis system and pruned to two 12-node canes with renewal

spurs. The vines are managed with regular hedging. Basal leaf removal is performed on

12

the east side of the rows within the fruiting zone before berry touch. The whole plot is tile

drained between the rows at a depth of 0.7 m (Marciniak, 2011).

Figure 3: Aerial photograph of the Thirty Bench Riesling plot examined in this

study (http://maps.google.ca/).

Global Positioning System (GPS)

A global positioning system (GPS900, Leica Geosystems) was used to geolocate

each individual sentinal vine used for data collection within the vineyard plot recording

13

latitude, longitude and elevation with an accuracy of approximately 5 cm. (Marciniak,

2011) The data was provided by Dr. Ralph Brown, University of Guelph.

Laser Range Finder (LIDAR)

An aircraft-based laser range finder was used to measure the variations in

topography across the vineyard plot at thousands of points at a high level of accuracy in

order to map solar aspect. Dr. Ralph Brown also provided this data.

Geographic Information Systems (GIS)

The GPS and LIDAR data were incorporated into a geographic information

system (GIS; ESIR ArcMap 9.3, MapInfo Corporation, Troy, NY) to generate a three-

dimensional solar aspect map of the vineyard plot. The GIS model was then used to

overlay the yield and quality data from the sentinel vines so that they could be divided

into sub plots based on solar aspect.

Experiment Design

A total of 440 sample (sentinel) vines were chosen randomly from a regular grid

pattern across the whole vineyard plot and GPS was used to geolocate their positions

14

within the plot (Marciniak, 2011). GIS ArcMap was used to generate a 3D map of the

vineyard plot from the LIDAR data. The aspect tool in the Raster Surface Toolbox of

ArcMap was then applied to generate a solar aspect map of the plot. The GPS positions

of the sentinel vines were then overlaid onto the solar aspect map so that they could be

categorized according to differences in solar aspect sets. Three different solar aspect sets

were identified: East-Northeast (E-NE set), North-Northwest (N-NW set), and West-

Southeast (W-SE set) (Figure 4).

Figure 4: Thirty Bench solar aspect map with main group sentinel vines categorized into E-

NE, N-NW, and W-SE aspect sets.

15

There were 17 sentinel vines in the W-SE set, 163 in the E-NE set and 261 in the

N-NW set. The sample size for sentinel vines located on the W-SE aspect was considered

to be too small for a worthwhile comparison and was therefore not included in this study.

A smaller subset of sentinel vines was then generated from the data of the E-NE set and

the N-NW set to explore the scenarios with the greatest difference in solar aspect: a

predominantly North aspect subset (26 vines) and a predominantly East aspect subset (28

vines) were derived based on the information in Figure 5.

Figure 5: Thirty Bench solar aspect map with select group sentinel vines categorized into a

predominantly North aspect and a predominantly East aspect subsets.

16

For both the main aspect sets (E-NE and N-NW) and the “extreme” aspect subsets

(predominantly North and predominately East) the effects of aspect on soil moisture

content, as well as yield and quality of the vines were examined. The yield variables

included: cluster number, the number of clusters per vine; yield, the total volume of

grapes per vine; and average grape weight, the average weight of the grapes on each vine,

based on an approximately 100-grape subsample. The quality variables included: oBrix, a

measurement of mass ratio of dissolved sucrose to water in a liquid; pH, a measure of

acidity of an aqueous solution; titratable acidity, the total concentration of protons

available in a juice or wine expressed as g/L tartaric acid; monoterpene concentration

(mg/kg), compounds which influence aroma and flavour of wine; free volatile terpene

(FVT) concentration (mg/kg), compounds which influence wine aroma; and potential

volatile terpene (PVT) concentration (mg/kg), a measure of the potential for formation of

free volatile terpene.

Determination of Soil Moisture, as well as Yield and Quality Parameters

This data on soil moisture, yield and quality were provided by Dr. Andrew

Reynolds, Brock University. The methods are summarized here for continuity.

The moisture content of the soil for each sentinel vine was determined every two

weeks over the growing season using a Field Scout TDR 300 Soil moisture probe

(Spectrum Technologies Inc., Plainfield, IL) which accurately measures the water to soil

volume ratio expressed as m³water/ m³soil (Marciniak 2011).

17

The study plot was harvested between September 30 and October 2, 2006. The total

yield was measured (weight, Mettler Toledo SB32000 electronic scale) and recorded, as

was the number of clusters per vine (Marciniak, 2011). Samples of 100 grapes were taken

at random, including some from each cluster, for each of the 440 sentinel vines. Each of

these samples was weighed to determine the mean grape weight for each vine and then

placed in plastic bags and frozen (-25°C) for later analysis. Samples of 400 grapes were

also taken from a subset of 116 sentinel vines in order to determine the grape’s volatile

monoterpene concentrations. These samples were also placed in plastic bags and frozen

at -25°C pending analysis.

The 100-grape samples were crushed to produce both clarified and unclarified juice

(Reynolds et al., 2010b; Marciniak, 2011). The unclarified grape juice samples were

measured for their soluble solids (oBrix) using a temperature-compensated Abbé bench

refractometer (American Optical Corp., Model 10450, Buffalo NY). The pH was

measured with an Accumet pH/ion meter Model AR50 (Fisher Scientific, Ottawa, ON).

The clarified grape juice samples were tested for their titratable acidity using a Man-Tech

PC-Titrate autotitrator (Man-Tech Associates Inc., Model PC-1300-475, Guelph, ON).

Results were expressed as tartaric acid equivalents (g/L). Please refer to Marciniak

(2011) for further details of analysis.

Terpene analysis (Marciniak, 2011) was undertaken in duplicate on the 400-grape

sample from each of the 116 sentinel vines using the method initially developed by

Dimitriadis and Williams (1984) as modified by Reynolds and Wardle (1989) and

reported by Reynolds et al. (2007; 2010a,b). Each sample was homogenized followed by

distillation to isolate the free volative terpenes (FVT) and potential volatile

18

(glycosidically bound) terpenes (PVT). The concentrations (mg/kg) of FVT and PVT

were determined by subjecting the distillates to uv/visible spectrophotometry with

linalool as the standard. The monterpene concentration (mg/kg) also used in this thesis is

the sum of the concentrations of FVT and PVT in each sample.

Data Analysis and Presentation

In order to explore the impact of solar aspect on the variables of soil moisture, as well as

grape quantity and quality, the data for the 440 vine group was subsampled based on

solar aspect (see above) to generate E-NE and N-NW sets. For each variable the mean for

the set was determined and expressed as a percent ratio of the mean for that variable for

the entire 440 vine group. The data are presented in tables and figures to allow

comparison of the impact of solar aspect on each variable. A subset of each set

(predominantly East for the E-NE set and predominantly North for the N-NW set) was

also developed to examine the vines with the greatest (most extreme) differences in solar

aspect (see Experimental Design above). For these, the means for each variable are also

presented in tables and figures for comparison, and the means have been expressed as

percent ratios of the overall mean for the two subsets.

The statistical significance of the differences between the means of the measured

variables for the E-NE set relative to the N-NW set and for the predominantly North

subset relative to the predominantly East subset were determined with t-tests. The t-test

19

function in Microsoft Excel was used, assuming a two-tailed distribution with unequal

sample size and homogeneity of variability.

20

CHAPTER FOUR

RESULTS AND DISCUSSION

The initial group of sentinel vines were categorized into an East-Northeast (E-NE)

solar aspect set, a North-Northwest (N-NW) solar aspect set and West-Southeast (W-SE)

solar aspect set (Figure 4). The data from the W-SE aspect set was excluded due to too

small a sample size compared to the other two solar aspects sets. The data from each of

these two sets (Table 1) were converted into a percent ratio of the individual set mean

over the mean of the entire group (Table 2). These data were then formatted into a

graphic representation to demonstrate visually the differences in values between the N-

NW and E-NE sets (Figure 6).

21


characteristics of vines and grapes at Thirty Bench Vineyard. The data are

expressed as means. Only 39 of the 162 E-NE sentinel vines and 54 of the 259 N-NW

Sentinel vines were sampled for monoterpenes.

DataTypeN‐NWDataMean

(SD,n)E‐NEDataMean

(SD,n)

Pvalue,t‐testofN‐NWversus

E‐NE

MeanofAllDataforGraphing

ClustNumb 50.2(16.1,259) 49.6(15.2,162) 0.74 49.8Yield(kg) 4.89(2.01,259) 5.23(1.86,162) 0.08* 5.00Brix 19.7(1.85,259) 19.5(1.91162) 0.47 19.6pH 3.33(0.22,259) 3.35(0.28,162) 0.44 3.33TA(g/L) 10.9(1.24,259) 11.1(1.11,162) 0.09* 11.0Avg_wgt(g) 1.68(0.27,259) 1.69(0.24,162) 0.65 1.68FVT(mg/kg) 0.63(0.35,54) 0.66(0.34,39) 0.67 0.64PVT(mg/kg) 2.48(0.60,54) 2.47(0.58,39) 0.99 2.49Monoterpene(mg/kg) 3.10(0.70,54) 3.13(0.83,39) 0.86 3.13Mean_SM 27.7(5.58,259) 25.9(6.06,162) 0.002* 27.0

ClustNumb – Number of clusters of grapes on vine,Yield – Weight of grapes on vine, Brix - oBrix, pH – pH, TA – Titratable acidity, Avg_wgt - Mean weight of berries on vine, FVT - Free volatile terpene concentration, PVT- Potential volatile terpene concentration, Monoterpene - monoterpene concentration, Mean_SM – Mean soil moisture, SD – Standard deviation, n – Sample size. Statistical significance, indicated with *, is defined at p ≤ 0.10.

22

Table 2: The impact of solar aspect (N-NW and E-NE) on soil moisture and quantity

and quality characteristics of vines and grapes at Thirty Bench Vineyard. The data

are expressed as percent ratios.

DataType N‐NWPercentRatio E‐NEPercentRatio

ClustNumb 1.01 1.00Yield 0.98 1.05Brix 1.00 1.00pH 1.00 1.00TA 0.99 1.01Avg_wgt 1.00 1.01FVT 0.97 1.02PVT 0.99 1.00Monoterpene 0.99 1.01Mean_SM 1.03 0.96

23

Figure 6: The impact of solar aspect (N-NW and E-NE) on soil moisture and

quantity and quality characteristics of vines and grapes at Thirty Bench Vineyard.

The data are expressed as percent ratios.

The main hypothesis was to determine if the sentinel vines with different aspect

(microclimate), but within the same vineyard, have differences in yield and berry quality.

In Table 1 and Figure 6 the data show a difference in yield (p = 0.08), TA (p = 0.09) and

mean soil moisture (p = 0.002). Relative to the entire group, there were, respectively, 5

and 1% increases in yield and FVT concentrations, and a 4% decrease in soil moisture for

the E-NE aspect sentinel vines. For the N-NW aspect group there were, respectively, 2%

and 1% decreases in yield and TA concentration, and a 3% increase in soil moisture.

Although there appears to be a trend for increased FVT concentions in E-NE sentinal

vines (Figure 6) the difference is not statically significant. Cluster number, ◦Brix, pH,

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

N‐NWAspect

E‐NEAspect

24

average weight of grape per vine, PVT concentration and monoterpene concentration

showed no apparent difference between the two aspects.

In order to further investigate the influence of solar aspect exposure

(microclimate) on monoterpene levels in Riesling, smaller subsets of sentinel vines were

generated from the data of the E-NE set and the N-NW set, to explore the scenarios with

the greatest difference in solar aspect: a predominantly North aspect (26 vines) and a

predominantly East aspect (29 vines) (Figure 5). The means of these subsets were

tabulated (Table 3), converted into percent ratios (Table 4), and presented graphically

(Figure 7).

25


characteristics of vines and grapes at Thirty Bench Vineyard. The data are

expressed as means. Only 7 of the 26 predominantly North sentinel vines and 8 of

the 28 predominantly East sentinel were sampled for monoterpenes.

DataTypePredominantly

NorthDataMean(SD,n)

PredominantlyEastDataMean

(SD,n)

Pvalue,t‐testofNorthver‐

susEast

MeanofAllDataforGraphing

ClustNumb 53.1(11.8,26) 47.8(15.1,29) 0.16 50.3Yield(kg) 5.39(1.31,26) 5.04(1.62,29) 0.39 5.21Brix 19.5(1.37,26) 19.5(0.98,29) 0.97 19.5pH 3.36(0.09,26) 3.41(0.09,29) 0.05* 3.39TA(g/L) 11.2(0.70,26) 10.76(0.78,29) 0.04* 11.0Avg_wgt(g) 1.69(0.17,26) 1.72(0.18,29) 0.54 1.71FVT(mg/kg) 0.88(0.48,7) 0.54(0.23,8) 0.09* 0.69PVT(mg/kg) 2.11(0.66,7) 2.68(0.47,8) 0.07* 2.41Monoterpene(mg/kg) 2.99(0.82,7) 3.21(0.63,8) 0.56 3.11Mean_SM 28.5(5.53,7) 26.8(6.74,29) 0.28 27.6

ClustNumb – Number of clusters of grapes on vine,Yield – Weight of grapes on vine, Brix - oBrix, pH – pH, TA – Titratable acidity, Avg_wgt - Mean weight of berries on vine, FVT - Free volatile terpene concentration, PVT- Potential volatile terpene concentration, Monoterpene - monoterpene concentration, Mean_SM – Mean soil moisture, SD – Standard deviation, n – Sample size. Statistical significance, indicated with *, is defined at p ≤ 0.10.

26

Table 4: The impact of solar aspect (predominantly North and predominantly East)

on soil moisture and quantity and quality characteristics of vines and grapes at

Thirty Bench Vineyard. The data are expressed as percent ratios.

DataTypePredominantlyNorth

PercentRatioPredominantlyEast

PercentRatioClustNumb 1.06 0.95Yield 1.04 0.97Brix 1.00 1.00pH 0.99 1.01TA 1.02 0.98Avg_wgt 0.99 1.01FVT 1.27 0.77PVT 0.87 1.11Monoterpene 0.96 1.03Mean_SM 1.03 0.97

27

Figure 7. The impact of solar aspect (predominantly North and predominantly East)

on soil moisture and quantity and quality characteristics of vines and grapes at

Thirty Bench Vineyard. The data are expressed as percent ratios.

Only 7 of the 26 predominantly North aspect vines and 8 of the 29 predominantly

East aspect vines had data available for monoterpene concentrations. Of the variables

measured for these two small subsets of vines (Figure 7), ◦Brix, and average weight of

grape per vine showed essentially no differences between the two aspects. There were

small reductions in cluster number (5%), yield (3%) and soil moisture content (3%) for

the East aspect vines and a small increase in cluster number (6%), yield (4%) and soil

moisture content (3%) for the North aspect vines. These differences were not however

statistically significant (respectively, p = 0.16, 0.39 and 0.28. Table 3). However, the

most pronounced differences were found in pH (p=0.05), TA (p=0.04), as well as

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

NorthAspect

EastAspect

28

monoterpene concentration and its two components FVT (P = 0.09) and PVT (p = 0.07).

The predominantly East aspect vines showed increased pH (1%), decreased TA (2%), and

decreased FVT concentrations (33%) but increased PVT concentrations (11%), while the

predominantly North aspect vines showed decreased pH (1%), increased TA (2%),

increased FVT concentrations (27%) but decreased PVT concentrations (17%). It should

be noted that the trend for FVT concentration relative to solar aspect is the reverse of that

noted for the larger N-NW and E-NE data sets.

Despite the small sample size (7 vines predominantly North Aspect and 8 vines

predominantly East Aspect), the effects of solar aspect were much more pronounced than

they were in the sets from which they were derived. This second select subset

(predominantly North and predominantly East) was included in this study as a point of

interest and should be interpreted with caution because of the small sample size.

The Thirty Bench vineyard is located on the Beamsville bench. This bench is

composed of gentle slopes and swales cut by small streams and ravines creating a

complex landscape of mostly north to east facing slopes. (VQA Beamsville Bench

http://vqaontario.com/Appellations). The differences in the soil moisture data presented

above appear to correlate to the fact that the vines are exposed to more sunlight on the E-

NE aspect, particularly early in the day when temperatures are at their coldest, compared

to the vines on the N-NW aspect, based on the path that the sun travels over the vineyard

(Robinson, 1999). Due to this increase in availability of direct sunlight, and therefore

higher soil temperatures, moisture evaporation is increased resulting in lower soil

moisture values in the E-NE as compared to N-NW aspect. The higher yield of vines on

the E-NE aspect compared to the vines of the N-NW aspect can perhaps be attributed to

29

an increase in photosynthesis brought on by greater solar exposure (Robinson, 1999).

Monoterpenes, such as FVT, are biosynthesized in the plastids of grape skin cells and

sunlight exposure is thought to stimulate the deoxyxylulose 5-phosphate pathways that

produce monoterpenes within them (Skinkis et al., 2010). Therefore, the trend to higher

concentrations of FVT of the berries, although not statistically significant, can be

attributed to the increase of solar exposure that the grapes receive in the E-NE aspect

compared to the N-NW aspect. The increased titratable acidity shown here by grapes in

the E-NE aspect set (along with the reduced soil moisture) is consistent with the work of

Morlat and Bodin (2006) who demonstrated increased acidity in grapes from vines grown

on soils with reduced soil moisture.

The results for the monoterpenes in the predominantly East and predominantly

North subsets are not fully consistent with the above. While PVT concentrations were

increased in the predominantly East subset (which received more sunlight) the FVT

concentration was reduced. This is consistent with the work of Reynolds and Wardle

(1989) who reported the lowest levels of FVT in the shaded grapes, the highest levels in

partial shaded grapes, and moderate levels in grapes in the full sun. The temperature in

the moderate level is adequate to stimulate FVT production but not high enough to cause

excessive volatilization. In the case of PVT they are not as susceptible to volatilization so

there is a steady increase in their levels in accordance with level of sun exposure the

grapes are receiving (Reynolds and Wardle, 1989).

In the study done by Skinkis et al. (2010) it was found that the monoterpene levels

in Traminette grapes were also stimulated by sunlight exposure. Four levels of shading

were tested (No shade, Low shade, Medium shade and High shade) during the ripening

30

period and the level of monoterpenes increased according to the amount of sunlight they

were exposed to but the ratio of FVT to PVT decreased as they were exposed to more

light. This is consistent with the work reported here.

Monoterpenes contribute to the floral and varietal aromas of Muscat, Riesling and

Gewürztraminer wines (Macaulay and Morris, 1993; Reynolds et al., 1996a,b). Reynolds

and Wardle (1989) determined that the levels of monoterpenes in Gewürztraminer berries

were strongly influenced by exposure to the sun. Sun-exposed berries showed the highest

PVT levels, and shaded berries the lowest PVT levels over two seasons of data collection.

Berry temperature appeared to be critical for maximizing monoterpene concentration

levels of Chilean Muscat grapes in a study done by Belancic et al. (1997). It appears then,

that the exposure of grape clusters to sunlight has been determined to increase fruit

quality (Skinkis et al., 2010). Therefore maximizing the sun exposure, or microclimate,

of a vine has the potential to increase monoterpene levels and in turn, enhance the

sensory characteristic of a wine. A number of studies have looked at training systems,

vine spacing, basal leaf removal and crop level reduction, to name a few, to influence the

microclimate of the berries to maximize monoterpene levels (Zoecklein et al., 1998;

Reynolds et al., 1996a,b; Reynolds et al., 2004). Leaf canopy thinning of vines was first

used in the past to control disease and bunch rot on vines (English 1989). The effect of

solar exposure (microclimate) can also be altered by covering the rows of soil between

the vines with reflective plastic or fabric/sheeting to influence soil moisture (Rana, 2004).

Netting can also be placed above the vines to create differing levels of shading (Skinkis

et al., 2010).

31

It is apparent that the ability to map variations in yield and grape quality within a

vineyard by using remote sensing tools is becoming an effective approach to defining and

possibly managing vineyard microclimate. When these tools are more readily available to

grape growers and wine producers, they will have a significant impact on the ability to

allocate grapes from specific areas and vines within a vineyard to terroir-specific wines.

32

CHAPTER FIVE

IMPLICATIONS FOR AND USES WITHIN LANDSCAPE ARCHITECTURE

This research demonstrating that GPS and LIDAR data can be combined using

GIS to characterize within-vineyard variability in solar aspect, and hence variability in

grape characteristics, has significant implications for landscape architecture. There are

three different areas in which the findings of this research could be interpreted and

applied to landscape architecture. The first involves optimizing the way in which wine is

produced from existing vineyards based on the variations described in this study. The

second application is in the design of new vineyards by sculpting the landscape through

grading in order to achieve specific solar aspects and microclimatic conditions. Finally,

the approach could be used on a regional basis to identify potential vineyard locations

based on remote-sensing data of regional topography to characterize microclimate.

By using the location of vines with grapes differing in monoterpene ratios across

the vineyard discussed earlier (E-NE aspect and N-NW aspect), selective harvesting

could be used to create separate lots of wine from a single plot with discernible difference

in aroma, and potentially flavor composition and intensity. This has already been done at

the Thirty Bench winery, based on soil moisture in accordance with the study done by

Marciniak (2011). Thirty Bench produce two small-lot Rieslings based on the steel post

and wood post sub plots identified in the Marciniak study

(http://www.thirtybench.com/overview.php). The wood post and steel post plots are

rectangular sections and are therefore easily harvested. In the case of the E-NE aspect and

N-NW aspect vines, the distribution across the plot is more variable. Marking the

33

changes from vine to vine with flagging tape along each row, in order to distinguish the

two during harvest could solve this problem for hand harvesting. In the case of large-

scale bulk wine producers, most already use GPS guided harvesters that are directed by

GIS maps (Bramley, 2001). These harvesters could be modified to place grapes in

separate hoppers depending on the GIS map. These grape lots could be vinted separately

and marketed as small-lot premium wines with specific characteristics. Alternatively the

wine lots could be used for blending to create wines with other intermediate

characteristics. While the work reported in this thesis is specific to Riesling, the same

approach could be used for any grape variety in any vineyard, assuming that the grape

variety had characteristics of quantity or quality influenced by solar aspect.

With respect to the creation of new vineyards in an existing wine region, the

topography could be modified through grading to turn a plot of land undesirable for

growing grapes into a plot specifically designed to grow a particular grape variety, and to

create specific traits within that variety. Alternatively, existing plots could be improved

by regrading and replanting of the vines. In regards to Riesling and other monoterpene

rich grape varieties, the aspect and slope of the plot could be oriented so that the morning

sun would stimulate the production of monoterpenes but, as the day went on, the grapes

would be shaded out by the rows of vines to keep temperatures down in order to preserve

FVT derived aromas. This could be done by changing the grading homogenous aspect

across the plot or by creating a series of repetitive berms that faced in the appropriate

direction. If you were trying to grow Riesling grapes with higher levels of FVT in the

northern hemisphere, at a longitude similar to the Niagara wine region, it would be best

34

to grade the plot to an East to Northeast aspect. Obviously other elements of terroir would

also have to be taken into consideration.

The concept of remote sensing can also be refined in the future to identify optimal

solar aspect for all grape varieties. This information could then be used to locate existing

topography in developing wine regions or even identify new ones, in order to deduce

what grape variety would do well in that location. This would enable new wine growing

regions to develop more quickly, and compete more effectively, compared to those long

established regions in the world that are famous for wine but have had centuries to

develop and refine the growing of grapes and vinification that allows them to produce

such high quality and distinctive wines. This could be particularly useful in emerging

wine growing regions such as China.

The refinement and application of the techniques explored in this thesis provides a

potentially new market for the discipline of landscape architecture. While the use of

remote sensing data in conjunction with GIS would not be inexpensive when applied to

either the refinement of existing, or the identification of new vineyards, both the land to

grow grapes and the wine produced are high value commodities. The use of the

applications outlined here could provide a significant advantage to wine producers in an

increasingly competitive market.

35

CHAPTER SIX

CONCLUSION

This study demonstrated that differences in solar aspect have a statistically

significant impact on microclimate, resulting in variations in the yield and monoterpene

concentration and composition of Riesling wine grapes at the Thirty Bench vineyard.

This can affect both the quantity of wine produced and the aroma and flavor

characteristics of a specific wine.

There were a number of limitations to this study. Since the data were originally

collected for another study, the questions that could be asked were somewhat limited.

While the predominantly East and North subset data showed significant impact of solar

aspect on several variables, the study could have benefited from a larger sample size, as

well as having monoterpene data collected for all of the sentinel vines. It would also have

been beneficial to have the individual vines sampled at three separate levels, bottom, top

and middle, to see if there was an impact on the grape quality and quantity variables.

Temperature monitors could be placed at these same three heights and one buried in the

soil at each sample vine in order to create accurate and thorough maps of the temperature

variations in air and soil across the vineyard plot. A soils map with a level of resolution

similar to that of the sentinel vines would allow the examination of the interaction

between soil and microclimate to a greater degree. It would also be beneficial to refine

the experiment design to include the slight changes in slope, as well as more divisions

within the aspect groups, in order to understand the relationships between these variables

to a greater extent. It would also be useful to examine solar aspect for the specific period

36

of time associated with berry ripening and monoterpene production as a further

refinement of this study.

Through various methods of microclimate management the variables responsible for

these differences can be controlled to produce a more consistent and specific end product.

If these effects were researched to a point where the impact can be mapped using only

remote sensing, the result would be a more cost effective management system for

viticulturists.

37

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Appendix A The impact of solar aspect on soil moisture and quantity and quality characteristics of vines and grapes at Thirty Bench Vineyard.

E-NE Aspect East 622320.185 622320.265 622320.410 622320.234 622320.078 North 4780359.094 4780348.079 4780324.174 4780312.190 4780300.504 Elevation 95.818 96.277 97.15 97.467 97.739 Vine_ID 1 2 4 5 6 ClustNumb 28 37 66 59 59 Yield (kg) 2.9 2.7 5.2 4.4 4.3 Brix 21.5 18 19.3 20.7 18.5 pH 3.4 3.19 3.37 3.39 3.29 TA (g/L) 11.16 11.25 11.23 11.03 11.69 Avg_wgt (g) 1.65 1.47 1.64 1.57 1.44 FVT (mg/kg) 1.45 PVT (mg/kg) 2.83 Mono (mg/kg) 4.28 Mean_SM 25.83 28.53 29.53 30.37 28.13 N-NW Aspect

East 622327.046 622334.639 622334.632 622341.819 622341.901 North 4780300.329 4780311.241 4780298.860 4780323.901 4780311.952 Elevation 97.582 97.048 97.458 96.522 96.931 Vine_ID 12 17 18 22 23 ClustNumb 54 35 40 38 30 Yield (kg) 3.8 5.4 3.7 3.7 2.9 Brix 19.7 20.3 22.5 20.5 18.1 pH 3.21 3.45 3.44 3.49 3.18 TA (g/L) 11.17 12.5 11.67 10.33 11.2 Avg_wgt (g) 1.37 1.94 1.86 1.29 1.34 FVT (mg/kg) 1.7 1.66 PVT (mg/kg) 2.97 2.14 Mono (mg/kg) 4.67 3.81 Mean_SM 30.40 32.60 36.67 30.03 30.47

40


E-NE Aspect East 622327.062 622327.178 622327.073 622327.142 622327.018 North 4780357.874 4780348.282 4780337.272 4780323.695 4780312.253 Elevation 95.635 96.03 96.503 96.918 97.242 Vine_ID 7 8 9 10 11 ClustNumb 75 60 58 51 65 Yield (kg) 6.5 5.8 5.2 3.1 5.6 Brix 19.6 19.6 19.6 19.3 20.9 pH 3.26 3.28 3.34 3.18 3.4 TA (g/L) 12.14 11.4 10.84 11.22 11.23 Avg_wgt (g) 1.75 1.65 1.56 1.21 1.77 FVT (mg/kg) 1.66 PVT (mg/kg) 4.13 Mono (mg/kg) 5.79 Mean_SM 33.60 32.33 35.00 25.10 37.67 N-NW Aspect

East 622341.847 622349.284 622349.116 622349.120 622349.103 North 4780298.705 4780335.985 4780323.016 4780311.250 4780298.144 Elevation 97.478 95.896 96.52 96.975 97.52 Vine_ID 24 27 28 29 30 ClustNumb 47 49 53 44 62 Yield (kg) 6 5.5 5.7 5.8 5 Brix 22.2 18.2 17.8 19.7 21.6 pH 3.37 3.35 3.29 3.34 3.31 TA (g/L) 10.89 11.11 11.37 10.98 11.26 Avg_wgt (g) 1.65 1.95 1.48 1.77 1.98 FVT (mg/kg) 1.75 PVT (mg/kg) 1.94 Mono (mg/kg) 3.69 Mean_SM 30.20 30.37 28.53 33.73 33.07

41


E-NE Aspect East 622334.705 622334.721 622334.732 622334.738 622341.784 North 4780357.836 4780348.218 4780334.004 4780324.305 4780358.892 Elevation 95.499 95.878 96.434 96.631 95.29 Vine_ID 13 14 15 16 19 ClustNumb 46 30 45 67 50 Yield (kg) 3.6 4 5.5 8.5 6.9 Brix 21.8 19.8 20.4 20.4 20.4 pH 3.27 3.29 3.38 3.37 3.48 TA (g/L) 11.27 11.53 11.25 11.28 11.54 Avg_wgt (g) 1.51 1.7 1.97 1.78 1.81 FVT (mg/kg) 1.42 1.58 PVT (mg/kg) 2.74 3.32 Mono (mg/kg) 4.16 4.89 Mean_SM 34.83 32.50 32.40 33.87 25.00 N-NW Aspect

East 622356.654 622356.507 622356.501 622356.518 622356.407 North 4780347.141 4780334.954 4780322.488 4780311.638 4780299.618 Elevation 95.177 95.899 96.438 96.834 97.286 Vine_ID 32 33 34 35 36 ClustNumb 56 44 39 43 32 Yield (kg) 5 4 3.3 4.2 3 Brix 19.9 21.2 21.2 21.2 21.2 pH 3.38 3.46 3.38 3.32 3.4 TA (g/L) 11.11 11.84 11.43 11.58 10.98 Avg_wgt (g) 1.77 1.7 1.85 1.84 2.06 FVT (mg/kg) 1.31 PVT (mg/kg) 1.94 Mono (mg/kg) 3.24 Mean_SM 32.97 43.57 26.33 37.13 41.77

42


E-NE Aspect East 622341.876 622341.972 622349.178 622349.162 622356.590 North 4780347.776 4780334.505 4780357.581 4780344.812 4780358.049 Elevation 95.659 96.103 95.13 95.564 94.879 Vine_ID 20 21 25 26 31 ClustNumb 35 57 62 61 51 Yield (kg) 5.1 6.7 6.2 6.4 5.5 Brix 21.4 20.4 18.8 18.2 19.8 pH 3.51 3.37 3.14 3.22 3.37 TA (g/L) 10.43 10.72 10.41 10.71 11.68 Avg_wgt (g) 1.75 1.58 1.53 1.65 1.71 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 31.80 30.00 25.00 27.13 26.97 N-NW Aspect

East 622364.422 622364.419 622364.307 622364.408 622364.403 North 4780358.280 4780346.626 4780334.426 4780323.651 4780310.730 Elevation 94.743 95.081 95.643 96.16 96.694 Vine_ID 37 38 39 40 41 ClustNumb 84 56 16 15 28 Yield (kg) 7.4 5 0.7 0.5 0.6 Brix 19.2 19.7 20.9 23.7 23.7 pH 3.27 3.24 3.32 3.42 3.39 TA (g/L) 11.77 11.33 10.93 11.14 11.07 Avg_wgt (g) 1.75 1.74 1.48 1.19 0.91 FVT (mg/kg) 1.06 PVT (mg/kg) 2.4 Mono (mg/kg) 3.46 Mean_SM 27.03 35.27 41.13 35.20 37.33

43


E-NE Aspect East 622435.146 622435.223 622442.991 622442.930 622442.893 North 4780318.052 4780304.604 4780327.453 4780316.527 4780304.257 Elevation 95.524 95.984 95.049 95.414 95.781 Vine_ID 95 96 100 101 102 ClustNumb 56 62 54 45 42 Yield (kg) 8.1 5.5 6.1 5.5 4.7 Brix 19.3 20.3 20.2 18.7 19.7 pH 3.29 3.31 3.36 3.33 3.42 TA (g/L) 11.34 11.79 11.64 11.09 12 Avg_wgt (g) 1.82 1.79 1.97 1.79 1.87 FVT (mg/kg) 0.63 0.48 PVT (mg/kg) 2.61 2.59 Mono (mg/kg) 3.24 3.07 Mean_SM 26.40 27.13 23.97 25.17 25.83 N-NW Aspect

East 622364.338 622371.314 622371.442 622371.414 622371.439 North 4780298.266 4780358.964 4780347.913 4780334.452 4780322.627 Elevation 97.173 94.579 94.992 95.595 96.104 Vine_ID 42 43 44 45 46 ClustNumb 41 56 78 76 38 Yield (kg) 1.7 4.9 7.1 5 2.6 Brix 23.6 18.8 17.9 20.5 19.9 pH 3.38 3.36 3.2 3.31 3.41 TA (g/L) 11.8 12.05 12.32 10.99 11.24 Avg_wgt (g) 1.23 1.72 2.08 1.71 1.92 FVT (mg/kg) 1.21 0.81 PVT (mg/kg) 3.82 1.52 Mono (mg/kg) 5.03 2.32 Mean_SM 30.90 25.20 25.83 26.13 25.10

44


E-NE Aspect East 622449.665 622449.714 622449.678 622449.705 622457.355 North 4780341.840 4780328.747 4780316.714 4780304.501 4780365.335 Elevation 94.476 94.901 95.204 95.597 93.359 Vine_ID 105 106 107 108 109 ClustNumb 46 58 53 52 16 Yield (kg) 5.3 7.2 5.4 5 1.3 Brix 17.3 18.6 19.1 17.9 20.9 pH 3.25 3.26 3.33 3.37 3.3 TA (g/L) 12.08 11.34 11.42 12.43 10.86 Avg_wgt (g) 1.75 2.05 1.81 1.76 1.35 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 28.73 21.20 27.70 30.40 26.20 N-NW Aspect

East 622371.431 622371.349 622378.692 622378.594 622378.552 North 4780310.453 4780299.712 4780359.475 4780346.067 4780335.179 Elevation 96.527 96.872 94.516 94.977 95.472 Vine_ID 47 48 49 50 51 ClustNumb 26 39 49 48 33 Yield (kg) 1.1 4.4 5.3 3.9 3 Brix 18.5 20.8 18.7 22.3 17.9 pH 3.42 3.34 3.42 3.37 3.21 TA (g/L) 10.52 10.98 12.2 11.3 11.78 Avg_wgt (g) 1.27 2.26 1.75 1.56 1.68 FVT (mg/kg) 1.05 PVT (mg/kg) 2.35 Mono (mg/kg) 3.4 Mean_SM 26.57 25.20 26.93 26.03 23.97

45




46


E-NE Aspect East 622464.367 622464.407 622464.233 622464.077 622472.049 North 4780352.274 4780340.126 4780328.541 4780316.637 4780365.118 Elevation 93.764 94.27 94.675 94.976 93.022 Vine_ID 116 117 118 119 121 ClustNumb 52 31 70 61 74 Yield (kg) 2.3 2 5.3 6.3 2.8 Brix 22.4 21.7 21.3 19.7 21.1 pH 3.4 3.25 3.35 3.37 3.28 TA (g/L) 11.56 11.51 11.08 11.96 11.43 Avg_wgt (g) 1.18 1.2 1.81 1.92 1.09 FVT (mg/kg) 0.38 PVT (mg/kg) 1.79 Mono (mg/kg) 2.17 Mean_SM 31.73 28.73 24.90 32.77 26.67 N-NW Aspect

East 622386.395 622386.260 622386.287 622386.319 622393.826 North 4780334.527 4780322.473 4780310.833 4780298.700 4780358.972 Elevation 95.557 95.936 96.304 96.781 94.422 Vine_ID 57 58 59 60 61 ClustNumb 48 47 31 41 22 Yield (kg) 5.9 4.6 4 4.3 1.7 Brix 19.1 21.3 21.2 20 21.9 pH 3.37 3.35 3.41 3.33 3.37 TA (g/L) 11.45 11.17 10.92 11.39 11.62 Avg_wgt (g) 1.61 1.48 1.56 1.53 1.4 FVT (mg/kg) 0.41 PVT (mg/kg) 1.17 Mono (mg/kg) 1.58 Mean_SM 27.80 29.27 34.63 32.87 28.07

47



East 622393.873 622393.820 622393.685 622393.716 622393.936 North 4780347.737 4780333.909 4780325.092 4780313.701 4780298.725 Elevation 94.816 95.414 95.764 96.159 96.723 Vine_ID 62 63 64 65 66 ClustNumb 63 44 37 30 56 Yield (kg) 5 2.2 1.1 2.2 3.8 Brix 20.8 21.5 21.4 21 21 pH 3.36 3.34 3.4 3.26 3.3 TA (g/L) 11.76 11.63 11.04 11.11 10.13 Avg_wgt (g) 1.71 1.51 0.97 1.67 1.72 FVT (mg/kg) 0.51 0.79 PVT (mg/kg) 2.9 3.17 Mono (mg/kg) 3.41 3.95 Mean_SM 33.33 33.43 31.83 27.40 24.17

48


E-NE Aspect East 622479.086 622479.142 622486.799 622486.774 622486.729 North 4780340.147 4780328.474 4780366.162 4780352.863 4780341.041 Elevation 93.831 94.208 92.691 93.091 93.542 Vine_ID 129 130 133 134 135 ClustNumb 70 22 55 58 47 Yield (kg) 6 2.7 6 5.6 5.4 Brix 17.7 20.4 19.2 17.8 17.8 pH 3.16 3.37 3.18 3.22 3.19 TA (g/L) 9.75 11.76 12.13 12.54 11.21 Avg_wgt (g) 1.54 1.99 1.86 1.78 1.98 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 28.33 28.63 25.30 25.87 30.73 N-NW Aspect

East 622400.718 622400.693 622400.661 622400.624 622400.675 North 4780358.286 4780347.570 4780334.677 4780323.215 4780310.256 Elevation 94.372 94.819 95.328 95.785 96.284 Vine_ID 67 68 69 70 71 ClustNumb 51 53 41 39 25 Yield (kg) 5.4 5.1 3 1.8 1.5 Brix 21 18.9 21.7 21.5 22.9 pH 3.33 3.3 3.31 3.33 3.29 TA (g/L) 10.72 11.46 10.73 11.31 11.4 Avg_wgt (g) 1.77 1.8 1.5 1.39 1.21 FVT (mg/kg) 0.6 PVT (mg/kg) 3.75 Mono (mg/kg) 4.35 Mean_SM 26.33 30.93 31.50 27.97 31.87

49


E-NE Aspect East 622486.725 622493.845 622493.826 622493.724 622493.661 North 4780329.365 4780365.079 4780352.930 4780342.472 4780329.604 Elevation 93.882 92.526 92.879 93.269 93.674 Vine_ID 136 139 140 141 142 ClustNumb 61 45 47 53 67 Yield (kg) 4.5 5.2 5.7 6.1 8.5 Brix 17.8 18.7 19.4 19.4 18.4 pH 3.33 3.27 3.2 3.23 3.33 TA (g/L) 11.69 12.24 11.52 11.15 12.23 Avg_wgt (g) 1.88 1.64 1.74 1.79 1.87 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 29.80 30.77 29.73 29.37 33.53 N-NW Aspect

East 622400.734 622413.558 622413.552 622413.473 622413.541 North 4780298.737 4780365.699 4780353.072 4780342.160 4780329.400 Elevation 96.751 94.015 94.502 94.879 95.344 Vine_ID 72 73 74 75 76 ClustNumb 15 42 58 36 57 Yield (kg) 1.1 4.9 2.9 2.3 4.5 Brix 21.2 19.5 19.5 23.1 20.8 pH 3.24 3.34 3.33 3.26 3.34 TA (g/L) 10.58 11.75 11.02 11.18 10.74 Avg_wgt (g) 1.28 1.79 1.32 1.38 1.6 FVT (mg/kg) 0.66 PVT (mg/kg) 3.06 Mono (mg/kg) 3.72 Mean_SM 28.73 27.60 31.50 30.27 26.23

50


E-NE Aspect East 622334.669 622334.773 622334.815 622341.726 622341.897 North 4780222.501 4780202.515 4780183.600 4780220.619 4780201.899 Elevation 92.191 93.075 93.932 91.916 92.748 Vine_ID 164 165 166 172 173 ClustNumb 35 29 42 51 36 Yield (kg) 3.1 2.2 3.8 6 2.8 Brix 18.6 18.7 20.3 19.7 17.6 pH 3.21 3.25 3.36 3.34 3.09 TA (g/L) 10.16 10.97 10.48 11.26 11.22 Avg_wgt (g) 1.78 1.47 1.62 1.63 1.36 FVT (mg/kg) 0.43 0.4 PVT (mg/kg) 1.77 1.53 Mono (mg/kg) 2.2 1.93 Mean_SM 28.60 39.63 26.80 22.70 26.10 N-NW Aspect

East 622413.458 622413.499 622420.730 622420.693 622420.619 North 4780317.588 4780304.700 4780364.787 4780353.732 4780340.978 Elevation 95.788 96.284 93.94 94.392 94.858 Vine_ID 77 78 79 80 81 ClustNumb 21 25 54 46 35 Yield (kg) 0.5 1.1 4.8 4 4.2 Brix 23.7 24 18.5 19.9 19.3 pH 3.34 3.32 3.25 3.34 3.26 TA (g/L) 11.86 10.03 12.07 11.26 11.41 Avg_wgt (g) 1.14 0.98 1.53 1.73 1.77 FVT (mg/kg) 0.48 PVT (mg/kg) 1.9 Mono (mg/kg) 2.38 Mean_SM 33.40 31.40 22.33 27.70 28.13

51


E-NE Aspect East 622341.853 622342.056 622349.116 622349.112 622349.199 North 4780184.220 4780166.951 4780236.833 4780220.323 4780200.822 Elevation 93.595 94.447 91.003 91.704 92.478 Vine_ID 174 175 179 180 181 ClustNumb 59 11 57 13 51 Yield (kg) 3.3 1.4 5.2 0.9 5.4 Brix 19 21.2 19.6 18.5 18.5 pH 3.21 3.49 3.24 3.32 3.26 TA (g/L) 10.11 10.73 11.61 11.49 10.72 Avg_wgt (g) 1.37 1.56 1.85 1.37 1.7 FVT (mg/kg) 0.42 PVT (mg/kg) 2.03 Mono (mg/kg) 2.45 Mean_SM 25.57 30.90 37.70 28.90 27.53 N-NW Aspect


52




53


E-NE Aspect East 622356.567 622356.547 622364.338 622364.278 622364.273 North 4780201.157 4780182.307 4780275.044 4780257.354 4780238.309 Elevation 92.179 92.962 89.168 89.659 90.304 Vine_ID 189 190 193 194 195 ClustNumb 27 40 38 18 78 Yield (kg) 5 4.8 2.8 2.3 9 Brix 19.7 19.7 20.1 19.5 19.5 pH 3.3 3.32 3.28 3.25 3.3 TA (g/L) 10.7 10.78 10.61 11.86 12.45 Avg_wgt (g) 1.81 1.5 1.66 1.82 1.91 FVT (mg/kg) 0.57 PVT (mg/kg) 2.53 Mono (mg/kg) 3.09 Mean_SM 30.00 34.73 49.17 39.83 33.70 N-NW Aspect

East 622435.081 622435.037 622435.172 622442.666 622442.804 North 4780352.557 4780340.667 4780329.972 4780365.835 4780352.675 Elevation 94.294 94.827 95.158 93.609 94.191 Vine_ID 92 93 94 97 98 ClustNumb 39 65 56 38 49 Yield (kg) 4 6 6.6 3.6 6.3 Brix 21.5 19.4 18.3 20.4 19.8 pH 3.28 3.37 3.28 3.21 3.32 TA (g/L) 11.45 11.54 12.16 11.83 12.08 Avg_wgt (g) 1.91 1.79 1.96 1.73 1.81 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 26.70 23.67 27.23 23.53 22.97

54


E-NE Aspect East 622364.216 622364.256 622371.261 622379.056 622386.237 North 4780220.370 4780201.405 4780275.629 4780145.625 4780146.777 Elevation 90.866 91.575 89.015 94.34 93.899 Vine_ID 196 197 201 216 224 ClustNumb 82 74 63 19 24 Yield (kg) 8.9 6.2 5.4 2.6 3 Brix 20 19.4 22 19.5 18.9 pH 3.45 3.36 3.29 3.49 3.45 TA (g/L) 11.86 11.8 11.04 11.54 11.91 Avg_wgt (g) 1.75 1.82 1.77 1.74 1.61 FVT (mg/kg) 0.65 0.58 PVT (mg/kg) 2.51 2.93 Mono (mg/kg) 3.15 3.51 Mean_SM 34.63 36.33 22.70 19.43 29.20 N-NW Aspect


55


E-NE Aspect East 622394.303 622394.277 622401.047 622401.175 622405.986 North 4780163.752 4780147.611 4780176.791 4780157.738 4780176.252 Elevation 92.59 93.324 91.643 92.367 91.374 Vine_ID 231 232 238 239 246 ClustNumb 52 35 52 30 51 Yield (kg) 4.7 3.9 5.6 3.6 4.5 Brix 17.7 15.9 17.3 18.5 19 pH 3.23 3.35 3.38 3.42 3.39 TA (g/L) 9.43 11.93 11.33 11.93 11.15 Avg_wgt (g) 1.7 1.86 1.81 1.83 1.85 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 31.87 26.10 27.50 26.27 40.50 N-NW Aspect

East 622471.939 622472.085 622479.212 622479.022 622486.715 North 4780316.453 4780304.393 4780316.583 4780304.508 4780316.553 Elevation 94.799 95.413 94.691 95.251 94.366 Vine_ID 125 126 131 132 137 ClustNumb 77 56 52 40 68 Yield (kg) 6.9 3.4 5.2 5.8 6.7 Brix 18.5 17.5 19.1 17.7 19.4 pH 3.27 3.17 3.39 3.35 3.4 TA (g/L) 11.98 12.21 12.67 11.96 12.44 Avg_wgt (g) 1.79 1.59 1.95 1.76 1.93 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 33.53 32.40 30.37 26.50 24.90

56


E-NE Aspect East 622405.949 622413.624 622428.430 622428.314 622428.261 North 4780156.450 4780177.594 4780286.766 4780268.362 4780250.184 Elevation 92.136 90.896 87.992 88.505 89.007 Vine_ID 247 254 261 262 263 ClustNumb 51 51 51 76 79 Yield (kg) 4.7 4 4.2 8.4 6.8 Brix 16.9 17.5 19.1 19.2 19.1 pH 3.38 3.33 3.37 3.41 3.33 TA (g/L) 12.18 11.26 11.4 11.68 11.9 Avg_wgt (g) 1.71 1.66 1.89 1.84 1.89 FVT (mg/kg) 0.79 PVT (mg/kg) 2.11 Mono (mg/kg) 2.9 Mean_SM 27.80 36.13 27.17 24.60 25.93 N-NW Aspect


57


E-NE Aspect East 622428.307 622435.341 622435.411 622442.882 622478.991 North 4780232.645 4780287.567 4780269.319 4780286.522 4780267.821 Elevation 89.564 87.777 88.274 87.608 87.873 Vine_ID 264 266 267 271 296 ClustNumb 46 49 48 29 44 Yield (kg) 5 5.7 6.1 3.6 5.4 Brix 19.1 19.4 19.2 19.8 20.2 pH 3.38 3.38 3.4 3.51 3.38 TA (g/L) 11.49 11.92 12.04 11.73 12.01 Avg_wgt (g) 1.79 1.82 2.01 1.84 1.76 FVT (mg/kg) 0.84 PVT (mg/kg) 2.28 Mono (mg/kg) 3.12 Mean_SM 18.23 24.17 27.80 32.53 17.60 N-NW Aspect

East 622320.136 622320.215 622320.366 622320.351 622320.574 North 4780238.867 4780221.584 4780203.053 4780186.098 4780166.666 Elevation 91.712 92.768 93.556 94.258 95.089 Vine_ID 147 148 149 150 151 ClustNumb 38 48 63 51 43 Yield (kg) 2.7 4.5 4.5 3.5 2.1 Brix 21.4 21.3 21 20.8 21.9 pH 3.35 3.38 3.35 3.37 3.35 TA (g/L) 11.01 10.77 11.05 10.53 10.82 Avg_wgt (g) 1.49 1.76 1.75 1.52 1.64 FVT (mg/kg) 0.53 PVT (mg/kg) 2.39 Mono (mg/kg) 2.93 Mean_SM 32.60 28.13 29.27 24.53 27.07

58


E-NE Aspect East 622486.588 622486.585 622493.610 622493.507 622514.093 North 4780286.393 4780267.649 4780286.258 4780267.499 4780364.529 Elevation 87.249 87.692 86.99 87.416 92.035 Vine_ID 299 300 303 304 307 ClustNumb 73 67 56 74 44 Yield (kg) 6.4 7.3 6.9 7.6 2.1 Brix 19.7 19.2 19.8 16.8 20.3 pH 3.36 3.44 3.34 3.35 3.23 TA (g/L) 12.02 12.25 11.78 13.18 8.59 Avg_wgt (g) 1.79 1.78 1.87 1.59 1.1 FVT (mg/kg) 0.64 0.72 PVT (mg/kg) 1.6 2.39 Mono (mg/kg) 2.24 3.11 Mean_SM 23.17 19.53 23.97 23.33 28.23 N-NW Aspect

East 622320.692 622326.985 622326.996 622327.043 622327.140 North 4780149.966 4780275.571 4780258.572 4780239.189 4780220.833 Elevation 95.82 89.74 90.468 91.418 92.592 Vine_ID 152 153 154 155 156 ClustNumb 33 68 89 62 57 Yield (kg) 2 5.7 6.9 4.2 4 Brix 20.8 18.4 20.9 19.3 20.8 pH 3.38 3.33 3.4 3.25 3.32 TA (g/L) 10.51 9.52 11.36 11.61 10.89 Avg_wgt (g) 1.73 1.94 1.58 1.41 1.73 FVT (mg/kg) 0.72 0.4 PVT (mg/kg) 1.87 1.59 Mono (mg/kg) 2.59 1.98 Mean_SM 37.53 26.77 24.17 23.17 34.53

59


E-NE Aspect East 622514.137 622520.265 622520.268 622527.035 622539.554 North 4780351.968 4780362.851 4780353.595 4780366.413 4780272.190 Elevation 92.175 91.711 91.951 91.335 94.823 Vine_ID 308 313 314 319 414 ClustNumb 44 45 17 70 64 Yield (kg) 5.4 5.4 2 7 8.7 Brix 20.6 20 20 20 19.1 pH 3.44 3.37 3.67 3.41 3.4 TA (g/L) 10.17 9.93 10.77 10.12 10.8 Avg_wgt (g) 1.69 1.61 1.44 1.89 1.59 FVT (mg/kg) 1.04 0.23 0.54 PVT (mg/kg) 3.34 2.55 2.08 Mono (mg/kg) 4.37 2.78 2.62 Mean_SM 29.17 32.17 33.97 35.57 20.03 N-NW Aspect

East 622327.217 622327.397 622327.483 622327.582 622334.584 North 4780202.649 4780184.137 4780166.371 4780148.967 4780276.318 Elevation 93.354 94.106 94.879 95.687 89.71 Vine_ID 157 158 159 160 161 ClustNumb 72 30 46 43 38 Yield (kg) 6.4 2.2 2.4 3.5 3.6 Brix 19.6 19.6 21.9 19.4 20.8 pH 3.36 3.36 3.42 3.38 3.37 TA (g/L) 11.01 11.31 10.8 10.68 11 Avg_wgt (g) 1.97 1.71 1.44 1.7 1.65 FVT (mg/kg) 0.52 PVT (mg/kg) 2.7 Mono (mg/kg) 3.22 Mean_SM 28.97 24.63 29.43 41.87 25.27

60



East 622334.410 622334.518 622334.843 622335.009 622341.744 North 4780257.884 4780238.246 4780165.796 4780149.283 4780275.932 Elevation 90.435 91.357 94.786 95.553 89.688 Vine_ID 162 163 167 168 169 ClustNumb 47 38 45 40 38 Yield (kg) 5.6 3.4 2.5 3.4 4.5 Brix 20.4 20.2 18 17.7 20.4 pH 3.32 3.24 3.01 3.45 3.34 TA (g/L) 11.08 10.87 11.21 11.25 10.06 Avg_wgt (g) 1.82 1.84 1.53 1.67 1.56 FVT (mg/kg) 0.38 0.6 PVT (mg/kg) 1.86 3.09 Mono (mg/kg) 2.24 3.69 Mean_SM 31.03 32.97 35.57 34.80 22.03

61


E-NE Aspect East 622545.942 622552.866 622552.556 622552.505 622552.454 North 4780242.974 4780297.642 4780285.222 4780274.168 4780258.518 Elevation 95.469 93.293 93.757 94.123 94.655 Vine_ID 420 348 421 422 423 ClustNumb 60 29 56 38 64 Yield (kg) 7 3.4 5 6.9 9.7 Brix 20.5 20.8 21.2 21.2 19.7 pH 3.51 3.41 3.54 3.59 3.51 TA (g/L) 10.9 10.85 10.94 10.8 11.37 Avg_wgt (g) 1.68 1.81 1.46 2.34 1.62 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 21.20 20.07 22.13 20.57 16.40 N-NW Aspect

East 622341.613 622341.707 622342.059 622349.158 622349.132 North 4780256.794 4780238.149 4780148.253 4780275.620 4780256.406 Elevation 90.314 91.116 95.335 89.595 90.151 Vine_ID 170 171 176 177 178 ClustNumb 31 46 45 51 49 Yield (kg) 5 2.8 2.6 4.4 4 Brix 20.4 20.9 20.9 17.1 20.5 pH 3.42 3.33 3.38 3.12 3.38 TA (g/L) 9.94 9.91 11.3 11.61 10.59 Avg_wgt (g) 1.56 1.57 1.51 1.53 1.53 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 31.87 30.57 31.67 19.47 35.17

62


E-NE Aspect East 622552.401 622558.855 622558.987 622558.848 622558.665 North 4780243.025 4780310.088 4780297.775 4780285.164 4780269.688 Elevation 95.136 92.702 93.038 93.476 93.995 Vine_ID 424 353 354 425 426 ClustNumb 49 49 47 39 64 Yield (kg) 7.1 4.6 5.4 5.7 8 Brix 19.7 20.8 20.6 20.4 21.4 pH 3.52 3.38 3.36 3.47 3.56 TA (g/L) 11.23 10.39 10.15 10.73 10.68 Avg_wgt (g) 1.78 1.68 1.78 1.74 1.69 FVT (mg/kg) 0.51 PVT (mg/kg) 1.74 Mono (mg/kg) 2.25 Mean_SM 22.23 27.30 18.57 21.50 20.20 N-NW Aspect

East 622349.379 622349.375 622356.563 622356.384 622364.260 North 4780163.698 4780147.628 4780164.464 4780147.682 4780183.090 Elevation 94.171 95.064 93.914 94.943 92.501 Vine_ID 183 184 191 192 198 ClustNumb 25 54 30 18 76 Yield (kg) 2.1 3.9 4.4 2.3 7.7 Brix 19.8 19.5 19.8 20.9 18.4 pH 3.31 3.33 3.41 3.34 3.3 TA (g/L) 10.94 11.15 10.81 10.42 10.98 Avg_wgt (g) 1.97 1.51 1.65 1.64 1.83 FVT (mg/kg) 0.49 0.69 0.61 PVT (mg/kg) 2.36 2.69 1.7 Mono (mg/kg) 2.85 3.38 2.31 Mean_SM 29.73 32.30 39.80 33.27 29.33

63


E-NE Aspect East 622558.532 622558.484 622565.647 622565.456 622565.426 North 4780260.041 4780244.708 4780312.744 4780298.990 4780285.341 Elevation 94.318 94.903 92.281 92.681 93.171 Vine_ID 427 428 359 360 429 ClustNumb 50 44 50 41 58 Yield (kg) 7.1 6.7 6.1 3.6 6.9 Brix 19.9 19.9 21 21 19.4 pH 3.52 3.5 3.42 3.46 3.42 TA (g/L) 10.53 12.06 10.48 11.2 10.57 Avg_wgt (g) 1.8 1.81 1.69 1.8 1.73 FVT (mg/kg) 0.56 PVT (mg/kg) 2.57 Mono (mg/kg) 3.13 Mean_SM 18.70 18.97 19.93 18.73 18.70 N-NW Aspect

East 622364.312 622364.189 622371.166 622371.331 622371.379 North 4780165.634 4780148.260 4780256.927 4780237.980 4780220.617 Elevation 93.66 94.867 89.507 90.102 90.755 Vine_ID 199 200 202 203 204 ClustNumb 95 62 53 49 67 Yield (kg) 8.4 4.4 4.5 4.7 5.6 Brix 20.3 20.6 19.4 18.2 19.4 pH 3.27 3.34 3.46 3.18 3.19 TA (g/L) 12.58 11.92 10.87 11 11.33 Avg_wgt (g) 1.81 1.81 1.91 2.1 2.01 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 33.80 35.93 25.10 27.20 26.60

64


E-NE Aspect East 622565.396 622565.336 622565.269 622571.697 622571.603 North 4780271.079 4780258.269 4780246.072 4780314.333 4780299.436 Elevation 93.612 94.036 94.464 92.056 92.455 Vine_ID 430 431 432 365 366 ClustNumb 42 35 0 57 69 Yield (kg) 6.3 4.5 0 5.3 7.6 Brix 20.2 19.1 0 21.6 20.6 pH 3.46 3.42 0 3.41 3.42 TA (g/L) 10.71 10.42 0 10.38 10.68 Avg_wgt (g) 1.68 1.69 0 1.73 1.67 FVT (mg/kg) 0.56 0.72 PVT (mg/kg) 2.57 2.59 Mono (mg/kg) 3.13 3.31 Mean_SM 15.63 19.83 20.07 18.27 18.13 N-NW Aspect


65


E-NE Aspect East 622571.693 622571.648 622571.449 622578.675 622578.497 North 4780288.137 4780270.336 4780257.609 4780326.689 4780316.200 Elevation 92.426 92.944 93.358 91.423 91.655 Vine_ID 433 434 435 370 371 ClustNumb 54 55 67 30 44 Yield (kg) 7.1 6.4 8.4 3.4 5.8 Brix 18.7 19.5 18.3 21.1 20 pH 3.37 3.38 3.41 3.51 3.53 TA (g/L) 10.28 9.96 10.88 10.16 10.95 Avg_wgt (g) 1.6 1.79 1.78 1.88 1.79 FVT (mg/kg) 0.51 0.58 PVT (mg/kg) 1.88 2.8 Mono (mg/kg) 2.39 3.38 Mean_SM 22.87 22.03 18.60 20.43 21.40 N-NW Aspect


66


E-NE Aspect East 622578.579 622578.166 622578.172 622578.191 622584.883 North 4780299.160 4780283.860 4780271.250 4780257.366 4780327.289 Elevation 92.188 92.674 93.012 93.33 91.15 Vine_ID 372 436 437 438 376 ClustNumb 55 46 28 48 33 Yield (kg) 4.8 5.8 3.9 5.1 4.8 Brix 19.5 17.8 17.2 19.4 20.4 pH 3.37 3.32 3.36 3.37 3.47 TA (g/L) 10.96 11.19 11.67 11.7 10.26 Avg_wgt (g) 1.69 1.59 1.57 1.95 1.94 FVT (mg/kg) 0.46 0.7 PVT (mg/kg) 1.73 3.73 Mono (mg/kg) 2.19 4.42 Mean_SM 18.90 18.23 21.20 22.40 21.97 N-NW Aspect


67




68



East 622393.929 622394.201 622394.278 622394.474 622401.006 North 4780238.169 4780220.717 4780201.527 4780182.607 4780275.571 Elevation 89.614 90.263 90.945 91.879 88.618 Vine_ID 227 228 229 230 233 ClustNumb 77 57 50 57 77 Yield (kg) 8.2 6 4.2 4.2 8.6 Brix 18.8 18.9 20.2 20.6 19.1 pH 3.38 3.39 3.4 3.38 3.32 TA (g/L) 11.06 11.05 10.95 10.38 9.94 Avg_wgt (g) 1.84 1.83 1.89 1.87 1.48 FVT (mg/kg) 0.5 0.61 PVT (mg/kg) 2.48 2.91 Mono (mg/kg) 2.98 3.52 Mean_SM 29.33 33.53 39.37 34.47 31.50

69



East 622400.841 622400.799 622400.844 622400.840 622405.738 North 4780256.863 4780238.018 4780220.336 4780200.431 4780286.956 Elevation 89.021 89.496 90.124 90.926 88.224 Vine_ID 234 235 236 237 240 ClustNumb 53 79 45 62 54 Yield (kg) 7.5 8.8 6 9.3 5 Brix 18.8 19 18.8 19.4 17 pH 3.34 3.33 3.38 3.45 3.25 TA (g/L) 10.61 11.49 10.75 11.58 10.38 Avg_wgt (g) 1.78 2.01 1.73 1.89 1.61 FVT (mg/kg) 0.6 0.58 PVT (mg/kg) 3.19 2.65 Mono (mg/kg) 3.79 3.24 Mean_SM 29.73 25.57 34.53 38.63 29.67

70


E-NE Aspect East 622598.006 622604.734 622604.858 622604.764 622605.044 North 4780300.179 4780365.337 4780354.290 4780338.912 4780324.875 Elevation 91.179 89.451 89.59 89.734 89.973 Vine_ID 390 391 392 393 394 ClustNumb 63 64 34 37 44 Yield (kg) 5.7 5.5 3 4.7 4.8 Brix 20.1 18.3 19.5 18.8 20.3 pH 3.45 3.34 3.41 3.47 3.45 TA (g/L) 11.4 11.06 11.54 10.36 11 Avg_wgt (g) 1.61 1.65 1.82 1.95 1.78 FVT (mg/kg) 0.47 0.45 PVT (mg/kg) 2.42 1.78 Mono (mg/kg) 2.89 2.23 Mean_SM 15.63 19.53 21.30 26.20 21.77 N-NW Aspect

East 622405.737 622405.850 622405.843 622405.780 622405.786 North 4780268.968 4780251.819 4780231.337 4780213.852 4780195.500 Elevation 88.644 89.078 89.73 90.422 91.048 Vine_ID 241 242 243 244 245 ClustNumb 31 76 65 81 81 Yield (kg) 4.9 7.9 5.8 7.6 6.8 Brix 17.6 16.8 18.4 18 20 pH 3.28 3.29 3.31 3.45 3.42 TA (g/L) 10.97 10.52 10.33 10.57 10.98 Avg_wgt (g) 1.68 1.58 1.8 1.64 1.63 FVT (mg/kg) 0.53 PVT (mg/kg) 2.77 Mono (mg/kg) 3.3 Mean_SM 38.53 28.93 36.97 31.87 33.07

71


E-NE Aspect East 622605.006 622605.089 North 4780315.308 4780301.406 Elevation 90.263 90.489 Vine_ID 395 396 ClustNumb 46 48 Yield (kg) 4.2 5.4 Brix 20.3 20.3 pH 3.42 3.46 TA (g/L) 9.97 10.89 Avg_wgt (g) 1.68 1.99 FVT (mg/kg) 0.48 PVT (mg/kg) 3 Mono (mg/kg) 3.47 Mean_SM 21.57 24.53 N-NW Aspect

East 622413.416 622413.505 622413.636 622413.515 622413.518 North 4780286.949 4780269.964 4780250.524 4780232.963 4780214.433 Elevation 88.104 88.557 89.038 89.625 90.303 Vine_ID 248 249 250 251 252 ClustNumb 49 61 61 47 63 Yield (kg) 4 5.9 7.9 5 6 Brix 18.5 19.2 17.8 19.6 17.5 pH 3.22 3.28 3.41 3.35 3.37 TA (g/L) 10.92 11.12 11.42 10.48 12.3 Avg_wgt (g) 1.92 1.83 1.77 1.71 1.73 FVT (mg/kg) 0.75 PVT (mg/kg) 3.34 Mono (mg/kg) 4.1 Mean_SM 24.23 27.33 30.20 33.40 28.33

72


E-NE Aspect East North Elevation Vine_ID ClustNumb Yield (kg) Brix pH TA (g/L) Avg_wgt (g) FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM N-NW Aspect

East 622413.510 622420.650 622420.665 622420.840 622420.900 North 4780194.914 4780286.772 4780268.250 4780250.003 4780231.816 Elevation 90.89 88.094 88.564 89.107 89.768 Vine_ID 253 255 256 257 258 ClustNumb 38 55 47 45 64 Yield (kg) 4 5.3 5 6.4 5.6 Brix 19 19.7 20 19.5 19.2 pH 3.37 3.52 3.49 3.28 3.36 TA (g/L) 10.7 11.51 11.45 11.3 11.48 Avg_wgt (g) 1.62 1.73 1.89 1.9 1.78 FVT (mg/kg) 0.55 PVT (mg/kg) 2.34 Mono (mg/kg) 2.89 Mean_SM 25.77 22.70 27.50 23.00 21.87

73




74




75




76



East 622457.429 622457.396 622464.102 622464.355 622464.329 North 4780250.444 4780231.922 4780286.146 4780267.744 4780248.903 Elevation 88.64 88.933 87.532 88.067 88.49 Vine_ID 283 284 286 287 288 ClustNumb 49 50 66 42 69 Yield (kg) 6.4 7.3 4.5 3.5 6.3 Brix 19 20 18.9 19.6 18.7 pH 3.38 3.4 3.21 3.39 3.39 TA (g/L) 11.58 11.16 10.31 11.41 12.37 Avg_wgt (g) 1.74 1.71 1.91 1.6 1.72 FVT (mg/kg) 0.71 PVT (mg/kg) 2.23 Mono (mg/kg) 2.95 Mean_SM 18.23 25.40 25.17 21.20 20.73

77



East 622472.019 622472.008 622479.102 622513.960 622514.021 North 4780285.214 4780267.774 4780286.531 4780341.388 4780327.397 Elevation 87.539 87.951 87.41 92.436 92.94 Vine_ID 291 292 295 309 310 ClustNumb 64 58 58 62 67 Yield (kg) 6.7 5.9 6.6 4.8 6.6 Brix 19.6 21 19 18.5 18.9 pH 3.41 3.47 3.31 3.34 3.42 TA (g/L) 10.9 10.85 12.68 9.87 10.22 Avg_wgt (g) 1.77 1.76 1.75 1.59 1.7 FVT (mg/kg) 0.64 PVT (mg/kg) 2.5 Mono (mg/kg) 3.14 Mean_SM 22.50 17.30 15.47 26.77 35.50

78



East 622513.858 622513.873 622513.689 622513.896 622513.711 North 4780312.135 4780296.664 4780284.440 4780270.730 4780256.872 Elevation 93.862 94.551 94.977 95.573 95.976 Vine_ID 311 312 397 398 399 ClustNumb 55 21 37 54 50 Yield (kg) 3.6 1.6 4.3 6.3 5.8 Brix 19.4 20 19 14.4 18.3 pH 3.29 3.44 3.35 3.38 3.32 TA (g/L) 10.2 10.87 11.39 11.4 11.06 Avg_wgt (g) 1.77 1.78 1.68 1.42 1.66 FVT (mg/kg) 0.46 PVT (mg/kg) 2.53 Mono (mg/kg) 2.99 Mean_SM 28.37 19.17 20.67 17.13 16.47

79



East 622520.215 622520.021 622519.965 622519.942 622519.853 North 4780338.410 4780325.997 4780310.541 4780298.042 4780284.524 Elevation 92.454 93.008 93.831 94.322 94.811 Vine_ID 315 316 317 318 401 ClustNumb 46 63 45 71 26 Yield (kg) 6.1 5.4 5.5 7.6 6.6 Brix 20 16.2 19 19 19 pH 3.48 3.2 3.47 3.43 3.26 TA (g/L) 9.51 10.22 10.28 10.98 9.64 Avg_wgt (g) 1.8 1.32 1.46 1.41 1.55 FVT (mg/kg) 0.26 PVT (mg/kg) 2.53 Mono (mg/kg) 2.79 Mean_SM 32.87 35.17 39.43 17.10 20.50

80




81



East 622526.942 622526.823 622526.815 622526.740 622526.718 North 4780312.429 4780298.564 4780284.686 4780267.675 4780257.000 Elevation 93.739 94.183 94.71 95.435 95.732 Vine_ID 323 324 405 406 407 ClustNumb 52 58 56 45 50 Yield (kg) 6.7 7.3 7.9 5.5 6.4 Brix 19 19.1 21 20.5 18.9 pH 3.53 3.43 3.37 3.31 3.32 TA (g/L) 10.68 11.08 10.54 10.03 10.57 Avg_wgt (g) 1.77 1.72 1.8 1.65 1.92 FVT (mg/kg) 0.16 PVT (mg/kg) 1.8 Mono (mg/kg) 1.96 Mean_SM 26.83 18.90 16.10 18.40 18.27

82



East 622531.390 622533.047 622532.978 622533.036 622532.943 North 4780363.415 4780353.884 4780338.481 4780326.390 4780311.013 Elevation 91.298 91.615 92.219 92.879 93.634 Vine_ID 325 326 327 328 329 ClustNumb 50 75 110 60 18 Yield (kg) 5.8 9.2 11.9 6.3 1.5 Brix 19.2 19.6 19.3 19 21.1 pH 3.32 3.37 3.35 3.3 3.53 TA (g/L) 9.04 9.76 10.19 9.29 10.55 Avg_wgt (g) 1.48 1.78 1.8 1.55 1.73 FVT (mg/kg) 0.15 PVT (mg/kg) 3.48 Mono (mg/kg) 3.63 Mean_SM 28.47 30.00 37.80 27.70 21.13

83



East 622532.938 622533.013 622532.884 622532.858 622539.928 North 4780298.442 4780284.685 4780270.801 4780258.233 4780363.752 Elevation 94.065 94.605 95.127 95.53 91.063 Vine_ID 330 409 410 411 331 ClustNumb 47 43 54 69 65 Yield (kg) 5.3 5.9 7.3 8.3 7 Brix 19.9 20.5 20.5 20.6 20.6 pH 3.47 3.48 3.47 3.49 3.39 TA (g/L) 10.81 10.85 10.99 9.99 10.15 Avg_wgt (g) 1.94 1.66 1.69 1.64 1.77 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 18.23 20.03 16.83 22.97 35.73

84



East 622539.839 622539.841 622539.723 622539.801 622539.644 North 4780351.111 4780338.868 4780313.908 4780298.565 4780287.505 Elevation 91.626 92.157 93.271 93.745 94.28 Vine_ID 332 333 335 336 413 ClustNumb 59 48 75 63 58 Yield (kg) 4.6 4 5.7 6.7 8.4 Brix 18.5 19.8 20.3 20.2 19.8 pH 3.33 3.3 3.38 3.42 3.39 TA (g/L) 9.86 9.18 10.04 10.47 10.31 Avg_wgt (g) 1.74 1.71 1.64 1.73 1.96 FVT (mg/kg) 0.16 PVT (mg/kg) 3 Mono (mg/kg) 3.16 Mean_SM 28.73 30.67 18.80 21.87 20.47

85




86



East 622545.996 622552.900 622552.901 622552.815 622552.744 North 4780297.844 4780365.715 4780354.686 4780340.813 4780325.173 Elevation 93.602 90.872 91.316 91.762 92.411 Vine_ID 342 343 344 345 346 ClustNumb 80 60 28 41 49 Yield (kg) 5.7 4.3 1.9 2.9 2.6 Brix 19 19.3 21 19.4 20.8 pH 3.24 3.2 3.27 3.15 3.29 TA (g/L) 9.02 9.2 8.03 9.05 8.81 Avg_wgt (g) 1.81 1.65 1.18 1.29 1.54 FVT (mg/kg) 0.17 PVT (mg/kg) 2.27 Mono (mg/kg) 2.44 Mean_SM 23.90 25.77 28.73 24.17 23.43

87



East 622552.853 622559.102 622559.042 622558.953 622558.845 North 4780315.676 4780364.449 4780354.587 4780339.136 4780326.811 Elevation 92.749 90.87 91.254 91.59 92.159 Vine_ID 347 349 350 351 352 ClustNumb 59 40 35 43 60 Yield (kg) 6.8 3.4 1.6 2 5 Brix 18.3 18.2 21.4 21 18.7 pH 3.22 3.18 3.24 3.19 3.18 TA (g/L) 9.69 9 8.22 9.96 9.32 Avg_wgt (g) 1.68 1.61 1.03 1.34 1.84 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 19.27 24.57 28.23 23.23 21.90

88



East 622565.815 622565.841 622565.764 622565.652 622570.049 North 4780363.095 4780355.964 4780340.805 4780328.004 4780365.767 Elevation 90.73 90.964 91.383 91.937 90.639 Vine_ID 355 356 357 358 361 ClustNumb 34 0 28 64 63 Yield (kg) 2.1 0 1.5 8.3 7.3 Brix 20.5 0 20.5 22.8 19.5 pH 3.25 0 3.4 3.39 3.3 TA (g/L) 9.12 0 11.59 10.09 10.5 Avg_wgt (g) 1.35 0 1.21 2.19 1.67 FVT (mg/kg) 0.24 PVT (mg/kg) 3.54 Mono (mg/kg) 3.78 Mean_SM 28.23 31.87 25.10 18.80 31.67

89



East 622571.921 622571.831 622571.696 622578.771 622578.748 North 4780351.870 4780342.285 4780328.409 4780366.136 4780353.512 Elevation 90.964 91.185 91.619 90.483 90.766 Vine_ID 362 363 364 367 368 ClustNumb 10 16 72 53 66 Yield (kg) 0.6 1 7.4 6.3 5.2 Brix 21.7 22.4 20.2 20.5 21 pH 3.36 3.35 3.38 3.37 3.32 TA (g/L) 8.35 8.67 10.67 10.28 9.76 Avg_wgt (g) 1.07 0.94 1.87 1.85 1.54 FVT (mg/kg) 0.27 0.59 PVT (mg/kg) 3.35 2.74 Mono (mg/kg) 3.62 3.33 Mean_SM 26.67 35.83 18.87 25.07 19.33

90



East 622578.601 622584.984 622584.987 622584.828 North 4780340.847 4780365.131 4780353.712 4780339.867 Elevation 91.106 90.303 90.563 90.977 Vine_ID 369 373 374 375 ClustNumb 49 45 82 39 Yield (kg) 5.2 4.9 10.7 2.7 Brix 21.3 17.7 18.5 21.9 pH 3.4 3.38 3.35 3.45 TA (g/L) 9.26 3.38 11.45 10.99 Avg_wgt (g) 1.85 3.38 1.84 1.71 FVT (mg/kg) PVT (mg/kg) Mono (mg/kg) Mean_SM 17.13 22.17 19.73 21.57

91

Appendix B The impact of solar aspect on soil moisture and quantity and quality

characteristics of vines and grapes at Thirty Bench Vineyard select group.

Predominantly North Aspect

East 622378.594 622386.418 622386.395 622378.552 622371.414 North 4780346.067 4780347.815 4780334.527 4780335.179 4780334.452 Elevation 94.977 94.875 95.557 95.472 95.595 Vine_ID 50 56 57 51 45 ClustNumb 48 39 48 33 76 Yield (kg) 3.9 3.5 5.9 3 5 Brix 22.3 20.9 19.1 17.9 20.5 pH 3.37 3.41 3.37 3.21 3.31 TA (g/L) 11.3 10.48 11.45 11.78 10.99 Avg_wgt (g) 1.56 1.54 1.61 1.68 1.71 FVT (mg/kg) 0.41 PVT (mg/kg) 1.17 Mono(mg/kg) 1.58 Mean_SM 26.03 25.30 27.80 23.97 26.13 Predominantly

East Aspect East 622356.398 622356.426 622364.216 622356.567 622364.256 North 4780237.324 4780219.784 4780220.37 4780201.157 4780201.405 Elevation 90.709 91.409 90.866 92.179 91.575 Vine_ID 187 188 196 189 197 ClustNumb 43 48 82 27 74 Yield (kg) 4.8 5.9 8.9 5 6.2 Brix 20.5 17.8 20 19.7 19.4 pH 3.45 3.47 3.45 3.3 3.36 TA (g/L) 11.11 11.68 11.86 10.7 11.8 Avg_wgt (g) 1.92 2.05 1.75 1.81 1.82 FVT (mg/kg) 0.5 PVT (mg/kg) 2.83 Mono(mg/kg) 3.33 Mean_SM 28.50 29.30 34.63 30.00 36.33

92




East 622356.507 622349.116 622349.12 622341.819 622341.847 North 4780334.954 4780323.016 4780311.25 4780323.901 4780298.705 Elevation 95.899 96.52 96.975 96.522 97.478 Vine_ID 33 28 29 22 24 ClustNumb 44 53 44 38 47 Yield (kg) 4 5.7 5.8 3.7 6 Brix 21.2 17.8 19.7 20.5 22.2 pH 3.46 3.29 3.34 3.49 3.37 TA (g/L) 11.84 11.37 10.98 10.33 10.89 Avg_wgt (g) 1.7 1.48 1.77 1.29 1.65 FVT (mg/kg) 1.31 1.75 PVT (mg/kg) 1.94 1.94 Mono(mg/kg) 3.24 3.69 Mean_SM 43.57 28.53 33.73 30.03 30.20 Predominantly

East Aspect East 622379.056 622386.237 622394.303 622401.047 622405.986 North 4780145.625 4780146.777 4780163.752 4780176.791 4780176.252 Elevation 94.34 93.899 92.59 91.643 91.374 Vine_ID 216 224 231 238 246 ClustNumb 19 24 52 52 51 Yield (kg) 2.6 3 4.7 5.6 4.5 Brix 19.5 18.9 17.7 17.3 19 pH 3.49 3.45 3.23 3.38 3.39 TA (g/L) 11.54 11.91 9.43 11.33 11.15 Avg_wgt (g) 1.74 1.61 1.7 1.81 1.85 FVT (mg/kg) 0.58 PVT (mg/kg) 2.93 Mono(mg/kg) 3.51 Mean_SM 19.43 29.20 31.87 27.50 40.50

93




East 622420.739 622420.836 622420.665 622420.84 622413.636 North 4780316.68 4780304.86 4780268.25 4780250.003 4780250.524 Elevation 95.68 96.189 88.564 89.107 89.038 Vine_ID 83 84 256 257 250 ClustNumb 53 56 47 45 61 Yield (kg) 5 6.5 5 6.4 7.9 Brix 20.7 18.6 20 19.5 17.8 pH 3.32 3.29 3.49 3.28 3.41 TA (g/L) 11.91 12.38 11.45 11.3 11.42 Avg_wgt (g) 1.83 1.87 1.89 1.9 1.77 FVT (mg/kg) 0.55 PVT (mg/kg) 2.34 Mono(mg/kg) 2.89 Mean_SM 22.87 21.87 27.50 23.00 30.20 Predominantly

East Aspect East 622413.624 622520.268 622514.137 622514.093 622520.265 North 4780177.594 4780353.595 4780351.968 4780364.529 4780362.851 Elevation 90.896 91.951 92.175 92.035 91.711 Vine_ID 254 314 308 307 313 ClustNumb 51 17 44 44 45 Yield (kg) 4 2 5.4 2.1 5.4 Brix 17.5 20 20.6 20.3 20 pH 3.33 3.67 3.44 3.23 3.37 TA (g/L) 11.26 10.77 10.17 8.59 9.93 Avg_wgt (g) 1.66 1.44 1.69 1.1 1.61 FVT (mg/kg) 1.04 PVT (mg/kg) 3.34 Mono(mg/kg) 4.37 Mean_SM 36.13 33.97 29.17 28.23 32.17

94




East 622420.9 622413.515 622464.205 622464.102 622457.525 North 4780231.816 4780232.963 4780304.163 4780286.146 4780286.404 Elevation 89.768 89.625 95.489 87.532 87.43 Vine_ID 258 251 120 286 281 ClustNumb 64 47 76 66 72 Yield (kg) 5.6 5 6.1 4.5 8.4 Brix 19.2 19.6 19.7 18.9 19.4 pH 3.36 3.35 3.41 3.21 3.4 TA (g/L) 11.48 10.48 12.08 10.31 12.18 Avg_wgt (g) 1.78 1.71 1.91 1.91 1.88 FVT (mg/kg) 0.75 0.71 PVT (mg/kg) 3.34 2.23 Mono(mg/kg) 4.1 2.95 Mean_SM 21.87 33.40 29.37 25.17 22.87 Predominantly

East Aspect East 622527.035 622597.998 622604.734 622598.046 622604.858 North 4780366.413 4780365.517 4780365.337 4780354.101 4780354.29 Elevation 91.335 89.814 89.451 90.015 89.59 Vine_ID 319 385 391 386 392 ClustNumb 70 58 64 42 34 Yield (kg) 7 5.6 5.5 4.5 3 Brix 20 18.6 18.3 19.7 19.5 pH 3.41 3.26 3.34 3.46 3.41 TA (g/L) 10.12 10.88 11.06 10.2 11.54 Avg_wgt (g) 1.89 1.52 1.65 1.58 1.82 FVT (mg/kg) 0.23 0.45 0.53 PVT (mg/kg) 2.55 1.78 2.58 Mono(mg/kg) 2.78 2.23 3.12 Mean_SM 35.57 23.80 19.53 16.67 21.30

95




East 622457.587 622449.855 622514.021 622520.021 622513.858 North 4780268.06 4780268.181 4780327.397 4780325.997 4780312.135 Elevation 88.124 88.101 92.94 93.008 93.862 Vine_ID 282 277 310 316 311 ClustNumb 52 41 67 63 55 Yield (kg) 6.7 5.4 6.6 5.4 3.6 Brix 18.2 19.3 18.9 16.2 19.4 pH 3.37 3.49 3.42 3.2 3.29 TA (g/L) 12.09 11.48 10.22 10.22 10.2 Avg_wgt (g) 1.59 1.69 1.7 1.32 1.77 FVT (mg/kg) 0.65 PVT (mg/kg) 1.79 Mono(mg/kg) 2.45 Mean_SM 23.53 26.50 35.50 35.17 28.37 Predominantly

East Aspect East 622598.058 622604.764 622591.558 622598.054 622605.044 North 4780341.369 4780338.912 4780328.856 4780327.854 4780324.875 Elevation 90.207 89.734 90.746 90.448 89.973 Vine_ID 387 393 382 388 394 ClustNumb 60 37 54 44 44 Yield (kg) 8.9 4.7 5.6 5.5 4.8 Brix 20.2 18.8 20.4 20.5 20.3 pH 3.42 3.47 3.45 3.46 3.45 TA (g/L) 10.53 10.36 9.93 10.53 11 Avg_wgt (g) 1.68 1.95 1.78 1.62 1.78 FVT (mg/kg) PVT (mg/kg) Mono(mg/kg) Mean_SM 15.63 26.20 22.50 21.87 21.77

96




East 622519.965 North 4780310.541 Elevation 93.831 Vine_ID 317 ClustNumb 45 Yield (kg) 5.5 Brix 19 pH 3.47 TA (g/L) 10.28 Avg_wgt (g) 1.46 FVT (mg/kg) PVT (mg/kg) Mono(mg/kg) Mean_SM 39.43 Predominantly

East Aspect East 622605.006 622605.089 622598.006 622584.631 North 4780315.308 4780301.406 4780300.179 4780271.218 Elevation 90.263 90.489 91.179 92.636 Vine_ID 395 396 390 440 ClustNumb 46 48 63 48 Yield (kg) 4.2 5.4 5.7 5.7 Brix 20.3 20.3 20.1 19.4 pH 3.42 3.46 3.45 3.36 TA (g/L) 9.97 10.89 11.4 10.43 Avg_wgt (g) 1.68 1.99 1.61 1.79 FVT (mg/kg) 0.48 0.47 PVT (mg/kg) 3 2.42 Mono(mg/kg) 3.47 2.89 Mean_SM 21.57 24.53 15.63 21.30

97

a microclimate analysis of a niagara peninsula vineyard

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