Simulating and assessing salinisation in the lower Namoi valley · 2016. 6. 16. · Mohammad Faruque Ahmed . M.F. AHMED ii. Simulating and assessing salinisation in the lower Namoi

Simulating and assessing salinisation in the lower Namoi valley

Mohammad Faruque Ahmed B.Sc.Ag(Hons.); M.Sc.Ag.(Soil Science) Bangladesh Agricultural University

Mymensingh, Bangladesh

Department of Agricultural Chemistry and Soil Science The University of Sydney

New South Wales Australia

A THESIS SUBMITTED IN THE FULFILMENT OF THE REQUIREMENTS FOR

THE DEGREE OF MASTERS OF SCIENCE IN AGRICULTURE

THE UNIVERSITY OF SYDNEY

MMI


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CERTIFICATE OF ORGINALITY

The text of this thesis contains no material which has been accepted as part of the requirements for any other degree or diploma at any University or any material previously published or written, unless due reference to the material has been made.

Mohammad Faruque Ahmed

M.F. AHMED

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FOR MY PARENTS

M.F. AHMED

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The Sydney Morning Herald, 19 March, 2001


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ABSTRACT

Dryland salinity is increasing in the upper catchments of central and northern New South

Wales, Australia. Consequently, salts may be exported downstream, which could adversely affect

cotton irrigated-farming systems. In order to assess the potential threat of salinity a simple salt

balance model based on progressively saline water (i.e., ECiw 0.4, 1.5, 4.0 and 9.0 dS/m) was used to

simulate the potential impact of salinisation due to the farming systems. The study was carried out in

the lower Namoi valley of northern New South Wales, Australia. A comparison has been made of the

various non-linear techniques (indicator kriging, multiple indicator kriging and disjunctive kriging) to

determine an optimal simulation method for the risk assessment. The simulation results indicate that

potential salinisation due to application of the water currently used for irrigation (ECiw) is minimal and

may not pose any problems to sustainability of irrigated agriculture. The same results were obtained

by simulation based on irrigation using slightly more saline water (ECiw 1.4 dS/m). However,

simulations based on irrigation using water of even lower quality (ECiw of 4 and 9.0 dS/m), shows

potential high salinisation, which will require management inputs for sustainable cropping systems,

especially legumes and wheat, which are used extensively in rotation with cotton. Disjunctive kriging

was the best simulation method, as it produced fewer misclassifications in comparison with multiple -

indicator kriging and indicator kriging. This study thus demonstrates that we can predict the salinity

risk due to application of irrigation water of lower quality than that of the current water used. In

addition, the results suggest where problems of excessive deep drainage and inefficient use of water

might be a problem.

The second part of this thesis deals with soil information required at the field scale for

management practices particularly in areas where deep drainage is large. Unfortunately, traditional

methods of soil inventory at the field level involve the design and adoption of sampling regimes and

laboratory analysis that are time-consuming and costly. Because of this more often than not only

limited data are collected. In areas where soil salinity is prevalent, detailed quantitative information

for determining its cause is required to prescribe management solutions. This part deals with the

description of a Mobile Electromagnetic Sensing System (MESS) and its application in an irrigated-

cotton field suspected of exhibiting soil salinity. The field is within the study area of part one of this

thesis-located about 2 km south west of Wee Waa. The EM38 and EM31 (ECa) data provide

information, which was used in deciding where soil sample sites could be located in the field. The

ECa data measured by the EM38 instrument was highly correlated with the effective cation exchange

capacity. This relationship can be explained by soil mineralogy. Using different soil chemical

properties (i.e. ESP and Ca/Mg ratio) a detailed transect study was undertaken to measure soil salinity

adjoining the water storage. It is concluded that the most appropriate management option to

remediation of the problem would be to excavate the soil directly beneath the storage floor where

leakage is suspected. It is recommended that the dam not be enlarged from its current size owing to

the unfavourable soil mineralogy (i.e. kaolin/illite) located in the area where it is located.

M.F. AHMED

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ACKNOWLEDGMENTS

There are many people who assisted me in completing this body of work. Firstly, I

am indebted to my supervisor Dr. John Triantafilis (Australian Cotton Cooperative Research

Centre-CRC). I would like to express my thanks for his invaluable guidance in the field and

laboratory and constructive criticism in helping me write this thesis. In addition, I am

grateful to him for encouraging and allowing me to undertake this study as part of my full-

time employment by the Australian Cotton Cooperative Research Centre (within the

Department of Agricultural Chemistry and Soil Science).

In terms of writing and interpreting of results in this thesis I need to acknowledge

several members of the academic staff of the Department of Agricultural Chemistry and Soil

Science. Firstly, I acknowledge the assistance of Dr Inakwu Odeh (Australian Cotton CRC)

and Mr Benjamin Warr (visiting student from Reading University) in carrying out some of

the geostatistical analysis and interpretation of results shown in Chapter 4. Thanks is also due

to Dr Odeh who allowed me to use the samples he collected in the Wee Waa district for this

study.

Mr Harold Geering and Dr Balwant Singh are acknowledged for their assistance in

advising me on various methods of laboratory analysis and interpreting results of x-ray

diffraction patterns and discussions on clay mineralogy described in Chapter 5. I would also

like to thank Dr. Edith Lees (Post-graduate coordinator) for assisting and encouraging me

along the way and for advising me about enrolment options throughout my candidature. I

also thank her for reviewing Chapter 2 and editorial comments of Chapter 3-5. To Professor

Alex McBratney, (Head of Dept. Ag. Chem. and Soil Sci.) I have the following answer to a

question he continually asked me over the last 3 years of my candidature: “How is your thesis

going, Faruque?” As at the 31/03/2001, I can say: “Alex…it’s finished!” I also thank him

and Dr Julie Markus and Dr Budiman Minasny for providing details about the use of the

MIK, program.

Various other people in the department also helped me greatly, particularly in respect

to laboratory and computational assistance. Technically, I need to acknowledge Mr. Chris

Conoley (Senior Technical Officer of Department of Agricultural Chemistry and Soil

Science) for his help in tracking down various pieces of laboratory equipment and advising

me of where I could buy the items we did not have. I also thank him for training me in the

use of the Atomic Absorption Spectrophotometer and various other pieces of equipment.


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Thanks also go to Mr. Adam Adam Sikorski, (Senior Technical Officer of Electron

Microscope Unit) for teaching me how to use the X-ray diffraction sensor. Computationally,

many thanks are extended to Ms. Marian Dunbar (Ph.D student) and Dr. Paco Sanchez Bayo

(Post-Doctoral Fellow) for helping me understand the intricacies of various software

packages and sort out various problems with my computer, respectively.

The field contribution of Mr. Andrew Huckel is also acknowledged. In particular his

field assistance in helping Dr Odeh collect the soil samples in the Wee Waa district and also

for helping me collect water samples and in carrying out the MESS survey of Cumberdeen

Field. Thanks is also due to Ms Esta Kokkoris for sharing an office with me.

Personally, I should extend my sincere thanks to all my friends, who always

encouraged me to complete this thesis. Last but not least I should like to acknowledge the

emotional support of my family, my wife Nasrin, son Rihab and daughter Rabita. Thanks for

allowing me to be away for extended periods in the field and in completing this thesis. Most

of the time my wife Nasrin demonstrated some special cooking for me. Many thanks Nasrin!

Thanks my youngest brother Shamsuddin for mitigating my workload at home.

This research was funded by Cooperative Research Centre for Sustainable Cotton

Production, The National Heritage Trust (via the Coordinating Committee of Namoi valley

water users association) and the Cotton Research and Development Corporation. I am

grateful to these funding organizations for their support.

M.F. AHMED

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CONTENTS

CHAPTER 1- GENERAL INTRODUCTION……………………………………………………………….. 1 ___________________________________________________________________________

SECTION 1: BACKGROUND ___________________________________________________________________________ CHAPTER 2- BIOPHYSICAL BACKGROUND 2.1 INTRODUCTION.......................................................................................................................................................... 5 2.2 HISTORY OF AGRICULTURAL DEVELOPMENT AND COTTON PRODUCTION................................. 6 2. 3 SOILS................................................................................................................................................................................ 7 2. 4 SOIL MAP UNITS......................................................................................................................................................... 8

2.4.1 Soil map units (Northcote, 1966) .................................................................................................................... 9 2.4.2 Soil map units (Stannard and Kelly, 1977)...................................................................................................... 11

2. 5 PHYSIOGRAPHY.......................................................................................................................................................... 12 2. 6 HYDROGEOLOGY...................................................................................................................................................... 15 2. 7 CLIMATE........................................................................................................................................................................ 16

2.7.1 Rainfall............................................................................................................................................................. 16 2.7.2 Temperature .................................................................................................................................................... 17

2.8 NATURAL VEGETATION IN THE LOWER NAMOI VALLEY ........................................................................ 18 ___________________________________________________________________________ CHAPTER 3- LITERATURE REVIEW 3.1 INTRODUCTION.......................................................................................................................................................... 19 3.2 INTERACTION BETWEEN SALINITY WITH SOILS AND PLANTS............................................................... 19

3.2.1 Introduction .................................................................................................................................................. 19 3.2.2 Effects of salinity on soils ............................................................................................................................ 19 3.2.3 Effects of salinity to plants.......................................................................................................................... 21

3.2.3.1 Osmotic effects....................................................................................................................... 21 3.2.3.2 Specific ion effects: Toxicity .................................................................................................. 22 3.2.3.3 Specific ion effects: Nutrition................................................................................................. 22

3.2.4 Salt tolerance of plants.................................................................................................................................. 23 3.2.5 Suitability of saline water for irrigation ........................................................................................................ 24

3.3 SALT BALANCE MODELS ......................................................................................................................................... 26 3.3.1 Introduction..................................................................................................................................................... 26 3.3.2 Models............................................................................................................................................................. 27

3.3.2.1 Leaching Requirement (LR) model......................................................................................... 27 3.3.2.2 Solute Dynamics in Irrigated Clay Soils (SODICS) model................................................... 29 3.3.2.3 Salinity and Leaching Fraction (SaLF) model ......................................................................... 30

3.4 GEOSTATISTICAL METHODS................................................................................................................................. 33 3.4.1 Introduction..................................................................................................................................................... 33 3.4.2 Variography and the intrinsic hypothesis ....................................................................................................... 34 3.4.3. Linear kriging................................................................................................................................................... 36 3.4.4. Non-linear kriging............................................................................................................................................ 39

3.4.4.1 Disjunctive kriging (DK) ........................................................................................................ 39 3.4.4.2 Indicator kriging (IK)............................................................................................................... 41

3.5 ELECTROMAGENETIC INDUCTION.................................................................................................................... 44 3.5.1 Introduction...................................................................................................................................................... 44 3.5.1 Root zone assessment ..................................................................................................................................... 45 3.5.2 Subsoil assessment........................................................................................................................................... 47

___________________________________________________________________________


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___________________________________________________________________________

SECTION 2: RESEARCH ___________________________________________________________________________ CHAPTER 4- SIMULATION AND MAPPING OF SOIL SALINITY 4.1 INTRODUCTION .......................................................................................................................................................... 49 4.2 MATERIALS AND METHODS.................................................................................................................................. 50

4.2.1 Soil and water data ........................................................................................................................................... 50 4.2.2 SaLF modeling.................................................................................................................................................. 52 4.2.3 Geostatistical methods..................................................................................................................................... 52 4.2.4 Prediction of conditional probability................................................................................................................ 56 4.2.5 Validation ......................................................................................................................................................... 57

4.3 RESULTS AND DISCUSSION...................................................................................................................................... 58 4.3.1 Estimates of soil salinity.................................................................................................................................. 58 4.3.2 Spatial distribution of cut -off EC e values for crop production........................................................................ 58 4.3.3 Misclassification............................................................................................................................................... 60 4.3.4 Spatial comparison of conditional probability................................................................................................. 60 4.3.5 Spatial comparison of conditional probability: change in irrigation quantity................................................... 68 4.3.6 Sensitivity analysis: change in irrigation quantity and water quality............................................................... 70

4.4 CONCLUSION................................................................................................................................................................. 72 ___________________________________________________________________________ CHAPTER 5- ASSESSMENT OF SOIL SALINITY AT THE FIELD SCALE

5.1 INTRODUCTION .......................................................................................................................................................... 73 5.2 MATERIALS AND METHODS.................................................................................................................................. 74

5.2.1 Mobile EM Sensing System (MESS).............................................................................................................. 74 5.2.1.1 EM38-instrument for measuring root zone ECa .......................................................................... 76 5.2.1.2 EM31-instrument for vadose zone EC a........................................................................................ 76 5.2.1.3 Field Guidance and Ag 132-guidance............................................................................................ 77 5.2.1.4 Data logging and MESS control - instrument set up .................................................................... 78

5.2.2 Case Study “Cumberdeen” lower Namoi Valley............................................................................................ 78 5.2.3 EC a survey ....................................................................................................................................................... 80 5.2.4 Soil sampling and laboratory analysis.............................................................................................................. 80

5.3 RESULTS AND DISCUSSION...................................................................................................................................... 81 5.3.1 Frequency distribution and correlation between ECa ...................................................................................... 81 5.3.2 Comparison of ECa with measured soil attributes.......................................................................................... 84 5.3.3 Spatial distribution of soil attributes along a transect ...................................................................................... 88

5.4 CONCLUSIONS............................................................................................................................................................... 91 ___________________________________________________________________________

SECTION 3: DISCUSSION, CONCLUSION AND FUTURE DIRECTION ___________________________________________________________________________ CHAPTER 6- DISCUSSION, CONCLUSION AND FUTURE DIRECTION 6.1 DISCUSSION .................................................................................................................................................................... 93 6.2 CONCLUSIONS............................................................................................................................................................... 97 6.3 FUTURE RESEARCH .................................................................................................................................................... 98 ___________________________________________________________________________ BIBLIOGRAPHY .................................................................................................................................................................. 99 APPENDICES Appendix 1: Input values for SaLF model................................................................................................................................113 Appendix 2: Soil variables used for interpreting EM data........................................................................................................117

M.F. AHMED

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LIST OF FIGURES

CHAPTER 1 Figure 1.1 Aerial view of salinity in lake Eyre..................................................................................................................................... 1 Figure 1.2 Infra-red aerial photo of dryland soil salinity in the upper Macqaurie valley................................................................................ 1 Figure 1.3 Soil salinity in an irrigated cotton field in the lower Macquarie valley. ..................................................................................... 2 Figure 1.4 Saline seep adjacent to a earthen storage near Bourke, Darling River......................................................................................... 2

___________________________________________________________________________ CHAPTER 2 Figure 2.1 Location of the study area ................................ ................................ ................................ ................................ ................. 5 Figure 2.2 Soils of the lower Namoi valley (after Northcote, 1966) ......................................................................................................... 8 Figure 2.3 Soils of lower Namoi valley (after Stannard and Kelly, 1977)..................................................................................................12 Figure 2.4 Physiographic units of the lower Namoi valley (after Stannard and Kelly, 1977).........................................................................13 Figure 2.5 Hydrogeology of the lower Namoi valley (Department of Water Resources, 1988) ......................................................................15 Figure 2.6 Average monthly rainfall (mm) at Narrabri, Gunnedah and Walgett over a period of 115 years (Bureau of Meteorology, 1996)..........................................................................................................................................16 Figure 2.7 Average monthly temperature (0C) at Narrabri, Gunnedah and Walgett over a period of 114 years (Bureau of Meteorology, 1996)..........................................................................................................................................17 ___________________________________________________________________________ CHAPTER 3 Figure 3.1 Relationship between ECe (electrical conductivity of a saturation extract), ECiw (electrical conductivity of water) and LF

(leaching fraction) under conventional irrigation management................................. ................................ ................................ .25 Figure 3.2 Relationship between deep drainage (DD) predicted for annual rainfall of 800 mm from the model of Shaw and Thorburn

(1985) and ponded infiltration rate (IRp) for a range of soils in north eastern Australia..................................................................32 Figure 3.3 Contour maps of SAR estimated by a) kriging with 898 SAR data, b) cokriging with 200 SAR and 898 in-situ ECa data, c)

kriging with 200 SAR data, and d) cokriging with 100 SAR and 898 in-situ ECa data (after Pozdnyakova and Zhang, 1999). ............38 Figure 3.4 Maps of the conditional probability that ECe > 4 dS/m (a) in November 1985 and (b) in March 1986 (Wood et al., 1990)...............41 Figure 3.5 IK maps showing the conditional probability of finding a saline subsoil layer with soil ECe value exceeding 5 dS/m in the

lower Namoi valley (after Triantafilis, 1996)................................. ................................ ................................ .......................43 Figure 3.6 EM38 instrument...........................................................................................................................................................45 Figure 3.7 Four-probe electrode................................. ................................ ................................ ................................ .......................46 Figure 3.8 EM31 instrument...........................................................................................................................................................47 ___________________________________________________________________________ CHAPTER 4 Figure 4.1 Prediction and validation sites of the Edgeroi and Wee Waa districts. Note: Location of sites ed109, ed126, ed143 and

ed160, which lie on a short transect north east of Wee Waa....................................................................................................51 Figure 4.2 Schematic representation of various interpolation methods used ..............................................................................................56 Figure 4.3 Blob plots and frequency distribution showing concentration of ECe in the lower Namoi valley as predicted using SaLF when ECw of a) 0.435, b) 1.5, c) 4.0, and d) 9.0 dS/m was simulated .....................................................................59 Figure 4.4 The percentage of sites misclassified as either ‘no risk’ and ‘at risk’ by each of the methods for scenarios were ECw and ECe

cut-off were respectively: a) 1.5 and 2; b) 4 and 2; c) 4 and 4; d) 4 and 6; e) 9 and 4; f) 9 and 6; and, g) 9 and 7 dS/m. IK = indicator kriging; MIK = multiple indicator kriging; and, DK = disjunctive kriging...................................................................61

Figure 4.5 Spatial and frequency distribution of the conditional probability that soil ECe at steady state exceeds 2 dS/m if ECw = 1.5 dS/m was simulated using a) IK, b) MIK and c) DK. IK = indicator kriging; MIK = multiple indicator kriging; and, DK = disjunctive kriging ..........................................................................................................................................................63

Figure4.6 Spatial and frequency distribution of the conditional probability that soil ECe at steady state exceeds 6 dS/m if ECw = 4.0 dS/m was simulated using a) IK, b) MIK and c) DK. IK = indicator kriging; MIK = multiple indicator kriging; and, DK = disjunctive kriging ..........................................................................................................................................................64



Figure 4.9 Spatial and frequency distribution of the conditional probability that soil ECe at steady state exceeds 7.7 dS/m if ECw = 9.0 dS/m was simulated using a) IK, b) MIK and c) DK. IK = indicator kriging; MIK = multiple indicator kriging; and, DK = disjunctive kriging ..........................................................................................................................................................67


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Figure 4.10. Spatial distribution of the conditional probability that soil ECe at steady state exceeds 2 dS/m if EC iw = 4.0 dS/m was

simulated using MIK and a) 600, b) 300 and c) 150 and soil ECe at steady state exceeds 4 dS/m was simulated using MIK and a) 600, b) 300 and c) 150 mm of irrigation water ................................................................................................................ 69

Figure 4.11 Sensitivity analysis of changing water quantity (mm/year) versus water quality (ECiw dS/m) for sample sites a) ed109, b) 126,

c) ed143, and d) ed160.................................................................................................................................................... 70

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CHAPTER 5 Figure 5.1 Mobile Electromagnetic Sensing System: a) with EM31 at front and EM38 with mast and polyvinyl tube at rear; b) close up

of EM38 inserted inside polyvinyl tube; c) close up of rotating mechanism of; and d) EM38486 computer data logger, TrimbleTM Fieldguide Moving Map Display, GPS410 and Ag13........................................................................................... 75

Figure 5.2 “Cumberdeen” field showing: a) aerial photograph and b) 18 transects covered by the MESS. ..................................................... 79 Figure 5.3 a) Frequency distribution ECa measured by (a) EM38, b) EM31 in vertical mode of operation, and c) relationship of ECa

measured by EM38 and EM31......................................................................................................................................... 81 Figure 5.4 ECa distributions along transects a) 3, b) 8 and c) 13 for EM31; EM38 in vertical mode of operation ............................................ 82 Figure 5.5 Spatial distribution of ECa across the field for: a) EM38; and, b) EM31 in vertical mode of operation. .......................................... 83 Figure 5.6 Regression relationships between ECa (EM38) and average profile (0-2.0 m); a) field moisture content (%), b) clay content

(%), c) ECe (dS/m), and d) effective cation exchange capacity (cmol(+)/kg of soil solids). ............................................................ 85 Figure 5.7 Relationship between average profile (0-2.0 m); a) clay content versus effective cation exchange capacity (cmol(+)/kg of soil

solids), and b) soil ECa (EM38) versus ratio of effective cation Exchange Capacity and clay content (cmol(+)/kg of clay solids).......................................................................................................................................................................... 86

Figure 5. 8 X-ray diffraction patterns of samples sites a) 7, b) 15, c) 19 and d) 20....................................................................................... 87 Figure 5.9 Spatial distribution along transect 3 of average profile a) field moisture content (%), b) clay content (%), c) ECe dS/m, d)

ECEC (cmol(+)/kg of soil solids), and e) CCR (cmol(+)/kg of clay solids)............................................................................... 89 Figure 5. 10 Spatial distribution along transect 3 of average a) 0.6-1.2 m Ca/Mg, b) 0.6-1.2 m ESP, c) 1.8-2.0 m Ca/Mg and d) 1.8-2.0 m

ESP. ............................................................................................................................................................................ 90

CHAPTER 6

Figure 6.1 Forecasted areas containing lands of high hazard or risk of dryland salinity in 2050 (after National Land and Water Resources Audit, 2001)................................ ................................ ................................ ................................ ................... 92

LIST OF TABLES

CHAPTER 2 Table 2.1 Brief description of the soils of the lower Namoi study area ( after Northcote, 1966)................................. ................................ ... 9 Table 2.2 Brief description physiographic units (Stannard and Kelly, 1977). ........................................................................................... 14

CHAPTER 3

Table 3.1 Crop tolerance and yield potential of selected crops as influenced by irrigation water salinity (ECiw) or soil salinity (ECe) (Maas and Hoffman, 1977)............................................................................................................................................... 24

Table 3.1 Classification of saline waters (after Rhoades et al., 1992) ...................................................................................................... 24

CHAPTER 4

Table 4.1 Marginal probability of site exceeding soil salinity at various threshold values.......................................................................... 60

M.F. AHMED

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LIST OF SYMBOLS

SaLF Salinity and Leaching Fraction

SODICS Solute Dynamics in Irrigated Clay Soils

LR leaching requirement

LF leaching fraction

ECa apparent electrical conductivity (dS/m)

ECe electrical conductivity of saturated paste (dS/m)

EC1:5 electrical conductivity of 1 part soil 5 part water extract (dS/m)

ECs electrical conductivity at maximum field moisture content (dS/m)

ECiw electrical conductivity of irrigation water (dS/m)

ECdw electrical conductivity of drainage water (dS/m)

Ddw depth of drainage water

Diw depth of drainage water

CoK cokriging

DK disjunctive kriging

IK indicator kriging

MIK multiple indicator kriging

OK ordinary kriging

EM Electromagnetic

MESS Mobile Electromagnetic Sensing System

ESP exchangeable sodium percentage

SAR sodium adsorption ratio

ECEC effective cation exchange capacity

CCR ratio of ECEC and clay percentage

GPS global positioning system

MMD moving map display

CHAPTER 1

GENERAL INTRODUCTION

CHAPTER 1 - GENERAL INTRODUCTION

1

Soil salinisation results from the accumulation of soluble salts in the root zone. In the

Australian environment, large quantities of stored soluble salts have accumulated. This

natural process is termed primary salinisation, a process that is attributable to arid to semi-

arid moisture regimes that prevail across the continent. The northwest part of Victoria,

western New South Wales, southwest Queensland, northeast South Australia, the southern

parts of the Northern Territory and the central and southwest parts of Western Australia are

all affected by primary soil salinity. The sources of these salts are mostly attributable to

cyclical deposition through rainfall, but also to weathering of saline materials and from fossil

or connate salts stored in the soil or entrapped waters laid down as marine sediments in earlier

geological times (Bresler 1982). As shown in Figure 1.1, Lake Eyre, located in central

Australia, is an example of natural salinity.

The activity of humans also causes soil salinisation. This is termed secondary

salinisation. Dryland salinity usually occurs when native vegetation is replaced with pastures

and/or cropping. As a result, less water is used (in particular during fallows); the remainder

drains beyond the root-zone. This is termed deep drainage. This excess water often

recharges ground-water reservoirs and thus causes water tables to rise if there is no outlet.

Any salt stored between the root-zone and the ground water, known as the vadose zone, is

generally mobilised in this process and brought to the surface. Through capillary action salts

are concentrated in the root-zone, and in time, will be great enough to cause soil salinity.

Examples of dryland salinity include Balfes Creek in Queensland (Gordon, 1998), the

Liverpool Plains in New South Wales (Beasley, 1998) and the Chapman valley in Western

Australia (Speed, 1998). Figure 1.2, shows an aerial photograph of dryland soil salinity

apparent in the upper Macquarie valley.

Figure 1.1 Aerial view of salinity in lake Eyre;

an example of natural salinity. Figure 1.2 Infra-red aerial photo of dryland soil

salinity in the upper Macqaurie valley.

M.F. AHMED

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Secondary salinisation due to irrigation is similarly the result of significant changes to

the hydrological balance. The process is mostly due to the injudicious use of surface or

ground water resources for irrigation. The result is excessive deep drainage, which similarly

can result in rising or perched saline water tables. This has occurred in the rice growing areas

of the Murrumbidgee river valley (Murray-Darling Basin Commission, 1999) and in isolated

instances in irrigated cotton areas such as the lower Macquarie valley (Willis et al, 1997;

McKenzie, 1992). Figure 1.3 shows irrigated soil salinity, which is apparent in an irrigated

cotton-growing field in a lower Macquarie valley south-east of Trangie. Figure 1.4 shows a

saline seep adjacent to a large earthen storage located approximately 2 km west of the Darling

River. Alternatively, direct application of saline and/or sodic ground or surface waters can

cause irrigation salinity. This is commonly a problem on the Darling Downs where slightly

saline groundwater has been used for irrigation.

Figure 1.3 Soil salinity in an irrigated cotton field in the lower Macquarie valley.

Figure 1.4 Saline seep adjacent to a earthen storage near Bourke, Darling River.

In 1987 it was estimated that 96,000 hectares of irrigated lands of the Murray Darling

Basin were salinised and that 560,000 hectares had water tables within 2 m of the soil surface

(Murray-Darling Basin Commission, 1999). This is mainly due to inefficient irrigation

practices, which has led to deep drainage and rising water tables. Similarly, in 1996 it was

estimated that an area of 300,000 hectares, rising to as much as 6 million hectares, of areas

within the basin was affected by dryland salinisation. Some of these areas are associated with

the Great Dividing Range and its footslopes. This could adversely impact water quality for

communities and irrigation schemes located further to the west.

In the northern parts of New South Wales, irrigation and dryland salinity do not seem

to be as prevalent as in the south. However, these areas have not been cleared for dryland

production nor developed for irrigated agriculture for as long. Nevertheless, incipient traces

of salinity are becoming apparent. In the Namoi valley, dryland salinity is considered to be

CHAPTER 1 - GENERAL INTRODUCTION

3

increasing in the upper part of the catchment. This is manifested in decreasing quality of the

river water. At present average electrical conductivity (ECiw) of Namoi river water is 0.68 dS

m-1, which is predicted to rise to 1.55 dS m-1in 2100 (Murray-Darling Basin Commission,

1999). If water salinity increases to levels greater than this, the impact on the irrigated cotton

farming systems located around Wee Waa and Narrabri needs to be assessed. In addition,

isolated cases of irrigation salinity have also been recorded (Triantafilis et al., 2000a).

In order to determine the possible effect and long term sustainability of irrigated

agricultural production in a particular area two approaches are necessary. The first is

information about the spatial distribution of soil and water resources suitable and currently

for irrigation. This could be collected by reconnaissance soil surveys (Odeh et al., 1996) or

from existing soil and water quality information. Secondly, this information should be used

in soil/water balance models to estimate soil salinity build -up and deep drainage beyond the

root zone. Various worstcase water-quality scenarios can be assessed and mapped to

determine which soil types and their location are at risk of accumulating salts if water quality

deteriorates. Conversely, areas where excessive leaching occurs also need to be identified as

salts applied in irrigation water may end up in groundwater reserves. In addition, these areas

may already be experiencing soil salinity from irrigation inefficiencies and creation of rising

or perched water tables.

The work described in this thesis is aimed at developing methods of assessing and

managing the threat of irrigation soil salinity in the irrigated cotton growing area of the lower

Namoi valley at the district and field levels. At the district level use is made of a salt/water

balance model to simulate soil salinity build-up using increasingly saline water (i.e. ECiw 0.4,

1.4, 4.0 and 9.0 dS/m). The results were extrapolated using non-linear geostatistical methods

(i.e. indicator kriging, multiple indictor kriging and disjunctive kriging) to identify soil types

at risk. The geostatistical methods were also compared to determine an optimal approach for

interpolating the risk (i.e. conditional probability). As a result of this analysis several areas

were identified where, although the risk of accumulation of salts was low, the possibility of

excessive deep drainage was considered high owing to the permeable nature of the soil. One

of these areas was located in the southern part of the study area and associated with the Pilliga

Scrub complex (Stannard and Kelly, 1977).

In order to determine some of the natural resource management issues at the field

level an investigation was undertaken in this part of the study area. This was carried out

using a Mobile Electromagnetic (EM) Sensing System (i.e. MESS) in a small-irrigated cotton

field experiencing water logging and soil salinity problems. The preliminary survey enabled

the selection of calibration sites to determine what the EM instruments (i.e. EM38 and EM31)

M.F. AHMED

4

were responding with. The results suggested that mineralogical differences and structural

stability of soil types along a storage wall led to irrigation inefficiencies and an isolated case

of soil salinity in the district.

In summary, this thesis explores the regional background of the lower Namoi valley

study area (Chapter 2). Chapter 3 is a literature review of the various geophysical,

geostatistical and salt-balance models available and used in this thesis. Chapter 4 shows how

a simple salt-balance model in associa tion with reconnaissance soil survey and water quality

information can be used to identify areas of risk of salts accumulating in the lower Namoi

valley using various non-linear geostatistical methods. Chapter 5 illustrates how at the field

level the use of a Mobile Electromagnetic Sensing System can assist in determining where

soil samples can be taken in order to improve the natural resource management in areas where

problems with soil salinity are apparent in the study area. Chapter 6 provides a general

discussion, conclusions from this research and possible future research directions.

CHAPTER 2

BIOPHYSICAL BACKGROUND

CHAPTER 2 - BIOPHYSICAL BACKGROUND

5

2.1 INTRODUCTION

The Namoi valley is a part of the Murray-Darling drainage system (Figure 2.1). The

valley occupies an area of approximately 43,000 square kilometres and lies in northern New

South Wales, Australia. The valley extends westward, some 350 km from the Great Dividing

Range in the east to the Barwon River in the west. The area is bordered in the east by the

Nandewar range, and to the south by the Warrumbungle, Liverpool and Great Dividing

Ranges. The Pilliga Scrub, which is an alluvial extension of the Warrumbungle range, defines

the southern part of this valley and the Darling-Barwon River system forms the western

boundary. The northern boundary extends east from the Nandewar range, north of the Gwydir

River to the Barwon River (Triantafilis, 1996).

Figure 2.1 Location of the study area.

The largest town in the total Namoi valley catchment is Tamworth, which is located

on the Peel River in the eastern part of the upper catchment. The largest water storage, keepit

Dam, is located around 25 km northeast of the township. Gunnedah is the second largest town

and located around 70 km west of Tamworth. It stands on the banks of the Namoi River, with

the principal agricultural industries including natural and improved pastures for livestock (e.g.

sheep and cattle), and cropping including oil seed, cotton, wheat, barley and soybean.

M.F. AHMED

6

Quirindi, lies on the New England Tablelands, surrounded by the Liverpool Ranges.

The district was a soldier settlement area after World War II and now the land is used for

wool, lambs, grain, crops and various other agricultural activities. Manilla, another small town

of this valley is situated in picturesque country at the junction of the Namoi and Manilla

rivers. It is the centre of a district engaged in wheat growing, mixed farming, wool and cattle.

Narrabri is located at the junction of Narrabri Creek and the Namoi River. It is located

around 90 km north-northwest of Gunnedah. The Nandewar Range is situated to the northeast.

Narrabri is the commercial centre for an area supporting a great diversity of rural activities.

The major industry in the area is cotton and the town is the site of one of Australia’s biggest

oilseed crushing mills. Wee Waa is a small town and lies between Burren Junction and

Narrabri and is located about 40 km west of Narrabri. It is the self proclaimed cotton capital

of Australia.

Walgett is the most western town in the valley and is situated near the junction of the

Namoi and Barwon Rivers. Although Walgett is a small town, it is a railhead and stock centre

and is the centre of a vast pastoral area stretching to the Queensland border. The major towns

in the lower Namoi valley area are Narrabri and Wee Waa and other settlements are Burren

Junction, Edgeroi and Pilliga.

2.2 HISTORY OF AGRICULTURAL DEVELOPMENT AND COTTON PRODUCTION

Thomas Mitchell set out in the early 1830s from the Hawkesbury River and explored

the lower Namoi valley near Narrabri and across the plains in the vicinity of Moree. Soon

after this expedition squatters followed. By the mid 1840s this area quickly developed into

agricultural communities dependent upon sheep and cattle grazing. The other main system

evident at this time was wheat-sheep enterprises (Irwin, 1972).

In the 1960s the Namoi area was predominantly used for sheep-raising for fine wool

production. In order to determine a profitable farming system for this area, using irrigation

water drawn from the Namoi River, the Department of Agriculture established a research

station between Wee Waa and Narrabri during this period. This research station assisted in the

successful cultivation of cotton.

The cotton plant was introduced into Australia with the first fleet in 1788 and was

planted in Sydney area, with disappointing results due to the unfavorable climate. In the

1830s, Australia was able to export a small amount of cotton to Britain and by the 1950s

cotton became a pioneer crop under natural rainfall conditions in Queensland, from the

tropical north to the Darling Downs (McHugh, 1996). The modern cotton industry

commenced in Australia in the early 1960s because of the imposition of restrictions by the


7

Government of the United States of America, on crop production land used for cotton. For

this reason American growers became interested in the prospects of cotton growing outside

the United States. During that period the price of land suitable for irrigated cotton growing in

the Namoi valley was $100 to $125 per hectare, whilst the price of comparable land in

California was about six times or more. In 1961 two Californian farmers bought some land in

the Namoi valley and established a farming system based on irrigated cotton (Irwin, 1972).

The first irrigated cotton crop was 26 hectares and was harvested in 1962. The production of

cotton increased rapidly in the valley. Today, approximately 50,000 hectares is cultivated for

cotton production in the lower Namoi valley around Wee Waa. One reason for the rapid

expansion of cotton production is the availability of suitable soil types in this area.

2.3 SOILS

In several early studies soil types in the region were described and classified (Gibbons

and Hallsworth, 1950; McGarity, 1950; Hallsworth et al., 1955). Gibbons and Hallsworth

(1950) mapped several soil associations near Narrabri. Grey clays (described as chernozem-

like soils) and grey clays with gypsum (sierozem-like soils; these had lighter colour and better

self-mulching character) were separated from complexes of red-brown earths and solodized-

solonetz. Thin deposits of sand on grey clays between Narrabri and Wee Waa were regarded

as alluvial wash. Gilgai were common on the heavy soils to the southwest; here the subsoil

had been forced to the surface.

McGarity (1950) attributed soil variation on the plains of the Namoi and Gwydir rivers

mostly to climate change, pattern of flood deposition, and variations in soil drainage. Wind

had a major role only in the erosion of sandy duplex soils. He roughly mapped five soil

groups near Edgeroi, on the northern boundary of the Namoi valley, basing his separations on

the presence or absence of gypsum in the uppermost ~2 m of the profile, soil colour, and

parent material. Basaltic soils were separated from brown, grey and black alluvia. Hallsworth

et al. (1955) attributed gilgai development to seasonal wetting and drying and to the transfer

of topsoil to subsoil by its collapse into cracks.

In later studies Hallsworth and Waring (1964) argued that soil variation near Narrabri

was due to the distribution of sediment by floods. The sand-over-clay profiles were formed by

clay washed by rain from topsoil. Northcote et al. (1965), Northcote (1966) and Northcote

(1984) identified hard alkaline red and brown soils, seasonally cracking clays and cracking

clays with gilgai, and subdivided the clay soils according to colour and size of surface soil

aggregates. A major classification of soils of the region is included in Northcote (1966).

M.F. AHMED

8

Stannard and Kelly (1977) distinguished physiographic provinces (Pilliga Scrub,

Alluvial Plain, etc.), and related the alluvial soils to flood plains and prior streams, grouping

them by texture profile, soil colour and self-mulching character. Van Dijk (1980, 1984)

inferred that the sodium chloride in the soils had originated by deposition from the air or by

weathering at depth, and stated that the cracking clays had developed on several ancient

landscapes.

2.4 SOIL MAP UNITS

Several surveys have specifically described the soil of lower Namoi valley. In the

following section a brief summary of the Northcote (1966) and Stannard and Kelly (1977)

surveys are produced.

Figure 2.2 Soils of the lower Namoi valley (after Northcote, 1966).


9

2.4.1 Soil map units (Northcote, 1966)

Northcote (1966) mapped the soils of Australia at a scale of 1:2,000,000. The soil

mapping units for the area of this study described by Northcote (1966), are shown in Figure

2.2. It is apparent that cracking clay profiles dominate the lower Namoi valley.

The major soil map unit in this area is CC16, which is a cracking clay soil (Ug5) and

contains self-mulching grey (Ug5.2) and deep forms, usually Ug5.24 and 5.25. CC17 is the

second major soil map unit in this area and dominates the southern part. This is also a

cracking clay soil and associated features are many small, low domes or rises related to old

drainage-ways, with soils of units B10, Si2, and Ro3 north of the Collarenbari-Moree line,

and of unit Oc12 south of about this line. The soil map unit Earths (Ms1) is located in the

eastern part of the study area. This includes yellow earths (Gn2.2) with an acid reaction trend

through profile (Gn2.21) and with no A2 horizon (Ms1). More detailed information about

each of the units shown are described in Table2.1

Table2.1 Brief description of the soils of the lower Namoi study area ( after Northcote, 1966).

Map Unit Description

Cracking clay soils (Ug5) I. Self-mulching (b) Grey (Ug5.2) (ii) Deep forms, commonly (Ug5.24, Ug5.25)

CC16

Plains associated with major and minor functional and non-functional drainage-ways: slightly gilgaied (few inches) plains of cracking clays-chiefly grey clays (Ug5.24 and Ug5.25) on the open plains and in depressions, and brown clays (Ug5.35) on slight elevations-alternating with low domes or rises in the field of variable extent and having hard alkaline red and brown soils (Dr2.33) and Bd1.33) with other (D) soils as found in un it Oc12. Other features include small areas of unit B10 and possibly small areas of red earths (Gn2.13) as for unit Mx5. There is some local variation in soil dominance between the (Ug5) and the (D) soils. This unit may represent a broad transition between units Oc12 and CC17.

CC17

Plains of slightly gilgaied cracking clays associated with major and minor functional and non-functional drainage -ways. Major soils included in this class are Grey clays (Ug5.24 and Ug5.25) and Brown Clays (Ug5.34 and Ug5.35).Associated features and soils are: many small, low domes or rises related to old drainage-ways, with soils of units B10, Si2, and Ro3 north of about the Collarenbri-Moree line, and of unit Oc12 south of about this line-note that these low domes may give rise to apparent toposequences of soil locally; small areas of (Dd1.33 and Dd1.43) soils along the eastern mergins of this unit some areas of dark clays (Ug5.15 and Ug5.16); especially in the Moree-Narrabri localities and possibly related to adjacent elevated basalt areas; some repeatedly flooded areas with grey clays (Ug5.5) having a massive surface and also some swamps and marshes with undescribed soils.

CC20

Gently undulating cracking clay plains with moderate to strong (2-4 ft) gilgai microrelife: chief soils are deep grey clays (Ug5.24) with smaller areas of (Ug5.25) and Ug5.28) and some brown clays (Ug5.34). soil reaction values of these cracking clays vary and comprise alkaline or neutral surfaces with acid subsoils (common), acid throughout (fairly common), and alkaline throughout (rare). In some areas (Dy2.33 and Dy2.43) soils occur on the slightly raised flat areas between gilgai depressions or adjacent to small drainage lines.

M.F. AHMED

10

Table 2.1 (continued).

(a) Dark-coloured (Ug5.1) (ii)Moderately deep forms (Ug5.13, Ug5.14, Ug5.15, Ug5.16)

Kc5

This soils are dissected basaltic plateau with hills and flat -topped ridges, sometimes bouldery: gently rolling to rolling terrain of dark cracking clays, principally (Ug5.13, Ug5.14, Ug5.15) and red-brown cracking clays (Ug5.37 and Ug5.38) some of which approach Ug5.6 soils. Um6.2, Db3.12 and Ug5.12 soils are associated on hills, knolls, and ridges, principally the red and yellow earths (Gn2.12) and (Gn2.21) on slopes, principally D soils such as (Dr2.43); and the D soils of unit R02 on lower slopes adjacent to that unit.

(vi) Deep forms with grey subsoils (Ug5.16) with alkaline (D) soils in non-self-mulching areas

Kh2

These cracking clay soils are dark coloured, deep forms with grey subsoils, flat to gently undulating plains showing slight gilgai features: chief soils are dark cracking clays (Ug5.16) and hard alkaline dark soils (Dd1.33 and Dd1.43) associated are cracking grey and brown clays (Ug5.2 and Ug5.3)and some D soils such as (Db1.33and Db1.43).some deep subsoil and / or D horizon layers may be strongly acid.

Sandy soils with mottled yellow clayey subsoils (Dy5) (i)Acid reaction trend through profile (i) Peds evident in subsoil (iv) Bleached A2 horizon (Dy5.41)

Wa12

Broken topography-undulating to hilly with sandstone ledges rock out crops towards the ranges (east), and long sandy ridges running out towards the plains (west) chief soils are sandy acidic and neutral yellow mottled soils (Dy5.41 and dy5.42) often containing ironstone gravel’s. Associated are yellow earths (Gn2.21 and Gn2.22) and possibly other undescribed soils.

(iii)Alkaline reaction trend through profile (iv) Bleached A2 horizon (Dy5.43)

Ya25

Gently undulating sandy plains: chief soils are sandy alkaline and neutral yellow mottled soils (Dy5.43 and Dy5.42). Associated are: slightly lower-lying, often wind-deflated plains of hard alkaline and neutral brown soils (Dn1.33 and Db1.32) and (Db1.43), and occasionally similar (Dr) soils; some areas of grey cracking clays (Ug5.2) along present stream courses; very irregular dunes (? Associated with priormstreams) of siliceous sands (Uc1.2); and possibly some sand sheets or dunes of earthy sands (Uc5.21)

Hard-setting loamy soils with brown clayey subsoils (Db1) (iii) Alk aline reaction trend through profile (iii) Sporadically bleached A2 horizon (Db1.33)

Ro1

Plains: chief soils are hard alkaline brown soils (Db1.33 and Db1.43) and similar (Dy), Dd), and less commonly (Dr) soils. Associated are: sandy neutral yellow mottled soils (Dy5.42); siliceous sands (Uc1.2) in the vicinity of creek; and low irregular dunes and/or sand shets of red earthy sands (Uc5.21) which may be extensive in some localities.

Ro2

Plains: chief soils are hard alkaline brown soils (Db1.33 and Db1.43) and smaller areas of cracking clays such as (Ug5.15 and Ug5.16). These soils may form soil complexes locally. Associated are: (Dr2.33 on slightly elevated portions of the plain; (Um6.21) soils on low limestone ridges.

Earths (Gn2) (b) Yellow earths (Gn2.2) (1) No A2 horizon (i) Acid reaction trend through profile (Gn2.21)

Ms1

Undulating to hilly with some fairly broad flat areas, often broken by rocky knolls and ridges, some may be step; chief soils are sandy acid yellow earths (Gn2.21), sandy acid and neutral red earths (Gn2.11 and Gn2.12), and shallow sand soils (Uc4.1 and Uc4.2) on ridges and slopes where ferruginized rock and ironstone gravels are common. Associated are: flatter and lower lying areas of various hard-setting (D) soils, such as Db2.42, Dy3.42 and Dy3.42; some sandy (D) soils, such as Dy5.42; some slopes and flatter areas especially in northern expressions of the unit of Dr2.43 and Dy3.43 soils sometimes with cracking clays (Ug5); small areas of Uc5.2 soils; and small areas of soils, such as Dr4.12, associated with small basaltic flat tops and ridges. Small areas of units Mz1, Mo5, Kb2, Ke11, and Kc5 may be included.

Friable (highly structured) porous earths (Gn4) (a) Brown friable porous earths (Gn4.31)

Mh7

Plateau remnants at high elevation (4000 ft) gently undulating with some deeply dissected valleys and swampy flats in the headwaters of some streams; chief soils are brown friable porous earths (Gn4.31) with red friable porous earths (Gn4.11). Associated acid peats (O) and various (Um) in the flats.

Other sand soils (Uc4) (a) No B horizon present (Uc4.1)

JJ7

Mountainous-steep to rugged terrian with rock outcrops and rock walls; shallow sandy soils (Uc4.1) and (Ucl.2) with various (Dr) and (Dy) soils and some red earths (Gn2.14).


11

2.4.2 Soil map units (Stannard and Kelly, 1977)

An alternative description of the soils of the lower Namoi valley has been provided by

Stannrd and Kelly (1977), as shown in Figure 2.3. They identified 8 soil types of which the

grey self muching clay soil types was the predominant soil identified.

Red-Brown Earths encompasses the major prior stream systems and includes the

following soils in order of importance; (a) Red-brown earths, (b) Transitional red-brown

earths, (c) Non self-mulching cla ys, (d) Self-Mulching clays and e) Deep sandy soils. These

soils, with the exception of the self-mulching clays are predominantly brown in colour, with

the grey counterparts of limited extent in lower situations.

The Non Self-Mulching Clays unit occupies the minor prior stream formations and

some of the land marginal to the major prior streams. The soils in order of dominance are a)

Non self-mulching clays b) Transitional red-brown earths c) weakly self-mulching clays d)

Self-mulching clays. The self-mulching and weakly self-mulching clays are predominantly

grey in colour whilst the other two soils are predominantly brown with, however, some fairly

extensive areas of grey non self-mulching clay.

Grey Self-Mulching Clays with small areas of Non Self-Mulching Clays unit occurs

on the plain where scattered, small, slightly elevated areas of brown non self-mulching clay

are present, and also along the terminal prior stream floodways. In the latter case, the

predominant grey self-mulching clays largely comprise the stream channels which carry

floodwaters, whilst the brown non self-mulching clays occurs as small islands of levee

remnants beside the stream channel.

Weakly Self-Mulching Clays occupy only small areas on the plain, and margins of the

minor prior stream formations. The soils are grey in colour and, apart from weakly self-

mulching clays, contain only very small areas of grey self-mulching clay.

The Brown Self-Mulching Clays unit occurs on that part of the plain apparently not

subject to inundation and only in the west of the region. Apart from the predominant brown

soils it contains very limited areas of grey self-mulching clays and brown non self-mulching

clays.

The Grey Self-Mulching Clays unit covers the greatest part of the area surveyed and

shows generally little variation except for very occasional small areas of weakly self-

mulching clays and non self- mulching clays. The predominant soils vary, in colour from grey

to grey-brown, in the lime content of the upper horizons, in gypsum content in the lower

horizons and in the nature of the self-mulching surface, as has been described above.

M.F. AHMED

12

Figure 2.3 Soils of lower Namoi valley (after Stannard and Kelly, 1977)

The remaining three soil mapping units, namely the Pilliga Scrub, Hilly and

Undulating Land and the Dissected Coarse Textured Flood plain was mapped as soil

complexes.

2.5 PHYSIOGRAPHY

The lower Namoi valley is a section of the total Namoi valley physiographic unit. This

is a typical riverine plain, bordered on the east by the Nandewar and Warrumbungle Ranges

and the south by the Cobar Shield. The valley from the longitude of Narrabri westward to the

Barwon River is an alluvial extension of the Warrumbungle Range (Stannard and Kelly,

1977).

The valley slopes gently to the west at a gradient of about 1:2,000, reducing slowly to

about half that slope towards Walgett. There are some minor variations in contour with

alluvial deposition along relic stream courses and dissection along present streams.


13

Downstream from Narrabri, the whole of the valley is of relatively level terrain intersected by

a number of distributaries and some minor streams rising on the western flanks of the

Nandewar Range which normally flow into the river. The Pilliga slopes generally to the north

and many minor streams rising in this area discharge into the river or associated distributaries

during times of high rainfall.

Stannard and Kelly (1977) identified eight physiographic units in the lower Namoi

valley including the clay plains and the prior stream formations. The physiographic units in

the lower Namoi valley are shown in Figure 2.4 (Stannard and Kelly, 1977).

The clay plain is the dominant soil physiographic unit. Particularly, from Narrabri to

Burren junction. The clay plain is generally uniform in topography except where dissected by

present streams. The upper most sediments are of fine texture on which clay soils of generally

self-mulching character have developed, varying in colour from dark grey to brown. Grey

soils are predominant in the unit.

Figure 2.4 Physiographic units of the lower Namoi valley (after Stannard and Kelly, 1977)

M.F. AHMED

14

The second largest unit is the Prior stream formations. These occur mostly as

continuous belts of slightly elevated and undulating land. The uppermost materials are coarse

textured compared with the clay plain. The relic stream channels and levees are clearly

distinguishable, the former underlain by coarse channel sediments. These systems are readily

distinguished from normal effluent by their wide meander belt and shallow channels, as

against the narrow meander belt and narrow relatively deep channels of the effluents. These

two units now form the principal irrigation districts of the valley, supported with water

supplied by the many dams that are now used to regulate the flow of the Namoi River. The

study area of the lower Namoi valley is surrounded by the Pilliga scrub and hilly and

undulating land in the southern and eastern part, respectively. A summary of the

physiographic units described by Stannard and Kelly (1977) are shown in Table 2.2.

Table 2.2 Brief description physiographic units (Stannard and Kelly, 1977).

Physiographic units Description

Clay Plains This unit is of generally uniform topography except where dissected by present streams. The uppermost sediments are of fine texture on which clay soils of generally self-mulching character have developed, varying in colour from dark grey to brown. The grey soils are predominant in this unit and are liable to inundation of varying depth and duration from river flooding. The brown soil profiles occur in small slightly elevated areas, which are not liable to inundation and located along minor prior stream formations.

Prior stream formations These formations mostly occur in continuous belts of slightly elevated and undulating land, generally the uppermost materials of these formations are coarser textured than the clay plain. The relic stream channels and levees are clearly distinguishable, the former being underlain by coarse channel sediments. In some cases, the stream channels are slightly lower than the plain and under present conditions are preferential paths for floodwaters. In these situation the uppermost sediments are of fine texture. These stream systems are easily distinguishable from normal effluent by their wide meander belt and broad and shallow channels.

Terminal prior stream flood

ways

These are an extension of the prior stream formations. They possess a wide, shallow, broadly meandering channel of grey clay, with associated numerous small slight rises of brown clayey material which are discontinuous levee formations. It carries a considerable volume of water during flood times. In the south of the region these formations run in a south-westerly direction from the Namoi towards the Castlereagh river west of the Pilliga Scrub, but any connection to the prior streams to the east has been obliterated by activity of the Namoi River and Baradine Creek.

Edgeroi scrub plain This unit is located in a single area near the village of Edgeroi. The main feature of this unit is a dense tall scrub, principally belah, most of which has been cleared for agriculture. Generally level landscape but appears to be slightly depressed in relation to the adjacent clay plain. Where such scrub covers is not present. Soils are mostly self-mulching grey clays with a higher content of coarse sand throughout the profile.

Coarse texture dissected low floodplain

The river passes alongside the northern margin of the Pilliga Scrub near Narrabri and between Narrabri and Wee Waa, along or close to the course of a prior stream formation. Generally, coarse textured low flood plain has been developed with the greater part of the material provided by the river. The low dissected land associated with the river and its effluent elsewhere is of uniformly fine texture.

The Pilliga Scrub

Uniformly gentle slopes of about 1:400 is usually found in this unit. Coarse to intermediate texture are generally found in the uppermost area where plenty of dense timber like eucalyptus and white pine are available. An intense network of ephemeral creeks of varying size drain this unit among which three creeks would discharge any significant quantity of water except during the periods of heavy rainfall. The soils of the margin of this unit are of fine texture and often exhibit an extreme development of gilgai formation. A wide marginal belt of such land is present at the western extremity of the Pilliga Scrub.

Barwon flood plain This unit form the western boundary of the Namoi valley, which is depressed in relation to the adjacent Namoi clay plains and severely, dissected by flood channels. The sediments are predominantly of fine texture, non self-mulching clays are fairly widespread which differ this unit from the clay plains.

Hilly and undulating land

The hilly and undulating land is principally developed on basalt rock but some sedimentary rocks outcrops occur at shallow depth. The lower slopes of this land, possess uppermost materials of fine texture with dark self-mulching clay soils. Where sedimentary rocks are present, the weathered and colluvial material is usually of coarse to intermediate texture on which lighter coloured soils have developed.


15

2.6 HYDROGEOLOGY

Of the three major types of rock formation that can yield useful quantities of water

(i.e., aquifers) only unconsolidated sediments exist of west Wee Waa. However, to the east

and located on the footslopes of the Nandewar Range, water can be obtained from

sedimentary and fractured rocks. Figure 2.5 shows the hydrogeological map of the salinity

and yields of the surficial (~25-35 m) aquifer system (Department of Water Resources, 1988).

The largest of the alluvial aquifers contains the highest yielding (0.50 L/s) and best quality

water (<500 mg/L TDS). Many farmers exploit this for stock and domestic purposes. Also of

interest, is the progressively more saline groundwater located near the northeast corner of the

Figure 2.5 Hydrogeology of the lower Namoi valley (Department of Water Resources, 1988).

M.F. AHMED

16

lower Namoi valley. One of the smaller alluvial aquifers is the most saline (i.e., 1,500-3,000

mg/L TDS) yielding 5-50L/s.

Colluvial aquifers dominate the northern areas. These are recent age with some

remnants of older formations. They are the result of weathering products and overlie various

rock types deposited by minor tributaries. These are lower yielding than the alluvial aquifers

(0.5-5 L/s) with salinity ranging from 0 to 1,500 mg/L TDS.

2.7 CLIMATE

Climate data are included from two major towns in the general area the lower Namoi

valley Narrabri in the east and Walgett, in the west. The Bureau of Meteorology (1996) has a

comprehensive history of climatic data of the Namoi region. Average annual rainfall generally

decreases westward is 610 mm at Narrabri and 432 mm at Walgett. Of this, about 34 per cent

falls between December and February. The potential evaporation as measured by the various

indices is relatively high during this period, because of a high percentage of cloud free days

and the high-intensity solar radiation received. The ratios of annual evaporation to annual

rainfall are a 3.3 and 4.4 for Narrabri and Walgett, respectively. The highest mean daily

maximum temperature recorded was 35.5 0C at Walgett Post Office (Bureau of Meteorology,

1996).

2.7.1 Rainfall

In the Namoi valley, the summer months especially December and January have the

highest monthly rainfalls. Although the rainfall varies year-to-year and month-to-month, the

Figure 2.6 Average monthly rainfall (mm) at Narrabri, Gunnedah and Walgett over a period of 115 years (Bureau of Meteorology, 1996).

1220

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7 8 9 10 11

NarrabriGunnedahWalgett

MonthJan Feb MarApr May Jun Jul Aug Sep Oct Nov Dec


17

rainfall in summer is more consistent. In Narrabri, the highest mean rainfall and the highest

monthly rainfall were recorded in January, 87.1 mm and 307 mm, respectively. In Walgett,

the highest mean rainfall and highest monthly rainfall were 63.3 mm and 345.4 mm,

respectively also recorded in January. Generally, rainfall in the study area tends to decrease

towards the west with more easterly areas experiencing a greater incidence of thunderstorms

and hail. In Figure 2.6, the average monthly rainfall in the area, determined over a period of

115 years is shown.

2.7.2 Temperature

Temperatures are very high. It is very hot throughout the valley during summer.

January is the hottest month with mean daily maxima and minima of 33.6/19.20C and

35.5/20.50C in Narrabri and Walgett, respectively. The coldest month is July with mean daily

maxima and minima of 17.8/3.60C and 17.5/4.40C at Narrabri and Walgett, respectively.

Average monthly temperature for Narrabri, Gunnedah and Walgett are shown in Figure 2.7

The lowest minimum temperature was recorded in July. Frosts are most likely during

July. June and August have only a slightly lower frequency of occurrence (Bureau of

Meteorology, 1972). The lowest minimum temperature in Narrabri was -4.40C whereas in

Walgett it was –3.80C (Bureau of Meterology, 1996).

Figure 2.7 Average monthly temperature (0C) at Narrabri, Gunnedah and Walgett over a period of 114 years (Bureau of Meteorology, 1996)

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8 9 10 11

Month

NarrabriGunnedahWalgett

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec12

M.F. AHMED

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2.8 NATURAL VEGETATION IN THE LOWER NAMOI VALLEY

The natural vegetation remaining in the area is varied and considerable and depends

on the various physiographic units. It appears to be influenced mostly by soil characteristics

and liability to inundation. In the physiographic units described by Stannard and Kelly (1977)

a wide range of plant species have been identified.

Coolabah (Eucalyptus microtheca ) is the dominant tree species in the clay plains unit.

In the more flood liable areas this species is relatively dense, with varying contribution of

river cooba (Acacia harpophylla ), black box (Eucalyptus largiflorens), belah (Casuarina

cristata ) and boree (Acacia pendula ). Eremophila bignoniifolia is present in some locations

towards the west. Rosewood (Heterodenderon oleifolium) and whitewood (Atalya

hemilgauca ) are fairly common with the above mentioned species in the low flood land.

Timber cover is limited to scattered bimble box (Eucalyptus populnea), rosewood

(Heterodenderon oleifolium) and whitewood (Atalya hemilgauca ) on the isolated islands of

non self-mulching clay.

In general, timber cover along the prior stream formations includes wilga (Geigera

parviflora), budda (Eremophila mitchelli), rosewood (Heterodenderon oleifolium) and belah

(Casuarina cristata), with varying amounts of bimble box (Eucalyptus populnea) and white

pine (Callitris columellaris) are densely populated trees. Some ground cover such as

windmill (Chloris spp.) and spear grasses (Stipa spp .) and various Atriplex and Bassia species

are associated with the above mentioned trees.

The terminal prior streams support coolabah (Eucalyptus microtheca ) in the channels

and bimble box (Euclayptus populnea ) on isolated levee remnants. The Edgeroi scrub plain

support dense timber stands, mainly of tall belah (Casuarina cristata), with wilga (Geigera

parviflora), budda (Eremophila mitchelli) and rosewood (Heterodenderon oleifolium). The

coarse textured dissected flood plain is dominated by river red gum (Eucalyptus

camaldulensis) in lower areas with coolabah (Eucalyptus microtheca ) in higher aspects.

White pine (Callitris columellaris) and ironbark (Eucalyptus melanophloia ) including

some other shrubs and trees dominate the deeper and sandier soils in the Pilliga Scrub whilst

the shallower soils are dominated by bimble box (Eucalyptus populnea ) with a varie ty of

other species, including shrubs and trees. The land with strong gilgai development around the

margins of the Pilliga Scrub mainly supports brigalow (Acacia harpophylla ), boree (Acacia

pendula) and belah (Casuarina cristata). Coolabah (Eucalyptus microtheca), river red gum

(Eucalyptus camaldulensis) and black box (Eucalyptus largiflorens) are available with

different density in the Barwon flood plain.

CHAPTER 3

LITERATURE REVIEW

CHAPTER 3- LITERATURE REVIEW

19

3.1 INTRODUCTION

The subject of soil and water salinity is wide and varied. A comprehensive literatrure

review is therefore difficult. In the following review, the literature is confined to the areas

which directly relate to the research carried out in this thesis. First, the interaction between

soil and water salinity with respect to soil and plants is briefly discussed (Section 3.2). Models

which estimate the consequences of saline water application to soil salinity levels are then

described (Section 3.3). These models are usually applied at point scales so they are of limited

use at the field or district levels. Geostatistical techniques can be used to interpolate these

estimates and these are introduced in the next section (Section 3.4). The use of geostatistics is

limited however when only limited soil information is available. This is often the problem at

the fieldscale. The use of electromagnetic (EM) induction methods, which measure soil

electrical conductivity (ECa), has revolutionsied the way this information can be collected and

can significantly inprove the interpretation of salinity. This is discussed in the final section of

this review (Section 3.5). Conclusions are briefly presented in Section 3.6.

3.2 INTERACTION BETWEEN SALINITY WITH SOILS AND PLANTS

3.2.1 Introduction

A saline soil contains sufficient water-soluble salts (electrolytes) to adversely affect

the growth of most plants. The water-soluble salts are mainly of sodium (Na+), calcium

(Ca2+), magnesium (Mg2+) and potassium (K+) and may be chlorides (Cl1-), sulphates (SO42-)

or carbonates (CO32-). In general, moderate soil salinity improves soil structure, but Ca2+ and

Mg2+ improve soil structure and Na+ adversely affects it (Tanji, 1990). The development of

effective salinity control practices and management requires an understanding of effects of

salinity on soil and plants.

3.2.2 Effects of salinity on soils

The negatively charged colloidal clay particles in the soil interact with cations to form

an exchangeable cation envelope. The bound cations are exchangeable with their counterparts

in the external bulk solution. The envelope of exchangeable cations is subject to two opposing

processes. The cations are attracted to the negatively charged clay surface in proportion to

their charge and they tend to diffuse from the surface of the clay, where their concentration is

higher, and into the bulk of the solution, where their concentration is lower. These two

M.F.AHMED

20

opposing processes result in an approximately exponential decrease in cation concentration

with distance from the clay surface to the bulk solution (Rhoades, 1990).

Divalent cations such as Ca2+ and Mg2+ are attracted to the surface of clay with a force

twice as great as Na+ and other monovalent cations. Thus for soils containing high proportions

of Ca+ and Mg+ relative to Na+ or a saline soil, the exchangeable cation envelope is

compressed toward the clay surface. Consequently, the repulsion forces between the like-

charged envelopes decrease and the particles can approach closely enough to permit their

cohesion into aggregates. The packing of aggregates yields a matrix of greater pore size

compared to that of individual particles, which is the reason for better permeability and tilth

of aggregate soils than those of non-aggregated soils (Rhoades, 1990).

The repulsion between clay particles allows solution to be imbibed between them,

which is referred to as swelling. The physical characteristics of clays change with their

chemical composition. Some clay minerals swell when wet. In particular, soil types in which

the clay fraction is dominated by clay minerals that swell when wet show strong shrink-swell

behavior (McKenzie, 1998). For example, kaolinite and illite shows little expansion on

absorption of water. Smectite on the other hand, shows large expansion on absorption of

water (McNeal and Coleman, 1966). Frenkel et al. (1978) could not distinguish between non-

acid kaolinite, vermiculite and montomorillonite in soils with low clay contents. Cracking

clay soil types contain a large proportion of smectite in their clay fraction and swell markedly

when they become wet. It is known that the lower Namoi valley is dominated by cracking

clay soils (section 2.4), which infers the presence of significant amounts of smectite.

An excess of exchangeable sodium relative to other exchangeable cations causes soil

to disperse because of its effects on the thickness of the diffuse double layer. A large amount

of exchangeable sodium relative to other exchangeable cations results in a thick double layer,

while a large amount of exchangeable calcium relative to other exchangeable cations results

in a thin double layer. A thick double layer promotes dispersion because negatively charged

clay surfaces are held together inadequately by the positively charged cations that lie between

them (McKenzie, 1998). The release of individual clay platelets from aggregates, and slaking,

the breakdown of aggregates into sub-aggregate assemblages, can occur at ESP values of

lower than 15 if the electrolyte concentration is low (Rhoades, 1990).

The amount of micro-pores in a soil and the soil permeability can be reduced due to

slaking (Abu-Sharar et al., 1987). As Na+ disperses soil aggregates, high Na+ levels combined

with low soil-water electrical conductivity can lower soil permeability and decrease

infiltration capacity through the swelling and dispersion of clays and the slaking of


21

aggregates. For example, if a soil has an ESP as low as 2, it may disperse if the EC of the soil

is negligible.

Water infiltration depends on the combined properties of water and topsoil, because

water must pass through the soil surface. Thus soil permeability and tilth problems must be

evaluated in terms of both the salinity of infiltrating water and the ESP of the topsoil.

3.2.3 Effects of salinity on plants

In saline conditions, plants can not express fully their genetic potential for growth,

development, and reproduction (Lauchli and Epstein, 1990). On the other hand, salinity may

promote the growth or enhance the yield or quality of crops (Pasternak, 1987). Moderate

salinity may increase the yields of cotton (Pasternak et al., 1979), raise the concentration of

total dissolved solids in tomatoes, a quality feature (Rush and Epstein, 1981) and increase the

freezing tolerance of citrus (Syversten and Yelenosky, 1988). Plants have different degrees of

tolerance or sensitivity to salinity.

Physiologically salinity affects plants in many ways and under extreme salinisation

overt injury symptoms can be found. Salt-affected plants are stunted and may have darker

green leaves which, in some cases, are thicker and more succulent (Lauchil and Epstein,

1990). In the case of woody species, toxic accumulations of Cl- or Na+ may cause leaf burn,

necrosis and defoliation. In contrast most herbaceous plants do not exhibit leaf injury

symptoms even though some accumulate Cl- and Na+ to the levels as high as those causing

injury in woody species (Maas and Hoffman, 1977).

In the case of cereal crops, for example rice (Pearson, 1960) and corn (Kaddah and

Ghowail, 1964), grain yields may be greatly reduced without any appreciable effect on straw

yield. Although cotton is classified as a salt tolerant crop based on relative crop yield under

field conditions and imposition of salinity after seed emergence and stand establishment

(Maas, 1984), it is quite salt-sensitive in the seedling stage (Kent and Lauchli, 1985; Cassman

et al., 1990).

3.2.3.1 Osmotic effects

Plants extract water from the soil by exerting an adsorptive force greater than that

which holds water to the soil. When plants can not make sufficient internal adjustment and

exert enough force, they are not able to extract sufficient water and subsequently will suffer

water stress. Salt in the soil water increases the force the plant must exert to extract water and

this additional force is referred to as the osmotic effect or potential (Ayers and Westcot,

M.F.AHMED

22

1985). When the osmotic potential of the medium becomes lower than that of the plants’ cells,

the latter will suffer osmotic desiccation. In a medium of high salinity, plants must build up

higher internal solute concentrations for survival.

The salt-accumulating halophytes, which can live successfully in saline habitats, are

adapted by absorbing salt from the medium and using it as a major internal osmoticum

(Flowers et al., 1977). However, salt in plant cells can be dangerous as high salt

concentrations in the cytoplasm damage enzymes and organelles (Greenway and Munns,1980;

Munns et al., 1983). The tonoplast transports salts into the vacuole, builds up a high

concentration of the salt there and prevents any substantial leakage of organic osmolytes from

the cytoplasm into the vacuole (Lauchli and Epstein, 1990). Glycophytes or non-halophytes

are ill equipped to cope with the stresses of saline and sodic conditions. They tend to exclude

salt and sequester what salt they absorb in the roots and stems, thus minimizing the exposure

of the leaf cells and hence the photosynthetic apparatus, to salt. They regulate ion fluxes less

effectively at the cellular level than do halophytes and are generally less tolerant of saline

conditions (Lauchli and Epstein, 1990).

3.2.3.2 Specific ion effects: toxicity

Toxic effects of salinity can occur when certain ions are taken up with the soil-water

and accumulate in the leaves during water transpiration. The degree of damage depends upon

the time of exposure in the growth cycle, concentration, crop sensitivity and crop water use,

and if damage is severe enough, crop yield is reduced. Woody plants show toxic effects at

even moderate concentrations of some ions. In several fruit crops leaf injury indicated by leaf

colour changes was observed in response to moderate Na+ and Cl- levels (Bernstein, 1965). In

general, the major toxic ions in irrigation water, which can cause toxicity individually or in

combination, are Cl-, Na+ and boron. In different plants the toxic symptoms of toxic salinity

may be different. For example, Grattan and Maas (1984, 1988) showed apparent phosphate

toxicity in soybean plants was caused by salinity. Kingsbury and Epstein (1986) found that

the growth of a salt-sensitive line of wheat was adversely affected by nutrient solutions

containing high concentrations of Na+ (100 mM), but not by isosmotic solutions without high

Na+ concentrations.

3.2.3.3 Specific ion effects: nutrition

The nutritional effects of Na+ are not simply related to the ESP but depend upon the

concentrations of Na+, Ca2+ and Mg2+ in the soil solution. In sodic, nonsaline soils, total

soluble salt concentrations are low and consequently, Ca2+ and/or Mg2+ concentrations are


23

often nutritionally inadequate. Since Na+ uptake by plants is strongly regulated by Ca2+ in the

soil solution, the presence of sufficient Ca2+ is essential to prevent the accumulation of toxic

levels of Na+. Inadequate concentrations of Ca2+ may adversely affect membrane function and

growth within minutes (Cramer et al., 1988). Sodium ions have been shown to cause

disturbances in Ca2+ nutrition. Nutritional disorders involving other elements may be linked

to the effects of salinity on the transport and metabolism of Ca2+ (Kent and Lauchli, 1985).

When external Ca2+ concentrations are high, they may mitigate the effects of salinity.

3.2.4 Salt tolerance of plants

Salt tolerance of plants varies considerably among species and also depends heavily

upon the cultural conditions under which the crop is grown. Many plant, soil, water and

environmental variables interact to influence the salt tolerance of plants. Plant tolerance to

salinity is usually appraised in one of three ways: (1) the ability of a plant to survive on saline

soils, (2) the absolute plant growth or yields, and (3) the relative growth or yield on saline soil

as compared with that on non-saline soil (Maas, 1990).

In addition, crop salt tolerance sensitivity can vary through the life of a plant. For

example barley, wheat and corn are more sensitive to salinity during emergence and early

seedling growth than during germination and later stages of growth and grain development

(Ayers, 1953; Ayers et al., 1952; Kaddah and Ghowail, 1964). Variation has been observed

between different wheat cultivars (Torres and Bingham, 1973).

Salt tolerance also varies with soil fertility. Crops grown on infertile soils generally

have abnormally high salt tolerance as compared with crops grown on fertile soils because

yields on nonsaline soil are severely limited by inadequate fertility rather than soil salinity

(Maas, 1984; Lunin and Gallatin, 1965; Ravikovitch and Porath,1973; Ravikovitch and Yoles,

1971). Apparent decreases in salt tolerance with excess N applications have been reported for

cotton (Khalil et al., 1967) and wheat (Luken, 1962). However, no significant change in

relative salt tolerance was found for beans (Lunin and Gallatin, 1965).

Climate may also significantly influence plant response to salinity. Most crops are

more sensitive to salinity under hot, dry conditions than under cool and humid ones. Air

pollution, especially ozone, increases the apparent salt tolerance of oxidant-sensitive crops.

Mass and Hoffman (1977) provided a review of crop yield potentiality in terms of different

salinity levels, part of which is shown in Table 3.1.

M.F.AHMED

24

Table 3.1 Crop tolerance and yield potential of selected crops as influenced by irrigation water salinity (ECiw) or soil salinity (ECe) (Maas and Hoffman, 1977).

Field Crops 100%

Yield

90%

Yield

75%

Yield

50%

Yield

0%

Yield

Crops EC e EC iw EC e EC iw ECe EC iw EC e EC iw ECe EC iw

Cotton (Gossypium hirsutum) 7.7 5.1 9.6 6.4 13.0 8.4 17.0 12.0 27.0 18.0

Barley (Hordeum vulgare) 8.0 5.3 10 6.7 13.0 8.7 18.0 12.0 28.0 19.0

Sugarbeet (Beta vulgaris) 7.0 4.7 8.7 5.8 11.0 7.5 15.0 10.0 24.0 16.0

Wheat (Triticum aestivum) 6.0 4.0 7.4 4.9 9.5 6.3 13.0 8.7 20.0 13.0

Wheat,durum (T. turgidum ) 5.7 3.8 7.6 5.0 10.0 6.9 15.0 10.0 24.0 16.0

Soybean (Glycine max) 5.0 3.3 5.5 3.7 6.3 4.2 7.5 5.0 10.0 6.7

Corn (Zea mays) 1.7 1.1 2.5 1.7 3.8 2.5 5.9 3.9 10.0 6.7

Broadbean (Vicia faba) 1.5 1.1 2.6 1.8 4.2 2.0 6.8 4.5 12.0 8.0

Bean (Phaseolus vulgaris) 1.0 0.7 1.5 1.0 2.3 1.5 3.6 2.4 6.3 4.2

Cowpea (Vigna unguicultata) 4.9 3.3 5.7 3.8 7.0 4.7 9.1 c6.0 13 8.8

3.2.5 Suitability of saline waters for irrigation

The suitability of irrigation water is dependent upon the conditions of use, including

crop, climate, irrigation method and management practices. Therefore water quality

classifications are not advised for assessing water suitability for irrigation (Ayers and

Westcott, 1985). For the purpose of identifying levels of ECiw, a useful classification scheme

is shown in Table 3.2. Such a classification is given in terms of total salt concentration. This

is the major quality factor generally limiting the use of saline waters for crop production

(Rhoades et al., 1992)..

Table 3.2 Classification of saline waters (after Rhoades et al., 1992). Water class Electrical

conductivity of water (ECiw) dS/m

Salt concentration mg/l

Type of water

Non-saline <0.7 <500 Drinking and irrigation water

Slightly saline 0.7-2 500-1500 Irrigation water Moderately saline 2-10 1500-7000 Primary drainage water and

ground water Highly saline 10-25 7000-15000 Secondary drainage water

and groundwater Very highly saline 25-45 15000-35000 Very saline groundwater Brine >45 >35000 Seawater


25

Figure 3.1 shows the relationship between ECe and ECiw and the leaching fraction (LF)

required under conventional irrigation management. It is evident that the most tolerant crops

can be produced with waters that exceed about 10 dS/m (i.e. EC iw). Many drainage waters,

including shallow groundwaters underlying irrigated lands, fall in the range 1-10 dS/m in

ECiw. Such waters are in ample supply in many developed irrigated lands and have good

potential for selected crop production. Reuse of second-generation drainage waters for

irrigation is also sometimes possible and useful, especially for purposes of reducing drainage

volume is preparation for ultimate disposal or treatment. Such waters will generally have

ECiw in the range 10-25 dS/m (Rhoades et al., 1992).

Figure 3.1 Relationship between ECe (electrical conductivity of a saturation extract), ECiw (electrical

conductivity of water) and LF (leaching fraction) under conventional irrigation management.

M.F.AHMED

26

3.3 SALT-BALANCE MODELS

3.3.1 Introduction

As described in the previous section there are various factors, which can affect soil

condition and plant potential as a consequence of the soil and water. However, assessing

which parameters and which salts are of concern would require extensive and exhaustive

studies in each area of interest. The use of salt balance models, which incorporate much of the

existing information, is of some use in simulation studies, however.

Several models have been developed in recent years. The conceptual approach and

degree of complexity of these models vary widely. They are strongly influenced by the

environment, training and specific interests of their developers. There are two principal types

of leaching models; namely deterministic and stochastic models.

Deterministic models presume that a system or process operates such that the

occurrence of a given set of events leads to a uniquely definable outcome (Addiscott and

Wagenet, 1985). A mechanistic model is one sort of deterministic model, which is usually

based on rate parameters. These models incorporate the most fundamental mechanisms of the

process. For leaching, this implies the use of equations derived from Darcy’s law for water

flow and the expression of resulting solute transport as the combination of the mechanisms of

mass flow and diffusion-dispersion. Functional model is additional type of deterministic

model. These are based on capacity parameters, incorporating simplified treatments of solute

and water flow. Therefore these models require less input data and computer expertise for

their use (Addiscott and Wagenet, 1985) than the mechanistic models.

Stochastic models presuppose the outcome to be uncertain and are structured to

account for this uncertainty. Models in this category consider solute displacement in soil to be

the result of the physical process of convection, or mass flow, of water and the chemical

process of diffusion in response to a concentration gradient. Although there are many detailed

models of water and solute transport available, generally only the simpler and less

mechanistic are suitable for field application. As opposed to rate models that are driven by

time, capacity models are usually driven by the amounts of rainfall, evaporation, or irrigation

and consider time only indirectly by using, for example, annual amounts of irrigation. Hence,

a capacity model defines changes (rather than rates of changes) in amounts of water and

solute content. The main disadvantage of a capacity-type model is that the dynamic nature of

soil water movement tends to be oversimplified, leading to the inaccurate estimation of water

fluxes (Addiscott and Wagenet, 1985).


27

In the following section a description of each of the various models available is made

and the quantity of required input data, depth of consideration of basic processes, sensitivity

and accuracy of simulations are discussed.

3.3.2 Models

3.3.2.1 Leaching Requirement (LR) model

The removal of salt from the root zone to maintain the soil solution at a salinity level

compatible with a specific cropping system is commonly achieved by applying irrigation

water over and above the evapotranspiration needs of the crop. This excess water is referred

to as the leaching requirement (LR). It can be defined as the fraction of the irrigation water

that must be leached through the root zone to control soil salinity at any specified level (U.S.

Salinity Laboratory Staff, 1954). A LR model is a deterministic -functional model, which are

usually based on capacity parameters.

The LR will depend on the salt concentration of the irrigation water and on the

maximum concentration permissible in the soil solution. The maximum salt concentration,

except for salt crusts formed by surface evaporation, will occur at the bottom of the root zone

and will be the same as the concentration of drainage water from a soil where irrigation water

is applied with areal uniformity and with no excess leaching. An increase in the concentration

of salts from the values existing in the irrigation water to the value occurring in the drainage

water is related directly to water consumption (U.S. Salinity Laboratory Staff, 1954).

There are some assumptions that need to be considered for the application of this

model. These include uniform area application of irrigation water, no rainfall, no removal of

salt in the harvested crop and no precipitation of soluble constituents in the soil. The

calc ulation is also based on the assumption that there is a steady-state water flow rate or the

total depths of irrigation and drainage water used over a long period of time. With these

assumptions, moisture and salt storage in the soil, depth of root zone, cation-exchange

reactions, and drainage condition of the soil do not need to be considered provided that

drainage will permit the specified leaching (U.S. Salinity Laboratory Staff, 1954).

The LR as discussed above, is the ratio of the equivalent depth of the drainage water

(Ddw) to the depth of irrigation water (Diw) and may be expressed as a fraction or as per cent.

Under the assumed conditions, this ratio is equal to the inverse ratio of the corresponding

electrical conductivities, that is:

dw

iw

iw

dw

ECEC

DD

LR == (3.1)

M.F.AHMED

28

where, ECiw = electrical conductivity of irrigation water, ECdw = electrical conductivity of

drainage water. For an example: in the case of field crops where a value of ECdw = 8 dSm-1

can be tolerated, the equation would be LR = Ddw/Diw = ECiw/8. For irrigation waters with

conductivities of 1, 2 and 3 dS m-1, respectively, the leaching requirements will be 13, 25, and

38 per cent.

Some care must be exercised in using Eq. (3.1), to make sure that the condition of

steady state or long time average is understood. This is because it is based on the use of

irrigation water only. In cases where there is rainfall the conductivity value used for the

irrigation water must be changed. As an average over a long period of time, the conductivity

of the irrigation water used in Eq. (3.1) should be a weighted average of the conductivities of

the rain water (EC rw), and the irrigation water (EC iw), that is;

iwrw

iwiwrwrwiw)(rw DD

ECDECDEC

++

=+ (3.2)

where Drw and Diw are the depths, respectively, of the rainwater and irrigation water entering

the soil; and ECrw is the electrical conductivity of the rain water. The depth of irrigation water

(Diw) is related to consumptive use, (Dcw) and the equivalent depth of drainage water (Ddw)

by the equation:

dwcwiw DDD += (3.3)

Using Eq. (3.1) to eliminate Ddw from Eq. (3.3) gives

( )LR1/DD cwiw −= (3.4)

Expressing the LR in this equation in terms of the conductivity ratio in Eq. (3.1) gives:

cwiwdw

dwiw D

ECECEC

D

−

= (3.5)

The depth of irrigation water (Diw) is thus expressed in terms of the electrical

conductivity of the irrigation water and other conditions determined by crop and climate;

namely, consumptive use and salt tolerance of the crop.

An important advantage of the LR model is that it requires very modest input data to

calculate leaching flux at the bottom of the root zone. Because of its simplicity, it is widely

used (Frenkel 1984; Oster, 1984). Some models, including the SaLF, have been developed on

the basis of the application of this model.


29

3.3.2.2 Solute Dynamics in Irrigated Clay Soils (SODICS)

Another deterministic -mechanistic model is the Solute Dynamics in Irrigated Clay

Soils (SODICS) model, which considers simple physical aspects of transport connection and

restricts consideration to one-dimensional situations. Rose et al., (1979) developed this model.

It is a transient mass balance model usually based on a rate parameter. That is, if soil salinity

level has not reached steady state, the SODICS model is applicable. With this model, the

change in mass storage of solute (e.g. chloride) within a soil profile is related to the difference

in the mass flux of solute applied to, and leaving the soil profile.

In explaining the SODICS models, Thorburn et al. (1990) expressed the LR model in

terms of a non-sorbed solute that undergoes no chemical transformation and negligible plant

uptake (e.g. chloride) as

zLsIC i − (3.6)

where, I = average infiltration rate (irrigation + rain) (mm y-1); L = leaching flux (mm y-1),

past some soil depth z (mm); Ci = solute concentration of infiltrating water (mmolc l-1); and, sz

= solute concentration (mmolc l-1) of soil water, at depth z, at the moisture content at which

leaching occurs.

The change in mass storage of solutes, such as chloride, in a soil profile can be related

to the difference in mass flux of solute applied to and leaving the profile. The model can be

expressed differentially as

zLsIC/dtsdz? it −= (3.7)

where =s average value of sz to depth z; =t time (y); and, θ t the moisture content at which

leaching occurs (m3 m-3)

By defining a non-dimensional parameter (P) as

s/zsP = (3.8)

and substituting P s for sz in Eq. 3.7, the value of s at any time ( ts ) is given by

( ) ( )[ ] ( )[ ]z/texps/ss tt θ−−−+= LP1LPIC 0i0 (3.9)

where s o is the value of s at 0=t

Provided I, Ci, L and P remain constant Eq. (3.9) can be used to calculate the time

course of s . There are several general forms this relationship can take. In one form, if L 0≤ ,

as would occur if solute moved upward into the root-zone from a water table, s t increases

M.F.AHMED

30

continually with time, unless downward diffusion of solute equals or exceeds the upward

solute flux. In a second form, if L ,0> s t approaches a final steady-state value (s f )

asymptotically. s f may be either greater or less than ,0s depending on the relativity between

the mass flux of solute applied to and leaving the profile. The value of s f is given by solving

Eqs. (3.7) and (3.8) for 0/ =dtsd , that is,

LPICs if /= (3.10)

To use this equation for estimating deep drainage and soil salinity in the root zone, soil

parameters like chloride (mg kg-1) of 1:5 soil:water suspension, air dry moisture content and

1500 kPa moisture content are needed. In addition to parameters for the water variables,

average depth of irrigation water (mm y-1), average depth of rain water (mm y-1), solute

concentration of irrigation water ( wC , mmolc l-1), solute concentration of rain water

( rC mmolc l-1) and the number of irrigation years are required.

This transient model is preferred to the steady-state LR model for assessing the effect

of irrigation on soil salinity, because of its ability to provid e predictions of future soil chloride

levels under non-steady conditions, and where leaching flux values are ≤ 0. Although this is a

reliable method for estimating deep drainage and salinity, this model does have some

drawbacks. The solute is restricted to one-dimensional transport and assumes no interaction

between salinity and soil. Another assumption is that there are no chemical transformations,

which could invalidate simple mass conservation and transport data. This model needs much

more data than the LR model. In addition, analysis of chloride is more difficult than

determination of EC.

3.3.2.3 Salinity and Leaching Fraction (SaLF) model

Shaw and Thorburn (1985) developed a functional deterministic model named Salinity

and Leaching Fraction (SaLF). This empirical model is based on data from 766 soils,

collected from a wide range of soils in Queensland, Australia. The profiles were dominantly

Vertisols and Alfisols with smaller occurrences of Ultisol, Inceptisols, Ardisols, Oxisols,

Mollisols and Entisols (Soil Survey Staff, 1975). It is based on the assumption that soil

leaching or deep drainage is related to hydraulic conductivity, which in turn is influenced by

the per cent clay, clay mineralogy (Effective Cation Exchange Capacity/Clay per cent) and

exchangeable sodium percentage (ESP). This model is usually based on the capacity

parameter. It can be used for predicting soil salinity levels for a wide range of crops including

cotton and wheat.


31

The basis of the SaLF model is the prediction of the steady-state leaching fraction

(LF) and deep drainage (DD) from the above mentioned soil properties, together with rainfall

and plant water use estimates. To account for irrigation events, the LF is initially predicted

for the non-irrigated situation and then adjusted for the effect of increased salinity and

sodicity of the irrigation water. A salinity correction term (ECrain + irrigation / ECrain) is used to

account for the flocculation effects of the increased salinity of the irrigation waters.

Essentially, the model predicts the electrical conductivity at maximum field water content

(ECs) at the bottom of the root-zone (taken to be 0.9m) and subsequently calculates the

leaching fraction based on the steady-state relationship given by Eq. (3.1).

The 0.9m depth was selected after examining a wide range of soil profiles where, for

the majority of soils, salt concentration had essentially reached equilibrium by a depth of

0.9m. Thus this depth was considered to be below the seasonally cyclical depth of wetting

and drying for most situations. As an average over a long time, the conductivity of the

irrigation water used in Eq. 3.3 should be a weighted average for the conductivities of the

rainwater (ECrw), and the irrigation water (EC iw). The weighted average will be similar to Eq.

(3.2). A basic assumption of the SaLF model is that salt concentration of the drainage water

(ECdw) is in equilibrium with, and hence equal to electrical conductivity at maximum field

moisture content (EC s) (Shaw and Thorburn, 1985). Indeed, soil properties and water

parameters were found to be highly significant in explaining 70 to 80 per cent of the variation

in salt concentration in the soil at 0.9m (Shaw, 1988).

In developing the model, the data of all 766 soil profiles were stratified into groups

based on clay content and mineralogy, to account for the complexity of interactions between

soil properties. The data within each soil group were regressed as a simple linear regression in

the form of Eq. (3.11), with ESP being the dominant soil attribute influencing drainage.

Varying the exponent of ESP linearized the variable influence of ESP on leaching, so that:

ba +

==C

rw

s

rwr ESP

DECEC

LF (3.11)

where LF r is the predicted leaching fraction under rainfall, a and b are regression coefficients,

Drw is the annual rainfall, and c is an exponent to linearize the effect of ESP (Shaw and

Thorburn, 1985). A leaching fraction under irrigation (LF iw) can then be calculated by

substituting (Diw + rw) for Drw in Eq. (3.11). As previously mentioned, a salinity correction

term (EC rw+iw/ECrw) is used to make an adjustment to LF iw, since a change in electrolyte

concentration (due to the addition of irrigation water) will result in a change in leaching for a

given soil ESP. Such an adjustment to LF iw is represented by:

M.F.AHMED

32

= +

rw

rwiwrwiw EC

ECLFLF (3.12)

where ECiw+rw is the weighted EC of input water for irrigation (i) and rainfall (r).

Shaw and Thorburn (1985) also related predicted deep drainage to measured ponded

infiltration rate (IRp). The result of this relationship is shown below in Figure 3.1 (r2 = 0.73).

Such a result indicates a satisfactory prediction from the SaLF model.

Figure 3.2 Relationship between deep drainage (DD) predicted for annual rainfall of 800 mm from the model of Shaw and Thorburn (1985) and ponded infiltration rate (IRp) for a range of soils in north eastern Australia.

Therefore, by considering a wide range of soil types under a variety of rainfall

regimes, the influence of soil properties on salt leaching under varying inputs has been

empirically quantified. Indeed, Shaw and Thorburn (1985) found the SaLF model provided

good predictions of leaching fraction (and hence deep drainage) for two irrigation regions

with widely differing soil properties. In addition, the model also showed that soils with high

clay content (over 55 per cent) and high CEC/clay ratios can be quite permeable at low ESP

values and thus can be used for irrigation with quite saline irrigation waters.

Basic assumptions used in developing this model (Shaw and Thorburn, 1985) were (a)

within a given management system (ECiwDiw) such as natural rainfall, ECdw can be related to

Ddw which, in turn, is a function of soil properties influencing soil hydraulic conductivity; (b)

the soil salinity-ESP relationships at the bottom of the root zone for dry land soils are in


33

equilibrium with rainfall and thus initial variations due to parent material salt composition and

content are no longer significant; (c) there is no significant contribution to salinity from

mineral weathering; (d) short-term variations in salt content of rainfall are insignificant in

determining the equilibrium soil salinity and, (e) the salt content of the drainage water (ECdw)

is in equilibrium with, and hence equal to ECs which can be related to soil properties

influencing Ddw.

To use the program based on this model, required soil parameters are cation exchange

capacity (CEC cmol+/kg), per cent clay and exchangeable sodium (cmol+/kg) at rooting

depth for a particular crop. Water parameters required are depth (mm) and EC (dS/m) of

irrigation water and rainfall.

The advantages of this model are (a) the model is applicable for dryland soils under

rainfall as well as irrigated conditions; (b) it needs easily measured soil properties related to

hydraulic conductivity; (c) the model predicts the electrical conductivity at maximum field

water content (ECs) at the bottom of the root zone from which leaching fraction is calculated;

and (d) this model was derived using data from semiarid areas which are dominantly neutral

to alkaline in pH, and is applicable in similar areas.

Some disadvantages of this model are (a) steady state conditions apply and the use of

annual rainfall will result in average values; (b) the covering range of rainfall values for the

relationship is 200-2000mm and outside this range (or in some cases close to these figures)

derived values will probably be in error; (c) electrical conductivity rather than chloride

content is used as the sum of all salts present which has an effect on soil permeability; and (d)

because the SaLF model has been derived mainly on soils in semi arid areas, which are

dominantly neutral to alkaline in pH, it is unlikely to give good results for other soil types. It

is suggested that this model can be applied in irrigated field which is equilibrium due to long

time irrigation.

3.4 GEOSTATISTICAL METHODS

3.4.1 Introduction

Geostatistics is a rapidly evolving branch of applied mathematics, which originated in

the mining industry in the early fifties to help improve ore reserve calculation. The first steps

were taken in South Africa, with the work of mining engineer DG Krige and the statistician

HS Sichel. Though this technique has developed to estimate ore reserves, it has spread into

other areas of the earth sciences with the advent of high-speed computers. Nowadays it is

M.F.AHMED

34

used in many fields of science where there is a need for evaluating spatially or temporally

correlated data.

The innovative concepts of Krige were set in a single framework with the theory of

regionalized variables (Matheron, 1965). The properties, which are used to estimate the

variation of a random variable in space, are known as regionalized variables. Recent

developments of statistical theory based on regionalized variable theory enable spatial

relationships among sample values to be quantified and used for interpolation of values at

unsampled locations. Regionalized variables provide the statistical tools for describing

variation over the earth’s surface, for estimating its attributes precisely, and for designing

efficient sampling schemes.

The theory of regionalized variables (Matheron, 1965) has been applied successfully

in soil studies. This includes kriging and its various forms ordinary, simple, lognormal and

disjunctive kriging (McBratney et al., 1981; McBratney and Webster, 1981; Burgess and

Webster, 1980a and b; Trangmar et al., 1985), cokriging (McBratney and Webster, 1983;

Yates and Warrick, 1987), universal kriging (Webster and Burgess, 1980) and regression

kriging (Knotters et al., 1995).

Kriging is an interpolation technique which uses the semivariogram to estimate data at

locations where there is no measured data (Mulla, 1989). Kriging and co-kriging utilize

sample semivariogram and co-semivariogram, respectively (in preference to auto-correlogram

and co-correlograms) for optimal interpolation of regionalized variables. Both of these

methods are considered to be unbiased in the sense that they give exact interpolation. This

means that the estimated value is identical to the observed value when a kriged or cokriged

site coincides with an observed site. The main disadvantage of kriging is that it requires large

data sets.

3.4.2 Variography and the intrinsic hypothesis

Geostatistical studies are almost always based on the variogram model. It is the key to

describing variation quantitatively and to understanding and prediction of soil properties. The

traditional variogram estimator is very sensitive to trends in the data; consequently simple

variograms may be either misleading or useless. To have an accurate model of the underlying

spatial continuity it is necessary to examine many sample variograms in different directions

throughout two or three-dimensional space.

A soil property can be related to itself in the sense that values at different places are

related to one another. This property is then said to be autocorrelated or spatially dependent

(Webster and Oliver, 1990). The only practicable approach is to regard a property as a random


35

variable and to treat its variation in space statistically. Considering two sites some distance

apart at which a property, Z, has the values z1 and z2 the relationship between the two values

can be defined by different means, such as algebraic difference or absolute value, or

variances. Using the variance equation,

( ) ( )22

21

2 zzzzs −+−= (3.13)

( )2212

1 zz −= (3.14)

where z is the mean of 1z and 2z

Considering the location of two sites x and x+h, where x denotes the coordinates of a

site in one or two or three dimensions, and h is a vector, known as the lag which denotes both

distance and direction then equation (3.13) converts to

2212 )()( hxzxzs +−= (3.15)

If z has been measured at numerous places in a region and there are n pairs of sites

separated by the vector h , the average semi-variance at the lag h can be calculated from

2

1212 )()(∑

=

+−=n

iiin hxzxzs (3.16)

This generally shows that the soil variability increases with increasing separation

distance. The actual value of a property at any place is regarded as one of the many values

that might have been generated by some random process; i.e., it is a random variable z(x) with

an actual value z(x) at x. This formality assigns an expectation to Z at x. With this there are

some assumptions (Webster and Oliver, 1990). First, the expected value of the variable,

[ ] µ=Ε )(xz , (3.17)

is constant and does not depend on the position x. It is said to be stationary in the mean, when

it holds the regionalized variable. Second, the expected squared difference between values at

places separated by the lag h is finite and depends only on h and not on x., i.e., the variance of

the difference is stationary for any given lag.

[ ] )(2)()( 2 hhxzxzE γ=+− (3.18)

The quantity )(hγ is the expectation of 2s at h . The term )(hγ is known as the semi-

variance. It is the variance per site when sites are considered in pairs (Yates 1948). As above,

γ depends on h, and the function relating the two is known as the semi-variogram, or

M.F.AHMED

36

increasingly as just the variogram (Webster and Oliver, 1990). These assumptions are said to

constitute the intrinsic hypothesis (Journel and Huijbregts, 1978). McBratney and Webster

(1986) show examples of how to choose and fit functions for variograms.

3.4.3 Linear kriging

Within the last two decades, geostatistical methods, such as kriging, have been

introduced into soil science to provide best linear unbiased estimators (BLUE) at unsampled

locations (Burgess and Webster, 1980a; Odeh et al., 1995). The difference between

geostatistics and classical statistics is that the former allows for the direct modeling of the

inherent spatial data correlation. This is achieved through the initial calculation of a

variogram, which acts as a quantified summary of all the available structural information of

one or more random functions (Journel and Huijbregts, 1978). Various forms exist and

include ordinary kriging (OK), cokriging (CoK) and regression kriging (RK) to name a few.

With respect to soil salinity assessment and in comparing classical statistical and

geostatistical methods, Hajrasuliha et al. (1980) found that the spatial dependent nature of the

variance structure in some fields allowed geostatistical techniques to produce better

predictions of soil salinity. Gallichand et al. (1992) produced similar results when comparing

geostatistical methods with moving and weighted moving average techniques. The results

suggested that not only were the resultant kriged maps more accurate but more readily

interpretable.

Russo (1984) used a geostatistical approach to investigate the spatial variability of

several soil properties (i.e. saturated hydraulic conductivity, soil characteristic and initial soil

salinity (EC0). These properties were used as input parameters into a simplified water and salt

flow model. The results were coupled with the conditional simulation method to analyze the

salinity profile and its spatial distribution during leaching in a 187-ha plot land in the Arava

valley of Israel. Results showed that in theory 107 hours of continuous leaching (6,527 m3/ha)

would be required to obtain an average salinity of EC e = 5 dS/m for the field layer between

the soil surface and 40 cm depth. By considering the leaching of the different sites in the field,

rather than that of the entire field, the amount of water for leaching required to obtain ECe = 5

dS/m uniformly across the field could be reduced to 4,038 m/ha (a reduction of 38 per cent).

Lesch et al. (1992) mapped spatial variation of soil salinity on the field scale using

geostatistical approaches. However, where there is a lack of spatial auto-correlation, a

statistical calibration technique based on multiple-linear regression (MLR) for predicting

multiple depth, field scale spatial distribution of soil ECa, is more appropriate (Lesch et al.,

1995a). In their study of fields in Hanford area and the Westland Water District in California,


37

OK was used to predict soil salinity at the field scale using results obtained from a MLR

model. The approach was compared with CoK and was found to be cost-effective, allowed

multiple prediction capabilities and significantly reduced the primary attribute (ECe) sample

size necessary. However, the selection of the sample sites need to be selected carefully in

order to ensure the collection of calibration data that can be used to effectively identify and

estimate an appropriate MLR model.

Utset et al. (1998) carried out a calibration of the four-electrode probe in order to

assess its use as an inexpensive and indirect determination of soil salinity in a plot at Cauto

valley, Cuba. Two transects were made in the north-south and east-west directions.

Laboratory measurements of soil electrical conductivity of a 1 part soil to five parts water

extract (EC 1:5) were made from samples taken on a 50-m spaced square grid. A linear

semivariogram was obtained for the four-electrode probe measurements (EC a) along the east-

west transect. The results agreed with the topographical perceptions of the plot and with the

expected EC1:5 variation. It also coincided with the spatial structure of laboratory-measured

soil EC1:5. A cross-validation analysis showed that ECa semivariograms characterised the

spatial variation of EC in a similar way as semivariograms of laboratory-measured EC1:5.

They concluded that the distance between samples for EC 1:5 maps can be based on the

semivariogram’s range of EC a measurements.

Pozdnyakova and Zhang (1999) compared geostatistical methods such as OK and CoK

to estimate sodium adsorption ratio (SAR) in a 3,375 ha area of irrigated farmland within the

South-Fork Kings River Watershed in central California. The ratio of Na+ to the square root

of half the sum of Ca2+ plus Mg2+ is defined as the sodium adsorption ratio (SAR). In CoK,

the more easily measured ECa data, as measured with the four-electrode technique (Rhaodes

et al., 1990), was incorporated to improve the estimation of SAR. The estimated spatial

distributions of SAR using the geostatistical methods with various reduced data sets were

compared with the extensive salinity measurements in the large field. The results suggested

that sampling costs could be dramatically reduced and estimation can be significantly

improved using CoK. Compared with the kriging results using total SAR data alone, CoK

with reduced data sets of SAR improved the estimations greatly by reducing mean squared

error and kriging variance up to 70% and increasing correlation of estimates and

measurements about 60 per cent. They found that the sampling costs for SAR estimation

could be reduced by approximately 80 per cent using extensive ECa data together with a small

portion of SAR data using CoK. Contour maps produced with varying amounts of input data

and methods of interpolation are shown in Figure 3.3.

M.F.AHMED

38

Souza et al. (2000) examined the spatial variability of pH of saturation paste (pHe),

ECe and the exchangeable sodium percentage (ESP) in an alluvial salt affected soil in an area

of the Irrigation Project of Capoeira, Sao Jose do Bonfim – Paraiba, Brazil. Low variability

was observed for pHe (CV<12%) in contrast to EC e and ESP (CV>60%). Spherical and

gaussian models were adjusted to the experimental semivariograms of the variables that

presented a spatial dependence structure. Structures of spatial dependence, with range varying

from 20 to 40 m were observed. Maps of contours of the combination of ECe and ESP

illustrated the variation in salinity and sodicity, constituting a tool for the definition of soil

reclamation of the area affected and hence for improved natural resource management.

Agrawal et al. (1995) described the spatial variability of soil salinity and hydraulic

conductivity at a site in Haryana, India using geostatistical approaches. Measurements were

taken at distances of 50, 150 and 200 m along the x-axis and at intervals of 12.5 m for the first

300 m and then at intervals of 25 m from 300 to 375 m along the y-axis. Variograms and

autocorrelograms were used to identify the degree of dependency of EC e between pairs of

measurements. Cross-semivariograms yielded better structured spatial variability than

semivariograms for all three years (1984, 1987 and 1990) of observation. Mapping of ECe and

hydraulic conductivity showed the effect of drain spacing with the varied range of hydraulic

conductivity and soil texture in the region.

Figure 3.3 Contour maps of SAR estimated by a) kriging with 898 SAR data, b) cokriging with 200 SAR and 898 in-situ ECa data, c) kriging with 200 SAR data, and d) cokriging with 100 SAR and 898 in-situ ECa data (after Pozdnyakova and Zhang, 1999).


39

3.4.4. Non-linear kriging

Nonlinear kriging or kriging is also of use in soil science. Non-linear kriging

algorithms are linear approaches that are applied to non-linear transforms of the original data.

Transformation to normality prior to geostatistical analysis results in a nonlinear function of

the original data, so that kriging estimate may not be made with minimum estimation variance

and without bias. There are different types of non-linear kriging such as: lognormal kriging

or kriging applied to logarithms of the data, multiGussian kriging or kriging applied to normal

score transforms; indicator kriging or kriging of indicator transforms; and disjunctive kriging,

or kriging of specific polynomial transforms (Deutsch and Journel, 1992).

3.4.4.1 Disjunctive kriging

Disjunctive kriging (DK) is a nonlinear estimation technique that allows the

conditional probability that the value of a spatially variable management parameter is greater

than a cut-off level to be calculated (Yates and Yates, 1988). DK can be defined as a

specialized form of kriging which differs from OK by requiring consideration of the statistical

distribution of the data (Trangmar et al., 1985). To use this technique, two input parameters

are required which are (a) the critical level at which the variable becomes a threat; in fact,

which is the cut-off level and (b) the critical probability level.

DK therefore is useful in making management decisions to help determine when some

reclamation is necessary (Yates and Yates, 1988). DK is a better estimator than linear

estimators (i.e., OK) in the sense of reduced variance of errors and average kriging variance

even when the data are approximately normally or lognormally distributed, and an estimation

of conditional probability (that the unknown value is greater than a specified cutoff value) can

be achieved using this method (Yates et al, 1986a).

Yates et al. (1986a) reviewed the theoretical basis of the DK method as it relates to

estimating the value of a random function at an unsampled location and obtaining the

conditional probability that the value of a random function at a point will be above a given

cutoff level. Yates et al. (1986b) compared DK and OK with respect to the accuracy of the

estimators and the computational requirements. The variable of interest was ECe. The results

indicated that DK was a better estimator than OK in the sense of reduced variance of errors

and average kriging variance even when the data are approximately normally or lognormally

distributed.

Yates (1986) presented an example using disjunctive cokriging (DCK) and ordinary

cokriging (OCK) estimators. In their analysis the surface gravimetric moisture content was

M.F.AHMED

40

estimated using the bare soil temperature as an auxiliary random function. The results

indicated that the DCK procedure produced a better estimator than OCK in terms of reduced

variance of errors and exactness of estimation. The DCK method had an average kriging

variance, which was 7 per cent lower than for OCK.

Yates and Yates (1988), applied DK as a decision making tool for the regulation of

septic tank setback distance as a means for minimizing the contamination of ground water by

viruses. In their experiment two examples were described: given a setback distance, the

spatial distribution of the conditional probability that the virus concentration will be greater

than acceptable levels was calculated; for a given critical probability level, the spatial

distribution of setback distances which satisfy that probability level was calculated. The study

showed that with 90 per cent confidence the virus concentrations would be within acceptable

limits. In many areas the setback distance would have to be several fold higher than

prescribed by the regulations applying when the experiment was carried out.

Wood et al. (1990) applied the technique of the DK to estimate and map ECe of soil in

the Bet Shean Valley of Israel. This was done at two separate times. The estimated ECe of the

soil exceeded 4 dS/m throughout most of the region, and in only a small area was the

probability of salinity less than 0.2 in November 1985 (Figure 3.4a). By March 1986, the ECe

had declined everywhere to less than 4 dS/m, and the conditional probability of exceeding this

value nowhere exceeded 0.25 (Figure 3.4b). The results suggested that winter wheat was

likely to germinate poorly in the saltier parts of the region though its later growth should not

be seriously impaired, and that Lucerne (alfalfa, Medicago sativa ) which is more susceptible

to salinity was unlikely to achieve its maximum yield in the area. In addition, the results

suggested that cotton, a salt-tolerant summer crop sown in spring, could grow successfully

without suffering from salinity.

Webster and Oliver (1989) applied DK for pH, exchangeable potassium and available

phosphorus which were mapped over 77 ha of the Boom’s Bran Farm and in the eastern

Border Region of Scotland the available copper and cobalt in the top soil were mapped. The

conditional probabilities that copper was less than 1.0 mg kg-1 and cobalt was less than 0.25

mg/kg were estimated simultaneously over the same grid and contoured. The map of copper

concentration showed that over most of the region there is ample copper in the topsoil. The

probability that the cobalt concentration is less than the critical value of 0.25mg kg-1 is

appreciable over most of the region. The map they prepared using DK for the distributions of

the pH and nutrient elements appeared very similar to those obtained previously (Webster and

McBratney, 1987; McBratney et al., 1982) using OK.


41

Figure 3.4 Maps of the conditional probability that ECe 4≥ dS/m (a) in November 1985 and (b) in March 1986 (after Wood et al., 1990).

3.4.4.2 Indicator kriging (IK)

Isaaks and Srivastava (1989) state that the OK of indicators at several cutoffs, using a

separate variogram model for each cutoff, is usually referred to simply as indicator kriging

(IK). That is IK indicates the presence or absence of a particular attribute of interest. The IK

approach can therefore be used for predicting categorical soil data (Bierkens and Burrough,

1993a). The approximation to IK consists of using the same variogram model for the

estimation at all cutoffs. The variogram model chosen for all cutoffs is most commonly

developed from the indicator data at a cutoff close to the median.

Istok and Rautman (1996), applied IK to define the spatial extent and severity of

nitrate and Dacthal (dimethyl tetrachloroterephthalate or DCPA, a herbicide) contamination in

the unsaturated and saturated zones for a 150 km2 site near Ontario, Oregon. The results

demonstrated that interpretations of site characterization data to determine the extent and

magnitude of contamination at a site vary depending upon the level of uncertainty that will be

tolerated by the decision maker.

M.F.AHMED

42

Bierkens and Burrough (1993b) used IK to map water table classes. This method

performs well when validated on an independent data set and appears robust with regard to

the number of observations. There was little difference between the original map of water

table classes that was available from the Soil Survey Institute and the map produced by this

method. Sequential indicator simulation was used to generate equi-probable maps of waters of

water table classes, which were then used in a land use suitability analysis for pasture. A

measure for the errors in this analysis that arise from impurities in the map of water table

classes was obtained.

Simple IK with varying means and Markov-Bayes algorithm were used to evaluate

probabilities for copper and cobalt deficiencies in the Borders Region of Scotland (Goovaerts

and Journel, 1995). Results were compared with maps obtained by the polygonal method

(Thiessen polygons) and an algorithm that does not use soil map information. The best results

were obtained with the more elaborate indicator cokriging (ICK) algorithm, with the simpler

co-located CoK algorithm being a close second. Markov approximation, however, was shown

to be inappropriate in the study and it is likely to be so when the soft information consists of

categorical variables that are more continuous in space than the measurements themselves.

Investigations were made of the utility of three interpolation techniques that ignored

descriptive ‘soft’ information and one that used it for mapping topsoil texture classes

(Oberthur et al., 1999). Thiessen polygons, and classification of probability vectors were

estimated by ordinary IK and simple IK with local prior means. The results were compared

with texture maps based on a classification of kriged maps of particle size distribution. It was

found that IK was technically feasible for mapping categorical data at regional scales in rain-

fed and irrigated environments and in particular was better than simpler interpolation

techniques where change is gradual. In heterogeneous environments such as Northeast

Thailand simple interpolation was almost as good as the more complex IK (Oberthur et al.,

1999).

Triantafilis (1996) used IK to describe the spatial distribution of a subsoil saline layer

in the lower Namoi valley of northern New South Wales. As a result the origin, nature and

spatial distribution of the salts in the profiles was determined. The reason for the

accumulation in the irrigated fields studied was not as a consequence of the application of

salts from irrigation waters, but occurred because the area acts as a local drainage basin. Over

a prolonged period of time the nearby Galathera Creek had dispensed sediments and salts in

the area now developed for irrigated cotton production. Owing to the low gradients, the lack

of size of the upper catchment and the lack of energy of the flowing waters, the salts are


43

deposited because of insufficient through flow of waters. Figure 3.5 shows the IK maps of

soil ECe exceeding 5 dS/m at depths of 0.9-1.2, 1.2-1.5 and 1.8-2.1 m.

Figure 3.5 IK maps showing the conditional probability of finding a saline subsoil layer with soil ECe

value exceeding 5 dS/m in the lower Namoi valley (after Triantafilis, 1996).

Multiple Indicator Kriging (MIK ) is similar to IK in that the data used has a value of 0

or 1: depending on whether the attribute is greater or less than one or more thresholds

(Markus, 2000). The difference is that the n data points are sorted in order of increasing

magnitude and discretized as many as 100 classes by threshold values Uk, where k = 1,…,100.

For every threshold the data are then transformed into values of 1 and 0 according to whether

they are greater or less than the threshold concentration. Markus (2000) used MIK for

predicting Pb ‘contaminated’ or ‘cleaned’ sites in Glebe and Camperdown in Sydney,

Australia. A comparison was made with the method known as the cumulative distribution

function (CDF) of order statistics. The results identified MIK produces the most accurate

predictions for delineating ‘clean’ soil from ‘contaminated’soil.

M.F.AHMED

44

3.5 ELECTROMAGNETIC INDUCTION

3.5.1 Introduction

Despite the adoption of geostatistical techniques into soil science, the problem still

remains the collection and analysis of an adequate number of soil samples across the various

levels of interest to account for the spatial variability of the property of interest. At the field

level this information is required in order to determine optimal management strategies to be

developed. This is similarly the case at the farm and district levels where information is

required for improved natural resource management by farmers and agency staff.

In Australia this is becoming increasingly important with respect to describing the

spatial distribution of soil salinity. The most commonly used approach is the measurement of

soil electrical conductivity (EC) of a suspension of a 1 part mass equivalent of oven-dry soil

to 5 parts mass of deionised water (i.e. EC1:5). However, the generally accepted standard

measure of soil salinity is the EC of a saturated soil paste extract (EC e). This approach is far

more time consuming and requires a paste to be prepared and left to stand over night, before

the solution is extracted for measurement. EC1:5, requires less soil and requires the sample to

be spun for 30 minutes before being left to stand for a similar period of time prior to

measurement.

In the latter part of the 20th century, electromagnetic (EM) measurements have proved

very useful as a surrogate measure of EC1:5 and ECe, because they are closely related to soil

salinity. EM instruments measure what is termed the bulk apparent electrical conductivity

(ECa), since they are influenced by the EC of the field soil water as well as other factors

(Corwin and Rhoades, 1982). Their increasing use is due to their robust construction of the

instrumentation, its compact design, ease of use and, most significantly, the non-contacting

nature of the measurement (Triantafilis, 1996).

The theory of operation is described be McNeill (1980). Briefly, the instruments

consist of a transmitting and receiving set of induction coils located at either end of the

device. An alternating current is used to energise the transmitter coil. This generates a

magnetic field that induces eddy currents in the soil. The receiver coil, measures both the

primary and secondary (significantly smaller) field strengths, that at low induction numbers is

proportional to the current flow and hence the apparent ECa (McNeill, 1986). Several

commercial models are available and include the GeonicsTM EM38 and EM31. Other

instruments include the EM34-3, which consists of two independent coils, and the EM39

which is used in down-hole investigations.


45

3.5.2 Root zone assessment

The EM38 has an intercoil spacing of 1 m and operates at a frequency of 13.2 kHz. It

was designed to measure ECa within the agriculturally significant part of the root zone and

has a theoretical depth of penetration of 1.0 and 2.0 m, depending on whether the instrument

is held in the horizontal or vertical mode of operation, respectively (McNeill, 1980).

Application of the EM38 for soil salinity assessment on the field scale include: Cameron et al.

(1981); Rhoades et al. (1989); McKenzie et al. (1989); Slavich and Petterson (1990); and,

Lesch et al. (1992). The EM38 has also been used to estimate leaching rate at the field level

from a chloride mass balance model (Slavich and Yang, 1990). Figure 3.6 shows the EM38

being used in a small plot and in the horizontal mode of operation.

The success of these studies depended on the establishment of calibration equations

relating soil ECe, EC1:5 or ECa as measured to EM measurements. Corwin and Rhoades

(1990) found that the prediction of EC a using EM38 induction techniques was reasonably

accurate particularly within the top 0-30 cm and 0-60 cm of soil which are the most relevant

depths to the production of field crops. The values of ECa within these depths predicted from

the EM38 gave reasonable estimates that provided more meaningful information with which

to interpret soil salinity within the plant root zone than the EM values themselves.

Figure 3.6 EM38 instrument.

Various other approaches have been suggested and include multiple regression

coefficients (Rhoades and Corwin, 1981); simple depth weighted coefficients (Wollenhaupt et

al., 1986); established coefficients for normal salinity (Corwin and Rhoades, 1982) and

inverted salinity profiles (Corwin and Rhoades, 1984); modelled coefficients (Slavich, 1990);

and mathematical coefficients (Cook and Walker, 1992).

In order to determine an optimal method of calibration for the irrigated cotton growing

soil of the Edgeroi district of the lower Namoi valley Triantafilis (1996) and Triantafilis et al.

(2000) compared various calibration approaches. This included the modelled coefficient

M.F.AHMED

46

approach developed by Slavich (1990), the established coefficients approach of Corwin and

Rhoades (1984), a linear regression approach and a logistic model. They found the fitted

salinity profiles were locally erratic, although global trends could be recognized. The simple

linear regression on the data was also unsatisfactory, and in fact, was not different from the

established-coefficients method on the data.

Figure 3.7 Four-probe electrode.

As a result an alternative and more rigorous approach was used in which a logistic

curve is fitted to each calibration hole. This mixed random and fixed effects nonlinear model

predicts ECe from measurements generated by an electromagnetic instrument (EM38) held in

the horizontal mode of operation. The logistic profile model fitted the data well, with no

obvious patterns in the residuals, and depended on far fewer parameters than the two

alternative methods. In addition, unlike the established-coefficient approach, the logistic

model provided meaningful prediction errors.

In terms of mapping, the EM38 has been used to provide information on the nature,

origin and spatial distribution of soil salinity at the field level (e.g. Boivin et al., 1989 and,

Lesch et al., 1995b). Cameron et al. (1981) and van der Lelij (1983) used an EM38 to

describe the spatial variability of salinity at the field scale, producing choropleth maps

showing areas of low, medium and high average salinity. Lesch et al. (1992) mapped spatial

variation of soil salinity on the field scale using geostatistical approaches. Most recently and

in order to map the spatial distribution of ECe in the lower Namoi valley, Triantafilis et al.

(2001a) undertook a comparative study to compare a number of kriging methods including;

OK, CoK, RK and three-dimensional kriging (3DK). The results of spatial prediction suggest

that regardless of what method was used, reasonable estimates of soil ECe were achievable.

Vaughan et al. (1995) similarly combined a geostatistical analysis of ECe and ECa in

an agricultural area in the San Joaquin Valley, California, USA. Their study involved

measurements of ECe and water content of soil samples supplemented by surface


47

measurements of EC a as measured by an EM38 in the horizontal mode of operation.

Prediction of soil salinity at unsampled points by CoK loge(ECe) and ECa was found to be

worthwhile because the EM38 measurements were easier to take as compared with soil

sampling and laboratory analysis for determination of ECe.

3.5.3 Sub-soil assessment

The EM31 has an intercoil spacing of 3.7 m and operates at a frequency of 9.8 kHz.

These specifications yield an effective depth of exploration of approximately 6 and 3 m when

the instrument is positioned 1 m above the ground surface and in the vertical and horizontal

modes of operation, respectively (McNeill, 1980). The EM31 unit is responsive to the ECa of

earth material below the root-zone depth. About 50 per cent of its reading is derived from

material below 2.75 m. The unit is therefore useful for saline seep diagnosis (McNeill, 1986).

Figure 3.8 shows an EM31 mounted on a small trolley, which is towed behind an all terrain

vehicle and allows more rapid data collection.

Owing to its depth of measurement the EM31 has been used sparingly in topsoil

investigations. However, McBride et al. (1990) used the device to determine forest soil

quality in Ontario, Canada. They found using multiple stepwise regression that variations in

exchangeable Ca and Kjeldahl N concentrations accounted for a large proportion of the EM31

instruments response (r2 = 0.87).

Figure 3.8 EM31 instrument.

With respect to soil salinity investigations, good correlations were obtained between

the apparent EC a measured by the EM31 and ECe or ECa as measured by the Wenner four-

probe (see Figure 3.7) method (De Jong et al., 1979). During the study it was concluded that

less detailed information on salinity changes with depth was available using the EM31 as

compared with the Wenner four-probe array, but the EM31 was faster and provided a

continuous record of salinity change along a transect much more rapidly.

M.F.AHMED

48

This was similarly the conclusion reached by Zalasiewicz et al., 1985. In their study

they found the use of the EM31 was able to delineate geological boundaries much more

rapidly and accurately than traditional geophysical mapping methods. Consequently, the

EM31 measurements can supplement conventional field mapping techniques in areas where

these techniques prove generally effective. They found the greatest contrast between well-

defined units of sand, gravel or limestone (low conductivity) and clay (high conductivity).

Kachanoski et al. (1988a and b) estimated the spatial variability of soil water content

stored in the top 0.5 m of a 1.8 ha field near Brantford, Ontario, Canada with an EM31

instrument. Soil water content measurement was similarly investigated using an

electromagnetic induction method in an arid region of southern New Mexico (Sheets and

Hendrickx, 1995). Soil water content was measured monthly using a neutron moisture meter

at 65 equally spaced stations along a 1,950 m transect. In this detailed investigation

measurements of the soil were made with the EM-31 over a 16 months period. A simple linear

relationship between bulk soil electrical conductivity and total soil water content in the top 1.5

m of the profile was developed. Compared with the neutron scattering method, the study

demonstrated that the EM31 is a quite viable tool (accuracy approximately 0.02m3m-3) for

measuring total soil water content in the soil profile over long periods of time if the

measurements are calibrated for soil temperature. The rapid, easy, inexpensive method

combined with the accuracy of the measurements make the ground conductivity meter a

valuable prospective tool for measuring soil moisture/water changes over time.

3.6 CONCLUSIONS

The application of saline water for irrigation will create problems in the root zone.

Knowledge of how much salt may build up in the soil is therefore necessary. Where limited

soil information is available a salt balance model, such as SaLF, can provide estimates of soil

ECe after applying a certain quality of ECiw. This model was used for these reasons and

because the model was developed for estimating the risk of soil salinisation in the heavy grey

cracking clay soil profiles which characterise the soils in the lower Namoi valley study area.

In terms of assessing the risk (or probability of exceeding a particular ECe threshold) across

an irrigated district the information collected at a point needs to be interpolated. The use of

non-linear kriging methods such as DK, MIK and IK would seem to be appropriate. In areas

where salinity is already a problem, the use of EM instruments such as an EM38 and EM31,

could provide useful information at a fieldscale, to identify soil sampling locations and assist

in assessing the cause of the problem. In the following chapter each of these methods is

explored.

CHAPTER 4

SIMULATION AND MAPPING OF SALINISATION RISK

CHAPTER 4-SIMULATION AND MAPPING OF SALINISATION RISK

49

4.1 INTRODUCTION

Dryland salinity is increasing in the upper catchments of the central and northern

river valleys of New South Wales (Murray-Darling Basin Ministerial Council, 1999). The

consequence of this is increased salinity in the river water. This could adversely affect

irrigated schemes further downstream, as irrigation with moderate to highly saline water can

lead to increased salinity in the root-zone, if there is insufficient leaching. In order to

determine the potential impact and long term sustainability of irrigated cotton production it is

necessary to know the spatial distribution of soil and the suitability and effect of the water

quality being used for irrigation. This information can be obtained from soil databases or

from reconnaissance soil and water inventories. Important also is the soil-water balance,

which needs to be modelled in order to provide estimates of potential soil salinity

accumulation and deep drainage as affected by the current quality of irrigation water. Worst

case scenarios can be applicable in this case.

There are several models used for salinity prediction and estimation of leaching

fraction and deep drainage. As described in Chapter 3, these include the steady-state leaching

requirement or LR model (United Stated Salinity Laboratory, 1954), SODICS-a transient

mass balance model (Rose et al., 1979) and the Salt and Leaching Fraction model SaLF

(Shaw and Thorburn, 1985). The SaLF model is based on the assumption that soil leaching or

deep drainage is related to the soil hydraulic conductivity, which in turn is influenced by the

amount of clay (%), clay mineralogy (defined by ECEC/Clay %) and ESP. Once these soil

properties and water quality and quantity parameters have been determined the empirically-

based model can be used to estimate the average root-zone ECe at steady-state using

progressively more saline water. Spatial extension of the model can be achieved by

interpolation in order to predict areas of risk.

Several interpolation techniques for risk analysis exist: 1) disjunctive- (DK), 2)

indicator- (IK), and 3) multiple indicator- (MIK ) kriging. DK can be used to estimate the

conditional probability that an indicator variable exceeds a critical tolerance level (Yates et

al., 1986a). The conditional probability can be targeted to the level at which some form of

management action is needed. Various applications of DK exist in the soil science literature.

Yates et al. (1986a) and Wood et al. (1990) used DK to estimate the conditional probability

for soil ECe. Webster and Oliver (1989) applied the method to determine when and where

remedial action would be necessary for soil pH and various nutrient elements. More recently,

Finke and Stein (1994) used DK and its CoK equivalent to estimate LF and crop production at

the field scale.

M.F. AHMED

50

IK, or kriging of indicator transforms, on the other hand is a form of kriging that

indicates the presence or absence of an attribute of interest (Journel, 1983). Examples of its

use in soil science include Bierkens and Burrough (1993a) who showed its application to

predict categorical soil data. In a subsequent paper Bierkens and Burrough (1993b) applied

IK to water-table mapping and land use suitability assessment. Bierkens and Weerts (1994)

also applied IK to modeling of lithological properties of a complex confining layer in the

Netherlands. Goovaerts (1994) compared the performance of CoK, OK and MIK in predicting

soil indicators. Similarly, Goovaerts and Journel (1995) applied various indicator methods to

evaluate probabilities for copper and cobalt deficiencies in Scotland. More recently, Oberthur

et al. (1999) used IK to interpolate soil texture classes in the Philippines and Thailand.

In this study the SaLF model predictions of ECe with DK, MIK and IK, have been

combined to assess the current status and potential threat of soil salinity using data from soil

and water surveys in the lower Namoi valley of northern NSW, Australia. Different

simulations based on the application of water of variable quality (i.e., EC iw 0.4, 1.5, 4.0 and

9.0 dS/m) have been tested. The best method for interpolation of point estimates was also

identified with the results interpreted in terms of best management strategies to forestall the

threat of soil salinity to irrigation.

4.2 Materials and methods

4.2.1 Soil and water data

The soil data used for this study were from two sources: 1) the Edgeroi data set

(McGarry et al., 1989), and 2) soil survey data determined for samples from the area west of

Edgeroi. The Edgeroi data consist of analyses of samples from 210 sites arranged in a

systematic equilateral triangular grid with an approximate 2.8 km spacing. In this study

profiles east of the Newell Highway (Figure 2.2), which are associated with the hilly and

undulating areas of the landscape close to the Nandewar Ranges were not used. Additional

sites were drilled by CSIRO, Division of Soils in the 1980s, at the Australian Cotton Research

Institute (19 sites), and the I.A. Watson Wheat Research Centre (17). Detailed transects were

also taken at "Llano" (5 sites), "Noelurma" (7 sites), "Oakvale " (5 sites) and a long transect

north of Wee Waa (11 sites). For the Edgeroi data set at each location a core was recovered

and sampled for laboratory analysis at depths of 0.0-0.1, 0.1-0.2, 0.3-0.4, 0.7-0.8, 1.2-1.3, and

2.5-2.6 m. The samples were air-dried and ground to pass a 2-mm sieve.


51

Figure 4.1 Prediction and validation sites of the Edgeroi and Wee Waa districts. Note: Location of sites ed109, ed126, ed143 and ed160, which lie on a short transect north east of Wee Waa.

The soil was analysed for exchangeable cations (mmol(+)/kg) based on Tucker’s

(1974) method using a mechanical leaching device (Holmgren et al., 1977); silt and clay per

cent were determined using the pipette method (Coventry and Fett, 1979).

The soil survey data for the area west of the Edgeroi consists of 125 sites which were

selected using a stratified simple random sampling design with site spacings ranging from 1 to

10 km (Figure 4.1). To be consistent with the Edgeroi data, soil samples were collected from

four layers and depths of 0.0-0.1, 0.3-0.4, 0.7-0.8, 1.2-1.3 m. The clay and silt content and

cation exchange capacity (ECEC) were determined using the same methods as described for

the Edgeroi data set (see Appendix 1).

Data on current water quality were obtained during a reconnaissance water survey in

the Namoi valley. Two samples were collected from along the Namoi River: one from

Collins Bridge (near Wee Waa) and the other in the township of Narrabri. Another sample

was taken from the Mooki River. The ECiw was determined for each sample. At Collins

Bridge and at Narrabri water salinity measurement (ECiw) was 0.569 dS/m and 0.366 dS/m,

respectively. The EC iw sample from the Mooki River (a tributary of the Namoi, see Figure

2.1) was 0.759 dS/m. These values suggest that by the time the water reach Narrabri and Wee

Waa (avg. 0.468 dS/m) the Namoi River water and other tributaries have diluted salts

received from the Mooki River. As a result the water available to irrigators is of good quality

(avg. 0.435 dS/m).

Northings (m)

0 10 20 km

BURRENJUNCTION

BUGILBONE

PILLIGA

NARRABRIN

Namoi River

Roads

Prediction sites(i.e. 271)

WEE WAA Validation sites(i.e. 58)

6690000

6680000

6670000

6660000

6650000

6640000

Eastings (m)660000 680000 700000 740000 760000 780000

ed109ed126

ed143ed160

M.F. AHMED

52

4.2.2 SaLF Modeling

In order to determine the salinity risk associated with irrigated farming systems three

examples of progressively more saline water were chosen for simulation studies using the

SaLF model. The three EC iw values chosen were 1.5, 4.0 and 9.0 dS/m. The first value is

predicted to be the salinity of water in the Namoi valley and available for irrigation in the year

2100 (Murray-Darling Basin Ministerial Council, 1999). The other two values of salinity are

used for irrigation in Colorado and Tunisia, respectively (Rhoades et al., 1992). Each of the

329 sites were inputted to the SaLF program, including the attributes of clay content and

ECEC at four depths (i.e. 0-0.1, 0.3-0.4, 0.6-0.7 and 1.2-1.3m) and exchangeable sodium at a

depth of 1.2m. An ECiw of 0.468 dS/m was used for estimating current salinity status of the

study area. In the SaLF model, it was assumed that the average annual rainfall and irrigation

water were 584 mm and 600 mm, respectively. Estimates of average root zone ECe at steady-

state were then calculated.

4.2.3 Geostatistical methods

Geostatistical methods of spatial interpolation are not new to soil science. Various

methods – ranging from linear (OK, CoK and kriging with an external drift) and non-linear

methods (DK, IK) have been used. The non-linear methods make predictions using the

conditional probability of distribution of the underlying random field producing an error

variance for each estimate.

In this section the relevant methods used in the studies are introduced. OK is one of

the most basic of the geostatistical kriging methods. It provides an estimate at an unobserved

location of a variable z based on the weighted average of adjacent observed sites within a

given area. The theory is derived from that of regionalized variables (Matheron, 1965 &

1971) and can be briefly described by considering an intrinsic random function denoted by

Z(xi), where xi represents all sample locations, i=1,..., n . An estimate of the weighted average

given by the ordinary kriging predictor at an unsampled site, z(x0), is defined by:

( ) ( )∑=

=n

i ixzi?oxz*1

(4.1)

where, λi are the weights assigned to each of the observed sample sites. These weights sum to

unity so that the predictor provides an unbiased estimation.


53

∑=

=n

j i?11 (4.2)

The weights are calculated from the matrix equation

c = A-1b (4.3)

where A is a matrix of semivariances between the data points; b is a vector of estimated

semivariances between the data points and the points at which we wish to predict the variable

z; and c is the resulting weights and the lagrange multipliers Ψ. The same principle of

intrinsic random functions can be extended to CoK – whereby two or more variables are

spatially correlated.

As Bierkens and Burrough (1993a) point out these methods do not estimate the

conditional distribution itself. Additionally, unless a parametric distribution of spatial errors

is assumed, an error variance falls short of providing confidence intervals and the error

probability distribution required for risk assessment (Duetsch and Journel, 1992). Non-linear

kriging algorithms are applied to non-linear transforms of the original data. Examples include

lognormal kriging; multi-Gaussian kriging or kriging applied to normal score transforms; IK

or kriging of indicator transforms; and DK, or kriging of specific polynomial transforms

(Deutsch and Journel, 1992). DK, IK and MIK are used here.

DK estimates the conditional probability that a measured indicator variable exceeds a

critical tolerance level (Yates et al., 1986a). The conditional probability can be used in

management decision-making to determine the level at which some form of management

action needs to be implemented. Two types of information are required: the first is the critical

level at which the variable becomes a threat (i.e. the cut-off); the second is the probability

level that spurs management action (Yates and Yates, 1988). Many examples of its use exist

in soil science (Yates et al., 1986a and b: Yates, 1986; Webster and Oliver, 1989; and Finke

and Stein, 1994).

DK can be described by considering Eq. (4.1) as a special case of a more general

estimator:

( )1

∑=

=n

i ixzif)o(xdz , (4.4)

where each fi is a function of Z at xi. The task of DK is to find the functions that minimise the

estimation variance, whereas in simple kriging only coefficients were needed. The

assumptions underlying DK are that the property, z(x), is the outcome of a second order

stationary process with mean µ and variance σ2. Thus it is consistent in both mean and

M.F. AHMED

54

variance and spatial covariance, C(h), and auto-correlation, ρ(h), exist. It is also assumed that

the bivariate probability distribution is known and that it is stationary throughout the region of

interest. The data is then transformed to

,)()( ii xyxz ϕ= (4.5)

where y(xi) is the transform of z(xi) and the function φ is a linear combination of Hermite

polynomials, such that

.)()( ∑∞

=

=ok

ikki xyHQxyϕ (4.6)

an infinite series of Hermite polynomials (Hk) and coefficients (Qk) are evaluated by Hermite

integration. The estimate is of the form:

( ) ∑∞

==

1)(ˆ

i ixy

kH

kQ

ox

dz λ (4.7)

which in practice can be truncated to the sum of no more than M = 10 terms because the

contributions from the higher order terms diminish to almost zero (Rendu, 1980). An

estimate of Hky(so) is computed using

( ) ∑=

=n

i ixykHikoxykH1

)()( λ)

(4.8)

which is the weighted sum of the Hermite polynomials of the same values. The λik are

weights, which are found in a similar fashion to that used in OK (i.e. Eq. 4.1). It can be

solved using the linear set of equations

k

jxoxn

i

k

jxixik

=∑

=

),(

1),( ρρλ for j = 1,2,…,n (4.9)

with the solution producing k times per estimate instead of once, where ρ (xi, xj) is the spatial

autocorrelation coefficient between the ith and the jth sampling points and ρ (xo, xj) is the

autocorrelation between the jth sampling point and the place for which the estimate is

required.

The conditional probability that the critical value has exceeded the threshold Zc at x0

requires an indicator function to be defined. This is Ω[z(x0) ≥ Zc], which takes the value of 1

if z(x0) ≥ Zc and zero otherwise. Its Hermite expansion is


55

( )[ ] ( )[ ] ( ) ( ) ( ) kxyHYHYgYGYxyZxz k

M

kckcccc /ˆ)(1 0

1100 ∑

=−−−=≥Ω=≥Ω (4.10)

where G(Yc) is the probability integral for the normal distribution and g(Yc) is the probability

density. Eq. 4.10 provides the DK estimate P(x0) of the probability that the value of Z at x0

equals or exceeds the threshold Zc.

IK, as the name suggests, is the kriging of indicators of the presence or absence of a

particular attribute of interest after transformation. IK can be explained by considering a field

where m mutually exclusive classes exist. At each point, an observation would be classified

as a member of one of the possible classes U1,…,Um. From these potential classes it is not

known a priori which one will be found at s0. A random variable N(s0) can be introduced to

model the uncertainty at this point, taking any of the m values U1,…,Um. For these classes

an order is chosen and maintained, the probability

;,...)([P);U(F 1 kookN UUsNs ∈= k = 1,…,m (4.11)

is called the cumulative probability distribution function (CPDF) of N(so) at location so. The

function N(so) is then said to be a random field with categorical outcomes. If n observations

are made then the occurrence of a class can be predicted from estimation of the conditional

probability of each of the previous m classes. An observation of a class can be denoted by ni,

i E(n ), and is written as:

)].(,,,...)([P)(,,;U(F 1 nisnUUsNnisns iikoiiokN ∈∈=∈ (4.12)

Estimation of this conditional probability, requires a new random variable to be

introduced. This is the indicator random function or indicator transform I(Uk:s) of N(s0) and

is defined as :

∈∈

=+ mk

kk UUsNif

UUsNifxUI

,...,)(1,...,)(0

);(1

1 . (4.13)

Since the random function I(Uk:s) has a value of ‘1’ for all classes that have a chosen

rank order less than or equal to k , the class Uk is sometimes referred to as the ‘threshold’ of

the indicator random function I(Uk:s) (Bierkens and Burrough, 1993a). Therefore, IK provides

an indication of whether or not a threshold value is exceeded. The indicator variogram

calculated from the transformed data is used to estimate values at other sites that range from 0

to 1. This corresponds to the probability that at an unknown location the value is greater or

less than the threshold specified. Owing to the binary nature of the data used in kriging, the

M.F. AHMED

56

method is resistant to the effects of outliers, which can affect variography, and is useful in

dealing with skewed data sets (Smith et al., 1993).

MIK is similar to IK in that the data used has a value of 0 or 1: depending on whether

the attribute is greater or less than one or more thresholds. The difference is that the n data

points are sorted in order of increasing magnitude and discretized into 100 classes by

threshold values Uk, where k = 1,…,100. For every threshold the data are then transformed

into values of 1 and 0 according to whether they are greater or less than the threshold

concentration.

4.2.4 Prediction of conditional probability

The primary aim was to apply various spatial prediction techniques to determine the

optimal method of interpolation. The methods used are shown in Figure 4.2. First, the data

were split into two sets, where the validation set consists of 58 sites and the prediction set

equaled 271 sites. The simplest approach (MIK) involved the automatic transformation of

estimated EC e data by SaLF data using the MIK program (Markus, 2000). The cut-off values

of ECe at steady state considered in this study included 2, 4, 6 and 7.7 dS/m (see Table 3.1).

Figure 4.2 Schematic representation of various interpolation methods used.

Data set(329 points)

Validation set(58 points)

Prediction set(271 points)

IK MiK

SaLFmodeling

DK

Krig. Indicatortransformof soil ECe

ConditionalProbability

SaLFmodeling

Krig. Multi. Ind.transform

of soil ECe


Estimate ofsoil ECe

D-Krig. estimatesof soil ECe

SaLFmodeling

SaLFmodeling


Transform ECeto indicator data


57

Respectively, these values represent the level of salinity where some form of soil, water or

crop management would be required to ensure the continued use of legumes (e.g., Dolichus

Lab Lab ), most crops (Bowers and Wilcox, 1965), grains (e.g. wheat) and cotton. Eq. (4.13)

was also used for manual transformation prior to kriging using the VESPER software package

(Minasney et al., 2000). DK was carried out with ISATIS program (Geovariances, 1994).

4.2.5 Validation

In order to determine optimal method of interpolation a statistical comparison of the

methods was carried out. This was achieved by splitting the data set of 329 sites into

validation and prediction sites: that is by considering whether at each of the 58 validation

sites, the prediction set (i.e. 271 sites) along with the use of the various geostatistical methods

was able to correctly determine whether or not the critical value was exceeded. The process

used involved four steps. To illustrate the approach the scenario of EC e exceeding 4 dS/m

was considered if ECiw of 9 dS/m is applied at each validation site:

1) Transformation of predicted probability using threshold values ranging from 0 to 1

at increments of 0.05. The data were back-transformed to an indicator of 0 or 1

>>

=otherwise

thresholdpmdSxzprobifpredictedpthreshold 0

))/4)(((1 (4.14)

Where z(x) is the soil ECe at location x predicted by 1 of the 3 methods.

2) Back-transformation of actual probability to an indicator of 0 or 1

>

=otherwise

mdSECeactualifactual

0

/41 (4.15)

3) Misclassification at each site was determined by the differences between

prediction and actual classes so that a value of 0 indicates correct classification.

Values of 1 or –1 indicate the location has been classified as exceeding or not

exceeding critical value, respectively.

)( pthresholdpthreshold predictedactualicationmisclassif −= (4.16)

4) Absolute mean misclassificationp threshold was determined to assess overall

prediction correctness

∑=

=n

ipthresholdpthreshold icationmisclassif

nicationmisclassifmean

1

1 (4.17)

M.F. AHMED

58

The mean misclassification does not indicate which type of incorrect prediction

occurred at any of the probability thresholds only that there was an incorrect prediction. This

was the approach of Lagacherie and Voltz (2000) for determining precision of soil class

prediction and Markus (2000).

4.3 RESULTS AND DISCUSSION

4.3.1 Estimates of soil salinity

In Figure 4.3a the frequency distribution of estimated soil ECe using the current water

quality (EC iw 0.435 dS m-1) is shown. The average salinity value is 0.74 dS/m. This suggests

ECe in the rootzone would not accumulate to levels deleterious for crops predominantly used

in the current irrigated farming system (i.e. Dolichus lab lab, wheat and cotton). This result is

consistent with the prevailing soil salinity values in the lower Namoi valley around Wee Waa.

With respect to the use of water of ECiw 1.5 dS/m in 2100 (Murray-Darling Basin Ministerial

Council, 1999), average ECe would be 1.86 dS/m. Figure 4.3b shows that sensitive

leguminous crops such as Dolichus Lab Lab may require some management in order to ensure

its continued use. This would be the case for just under half (i.e. 109 of 278) of the prediction

sites.

Figure 4.3c shows frequency distribution of soil EC e if water with quality of ECiw of

4.0 dS/m was applied. Potential average ECe across the district would be 3.88 dS/m.

Approximately 60 and 5 per cent of prediction sites would require some form of management

to enable inclusion of legumes (as a rotation crop) and wheat production as part of the

irrigated cotton farming system, respectively. If water quality of ECiw 9.0 dS/m was applied,

more than half the sites would require management to enable wheat cropping whilst all sites

would require management to enable production of leguminous crops. With respect to cotton

production about 30 per cent of the sites would need to be managed in some way to reduce

salt build up in the root-zone.

4.3.2 Spatial distribution of cut-off ECe values for crop production

Figure 4.3 also shows patterns of spatial distribution of ECe (SaLF estimates) using

EC iw values = 0.4, 1.5, 4 and 9 dS/m. It is apparent that responses in the Edgeroi and the Wee

Waa data sets are different. This is most obvious on either side of Spring Plains Road (see

Figure 2.2, for location). To the northeast, soil ECe is generally higher as compared with the

area to the southwest and around the township of Wee Waa. The reason for this is attributable

to the generally sandier and coarser textured sediments associated with the palaeochannel of


59

the Namoi River, which flowed in a northwesterly direction, parallel with Spring Plains Road

(Figure 2.2). As a consequence less soluble salts such as sodium have accumulated in these

areas and the soils are relatively well drained compared to the clay alluvial plains to the

northwest of Spring Plains Road (Triantafilis et al., 2000b and 2001a).

Figure 4.3 Blob plots and frequency distribution showing concentration of ECe in the lower Namoi valley as predicted using SaLF when ECiw of a) 0.435, b) 1.5, c) 4.0, and d) 9.0 dS/m was simulated.

N

N

6690000

6680000

6670000

6660000

6650000

6640000

N

6690000

6680000

6670000

6660000

6650000

6640000

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

66 90000

66 80000

6670000

6660000

6650000

6640000

Northings (m)

N

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

6690000

6680000

6670000

6660000

6650000

6640000

660000 680000 700000 740000 760000 780000Eastings (m)

Soil ECe (dS/m)

< 12< 10< 8< 6< 4< 2

Soil ECe (dS/m)

0.74

1.86 1.79 0.55

b)

3.88 3.74 1.28

7.01 6.82 2.48

c)

d)

a)

0.70 0.18

0

5

10

15

0

5

10

15

0

5

10

15

0

5

10

15meanmedianst. dev.

meanmedianst. dev.

meanmedianst. dev.

meanmedianst. dev.

a)

b)

c)

d)

M.F. AHMED

60

4.3.3 Misclassification

Figure 4.4 shows the plots of misclassification versus probability threshold for each of

the scenarios where water with quality of ECiw of 1.5, 4 and 9 dS/m was applied. Figure 4.4a

shows the per cent misclassification of each of the three methods of interpolation if ECiw of

1.5 dS/m was applied and ECe exceeded 2 dS/m. The percentage of validation sites

misclassified increases as the threshold approaches 0 and 1 for each method, and decreases at

intermediate threshold probabilities. As such the threshold probability is small, for example

0.2, then a location having a probability of 0.3 of exceeding 2 dS/m would be considered

“saline”. However, a probability of 0.2 would be considered relatively “low risk” and

therefore there is misclassification. The converse is the case when threshold probabilities take

on large values.

Goovaerts (1997) suggests that the optimal threshold probability (i.e. the smallest

percentage of misclassification) corresponds with the proportion of prediction sites that

exceed the critical value. This was termed the marginal probability. In Table 4.1 the number

of prediction sites that exceed the ECe cut-off value using the various water quality scenarios

are shown. The values of marginal probability are also calculated for each scenario. The

marginal probability, when an ECiw of 1.5 was applied and an ECe cut-off value of 2 was

considered, was 0.52. For DK misclassification was minimal at a threshold probability of

0.15, whilst for IK and MIK the minimum was reached at a threshold probability of 0.50. This

suggests an inherent difference in the way the conditional probability is estimated by each of

the three methods.

Table 4.1 Marginal probability of sites exceeding soil salinity at various threshold values.

Scenarios Cut-off units

Number of sites exceeding threshold

Marginal probability: proportion of sites exceeding threshold

1.5 2 30 0.52 4 2 54 0.93 4 31 0.47 6 3 0.05

9 4 53 0.91 6 42 0.78 7.7 28 0.48

The pattern of misclassification was similar for the scenarios where ECe exceeds 4

dS/m and ECiw of 4 dS/m was simulated (Figure 4.4c). Again, the DK method produced

fewer misclassifications across most threshold probabilities. This was the case at low

threshold probabilities (i.e. < 0.4), although at some thresholds MIK and IK produced fewer

misclassifications. This was generally the case around the marginal probability of 0.47.


61

Figure 4.4 The percentage of sites misclassified as either ‘no risk’ and ‘at risk’ by each of the methods for scenarios were ECiw and ECe cut-off were respectively: a) 1.5 and 2; b) 4 and 2; c) 4 and 4; d) 4 and 6; e) 9 and 4; f) 9 and 6; and, g) 9 and 7.7 dS/m. IK = indicator kriging; MIK = multiple indicator kriging; and, DK = disjunctive kriging.

However, MIK and IK do not perform as consistently across all threshold

probabilities. Figure 4.4f and 4.4g, show similar trends in misclassification when ECe

exceeds 6 and 7.7 dS/m when ECiw of 9 dS/m was simulated, respectively. It is also evident

from Figures 4.4a, c and g that the minimum number of misclassifications in each of these

scenarios is approximately 30 per cent regardless of the method used and probability

threshold considered.

Figure 4.4b, shows the percent misclassification when soil ECe exceeds 2 dS/m if

ECiw of 4 dS/m was applied. In this case misclassification increased with increasing threshold

probability regardless of which interpolation method was used. This was because a large

number of the validation sites actually exceed the critical value of 2 dS/m (i.e. marginal

probability of 0.93). It is reasonable to expect that most of the validation sites would be

correctly classified at low threshold probability values, and conversely with increasing

probability threshold a larger number of errors would be anticipated. At probabilities greater

than 0.90 the percentage misclassification is greater for IK and MIK as compared with DK for

this scenario. This was more the case with respect to IK. The reason for this was that IK is

not bounded and leads to over-smoothing of the predictions of conditional probability. This is

generally a problem with ordinary kriging (Triantafilis et al., 2001b). As a result the

conditional probability was underestimated. A similar result was obtained for the scenario

where EC iw applied is 9 dS/m and ECe exceeds 4 dS/m as shown in Figure 4.4e.

b) c)a)

Misclassification (%)

Probabili ty threshold0 0.2 0.4 0.6 0.8 1.0

MIkiIKDK

MIkiIkDK

e g)f)

d)

0 0.2 0.4 0.6 0.8 1.0 0 0.2 0.4 0.6 0.8 1.0

0 0.2 0.4 0.6 0.8 1.0

20

40

60

80

100

0

20

40

60

80

100

0

M.F. AHMED

62

The result of misclassification if soil ECe was greater than 6 dS/m and EC iw of 4

dS/m is shown in Figure 4.4d. In general, misclassification is much lower as compared with

all of the scenarios discussed. The reason for this was that very few of the prediction points

actually exceed the critical value of 6 dS/m, that is the marginal probability is 0.05. Overall,

and despite the similarity in the trends of misclassification as a function of threshold

probability it was obvious that DK provides fewer misclassifications. This was followed by

MIK, which was slightly better than IK.

4.3.4 Spatial comparison of conditional probability: change in irrigation quality

In order to better understand the results described above, the spatial distribution of

conditional probability produced by each of the methods was plotted along with the frequency

distributions for a few of the scenarios. The results are shown in Figures 4.5-4.9. Figure 4.5

shows the spatial distribution of conditional probability for each of the kriging methods where

soil ECe exceeded 2 dS/m if water with quality of EC iw 1.5 dS/m was simulated. The white

patches of each map indicate lowest risk (i.e. conditional probability < 0.2) whilst the

progressively darker gray scale areas indicate higher probabilities (darkest shade: conditional

probability > 0.8).

Regardless of which method of interpolation was used, much of the area surrounding

the township of Wee Waa exhibits a low conditional probability or risk that the critical value

for legume crops would be exceeded. The areas of highest risk are those associated with the

clay alluvial plains to the north of the Kamillaroi Highway and Spring Plains Road and north

and south of Burren Junction (Figure 2.2). In these areas, the continued use of sensitive crops

such as legumes in cotton farming systems would require some form of soil, crop or irrigation

management.

Despite the similarity of the spatial distribution of conditional probability, shown in

Figure 4.5 each map was slightly different. This was illustrated most clearly at either end of

the predicted conditional probability scale. Spatially, and at values close to 0, large tracts of

land around Wee Waa, south of Edgeroi and north of Pilliga and Burren Junction had

conditional probabilities less than 0.2 using the method of DK (Figure 4.5c) as compared with

IK (Figure 4.5a) and MIK (Figure 4.5b). Conversely, less of the area interpolated using DK

had conditional probabilities exceeding 0.4 in comparison to IK and MIK.


63

Figure 4.5 Spatial and frequency distribution of the conditional probability that soil ECe at steady state exceeds 2 dS/m if ECiw = 1.5 dS/m was simulated using a) IK, b) MIK and c) DK. IK = indicator kriging; MIK = multiple indicator kriging; and, DK = disjunctive kriging.

Figure 4.5 also shows the frequency distributions of conditional probability achieved

using each method. For IK the distribution is clearly bimodal producing two peaks of

conditional probability: 0.20-0.25 and 0.55-0.60. As previously stated this is because the

method of IK is unbounded and as a consequence there was over-smoothing. It should be

noted that the mean and median values of conditional probability were similar to the marginal

probability value (i.e., 0.4) for this method, however. This was not the case with either MIK

or DK. For DK the majority of sites had a conditional probability which ranged from 0.30-

0.35 whilst for MIK most values were in the range of 0.45-0.50. Using these methods, the

mean prediction of conditional probability was respectively 0.31 and 0.39. The result was

similar to the scenario where ECe exceeds 4 when application of ECiw of 4 was simulated,

(Figures not shown).

mean

median

mean

median

mean

median

N

N

660000 680000 700000 740000 760000 780000

669000 0

668000 0

6670000

6660000

6650000

6640000

N

669000 0

668000 0

6670000

6660000

6650000

6640000

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

6690000

6680000

66 70000

6660000

66 50000

66 40000

Northings (m)

Eastings (m)

4,824

4,084

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0

Frequency distributions

0

0.2

0.4

0.6

0.8

1.00.41

0.43

0.39

0.42

0.31

0.30

a)

b)

c)

2,786

Conditional Probability

> 0.8< 0.20 0.6 <C.P.< 0.80.4 <C.P.< 0.60.2 <C.P.< 0.4

M.F. AHMED

64


Figure 4.6 shows the spatial distribution of conditional probability when ECe exceeds

6 if ECiw of 4 dS/m was to be applied. IK (Figure 4.6a) overestimated the conditional

probability as compared with MIK and DK. As DK (Figure 4.6c) had the fewest

misclassifications (Figure 4.4d), the risk was low across the whole district (i.e. < 0.05). This

value of conditional probability was consistent with the marginal probability for this scenario,

0.05. Results of MIK (Figure 4.6b) showed that only a few isolated sites in the area to the

north and south of the Kamillaroi Highway (Figure 4.1) between Wee Waa and Narrabri and

to the south Edgeroi have low risk (i.e. < 0.4). With respect to IK (Figure 4.6a) the

conditional probability was high (i.e. < 0.6) in isolated areas to the south of Bugilbone, north

of Edgeroi and a small band of sites between Wee Waa and Narrabri.

Northings (m)

Eastings (m)

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

E DGEROI

N

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WE E WAA

EDGEROI

N

66000 0 680000 700000 740000 760000 780000

6690000

6680000

6670000

6660000

6650000

6640000

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

E DGEROI

N

6690000

6680000

6670000

6660000

6650000

6640000

0

0.2

0.4

0.6

0.8

1.0

18,989

21,266

28,109

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0


a)

b)

c)6690000

6680000

6670000

6660000

6650000

6640000

mean

median

0.06

0.02

0.04

0.03

0.01

0.00

mean

median

mean

median


> 0.8< 0.20 0.6 <C.P.< 0.80.4 <C.P.< 0.60.2 <C.P.< 0.4


65


Figures 4.7 shows the results when ECe exceeded 4 dS/m when EC iw of 9 dS/m was

applied. The result achieved here was similar to where ECe exceeds 2 dS/m and if ECiw of 4

dS/m was simulated (Figures not shown). In both cases the marginal probability was high

(i.e. respectively, 0.91 and 0.93). Figure 4.7 shows that IK and MIK again overestimated the

conditional probability as compared with DK. These two methods also underestimated the

risk. Spatially this is shown in the areas to the northwest and southeast of Wee Waa, and to a

lesser extent in isolated areas to the west of Pilliga and east of Bugilbone. This obviously has

implications for the final risk maps, whereby the use of the IK and MIK may lead to the belief

that the risk is low when in fact the risk is high around the township of Wee Waa.

Northings (m)

BURRE NJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

N

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

N

6 60000 680000 700000 740000 760000 780000

6690000

6680000

6670000

6 660000

6650000

6640000

BURRE NJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

N

6690000

6680000

6670000

6 660000

6650000

6640000

6690000

6680000

6670000

6660000

6650000

6640000

0

0.2

0.4

0.6

0.8

1.0 10,395

17,053

8,404

0.83

0.90

0.87

0.96

0.87

0.89

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0

Eastings (m)


a)

b)

c)

mean

median

mean

median

mean

median


> 0.8< 0.20 0.6 <C.P.< 0.80.4 <C.P.< 0.60.2 <C.P.< 0.4

M.F. AHMED

66


For this scenario only the frequency distribution of DK is consistent with the marginal

probability value of 0.84. Further the conditional probability distribution of DK was

negatively skewed rather than lognormal as produced using IK and MIK.

The results achieved if an ECe cut-off value of 6 dS/m was considered along with the

application of 9 dS/m water is shown in Figure 4.8. Clearly, the conditional probability

pattern achieved with each method was spatially similar. As with the previous scenario,

however, the frequency distribution shows the difference between the methods. For DK, over

6,000 predictions had a conditional probability between 0.60 and 0.65. This value of

conditional probability was consistent with the marginal probability for this scenario (0.78).

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

N

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

N

660000 680000 700000 74000 0 760000 780000

6690000

6680000

667000 0

666000 0

665000 0

664000 0

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

N

6690000

6680000

667000 0

666000 0

665000 0

664000 0

66 90000

66 80000

6670000

6660000

6650000

6640000

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0

3,896

6,037

3,194

Northings (m)

Eastings (m)


a)

b)

c)

0.64

0.67

0.63

0.64

0.63

0.67

mean

median

mean

median

mean

median


> 0.8< 0.20 0.6 <C.P.< 0.80.4 <C.P.< 0.60.2 <C.P.< 0.4


67

Figure 4.9 Spatial and frequency distribution of the conditional probability that soil ECe at steady state exceeds 7.7 dS/m if ECiw = 9.0 dS/m was simulated using a) IK, b) MIK and c) DK. IK = indicator kriging; MIK = multiple indicator kriging; and, DK = disjunctive kriging.

This was similarly the case for IK, although in the latter fewer than 4,000 values had

conditional probabilities between 0.65 and 0.70. MIK had fewer than 3,000 values in either of

these ranges with most occurring between 0.75 and 0.80 (i.e. 3,194). It is clear from these

figures that the risk, although approaching high values across most of the study area (i.e. >

0.60), conditional probability was again overestimated using IK and MIK , with respect to DK.

Figure 4.9 illustrates the results achieved when the ECe cut-off value was 7.7 dS/m

and ECiw of 9 dS/m was applied. It is clear, the area surrounding Wee Waa is at lowest risk

regardless of which method was used. However, it is also apparent that in this scenario IK

and MIK again tend to be smoothing the conditional probability as compared with DK. In the

case of IK this is most apparent by viewing the frequency distribution, which is slightly

bimodal as compared to MIK and DK. However, and although MIK produces fewer

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WE E WAA

EDGE ROI

N

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

N

660000 680000 700000 740000 760000 780000

66 90000

66 80000

6670000

6660000

6650000

6640000

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WE E WAA

EDGE ROI

N

66 90000

66 80000

6670000

6660000

6650000

6640000

6690000

6680000

6 670000

66 60000

6 650000

6 640000

4 ,625

3,003

6,070

Northings (m)

Eastings (m)


a)

b)

c)

x

s

x

s

x

s

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0

0.50

0.52

0.36

0.36

0.37

0.37

mean

median

mean

median

mean

median


> 0.8< 0.20 0.6 <C.P.< 0.80.4 <C.P.< 0.60.2 <C.P.< 0.4

M.F. AHMED

68

misclassifications in this scenario across the various probability thresholds than DK, the

former produces similar patterns of conditional probability to IK, except perhaps in the

western areas where it approximates the pattern achieved with DK.

The significance of this can be appreciated by comparing Figures 4.8b (MIK) and

4.8c (DK), with the physiography map shown in Figure 2.4 (Stannard and Kelly, 1977). It is

apparent that the Kamillaroi Highway runs in the same general direction as the prior stream

channel (Figure 4.1): from Wee Waa in a west-northwesterly direction to Bugilbone. The soil

here is generally coarser textured and sandier in nature and generally lower in soluble salts

(e.g., Na, etc.,.) as compared with the clay alluvial plains. Consequently, this part of the

landscape is likely to be more freely draining and hence possesses a lower risk of

accumulating salts applied with saline irrigation water than the clay alluvial plains. This

should be reflected in risk maps, in particular along the narrow corridor defined by the prior

stream channel shown in Figure 2.4.

Figure 4.9b shows that around Burren Junction and Bugilbone the conditional

probability produced using MIK is reasonably similar to the DK result, ranging from 0.20 and

0.40 between these two centres. This was also the case for the areas to the north and south of

these centres and to the east of Burren Junction where the conditional probability range is

0.40 and 0.60. With respect to the physiography map (Stannard and Kelly, 1977), there is

only a vague band of lower conditional probability or risk between Bugilbone and Burren

Junction along the route of the prior stream channel. It is apparent, however, that DK (Figure

4.9c) produces a corridor of lower risk (i.e., < 0.2) along this prior stream channel, extending

from Bugilbone through to the small township of Merah North (located approximately two-

thirds of the way to Wee Waa from Burren Junction). In addition, higher risk areas (0.4-0.8)

are also elucidated to the south of Bugilbone and Burren Junction and associated with the clay

alluvial plain using DK. This is not the case for MIK nor IK in these areas which suggest the

conditional probability or risk is lower than 0.40.

4.3.5 Spatial comparison of conditional probability: change in irrigation quantity

In addition to the change in the quality of water that may be available to the irrigators

in the lower Namoi valley over the next 100 years there is also the question of a change in the

quantity of water available due to the increasing pressure to increase environmental flows. In

order to assess what impact this may have on the salt build up in the root zone, simulations

were carried out. The results are shown in Figure 4.10 for ECiw of 4.0 dS/m only since the

conditional probabilities for ECiw 0.4 and 1.5 dS/m were negligible. As with the previous

simulations it was assumed that 584 mm falls as rainfall per annum.


69

Figure 4.10 and shows the results for an ECe cut-off of 2 dS/m when the quantity of

applied irrigation water is 600, 300 and 150 mm/year, respectively and using ECiw of 4 dS/m.

It is evident that with lower amount of water application the area with high conditional

probability (i.e. > 0.8) decreases. This is true for the area north of Doreen Land and “The

Gardens” in the central northern and southern parts of the study area, respectively, as well as

the areas to the north and south of Wee Waa and Bugilbone. The results mean some

management is required as water quantity increases. As the water quality decreases the

volume of water applied will need to be reduced in order to reduce the amount of salt

accumulation in the root zone. This is particularly important for sensitive crops such as

legumes. Similar results were obtained when an ECe cut off value of 4 dS/m was considered.

As shown in Figure 4.10e and f, if less water is applied the conditional probabilities suggest

most crops should be grown.

Figure 4.10 Spatial distribution of the conditional probability that soil ECe at steady state exceeds 2 dS/m if ECiw = 4.0 dS/m was simulated using MIK and a) 600mm, b) 300mm and c) 150mm and soil ECe at steady state exceeds 4 dS/m was simulated using MIK and a) 600, b) 300 and c) 150 mm of irrigation water.

Northings (m)

Eastings (m)

BURRENJUNCT ION

BUGIL BONE

PILLIGANARRABRI

WEE WAA

EDG EROI

N

BURRENJUNCTION

BUGILBONE

PILLIGANARRABRI

WEE WAA

EDGEROI

N

660000 680000 700000 740000 760000 780000

6690000

6680000

6670000

6660000

6650000

6640000

BURRENJUNCT ION

BUGIL BONE

PILLIGANARRABRI

WEE WAA

EDG EROI

N

6690000

6680000

6670000

6660000

6650000

6640000


a)

b)

c)6690000

6680000

6670000

6660000

6650000

6640000

BURRENJUNCTION

BUGILBONE

PILL IGANARRABRI

WEE WAA

EDGEROI

N

BURRENJUNCTION

BUGILBONE

PIL LIGANARRABRI

WEE WAA

EDGEROI

N

BURRENJUNCTION

BUGILBONE

PILL IGANARRABRI

WEE WAA

EDGEROI

N

d)

e)

f)

Eastings (m)660000 680000 700000 740000 760000 780000

> 0.8< 0.20 0.6 <C.P.< 0.80.4 <C.P.< 0.60.2 <C.P.< 0.4

M.F. AHMED

70

4.3.6 Sensitivity analysis: change in irrigation quantity and water quality

In order to better appreciate the management options available to the various irrigators

and better understand the way the SaLF model estimates salinisation in the root zone with

varying irrigation water quantity (i.e. mm/y) and quality (i.e. ECiw in dS/m), a sensitivity

analysis was carried using a short transect of soil profiles where change in risk was large. The

four profiles selected include from north to south ed109, ed126, ed143 and ed160. There

location is shown in Figure 4.1.

At each site the soil parameters shown in Appendix 1 were used in the SaLF model.

In addition, the following water quality parameters were used 0.5, 1.5, 2, 3, 4, 5, 6 and 9 dS/m

in combination with the following water quantity values of 150, 300, 450 and 600 mm/y. In

total 32 simulations were carried out at each site. The ECe (d/Sm) of each simulation was

plotted as shown in Figure 4.11.

Figure 4.11 Sensitivity analysis of changing water quantity (mm/year) versus water quality (ECiw

dS/m) for sample sites a) ed109, b) 126, c) ed143, and d) ed160.

<= 1

<= 4

<= 5<= 6<= 7

<= 8

<= 9

<= 10

> 10

ECiw( dS/m)

0 3 6 9

600

450

300

1500 3 6 9

600

450

300

150

Irrigation water quantity (mm/year)

ECe (dS/m) <= 2

<= 3


71

Of all the sites, sites ed109 would accumulate the most salts because of the sodic

nature of the subsoil (ESP = 16.5) but also the heavy clay in the soil profile (average clay

content = 68.5%). In terms of maintaining the inclusion of a legume crop into the current

cotton farming system without any form of soil or crop management, the grower who used

soil similar to that at site ed109 would need to irrigate with at least 300 mm/y and at ECiw of 2

dS/m or less. Alternatively, the grower has the option of using less than 300 mm/y of poorer

quality water (up to ECiw = 4 dS/m). The application of water with ECiw greater than 6 dS/m

and volumes exceeding approximately 300 mm/y will produce the levels of soil salinisation

(ECe > 7.7 dS/m) where even cotton may require some form of soil or crop management to

ensure its sustained production.

Figure 4.11b and c respectively indicate the level of soil salinisation at sites ed126 and

ed143. The results are similar since the average clay contents are 48.75 and 58.35 and ESP

values at 0.90-1.20 are 17.17 and 14.39, respectively. It is evident, however, that soil

salinisation is likely to be greater in ed126. This is because although there is 10 % less clay

on average compared with ed143, soil ESP is slightly higher. This will pose a problem when

ECiw exceeds 5 dS/m. It is also evident that in profile ed143 soil salinisation would not

exceed the levels (> 7.7 dS/m) where cotton would be affected as compared with ed126,

where this value would be exceeded if ECiw of 7 dS/m and volume of water applied exceeds

350 mm/y. However, some form of management would still be required if similar values of

ECiw and water applied at ed143 for wheat to be grown since at these values soil salinisation

would exceed 6.0 dS/m.

Of all the sites, the simulations carried out at ed160, as and presented in Figure 4.11d,

suggest that almost any saline water could be applied without soil salinisation occurring at a

level where the most sensitive component (i.e. legumes) of the current irrigated farming

system would be affected. This is because the level of soil salinisation would not exceed 2

dS/m. This value would only be exceeded if ECiw = 9 dS/m is applied using irrigation water

volumes above approximately 400 mm/y. The attribute values of this profile clearly illustrate

the reason for this is due to the fact that at this site the average clay content was

approximately 39 % and ESP at 0.90-1.20 m was 3.88 %. As a result the soil is likely to be

better structured owing to the low ESP value and more permeable owing to the lower clay

content compared to the other profiles. As a consequence profile ed160 is more likely to

enable sufficient leaching of salts applied in any irrigation water quality applied.

Conversely, it should be noted that irrigation of this type of soil would lead to an

increase in deep drainage and groundwater recharge rate. This could adversely impact on the

quality of groundwater, which could be used as a management option in those areas further to

M.F. AHMED

72

the west where poor quality river water could be supplemented with good quality

groundwater. That is despite the saline water that may be available from the river, good

quality water may still be available from the main aquifer shown in Figure 2.5 to leach the

salts added. This is a potential management option for those irrigators who hold conjunctive

water licences (i.e. ground and river water licences).

4.4 CONCLUSIONS

A step-by-step account on how the Salt and Leaching Fraction (SaLF) model in

conjunction with non-linear methods of kriging was implemented to identify salinity risk for

various worst-case scenarios in the lower Namoi valley. The results suggest the use of water

of good quality (ECiw = 0.4 dS/m) drawn from the Namoi River does not pose a threat to the

irrigated-cotton farming systems as it may not result in significant increase in soil salinity. If

water of slightly lower quality (ECiw = 1.5 dS/m) is used there may be some cause for concern

about the viability of growing Dolichus Lab Lab and other sensitive leguminous crops in the

valley. This is particularly the case for the irrigation farms located in the clay alluvial plains,

where large amounts of sodium are stored. As a consequence this is likely to impede deep

drainage and hence cause salts to accumulate in the root zone.

Further, if water of even lower quality is used (ECiw = 4 or 9 dS/m), the worst-case

scenarios may eventuate. Potential threat of soil salinity increases dramatically to levels

where some management would be necessary in order to continue with the inclusion of wheat

in the irrigated cotton farming system. The area to the northeast of Spring Plains Road

(Figure 4.1) is particularly vulnerable. Conversely, the areas associated with the prior-stream

channels and the Pilliga Scrub potentially have a lower risk of accumulation of salts.

However, here it is expected that deep drainage is higher and saline water may contaminate

the groundwater reserves.

Among the non-linear methods of predicting the conditional probability that were

tested here, the best was DK, since it consistently produced the lowest number of

misclassifications across the broad range of cut-off values and scenarios. The reason is that

DK uses a bounded model in estimating conditional probability, which reflected the original

data. MIK was better than IK. This is probably due to the fact that in using MIK, the indicator

transforms of the data for several cut-offs was used to model the cumulative distribution

function in contrast to a single cut-off value for IK. As a consequence the probability that a

variable is greater than a particular cut-off is an average of the indicator transformed values.

CHAPTER 5

ASSESSMENT OF SALINISATION AT THE FIELD SCALE

CHAPTER 5 – ASSESSMENT OF SALINISATION AT THE FIELD SCALE

73

5.1 INTRODUCTION

The traditional methods used for the acquisition of soil information involve intensive field

survey and laboratory analysis which are time-consuming and costly. As a consequence only limited

amounts of data are collected. Inferences about the spatial distribution of soil properties and soil

condition are made from soil maps produced, which may lead to errors in interpretation and possibly

soil management. For soil salinity assessment and determination of irrigation/drainage efficiency,

more detailed quantitative information is required.

The development of new technologies has revolutionised the way in which the soil

information can be obtained more efficiently. One of these technologies is an electromagnetic (EM)

induction instrument, which measures the apparent soil electrical conductivity (ECa). The instrument

works by emitting an alternating current through a transmitter coil, which induces a primary magnetic

field to pass perpendicular to the coil orientation and into the adjacent media. As this current passes

through the soil, eddy currents are generated. The strength of these is a function of: a) amount of

negative charge on the clay particle; b) clay content; c) concentrations of salts in the soil solution;

and, d) soil moisture content. The more conductive the soil, the greater the secondary magnetic field

produced. The ratio of the two magnetic fields (i.e. primary and secondary) determines the electrical

conductivity (ECa).

EM instruments have been used extensively to determine the spatial distribution of soil

attributes including clay content (Williams and Hoey, 1987; Triantafilis et al., 2001b); moisture

(Kachanoski, et al., 1988a and b) and soil salinity (Lesch et al., 1995; Vaughan et al., 1995). In

other studies EM instruments have been used to determine the nutrient status of soil (Suddeth et al.,

1995), depth to a clay layer (Brus et al., 1992; Doolittle et al., 1994), for identification of high SAR

(Ammons et al., 1989) and ESP (Nettleton et al., 1994) soil types and determination of organic

carbon fraction (Jaynes et al., 1995).

All of these studies were based on the initial calibration, which determines the soil attributes

that most contribute to the response of the instrument. For example, Triantafilis et al. (2001b) found

that when moisture content is uniform (i.e. recent heavy rainfall or irrigation) and clay content

differences negligible most of the instrument’s response can be attributed to soil salinity. As a result

they were able to calibrate an EM38 instrument for the purpose of describing the spatial distribution

of soil salinity at the field scale (Triantafilis et al., 2001a).

M.F. AHMED

74

With the advent of Precision Agriculture (Emmott et al., 1997) the ability to generate a large

amount of information rapidly is becoming more important than ever. To improve the efficiency of

field measurement of ECa, Rhoades (1992) and Cannon et al. (1994) incorporated Global

Positioning Systems (GPS) and EM instruments onto small tractors (i.e. Mobile EM Sensing

Systems, MESS). The system developed by the United States Salinity Laboratory (Carter et al.,

1993) was designed to assist in the collection of data that could be used to describe the spatial

distribution of soil salinity in the root-zone. Their system includes a GPS, EM38 and a Wenner

array. The system operates by travelling along a traverse of interest, with measurements made at

intervals of 50 m. The system of Cannon et al. (1994) is similar their instrumentation includes a GPS

and an EM31. Measurement is made on-the-go, and is therefore similar to the tractor driven four-

electrode probe system described by Rhoades (1992).

With the ECa data generated, soil-sampling sites can strategically be selected to determine

the reasons for the spatial variation in EC a, and hence soil properties, across a given field. These

measurements can be used in deciding the location of soil samples sites to calibrate the EM

instrument. Once a suitable relationship is established between one or a number of soil attributes a

map can be produced. As such ECa values add to the limited soil information collected using

traditional methods. A description of the components of a MESS is described. A case study is

shown which demonstrates how ECa data were generated and used to assist in the selection of soil

sampling sites for calibration of a EM38 instrument in an irrigated cotton field located in the lower

Namoi valley south east of Wee Waa. The results of the laboratory analysis are used to elucidate

the cause of soil salinity and the likely management options required to resolve the problem.

5.2 MATERIALS AND METHODS

5.2.1 Mobile EM sensing system (MESS)

Salinisation due to irrigation in the cotton-producing regions in Australia is of increasing

concern. Accurate and reliable salinity information is therefore needed in order to develop

management strategies to minimise salinisation. In order to generate the necessary soil data more

efficiently at the field level, a MESS was constructed.

Prior to the construction of the MESS, a number of issues were considered:


75

a) The MESS would be used in the irrigated-cotton fields. It therefore needed to fit

perfectly within a two metre bed - the common bed/furrow configuration used in the

Australian cotton industry;

b) The MESS needed to simultaneously carry a number of EM instruments (to be placed

some distance from each other and any metallic objects to avoid interference), in

addition to two Global Positioning System (GPS) units, a GPS antennae, and a custom-

built controller and data logger;

c) The MESS needed to lightweight for easy transportation behind a 2.75 ton 4Wheel

Drive vehicle.

With these caveats, a lightweight articulated tractor with four-wheel hydrostatic drive was used as

the MESS platform. This is similar to that used by the United States Salinity Laboratory (Carter et

al., 1993). It satisfies the above requirements because the tyre spacing can be altered to the

required 2.0 m width, adjustable to a minimum of 1.85 m and loaded onto a custom built trailer. It is

also relatively light (approx. 900 kg), and in combination with the weight of the trailer (approx. 920

kg), it is a legally allowable tow behind most 4WD vehicles (i.e. < 75% Gross Vehicle Mass). The

size of the tractor and its sturdy construction was also amenable to the various components required

to be manufactured and attached to support the various EM instruments (Figure 5.1a) and the Global

Positioning System (GPS).

M.F. AHMED

76

Figure 5.1 Mobile Electromagnetic Sensing System (MESS): a) with EM31 at front and EM38 with mast and polyvinyl tube at rear; b) close up of EM38 inserted in polyvinyl tube; c) close up of rotating mechanism of EM38 and, d) 486 computer data logger, Trimble T M Fieldguide Moving Map Display, GPS410 and Ag132.


77

5.2.1.1 EM38 instrument for measuring root zone ECa

To enable measurement of EC a within the agriculturally significant portion of the root-zone,

Geonics (McNeill, 1980) developed the EM38 instrument. As the EM38 consists of two coils,

spaced 1 m apart, and operates at a frequency of 13.2 kHz, the instrument measures ECa to 0.75 or

1.5 m depending on whether it is held in the horizontal or vertical mode of operation, respectively

(McNeill, 1986). The EM38 is enclosed within a 2.5 m hollow vinyl-ester tube, with an internal

diameter of 15.9 cm and thickness of 0.45 cm. At the end of the tube furthest from the tractor a slot

was cut, wide enough to allow the EM38 to be positioned inside (Figure 5.1b). The instrument is

secured inside a small wooden cradle and can be strapped down using plastic taping or flexible

straps.

The tube is aligned parallel to the direction of travel, affixed to a stainless steel cylinder. To

ensure that the tractor does not interfere with the instrument, the transmitter is located at the end of

the vinyl-ester tube with the receiver located no closer than 1.5 m from the tractor. The tube is

attached to the tractor via a short piece of steel tubing, which is attached to a hydraulically driven

chain mechanism. This allows the EM38 to be raised from a minimum height of 0.10 m above the

ground surface to a maximum of 1.60 m. It also allows the EM38 to be positioned at various

heights. A small rotating arm, which is also attached to the steel cylinder, enables 90o rotation

(Figure 5.1c). The rotatable EM38 allows measurements to be made in a vertical or horizontal

mode of operation. The raising and lowering and rotation of the EM38 can all be carried out by the

operator whilst seated in front of the 486 data logger/control box.

5.2.1.2 EM31 instrument for measuring vadose zone ECa

The Geonic s® EM31 instrument has an intercoil spacing of 3.8 m, operating at a frequency

of 6.4 kHz. The theoretical depth of EC a measurement achieved by the instrument is approximately

6.0 and 3.0 m, (McNeill, 1986), when the instrument is held at 1.0 m above the ground and in the

vertical and horizontal modes of operation, respectively (Figure 5.1a). The EM31 mainly provides

information about the shallow vadose zone (i.e. depths beyond 1.5 m). It is positioned

approximately 1.65 m in front of the tractor and suspended 1.0 m above the ground. The positioning

is achieved using a PVC cradle.

The cradle is suspended at the front of the tractor, using nylon guys that are attached to the

roof of the tractor and cradle (Figure 5.1a). The cradle is connected to the main frame of the tractor

M.F. AHMED

78

via two perpendicular pieces of PVC piping, at two points that are spaced approximately 1.0 m

apart. To further enhance instrument stability whilst the vehicle is moving on-the-go mode of

operation, an additional flexible strap is pulled tight and then used to secure the PVC cradle against

the bulkhead, which supports the 486 data logger/control box, GPS computer hardware and the two

EM31 PVC cradle supports.

Unlike the EM38, the EM31 needs to be manually positioned into the vertical or horizontal

mode. The EM31 is fixed in place at four points along the cradle. To ensure that as little wear and

tear occurs on the EM31 fiberglass, four pieces of air-conditioner ducts have been affixed to the

EM31. Each coincides with points on the PVC cradle where the instrument rests. The EM31 is

held in place by four pieces of flexible cord, which are used to strap the instrument firmly onto the

cradle.

5.2.1.3 FieldGuide and Ag132 – guidance

The Trimble® FieldGuide provides positioning and guidance. The system consists of a

GPS400 receiver, a moving map display (MMD), a light-bar and an antenna. The receiver

incorporates a GPS with a power supply. It is powered by a 12 V battery. The GPS is connected

to the MMD and the lightbar, which are both contained within a detachable custom-built case affixed

in front of the operator (Figure 5.1d). The MMD has three windows. The largest is the graphical

window, which indicates the position of the MESS relative to the field boundary, the satellites

available, the differential status and the speed of the vehicle. The other is the message window,

located beneath the graphical window. It provides information on the name of the farm, the name of

field, line spacing and the area covered during survey. The third window is known as the information

column, located along the left-hand side of the screen. It indicates the direction of travel as well as

five lines, which are user-definable and can indicate latitude, longitude, time, etc. The GPS is

positioned adjacent to the data logger and housed in a removable strongbox situated in front of the

operator.

The light-bar is also positioned inside the strongbox above the MMD. It assists in navigation

by indicating to the operator where the current pass is and how to maintain the correct bearing. It

also assists with locating and aligning the MESS to where the next pass should be made. The GPS

antenna is directly above the operator. A second GPS provides wide-area differential correction to

ensure sub-meter accuracy. The GPS (Trimble® AG132) is a 12-channel differential receiver that

uses either free public or subscription-based private differential correction to calculate sub-meter


79

positions in real-time. RACAL satellite differential signal is used. The AG132 unit is also linked to

the GPS400 (powered by the 12 Volt car battery). The Ag132 is mounted on top of the GPS400

unit.

5.2.1.4 Data logger and MESS control – instrument set up

The EM38, EM31 and MMD are connected to a computer that integrates and logs the

incoming measurements into a single file. The file can be downloaded at the end of a day using Lap

Link Version 5. The file contains Eastings and Northings (m) in the Australian Map Grid Coordinate

System (AMG), and EC a (mS/m) measurements by the EM38 and EM31 instruments. Data on

whether the instrument is in the vertical or horizontal mode of operation, height of the EM38, as well

as the date and time of a measurement are also logged. The computer also acts as the controller

allowing the operator to designate whether vertical or horizontal data is collected and the height at

which EM38 measurements are made. The 486 computer is affixed inside the custom-built strong

box.

The MESS can be operated in a stop-and-go or fully automated on-the-go mode, logging

GPS and EM instruments at 1 reading per second. The travelling speed for the system is

approximately 1-2 m/s (or 5-10 kmh). So that a large amount of data can be logged very quickly.

Positional corrections of the EM38 and EM31 measurements need to be made after the survey has

been completed. This is achieved manually.

5.2.2 Case Study – “Cumberdeen,” lower Namoi valley

In order to demonstrate the application of the MESS and how it can be used to provide

information for managing soil salinity, a field experiencing salinity problems was selected. The field is

located at “Cumberdeen,” an irrigated cotton-growing farm situated 2 km southeast of Wee Waa

in the lower Namoi valley (Figure 2.2). The farmer had observed a shallow water table, and

suspected that saline and sodic soil condition had affected cotton production. The field covers

approximately 26 ha. Figure 5.2a is an aerial photograph showing the field. The air-photo shows

the location of the water storage adjacent to the southwest corner of the field. It was constructed by

scraping soil in the middle part of the storage (to depths of approximately 0.9 m), and then pushing

the material up to form the storage walls. The western part of the field contains flood-irrigation runs

that are between 600 m (transect 1) and 800 m long (transect 10), whilst the eastern part contains

runs that are about 540 m in long (Figure 5.2b). The width of the field is approximately 408 m.

Other significant landmarks around the field include two bore pumps.

M.F. AHMED

80

Figure 5.2 “Cumberdeen” field showing: a) aerial photograph and b) 18 transects covered by the

MESS. Note location of the 22 soil sampling sites.

As shown in the airphoto, one of these is near a palaeo channel located just outside the

northwest corner of the field. The other is situated in the middle of the field, near the head ditch and

where the length of the irrigation runs are shorter.

The farm is located at the northern edge of the Pilliga Scrub. Little detailed information exists

about the soil of the Pilliga Scrub apart from the cursory work of Stannard and Kelly (1977). They

recognised several soil types: a) contorted gilgai soil, b) Red-Brown earths and transitional Red-

Brown earths (Inceptisols), c) Solodized Solonetz and d) Deep Sands (Entisols).

The transitional Red-Brown earths are characterised by sharp texture contrast between the

surface horizons and the subsoil. Usually the B-horizon contains a lower clay content than in the self-

mulching clays, upon which most irrigated cotton production is based on in the lower Namoi valley

(Triantafilis et al., 2001c and d). The fine sand and silt fractions also increase with depth, but

essentially the profiles are relatively fine textured (Stannard and Kelly, 1977). However, the texture

is sandy, and the subsoil horizon is generally weakly developed.

The Red-Brown earths exhibit strong texture contrasts, but the surface horizon varies

considerably in depth and texture. The subsoil possesses similar clay contents to the transitional red-

brown earths, but usually clay content decreases significantly with depth. The deep sandy soil


81

profiles, by comparison, were generally obtained in the channels of the more prominent palaeo

formations. These profiles are dominated by the sand fraction throughout.

5.2.3 ECa survey

The MESS survey involved the measurement of EC a by EM31 and EM38, both in the

vertical mode of operation across the field. A total of 18 transects were traversed starting from the

western corner adjacent to the water storage (Figure 5.2b). Each transect was situated

approximately 24 m apart. This was done to align the MESS in the appropriate traffic lanes and also

to avoid compacted or controlled traffic furrows, which could result in non-representative ECa

measurements. Approximately 20,000 measurements were recorded with each instrument. The

survey, which took one day to complete, involved approximately 11,000 m of travel.

5.2.4 Soil sampling and laboratory analysis

In order to determine the location of the sampling sites, and thus the soil properties which

influenced the EM instruments, the EM38 measurements of ECa were plotted against the EM31 at

each of the 20,000 locations. Two criteria needed to be met. The first was that the low,

intermediate and high values of soil EC a should be sampled. The second was that the sampling sites

needed to be spaced evenly across the field in order to characterise as much of the spatial variation

in ECa as possible. A total of 13 calibration sites (i.e. sites 1-13) spaced evenly across the field

were selected (Figure 5.2b). In order to optimize the selection of the calibration sites and thus assist

in elucidating the cause of soil salinity at the southern end of the field 9 extra calibration sites (i.e.

Sites 14-22) were taken along a single south north transect (i.e. transect 3). These additional sites

were examined for other reasons. The first was that the farmer had suggested the storage might be

extended along the entire western boundary of the field. This therefore required knowledge of soil

types in the field. A second reason was that a marked decrease in ECa from the southern to northern

end was evident.

At each of the 22 calibration sites an intact soil core to a depth of 2 m was collected and

subsampled in 0.30-m increments. The ECa was measured directly above each of the sites. The

sub-samples were analysed for the following: field moisture content (%); pH and ECe (United States

Salinity Laboratory, 1954); clay, silt and sand content (%) using the hydrometer method; and

effective cation exchange capacity (ECEC-mmol(+)/kg) (Tucker, 1974) using a mechanical leaching

device (Holmgren et al., 1977). Data collected at Cumberdeen Field 4 is shown in Appendix 2.

M.F. AHMED

82

5.3 RESULTS AND DISCUSSION

5.3.1 Frequency distribution and correlation between ECa

Figure 5.3 shows the frequency distribution of ECa measured in the vertical mode of

operation along the 18 transects. The distributions of EC a for the EM38 (Figure 5.3a) and EM31

(Figure 5.3b) were bimodal. Just over 4,000 EC a measurements were between the range of 30-35

mS/m for the EM38, whilst 4,500 measurements were between the range of 55-60 mS/m for the

EM31. In general ECa, as measured by the EM31, was larger than ECa measured by the EM38.

This suggests that the EM31 may be responding to a more conductive subsoil material. However, as

illustrated in Figure 5.3c, the relationship between the two instruments was highly correlated (ie.

r=0.947). The bimodal nature of the data suggests that the field consists of two very distinct ECa

populations.

Figure 5.3 a) Frequency distribution ECa measured by (a) EM38, b) EM31 in vertical mode of operation, and c) relationship of ECa measured by EM31 versus EM38.

0

25

50

75

100

125

150

EC mS/ma

0

50

100

150

EC mS/ma

0 50 100 150

c)

EM31

EM38 = 0.826 x EM31 - 15.49

r = 0.9472

Frequency0 5000 0 5000

EC mS/ma

a) b)


83

Figure 5.4 ECa distributions along transects a) 3, b) 8 and c) 13 determined by EM31and EM38 in vertical mode of operation.

Figure 5.4, which illustrates the distribution of EC a as recorded along three transects, shows

the bimodal nature of the field more clearly. Transect 3 (Figure 5.4a) was located approximately 75

m from the western side of the field, with the head ditch end lying in close proximity to the large

earthen water storage. Transect 8 (Figure 5.4b) was located a further 120 m away, whilst transect

13 (Figure 5.4c) is located another 120 m to the east, about 100 m from the eastern field boundary.

It is clear from these transects that EC a was greater near the head ditch than near to the tail ditch or

northern end.

Along transects 8 (Northing of 6651850) and 13 (Northing of 6651700) ECa for both

instruments decreases quite sharply. This explains the bimodal nature of the ECa distributions shown

in Figure 5.3. Along transect 3, ECa changes more gradually. Nevertheless, at a Northing of

6652100 ECa was equivalent to that at the northern end of transects 8 and 13. At a Northing of

6651950 along transect 8, a small trough in EC a is obvious. Here, the values of ECa were the lowest

recorded across the entire field.

0

50

100

150

c)

0

50

100

150

Head ditch Tail ditchEM38

EM31

Transect 8

EM38

EM31

0

50

100

150

66517006651500 6651900 6652100 6652300

Northing (m)

EM38

EM31

EC mS/ma

b)

Head ditch Tail ditch

a)

Head ditch Tail ditch

M.F. AHMED

84

Figure 5.5 Spatial distribution of ECa across the field for: a) EM38; and, b) EM31 in vertical mode of

operation. Note: WS = water storage,

The sharp drop in EC a is better appreciated graphically across the field. The spatial

distributions of ECa are shown for the EM38 (Figure 5.5a) and EM31 (Figure 5.5b). In the

southwest corner of the field near the head ditch and the eastern storage wall, EC a was consistently

much higher (e.g. EM31 > 100 mS/m) than at the northern or tail-ditch end (EM31 < 70 mS/m).

There was a sharp drop in EC a, at approximately halfway between the head ditch and tail ditches

(Figure 5.5b).

There was also a small band of low EC a (i.e. 50 ≤ ECa < 60 mS/m) which lies perpendicular

to the eastern storage wall at an approximate Northing of 6651750. In Figure 5.5a, ECa as

recorded by the EM38, was similarly low in this area (i.e. 40 ≤ ECa < 50 mS/m). It is also apparent

that adjacent to this lower band, ECa was larger than 60 mS/m to the north east near the head ditch

of the shorter transects and south adjacent to the eastern storage wall and near the head ditch. The

largest EC a values measured by the EM31 were similarly recorded in the general vicinity of the large

earthen water storage.


85

5.3.2 Comparison of ECa with measured soil attributes

In order to determine which soil attributes influence ECa an average profile value for clay

content, soil moisture, ECEC and ECe was calculated and compared. Differences in soil mineralogy

were inferred from ECEC and the ratio of ECEC and clay percentage (CCR). The results are

shown for the EM38 only, since the EM31 results were similar. Figure 5.6a and b shows that ECa

was generally not well correlated with either of the average field moisture or clay content. With

respect to field moisture content the lack of correlation is consistent with

a) the field being managed in the same way (i.e. furrow irrigated) and b) heavy rain fell prior to the

MESS survey.

Figure 5.6c, on the other hand, shows a significant correlation between ECa and ECe and, in

fact two distinct salinity populations. The profiles with low salinity were located in the northern half,

near the tail ditch. More saline profiles characterise the southern half near the storage. This suggests

differences in drainage between the southern and northern ends of the field. Significantly site 19,

which was located at the northern end and adjacent to the northeast corner of the storage, is not one

of the more saline profiles.

ECEC was also strongly correlated with ECa (Figure 5.6d). The relationship between clay

content and exchangeable cations is often strong and as a consequence if ECa is correlated with one

it can be expected to be correlated with the other. In this situation, the reason for the poor

relationship between ECa and clay content in contrast to the correlation ECEC. was attributable in

this case to mineralogical differences. Figure 5.7a shows average ECEC and clay content for each

of the 22 calibration profiles and illustrates the relationship between ECa and CCR.

In Figure 5.7a and b, site 7 was shown to be distinctly different from the other profiles. The

reason for this is that this site was located in a palaeochannel. This was revealed along transect 8

(Figure 5.4b), and in Figures 5.5a and b, where this profile was taken within a band of low EC a (i.e.

EM38 ≤ 20 mS/m) and (EM31 ≤ 40 mS/m). The aerial photo shown in Figure 5.2a confirms the

existence of such a channel, which was evident beyond the northwest corner of the Field. Stannard

and Kelly (1977) found in such areas, the soil is sandy in nature and the coarse sand fraction

dominates (i.e. at site number 7 this is ~ > 60 %). In a similar study in the nearby lower Gwydir

valley, Triantafilis et al. (2001b) found that low values of ECa correlated well with the location of a

well-defined, sandy palaeo channel. The large amount of charge suggested in Figure 5.7b is biased

somewhat by the small clay content measured at site 7 and shown in Figure 5.6b.

M.F. AHMED

86

Figure 5.6 Regression relationships between ECa (EM38) and average profile (0-2.0 m); a) field

moisture content (%), b) clay content (%), c) ECe (dS/m), and d) effective cation exchange capacity (cmol(+)/kg of soil solids).

The remaining 21 profiles, can be partitioned into two and possibly even three groups based

on the relationship between average ECEC and clay content. The first group was associated with

the southern end of the field and includes sites 1, 2, 8, 10, 18 to 22. All of these have average CCR

values greater than 35 and as much as 55 cmol(+)/kg of clay solids. Pure smectite clays have cation

exchange capacities between 80-90 cmol(+)/kg. By comparison, pure kaolin and illite clays are

characterized by CCR range of between 5-20 and 20-40 cmol(+)/kg, respectively. This suggests

these profiles are probably composed of a mixture of kaolin and illite, but also contain some

smectite. By contrast, the second largest group of profiles are characterised by lower values of

CCR (i.e. < 30 cmol(+)/kg of clay solids) and were located predominantly in the northern part of the

field. This group includes sites 4, 5, 9 and 11 to 16. A mixture of kaolin and illite clays is typically

expected at such values of CCR. The third and smallest group of sites (i.e. 3, 6 and 17) were

1

2

3

4

56

7

8

9

1810

11

12

131415

16

17

19

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21

22

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0 20 40 60 80 1000 20 40 60 80 100

a) b)

c) d)

Moisture (%) Clay (%)

ECe (mS/m) ECEC (cmol (+)/kg of soil solids

Soil ECa(ms/m)


87

situated geographically midway between the northern and southern ends, and although they probably

also contain interstratified clay minerals they act as intergrades between the two types of profiles.

Figure 5.7 Relationship between average profile (0-2.0 m); a) clay content versus effective cation exchange capacity (cmol(+)/kg of soil solids), and b) soil ECa (EM38) versus ratio of effective cation Exchange Capacity and clay content (cmol(+)/kg of clay solids).

The hypotheses about soil mineralogy were confirmed in X-ray diffraction patterns of four

samples obtained from two northern profile (i.e. site 7 and 15) and two southern profiles (i.e. sites

19 and 20) at depths of 0.6-0.9 m. The diffraction patterns shown in Figure 8 suggest that at both

site 7 (Figure 5.8a) and 15 (Figure 5.8b) kaolin and illite are the dominant clay minerals. Similarly

the diffraction patterns obtained from profiles 19 and 20 (Figures 5.8c and d, respectively) suggest

that kaolin and illite clays are also dominant, although the presence of interstratified clay minerals is

evident.

The significance of this is that profiles 19 to 22 are all likely to contain interstratified clays,

which have a shrink-swell character. In practical terms, this perhaps suggests the reason why the

southern end of the field was chosen to site the storage. However, it is evident from the cores drilled

at the southern end that water-logged soil condition exists. The water is suspected to have originated

from the storage, the location and extent of which is unknown. Conversely, extension of the storage

from its current location towards the northern end of the field is probably inappropriate owing to the

lack of shrink swell clays in the soil.

b)

0 20 40 60 80 100

12

3

45

6

7

9

10

1113

14

15

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1920

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22

1

5

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20

10 20 30 40 50

a)

Clay content (%)

ECEC cmol(+)/kg CCR cmol(+)/kg of clay solids

Soil ECa (mS/m)

M.F. AHMED

88

Figure 5. 8 X-ray diffraction patterns of samples sites a) 7, b) 15, c) 19 and d) 20.

k335k550kadmgglymgad

kaolinite

illite

0

100

200

300

400

500

600

700


kaolinite

illite

2θ (Cukα)

0

100

200

300

400

500

600

700


kaolinite

illite

0

100

200

300

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700


kaolinite

illite

2θ (Cukα)3 10 20 30

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1100

b)

a)

c)

d)


89

5.3.3 Spatial distribution of soil attributes along a transect

In order to elucidate where the storage dam may have leaked and why free water was found

at depth, the average soil variables measured along transect 3 against the length of the transect was

plotted. Along this transect, site 22 was located approximately 10 m north of the head ditch. Sites

21, 20 19 and 18 lie approximately 80, 100, 160 and 230 m, respectively from site 22. Sites 17 to

14 lie in the northern half of the field. Figure 5.9a shows the similarity of average moisture content at

each of the nine sites. It does not reveal any real indication of the source of the water, which was

accumulating. Similarly, Figure 5.9b provides no real indication of any textural discontinuities since

average clay content between sites 22 and 19 are similar although it is obvious that at site 22 clay

content is slightly smaller. The lack of clay content at sites 21 and 22 appears to be compensated

somewhat by the fact that CCR (Figure 5.9d) was larger at these sites in comparison to 19 and 20

and hence probably contain slightly larger quantities of interstratified clay minerals. As such we could

reasonably expect high shrink-swell capacity of the soil at these two sites and perhaps better water

retaining properties.

Average EC e, shown in Figure 5.9c, provides the best indicator of where the water is

leaking. At site 19, average soil profile ECe (i.e. 1.16 dS/m) is one of the smallest with respect to the

other southern profiles that lie adjacent to the water storage. By comparison sites 18 and 20

(located in either side of and within 70 m of site 19) have ECe values of between two to four times

that of site 19. This suggests that site 19 is in a discharge area. Conversely, sites 18 and 20 are

located in recharge areas where salts are accumulating.

It appears that site 19 is the main area where leakage occurs. This can be explained by

considering two indicators of soil structural stability. That is ESP and the ratio of Ca and Mg. These

are shown in Figure 5.10a and b (at depths of 0.6-1.2 m). In Australia, soil with ESP values, which

exceed 6 are considered as sodic and the soil is likely to be dispersive. Soil dispersion also occurs

when the Ca/Mg ratio is below 2, although it is more likely to be so where this value is less than 1

(McKenzie, 1998). At site 19 neither of these conditions is satisfied. It is possible that the soil at

site 19 may be structurally more stable than the soil at sites 20, 21 and 22. Consequently, and

assuming that the site was representative of the soil in the vicinity with similarly low ECa (i.e. near the

storage wall), we can conclude this area of the field acts as the conduit of water from the storage to

the field.

M.F. AHMED

90

Figure 5.9 Spatial distribution along transect 3 of average profile a) field moisture content (%), b)

clay content (%), c) ECe dS/m, and d) CCR (cmol(+)/kg of clay solids).

The reason why the salts do not drain freely beyond the root-zone can also be explained by

Figure 5.10, which shows the variability of Ca/Mg and ESP at 1.8-2.0 m depth, along transect 3.

The Ca/Mg ratios (Figure 5.10 c) are all below 1 at the southern end of the field. The soils at these

depths are therefore likely to be structurally unstable and hence can easily be dispersed. Thus deep

drainage may be impeded. This is consistent with the mottling characteristics at depths beyond 1.2

m in the northern profiles as recorded during the field survey. On the other hand, spatial distribution

25

35

45

22

2120

18

19

17 16

15

14

b)

Clay content(%)

2

5

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20

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1716 15 14

0

c)4

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1

ECe (dS/m)

CCR (cmol(+)/kg of clay solids)

20

40

50

60

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2018

1917

16 1514

66517006651500 6651900 6652100 6652300

Northing (m)

d)

5

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201819

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15 14

a)

Field moisture content (%)


91

of ESP (Figure 5.10d) suggests that these results are equivocal, particularly at site 19 and 21, both

of which are non-sodic.

Figure 5.10 Spatial distribution along transect 3 of average a) 0.6-1.2 m Ca/Mg, b) 0.6-1.2 m ESP, c)

1.8-2.0 m Ca/Mg and d) 1.8-2.0 m ESP.

The soil mottling at depth is indicative of imperfect drainage, and it may well be that in some

circumstances, water drains beyond 2.0 m. This is the case because the bore water used to irrigate

this field is slightly saline (ECw = 0.57 dS/m) and as a result may periodically result in improved soil

0

2

3

1

stable

unstable

a)

2221

20 18

19 17

16

15

14

Ca/Mg 0.6-1.2m

ESP 0.6-1.2m15

10

strongly sodic

sodic

non-sodic

b)

22 2120

18

19

1716

15 145

0

66517006651500 6651900 6652100 6652300

Northing (m)

22

21

20

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1917

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15 14

25

15

5

0

d)

strongly sodic

sodic

non-sodic

ESP 1.8-2.0m

22 21 20 1819

17

16

15 14

3

stable

unstable

c)

2

1

0

Ca/Mg 1.8-2.0m

M.F. AHMED

92

structural stability that probably enables deep drainage. This may be the case at sites 21, 19 and 18

where soil ESP borders on what is considered non-sodic and therefore structurally stable.


93

5.4 CONCLUSIONS

The MESS described provides a rapid method for measuring ECa at the field scale. In this

study, an example of how the MESS was used at “Cumberdeen” Field 4 was shown. The spatial

distribution of ECa collected using the EM31 and EM38 along with the correlation between the

instruments, enabled the design of a suitable soil sampling strategy to calibrate the instruments in this

field. In addition, the collection of a detailed set of profiles along a single transect allowed the likely

area of leakage from the storage dam and hence the cause of soil salinity to be determined.

The calibration of the EM38 instrument was achieved by simple linear regression of average

soil profile properties. This included comparison of EC a with field moisture and clay content (%),

salt concentration (EC e-dS/m) and effective cation exchange capacity (ECEC-cmol+/kg). The

results suggested that ECe and ECEC were the most strongly correlated with EC a in this field. This

was particularly the case with respect to ECEC. As a result interpretations about the likely cause of

leakage from an earthen storage and effects of soil salinity at the field-scale could be determined.

Further work could involve corroborating the results obtained here by carrying out a MESS

survey within the dam and taking more detailed soil samples at the base of the eastern storage wall.

In terms of management, the grower has several options relating to improving water use efficiency.

The first is the relocation of the storage to another portion of the farm more suited for this purpose.

This is a rather costly proposition. An alternative to this is to dig a trench beneath the northwest

corner of the storage wall and insert either a geo-membrane, such as a bentonite curtain, or soil

imported from the nearby clay plains. Either way the wall is likely to be sealed and prevent further

leakage. In terms of increasing the size of the storage to the north, this option is not recommended.

This is because the soil located in the area proposed does not contain the necessary clay minerals

(e.g. smectite) which are suited for this purpose.

CHAPTER 6

DISCUSSION, CONCLUSIONS AND FUTURE RESEARCH

CHAPTER 6 - DISCUSSION, CONCULSION & FUTURE RESEARCH

93

6.1 DISCUSSION

There is evidence that risk assessment of dryland salinity suggests that salinity is on the

increase in the northern catchments of the Murray-Darling Basin, especially at or near the foot-slopes

of the Great Dividing Range as shown in Figure 6.1 (National Land and Water Resources Audit,

2001). This could put at risk not only the dryland farming systems but also irrigated agriculture

located downstream. In addition, isolated cases of soil salinity in the irrigated-cotton fields are

becoming apparent. The work described here is an attempt to collect natural resource information at

the field and regional scales, which can be used to assess and simulate potential soil salinity in the

cotton growing areas of the lower Namoi valley. Management options most appropriate to ensure

they remain sustainable are discussed.

Figure 6.1 Forecasted areas containing lands of high hazard or risk of dryland salinity in 2050 (after National Land and Water Resources Audit, 2001).

In Chapter 4, risk assessment of the irrigated farming systems in the lower Namoi valley was

carried out based on simulations using a steady-state mass-balance model called SaLF (Thorburn

and Shaw, 1988). The use of this type of model is consistent with the approach of Rhoades et al.

(1992). They advocated the use of a steady-state composition that better reflects the worst-case

scenario (i.e. maximum build up of salinity and sodicity) that would result from the use of saline

M.F. AHMED

94

irrigation water. This is in contrast to transient state models, which although preferable, require a

large number of inputs which are generally not available in most practical conditions (i.e. how to

relate crop response to time- and space varying salinity).

The results of SaLF simulations suggest that the use of the current river water (i.e. ECiw =

0.4 dS/m) for irrigated cotton production should not increase root zone salinity in the foreseeable

future. Further based on the worst case scenario the use of moderate to high saline water (ECiw of

4.0 and 9.0dS/m) would require a combination of suitable soil, crop and irrigation management

practices. With respect to soil management, irrigation with saline water would require changes in the

way the bed is designed. Specifically, salinity is likely to increase if the rows were furrow irrigated,

round-topped and aligned in single rows (Rhoades et al., 1992). Fortunately, in the lower Namoi

valley cotton-growing region, 2-metre bed design is the standard. However, some machining and

engineering would be required in order to manufacture the necessary equipment for beds with long

side slopes, so that the crops could be planted on mid -slope positions away from areas where salts

would accumulate.

Of major concern is the excessive deep drainage that may result from using highly saline

water for irrigation, as simulated here, and the potential for the creation of shallow saline water

tables. This is despite the fact that most of the irrigated infrastructure is located on the heavier clay

plain alluvial soils (i.e. Vertosols). However, soil salinity generally improves the tilth and hence the

permeability of the soil. The areas of greatest concern, however, would be where irrigation

infrastructure, such as water storages and supply channels, have been developed and the soil types

used in their construction. In these situations, several options are available and include the lining of

sections of water storages with geo-membranes (e.g. bentonite) or their relocation onto more

suitable soil types. With respect to channels and head ditches, these could be re-routed to run

across soil types, which contain more reactive clays.

Discussion of salinity management strategies with respect to the use of saline water is a little

premature in the lower Namoi valley. This is because ECiw of the Namoi valley river water is not

predicted to exceed 1.4 dS/m until 2100. What is necessary, however, is the constant monitoring of

water quality along the entire length of River including its tributaries. This is particularly the case for

the Mooki River, which regularly exports a large quantity of salt from upstream areas affected by

dryland salinity. In this sense, resources for identifying causes and controls of salinity are most

urgently required in the upper Namoi valley as diminishing water quality is probably the greatest


95

threat to the sustainability of the irrigated farming systems southeast of Gunnedah, west of Walgett

and around Wee Waa.

As shown in Figure 6.1 the problem of dryland salinity risk is greatest along most of the

Great Dividing Range of New South Wales. In these areas, many dams have been constructed and

are used to supply water for irrigation downstream. It is suggested that the approach used in this

study of lower Namoi valley should be extended to other areas, such as the lower Gwydir valley

located to the north and the lower Macquarie valley located to the south, in order to determine the

spatial distribution of soil types which are susceptible to increasing amounts of salts in irrigation

waters. This is particularly the case for the Macquarie valley where EC iw is predicted to reach levels

of 2.11 dS/m by the year 2100 (Murray-Darling Basin Commission, 1999) and where soil salinity

caused by irrigation inefficiencies are already apparent (Willis et al., 1997).

The conditional probability maps produced here using the various methods were essentially

similar, despite the fact that DK produced slightly fewer misclassifications as compared with MIK

and IK. As a result any of these approaches is suitable for estimating the risk of soil salinity using the

SaLF model and various worst-case water quality scenarios. As a result the computational

efficiency of each of the interpolation methods used to predict conditional probability needs to be

considered.

DK takes the longest to perform, particularly in the initial phase when modelling the

anamorphosis and its expansion in Hermite Polynomials is required. For these calculations, DK

needs an anamorphosis modelled on the block support and a variogram model. This is achieved in

several steps: 1) modelling the raw punctual variogram; 2) regularization on the block support of the

variogram; 3) transformation of this regularized variogram in the corresponding gaussian variogram

using the block anamorphosis, and; 4) modelling this last discretized variogram.

IK was carried out using two steps: 1) data converted into 0 and 1 using cut-off value (i.e.

ECe = 2, 4, 6 and 7.7 dS/m); and, 2) estimation of conditional probability using a local variogram. If

a global kriging is to be carried out variograms for each transformation will be required. By

comparison MIK was the computationally most efficient as it only requires the input of the raw ECe

data into the MIK program (Markus 2000). Computationally, MIK is time-consuming to produce

the final maps since each of the cut-offs used is modelled. However, the advantage of MIK is that it

can more efficiently provide up to 50 realisations of the conditional probability than DK and IK.

In order to enhance the accuracy of the risk maps produced here, the approach described

could be improved via the use of EM instruments. That is, at each of the soil sampling sites used to

M.F. AHMED

96

collect the input parameters required for the SaLF model, measurements taken with the EM38 for

example, could be calibrated to estimate soil ECe or deep drainage as produced by the model.

Additional EM measurements taken at unsampled locations should lead to an improvement in the

prediction of conditional probability of soil ECe or deep drainage exceeding a critical value and the

accuracy of the maps produced.

Where salinity management is required in the lower Namoi valley, it is confined to isolated

instances as found at ‘Cumberdeen’. As determined by the information gathered from a Mobile EM

Sensing System (MESS) and soil samples strategically sampled across the field, the soil types used

here for irrigated agriculture are unique compared with the remainder of the district. This is

evidenced by the low clay contents (i.e. <35 %) as compared to the clay alluvial plains (i.e. > 45 %).

In addition, the type of clays found were dominated by kaolin and illite minerals which are non

shrink-swell minerals and are generally not suitable for the construction of water storages and supply

channels. As a result, leakage has occurred and caused some soil salinisation. From the data

collected it is evident that the extension of the storage from its current position toward the tail ditch is

not recommended. In addition and in this instance the most inexpensive approach to remediate the

problem of leakage is to excavate a trench beneath the storage wall and line it with a geo-membrane

before reconstructing the wall.

In this study, the establishment of a linear relationship between ECEC and ECa (i.e. EM38)

was not examined further, in terms of its use in estimating soil fertility across the field. This could be

looked at in the future along with the ECe data and the effect higher salinity has on cotton yield. In

addition, future sampling could be carried out at various parts of the growing season, particularly with

respect to ECe along transect 3, in order to determine how sensitive the EM instruments are to

changes in profile soil salinity with time.

It is also suggested that soil sodicity problems apparent in some irrigated cotton fields in the

district could be investigated with the MESS. In the northern part of the Edgeroi district the use of

groundwater that is characterised by high sodium adsorption ratios (SAR), has resulted in soil

profiles with high values of ESP (i.e. 18-27 %). These farms are located in the northern parts of the

area. It would be useful to test the ability of the MESS to identify management units, which could be

used to determine variable/optimal rates of gypsum to ameliorate the strongly sodic nature of the soil.


97

6.2 CONCLUSIONS

With respect to salinity risk assessment in the lower Namoi valley:

1). The SaLF model provides point estimates on the consequence of increasing saline water

application on a wide variety of soil types. Results suggested that the clay alluvial plains are most

susceptible to build-up of salts, whilst the sandier soil types associated with the prior stream channels

and Pilliga Scrub complex are likely to be conducive to excessive deep drainage.

2). The current water quality of EC iw 0.5 and ECiw 1.4 dS/m (that forecast for 2001) should not

lead to increased levels of soil salinity which will affect the irrigated cotton farming system. If ECiw

exceeds 4 dS/m it is likely that soil ECe will exceed the critical limit for legume production and will

therefore require some management. At an ECiw of 9 dS/m the threat of soil salinity increases

dramatically, where some management would be necessary in order to continue with the inclusion of

wheat and perhaps some cotton cultivars in the irrigated cotton farming system. This is the case on

the clay alluvial plains.

3). Non-linear geostatistical methods allowed point estimates to be used to map areas of risk at

unsampled locals. Of these DK was optimal, followed by MIK and IK. Computationally, MIK is

the most time consuming method as compared to DK and IK. However, it can produce more

efficiently up to 50 realisations of the conditional probability than DK and IK.

With respect to cause and control of soil salinity in the lower Namoi valley:

1). The MESS provided information about the spatial distribution of EC a which enabled soil

sampling sites to be selected to determine the cause and control of soil salinity in an irrigated cotton

field.

2). EC a as measured by the EM38 was highly correlated with ECEC and to a lesser extent ECe.

Consequently, it was found that this difference in mineralogy as well as chemical properties of soil

along the storage wall enable site of leaking storage to be allocated.

3). Based on the results achieved here it is recommended that the northeastern corner of the

storage wall should be excavated and sealed with a bentonite curtain or with the use of reactive clays

quarried from the clay plains located to the north.

M.F. AHMED

98

6.3 FUTURE RESEARCH

The results achieved here point clearly to future research directions. With respect to

assessment of salinity risk in the cotton growing areas of southeastern Australia this includes:

1). The use of the SaLF model and various worst-case scenarios used here should be applied

across irrigated cotton growing districts such as the lower Macquarie valley where by the year 2100,

EC iw is expected to be 2.11 dS/m as a result of increasing dryland salinity (The Salinity Audit,

1999).

2). Testing whether improvements in interpolation can be achieved by calibrating these estimates

using EM instruments particularly with an EM38.

3). Carry out simulations to estimate the threat of excessive deep drainage losses using SaLF or

groundwater recharge rate using a chloride mass balance model.

4). Determine salt tolerance of various varieties of crops (i.e. legumes, wheat and cotton)

currently used in cotton farming systems to determine where their use may be appropriate in areas

currently experiencing soil salinity.

With respect to field assessment, determine the usefulness of the Mobile EM Sensing System

to determine suitable management practices:

1). Determine suitability of system for identifying areas in fields where irrigation inefficiencies may

occur due to excessive deep drainage using salt/water balance model

2). Measure and map the spatial distribution of soil properties such as clay content and soil

salinity using various geostatistical methods.

3). Identify soil sodicity management units in fields where high Sodium Adsorption Ratio (SAR)

waters are used for irrigation in the lower Namoi valley. This is particularly the case in the areas to

the north of Spring Plain Road where sodic groundwater is extracted for irrigated agriculture.

4). Develop a more robust system for consultancies, particularly assessment of suitability of

ground for development into large earthen storages and supply channels.

BIBLIOGRAPHY

BIBLIOGRAPHY

99

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148.

APPENDICES

APPENDIX 1 – INPUT VALUES FOR SaLF MODEL

113

Appendix_1: Input values for SaLF model

Clay content (%), effective cation exchange capacity (cmol(+)/kg) and exchangeable Na (cmol(+)/kg).

Site Coordinates Clay content (%)

Effective Cation Exchange Capacity (cmol(+)/kg)

Exch. Na (cmol(+)/kg)

ID Eastings m

Northingsm

0.0-0.1 m

0.3-0.4 m

0.7-0.8 m

1.2-1.3 m

0.0-0.1 m

0.3-0.4 m

0.7-0.8 m

1.2-1.3 m

1.2-1.3 m

nm001 736955 6656565 53.20 48.71 40.09 25.25 37.76 30.39 29.89 24.36 0.20 nm002 734239 6654621 55.12 62.88 61.29 61.18 39.55 42.55 44.86 42.00 4.54 nm003 738261 6654161 59.63 64.27 63.74 64.14 39.72 39.51 42.05 36.89 4.55 nm004 738807 6645055 11.51 24.71 22.91 53.45 6.91 7.12 7.90 15.46 0.89 nm005 735555 6648246 39.02 45.17 48.63 52.73 25.07 27.98 34.28 28.72 8.36 nm006 737703 6651313 32.24 44.07 40.80 44.09 18.26 21.42 22.26 22.14 0.85 nm007 730568 6651754 50.21 59.88 60.03 50.37 33.86 36.69 40.49 31.58 4.19 nm008 732125 6649795 44.95 50.90 63.53 43.84 32.19 31.39 41.11 26.58 1.22 nm009 725782 6642530 60.29 44.67 35.29 31.95 20.66 14.54 9.91 10.43 1.24 nm011 719751 6644648 13.50 31.30 35.79 35.75 4.76 16.13 17.81 19.11 5.22 nm012 721375 6644355 19.28 36.85 55.94 53.52 10.56 19.21 33.91 20.80 1.75 nm013 726504 6639570 39.16 42.04 38.72 30.54 16.04 16.03 16.00 17.94 1.10 nm014 715418 6643430 7.67 24.13 24.79 18.38 2.69 13.88 17.11 13.16 8.01 nm018 688567 6643133 48.19 46.51 38.68 41.07 30.69 31.44 28.98 24.67 3.32 nm020 708267 6641513 7.87 20.27 26.34 13.74 1.80 9.99 7.68 11.29 8.17 nm021 732189 6655216 71.81 74.96 73.69 72.51 53.09 55.97 50.91 43.56 4.10 nm022 726246 6652717 61.07 65.96 63.17 54.55 42.02 49.67 43.49 32.68 4.23 nm023 724258 6648128 60.81 66.47 61.19 52.02 43.23 48.48 43.56 27.28 4.11 nm024 721307 6647576 60.10 64.18 57.64 52.42 44.69 48.72 40.45 24.48 1.80 nm025 724403 6651892 71.79 78.24 76.00 78.11 49.20 58.60 52.29 38.66 1.49 nm026 719779 6651577 60.55 65.59 61.96 60.26 43.16 49.93 47.15 34.32 2.53 nm027 716234 6647830 55.99 61.34 61.69 44.90 38.83 46.04 41.43 20.64 2.83 nm028 716045 6652663 54.97 64.36 64.18 64.74 41.63 51.86 48.84 37.28 4.69 nm029 719306 6648797 51.95 56.21 48.45 46.84 37.58 44.04 37.56 24.60 0.57 nm031 727435 6655288 27.14 46.16 45.35 44.07 16.98 30.28 31.16 17.68 0.02 nm032 721514 6658108 69.21 77.42 74.33 73.06 53.25 65.16 56.21 43.77 2.56 nm033 716443 6654818 57.21 63.99 62.91 59.88 40.74 49.36 47.92 26.32 2.54 nm034 718904 6657360 52.29 57.48 54.46 51.69 33.22 44.34 40.77 30.72 0.84 nm036 710577 6655449 64.71 67.52 66.74 64.54 45.84 45.06 44.28 38.28 7.45 nm037 708908 6652319 58.32 65.22 66.69 58.49 40.25 49.08 43.87 37.00 9.78 nm038 704110 6649397 55.90 57.41 62.39 56.88 37.61 43.86 37.81 32.55 7.39 nm039 700973 6655778 46.54 50.71 49.74 50.83 32.83 40.67 32.32 30.58 1.06 nm040 699350 6656615 63.94 66.87 68.71 64.85 44.16 54.66 45.12 36.74 7.66 nm041 694963 6647704 54.55 60.44 56.32 49.72 40.44 47.97 38.73 31.85 6.95 nm042 690101 6649054 52.21 57.95 59.20 58.71 37.66 51.43 39.45 34.78 8.83 nm043 683764 6653598 51.33 56.95 46.95 46.89 36.15 40.73 29.59 29.63 4.58 nm044 677681 6651807 58.36 63.91 65.77 64.40 44.38 44.66 42.58 46.69 5.14 nm046 672823 6658515 45.63 54.05 47.66 45.60 33.05 48.01 33.40 25.29 5.17 nm047 668223 6652637 62.14 64.87 63.44 64.11 41.22 52.00 27.95 35.04 1.95 nm048 683739 6640292 55.75 68.60 69.73 65.70 29.87 48.39 37.85 28.59 3.65 nm049 668718 6638606 49.99 62.25 30.53 42.11 19.61 34.86 12.40 12.81 1.62 nm050 676607 6645813 51.89 52.37 60.62 49.28 37.26 43.64 40.80 26.87 1.49 nm051 666536 6649738 55.66 53.48 62.11 59.51 38.02 43.99 39.93 35.45 3.02 nm052 668828 6666333 39.11 49.45 46.04 31.94 24.51 37.66 31.05 23.68 8.10 nm054 676800 6662492 66.32 71.74 72.33 73.21 48.24 64.18 57.79 54.20 11.83 nm055 680926 6673780 26.46 60.67 56.12 54.38 14.90 47.52 35.17 35.49 2.17 nm056 676171 6680611 48.36 47.81 35.34 42.12 26.10 36.44 30.37 30.21 4.87 nm057 674562 6675594 49.03 45.42 44.71 30.88 29.56 37.43 27.08 23.39 1.77 nm058 670167 6675361 41.37 45.58 48.83 45.26 26.27 35.55 32.30 29.03 0.69 nm059 667362 6682701 46.37 46.58 47.86 53.79 28.87 36.79 34.17 33.12 6.73 nm061 668754 6688378 58.70 59.53 59.03 59.38 35.37 45.77 43.69 35.09 8.37 nm062 679763 6690451 62.28 62.85 65.24 65.77 35.77 42.81 48.38 38.36 4.47 nm063 682696 6690160 47.27 57.86 54.65 56.08 26.99 41.76 34.60 34.28 3.13 nm064 690388 6688342 36.66 61.42 48.54 53.98 20.41 43.68 32.18 32.75 4.17 nm065 691955 6684838 44.00 37.46 35.75 44.41 30.68 28.79 31.56 26.42 5.93 nm068 694853 6677474 47.17 47.44 50.13 47.15 32.48 34.10 41.59 26.35 5.31 nm069 701926 6675251 56.40 59.06 64.45 58.08 37.73 41.67 47.10 30.67 7.34 nm071 696096 6670582 49.19 54.11 57.35 42.77 31.97 44.13 46.13 26.46 2.49 nm072 690006 6669665 38.65 34.58 28.57 26.94 24.22 30.74 25.85 19.68 2.71 nm073 686817 6667419 53.47 58.10 56.83 41.35 37.37 42.97 43.39 25.02 4.63 nm074 686186 6661862 51.34 43.82 45.59 42.64 30.79 35.42 28.81 25.06 1.64 nm075 692324 6662404 42.33 47.09 31.85 36.78 24.03 33.44 22.22 21.83 2.57 nm076 690942 6663648 55.55 58.20 57.97 51.64 33.36 40.01 42.88 27.50 5.49 nm077 696736 6662683 60.34 61.24 63.37 59.53 40.00 45.36 46.76 32.44 7.03 nm078 698989 6659199 55.32 56.09 59.87 58.21 35.87 39.37 44.62 33.16 8.89

M.F. AHMED

114

nm080 700895 6662900 64.41 66.86 69.05 68.34 45.03 39.20 46.68 39.81 7.07 nm081 714415 6679372 48.86 51.03 52.89 51.97 33.80 37.22 30.73 29.96 7.47 nm082 705863 6680518 43.41 52.30 55.39 54.18 28.72 42.51 36.76 35.13 4.39 nm083 708420 6688517 57.44 63.82 67.24 64.57 35.19 42.65 49.48 41.18 12.99 nm084 715972 6688551 19.60 41.32 37.98 37.98 11.99 29.14 22.74 22.74 22.78


115




ID Eastings m

Northingsm

0.0-0.1 m

0.3-0.4 m

0.7-0.8 m

1.2-1.3 m

0.0-0.1 m

0.3-0.4 m

0.7-0.8 m

1.2-1.3 m

1.2-1.3 m

nm085 720571 6692340 51.61 53.74 53.66 53.25 34.13 38.20 29.23 30.01 4.93 nm088 722619 6676500 54.49 58.48 60.14 62.20 37.01 42.05 33.60 34.61 5.04 nm090 730956 6684502 46.82 51.55 53.56 57.03 32.98 38.74 32.81 33.61 2.01 nm091 730424 6679060 34.92 40.50 41.07 34.59 20.14 27.82 25.47 29.59 5.05 nm092 736491 6677211 55.52 57.24 52.88 61.77 30.43 38.44 31.12 32.56 5.17 nm093 736059 6690852 40.30 31.46 22.51 49.36 26.79 27.29 13.20 29.73 5.67 nm094 704729 6665000 62.73 66.38 67.48 67.01 42.97 50.82 41.48 41.50 4.29 nm096 711666 6672168 55.00 60.06 66.57 65.99 38.93 43.88 38.97 36.98 4.85 nm097 705032 6671344 41.69 48.66 55.23 56.80 30.61 37.67 39.94 37.03 3.68 nm098 708313 6665932 55.84 61.07 68.96 64.97 39.87 50.46 52.89 42.77 4.82 nm099 708681 6669847 59.26 63.39 66.15 61.20 38.42 48.77 49.21 40.30 4.67 nm100 710918 6660691 63.15 62.58 64.56 60.99 47.59 47.43 46.98 38.11 6.47 nm101 709620 6662010 44.42 46.80 45.10 38.79 33.61 37.13 35.94 25.28 3.57 nm102 714260 6668343 66.02 66.61 67.51 67.70 47.31 49.56 51.02 45.12 8.89 nm104 713558 6659977 68.92 72.74 70.79 65.13 48.12 53.89 52.89 42.77 6.06 nm105 715850 6665944 55.94 59.04 64.92 56.59 44.42 49.48 48.79 42.73 3.51 nm106 715754 6660314 48.82 43.53 32.28 30.12 34.23 33.52 24.02 25.91 3.61 nm107 717714 6664619 59.97 60.52 64.84 43.35 38.91 44.08 43.89 28.22 2.98 nm108 718512 6661678 67.25 69.44 70.47 61.59 47.65 54.37 46.17 45.89 1.36 nm109 719402 6671144 60.84 57.74 46.69 58.67 43.42 46.75 36.07 40.55 7.13 nm110 737673 6659715 65.37 66.90 53.84 63.30 48.55 51.32 42.64 37.53 0.83 nm111 734264 6662007 54.94 58.02 46.10 55.43 38.50 46.02 37.34 38.02 2.32 nm112 736976 6664552 51.92 57.15 44.82 50.67 36.50 44.47 33.51 30.80 1.47 nm114 738784 6666674 42.40 34.14 37.77 48.87 28.32 29.21 27.47 27.60 5.28 nm115 733405 6668766 50.88 53.15 48.77 60.60 37.30 45.69 36.23 25.69 2.29 nm116 725436 6666676 60.97 65.20 58.93 70.12 43.73 53.36 47.00 32.10 1.46 nm117 731990 6671652 62.59 65.62 59.45 65.11 40.95 53.74 42.01 39.17 1.08 nm118 725344 6671918 59.38 63.17 25.60 62.69 41.42 46.39 45.37 36.37 3.33 nm119 732051 6657768 68.92 72.48 61.18 60.48 48.64 54.04 48.43 38.22 3.27 nm120 729386 6663864 58.56 58.15 50.85 54.47 41.58 44.65 35.56 32.09 0.45 nm120 729386 6663864 58.56 58.15 50.85 54.47 41.58 44.65 35.56 32.09 0.45 nm121 723002 6661469 65.88 69.65 61.41 63.53 47.19 50.15 44.20 43.24 2.33 nm123 723419 6671392 63.24 64.89 63.04 63.95 43.35 46.42 44.64 38.51 3.13 nm124 726948 6658011 62.48 66.49 58.84 53.58 44.36 49.69 49.12 31.60 3.69 bu002 723800 6684500 52.70 60.40 58.50 56.20 36.49 47.68 50.73 45.54 11.68 ed001 741800 6677900 52.80 51.90 49.70 55.60 37.25 47.80 40.67 47.79 10.00 ed002 744550 6677800 61.10 65.40 65.40 68.80 48.14 46.38 33.01 57.27 10.17 ed004 750100 6677750 66.70 69.20 55.10 72.60 46.82 58.46 54.36 58.38 13.76 ed005 752800 6677700 59.10 61.60 62.60 64.60 48.80 49.98 52.91 53.60 12.31 ed006 755600 6677700 50.90 50.50 59.40 63.70 37.64 45.82 53.53 55.72 12.64 ed007 758400 6677600 50.30 52.00 54.70 65.30 38.18 41.49 48.26 55.06 10.13 ed008 761200 6677500 49.60 54.50 62.20 70.10 39.22 43.93 54.14 61.89 16.82 ed009 763900 6677400 62.30 63.70 68.90 70.70 51.51 62.49 65.29 66.78 15.64 ed011 769500 6677200 70.60 68.40 65.80 65.30 51.84 54.97 52.09 54.56 13.70 ed012 772200 6677100 53.00 55.10 58.10 65.40 45.03 47.79 51.33 55.28 20.38 ed019 743200 6675500 52.80 56.50 48.50 42.10 43.70 47.96 39.62 31.44 9.20 ed020 745900 6675400 71.60 72.00 72.10 73.90 45.23 51.92 53.54 51.33 9.12 ed021 748700 6675400 65.00 64.10 67.60 73.20 47.71 49.18 55.52 61.04 15.64 ed023 754200 6675200 58.00 58.60 62.50 65.00 46.08 48.53 52.98 54.54 11.60 ed024 756900 6675200 50.50 53.10 52.50 53.20 47.37 50.25 45.35 46.00 8.21 ed025 759700 6675100 53.40 57.30 57.80 54.60 43.07 46.20 45.36 42.69 8.82 ed027 765300 6675000 44.30 46.70 51.00 58.00 31.00 38.62 40.01 45.49 9.04 ed028 768000 6674900 45.20 49.40 53.40 66.60 34.54 42.74 47.07 59.57 16.00 ed029 770800 6674800 51.00 50.70 55.40 43.30 40.77 46.80 45.53 42.90 13.94 ed036 741700 6673100 38.60 33.90 29.60 34.50 31.81 28.80 24.62 26.11 6.60 ed038 747300 6673000 60.20 59.90 62.60 62.10 37.87 40.89 44.92 43.92 7.59 ed039 750000 6672900 32.40 43.00 52.70 61.40 20.31 34.44 41.00 46.68 9.74 ed040 752750 6672900 39.50 46.20 55.70 58.30 29.55 39.49 46.45 48.28 8.92 ed041 755500 6672800 22.90 41.00 50.60 56.80 13.05 33.42 41.71 46.72 8.31 ed042 758300 6672750 40.40 50.20 54.90 58.50 26.95 36.72 40.92 44.86 6.71 ed043 761000 6672700 38.90 47.00 49.40 58.60 34.91 49.69 50.32 60.07 9.05 ed044 763800 6672600 47.70 48.40 54.90 58.90 32.59 38.21 43.69 47.29 8.93 ed045 766600 6672600 39.90 47.00 55.60 62.70 27.28 34.94 42.11 47.39 10.25 ed047 772000 6672100 51.40 56.90 55.20 51.40 46.30 53.41 51.95 45.73 9.30 ed054 743100 6670700 66.60 68.80 70.50 72.00 44.72 50.96 49.74 50.17 11.68 ed055 745800 6670700 65.20 64.20 66.60 66.90 41.95 45.30 47.25 45.66 9.72 ed056 748500 6670600 65.90 62.50 62.80 65.90 43.15 41.95 45.12 50.19 12.34 ed058 754100 6670500 24.70 48.50 48.20 56.30 11.24 34.91 37.76 40.98 7.54

M.F. AHMED

116

ed059 756800 6670400 46.40 51.80 52.50 53.10 57.49 62.56 63.53 60.89 4.90 ed060 759600 6670400 49.20 47.60 51.60 56.70 36.08 42.09 43.93 47.87 10.81 ed062 765100 6670200 44.30 49.30 52.30 58.40 27.62 40.02 40.68 43.50 5.02 ed063 767900 6670100 31.10 37.20 46.70 45.30 25.97 32.18 40.17 36.19 5.67 ed064 770700 6670100 28.40 32.00 41.20 50.30 28.73 28.51 39.85 42.26 6.08 ed071 741700 6668300 52.50 53.60 54.70 55.60 37.26 39.61 41.96 35.53 8.42


117

Site Coordinates Clay content

(%) Effective Cation Exchange Capacity

(cmol(+)/kg) Exch. Na

(cmol(+)/kg) ID Eastings

m Northings

m 0.0-0.1

m 0.3-0.4

m 0.7-0.8

m 1.2-1.3

m 0.0-0.1

m 0.3-0.4

m 0.7-0.8

m 1.2-1.3

m 1.2-1.3

m ed073 747100 6668200 60.60 62.20 58.00 55.80 48.93 48.33 47.82 47.88 9.00 ed074 749800 6668500 56.10 56.00 54.70 55.60 40.02 41.45 42.33 45.83 8.77 ed075 752700 6668100 55.20 58.70 61.20 66.00 40.83 47.51 49.87 51.99 10.86 ed076 755400 6668000 43.20 45.50 48.20 56.10 34.83 38.67 42.33 47.85 8.95 ed077 758200 6668000 49.30 52.20 52.40 60.60 32.87 40.33 43.78 54.52 10.93 ed078 760900 6667900 37.40 39.60 47.30 55.10 31.02 40.00 46.28 49.98 8.69 ed079 763700 6667800 26.10 30.80 36.30 31.07 20.03 25.21 30.31 25.18 3.91 ed080 766450 6667800 26.30 47.90 41.20 43.40 18.00 37.48 37.22 37.65 5.87 ed082 771900 6667700 32.90 36.20 40.40 45.50 27.04 33.00 37.38 40.83 8.42 ed089 743100 6666100 53.00 57.60 64.00 65.00 40.32 44.17 47.08 47.57 9.09 ed090 745700 6665900 61.90 63.50 64.10 66.20 44.29 43.39 45.33 45.73 6.93 ed091 748500 6665800 68.90 68.90 72.30 73.70 52.18 53.77 55.80 55.76 10.15 ed094 756700 6665600 40.00 42.90 52.50 57.90 29.67 34.76 40.60 42.07 7.10 ed095 759500 6665600 39.80 49.50 52.20 45.00 25.38 39.93 42.71 44.94 7.79 ed097 764900 6665000 32.70 41.60 49.90 51.20 22.83 36.31 45.56 38.52 8.41 ed098 767700 6665300 64.00 55.80 45.30 53.70 32.80 38.63 42.57 44.22 7.12 ed099 770500 6665100 33.00 43.80 42.80 44.10 25.53 46.22 39.98 38.88 8.01 ed106 741600 6663600 57.90 56.60 57.00 57.00 34.50 37.74 37.91 39.92 3.17 ed107 744300 6663500 58.00 58.90 59.00 55.00 39.25 40.97 41.92 36.27 5.54 ed108 747100 6663500 59.50 59.70 61.00 66.10 43.07 48.00 42.98 47.59 8.64 ed109 749800 6663400 66.90 68.80 67.80 70.50 50.57 51.91 50.79 52.96 8.74 ed110 752600 6663400 62.80 62.80 60.00 61.90 42.78 42.58 42.91 44.00 6.86 ed111 755300 6663200 51.60 53.80 58.70 62.60 34.71 39.60 43.64 45.05 8.19 ed113 760800 6663100 39.00 45.70 46.60 39.70 30.64 31.03 35.60 37.35 6.91 ed114 763600 6663200 46.90 50.60 54.70 52.80 29.21 37.99 42.84 40.79 3.78 ed115 766300 6663000 40.70 53.90 45.60 41.20 26.99 38.63 32.90 31.40 2.58 ed117 771800 6662900 20.60 32.60 36.40 29.10 9.05 27.98 32.09 23.28 2.86 ed124 742900 6661300 23.50 48.00 41.30 38.80 11.48 27.92 26.55 28.14 5.55 ed126 748400 6661100 49.90 51.50 47.70 45.90 32.04 35.47 32.81 26.27 4.51 ed127 751200 6661000 64.30 63.10 61.70 64.40 49.19 51.83 47.02 49.58 9.31 ed129 756700 6660900 56.20 57.20 56.70 59.30 41.65 47.14 46.03 45.24 7.58 ed130 759400 6660800 60.10 59.30 59.90 63.20 52.41 53.27 53.74 56.96 3.24 ed131 762100 6660800 59.60 61.70 56.00 49.30 64.84 66.87 64.02 66.96 3.06 ed132 764800 6660700 25.20 53.80 37.60 40.80 12.03 41.99 32.06 32.61 8.04 ed133 767600 6660600 33.00 48.00 35.70 27.50 18.31 33.97 26.40 24.87 5.51 ed134 770400 6660500 28.90 44.40 37.00 24.90 16.08 32.18 34.83 24.64 0.49 ed142 744300 6658800 68.70 74.10 73.60 72.60 43.55 46.06 45.57 47.18 7.82 ed143 747000 6658700 56.20 58.00 59.80 59.40 43.47 48.35 51.52 47.54 6.84 ed144 749700 6658700 70.50 70.30 69.00 72.00 46.94 48.54 47.08 47.73 13.80 ed145 752500 6658600 61.50 64.00 68.60 71.00 40.82 51.28 51.04 53.47 10.09 ed146 755300 6658500 65.10 66.40 68.80 71.10 51.16 49.19 52.60 50.93 10.54 ed147 758000 6658500 53.40 54.40 56.10 59.60 38.50 43.46 43.56 43.66 7.89 ed148 760700 6658400 31.90 40.20 43.90 47.20 22.95 30.64 32.13 33.18 9.60 ed150 766200 6658300 50.90 52.30 50.60 48.90 40.41 39.75 40.21 35.52 7.11 ed151 768900 6658200 37.50 54.40 48.10 48.20 20.54 40.38 34.56 30.82 2.32 ed152 771700 6658100 28.80 33.40 30.80 26.30 24.89 32.88 30.13 26.28 1.21 ed159 742800 6656500 40.60 39.60 34.60 40.00 28.47 29.11 24.65 27.66 0.33 ed160 745500 6656400 36.00 44.70 47.50 30.60 18.23 24.21 26.35 20.10 0.78 ed162 751030 6656200 56.10 54.30 54.70 60.70 36.91 34.18 34.02 36.38 4.69 ed163 753800 6656200 67.90 70.10 74.00 69.00 49.52 52.01 54.09 51.25 9.66 ed165 759300 6656100 35.10 40.60 52.00 45.20 21.84 31.24 36.59 31.86 7.96 ed166 762100 6656000 45.60 48.80 50.10 51.50 36.31 38.65 41.97 37.95 8.94 ed167 764800 6655900 52.70 52.60 51.70 34.90 43.44 44.56 42.20 30.73 4.40 ed168 767500 6655800 33.10 53.70 51.40 40.10 21.18 48.43 42.76 32.58 3.63 ed169 770200 6655800 54.60 58.40 59.80 61.00 37.18 45.50 46.28 44.44 3.94 ed176 741300 6654100 40.90 55.60 54.50 38.10 17.83 24.89 26.22 22.76 1.29 ed178 746800 6653900 62.30 58.70 58.60 57.10 36.42 37.62 38.47 37.83 0.74 ed179 749600 6653900 47.80 48.40 38.10 34.80 36.47 37.80 35.75 28.97 0.90 ed180 752300 6653800 48.10 54.90 56.90 43.30 25.17 33.78 35.05 28.22 1.64 ed181 755200 6653750 54.50 53.10 49.90 51.40 36.66 35.08 32.15 31.32 6.41 ed182 757800 665370v 44.70 47.50 45.90 51.70 30.65 33.39 32.54 34.72 16.48 ed183 760700 6653700 54.30 48.60 48.20 54.40 39.56 44.39 44.44 44.27 7.02 ed184 763400 6653600 38.40 39.60 50.90 53.00 27.83 37.05 44.06 45.99 5.51 ed185 766000 6653500 36.30 40.20 39.70 43.40 31.36 35.16 36.23 43.82 12.49 ed187 771400 6653500 11.90 12.90 37.90 47.80 4.41 2.79 13.77 21.41 2.40 ed194 742600 6651600 6.70 8.30 15.90 31.30 3.02 0.30 1.58 4.02 0.01 ed195 745400 6651600 24.60 28.60 44.60 34.90 11.84 16.16 26.40 22.26 0.48 ed196 748200 6651500 61.80 65.10 64.20 65.90 40.09 41.24 38.97 37.13 5.31 ed198 753700 6651350 54.80 56.90 58.20 50.10 31.58 38.42 40.54 36.96 1.52

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ed199 756400 6651400 34.80 46.90 48.30 39.20 21.11 34.09 36.09 31.78 0.72 ed200 759200 6651250 41.00 44.60 39.40 42.40 24.60 33.97 29.07 30.83 13.83 ed202 764600 6651200 43.00 46.10 44.00 38.20 41.60 36.19 42.59 35.77 3.48 ed203 767300 6651100 8.50 18.30 24.00 24.60 2.56 18.58 24.49 21.24 18.82 ed204 770100 6651000 35.80 48.50 45.20 47.90 27.06 35.41 34.66 25.89 3.60


119




ID Eastings m

Northingsm

0.0-0.1 m

0.3-0.4 m

0.7-0.8 m

1.2-1.3 m

0.0-0.1 m

0.3-0.4 m

0.7-0.8 m

1.2-1.3 m

1.2-1.3 m

ed212 758200 6669600 53.50 54.70 59.40 64.10 43.35 52.00 55.16 54.35 8.58 ed213 759500 6664000 37.50 46.00 48.80 38.90 26.23 36.46 37.22 29.71 6.55 ed214 759500 6664800 36.10 44.80 47.60 39.70 28.42 37.86 35.90 31.60 6.88 ed215 750270 6655680 46.80 47.50 49.60 50.30 35.02 36.45 36.79 37.99 4.23 ed216 749630 6655680 56.10 57.50 58.30 59.10 32.69 34.47 34.34 33.51 1.67 ed217 751030 6656850 66.70 67.40 68.60 64.60 44.94 44.67 43.57 40.37 7.74 ed218 749910 6655130 56.20 59.30 57.20 57.70 42.46 38.43 37.89 37.51 4.76 ed220 752070 6656230 62.50 62.50 61.70 62.30 44.13 44.80 43.07 37.75 7.35 ed221 749910 6656200 48.50 54.80 53.70 52.50 30.80 36.92 35.06 33.15 4.80 ed222 750420 6656840 52.90 52.60 53.10 43.90 33.72 34.86 32.30 25.03 5.14 ed223 752370 6656780 53.50 53.90 50.20 46.40 34.34 34.46 31.91 29.42 6.22 ed224 749300 665507v 37.70 57.00 57.50 52.90 16.41 34.48 31.18 31.64 4.99 ed227 760000 6675300 45.20 40.80 40.90 41.60 39.60 36.60 38.38 39.93 5.69 ed228 751400 6656300 56.80 58.20 58.60 59.90 38.91 38.14 39.26 37.86 6.48 ed229 751600 6655600 56.60 58.00 58.10 57.80 35.28 36.82 38.06 37.69 5.70 ed232 750250 6654700 42.00 48.60 49.20 43.30 25.89 35.01 35.21 35.17 0.82 ed233 749900 6654700 46.80 50.40 51.40 51.00 31.49 39.49 40.55 40.05 2.43 ed235 748600 6672100 39.20 48.80 53.90 64.90 22.77 38.27 47.55 52.32 11.25 ed239 759850 6661150 44.30 47.70 48.50 56.90 30.14 34.31 34.89 41.05 3.80 ed244 758800 6652300 21.30 48.20 42.90 31.40 9.90 34.66 34.52 28.30 3.44 ed330 763200 6654300 20.90 38.70 33.50 40.30 19.37 38.31 34.72 40.44 5.94 ed331 763200 6654200 27.50 38.90 41.20 47.30 19.15 39.02 40.83 43.82 8.51 ed332 763200 6654100 25.50 36.90 40.60 42.70 16.95 35.34 38.73 39.21 7.36 ed333 763200 6654000 43.80 52.10 43.70 48.80 33.27 48.81 44.20 43.29 8.58 ed334 763200 6653900 52.30 52.90 54.80 55.40 42.49 44.85 48.22 49.04 8.49 ed335 763200 6654400 24.80 39.60 47.90 46.50 14.27 36.23 43.78 41.82 6.98 ed336 763200 6654500 52.50 56.00 54.00 54.70 43.88 43.96 45.43 43.44 6.86 ed360 751750 6674810 57.90 51.50 59.10 63.00 44.94 51.57 52.64 56.63 14.78 ed361 752400 6672950 36.50 40.50 55.30 57.10 28.47 38.38 47.73 51.58 11.64 ed370 751950 6674200 47.70 50.10 56.20 56.20 33.84 42.31 52.55 51.33 13.11 ed371 752180 6673580 42.20 42.90 51.20 56.00 32.07 36.00 43.37 47.73 13.79 ed601 759930 6670320 0.00 52.80 58.00 57.80 20.99 41.92 47.30 52.72 6.29 na010 771760 6647780 31.90 44.70 41.80 38.60 17.54 30.80 27.40 24.35 1.87 na013 768950 6644720 60.20 60.20 60.00 57.40 47.97 53.08 54.85 57.65 12.66 na015 770000 6647930 19.70 25.50 25.20 30.40 8.36 14.87 19.47 22.68 8.07 na017 769660 6645900 55.30 54.20 55.20 57.40 44.74 44.86 48.40 50.86 9.72 na018 769100 6646300 41.50 46.60 53.00 55.80 37.00 47.82 52.52 50.12 16.06 na020 769450 6647080 38.20 44.50 52.30 37.10 23.32 30.32 42.26 26.14 3.84 na021 770400 6647050 43.00 50.20 49.90 54.90 42.22 47.43 46.14 52.08 23.37 na022 770610 6647400 23.70 28.50 31.20 27.80 18.78 27.15 27.27 23.68 0.62 na024 770400 6648120 14.70 17.35 20.00 22.70 9.05 12.02 13.93 14.46 0.44 na026 770420 6648570 16.70 35.00 35.60 38.60 8.85 25.68 25.55 23.68 6.42 na031 745700 6649150 36.70 46.90 48.10 59.70 9.19 16.16 15.85 21.24 0.72 na033 745770 6649240 2.50 1.70 1.40 20.00 1.95 0.81 0.19 9.20 0.99 na036 745600 6649000 28.20 44.80 49.30 68.30 8.68 14.61 17.65 24.27 0.94 we001 722800 6678000 15.60 16.30 17.60 19.00 43.93 41.34 40.61 38.03 1.61 we002 717600 6678900 21.10 37.50 39.60 37.30 14.83 31.50 31.20 31.46 8.16 we005 714500 6673700 57.40 59.60 62.10 61.00 45.42 46.70 50.99 59.31 17.87 we007 708800 6664300 59.90 62.90 61.40 56.70 47.16 53.03 57.56 49.82 12.61 we008 708700 6661300 43.50 54.40 45.20 30.50 28.36 42.26 35.80 26.90 3.00 we009 708400 6660700 12.20 12.30 50.50 33.90 43.58 42.28 35.78 27.92 1.15 we010 708200 6660400 33.30 38.80 38.90 27.60 21.43 26.08 27.00 18.21 0.60 we011 707300 6658800 20.60 21.60 22.30 58.80 27.20 22.53 16.17 45.81 19.20 we013 705500 6659000 40.00 47.00 44.30 32.30 29.05 37.59 31.93 24.81 1.44

APPENDIX 2 – SOIL VARIABLES USED FOR INTERPREATING EM DATA

117

Appendix 2.1: Clay content (%) Site Coordinates EM

measurements Clay content (%)

ID Eastings (m)

Northings (m)

31V mS/m

38V mS/m

0-0.3 (m)

0.3-0.6 (m)

0.6-0.9 (m)

0.9-1.2 (m)

1.2-1.5 (m)

1.5-1.8 (m)

1.8-2.0 (m)

Avg.

CUMB01 738283 6651956 99 71 23 24 19 27 25 25 25 24 CUMB02 738226 6651628 115 85 17 24 36 41 40 38 40 34

CUMB03 738307 6651946 91 60 15 15 33 50 50 51 49 38 CUMB04 738342 6652142 58 29 17 28 41 42 39 38 38 35 CUMB05 738441 6652160 46 21 19 28 35 46 50 42 33 36 CUMB06 738383 6651828 107 66 10 30 48 43 40 51 50 39 CUMB07 738450 6651934 38 14 3 8 5 6 8 10 53 13

CUMB08 738435 6651709 111 81 15 41 50 46 46 51 51 43 CUMB09 738526 6652086 76 45 11 11 38 44 43 43 36 32 CUMB10 738483 6651672 121 92 26 33 40 43 39 40 40 37 CUMB11 738550 6651767 57 36 15 26 30 38 43 39 39 33 CUMB12 738614 6651982 63 36 20 41 33 34 40 36 36 34 CUMB13 738638 6651721 53 29 20 30 40 46 50 39 37 37


CUMB19 738296 6651761 81 52 12 16 30 33 37 47 53 32 CUMB20 738286 6651705 124 85 33 29 29 27 23 37 37 31 CUMB21 738280 6651674 108 70 17 17 40 40 37 37 40 32 CUMB22 738268 6651606 97 62 13 13 29 33 33 40 35 28

Appendix 2.2: Sand content (%) Site Coordinates EM

measurements Sand content (%)

ID Eastings (m)

Northings (m)

31V mS/m

38V mS/m

0-0.3 (m)

0.3-0.6 (m)

0.6-0.9 (m)

0.9-1.2 (m)

1.2-1.5 (m)

1.5-1.8 (m)

1.8-2.0 (m)

Avg.

CUMB01 738283 6651956 99 71 54 53 57 51 53 53 53 53 CUMB02 738226 6651628 115 85 60 53 42 39 40 41 40 45 CUMB03 738307 6651946 91 60 61 61 46 32 31 30 32 42




CUMB20 738286 6651705 124 85 45 49 49 51 54 43 43 48 CUMB21 738280 6651674 108 70 59 42 39 41 42 42 40 44 CUMB22 738268 6651606 97 62 62 62 49 45 45 40 44 50

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Appendix 2.3: Silt content (%) Site Coordinates EM

measurements Silt content (%)

ID Eastings (m)

Northings (m)

31V mS/m

38V mS/m

0-0.3 (m)

0.3-0.6 (m)

0.6-0.9 (m)

0.9-1.2 (m)

1.2-1.5 (m)

1.5-1.8 (m)

1.8-2.0 (m)

Avg.

CUMB01 738283 6651956 99 71 23 23 24 22 23 23 23 23 CUMB02 738226 6651628 115 85 24 23 21 20 21 21 20 21




CUMB19 738296 6651761 81 52 25 24 22 22 21 21 19 22 CUMB20 738286 6651705 124 85 21 22 22 22 23 21 21 22 CUMB21 738280 6651674 108 70 24 41 21 18 21 21 20 24 CUMB22 738268 6651606 97 62 25 25 22 21 22 20 21 22

Appendix 2.4: Field moisture (%) Site Coordinates EM

measurements Field moisture (%)

ID Eastings (m)

Northings (m)

31V mS/m

38V mS/m

0-0.3 (m)

0.3-0.6 (m)

0.6-0.9 (m)

0.9-1.2 (m)

1.2-1.5 (m)

1.5-1.8 (m)

1.8-2.0 (m)

Avg.

CUMB01 738283 6651956 99 71 14.48 14.56 14.18 17.79 16.39 16.39 16.39 15.74 CUMB02 738226 6651628 115 85 13.22 19.07 21.91 23.46 25.88 22.93 20.15 20.95 CUMB03 738307 6651946 91 60 9.40 12.67 20.31 23.66 20.38 19.95 20.39 18.11 CUMB04 738342 6652142 58 29 9.48 14.13 18.61 18.13 15.98 15.52 14.70 15.22 CUMB05 738441 6652160 46 21 9.39 12.78 17.02 20.85 20.72 19.10 19.00 16.98



CUMB16 738339 6652000 66 36 8.01 31.80 18.76 17.82 18.06 18.04 16.66 18.45 CUMB17 738329 6651944 87 49 11.39 16.51 24.08 24.23 21.25 21.33 19.59 19.77 CUMB18 738309 6651833 105 72 9.56 23.00 19.33 18.23 21.61 6.07 18.39 16.60 CUMB19 738296 6651761 81 52 11.76 13.68 18.18 14.21 17.00 20.30 21.33 16.64 CUMB20 738286 6651705 124 85 11.41 12.06 12.99 15.78 16.66 16.73 16.21 14.55 CUMB21 738280 6651674 108 70 9.22 21.24 20.00 19.20 18.56 19.39 19.49 18.16

CUMB22 738268 6651606 97 62 10.28 10.32 17.21 19.01 18.69 18.85 18.21 16.08

APPENDIX 2 – SOIL VARIABLES USED FOR INTERPREATING EM DATA

119

Appendix 2.5: ECe (dS/m) Site Coordinates EM

measurements ECe (dS/m)

ID Eastings (m)

Northings (m)

31V mS/m

38V mS/m

0-0.3 (m)

0.3-0.6 (m)

0.6-0.9 (m)

0.9-1.2 (m)

1.2-1.5 (m)

1.5-1.8 (m)

1.8-2.0 (m)

Avg.

CUMB01 738283 6651956 99 71 1.19 1.28 1.22 1.49 1.93 1.93 1.93 1.57 CUMB02 738226 6651628 115 85 2.00 1.14 1.68 2.28 3.97 3.83 2.74 2.52




CUMB19 738296 6651761 81 52 0.57 0.70 0.85 0.79 0.77 0.69 0.71 0.72 CUMB20 738286 6651705 124 85 2.12 2.06 2.56 4.03 5.28 4.63 5.61 3.76 CUMB21 738280 6651674 108 70 2.03 2.73 1.52 2.07 2.06 2.09 2.46 2.14 CUMB22 738268 6651606 97 62 0.81 0.91 1.71 1.08 1.80 1.86 2.06 1.46

Appendix 2.6: Effective Cation Exchange Capacity (cmol(+)/kg) Site Coordinates EM

measurements Effective Cation Exchange Capacity (cmol(+)/kg)

ID Eastings (m)

Northings (m)

31V mS/m

38V mS/m

0-0.3 (m)

0.3-0.6 (m)

0.6-0.9 (m)

0.9-1.2 (m)

1.2-1.5 (m)

1.5-1.8 (m)

1.8-2.0 (m)

Avg.

CUMB01 738283 6651956 99 71 12.31 12.63 8.44 14.11 10.98 10.98 10.98 11.49





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Appendix 2.7: pH Site Coordinates EM

measurements pH

ID Eastings (m)

Northings (m)

31V mS/m

38V mS/m

0-0.3 (m)

0.3-0.6 (m)

0.6-0.9 (m)

0.9-1.2 (m)

1.2-1.5 (m)

1.5-1.8 (m)

1.8-2.0 (m)

Avg.


CUMB06 738383 6651828 107 66 8.5 7.96 8.16 8.52 8.59 8.98 9.04 8.54 CUMB07 738450 6651934 38 14 8.36 7.95 7.88 8.04 7.75 7.4 9.3 8.10 CUMB08 738435 6651709 111 81 8.05 7.62 8.03 8.87 9.16 9.18 9 8.56 CUMB09 738526 6652086 76 45 8.11 7.97 5.5 5.64 6.18 6.21 7.04 6.66 CUMB10 738483 6651672 121 92 8.84 8.72 9 9.38 9.56 9.6 9.77 9.27 CUMB11 738550 6651767 57 36 7.9 8.26 8.27 7.92 7.9 7.92 7.92 8.01



CUMB22 738268 6651606 97 62 8.49 7.71 6.96 7.97 9.15 9.3 9.42 8.43

Appendix 2.8: Exchangeable sodium percentage (ESP) Site Coordinates EM

measurements ESP

ID Eastings (m)

Northings (m)

31V mS/m

38V mS/m

0-0.3 (m)

0.3-0.6 (m)

0.6-0.9 (m)

0.9-1.2 (m)

1.2-1.5 (m)

1.5-1.8 (m)

1.8-2.0 (m)

Avg.

CUMB01 738283 6651956 99 71 3.25 5.30 5.69 4.75 4.28 4.28 4.28 4.55 CUMB02 738226 6651628 115 85 2.64 4.18 5.75 6.80 7.63 12.31 16.30 7.95 CUMB03 738307 6651946 91 60 3.47 5.85 3.71 3.44 3.12 2.98 3.47 3.72




CUMB20 738286 6651705 124 85 7.07 11.83 12.85 13.14 23.31 21.41 21.38 15.85 CUMB21 738280 6651674 108 70 6.10 7.00 6.96 6.92 7.97 3.90 4.79 6.23 CUMB22 738268 6651606 97 62 4.50 6.69 7.99 7.55 10.90 13.85 19.01 10.07

Documents

Simulating and assessing salinisation in the lower Namoi valley · 2016. 6. 16. · Mohammad Faruque Ahmed . M.F. AHMED ii. Simulating and assessing salinisation in the lower Namoi