impact of soil variation on a vineyard water balance · impact of soil variation on a vineyard...

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!”


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.


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


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


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


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 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 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


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


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


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.


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


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.




product.



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



Introduction 9



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).


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).




product.




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




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).



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).



Figure 1 Grapevine growth stages (Coombe 1995)



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.



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



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.



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).



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).



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



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).



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



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).



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).



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



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,



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



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



(~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



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).



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



(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



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).



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).



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).



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



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).



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).


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


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)



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.


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.



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.


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):

Φ±��

��

�

∂∂

∂∂−=

∂∂

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).


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).



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



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



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).



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).


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.


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



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.



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



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)



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.)



Figure 10 Installing the moisture probes at Bridgelands vineyard (Bennet pers comm.)

Figure 11 Moisture loggers in the ground (Bennet pers comm.)



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.



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.



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



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



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.



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.



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


0

50

100

150

200

250

300

0 0.1 0.2 0.3 0.4


Dept

h (c

m)


Figure 14 Water content across profile for Hole 2 in the absence of vegetation




0

50

100

150

200

250

300

0 0.1 0.2 0.3


Dept

h (c

m)


Figure 15 Water content across profile for Hole 3 in the absence of vegetation



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.


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


September til 5th October)


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






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




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





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



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



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.



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




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



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


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.



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



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



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



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.



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.


Conclusions 77

7 ConclusionWater supply to the vine is one of the key elements which determine wine quality. The amount of


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.


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.


Appendices 79

8 ReferencesAspin, S.J., and Bierwirth, P.N. 1997, ‘GIS analysis of the effects of forest Biomass on gamma-

radiometric images’, Proceedings of the 3rd National Forum on GIS in the Geosciences,

Australian Geological Survey Organisation Record 1997/36.

Baigent Geosciences. 2005, Project Chardonnay GPR and Radiometric Survey, Baigent

Geosciences, Perth.

Behboudian, M.H. and Singh, Z. 2000, Grapevine: Water Relations and Irrigation Scheduling,

Massey University NZ and Muresk Institute of Agriculture WA.

Bramley, R. 2004, 'Opportunities for improved agricultural management through soil and crop

sensing at high spatial resolutions', in Sir Mark Oliphant, Hawthorn.

Brooks, R.H. and Corey, A.T. 1964, ‘Hydraulic properties of porous media’, Hydrology Papers

No. 3, Colorado State University, Fort Collins, USA

Burdine, N.T. 1953, ‘Relative permeability calculations from pore-size distribution data’, Journal

of Petroleum Technology, vol 5, pp 71-78.

Bureau of Meteorology (BOM) 2005, Witchcliff (Margaret River District), Western Australia,

July-Ocober 2005 Daily Weather Observations, Australian Government: Bureau of Meteorology,

Available from: < http://www.bom.gov.au/climate/dwo/IDCJDW6081.latest.shtml> [15

November 2005]

Cai, J., McMechan, G.A., Fisher, M.A. 1996, ‘Application of gpr to investigation of near surface

fault properties in the San Fransisco Bay region’, Bulletin of the Seismological Society of

America, vol.86, no. 5, pp 1459-2470.


80 References

Campbell, G.S. 1974, ‘A simple method of determining unsaturated hydraulic conductivity from

moisture retention data’, Soil Science, vol. 117, pp 311-315.

Campbell-Clause, J. & Fisher, D. 2001, 'Irrigation techniques for winegrapes', Farmnote, vol. 66,

no. 99, Department of Agriculture, Perth.

Campbell, G.S. and Mulla, D.J. 1990, ‘Measurement of soil water content and potential’, in

Irrigation of Agricultural crops, eds. Stewart, B.A. and Neilsen, D.R. American Society of

Agronomy, Crop Science Society of America & Soil Society of America, U.S.A.

Carcione, J. 1996, ‘Ground-penetrating radar: wave theory and numerical simulation in lossy

anisotropic media’, Geophysics, pp. 1664-1667.

Celia, M.A., Bouloutas, E.T., & Zarba, R.L. 1990, ‘A general mass-conservation numerical

solution for the unsaturated flow equation’, Water Resources Research, Vol. 26, pp 1483-1496

Cresswell, H.P., Smiles, D.E. & Williams, J. 1992, ‘Soil structure, soil hydraulic properties and

the soil water balance’, Australian Journal of Soil Research, vol. 30, pp 265-283

Cook, S.E. Corner, R.J., Groves, P.R. & Grealish, G.J. 1996, ‘Use of gamma radiometric data for

soil mapping’, Australian Journal of Soil Research, vol. 34, pp. 183-194.

Coombe, B.G. 1995, Adoption of a system for identifying grapevine growth stages, Australian

Journal of Grape and Wine Research, 100-110.

Cresswell, H. P. and Paydar, Z. 1996, ’Water retention in Australian soils. I. description and

prediction using parametric functions’, Australian Journal of Soil Research, vol. 34, pp 195-212,

CSIRO Publishing, Canberra.

Cresswell, H. P., Smiles, D. E. & Williams, J. 1992, ‘ Soil Structure, Soil hydraulic properties

and the soil water balance’, Australian Journal of Soil Research, 30, pp 265-283.


Appendices 81

Cummins, T. 1987, Grapevine Growth Stages and Their Water Requirements. In ‘Efficient

Irrigation in Sunraysia, Information and Management Kit.’ Department of Agriculture and Rural

Affairs, Victoria.

Cummins, T. 1998, Irrigation Survival Requirements, The Department of Natural Resources and

Environment, Rosebank (NSW).

During, H. 1998, ‘Photochemical and non-photochemical response of glasshouse-grown grape to

combined light and water stress’, Vitis, vol. 37, pp. 1-4.

Elliot, J. 2005, Wine Grape Growing In Western Australia [online], Department of Agriculture,

Western Australia. Available from:

<http://agspsrv34.agric.wa.gov.au/programs/hort/viticulture/wine_grow.htm> [11 June 2005].

Freeman, B.M. & Kliewer, W.M. 1983, ‘Effect of Irrigation, Crop Level and Potassium

Fertilization on Carignane Vines. II. Grape and Wine Quality’, American Journal of Enology and

Viticulture, vol. 34, no.3, pp. 197-207.

Gaudillere, J., Van Leeuwen, C., & Ollat, N. 2002, ‘Carbon isotope composition of sugars in

grapevine, an integrated indicator of vineyard water status’, Journal of Experimental Botany, 53,

369, 757-763.

Geo-Graf. (January 2005), What is GPR? - Geophysical Equipment Descriptions - GPR

[Online], Available from: <D:\GPR\Geo-Graf, Inc_ - Geophysical Equipment Descriptions -

Ground Pen\Geo-Graf, Inc_ - Geophysical Equipment Descriptions - Ground.htm> [11 June

2005].

George, R. 1998, Toolibin, Western Australia, Agriculture, Fisheries and Forestry - Australia and

the National Salinity Program, Perth.


82 References

George, R. J., Beasley, R., Gordon, I., Heislers, D., Speed, R., Brodie, R., McConnell, C. and

Woodgate, P. 1998, Evaluation of Airborne Geophysics for Catchment Management, Agriculture,

Fisheries and Forestry Australia and the National Dryland Salinity Program, Canberra.

George, R. 2001, 'Airborne geophysics - a tool for salinity control', Farmnote, , no. 18.,

Department of Agriculture, Perth.

Ginestar, C., Eastham, J., Gray, S. & Iland, P. 1998, 'Use of sap-flow sensors to schedule

vineyard irrigation. 1. effects of post-veraison water deficits on water relations, Vine growth and

yield of shiraz grapevines', The American Society Journal for Enology and Viticulture, vol. 49,

no. 4, pp. 413-420.

Green, S., Clothier, B., Greven, M., Neal, S. & Davidson, P. 2004, 'A risk assessment of

irrigation needs and pesticide fate under vineyards', in SuperSoil 2004: 3rd Australian New

Zealand Soils Conference, Sydney.

Grimes, D.W. & Williams, L.E. 1990, ‘Irrigation effects on plant water-relations and productivity

of Thompson seedless grapevines’, Crop Science, vol. 30, pp. 255-260.

Goodwin, I. 1995, Irrigation of Vineyards: a Winegrape Grower's Guide to Irrigation Scheduling

and Regulated Deficit Irrigation, Institute of Sustainable Irrigated Agriculture, Tatura (Vic).

Gourlay, R. & Sparks, T. 1996, ‘Value adding to radiometrics for mapping soil properties’, 8th

Australasian Remote Sensing Conference, Canberra.

Hale, M.G. & Orcutt, D.M. 1987, The physiology of plants under stress, Wiley, New York.

Hauck, E.J. and Coles, N.A. (ed.) 1995, Farm Water Supply Reference Data for Raintanks and

Surface Water, WA

Hubbard, S., Grote, K. and Rubin, Y 2002, 'Mapping the volumetric soil water content of a

California vineyard using high-frequency GPR ground wave data', The Leading Edge.


Appendices 83

Hubbard S. and Rubin, Y. 2004, ’The Quest for Better Wines using Geophysics’, Geotimes,

August 2004, Available from: <http://www.geotimes.org/aug04/feature_goodwine.html#authors>

[12 June 2005].

Huisman J.A., Sperl C., Bouten W., Verstraten J.M. 2001, ‘Soil water content using

measurements at different scales: accuracy of time domain reflectometry and ground-penetrating

radar’, Journal of Hydrology, vol. 245, no. 1, pp. 48-58.

Johnson, L. F., Pierce, L., DeMartino, J., Youkhana, S., Nemani, R. & Bosch, D. 2003, ‘

Image-based decision tools for vineyard management’, in 2003 ASAE Annual Meeting, American

Society of Agricultural Engineers, St Joseph (Michigan).

Jones, H. G. 2004, 'Irrigation scheduling: advantages and pitfalls

of plant-based methods', Journal of Experimental Botany,, vol. 55, no. 407, pp. 2427-2436.

Kumar, C.P. and Purandara, B.K. 2003, ‘Modelling of soil moisture movement in a watershed

using SWIM’, Agricultural Engineering Journal, 84, pp 47-51.

Lamb, D and Bramley, R. 2001, ‘Managing and monitoring spatial variability in vineyard

production’, Innovations and Technology, 4, 1, 25-30.

Lombard, J., 2005, Dormancy and Restbreaking Practices for Table Grapes, ARC Infruitec-

Neitvoorbij, Stellenbosch, Available from: <http://www.safj.co.za/pdf/dormancy.PDF> [20

September 2005].

Luke, G. J., Burke, K. L. and O'Brien, T.M. 1987, Evaporation Data for Western Australia,

Department of Agriculture Western Australia, Perth.

Marks, P., 2002, ‘Managing variability in vineyards’, Grape and Wine Research and

Development Corporation (GWRDC) Strategic Planning Workshop on Precision Viticulture.

January 2002Marks, P. (ed) 2002, GWRDC Strategic Workshop on Precision Viticulture.


84 References

McKenzie, D, C. 2000, ‘Survey soil options prior to vineyard design’, The Australian Grape

Grower and Wine Maker, vol. 438a, pp. 144-151.

Minty, B. R. S. 1997, 'The fundamentals of airborne gamma ray spectrometry', AGSO Journal of

Australian Geology and Geophysics, vol. 17, no. 2, pp. 39-50.

Mitchell, P. D. & Goodwin, I. 1996, Irrigation of Vines and Fruit Trees, Agmedia, East

Melbourne.

Mualem, Y. 1976, ‘A new model for predicting the hydraulic conductivity of unsaturated porous

media’, Water Resources Research, vol. 12, pp 513-522

Oliveira, M. 2001, ‘Modeling water content of a vineyard soil in the Douro Region Portugal’,

Plant and Soil, vol. 233, pp. 213-221.

Oliver, Y.M. 2001, Field Measurement and estimation of soil water and chemical transport in

deep sands, Department of Soil Science and Plant Nutrition, The University of Western

Australia.

Oliver, Y. M. and Smettem, K. R. J. 2005, ‘Predicting water balance in a sandy soil: model

sensitivity to the variability of measured saturated and near saturated hydraulic properties’,

Australian Journal of Soil Research, 43, pp 87-96, CSIRO Publishing.

Parasnis, D.S. 1997, Principles of Applied Geophysics 5th Edition, Chapman & Hall.

Paydar, Z. and Cresswell, H. P. 1996, ‘Water retention in Australian soils. II. Prediction using

particle size, bulk density, and other proerties’, Australian Journal of Soil Research, 34, pp 679-

693.

Patakas, A.,Noitsakis, B.,Stavrakas, D. 1997, Adaptation of leaves of Vitis vinifera L. to

seasonal drought as affected by leaf age’, Vitis, vol. 36, no.1, pp. 11-14.


Appendices 85

Pescovitz, D. 2003, A High-Tech Toast to Better Wines, Berkeley College of Engineering,

Berkeley.

Peters, L., Daniels, J.J. & Young, J.D. 1994, ‘Radar as a susbsurface environmental sensing

tool’, Proceedings of the IEEE, vol. 82, no. 12, pp. 1802-1822. Available from

<http://ieeexplore.ieee.org/iel1/5/7932/00338072.pdf>. [10 August 2005].

Pickup, G. & Marks, A. 2000, 'Identifying large scale erosion and deposition processes from

airborne gamma radiometrics and digital elevation models in a weathered landscape', Earth

Surface Processes and Landforms, vol. 25, pp. 535-557.

Poulter, R. 2005, Investigating the Role of Soil Constraints on the Water Balance of Some

Annual and Perennial Systems in a Mediterranean Environment, thesis presented for the degree

of Doctor of Philosophy at the University of Western Australia, School of Agriculture and Earth

Sciences, April 2005.

Rampant, P. 2004, 'Rapid Assesment soil mapping tools for precision agriculture', in 8th Annual

Symposium on Precision Agriculture Research & Application in Australasia, Sydney.

Reynolds, A.G. & Naylor, A.P. 1994, ‘‘Pinot Noir’ and ‘Riesling’ grapevines respond to water

stress duration and soil water holding capacity’, Horticultural Science, pp. 1505-1510.

Rivasborge, M.A. 2003, The Effect of Soils and Climate on the Productivity of a Eucalyptus

globulus Plantation in a Water Deficit Environment, School of Earth and Geographical Sciences,

University of Western Australia.

Ross, P.J. 1990, ‘Efficient numerical methods for infiltration using Richards Equation’, Water

Resources Research, vol. 26, pp 279-290


86 References

Rubin, Y. 2003, Soil Water Monitoring Using GeophysicalTechniques : Development and

Applications in Agriculture and Water, Technical Completion Report (University of California

Water Resources Centre), Berkley.

Sever, M 2004, ’Wines Deeper History’, Geotimes, August 2004, Available from:

<http://www.geotimes.org/aug04/feature_goodwine.html#authors> [12 June 2005].

Smettem, K.R.J. and Pracilio, G. 2000, Application of gamma radiometrics for mapping the

surface physical properties of soils at catchment scale, European Geophysical Union Abstracts,

Vienna, April 2005.

Stoffregen, H., Yaramanci, U., Zenker, T & Wessolek, G. 2002, ‘Accuracy of soil water content

measurements using ground penetrating radar: comparison of gpr and lysimeter data’, Journal of

Hydrology (Amsterdam), vol 267, no3-4, pp. 201-206.

Stoll, M., Loveys, B. & Dry, P. 2000, ‘Hormonal changes induced by partial root zone drying of

irrigated grapevine’, Journal of Experimental Botany, vol. 51, pp. 1627-1634.

Taylor, M.J., Smettem, K., Pracilio, G., & Verboom, W. 2002 ‘Relationships between soil

properties and high-resolution radiometrics, central eastern Wheatbelt, Western Australia’,

Exploration Geophysics, Vol. 33, No. 2, pg 95-102.

Trambouze, W., Bertuzzi, P. & Voltz, M. 1998, Comparison of methods for estimating actual

evapotranspiration in a row-cropped vineyard, Agric. Forest Meteor., vol. 91, pp. 193-208.

Tzortzis, M., Tsertos, H., Christofides, S. & Christodoulides, G., (2 December 2002), Gamma

Ray Measurements of Naturally Occurring Radioactive Samples from Cyprus Characteristic

Rocks, [Online], Available from: <http://arxiv.org/ftp/physics/papers/0212/0212099.pdf> [12

August 2005].


Appendices 87

U.S. Army Corps of Engineers (U.S. ACE). 1995, Geophysical Exploration for Engineering and

Environmental Investigations, Engineer Manual 1110-1-1802, Department of the Army: U.S.

Army Corps of Engineers, Washington.

U.S. EPA Office of Superfund Remediation and Technology Innovation (OSRTI) (US EPA)

(January 2003), GPR Description/ System Components, [Online]. Available from: <

http://fate.clu-in.org> [10 August 2005].

United States Salinity Laboratory (USSL) 1999, Rosetta, ed. 1.0, [Online], Available from:

http://www.ussl.ars.usda.gov/MODELS/rosetta/rosetta.HTM [27 November 2005]

van Genuchten, M. Th. 1980, ’A closed-form equation for predicting the hydraulic conductivity

of unsaturated soils’, Soil Science America Journal, vol. 44, pp 892-898

van Genuchten, M. Th., Leij, F. J. and Yates, S. R. 1991, The RETC Code for Quantifying the

Hydraulic Functions of Unsaturated Soils, Report Number EPA/600/S2-91/065. U.S.

Environmental Protection Agency, Research adn Development, Springfield, VA.

van Leeuwen, C. & Seguin, G. 1994, ‘Incidences de l’alimentation en eau de la vigne, appreciee

par l’etat hydrique du feuillage, sur le development de l’appareil vegetatif et al maturation du

raisin’, Journal Sci. Vigne Vin., vol. 28, pp. 81-110.

Van Overmeeren, R.A., Sariowan, S.V. & and Gehrels, J.C., 1997 ‘Ground penetrating radar for

determining volumetric soil water content; results of comparative measurements at two test sites’,

Journal of Hydrology (Amsterdam), vol. 197, no. 1-4, pp. 316-338.

Water and Rivers Commission (WRC) 2003, Water Facts 7- The Water Cycle, Warter and

Rivers Commision, Perth.

Weiler, K.W., Steenhuis, T.S., Boll, J., Kung, K-JS., 1998, ‘Comparison of ground penetrating

radar and time domain reflectrometry as soil water sensors’, Soil Science Society of America

Journal, vol. 62, no. 5, pp. 1237-1239.


88 References

Wilford, J.R. 1995, ‘Airborne gamma-ray spectrometry as a tool for assessing relative landscape

activity and weathering development including soils’, AGSO Research Newsletter, vol.7, pp. 12-

14.

Wilford, J. 2002, Airborne gamma ray spectrometry, Geophysical and remote sensing methods

for regolith exploration, CRCLEME Open File Report 144, CRCLEME, Canberra.

Winkler, A.J., Cook, J.A., Kliwer, W.M. & Lider, L.A. 1974, General Viticulture, University of

California Press, Berkley.

Yunusa, I. A. M., Walker, R. R. & Blackmore, D. H. 1997a, 'Characterisation of water use by

Sultana grapevines (Vitis vinifera L.) on their own roots or on Ramsey rootstock drip-irrigated

with water of different salinities', Irrigation Science, vol. 17, pp. 77-86.

Yunusa, I. A. M., Walker, R. R. & Guy, J. R. 1997b, 'Partitioning of seasonal evapotranspiration

from a commercial furrow-irrigated Sultana vineyard', Irrigation Science, vol. 18, pp. 45-54.

Zhang, R. and van Genuchten, M.T. 1994, ‘New models for unsaturated soil hydraulic

properties’, Soil Science, vol. 158, pp 77-85.


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


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


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


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


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


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


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


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

impact of soil variation on a vineyard water balance · impact of soil variation on a vineyard...

Documents