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Impact of Soil Variation on a
Vineyard Water Balance
Geraldine Miles
Honours DissertationBachelor of Engineering
(Environmental)
Supervised byAssociate Professor
Keith Smettem
November 2005
Impact of Soil Variation on a Vineyard Water Balance
1
Acknowledgements
Firstly , I would like to thank my supervisor Keith Smettem for his help and support whilst
allowing me freedom to formulate my own ideas.
Many thanks to Tony Robertson and the Chardonay Project Team for providing me with the
opprotunity to be involved with this project.
Most importantly I must thank my friends and family (Thesis Suppot Team) for their continued
patience and friendship. Their help was integral in maintaining my sanity.
And finally to Peter : ”Mange tak for din tålmodighed og støtte. Du er vidunderlig!”
Impact of Soil Variation on a Vineyard Water Balance
1
AbstractGrapevines (Vitis vinifera L.) are often cultivated under mild water stress, caused by particular
pedo-climate conditions (rainfall timing or available soil water in the root zone). Such conditions
can also be induced through vineyard management with the intention to achieve optimal water in
order to enhance quality.
Water supply to the vine is one of the key elements which determine wine quality. The amount of
water that reaches the root system and the time for which the vine is “stressed” determine the
amount of soluble solids and acidity which ultimately affects the taste of the wine.
Variability within vineyards affects the resultant quality and quantity of produce. Precision
agriculture is concerned with better understanding the variability of the environment in which a
crop is grown, and the learnt knowledge can be used to manipulate the vines to obtain a desired
product.
Innovative methods for providing spatial variability maps of vineyard soils and water balances
are currently being explored. This report focuses on the use of radiometrics to map the surface
soil properties and Ground penetrating Radar (GPR) to provide a profile of the soil and patterns
of soil moisture. These methods are compared to more traditional point sampling methods of
vinyard soil survey. Direct measurement of soil moisture content using logged moisture probes
provide validation data for vineyard water balance modelling using rainfall and evaporative
variables. The use of a water balance model permits periods of vine stress to be identified under
natural rainfall conditions and allows irrigation application to be designed with allowance for the
spatial pattern of vineyard soils.
The model showed that during summer the actual to potential transpiration ratio dropped below
the ideal limit within a few days causing excessive stress on the vine. This is beneficial in a
vineyard situation for determining when irrigation should be applied to control stress and
therefore eventual wine quality.
Impact of Soil Variation on a Vineyard Water Balance
Table of Contents 1
Table of Contents
Acknowledgements....................................................................................................1
Abstract ......................................................................................................................1
Table of Contents.......................................................................................................1
List of Figures ............................................................................................................4
List of Tables..............................................................................................................5
Glossary.....................................................................................................................6
1 Introduction ............................................................................................................8
2 Literature Review .................................................................................................10
2.1 Vineyards ..............................................................................................................10
2.1.1 Overview of grapevine growth stages.......................................................11
2.1.2 General principles of vineyard water balance...........................................14
2.1.3 General principles of Vineyard irrigation management .............................16
2.1.4 Modern Methods of Irrigation management aimed at reducing water use
whilst maintaining quality..........................................................................19
2.2 Spatial variability of soils within vineyards.............................................................24
2.2.1 The advent of precision agriculture (viticulture) ........................................25
2.2.2 Applications of precision agriculture in the Margaret River region ............26
2.3 Soil sampling methods ..........................................................................................26
2.3.1 Airborne Radiometrics ..............................................................................27
2.3.2 Ground Penetrating Radar .......................................................................31
3 Soil Survey at Bridgelands Vineyard....................................................................38
Impact of Soil Variation on a Vineyard Water Balance
2 Table of Contents
3.1 Radiometric results ...............................................................................................38
3.2 Ground Penetrating Radar results.........................................................................40
3.3 Ground Truthing and Survey Summary.................................................................41
4 Parameterisation of the Richards equation..........................................................43
4.1 Richards Equation.................................................................................................43
4.2 The h(θ) relationship .............................................................................................44
4.3 The K (h) Relationship ..........................................................................................46
4.3.1 Introduction...............................................................................................46
4.3.2 Determining the hydraulic conductivity from WRC....................................46
4.4 SWIM model..........................................................................................................48
5 Modelling of the vineyard water balance..............................................................49
5.1 Introduction ...........................................................................................................49
5.2 Climatic data .........................................................................................................49
5.3 The soil water pressure head – water content relationship (h(θ) relation).............52
5.3.1 Measurements for water retention curves ...............................................52
5.3.2 In situ measuring of soil water content through time.................................54
5.3.3 Generation of water retention curves........................................................56
5.4 Hydraulic Conductivity – Soil Water Pressure relationship (K(h)) relationship ......56
5.4.1 Introduction...............................................................................................56
5.4.2 Hydraulic conductivity models ..................................................................56
5.4.3 Modelling the K(h) curves using WRC data..............................................57
5.5 Modelling the vineyard water balance using SWIM...............................................57
5.5.1 Introduction...............................................................................................57
5.5.2 Modelling water balance in the absense of vegetation .............................58
5.5.3 Validating model against field measurements and variability between
sites ..........................................................................................................64
Impact of Soil Variation on a Vineyard Water Balance
Table of Contents 3
5.5.4 Summer simulation of vineyard water balance .........................................69
6 Future Recomendations.......................................................................................76
7 Conclusion ...........................................................................................................77
8 References...........................................................................................................79
Appendix 1: Climate Data ........................................................................................89
Appendix 2: Soil Profiles ..........................................................................................91
Appendix 3: Water Retention Curves.......................................................................92
Appendix 4: Modelled and Logged Data..................................................................94
Appendix 5: Vegetation model inputs and outputs ..................................................96
Impact of Soil Variation on a Vineyard Water Balance
4 List of Figures
List of FiguresFigure 1 Grapevine growth stages (Coombe 1995) 13
Figure 2 The hydrological cycle of gravevines (WRC 2003) 15
Figure 3 Ternary legend used for total count image (Pracilio pers comm.) 39
Figure 4 Three class classification spatial distribution and total count image for Bridgelands vineyard
(Baigent Geosciences 2005) 39
Figure 5 A representative radargram for Bridgelands vineyard (Baigent Geosciences 2005) 40
Figure 6 Refelction density image for Bridgelands vineyard, red represents a high level of reflectance and blue
a low level of refectance (Baigent Geosciences 2005) 41
Figure 7 Graph showing the daily maximum and minimum temperatures and evapotranspiration at
Bridgelands vineyard for the period 25 May til 5 October 2005 52
Figure 8 Relation between clay content and volumetric water content at -1.5MPa (Smettem & Pracillio 2005)
53
Figure 9 The holes being dug for the installation of the moisture probes (Bennet pers comm.) 54
Figure 10 Installing the moisture probes at Bridgelands vineyard (Bennet pers comm.) 55
Figure 11 Moisture loggers in the ground (Bennet pers comm.) 55
Figure 12 Water components from modelling 492mm precipitation with no roots 61
Figure 13 Water content across soil profile for Hole 1 in the absence of vegetation 62
Figure 14 Water content across profile for Hole 2 in the absence of vegetation 62
Figure 15 Water content across profile for Hole 3 in the absence of vegetation 63
Figure 16 Comparison of modelled and logged water content at hole 1 and 5cm depth during late spring (7th
September til 5th October) 65
Figure 17 Comparison of modelled and logged water content at hole 1 and 40cm depth during late spring (7th
September til 5th October) 65
Figure 18 Comparison of modelled and logged water content at hole 2 and 5cm depth during late spring (7th
September til 5th October) 66
Figure 19 Comparison of modelled and logged water content at hole 2 and 40cm depth during late spring (7th
September til 5th October) 66
Figure 20 Water content at depth 5cm for each hole over the duration of the study period 67
Figure 21 Water content at depth 40cm for each hole over the duration of the study period 68
Figure 22 Ratio of actual and potential transpiration for deep and shallow root systems for hole 1 70
Figure 23 Ratio of actual and potential transpiration for deep and shallow root systems for hole 2 71
Figure 24 Ratio of actual and potential transpiration for deep and shallow root systems for hole 3 71
Figure 25 Water content at 15cm depth for each of the three holes 72
Figure 26 Water content at 150cm depth for each of the three holes 73
Impact of Soil Variation on a Vineyard Water Balance
List of Tables 5
Figure 27 The distribution of water from the model output for summer climate conditions and deep and
shallow roots 73
List of Tables
Table 1 Average annual water consumption of south west vineyards ( Luke, Burke & O'Brien 1987). 18
Table 2 The three identified classes and their respective potassium, uranium and thorium counts (Baigent
Geosciences 2005) 38
Table 3 Surface conditions for holes 1,2 & 3 58
Table 4 Hole 1: Initial conditions as inputted to the model 59
Table 5 Hole 2: Initial conditions as inputted to model 59
Table 6 Hole 3: Initial conditions as inputted to model 60
Impact of Soil Variation on a Vineyard Water Balance
6 Glossary
GlossaryAntecedent soil moisture - The amount of moisture present in the soil at the beginning of a
storm event, frequently expressed as an index corresponding to the weighted average of daily
rainfalls for a given period of time preceding the storm event.
Anthocyanins - these are the pigments found in the grape skins that are responsible for the
colour of red wines. To get this colour into red wine is one of the most important stages of red
winemaking. Anthocyanins in white wines tend to add flavour rather than colour.
Hydraulic conductivity (K) - a measure of the ability of soil or rock to transmit fluids,
measured as a velocity, generally cm/day or m/day.
Hysteresis - Hysteresis is a property of systems (usually physical systems) that do not instantly
follow the forces applied to them, but react slowly, or do not return completely to their original
state: that is, systems whose states depend on their immediate history
Irrigation scheduling - Irrigation scheduling is the process used by irrigation system managers
to determine the correct frequency and duration of watering.
Matric potential - A water potential component, always of negative value, results from
capillary, imbibitional and adsorptive forces.
Oenology - The science and study of wine and winemaking. Also spelled enology.
Phenolic - A molecule containing an aromatic ring that bears one or more hydroxyl groups is
referred to as 'phenolic.' Examples include flavonoids, isoflavonoids, and lignans. The word
comes from 'phenol,' the name for the structure below which has one hydroxyl group attached to
the ring
Impact of Soil Variation on a Vineyard Water Balance
Glossary 7
Stomata - Tiny pores on the surface of plant leaves that can open and close to take in and give
out water vapour
Terroir - Describes all the influences on the flavours in the wine that come from where the vines
grow, especially soil, climate, slope, the aspect of the slope. There is no exact translation in
English, but 'terroir' is an important concept in the expression of the origin of wine.
Unsaturated hydraulic conductivity - The proportionality constant between the volumetric
flux of water and the hydraulic gradient in a porous medium for cases of water content less than
saturation. Often expressed as a function of soil-water pressure head and/or water content, it
includes the effects of the water-filled pore structure and the viscosity and density of the water.
Vadose zone - The soil or other geologic material usually located between the land surface and a
saturated formation where the voids, spaces or cracks are filled with a combination of air and
water.
Veraison - Beginning of fruit ripening, recognized by berry softening and beginning of
pigmentation in colour varieties
Viticulture – the cultivation of grape vines
Water content - The percent of water in a soil relative to its dry weight.
Water potential - a measure of the ability of any object or substance to draw water into itself; an
object (such as a cell wall) that has a negative water potential will draw water into itself from any
other object that has less negative water potential
Water stress - The condition when plants are unable to absorb enough water to replace that lost
by transpiration. The results may be wilting, cessation of growth, or even death of the plant or
plant parts.
Impact of Soil Variation on a Vineyard Water Balance
8 Introduction
1 Introduction
The aim in an industry such as viticulture is to maximise the harvest load of grapes without
compromising the quality of the product. Quality and quantity are determined by environmental
variables including weather and soil.
Grapevines (Vitis vinifera L.) are often cultivated under mild water stress, caused by particular
pedo-climate conditions (rainfall timing or available soil water in the root zone). Such conditions
can also be induced through vineyard management with the intention to achieve optimal water
conditions in order to enhance quality (Gaudillere, van Leeuwen & Ollat 2002).
Water supply to the vine is one of the key elements which determine wine quality. The amount of
water that reaches the root system and the time for which the vine is “stressed” determine the
amount of soluble solids and acidity which ultimately affects the taste of the wine.
Water deficit can increase berry sugar and anthocyanin concentration which impacts on vine
development and berry composition and ultimately enhancing oenological quality potential,
whilst decreasing crop yield.
Variability within vineyards affects the resultant quality and quantity of produce. Precision
agriculture is concerned with better understanding the variability of the environment in which a
crop is grown, and the learnt knowledge can be used to manipulate the vines to obtain a desired
product.
Innovative methods for providing spatial variability maps of vineyard soils and water balances
are currently being explored. This report focuses on the use of radiometrics to map the surface
soil properties and ground penetrating radar (GPR) to provide a profile of the soil and patterns of
soil moisture. These methods are compared to more traditional point sampling methods of
vineyard soil survey. Direct measurement of soil moisture content using logged moisture probes
provide validation data for vineyard water balance modelling using rainfall and evaporative
Impact of Soil Variation on a Vineyard Water Balance
Introduction 9
variables. The use of a water balance model permits periods of vine stress to be identified under
natural rainfall conditions and allows irrigation application to be designed with allowance for the
spatial pattern of vineyard soils.
The wine industry in Australia may be a relatively new by world standards but is developing a
favourable reputation for its high quality produce. Annual wine exports alone exceed $1 billion
(Elliot, 2005). The youth of Australia’s wine making means it is not restricted by tradition,
allowing freedom for the exploration of new techniques. Rapid expansion of vineyards has
occurred in both the eastern states and Western Australia. Bridgelands vineyard in Margaret
River wine region of Western Australia was chosen for this study. The south-west of Western
Australia is considered to have the ideal climate and suitable soils for the production of quality
wine (Elliot 2005).
Impact of Soil Variation on a Vineyard Water Balance
10 Literature Review
2 Literature Review
2.1 Vineyards
The quality of a wine is determined by its terroir, a confluence of environmental factors including
climate, soil and topography. But the characteristics that constitute terroir are largely a reflection
of today’s landscape (Hubbard & Rubin 2004), Sever (2004) comments on the geologists David
Howell’s remark, that the character of a wine has a deep history: a geologic history and as such
great wine begins with great dirt.
Water supply to the vine is one of the key elements which determine wine quality (Trambouze,
Bertuzzi & Voltz 1998). The amount of water that reaches the root system and the time for which
the vine is “stressed” determine the amount of soluble solids and acidity which ultimately affects
the taste of the wine. Water deficit can increase berry sugar and anthocyanin concentration which
impacts on vine development and berry composition and ultimately enhancing oenological
quality potential (Gaudillere, van Leeuwen & Ollat 2002). Vines with access an abundant supply
of water have an increase in total production. Berry weight is higher, with lower concentrations
of sugars and of anthocyanins and phenolic compounds, and increased titrable acidity (Oliveira
2001; Freeman & Kliewer 1983); these characteristics can reduce wine grape quality. Vines
which undergo some water stress have increased soluble solids and reduced acidity, producing a
more favourable wine (Oliveira 2001).
Understanding the water balance of a vineyard is important for efficient management, especially
for grapes which benefit from mild water stress (Oliveira 2001).
Variability within vineyards affects the resultant quality and quantity of produce. Precision
agriculture is concerned with better understanding the variability of the environment in which a
crop is grown, and the learnt knowledge can be used to manipulate the vines to obtain a desired
product.
Innovative methods for providing spatial variability maps of vineyard soils and water balances
are currently being explored. This report focuses on the use of radiometrics to map the surface
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 11
soil properties and Ground Penetrating Radar (GPR) to provide a profile of the soil and patterns
of soil moisture. These methods are compared to more traditional point sampling methods of
vineyard soil survey. Direct measurement of soil moisture content using logged moisture probes
provide validation data for vineyard water balance modelling using rainfall and evaporative
variables. The use of a water balance model permits periods of vine stress to be identified under
natural rainfall conditions and allows irrigation application to be designed with allowance for the
spatial pattern of vineyard soils. Calibration and ground truthing remain essential if the
authenticities of the measurements are to be maintained (Lamb & Bramley 2001).
2.1.1 Overview of grapevine growth stages
It is important to understand the growth stages of grapevines when interpreting the water balance
in a vineyard and in provisioning for irrigation. The stages are often abbreviated to budburst,
flowering, fruit set and veraison. Each period requires differing amounts of water. Figure 1
details the stages of growth.
Grapevines undergo periods of dormancy and growth. The characteristics of each stage,
beginning from winter dormancy are:
Woolly Bud
Green growth is sensitive to cold damage, to overcome this, the winter bud is surrounded by a
protective woolly layer and hard bud scales that provide protection for the green bud material
(Lombard 2005).
Bud burst
Growth of vines shoots and leaves start (Campbell-Clause & Fisher 2001). The visible green tip
is the first leaf tissue.
Shoot growth
Shoots begin to appear at this stage. The growth of shoots occurs rapidly after bud burst. Nearly
50% of the final leaf and shoot growth has developed by the time flowering occurs (Goodwin,
1995; Campbell-Clause & Fisher 2001).
Impact of Soil Variation on a Vineyard Water Balance
12 Literature Review
Flowering
Flowers develop on the vine.
Fruit set
Also known as berry set. Flower caps have fallen off and berries begin to enlarge.
After flowering and fruit set
Berry growth is initially very rapid, and then slows (Campbell-Clause & Fisher 2001). Flowering
and fruit-set starts approximately two months after budburst. Yield is largely determined by
bunch numbers, berry numbers and berry size. Normally 25 to 30% of berries are set (Cummins
1987).
Berry growth has three distinct periods;
I. A period of rapid growth, lasts five to seven weeks, it is characterised by three to four
weeks of rapid cell division followed by rapid cell expansion.
II. Berry growth slows for two to four weeks.
III. Growth is due to cell expansion (Winkler 1974).
Veraison
Marks the beginning of berry ripening, it is the most sensitive growth stage (Grimes & Williams
1990). Berries begin to soften, change colour, accelerate in growth, accumulate sugar, and
decrease in acidity and increase pH (Campbell-Clause & Fisher 2001; Coombe 1995; Goodwin
1995). Lateral shoots may also develop during this period (Goodwin 1995).
Insufficient water during the development stages can cause vine deaths, reduce crop yield during
flowering, and reduce fruit maturity at the expense of quality in the later stages of ripening (Elliot
2005).
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 13
Figure 1 Grapevine growth stages (Coombe 1995)
Impact of Soil Variation on a Vineyard Water Balance
14 Literature Review
2.1.2 General principles of vineyard water balance
Understanding the water balance of a vineyard is important for efficient management, especially
for grapes which benefit from mild water stress (Oliveira 2001). Water stress during flowering
and fruit set will substantially decrease yield and should therefore be avoided (Mitchell &
Goodwin 1996). It is important to understand the vines water requirements during the growth
stages, to ensure sufficient water is available.
A grapevine’s water consumption is dependent on: the atmospheric demand for water that is
defined by the microclimate; the vine leaf area which is determined by the number of shoots and
the leaf area per shoot; and the response of the leaves to their aerial and soil environment (Green
et al. 2004).
The water balance of an irrigated vineyard can be represented as in Equation 1 (Yunusa, Walker
& Blackmore 1997):
∆S = P + I + Gw – Es – T – RO – D
Equation 1
Where
∆S is the change in the storage of soil water,
P is precipitation,
I is irrigation,
Gw is groundwater (water contributed to the root zone by the shallow water-table, if present),
Es is soil evaporation,
T is transpiration,
RO is surface run-off and
D is through-drainage.
A pictorial representation of the components of a vineyard water balance is given in Figure 2.
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 15
Figure 2 The hydrological cycle of gravevines (WRC 2003)
Transpiration
Water is absorbed by the plant through the roots and transported up the vine stem to the leaves
where it is lost to the atmosphere as water vapour (transpiration) (Behboudian & Singh 2000).
Water movement is driven by the water potential gradient, flowing from high to low gradient,
retardants are present within the system restricting water flow (Behboudian & Singh 2000). In a
vineyard water balance transpiration from both vines and cover crops needs to be considered.
Cover Crops
Cover crops are non-economic crops such as oats that are grown under the vines and between
vine rows to limit weed growth. They also absorb the soil water from the ground and transpire it
to the air, contributing to the total evapotranspiration of the vineyard.
It is essential to put more emphasis on characterizing the annual and not the seasonal water
balance since 30% of water used during the season is from antecedent soil water (Yunasa, Walker
& Blackmore 1997). It is often hard to try map a vineyard water balance. Typically, quantifying
through drainage (D) and transpiration (T) at the vineyard scale has been done using neutron
Impact of Soil Variation on a Vineyard Water Balance
16 Literature Review
probe and drainage lysimeters. Problems can occur when, for example, irrigation is frequent or
there is a presence of a perched aquifer (Yunusa, Walker & Guy 1997). Grapevines are
conservative in their water use. The low levels of interception and stomatal control define the low
transpiration rates that give rise to the relatively low water demand (Yunusa, Walker &
Blackmore 1997).
2.1.3 General principles of Vineyard irrigation management
Grapes are commonly grown in areas with low water supply, particularly in Mediterranean
countries, and often the vineyards are not irrigated, subjecting the vines to water stress during the
summer (Behboudian & Singh 2000; Patakas Noitsakis & Stavrakas 1997; During 1998). Water
stress results in reduced photosynthesis, cell division and expansion (Cummins 1998). Water
stress therefore, limits the growth of the vine. Water stress can be beneficial during grape
ripening to increase flavour and periods of rapid shoot growth to limit growth and water stress
can be detrimental during flowering, resulting in total yield loss. Irrigation can be used to harness
the effects of water stress.
Management of water is important in regions of low rainfall that support a large agricultural
industry (Behboudian & Singh 2000; Hubbard & Rubin 2004). Smart water management can
have a significant impact in dry agricultural areas which have high-value crops, such as the wine
grape growing regions of California and Australia (Hubbard & Rubin 2004). Increased water
shortages and cost of irrigation are driving the research to maximise water use efficiency (Jones
2004). The introduction of precision irrigation methods has reduced the water use in agricultural
and horticultural crops, and encouraged the development of accurate irrigation scheduling and
control (Jones 2004). Precision irrigation methods generally aim at delivering an accurate, target
amount of water so as to deliver the optimal amount of water while reducing excess water
delivery and therefore waste (e.g. trickle irrigation).
Farmers irrigate to optimize crop production (Cummins 1998). Irrigation can be used to influence
vine water status, canopy size, vineyard homogeneity, and grape yield (Ginestar et al. 1998;
Johnson et al. 2003). Irrigation is an important management tool for vineyards (Campbell-Clause
& Fisher 2001) as careful manipulation can lead to optimum crop results.
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 17
Careful irrigation management can have beneficial effects on winegrape crops (Hubbard & Rubin
2004). Efficient irrigation maintains soil moisture high enough to avoid water stress whilst low
enough to avoid water logging and excess drainage (Cummins 1998). Irrigation efficiency can be
improved by determining when to and how much to irrigate, enabling the farming to choose
between a reduced yield of high quality grapes and maximum yield per hectare (Green et al.
2004).
If rainfall is inefficient to meet evapotranspiration requirements, irrigation has been found to
increase productivity (Behboudian & Singh 2000; Hubbard & Rubin 2004). Irrigation scheduling
should attempt to minimise periods of low water status in plants by replacing an adequate
percentage of evapotranspiration loss (Behboudian & Singh 2000). Judicious irrigation therefore
necessitates estimation or measurement of ET (Behboudian & Singh 2000).
The choice of irrigation technique will determine how much water is needed, as well as
meteorological conditions and crop factors (Campbell-Clause & Fisher 2001). Meteorlogical
factors influencing crop water use include radiation; temperature, vapour pressure deficit and
wind speed (Bedboudian & Singh 2000) and vary between regions and seasons (Campbell-Clause
& Fisher 2001). Crop factors influencing water use include stomatal response, leaf morphology,
vine architecture, rootstock, crop load, trellis type and cultivar (Behboudian & Singh 2000;
Campbell-Clause & Fisher 2001). Other important factors which determine amount of irrigation
needed include antecedent soil water content, soil water holding capacity, root distribution and
density and minimum leaching requirements (Cummins 1998).
Table 1 shows the average annual water consumption of established vineyards in the south-west
of Western Australia. Margaret River vines consume less water per hectare than all the other
vineyards mentioned. Understanding water requirements of vines is vital for optimizing grape
production in areas reliant on irrigation (Hubbard & Rubin 2004).
Impact of Soil Variation on a Vineyard Water Balance
18 Literature Review
Table 1 Average annual water consumption of south west vineyards (Luke, Burke & O’Brien 1987).
Location Megalitres per hectare
Swan Valley 3.8
Wokalup 2.7
Rocky Gully 2.7
Donnybrook 2.5
Albany 2.4
Manjimup 2.4
Pemberton 2.1
Margaret River 1.8
Californian scientists created a water balance model for a vineyard to assist with irrigation
planning. The model combines leaf area with weather and soil data bases to predict soil moisture,
vine stress, and the water replacement requirement for the vineyard (Johnson et al. 2003). The
success of the model highlights the importance of all components of the water balance, and that
water balance in vineyards can be modelled.
A study undertaken by Green et al. (2004) determined the water balance and irrigation needs of a
vineyard in New Zealand, using a combination of sap flow measurements in the vine trunks, and
soil moisture measurements using Time Domain Reflectrometry (TDR) and neutron probes over
a period of three years. From this they were able to calculate vine irrigation requirement.
Silt loam: 2.4m spacing- 102mm/yr irrigation, vine needs irrigation 80% time
1.8m spacing- 152mm/yr reduced to 112mm/yr because of increase shade effect
reducing evaporation losses.
Research done using Shiraz grapevines in the Barrossa Valley, Australia, found that irrigation
applied post-veraison increased grape yields and changed leaf area to fruit weight ratio (Ginestar
et al. 1998).
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 19
If grape vines have abundant supplies of water, they produce a dense, shaded canopy. As growth
cycle gets longer, shoot growth increases in speed and size, resulting in an increased pruning
weight. Total production increases, the berry weight increases but they have a lower
concentration of sugars, and anthocyanins and phenolic compounds and titratable acidity
increases. This shift can result in a reduction in wine quality (van Leeuwen & Seguin 1994;
Reynolds & Naylor 1994).
Grapevines must undergo a certain degree of water stress to obtain an optimal wine quality.
Vinifera grapes produce better quality wines in regions that experience dry summers. Dry
summers force the vines to rely on the storage of winter rains within the soils, which produces a
certain amount of stress needed for optimal wine quality (Oliveira 2001).
Irrigation and soil types
Soil parameters are an important consideration for effective irrigation management. The
parameters influence the depth to which vine roots grow and the volume of water held within the
root zone (Hubbard & Rubin 2004). Sandy areas have a low water holding capacity due to the
large grain size, whilst clay soils have a higher capacity to store water (Hubbard & Rubin 2004).
Consequently for roots that are located in shallow clay-rich soils, the water will be held around
the root system and be more available then for roots which tap in to sandy soils, the water drains
freely and is not readily available for the roots. Since a vineyard is not uniform, it is difficult to
create a homogeneous quality of wine across the vineyard (Hubbard & Rubin 2004). The amount
of irrigation a plant requires is dependant on the plant and soil characteristics in addition to
climate (Hubbard & Rubin 2004).
2.1.4 Modern Methods of Irrigation management aimed at reducing wateruse whilst maintaining quality
Irrigation Scheduling
Irrigation scheduling refers to the practise of applying the correct amount of water at the correct
time to fulfil an objective of the irrigation strategy (Goodwin 1995). Objectives within viticulture
Impact of Soil Variation on a Vineyard Water Balance
20 Literature Review
include restricting excessive foliage growth or avoiding water stress during flowering. Irrigation
scheduling can be done by traditional water balance and soil moisture-based approach or by
sensing the plant response to water deficit (Jones 2004). An irrigation strategy is a selection of
the best scenarios of when and how much to irrigate during the season to meet an objective for a
particular vineyard (Goodwin 1995). Grapevines are predominately grown in dry climates,
therefore important to maximise use of applied water (Behboudian & Singh 2000).
Two types of irrigation techniques which can be effectively used on mature vines are Regulated
Deficit Irrigation (RDI) and Partial Root zone drying (PDI) (Campbell-Clause & Fisher 2001).
There are three recognized vine growth stages, establishment phase, young vines and established
vines. During each growth stage vines have require differing water requirements.
Establishment Phase
During establishment phase, soil should be kept moist to encourage root growth, but not too wet
as to inhibit growth (Campbell-Clause & Fisher 2001).
Young Vines
Young vines should be irrigated. Young vines are those 1-3 years old, during this stage growth
should be optimized; soil moisture should be kept similar to the establishment phase (Campbell-
Clause & Fisher 2001).
Established Vines
During establishment phase, soil water can be controlled using irrigation techniques such as RDI
and PRD, to control growth and manipulate fruit quality and quantity (Campbell-Clause & Fisher
2001). Moderate water stress on grapevines early in the growing season has a positive effect on
grape quality (Hubbard & Rubin 2004).
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 21
Regulated Deficit Irrigation
Regulated Deficit Irrigation (RDI) is the control and management of water stress through
irrigating at less then the full requirement of the vines and maintaining soil moisture at a
particularly dry level (Goodwin 1995; Campbell-Clause & Fisher 2001). It has been shown that if
a slight water deficit is maintained, fruit growth is encouraged and excessive vegetation growth
can be controlled (Jones 2004). RDI aims to reduce shoot growth or improve fruit quality
(Cummins 1998).
The aim of RDI is to maintain water stress at a particular level during certain growth stages,
limiting transpiration (Cummins 1998), so the response of the vine can be used to advantage the
vineyard (Campbell-Clause & Fisher 2001). The depth and timing of irrigation is controlled to
provoke a desired response (Campbell-Clause & Fisher 2001). Established vines with well
established root systems should be the recipients of RDI and only quality water should be used
(Campbell-Clause & Fisher 2001).
RDI evolved during the 1980’s as a result of research conducted on stone and pome fruit by the
Victorian Department of Agriculture at Tatura (Goodwin 1995). Water stress was used to
discourage excessive shoot and tree growth (Goodwin 1995). RDI was applied to winegrapes first
in the 1990’s with the intent to increase quality through manipulating water stress conditions
(Goodwin 1995; Cummins 1998).
RDI is difficult because rather then apply specific volumes of water, soil moisture needs to be
maintained within a narrow tolerance (Cummins 1998; Jones 2004); if excess water is applied,
the advantages of regulated deficit are lost and can result in more water used, whilst if the plants
are underwatered it can result in severe yield or quality loss (Jones 2004). For RDI to be
successful, accurate soil moisture or plant ‘stress’ sensing is required, as well infrastructure to
allow the dispensing of small amounts of water often on demand (Jones 2004).
Application of RDI throughout growth cycle
When RDI practices are applied to grapevines it can influence fruit quality (Cummins 1998). The
timing of RDI application is crucial, since water stress has different outcomes at different stages
Impact of Soil Variation on a Vineyard Water Balance
22 Literature Review
of growth. Water stress during any growth stage will affect the most active growth process
occurring at that time (Cummins 1998). At any time during the season, RDI will decrease yield
and titratable acid (Goodwin 1995; Campbell-Clause & Fisher 2001)
RDI during shoot growth
When RDI is applied during periods of rapid shoot growth; shoot growth will slow down
(Campbell-Clause & Fisher 2001). A thinner canopy allows better light penetration, to grape
buds which are usually located in the interior of the canopy. An open canopy decreases disease
risk, improves spray penetration, improves fruit quality by exposure to light.
RDI during flowering and fruit set
High levels of water stress during flowering and fruit set will reduce yield significantly, up to
50% (Goodwin 1995) in grapevines (Cummins 1998; Campbell-Clause & Fisher 2001)
RDI pre veraison
If applied pre-veraison vegetative growth will be considerably reduced, and yield only marginally
affected (Campbell-Clause & Fisher 2001; Goodwin 1995).
RDI post veraison
Applying RDI after veraison increases wine colour, phenolics and flavour, but yield and soluble
solids will be decreased. Later season RDI will slightly decrease soluble solids, yield and
vegetative growth (Campbell-Clause & Fisher 2001; Goodwin 1995).
Wineries prefer berries with high levels of soluble solids, and for some varieties high skin to
juice ratios to increase wine colour, aroma and flavour, RDI has proven successful for
winegrapes because it can assist in balancing these characteristics against decrease in yield
(Cummins 1998).
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 23
Partial Root Zone Drying
An alternative to RDI which also controls growth is partial root-zone drying (PRD) (Jones 2004).
PRD is a new irrigation technique and not yet been trialled in Western Australia (Campbell-
Clause & Fisher 2001).
PRD works by applying water to different parts of the root system on an alternate basis (Jones
2004; Campbell-Clause & Fisher 2001). As the part of the vine not receiving water dries out, a
hormone called abscisic acid is released, reducing stomatal conductance, photosynthesis and
growth of the vine (Campbell-Clause 2001). PRD reduces canopy density, increasing fruit
exposure and therefore quality, whilst reducing the total amount of water given to the vine
(Campbell-Clause & Fisher 2001). PRD does not reduce yield, and berry size is not changed
(Campbell-Clause & Fisher 2001). Precise irrigation control is less crucial then with RDI, as
plants always have access to adequate water from the watered side and the drying side aids to
manipulate growth and stomatal aperture (Stoll, Loveys & Dry 2000).
Water Stress
Stress in a plant refers to a physical or chemical factor that causes bodily tension and may be a
factor in disease causation; a state or condition caused by factors that tend to alter equilibrium;
the state or condition of strain; expressed quantitatively in units of force per unit area (Hale &
Orcutt 1987).
Water Stress of a vine is the physical reaction of a vine to a limitation of supply of water, this
predominantly occurs when the water loss from the leaf canopy exceeds the supply from the soil
(Goodwin 1995). The water demand is dependant on the weather and size and shape of the
canopy (Goodwin 1995). The supply of water to the vine is dependant on the soil water content,
root distribution and density, and the soil properties including hydraulic conductivity and water
holding characteristics (Goodwin 1995). Physical responses to this stress include closing of leaf
stomata, reduced photosynthesis, reduced cell division and loss of cell expansion
(Goodwin1995). These responses are dynamic and vary depending on the extent to which stress
is occurring (Goodwin 1995).
Impact of Soil Variation on a Vineyard Water Balance
24 Literature Review
Soil water content is the only factor which can be manipulated to maintain a level of water stress
(Goodwin 1995). Soil moisture tension is used as a proxy indicator for water stress (Goodwin
1995).
2.2 Spatial variability of soils within vineyards
The forgoing sections have outlined the general aspects of the stages of grapevine growth as well
as the principles of vineyard water balance and how irrigation is currently being managed. In
order to completely understand the processes it is important to understand the soil variability
within a vineyard. This idea of segmenting a field based on geological characteristics and farming
each area based on the characteristics is referred to as precision agriculture or precision
viticulture.
Soils play a significant role in determining the quality of wine produced (Hubbard & Rubin
2004), contributing to variability in fruit production across and within vineyards (Lamb &
Bramley 2001). Soil properties can vary laterally over distances as small as a few meters
(Hubbard & Rubin 2004) as a result of geological processes. It is important to have soil
characterized within a vineyard, so growth can be managed (McKenzie 2000).
In traditional wine growing areas, such as France, the variations of soils within vineyards and
subsequent winemaking processes have been learnt over hundreds of years through trial and
error, Newer winemaking areas such as Australia and California lack this historical information
and therefore require methods of mapping their soils in detail ‘quickly’ (Hubbard & Rubin 2004).
The conventional approach to map soils in vineyards is to dig pit-holes and record the soil types
at these point locations, the spacing of these holes is usually such that the soil can vary in-
between sample sites, making the conventional approach inadequate to precisely and effectively
managing a vineyard (Hubbard and Rubin 2004).
Research done by Rubin (2003) revealed that the effect range of near surface water content over
time was approximately 5m, and samples taken at more then 5m are of little significance for
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 25
spatial correlation. By this reasoning, conventional ‘point’ measurements used in agriculture has
a limited ability to create reliable soil water content spatial correlation models.
The advent of precision agriculture is driving research for better mapping of spatial and temporal
variation within vineyards (Hubbard & Rubin 2004). The variability within a vineyard stays
constant over time, so adapting farming practices based on natural variations has the ability to
provide more uniform fruit to wineries (Hubbard & Rubin 2004; Rampant 2004) and increased
predictability of produce each year.
Maps of soil texture and moisture could provide a guide to vineyard management and decisions
regarding plant varieties based on their preferred soil and water conditions (Hubbard & Rubin
2004). In established vineyards, soil mapping can be beneficial in targeting the specific irrigation
needs of vines, potentially increasing grape quality and decreasing water consumption (Hubbard
& Rubin 2004).
Remote sensing methods are being developed in preference to traditional methods, for acquisition
of high spatial density soil data (Lamb & Bramley 2001).
2.2.1 The advent of precision agriculture (viticulture)
Precision Agriculture (or Precision Viticulture) refers to a whole range of technologies that can
improve the management of agricultural (viticultural) production through recognising that the
potential productivity of land varies in space, often over distances of only a few metres (Marks
2002).
A large amount of data is required to characterize the spatial variation of soil, vine and fruit, if it
is to be useful enough to apply to precision agriculture practices, for this reason cheap and rapid
methods of data collection need to be developed (Lamb & Bramley 2001).
Precision viticulture tools are already commercially available, such as yield monitors,
electromagnetic sensors of bulk electrical soil conductivity and airborne and satellite imagery.
Such tools are becoming progressively cheaper, accessible and reliable. Integrating technologies,
Impact of Soil Variation on a Vineyard Water Balance
26 Literature Review
such as overlaying remote sensing imagery onto digital elevation models, can enhance the
interpretation of data for management decision-making (Marks 2002). Remote sensing can
provide a basis for decision support in vineyard management (Johnson et al. 2003).
However, more research is needed to better understand the factors that drive wine quality and to
identify early indicators of plant stress related to pests and disease, soil and water (Marks 2002).
Crop production is strongly influenced by pedogenic properties, biology, rooting depth, nutrition
and agronomic management, as well as the interaction of these factors with climate as well as
anthropogenic factors (Bramley 2004; Rampant 2004). Therefore, the productivity within an area
and between regions is highly variable (Bramley 2004).
Precipitation can not be controlled, but it can be measured, biology and anthropogenic factors can
also be managed, and the most constant factor over time is the soil characteristics (Rampant
2004).
2.2.2 Applications of precision agriculture in the Margaret River region
Precision agriculture for viticulture is a developing management tool, for this reason minimum
precision viticulture practises have been undertaken within the Margaret River Region. Only
broad scale soil maps are attainable quickly, detailed ground surveys are still the dominant means
to obtain a detailed soil map.
2.3 Soil sampling methods
Conventional methods of mapping soil types and profiles include airphoto interpretation, ground
geophysics and drilling regional mapping and satellite images, these methods are often time
consuming and expensive (George 1998).
Advances in geophysical equipment, types of airborne platforms and Global Positioning Systems
(GPS) are being applied to engineering and environmental studies (US ACE 1995). In situ
methods including airborne radiometrics and GPR are being utilized by agricultural groups since
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 27
they can provide accuracy at a paddock scale and can aid in soil and water mapping (US ACE
1995).
This project focuses on the use of radiometrics and GPR in collaboration with ground truthing to
map the soil and corresponding soil moisture with Bridgelands vineyard. Bramley (2004)
comments on GPR as being highly material dependent, and having the potential for identifying
variation in soil moisture content, and gamma radiometrics as being most useful for sensing
variation in clay mineralogy.
2.3.1 Airborne Radiometrics
Airborne radiometrics is also known as airborne gamma-ray spectrometry. Radiometrics is a
geophysics technology; the other dominant geophysical technologies are electromagnetics and
magnetics.
History
Since the 1950’s airborne geophysics has primarily been used for geological mapping and the
exploration of valuable minerals by the mining industry (George et al. 1998; George 2001). Over
the last ten years the technology has developed rapidly. Previously, development of airborne
geophysics for use in engineering and environmental research has been slow because it was
believed the data collected was not sufficiently detailed to be of use, nor economically viable
unless it was used over a large area with targets of ample anomaly strength (US ACE 1995).
Despite the apparent ability of radiometrics to describe surface materials it has not been readily
adopted for use in soil mapping (Cook et al. 1996). To date, there have been few attempts to
quantitatively assess the relationships between radiometric data and specific soil properties,
particularly in WA. This is an essential step if radiometrics is to be used as a soil property
mapping tool (Taylor et al. 2002).
Soil at Paddock scale
Gamma radiometrics can be used to rapidly map soil characteristics at a paddock scale (~100m)
(Rampant 2004; George 1998; George et al. 1998), rather then the existing regional scales
Impact of Soil Variation on a Vineyard Water Balance
28 Literature Review
(~1000-5000m) allowing farmers to compare soils, map similar units within catchments or
regions, improving farm maps (George 1998).
Delineating Landforms
Airborne radiometric surveying has significant advantages when compared to other soil mapping
techniques, as it is able to map soil variables at a high spatial resolution across the landscape,
instead of the traditional method of extrapolating from point samples (Wilford 1995).
Landscape hydrology has been conventionally mapped on paddock or regional scales using soil
analysis, airphoto interpretation, regional mapping, and ground geophysics and drilling, such
methods are expensive, labour intensive and are therefore not applicable for use in many
catchments (George 1998).
Airborne geophysics technology has the ability to produce images of features in the surface and
sub-surface of a catchment, providing significant information about the soils, geological
structures, groundwater processes and salt distribution (George et al. 1998).
Combined with conventional sources
Airborne radiometrics are used to map patterns of soil properties inc. mineralogy and texture, and
when integrated with conventional sources, do so more thoroughly then other landscape
interpretation technologies (George 1998).
Physical principles
Radiometrics provides information regarding the abundance and distribution of K, Th and U on
the earth surface. This information correlates with the soil landscape processes as well as soil
hydrology and plant growth.
Gamma ray measures radionuclides
Gamma ray spectrometry (radiometrics) measures gamma ray signals which are naturally emitted
from the decay of isotopes present in all soils (George 1998; George 2001; Cook et al. 1996;
Pickup & Marks 2000). The naturally occurring elements that emit gamma radiation with
sufficient intensity to be measured at airborne heights are potassium (P), uranium (U) and
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 29
thorium (Th) (Taylor et al. 2002; Wilford 2002). Radiometrics measures these and/or there
daughter products. The data collected can be used to produce images of the spatial concentrations
of radionuclides through the landscape (Taylor et al. 2002). The relative abundance or
concentration of particular radioelements in rocks and regolith materials at the surface is
determined by measuring the intensity of the emittance peaks (George 1998).
Radionuclides related to soil properties
Gamma ray signatures are largely determined by lithology but also change with weathering,
erosion and deposition, and therefore it is possible to be used as a partial surrogate for those
processes (Pickup & Marks 2000). The gamma ray signatures are related primarily to the
mineralogy and geochemistry of parent material, and secondarily to weathering processes leading
to soil formation (Taylor et al. 2002). Radiometric data therefore provide an opportunity to
remotely sense information relevant to soils (Gourlay & Sparks 1996).
Gamma rays
Naturally emitted gamma-rays are a form of high energy, short-wavelength electromagnetic
radiation that is emitted at different energy levels corresponding to the radioactive decay of
particular radioisotopes (George 1998). Gamma rays are absorbed as they pass through matter
due to interaction with electrons and atomic nuclei. Gamma rays can travel long distances
through air but only 30cm through most soils, therefore radiometrics only measures the surface
characteristics of the earth (Parasnis 1997; George 1998; George 2001; Minty 1997). The degree
of absorption depends on the electron density of the matter through which the gamma-ray passes
(Taylor et al. 2002).
Why measure gamma rays?
Unless vegetation is thick it generally has little influence on the gamma ray response, for this reason
spectrometric surveys are more useful in such terrain then other remotely sensed data such as landsat
and SPOT which have interpretation difficulties caused by fire scaring and vegetation masking the soil
regolith (Wilford 2002). Dense vegetation will only reduce elemental readings by 15% (Aspin and
Bierwirth 1997).
Impact of Soil Variation on a Vineyard Water Balance
30 Literature Review
Potassium, uranium and thorium
Only those radionuclides with half lives comparable to the earth’s, and there decay products can be
found on earth today (Tzortzis et al. 2002) these are thorium, uranium and potassium.
Potassium
Potassium abundance can be measured directly as gamma rays are emitted as K decays to argon
(Wilford 2002). Potassium has an average concentration in the earth’s crust of 2.3% and is present in
rock forming minerals including K-feldspars, micas and in clays including illite (Wilford 2002).
Thorium/Uranium
U and Th can not be measured directly (Wilford 2002). The daughter nuclides which are generated
during the decay of the parent elements are measured, and then the abundance of the parent element is
inferred (Wilford 2002) [more then one peak is present on the intensity verses energy table, only one
distinct peak exists for potassium]. Tl and Bi are the daughter products of Th and U respectively, as
such Th and U are expressed in equivalent parts per million (eU and eTh) (Wilford 2002).
Uranium and Thorium are much less abundant. Uranium has an average concentration of 3 ppm and is
mainly found in pegmatite, syenite, radioactive granites, some black shale, and many accessory
minerals (Wilford 2002). Thorium has an abundance of 12 ppm and is most common in accessory and
resistate minerals such as zircon, sphene, apatite, xenotime, monazite and epidote (Wilford 2002).
Geology
Using gamma ray signatures to distinguish between Soil types
Gamma rays emitted from the surface relate to the mineralogy and geochemistry of the bedrock
and weathered material; this includes soils, saprolite, alluvial and colluvial sediments (Wilford
2002).
Results from Taylor et al. (2002) study indicate that relationships exist between certain soil
properties and high resolution radiometric data. The mineralogy and geochemistry of parent
material and the weathering history of an area must be understood before interpretations can be
made about soil properties, as they have a strong influence on the radionuclide content of the soil
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 31
(Taylor et al. 2002). Of the data analysed particle size exhibited the strongest correlation with
radiometrics.
Silt and TC also exhibited a similar positive linear relationship, but with a reduced level of
correlation, when compared with percentage clay (Taylor et al. 2002). It has been shown that
radiometric data could differentiate areas of shallow soil over granitic bedrock and areas of
granitic outcrop within the catchment, as these features have distinctly high radiometric
signatures (Taylor et al. 2002).
2.3.2 Ground Penetrating Radar
History
Ground Penetrating Radar (GPR) is a geophysical method that has been developed for shallow,
high resolution, subsurface investigations of the earth (US EPA 2003).It is commonly used for
environmental, engineering, archeological, and other shallow investigations (Peters, Daniels &
Young 1994; US EPA 2003; Geo-Graf 2005). Originally developed by the army, GPR has been
in commercial use for over 30 years (Geo-Graf 2005). Ground penetrating radar has become
increasingly popular as researchers in a variety of disciplines strive to better understand near-
surface conditions (Hubbard, Grote & Rubin 2002).
GPR has been used to detect buried tanks, landfill debris, water levels, and contaminated fluids,
also military devises including land mines and unexploded ordnance, as well as being used to
examine archaeological sites (Peters, Daniels & Young 1994; US EPA 2003; U.S. ACE 1995). It
has also been used to map geological conditions that include depth to bedrock, depth to water
table, depth and thickness of soil and sediment strata on land and under freshwater bodies, and
the location of subsurface cavities and fractures in bedrock (US EPA 2003; U.S. ACE 1995).
Physical principles: How it works
GPR provides high resolution images of near surface earth structure; it is non-destructive, non-
intrusive, provides fast results and is economical (Cai, McMechan, & Fisher 1996; Geo-Graf
Impact of Soil Variation on a Vineyard Water Balance
32 Literature Review
2005; Pescovitz 2003). GPR can detect both metallic and non-metallic subsurface features and
penetrate most surfaces (Geo-Graf 2005).
Basic methodology
The basic concepts of GPR can be summarised as antennas, propagation, target scattering, and
mapping (Peters, Daniels & Young 1994). The GPR unit consists of a transmitting and receiving
antenna and a recording unit. It is either pulled along the surface by hand or vehicle.
Transmitting antenna
An antenna is positioned in close proximity to the ground emits high frequency electromagnetic
waves (pulses) (about 100 megahertz to 1000megahertz) to probe the subsurface and acquire
subsurface information (U.S. ACE 1995; Pescovitz 2003; Hubbard & Rubin 2004; Carcione
1996; US EPA 2003).
Electromagnetic wave is reflected
The wave is reflected when a dielectric difference is encountered.
The transmitted radar pulses are reflected from various interfaces within the ground (Huisman et
al. 2001; U.S. ACE 1995).
A GPR reflection occurs when significant dielectric difference occurs between adjoining layers,
either because of a bedrock layer or a change in soil or water content (Hubbard & Rubin 2004).
The dielectric constant is the ability of a material to store electrical energy under the influence of
an electric field (Pescovitz 2003). Reflecting interfaces may be soil horizons, the groundwater
surface, soil/rock interfaces, man-made objects, or any other interface possessing a contrast in
dielectric properties (Hubbard, Grote & Rubin 2002; U.S. ACE 1995).
Receiving antenna
Reflected signals are detected by the transmitting antenna or by the second, separate receiving
antenna. The received signals are processed and displayed on a graphic recorder as the unit
moves across the surface (U.S. ACE 1995). A cross section of the subsurface is recorded (U.S.
ACE 1995).
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 33
Dielectric properties and GPR frequency determine depth and velocity of penetration
The depth of penetration and velocity of GPR is dependent on the electrical conductivity of the
soil, the soil moisture, dielectric constant of the soil and the GPR antenna frequency (Rubin
2003; Pescovitz 2003). The dielectric properties of materials correlate with many of the
mechanical and geologic parameters of materials (U.S. ACE 1995).
In most earth materials GPR uses short wave lengths, resulting in good resolution of interfaces
and discrete objects (U.S. ACE 1995). However, the attenuation of the signals in the earth
materials is high and depths of penetration seldom exceed 10m. Water and clay soils increase the
attenuation decreasing the penetration (U.S. ACE 1995). Lower frequency signals sample a
thicker soil zone then higher frequency signals, and the soil zone of influence is thicker in drier
times (when the electromagnetic velocities are higher) than in wetter times (Hubbard, Grote &
Rubin 2002).
Water and clay influence dielectric properties of soil
The presence of water or clay greatly increases the dielectric constant of a material (U.S. ACE
1995). At GPR frequencies the polar nature of water molecules causes it to contribute
disproportionately to displacement currents which dominate the current flow at GPR frequencies
(U.S. ACE 1995). Thus, if significant amounts of water are present, the ε will be high and the
velocity of propagation lowered (U.S. ACE 1995). Clay materials with their trapped ions behave
similarly. Additionally, many clay minerals also retain water (U.S. ACE 1995).
Soil has a low dielectric constant, which is considerably elevated in the presence of water, the
signals travel time is then interpreted as a measure of the dielectric constant and corresponding
soil moisture (Pescovitz 2003; Hubbard & Rubin 2004). Dielectric constants range differs from 1
for air and 80 for water. The dielectric constant of dry soil is approximately 4 to 8, as the soil
pore spaces are filled with water; the corresponding dielectric constant increases (Hubbard &
Rubin 2004). In general, GPR performs better in unsaturated coarse or moderately coarse
textured soils (Hubbard, Grote & Rubin 2002).
Impact of Soil Variation on a Vineyard Water Balance
34 Literature Review
Shallow and deep groundwaves
GPR emits both shallow groundwaves and deep reflected waves in to the soil profile, the
different signals supply information regarding water content at different soil depths (Hubbard &
Rubin 2004).
At early times in the GPR signal propogation, spherical wavefronts propogate in to the ground.
Because the electromagnetic velocities of air and ground are different, boundary waves are
created when the spherical waves intercept with the ground surface. A ground wave is formed
that travels along the air ground interface (Hubbard, Grote & Rubin 2002). The ground wave
propagates through the top subsurface soil with a velocity dictated by the dielectric constant of
that soil (Hubbard, Grote & Rubin 2002). The soil zone of influence is function of the acquisition
parameters and the signal wavelength (and thus the electromagnetic velocity) (Hubbard, Grote &
Rubin 2002).
Limitations of GPR
The two dominant limitation of GPR include the accessibility of the site and the penetration
depth (Geo-Graf 2005) and the results are subjective since the technique requires operator
interpretation.
Accessibility of site
Because the GPR unit needs to be physically pulled along the ground surface, the area to be
surveyed needs to be accessible, i.e. relatively clear from underbrush, debris and equipment
(Geo-Graf 2005).
Depth of penetration
The medium through which the pulse travels, as well as the frequency of the transmitted wave are
contributing factors in the depth of signal penetration. Within the range of GPR antenna
frequencies, low frequencies penetrate more deeply then high frequencies. Low frequencies
produce poor resolution images, high frequencies result in good image resolution, but can not
penetrate far. Maximum depth of penetration is usually between 2-5m (Geo-Graf 2005). Depths
are dependent on the GPR antenna system used and the properties of the subsoil (Geo-Graf
2005).
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 35
Dependent on interpretation
Despite improved technology, GPR data interpretation is still highly dependent on the skill and
knowledge of the operator (Geo-Graf 2005).
Mapping soil water in vineyards using GPR
The importance of developing non-invasive, in-situ techniques for measuring soil water content
has been noted for monitoring and research of hydrological processes (Weiler et al. 1998).
Monitoring of soil water is imperative for precision agricultural and ecological programs and
water management (Rubin 2003). Surface geophysical techniques are a useful tool for precision
agriculture because it can investigate the subsurface with good spatial resolution in a non-
invasive manner (Hubbard & Rubin 2004).
GPR is unique because it maps soil water content at an intermediate scale in between point and
remote sensing measurements (Huisman et al. 2001; Stoffregen et al. 2002). GPR is
advantageous over other soil moisture measuring techniques because provides a continuous map
(van Overmeeren, Sariowan and Gehrels 1997; Rubin 2003) of high resolution (Rubin 2003).
GPR has been found to be as accurate as Time Domain Reflectrometry to map soil water content
(Huisman et al. 2001; Weiler et al. 1998). Weiler et al. (1998) noted some calibration differences
between the two methods were caused by a difference in frequency ranges.
Overmeeren, Sariowan and Gehrels (1997) discuss a trial where GPR was compared to
capacitance measurements. The inferred values from the two techniques were shown to be very
similar, and complemented each other well producing a realistic and reasonably complete image
of the vertical distribution of soil water distribution across the saturated.
Ground penetrating radar has been used to assess soils at potential vineyard sites, specifically
trying to locate shingle beds and aquifers (Lamb & Bramley 2001).
The spatial patterns of soil moisture are governed by soil texture (Rubin 2003) and so soil
moisture patterns can be complementary to soil maps. The soil-moisture patterns remain constant
Impact of Soil Variation on a Vineyard Water Balance
36 Literature Review
throughout time, though the moisture levels fluctuate with both irrigation and season (Hubbard &
Rubin 2004).
Comparison of the GPR images, and the corresponding water content information, demonstrated
that soil texture controls both water drainage and spatial distribution of soil moisture (Hubbard &
Rubin 2004).
GPR has been used to map soil water content within Californian vineyards (Hubbard & Rubin
2004) with the aim of developing a tool to manage stressed irrigation (Pescovitz 2003).
Rubin’s California study
Research is being undertaken by University of California to map soil water content within a
vineyard using ground penetrating radar (Pescovitz 2003). A study by Rubin (2003) investigated
the applicability of Ground Penetrating Radar for estimating soil water content within a vineyard.
The studies concluded that GPR was capable of estimating shallow surface water in high
resolution and non-invasive manner.
Robert Mondavi Winery
At the Robert Mondavi Winery in California GPR was used to estimate soil water content
(Hubbard & Rubin 2004). GPR with 900megahertz was used to estimate the dielectric constant
and corresponding soil water content in the top 20cm of soil (Hubbard & Rubin 1004).
Measurements were taken at 0.1m intervals providing a dense profile of the spatial variability.
Dehlinger Winery
Another vineyard, the Dehlinger Winery was also surveyed; focus was on deeper soil water
content (Hubbard & Rubin 2004). The vines were 20 year old Chardonnay vines and the soils
varied from sandy loam to sandy clay (Hubbard & Rubin 2004). The reflection technique was
used at the Dehlinger site, and an underground channel was located 1-2 m below the surface
(Hubbard & Rubin 2004).
Impact of Soil Variation on a Vineyard Water Balance
Literature Review 37
Results from the studies
- Temporally stable
For both the Mondavi and Dehlinger sites, the subsurface variations governed the water content
distributions consistently over time (Hubbard & Rubin 2004).
- Results support ground knowledge
The vineyard manager at the Dehlinger site provided spatial quantity and quality variations of the
produce which corresponded with the location of the sub-surface channel shaped feature
(Hubbard & Rubin 2004).
- Soil moisture varied seasonally
This soil moisture changed throughout the year due to natural meteorological conditions
(Hubbard & Rubin 2004) and consequently the dielectric constants differed seasonally. The
average water content could be mapped for different times of the year (Hubbard & Rubin 2004).
- GPR can map soil variation with high resolution
The Mondavi and Dehlinger vineyard research supports claims that surface geophysical methods
can be beneficial for accurately mapping soil variations in very high resolution (Hubbard &
Rubin 2004).
Future applications
If farmers can be provided with a detailed soil map, adjustments to management practices, such
as irrigation, can be made to ensure a uniform product of high quality (Hubbard & Rubin 2004).
As water becomes a more precious resource, precision agriculture will become a more favourable
practice (Hubbard & Rubin 2004). Better information will allow better correlations between soil,
vegetation and climate, allowing for better management practices and more efficient and reliable
produce (Hubbard & Rubin 2004).
Impact of Soil Variation on a Vineyard Water Balance
38 Soil Survey at Bridgelands Vineyard
3 Soil Survey at Bridgelands VineyardA GPR and radiometrics survey was undertaken by Baigent Geosciences at the Bridgelands
vineyard. The purpose of the GPR survey was to identify soil horizons and the depths which they
occur. Radiometric data obtained was used to identify soil types with similar mineral properties.
Bridgelands vineyard is located 115.19° East and 33.00° South in the Margaret River wine
region. The study area covers rows 1-37 and an area of 3.027 acres.
The GPR data was obtained at 0.2 seconds (approximately 0.66m) sample intervals with a line
spacing of 3 m. The radiometrics data had a sample interval of 2 seconds (approximately 4.0 m)
with a line spacing of 3 m (Baigent Geosciences 2005).
3.1 Radiometric results
A total count image of the vineyard was created, displaying the distribution of total count
intensity. The spatial distribution of total gamma radiation count is shown in Figure 4. A ternary
scale was used for the graphical representation (see Figure 3). The colour corresponds to a K, Th
or uranium signal and the intensity corresponds with the strength of the signal.
Three distinct soil ‘types’ were identified within the vineyard, the soils were grouped
corresponding to their mineral composition as determined by their thorium, potassium and
uranium counts. The classes and their corresponding gamma radiation counts are displayed in
Table 2.
Table 2 The three identified classes and their respective potassium, uranium and thorium counts (Baigent
Geosciences 2005)
Class Number Potassium
Counts
Uranium
Counts
Thorium
Counts
1 (Red) 6.25289 7.36378 1.54910
2 (Cyan) 8.42385 9.93451 2.73858
3 (Yellow) 2.36366 12.5269 24.1828
Impact of Soil Variation on a Vineyard Water Balance
Soil Survey at Bridgelands Vineyard 39
The thorium count had the largest influence on total count. The spatial distribution of the three
classes of soil type is shown for the Bridgelands vineyard in Figure 4.
Figure 3 Ternary legend used for total count image (Pracilio pers comm.)
3 Class classification Total Count Image
Figure 4 Three class classification spatial distribution and total count image for Bridgelands vineyard (Baigent
Geosciences 2005)
Impact of Soil Variation on a Vineyard Water Balance
40 Soil Survey at Bridgelands Vineyard
3.2 Ground Penetrating Radar results
Representative radargram
The radargram is a vertical profile of the vadose zone, provides an insight to the distribution of
dielectric properties. A radargram was produced for each row. At the Bridgelands site, horizons
were noted at 0.38m, 0.6m and 0.81m. An example of a radargram in shown in Figure 5.
Figure 5 A representative radargram for Bridgelands vineyard (Baigent Geosciences 2005)
Reflection density index image
A reflection density index is a measure of the number of predominant hard reflectors. More
reflectors are indicated with higher grid values. Rock is often the cause of strong reflectance. The
reflection density image provides a map of the swpatial distribution of hardness of soil surface.
The reflection density index image for Bridgelands vineyard is given in Figure 6.
Impact of Soil Variation on a Vineyard Water Balance
Soil Survey at Bridgelands Vineyard 41
Using the information obtained from the radiometric and GPR survey, three distinct sites have
been identified for detailed acquisition of soil moisture and variables with the intent of better
understanding the maintenance of the vineyards soil water balance.
Figure 6 Refelction density image for Bridgelands vineyard, red represents a high level of reflectance and blue a
low level of refectance (Baigent Geosciences 2005)
3.3 Ground Truthing and Survey Summary
Ground truthing was undertaken at the Bridgelands vineyard to validate the gamma radiometric
and GPR survey. Thirteen auger holes were excavated across the vineyard in a stratified
sampling, designed to incorporate the main units mapped by the radiometrics and any features of
interest revealed by the GPR transects.
Gamma-ray spectrometry reveals texture variations within the surface 30cm of soil. The low total
counts correspond to sands and high total counts at this site correspending to loams at this site.
Impact of Soil Variation on a Vineyard Water Balance
42 Soil Survey at Bridgelands Vineyard
This was confirmed by the soil samples extracted from the auger holes, with surface textures (0-
20 cm) ranging from sand (6 auger holes) to sandy loam (4 auger holes) to loam (3 auger holes).
Soil texture consistantly became finer with depth and light clay was generally encountered
between 400 and 600 cm. At six of the auger holes the light clay also contained appreciable sand
but this is unlikely to affect the hydraulic properties (which are dominated by clay).
The presence of roots was assesed as abundant in the top 20 cm, abundant to common from 20
cm to 60 cm and then varied to 1.8 m, with 5 sites having few roots and 8 sites having common
abundance.
The auger holes supported the supposition that gamma radiometrics and GPR could be used to
map the soil variation within a vineyard. Three soil types dominated the surface; sand, sandy
loam and loam. Soil profiles for holes 1, 2 and 3 can be viewed in Appendix 2.
Impact of Soil Variation on a Vineyard Water Balance
Parameterisation of the Richards Equation 43
4 Parameterisation of the Richards equationThe soil water balance is central to the understanding of vines accessibility to water and hence for
efficient irrigation management.
Richards’ equation which is derived by combining the mass balance of water in soils with
Darcy’s Law flow equation, relates the flux of water relative to the soil particles to the space
gradient of water potential (Cresswell, Smiles & Williams 1992)
4.1 Richards Equation
One-dimensional vertical water flow in unsaturated soil is traditionally described with the
Richards’ Equation (Ross 1990; van Genuchten, Leij & Yates 1991):
ֱ��
���
�
∂∂
∂∂−=
∂∂
zhK
ztθ
Equation 2
Where:
θ is the volumetric water content ( 33 −LL )
h is the soil water pressure head (L)
K is the unsaturated hydraulic conductivity ( 1−TL )
t is the time (T)
Z is the spatial coordinate (L)
Ф is the sink/source term ( 133 −− TLL ) which evaluates water uptake by plant roots
For the Richards’ equation, as with other mechanistic models, simulation of the water balance is
possible with knowledge of climate and two basic hydrological characteristics of soil;
- The relation between soil water pressure head – water content relation (h(θ)), described
as the water retention curve (WRC), and
- The hydraulic conductivity – water content (K(θ)) or hydraulic conductivity – soil water
pressure (K(h)) relationship (Cresswell & Paydar 1996; Oliver 2001).
Impact of Soil Variation on a Vineyard Water Balance
44 Parameterisation of the Richards Equation
These characteristics are often difficult and costly to measure and as such have restricted the
application of unsaturated flow theory to actual flow problems, resulting in extensive research
(eg. van Genuchten 1980) to devise means of measuring them more efficiently or predicting them
from more easily measured soil properties (Cresswell & Paydar 1996).
Parameters of the Richards’ equation vary spatially and temporally and between measurement
techniques (Oliver & Smettem 2005).
The condition of use of the Richards’ equation are that the hydraulic conductivity K, and the
water potential ψ, are well defined (and measurable) functions of the volumetric content, θ
(Oliver & Smettem 2005; Cresswell, Smiles & Williams 1992). If K(θ) and h(θ) are well defined,
it follows that k(h) is also well defined (Cresswell, Smiles & Williams 1992).
Soil water pressure head may be positive or negative but under unsaturated conditions is always
negative (Oliver & Smettem 2005). The term ‘negative soil water pressure head’ may be denoted
by positive notation and this convention is adopted throughout to avoid any confusion in
terminology.
4.2 The h(θ) relationship
Soil moisture curves
The relationship between soil water content, θ, and soil water pressure head, h, is known as the
water retention curve (WRC) or moisture characteristic (Oliver 2001; Poulter 2005).
This relationship can be measured within the field or laboratory (Poulter 2005). WRC data can be
fitted to water retention models using empirical equations that describe the relationship between
the soil water pressure head and the water content, such equations include the power function of
Campbell (1974) and Brooks and Corey (1964) and the asymmetrical sigmoidal curve of van
Genuchten (1980).
Impact of Soil Variation on a Vineyard Water Balance
Parameterisation of the Richards Equation 45
The widely used functional relationship of van Genuchten (1980) is written
( )mn
e
rsr
hh
��
�
�
��
�
�
���
�
�+
−+=
1
θθθθ h>0
sθθ = 0≤h
Equation 3
Where
θ is the volumetric water content ( )33 −LL
h is the soil water pressure head ( )L
rθ is the residual water content ( )33 −LL
sθ is the saturated water content ( )33 −LL
eh1 is a scaling parameter, defined as the inflection point of the curve where eh , ( )L is often
described as the air entry parameter.
n is a slope parameter
m is a symmetry parameter
Equation 3 can be used with n and m as independent variables or else unique relations between n
and m can be assumed (Cresswell & Paydar 1996).
The theoretical pore size distribution model of Mualem (Mualem 1976) places restrictions on the
value of m such that the product of n and m is constant at
��
���
�−=n
m 11
Equation 4
Impact of Soil Variation on a Vineyard Water Balance
46 Parameterisation of the Richards Equation
Similarly, Burdine (Burdine 1953) limits the value of m such that:
��
���
�−=n
m 21
Equation 5
The restrictions on m fix the shape of the water retention curve at the wet end.
4.3 The K (h) Relationship
4.3.1 Introduction
The hydraulic conductivity – water content, K(θ) or pressure, K(h) relationship are key
parameters in any quantitative description of water flow into and through the vadose zone (Oliver
2001; van Genuchten, Leij & Yates 1991).
Laboratory and field measurements of K(h) of K(θ) can be expensive, with cost increasing due to
the number of samples required to overcome the spatial and temporal variability within the field
(Oliver 2001). Predictive techniques which obtain the hydraulic conductivity from more easily
measured soil water retention data (Burdine 1953; Mualem 1976) offer a less expensive option
which can account for this variability (Oliver 2001).
Water balance models based on the Richards equation, e.g. SWIM (Ross 1990) specify the K(h)
relationship using function models derived from water retention parameters incorporating
measured water retention points (Zhang and van Genuchten 1994). The water retention and
hydraulic conductivity functions of Brooks and Corey (1964), smoothed Campbell (1974) and
van Genuchten with Burdine or Mualem restrictions (van Genuchten 1980) can be used to
calculate relative K(h) relationships ( )sr KKK = from WRC parameters.
4.3.2 Determining the hydraulic conductivity from WRC
Water retention curves can be used to indirectly determine the hydraulic conductivity using
simple exponential or power function models (Gardner 1958; Brooks and Corey 1964), or
Impact of Soil Variation on a Vineyard Water Balance
Parameterisation of the Richards Equation 47
statistical pore radius distribution function models (Burdine 1953; Mualem 1976; van Genuchten
1980).
For the van Genuchten model with a Mualem restriction, water retention function can be
rearranged such that (Kumar & Puranara 2003):
( )( )
mn
ers
re h
hS
−
��
�
�
��
�
�
���
�
�+=
−−
= 1θθθθ
h<0
Equation 6
and the hydraulic conductivity can then be described by:
( )[ ]2/12/1 11 mmees SSKK −−=
Equation 7
Where
eS is effective saturation
θ is the volumetric water content ( )33 −LL
rθ is the residual water content ( )33 −LL
sθ is the saturated water content ( )33 −LL
eh1 is a scaling parameter, defined as the inflection point of the curve where eh , ( )L is often
described as the air entry parameter.
m is a symmetry parameter
RETC
The water retention parameters can be obtained from measured h(θ) points by using the RETC
computer program (Poulter 2005). RETC (RETention Curve) evaluates the hydraulic properties
of unsaturated soils using non-linear least squares parameter optimization (van Genuchten, Leij &
Yates 1991).
Impact of Soil Variation on a Vineyard Water Balance
48 Parameterisation of the Richards Equation
The RETC code is useful for a variety of applications including (i) predicting the unsaturated
hydraulic properties from previously estimated soil hydraulic parameters, (ii) predicting the
unsaturated hydraulic conductivity functions from observed retention data, and (iii) quantifying
the hydraulic properties by simultaneous analysis of a limited number of soil water retention and
hydraulic conductivity data points (van Genuchten, Leij & Yates 1991).
The program uses the functions of Brooks-Corey or van Genuchten models to describe the soil
water retention curve, θ(h), and the theoretical pore-size distribution models of Mualem and
Burdine to predict the unsaturated hydraulic conductivity function, K(h) or K(θ), from observed
soil water retention data (Poulter 2005; van Genuchten, Leij & Yates 1991).
4.4 SWIM model
Soil Water Infiltration Movement (SWIM) model is an efficient numerical solver for Richards
equation (Ross 1990) that can be used to model the infiltration, redistribution and deep drainage
of water within the vadose zone (Cresswell, Smiles & Williams 1992). SWIM (Ross 1997) model
parameterization uses the widely accepted θ(h) and K(h) relationship of Campbell(1974) or van
Genuchten (1980).
The model deal with a one-dimensional vertical profile divided in to horizontal layers of arbitrary
thickness and calculates soil water content profiles for defined initial conditions and time
dependent rainfall and potential evaporation data (Cresswell, Smiles & Williams 1992;
Rivasborge 2003). The soil may be vertically inhomogenous but is assumed horizontally uniform
(Cresswell, Smiles & Williams 1992). The model calculates transient surface retention and
runoff, though horizontal fluxes of water on and off the soil surface can not be properly
accounted for in a one-dimensional model of vertical flow (Cresswell, Smiles & Williams 1992).
To minimize data requirements and increase versatility, SWIM neglects vapour flow in the soil,
temperature effects on liquid water movement and hysteresis in the soil moisture characteristic
and unsaturated hydraulic conductivity functions (Cresswell, Smiles & Williams 1992).
Impact of Soil Variation on a Vineyard Water Balance
Modelling of the Vineyard Water Balance 49
5 Modelling of the vineyard water balance
5.1 Introduction
The purpose of modelling the water balance was to investigate conditions underwhich the
vineyard might be expected to suffer from water stress and therefore aid in irrigation
management. Once the model has been calibrated for the vineyard it is possible to extend the
application to the summer conditions with higher evaporative demands to investigate the effect
on soil water stores during the sensitive vine growth periods.
The physically based SWIM model developed by Ross (1990) was used in this study. The model
utilizes the Richards equation for one-dimensional vertical flow to model infiltration,
redistribution and deep drainage in the vadose zone (Cresswell, Smiles & Williams 1992). The
vertical profile is divided in to horizontal layers of arbitrary thickness and calculates soil water
content profiles for defined initial conditions and time dependent rainfall and evaporation data.
The input parameters for the SWIM model include climate data, the soil water pressure head -
soil water content relation, and the hydraulic conductivity - water content or hydraulic
conductivity - soil water pressure relationship.
5.2 Climatic data
The climate in the Margaret River region of Western Australia is Mediteranean with cool wet
winters and hot dry summers. Average annual rainfall is 982 mm.
Precipitation and evaporation are the driving forces in determining the gains and losses of water
from the soil-plant system.
For the duration of the experimental period, an automated meteorological station, located on the
Bridgelands site was used to measure rainfall, wind speed, air temperautre, humidity and
radiation at 15 minute intervals. The daily potential evaporation was determined from the
Penman equation (Equation 8). Cumulative daily rainfall and Penman evapotranspiration data
over the study period are shown in Appendix 1.
Impact of Soil Variation on a Vineyard Water Balance
50 Modelling of the Vineyard Water Balance
( ) ( )( )20 0062.0143.6 uee
LHLHGR
ET as
sn
s
+−∆+
++
+∆∆=
γγ
γ
Equation 8
Where
ET is the evaporatranspiration ( )1−mmday
∆ is the slope of the saturated vapour pressure - temperature realtion ( )10 −CkPa
sγ is the psychrometer constant
nR is the net radiation ( )1−MJkg
G is the soil heat flux ( )12 −− dayMJm
LH is the Latent Heat of vaporisation ( )1−MJkg
2u is the wind speed at 2m above ground height ( )1−kmday0e is the saturation vapour pressure ( )kPa
ae is the actual vapour pressure ( )kPa
The measurements of temperature, humidity, and height of weather station are used to calculate
the Penman parameters (Equation 9 - Equation 12).
10003601.22501 avT
LH−
=
Equation 9
2.0
22���
����
�=
mm z
uu
Equation 10
���
����
�
+=
3.23727.17
exp6108.00
av
av
TT
e
Equation 11
Impact of Soil Variation on a Vineyard Water Balance
Modelling of the Vineyard Water Balance 51
1000 RHeea ∗=
Equation 12
Where
avT is the average daily air temperature ( )C0
mu is the wind speed ( )1−kmday measured at mz , where
mz is height above the ground ( )m
RH is the relative humidity ( )%
For the periods from the 7-22 July and 20 August till 7 September the on-site weather station
experienced technical difficulties and subsequently the data for these periods was obtained from
the Bureau of Meteorology’s Witchcliff site (BOM 2005). Witchcliff is the nearest of the BOM’s
station to the Bridgelands site and as such provides the best possible estimate. The
evaportranspiration was derived for this period using the daily averages and the Penmans
equation (Equation 8), some adjustments of wind speed were needed to keep values in line with
Bridgelands.
The combined Witchcliff data and on-site meteorological station temperatures and
evapotranspiration used in this study are displayed in Figure 7.
Impact of Soil Variation on a Vineyard Water Balance
52 Modelling of the Vineyard Water Balance
Temperature and ET at Bridgelands vineyard
0
1
2
3
4
5
6
25/05
/2005
1/06/2
005
8/06/2
005
15/06
/2005
22/06
/2005
29/06
/2005
6/07/2
005
13/07
/2005
20/07
/2005
27/07
/2005
3/08/2
005
10/08
/2005
17/08
/2005
24/08
/2005
31/08
/2005
7/09/2
005
14/09
/2005
21/09
/2005
28/09
/2005
5/10/2
005
Evap
otra
nspi
ratio
n (m
m/d
ay)
-5
0
5
10
15
20
25
30
35
Tem
pera
ture
(deg
. Cel
sius
)
ET T max T min
Figure 7 Graph showing the daily maximum and minimum temperatures and evapotranspiration at Bridgelands
vineyard for the period 25 May til 5 October 2005
5.3 The soil water pressure head – water content relationship (h(θ)relation)
5.3.1 Measurements for water retention curves
Three sites were identified to be sampled based on the radiometric and GPR results. Soil samples
were collected from different depths and the water contents were evaluated in the laboratory
using Tempe pressure cells.
Tempe Pressure cells
The water retention curve was measured from 0 to 1.0m negative soil water pressure head using
Tempe pressure cells attached to a U-tube. The Tempe cells require cores of 5 cm diameter by 3
cm height, which can be taken intact from the field or repacked in the laboratory. For this study,
cores were repacked in the laboratory. Samples were collected from three sampling sites at depths
Impact of Soil Variation on a Vineyard Water Balance
Modelling of the Vineyard Water Balance 53
of 5 and 40cm. These depths were chosen so that input parameters would be available at
equivalent depths as the locations of moisture probes.
The soil core was saturated by capillary rise using a 0.01 CaCl2 solution then weighed to measure
the saturated water content. The weight of the saturated soil cell was used to calculate saturated
moisture content.
The apparatus allowed small pressure increments to be applied to the top of the core using a
syringe, with pressure registered on a hanging water column. Pressures of 30, 60 and 100 cm
were applied; the mass of water expelled from the core was collected and weighed to obtain the
water loss over each pressure increment. Measurements near saturation are important because
they allow a more accurate determination of the air entry point and the shape of the water
retention cure near saturation (Oliver 2001).
% clay-15000cm relation
The higher soil water pressure head range was estimated using a known relationship between clay
content and volumetric water content at -1.5MPa or 150 m H20 (see Figure 8). Percentage clay
content can be inferred from airborne radiometrics data or by soil sample analysis.
Figure 8 Relation between clay content and volumetric water content at -1.5MPa (Smettem & Pracillio 2005)
Impact of Soil Variation on a Vineyard Water Balance
54 Modelling of the Vineyard Water Balance
5.3.2 In situ measuring of soil water content through time
Moisture probes were used to measure soil moisture at two locations within the vineyard with
probes paired at depths of 5 cm and 40 cm. MDA300 sensor boards attached to Echo20 moisture
probes were used. The loggers were installed on 8th September and continued to record moisture
every 12 minutes till the 5th October. The values obtained for the two probes were averaged for
each depth. This value was then compared to the models ouput to test appropriateness. Figure 9,
Figure 10 and Figure 11 show the installation of the moisture probes.
Figure 9 The holes being dug for the installation of the moisture probes (Bennet pers comm.)
Impact of Soil Variation on a Vineyard Water Balance
Modelling of the Vineyard Water Balance 55
Figure 10 Installing the moisture probes at Bridgelands vineyard (Bennet pers comm.)
Figure 11 Moisture loggers in the ground (Bennet pers comm.)
Impact of Soil Variation on a Vineyard Water Balance
56 Modelling of the Vineyard Water Balance
5.3.3 Generation of water retention curves
Water content-water potential pairs were measured on the Bridgelands topsoil and subsoil using
Tempe-pressure cells, and field techniques. Laboratory Tempe cells and literature data were
combined to produce WRC’s. The water retention curve’s for the three holes are shown in
Appendix 3.
Water retention data for each sample were input to RETC. RETC identifies an equation which
maximises the sum of squares associated with the model while minimising the residual sum of
squares (Poulter 2005). Curves were then fitted using van Genuchten retention (Equation 3) with
the Burdine restriction on the value of m (Equation 5) and hence θr, 1/air entry and n parameter
were determined.
The predicted values θs, 1/ eh and n, were used in the van Genuchten equation (Equation 3) with a
Burdine restriction (Equation 5) to calculate predicted water content values at the soil water
pressures used for determination of the measured soil water characteristics (Paydar & Cresswell
1996).
5.4 Hydraulic Conductivity – Soil Water Pressure relationship(K(h)) relationship
5.4.1 Introduction
This study followed the convention of predicting the hydraulic conductivity – pressure curves
from the more easily measured soil water retention data (Burdine 1953; Mualem 1976).
5.4.2 Hydraulic conductivity models
As mentioned previously (section 4.3.2) there are a number of equations that can be used to
calculate the K(h) relationship from WRC parameters. These include the water retention and
hyraulic conductivity functions of Brooks and Corey (1964), smoothed Campbell (1974) and van
Genuchten (1980) with Burdine or Mualem restrictions.
Impact of Soil Variation on a Vineyard Water Balance
Modelling of the Vineyard Water Balance 57
The average WRC parameters from the lab Tempe and %-clay – 150 MPa relationship was used
to produce K(h) curves. The K/Ks as calculated using RETC, was converted to K, using Ks
values derived from the Rosetta data base (USSL 1999).
5.4.3 Modelling the K(h) curves using WRC data
The K(h) curve are plotted on a log-log scale with the curve starting at K=Ks at soil water
pressures less then air entry. After air entry soil water pressure, the K sharply decreased with
increasing soil water pressure at a particular gradient for each of the models (Oliver 2001).
The gradient of the K(h) curve is dependent on the WRC parameters. Increasing the gradient of
the K(h) curve results in a large decrease in K for very small soil water pressure change (Oliver
2001). Where the point of inflection occurs is dependent on the air enrty parameter. Decreasing
the air enrty shifts the K(h) curve to left which causes a lower hudraulic conductivity for the same
pressure (Oliver 2001).
5.5 Modelling the vineyard water balance using SWIM
5.5.1 Introduction
To run the model all the parameters needed to be collated then input to the model. The model
then returns a visual display of water content across the soil profile, spreadsheets containing the
water content and matic potential for each layer for all specified days, and a summation of
precipitation, evapotranspiration, runoff, surface water, available water, unavailable water and
drainage.
The inputs for the model include upper and lower storage limits for each soil layer, which were
derived from soil texture maps, saturated hydraulic conductivity for each layer, which were
estimated using values obtained from RoseTTa data base (USSL 1999), initial soil moisture
which was measured and the root depth and root length density which was obtained from the soil
survey as described in section 3.3 and literature.
Impact of Soil Variation on a Vineyard Water Balance
58 Modelling of the Vineyard Water Balance
5.5.2 Modelling water balance in the absense of vegetation
The water balance model was used in the absense of vegetation, so the results could better reflect
the contribution of soil characteristics and provide a firm base on which to base the influence of
vegetation and irrigation. Three sites were chosen, one with a predominately sand profile, one
with a predominately loam profile and one with a predominately sandy loam profile.
Methods
To simulate the water balance within the vadose zone in the absense of vegetation, the vegetation
parameters in the input file were switched off. The daily rainfall and evapotranspiration as
tabulated in Appendix 1 were included for the period 25 May till 5 October. The surface
conditions were inputted as shown in Table 3 and the initial soil profile conditions for each hole
are displayed in Tables 4, 6 and 7.
Table 3 Surface conditions for holes 1, 2 & 3
CONDUCTANCEInitial soil surface conductance: 4 /hMinimum soil surfaceconductance: 0.02 /hPrecipitation constant: 2.5 cmEffectiveness parameter: 0.184 RUNOFFInitial soil surface storage: 2 cmMinimum soil surface storage: 1 cmPrecipitation constant: 5 cmRunoff rate factor: 2 (cm/h)/cm^PRunoff rate power P: 2Initial surface water depth: 0 cm
Impact of Soil Variation on a Vineyard Water Balance
Modelling of the Vineyard Water Balance 59
Table 4 Hole 1: Initial conditions as inputted to the model
Depth hi θs θr hg
m=1-2/n Ks
cm cm cm cm/h 0 -120 0.45 0.04 -7 0.25 3001 -120 0.45 0.04 -7 0.25 3005 -120 0.45 0.04 -7 0.25 300
15 -120 0.45 0.04 -7 0.25 30030 -120 0.45 0.04 -7 0.25 30040 -120 0.45 0.04 -7 0.25 30050 -120 0.45 0.04 -7 0.25 30060 -600 0.4 0 -50 0.4 275 -600 0.4 0 -50 0.4 290 -600 0.4 0 -50 0.4 2
110 -600 0.47 0 -330 0.09 3150 -600 0.47 0 -330 0.09 3180 -600 0.47 0 -330 0.09 3250 -600 0.47 0 -330 0.09 3
Table 5 Hole 2: Initial conditions as inputted to model
Depth hi θs θr hg
m=1-2/n Ks
cm cm cm cm/h 0 -120 0.43 0.08 -28 0.2 101 -120 0.43 0.08 -28 0.2 105 -120 0.43 0.08 -28 0.2 10
15 -120 0.43 0.08 -28 0.2 1030 -120 0.43 0.08 -28 0.2 1040 -120 0.43 0.08 -28 0.2 1050 -120 0.4 0 -50 0.09 260 -600 0.4 0 -50 0.09 275 -600 0.4 0 -50 0.09 290 -600 0.4 0 -50 0.09 2
110 -600 0.47 0 -330 0.09 3150 -600 0.47 0 -330 0.09 3180 -600 0.36 0.07 -300 0.11 3250 -600 0.36 0.07 -300 0.11 3
Impact of Soil Variation on a Vineyard Water Balance
60 Modelling of the Vineyard Water Balance
Table 6 Hole 3: Initial conditions as inputted to model
Depth hi θs θr hg
m=1-2/n Ks
cm cm cm cm/h 0 -120 0.41 0.06 -10 0.23 1461 -120 0.41 0.06 -10 0.23 1465 -120 0.41 0.06 -10 0.23 146
15 -120 0.41 0.06 -10 0.23 14630 -120 0.41 0.06 -10 0.23 14640 -120 0.41 0.06 -10 0.23 14650 -120 0.41 0.06 -10 0.23 14660 -120 0.41 0.06 -10 0.23 14675 -120 0.41 0.06 -10 0.23 14690 -600 0.41 0.06 -10 0.23 146
110 -600 0.36 0.07 -300 0.11 3150 -600 0.36 0.07 -300 0.11 3180 -600 0.15 0.04 -360 0.1 1250 -600 0.15 0.04 -360 0.1 1
The model was run using the initial conditions tabulated above (Table 3, 4, 5 and 6) and profiles
of the water content and matric potential were obtained for each hole.
Results
This analysis describes the effect of the major soil variations on the water balance at a time of the
year when roots are not active.
A simulation was run using a total of 492mm precipitation and no vegetation (Figure 12). This
simulation shows holes 1 and 2 have similar water components, whilst hole 3 has a greater
proportion of drainage and lower available and unavailable water. Interestingly the
evapotranspiration for all three holes was quite similar.
Impact of Soil Variation on a Vineyard Water Balance
Modelling of the Vineyard Water Balance 61
Model output with 492mm Precipitation and No Roots
0
200400
600
8001000
1200
1 2 3
Hole
mm
of w
ater
DrainageUnavailable WaterAvailable WaterSurface WaterRunoffEvapotranspiration
Figure 12 Water components from modelling 492mm precipitation with no roots
The water content profiles were plotted for each hole at the beginning of each month during the
winter period. The water content appears to be reflective of soil type. For example at hole 1
(Figure 13) there is a noticable shift in water content at a depth of 110cm, corresponding with a
shift from sandy clay loam to sandy light clay, the later holding more water. The water content
profiles for holes 2 and 3 (Figure 14 and Figure 15) show greater variation with depth then hole
1. Similarly this is reflective of soil types.
For all holes there is considerable variation of surface water content between each month. The
surface soil water is lowest in June then increases each month before peaking in august then
receding back to antecent levels in September, before increasing again in October.
Impact of Soil Variation on a Vineyard Water Balance
62 Modelling of the Vineyard Water Balance
Hole 1: Water Content During Winter Period
0
50
100
150
200
250
300
0 0.1 0.2 0.3 0.4
Water Content (vol/vol)
Dept
h (c
m)
1st June1st July1st August1st September1st October
Figure 13 Water content across soil profile for Hole 1 in the absence of vegetation
Hole 2: Water Content During Winter Period
0
50
100
150
200
250
300
0 0.1 0.2 0.3 0.4
Water Content (vol/vol)
Dept
h (c
m)
1st June1st July1st August1st September1st October
Figure 14 Water content across profile for Hole 2 in the absence of vegetation
Impact of Soil Variation on a Vineyard Water Balance
Modelling of the Vineyard Water Balance 63
Hole 3: Water Content During Winter Period
0
50
100
150
200
250
300
0 0.1 0.2 0.3
Water Content (vol/vol)
Dept
h (c
m)
1st June1st July1st August1st September1st October
Figure 15 Water content across profile for Hole 3 in the absence of vegetation
Impact of Soil Variation on a Vineyard Water Balance
64 Modelling of the Vineyard Water Balance
Discussion
Water content is influenced by soil properties and climate. This was demonstrated in hole 3
which had significantly higher drainage then holes 1 and 2. Hole 3 is unique since the top 1.10m
of soil is a sandy loam, below which is light clay followed by a heavy clay, both of which have
high gravel contents, 50% and 80% respectively. Across the soil profile of 2, the gravel content
remains below 5%. Likewise for hole 1 with the exception of a sandy clay loam layer with 20%
gravel. The elevated level of drainage in hole 3 may be attributable to this gravel.
A possible reason for the variation in soil water content throughout the winter period is the
characteristics of rainfall events. On the first of August there was a high rainfall event, paired
with low evapotranspiration, the culmination of which translates as increased water content. June
and September had similar water contents. The 1st June had low Evapotranspiration rates, and no
rainfall, presumably the antecedent soil water content was low since it was proceeding the winter
raibfall period. The 1st of September had higher evapotranspiration rates, and also recieved no
rainfall, but had more water storage due to the high August rains.
5.5.3 Validating model against field measurements and variabilitybetween sites
Methods
The water content as modelled by SWIM for depths 5cm and 40 cm, for sites 1 and 2 were
plotted agianst the averaged water content as measured by the moisture probes as previously
described in section 5.3.2. This was done to validate the applicability of the model for moisture
content modelling. Logged data was available form 7th September til the 5th October at holes 1
and 2. On the graph the days are in numbers consistant with the model inputs and the other
graphs displayed in this report.
Impact of Soil Variation on a Vineyard Water Balance
Future Recommendationse 65
Results: Model validation
Figure 16 and Figure 17 display the modelled and logged water content for hole 1 at depths 5cm
and 40cm respectively. Figure 18 and Figure 19 display the modelled and logged water content
for hole 2 at depths 5cm and 40cm respectively. A close correlation between the predicted and
measured water contents exist for all of the three figures.
Hole 1: Comparison of modelled and logged water content at 5 cm depth
00.020.040.060.080.1
0.120.14
105 110 115 120 125 130 135
Days
Wat
er C
onte
nt (v
ol/v
ol)
Model Logged
Figure 16 Comparison of modelled and logged water content at hole 1 and 5cm depth during late spring (7th
September til 5th October)
Hole 1: Comparison of modelled and logged water content at 40 cm depth
00.020.040.060.080.1
0.120.14
105 110 115 120 125 130 135
Days
Wat
er C
onte
nt (v
ol/v
ol)
Model Logged
Figure 17 Comparison of modelled and logged water content at hole 1 and 40cm depth during late spring (7th
September til 5th October)
Impact of Soil Variation on a Vineyard Water Balance
66 Modelling of the Vineyard Water Balance
Hole 2: Comparison of modelled and logged water content at 5 cm depth
0
0.05
0.1
0.15
0.2
105 110 115 120 125 130 135
Days
Wat
er C
onte
nt (v
ol/v
ol)
Model Logged
Figure 18 Comparison of modelled and logged water content at hole 2 and 5cm depth during late spring (7th
September til 5th October)
Hole 2: Comparison of modelled and logged water content at 40 cm depth
0
0.05
0.1
0.15
0.2
105 110 115 120 125 130 135
Days
Wat
er C
onte
nt (v
ol/v
ol)
Model Logged
Figure 19 Comparison of modelled and logged water content at hole 2 and 40cm depth during late spring (7th
September til 5th October)
Impact of Soil Variation on a Vineyard Water Balance
Future Recommendationse 67
The assumption that the SWIM model can be used to predict soil water content is validated with
Figure 16, Figure 17, Figure 18 and Figure 19. The modelled water content closely follows the
measured content with little variation.
Results: Variability between sites
Figure 20 and Figure 21 show the water content at depths 5cm and 40cm over the duration of the
study (25th May til 5th October) for the three holes. The minimum water content at depth 5cm
for hole 1 is 0.067, hole 2 is 0.125 and hole 3 is 0.089. The maximum water content at depth 5cm
for hole 1 is 0.153, hole 2 is 0.248 and hole 3 is 0.175. At 40cm depth, the minimum water
content for hole 1 is 0.082, hole 2 is 0.121 and hole 3 is 0.103. The maximum water content for
hole 1 is 0.138, hole 2 is 0.248 and hole 3 is 0.161.
Water Content at 5cm Depth
0
0.05
0.1
0.15
0.2
0.25
0.3
1 12 23 34 45 56 67 78 89 100 111 122 133
Days
Wat
er C
onte
nt (v
ol/v
ol)
Hole 1Hole 2Hole 3
Figure 20 Water content at depth 5cm for each hole over the duration of the study period
Impact of Soil Variation on a Vineyard Water Balance
68 Modelling of the Vineyard Water Balance
Water Content at 40cm Depth
0
0.05
0.1
0.15
0.2
0.25
0.3
1 11 21 31 41 51 61 71 81 91 101111121131
Days
Wat
er C
onte
nt (v
ol/v
ol)
Hole 1Hole 2Hole 3
Figure 21 Water content at depth 40cm for each hole over the duration of the study period
Discussion
For depth 5cm and 40cm hole 1 is sand, hole 2 loam and hole 3 sandy loam. As would be
expected based on soil properties, the sand (Hole 1) consistantly held more water followed by
sandy loam (Hole 3) and then loam (Hole 2). This held true for both depths. The rates of change
of water content for all three soils are very similar. The difference between water content for each
of the holes can be attributed to soil type.
The minimum water contents for both 5cm depth (Figure 20) and 40cm depth (Figure 21) are
similar, but at 5cm depth there is a greater variation of water content, with the maximum values
being much higher. The water content at 5cm depth fluctuates more then at 40cm depth. This
greater variation can be attributed to the close surface proximity allwing the water content to
respond rapidly to changes in the climate, such as precipitation and increased evaporation. The
water contents exhibited during the winter period would be the greater then that at any other time
of the year, since the grapevines are inactive, daily evaporation is lower and the majority of
yearly rainfall events occur.
The model closely matched the logged data, cementing it’s appropriateness as a valid predictive
tool. The largest error was ~14%, with most being ~2%, the data corresponding to this error is
Impact of Soil Variation on a Vineyard Water Balance
Future Recommendationse 69
located in Appendix 4. The model successfully simulates all types of variability throughout time.
For example the model closely follows the peaks and troughs of the measured moisture in Figure
16 and Figure 18, as well as the steady trend exhibited in Figure 17 and Figure 19.
5.5.4 Summer simulation of vineyard water balance
The study period occurred throughout the duration of winter extending in to early spring. During
this period the vines are dormant and as such the system behaves as if it is void of vegetation. A
positive aspect of the model is that it can be used to estimate soil water and matric potential
conditions in summer, given soil types and average climatic data is known.
Methods
Characteric of a mediterranean climate, the study site experiences hot dry summers. To simulate
this; precipitation was inputted as zero (ignoring the possibility of rain bearing summer storm
events) and evapotranspiration was increased to 8mm per day, inline with literature summer
average values for the Margaret River region (Hauck & Coles 1995).
The cover crop was excluded from the modelling of the vineyard water balance. This is possible
since the SWIM model ignores lateral flow and the study is primarily concerned with the soil
profile directly under the vines. To simulate a shallow root zone and a deep root zone, two
different vegetation types were inputted to the model, one with a shallow root system and the
other with a deep root system. The shallow rooted system is active at 10cm depth and the deep
rooted system is active at 150cm depth. The vegetative parameters input to the model are shown
in Appendix 5 all three sites had the same root parameters, so the results would be comparible.
The soil profileparameters and surface conditions used were not changed from the winter
modelling (see Table 3, 4, 5 and 6).
The summer scenario modelled had active vegetation with no irrigation. This corresponds to a
vineyard with growing vegetation, with deep and shallow root systems. The ratio of actual
transpiration to potential transpiration was determined for each site. The change in transpiration
dynamics between the two root systems was noted.
Impact of Soil Variation on a Vineyard Water Balance
70 Modelling of the Vineyard Water Balance
Results: Active vegetation with no irrigation
The ratio of actual transpiration to potential transpiration is plotted against time for hole 1, 2 and
3 in Figure 22, Figure 23 and Figure 24. For all three holes the shallow rooted system remained at
the potential transpiration rate for a few days before the rate rapidly decreased to zero. The rate of
decline varied between holes. Hole 3 had the slowest rate of decrease.
The deeper roots maintained potential transpiration rates for a longer duration then the shallow
roots. The ratio of actual to potential transpiration remained at 1 for 32 days for hole 1, 39 days
for hole 2 and 21 days for hole 3. The rate of transpiration decline of the deep roots was less then
the shallow roots for all three holes.
Transpiration (T) During Summer at Hole 1
0
0.2
0.4
0.6
0.8
1
1.2
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65
Days
Actu
al T
/Pot
entia
l T
Deeper Roots Surface Roots
Figure 22 Ratio of actual and potential transpiration for deep and shallow root systems for hole 1
Impact of Soil Variation on a Vineyard Water Balance
Future Recommendationse 71
Transpiration (T) During Summer at Hole 2
0
0.2
0.4
0.6
0.8
1
1.2
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65
Days
Actu
al T
/Pot
entia
l TDeeper Roots Shallow roots
Figure 23 Ratio of actual and potential transpiration for deep and shallow root systems for hole 2
Transpiration (T) During Summer at Hole 3
0
0.2
0.4
0.6
0.8
1
1.2
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65
Days
Actu
al T
/Pot
entia
l T
Deeper Roots Shallow Roots
Figure 24 Ratio of actual and potential transpiration for deep and shallow root systems for hole 3
Water conent was modelled at depths of 15cm and 150cm for all three holes over the summer
period. Figure 25 and Figure 26 display the water content measured in water volume per soil
volume.
Impact of Soil Variation on a Vineyard Water Balance
72 Modelling of the Vineyard Water Balance
The below graphs indicate that for all surface soil layers the transpiration of the surface roots
diminishes quickly in the presence of high evporation rates and not precipiation. The water
content of the soil area the shallow roots dominate is given in Figure 25; the water content of the
soil layer the deep roots dominate is shown in Figure 26. It appears transpiration is restricted by
the amount of available water, as the amount of available soil water dimishes, transpiration also
diminishes.
Water content at depth 15cm for three sites
0
0.05
0.1
0.15
0.2
0.25
0.3
1 6 11 16 21 26 31 36 41 46 51 56 61 66
Days
Wat
er C
onte
nt (v
ol/v
ol)
Hole 1Hole 2Hole 3
Figure 25 Water content at 15cm depth for each of the three holes
Impact of Soil Variation on a Vineyard Water Balance
Future Recommendationse 73
Water content at depth 150cm for three sites
00.05
0.10.15
0.20.25
0.30.35
0.40.45
1 6 11 16 21 26 31 36 41 46 51 56 61 66
Days
Wat
er C
onte
nt (v
ol/v
ol)
Hole 1Hole 2Hole 3
Figure 26 Water content at 150cm depth for each of the three holes
The model output of the summer scenario is shown in Figure 27. After the 60 days of summer
there is no available water for any of the sites. Hole 2 experiences the highest amount of
evapotranspiration, drainage and also unavailable water. Hole 3 has the lowest values for all
criteria and Hole 1 has intermediate values. The model outputs are tabulated in Appendix 5.
Model output with summer conditions and deep and shallow roots
0100200300400500600700800900
1 2 3
Hole
(mm
)
DrainageUnavailable WaterAvailable WaterSurface WaterRunoffEvapotranspirationPrecipitation
Figure 27 The distribution of water from the model output for summer climate conditions and deep and shallow
roots
Impact of Soil Variation on a Vineyard Water Balance
74 Modelling of the Vineyard Water Balance
Discussion
While the ratio remains at one, the plant are transpiring the maximum or potential amount, when
the ration falls below one (i.e. close to zero) the plant is becoming water stressed. In all the holes
the surface roots become stressed before the deeper roots. This is partly due to the deep roots
being less influenced by evaporative losses.
The deep roots occur at 150cm depth, hole 1 and 2 have sandy light clay with 5% gravel at this
depth. It can be expected that the water content and tranpiration rates be similar for these two
sites for the deep roots. The results support this presumption; see Figure 22 and Figure 23. Hole 3
shows a different trend with the deep roots becoming stressed sooner. This can be attributed to
the soil type at this location which varies from the other two. At hole 3 the soil type at 150cm
depth is light clay with 50% gravel. This soil combination makes it more difficult for the roots to
access water, compared to the other sites.
The shallow roots come under stress sooner then then deep roots. The water content and hence
transpiration rates are proporational to the hydraulic conductivity. Hole 1 has the highest
saturated hydraulic conductivty being sand, and also displays the earliest water stress.
Conversely, hole 2 has the lowest saturated hydraulic conductivity (loam) and experiences stress
at the latest time
The water content of the soil is linked to the transpiration rate and resultant stress on vegetation.
As the water content declines so does the rate of transpiration. The water content at 15cm depth
soil drops to a baseline level after 6-8 days (Figure 25), this corresponds to the time at which the
shallow rooted plants experience stress. At 150cm depth it takes longer for the water content to
drop to the baseline level (Figure 26); this reflects the extended time before the onset of stress for
the deeper roots.
During the summer simulations, the water holding capabilities of the soils have a greater
influence on total water storage. This is not as imperitive during winter as the high rainfall
replenishes the soil profile with water. This explains the differing output totals in summer, i.e. in
Impact of Soil Variation on a Vineyard Water Balance
Future Recommendationse 75
winter the summation of the water allocations were similar (Figure 12), whilst for summer they
varied (Figure 27).
Based on the times the shallow and deep roots come under stress, you would need to irrigate at
least weekly to prevent stress on the shallow root system but the simulations show that the deep
root system could continue to supply the vine for much longer. This hypothesis requires field
validation as the next step because the model does not allow for any physiological
compensation that might occur as the shallow roots start to stress i.e. the vine could
start to draw more heavily on the deeper roots.
Impact of Soil Variation on a Vineyard Water Balance
76 Modelling of the Vineyard Water Balance
6 Future RecomendationsThis study has shown that the SWIM model can adequately describe the soil water content across
soil profiles. The study falls short from validating the adequacy of the model to simulate the soil
water content in the vadose zone when vegetation is active. By comparing the ratio of actual to
potential transpiration it can be shown when the plant becomes stressed, however since the model
does not allow for any physical compensation that might occur, it is recommended that field
sampling be done in conjuction with the modelling to test the validity of the model.
It is also recommended that the summer scenario be extended to include irrigation of the active
vines. Precipitation could be introduced in small increments to the model and changes in water
balance and matric potential should be observed. A matric potential of -400cm could be used as a
benchmark for when stress occurs. Irrigating at this time could prevent the vine from
experiencing stress. Experimentation with amounts and timing of irrigation would further
strengthen the potential for implementing the SWIM model as a management tool.
Impact of Soil Variation on a Vineyard Water Balance
Conclusions 77
7 ConclusionWater supply to the vine is one of the key elements which determine wine quality. The amount of
water that reaches the root system and the time for which the vine is “stressed” determine the
amount of soluble solids and acidity which ultimately affects the taste of the wine. Variability
within vineyards affects the resultant quality and quantity of produce. Precision agriculture is
concerned with better understanding the variability of the environment in which a crop is grown,
and the learnt knowledge can be used to manipulate the vines to obtain a desired product.
This report focused on the use of radiometrics to map the surface soil properties and ground
penetrating radar (GPR) to provide a profile of the soil and patterns of soil moisture. These
methods were compared to more traditional point sampling methods of vineyard soil survey.
Direct measurement of soil moisture content using logged moisture probes provide validation
data for vineyard water balance modelling using rainfall and evaporative variables. The use of a
water balance model permits periods of vine stress to be identified under natural rainfall
conditions and allows irrigation application to be designed with allowance for the spatial pattern
of vineyard soils.
The Soil Water Infiltration and Movement (SWIM) model developed by Ross (1990) was used to
simulate the water content across soil profiles within the vineyard. Climatic data was collected
from an on site meteorological station, soil types were determined using airborne radiometrics,
ground penetrating radar and site sampling and lab tempe analysis in conjunction with the RETC
model were used to formulate water retention curves. These were all inputs for the SWIM model,
which outputted the soi profile water balance of the vineyard with time.
Initially the model was validated by matching the logged soil moisture with the modelled soil
moisture. The variability of water content across soil types was noted, emphasizing the
importance of detailed soil mapping across the vineyard. Since the study period occured during
winter when the vines are dormant, summer scenarios were run to estimate the effect of increased
evapotranspiration, reduced rainfall and active plant growth, on the water balance.
Impact of Soil Variation on a Vineyard Water Balance
78 Conclusions
The model showed that during summer the actual to potential transpiration ratio dropped below
the ideal limit within a few days causing excessive stress on the vine. This is beneficial in a
vineyard situation for determining when irrigation should be applied to control stress and
therefore eventual wine quality.
Impact of Soil Variation on a Vineyard Water Balance
Appendices 79
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Impact of Soil Variation on a Vineyard Water Balance
Appendices 89
Appendix 1: Climate Data Data retrieved from BOM (Witchcliff)
Date Rainfall ET T max T min Date Rainfall ET T max T min25/05/2005 0 1.65 22.7 12.7 1/08/2005 20.8 2.86 20.5 9.426/05/2005 0 2.35 24.1 13.4 2/08/2005 3.6 1.94 13.6 6.427/05/2005 0 1.71 21.3 11.9 3/08/05 0.2 1.2 14.1 8.128/05/2005 0 1.8 20.7 12.6 4/08/05 1.6 1.97 15.8 7.429/05/2005 0 1.98 25.6 14.9 5/08/05 0.6 1.97 16.6 7.230/05/2005 0 1.45 20.7 11.4 6/08/05 1.2 2.06 16.4 10.631/05/2005 0 1.63 18.6 12.5 7/08/05 0 1.88 17.1 6.11/06/2005 0 2 20.7 12.3 8/08/05 0.6 2.1 19.9 4.72/06/2005 1.2 1.34 17 12.8 9/08/05 0.2 1.79 16.6 73/06/2005 5.4 0.62 17.3 12.2 10/08/05 1.4 2.01 17.3 8.24/06/2005 0 0.98 16.7 9.3 11/08/05 0 1.94 17 75/06/2005 0 1.22 18.1 6.6 12/08/05 17.8 1.09 16.3 9.96/06/2005 17.8 0.42 15.4 5.4 13/08/05 12.6 2.05 15.5 7.57/06/2005 9 1.71 18.2 9.6 14/08/05 0.8 2.03 13.8 6.88/06/2005 7.6 1.21 17.3 11.8 15/08/05 2.8 2.69 16.8 10.49/06/2005 0 0.09 12 10.9 16/08/05 24 1.13 14.6 5.5
10/06/2005 10.2 1.63 16.1 6.3 17/08/05 16 1.41 12.9 5.411/06/2005 12 1.75 13.1 7.9 18/08/05 2.2 2.8 19.5 6.612/06/2005 12 1.27 11.4 6.2 19/08/05 0 1.71 21.6 8.713/06/2005 4.8 1.48 14.7 6.7 20/08/05 3.8 1.76 16.9 6.714/06/2005 6.6 1.1 16.6 7.5 21/08/05 0.2 1.69 18.8 3.715/06/2005 2.8 1.03 15.7 7.3 22/08/05 0.2 1.88 18.4 8.216/06/2005 4.4 0.85 13.6 9.8 23/08/05 0 2.01 18.1 7.717/06/2005 0.4 1.43 15.3 7.4 24/08/05 0.4 2.12 18.2 9.818/06/2005 5.2 1.58 14.4 8.8 25/08/05 0 2.13 19.2 10.319/06/2005 2.2 1.47 14.7 8.7 26/08/05 0 1.84 18.4 8.320/06/2005 1.4 1.03 15.3 6.6 27/08/05 2 1.84 18.5 8.721/06/2005 0 1.47 15.3 4.2 28/08/05 4.4 1.85 16.2 7.622/06/2005 0 2.23 15.1 5.3 29/08/05 2.8 2.29 15.3 10.323/06/2005 36.2 1.56 15.2 9.5 30/08/05 3.2 2 12.8 7.224/06/2005 1.4 1.71 16.3 7 31/08/05 0.2 1.7 13.7 325/06/2005 0 1.45 17.8 6.7 1/09/05 0 2.17 16 8.826/06/2005 0.2 2.02 18.8 9.7 2/09/05 7.8 1.8 17 927/06/2005 0 2.03 18.4 6.1 3/09/05 0.6 1.84 17 4.828/06/2005 0 1.98 17.8 4.5 4/09/05 1.6 2.43 17.6 12.529/06/2005 9 1.32 17.9 10.4 5/09/05 2.01 18.6 30/06/2005 1.2 1.73 14.8 6.4 6/09/05 32 3 16.5 10.61/07/2005 0 0.8 14.8 8.1 7/09/05 6.6 2.25 15.8 122/07/2005 0 1.75 17.6 11.6 8/09/05 0.6 2.3 11.3 93/07/2005 10 1.76 17.9 11.7 9/09/05 3.2 3.08 13.2 6.64/07/2005 4.2 2.12 13.5 8.6 10/09/05 0 2.76 14.7 7.85/07/2005 1 1.5 12.4 4.1 11/09/05 7.4 2.59 17.1 6.86/07/2005 0.2 1.16 11.8 1.7 12/09/05 1 4.04 26.6 7.37/07/2005 0 1.72 13.8 0.6 13/09/05 5 2.92 27.2 12.5
Impact of Soil Variation on a Vineyard Water Balance
90 Appendices
8/07/2005 0 1.74 13.8 -1 14/09/05 0 3.72 17.9 6.69/07/2005 0.2 1.83 16.6 -0.3 15/09/05 0 2.21 17 5.3
10/07/2005 0 1.77 16.9 1.2 16/09/05 0 3.65 18.9 6.211/07/2005 0 1.72 16.5 -0.8 17/09/05 11.8 3.5 17.7 9.912/07/2005 0.2 2.15 15.2 1.9 18/09/05 0 3.03 16.7 11.813/07/2005 15.2 2.22 14.5 9.7 19/09/05 0.4 3.01 19.8 12.714/07/2005 17.6 1.79 17 10.6 20/09/05 3 1.93 19.5 11.815/07/2005 5 1.96 15.6 10.8 21/09/05 0.2 1.54 15.7 11.716/07/2005 1.4 1.73 13.9 2.4 22/09/05 1.4 4.1 32.2 12.617/07/2005 0 1.86 17 2.3 23/09/05 0 4.56 31.7 9.218/07/2005 2 1.78 18.5 8.6 24/09/05 0 4.38 16.2 4.319/07/2005 3.6 2.44 19.2 10.6 25/09/05 0 2.39 16.2 4.220/07/2005 0 2.26 20.2 12.3 26/09/05 6.6 4.3 17.8 6.321/07/2005 23.6 2.29 15.1 10.7 27/09/05 6.6 5.14 12.8 7.322/07/2005 1 2.13 16.2 10.1 28/09/05 0.4 4.61 15.3 9.223/07/2005 0.2 1.5 16.2 12.2 29/09/05 4 3.76 14.9 11.224/07/2005 0.8 1.5 17.5 11.8 30/09/05 11 4.28 15.8 11.825/07/2005 0.2 1.12 16.2 12.7 1/10/05 3.8 0.96 29.2 13.126/07/2005 0 1.47 17.7 9.7 2/10/05 0.2 0.54 29.7 11.327/07/2005 0.8 2.28 19.1 8.3 3/10/05 7.6 3.83 19.3 1228/07/2005 0.2 1.82 16.9 10.4 4/10/05 4.4 5.33 15.6 6.829/07/2005 0.2 1.7 16.6 10.1 5/10/05 2.2 1.21 14.5 5.830/07/2005 0 1.34 16.1 9.231/07/2005 0 1.76 18.1 12
Impact of Soil Variation on a Vineyard Water Balance
Appendices 91
Appendix 2: Soil Profiles
Sand
SCL - Sandy Clay Loam
SLC - Sandy Light Clay
Loam
LC - Light Clay
SL - Sandy Loam
HC - Heavy Clay
Depth
(cm) Hole 1 Hole 2 Hole 3
0 Sand Loam SL
5 Sand Loam SL
10 Sand Loam SL
15 Sand Loam SL
20 Sand Loam SL
25 Sand Loam SL
30 Sand Loam SL
35 Sand Loam SL
40 Sand Loam SL
45 Sand Loam SL
50 Sand SCL SL
55 Sand SCL SL
60 SCL SCL SL
65 SCL SCL SL
70 SCL SCL SL
75 SCL SCL SL
80 SCL SCL SL
85 SCL SCL SL
90 SCL SCL SL
95 SCL SCL SL
100 SCL SCL SL
105 SCL SCL SL
110 SLC SLC LC
115 SLC SLC LC
120 SLC SLC LC
125 SLC SLC LC
130 SLC SLC LC
150 SLC SLC LC
155 SLC SLC LC
160 SLC SLC LC
165 SLC SLC LC
170 SLC SLC LC
175 SLC SLC LC
180 SLC LC HC
Impact of Soil Variation on a Vineyard Water Balance
92 Appendices
Appendix 3: Water Retention Curves
Hole 1: Water Retention Curve for each soil type
00.050.1
0.150.2
0.250.3
0.350.4
0.450.5
1 10 100 1000 10000 100000
Sand 30cm
Sandy Clay Loam60cmSandy Light Clay110cm
Hole 2: Water Retention Curve for each soil type
00.050.1
0.150.2
0.250.3
0.350.4
0.450.5
1 10 100 1000 10000 100000
Loam 15cm
Sandy Clay Loam50cmSandy Light Clay110cmLight Clay 180cm
Impact of Soil Variation on a Vineyard Water Balance
Appendices 93
Hole 3: Water Retention Curves for each soil type
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 10 100 1000 10000 100000
Sandy Loam 15cmLight Clay 110cmHeavy Clay 180cm
Impact of Soil Variation on a Vineyard Water Balance
94 Appendices
Appendix 4: Modelled and Logged DataHole 1 - 5cmdepth Hole 1 - 40cm depth Day Modelled Logged % Error Day Modelled Logged % Error107 0.108546 0.11 1.32 107 0.113544 0.13 12.66108 0.104722 0.11 4.80 108 0.107761 0.12 10.20109 0.089807 0.09 0.21 109 0.102611 0.11 6.72110 0.109885 0.1 9.89 110 0.101722 0.1 1.72111 0.090609 0.1 9.39 111 0.099253 0.1 0.75112 0.099663 0.1 0.34 112 0.097896 0.1 2.10113 0.082431 0.08 3.04 113 0.095655 0.1 4.35114 0.076322 0.08 4.60 114 0.093387 0.09 3.76115 0.073212 0.07 4.59 115 0.091392 0.09 1.55116 0.111117 0.12 7.40 116 0.09343 0.09 3.81117 0.089091 0.1 10.91 117 0.093747 0.09 4.16118 0.078953 0.08 1.31 118 0.092324 0.09 2.58119 0.08458 0.08 5.73 119 0.090937 0.09 1.04120 0.078568 0.07 12.24 120 0.089592 0.09 0.45121 0.073612 0.07 5.16 121 0.088304 0.09 1.88122 0.070924 0.07 1.32 122 0.087112 0.09 3.21123 0.069312 0.07 0.98 123 0.086028 0.09 4.41124 0.068157 0.07 2.63 124 0.085058 0.09 5.49125 0.082744 0.08 3.43 125 0.084305 0.09 6.33126 0.090572 0.09 0.64 126 0.084114 0.09 6.54127 0.077278 0.09 14.14 127 0.083868 0.09 6.81128 0.076362 0.08 4.55 128 0.083515 0.08 4.39129 0.107248 0.1 7.25 129 0.085941 0.08 7.43130 0.108747 0.1 8.75 130 0.090838 0.09 0.93131 0.099962 0.1 0.04 131 0.093061 0.09 3.40132 0.109687 0.11 0.28 132 0.096435 0.1 3.57133 0.098874 0.1 1.13 133 0.096628 0.1 3.37134 0.100283 0.1 0.28 134 0.096316 0.1 3.68
Impact of Soil Variation on a Vineyard Water Balance
Appendices 95
Hole 2 - 5cmdepth Hole 2 - 40cm depth Day Modelled Logged % Error Day Modelled Logged % Error107 0.188854 0.19 0.60 107 0.153127 0.16 4.30108 0.18198 0.19 4.22 108 0.148702 0.16 7.06109 0.163634 0.15 9.09 109 0.144251 0.14 3.04110 0.18445 0.19 2.92 110 0.142195 0.15 5.20111 0.162226 0.17 4.57 111 0.139766 0.14 0.17112 0.17074 0.17 0.44 112 0.137912 0.14 1.49113 0.14933 0.15 0.45 113 0.13575 0.12 13.12114 0.139617 0.15 6.92 114 0.133608 0.12 11.34115 0.134263 0.13 3.28 115 0.131664 0.12 9.72116 0.178291 0.19 6.16 116 0.130928 0.12 9.11117 0.154664 0.15 3.11 117 0.130301 0.12 8.58118 0.141599 0.14 1.14 118 0.129303 0.13 0.54119 0.145167 0.15 3.22 119 0.128241 0.13 1.35120 0.138936 0.14 0.76 120 0.12719 0.13 2.16121 0.133025 0.14 4.98 121 0.126179 0.13 2.94122 0.129227 0.13 0.59 122 0.125227 0.13 3.67123 0.126682 0.13 2.55 123 0.124342 0.12 3.62124 0.124815 0.13 3.99 124 0.123524 0.12 2.94125 0.137552 0.14 1.75 125 0.122775 0.12 2.31126 0.148715 0.15 0.86 126 0.122153 0.12 1.79127 0.136426 0.14 2.55 127 0.121595 0.12 1.33128 0.13364 0.14 4.54 128 0.121078 0.12 0.90129 0.17103 0.19 9.98 129 0.121015 0.12 0.85130 0.176007 0.18 2.22 130 0.121892 0.12 1.58131 0.16625 0.17 2.21 131 0.123047 0.12 2.54132 0.178708 0.19 5.94 132 0.125103 0.12 4.25133 0.16648 0.17 2.07 133 0.126286 0.12 5.24134 0.167621 0.17 1.40 134 0.126916 0.13 2.37
Impact of Soil Variation on a Vineyard Water Balance
96 Appendices
Appendix 5: Vegetation model inputs and outputsNumber of vegetation types (0 to 4): 2Vegetation type number: 1 2Minimum xylem potential: -150 m -150 mDepth constant for roots: 10 cm 150 cmMaximum effective root lengthdensity: 5 cm/cm^3 0.5 cm/cm^3Maximum fraction of PET: 0.5 0.5Fraction of above maxima: 0.9 0.9at time: 0 d 0 dFraction of above maxima: 0.95 0.95at time: 30 d 30 d Number of RLD depth/time sets: 0
Site 1 Site 2 Site 3
Precipitation (mm) 0 0 0
Evapotranspiration (mm) 169 210 122
Runoff (mm) 0 0 0
Surface Water (mm) 0 0 0
Available Water (mm) 1 0 0
Unavailable Water (mm) 355 410 256
Drainage (mm) 151 164 69
Recommended