Characterisation of porosity and root growth in a sodic ... · Characterisation of porosity and...

Characterisation of porosity and root growth in a sodic texture-contrast soil

By

Laurence Jassogne

This thesis is presented for the degree of Doctor of Philosophy at The University of Western Australia, School of Plant Biology.

September 2008

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Abstract

In Australia a class of soils known as sodic duplex soils covers approximately 20% of

the continent. Their defining characteristic is a sharp texture contrast between the A (or

E) and B horizon. The upper B horizon at the point of contact with the E horizon is

often highly sodic and of such a high strength that root growth and proliferation, water

conductivity, aeration, water storage and water uptake are restricted. Roots growing in

these soils rely on channels created by previous roots or cracks arising from shrink–

swell forces associated with seasonal wetting and drying. It has been suggested that by

increasing the number of these channels in the subsoil, the structure and permeability of

the subsoil would be increased as would be the number of preferential pathways for

following generation roots. A biological approach for improving soil macroporosity

would be to use plants that can grow through that hostile layer creating new channels.

This is known as the primer plant concept. This concept is based on a better

understanding of root soil interactions. It is accepted that root growth is influenced by

the soil structure and the soil structure is influenced by root growth. However, a lot of

these dynamics are still unknown. This project aims to contribute to improving that

knowledge by investigating the use of modern techniques to study plant/root

interactions in duplex soils. First macroporosity and mesoporosity were characterized in

three dimensions using medical computer tomography and micro-tomography. Then the

imaging methodology was improved by using a local and adaptive threshold technique

based on indicator kriging instead of a global threshold. Using this new methodology,

changes in porosity were analysed in intact samples when three different plant species

were grown for 12 weeks. The plants were canola (Brassica napus); lucerne (Medicago

sativum) and saltbush (Atriplex nummularia) hypothesizing saltbush would change the

porosity more because it is a native plant species based on the primer plant concept. The

results showed that the porosity changed significantly after root growth but no

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differences were found between plant species. The changes could also not all be

attributed to root growth because cracks were also formed after 12 weeks. Therefore,

the living roots were visualized and characterized using a new tracing algorithm

‘rootviz’. This revealed that saltbush was growing more roots down through the profile.

Lucerne seemed to grow roots down the profile as well but to a lesser extend. Both of

these plants seemed to have more geotropic features than canola that seemed to grow

more laterals and had a more exploratory behaviour.

In a second part of this study, the chemistry and distribution of some elemets (Ca, Mn,

Fe, Zn and Cu) around mesopores in duplex soils were analysed using synchrotron

based X-ray techniques like µ-X-ray fluorescent spectrosopy, µ X-ray absoprtion fine

structure spectroscopy (XAFS) and µ-X-ray-absorption near edge specrtoscopy

(XANES). There were some major differences among elements. Ca was always present

in the studied pores while Mn and Fe were always in the soil matrix. Mn sometimes

increased around the micropore but its distribution was always well correlated with Fe.

Zinc and Cu were always less correlated with Fe and almost always, there concentration

increased around the micropore.

In conclusion, this work shows that there are strong influences in sodic duplex soils

between root growth, soil structure and distribution of elements. Little is known about

the processes involved in these root/soil interactions and this thesis contributes in

improving that knowledge.

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Table of contents

ABSTRACT i

TABLE OF CONTENTS iii

ACKNOWLEDGEMENTS vi

LIST OF PUBLICATIONS vii

STUDENT DECLARATION viii

CHAPTER 1:

General Introduction 1

CHAPTER 2:

3D-visualization and analysis of macro- and meso-porosity of the upper horizons of

a sodic, texture contrast soil

Abstract 10

Introduction 11

Materials and methods 13

Results and Discussion 17

Conclusions 22

Acknowledgements 22

References 23

CHAPTER 3:

Using computer tomography to visualise and quantify macropores in intact soils,

the issue of thresholding

Abstract 41

Introduction 42


Results 51

Discussion 53

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Conclusions 61

Acknowledgements 62

References 62

CHAPTER 4:

Computed tomography of intact soils cores I. Spatial analysis of soil

macroporosity before and after root growth

Abstract 66

Introduction 67


Results 73

Discussion 78

Conclusions 87

Acknowledgements 88

References 89

CHAPTER 5:

Computed tomography of intact soil cores II. Spatial and temporal visualisations

of in situ root systems.

Abstract 93

Introduction 94


Results 100

Discussion 106

Conclusions 112

Acknowledgements 113

References 114

CHAPTER 6:

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Distribution and speciation of nutrient elements around micropores


Abstract 119

Introduction 121



Conclusions 148

References 149

CHAPTER 7:

Characterising the chemistry of micropores in a sodic soil with strong texture-

contrast using synchrotron X-ray techniques and LA-ICP-MS


Abstract 155

Introduction 157



Conclusions 181

References 181

CHAPTER 8:

Summary and general discussion 184

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Acknowledgements

I would like to thank my supervisors David Chittleborough, Ganga Hettiarachchi and Annie McNeill for the support and help during my PhD, for giving me the opportunity to travel to several conferences and to do the synchrotron experiments in Chicago. Ganga, thanks for not loosing patience with me and continuously making me questioning things and Annie, thank you for the Scotland adventure and thanks for keeping me not loosing sight of the big picture. I would also like to thank my supervisor Hans Lambers. This thesis would not have been possible without his support. I need to thank the CRC for plant based management of dryland salinity for funding of this project and for travel funding, the Australian government and UWA for my international postgraduate research scholarship, UWA for the student travel fund. The synchrotron experiments were done at the PNC/XOR facilities at the Advanced Photon Source, and research at these facilities, are supported by the US Department of Energy – Basic Energy Sciences, a major facilities access grant from NSERC, the University of Washington, Simon Fraser University and the Advanced Photon Source. Use of the Advanced Photon Source is also supported by the U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract DE-AC02-06CH11357. This work was supported by the Australian Synchrotron Research Program (ASRP) which is funded by the Commonwealth of Australia under Major National Research Facilities Program. Some of this work was performed at GeoSoilEnviroCARS (Sector 13), Advanced Photon Source (APS), Argonne National Laboratory. GeoSoilEnviroCARS is supported by the National Science Foundation - Earth Sciences (EAR-0622171) and Department of Energy - Geosciences (DE-FG02-94ER14466). I especially want to thank Matt Newville. This work was also supported by the Australian Synchrotron Research Program (ASRP) which is funded by the commonwealth of Australia under Major National Research Facilities Program. Thanks to UWA letting me be based in Adelaide and thanks to Soil and Land Systems, UA, for letting me use their facilities. Thanks to the SLS staff, especially Colin Rivers. A special thank to Damien Adcock for helping with fieldwork and for friendship and Rob Davidson for teaching me imaging and for being there when I had computer troubles. Thanks to Peter Self and Angus Netting at Adelaide Microscopy and the radiology department at the Queen Elizabeth Hospital. Thanks to my Adelaide family, the Adelaide Frenchies and all my friends in Adelaide and in other parts of the world. You kept me sane Finally, thanks to my parents for their eternal support and my brother Nicolas for his long distance great friendship. Thank you thank you thank you!

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List of publications arising from this thesis Chapter 2: ‘3D-visualisation and analysis of macro- and meso-porosity of the upper horizons of a sodic, texture-contrast soil’ L. Jassogne, A. McNeill and D. Chittleborough European Journal of Soil science, 58: 589-598. The images were acquired before the start of the PhD, and the methodology was established before the PhD as well. The data analysis and the manuscript were written after the PhD started. Laurence Jassogne was then a student from the School of Plant Biology at the University of Western Australia. The Australian government, UWA and Hans Lambers should have been acknowledged for the International Postgraduate Scholarship. Chapter 3: ‘Using computer tomography to visualise and quantify macropores in intact soils, the issue of threshold’ L. Jassogne, R. Davidson, A. McNeill, G. Hettiarachchi and D. Chittleborough This paper was submitted to Geoderma. Chapter 4: ‘Computer tomographic analysis of the effect on root growth on porosity’ L.Jassogne, R. Davidson, A. McNeill, G. Hettiarachchi, D. Chittleborough This paper will be submitted to Plant and Soil. Chapter 5: ‘Visualising root growth in 4 dimensions in intact soil cores’ L.Jassogne, R. Davidson, A. McNeill, G. Hettiarachchi, D. Chittleborough This paper will be submitted to Plant and Soil. Chapter 6: ‘Distribution and speciation of nutrient elements around micropores’ L. Jassogne, G. Hettiarachchi, D. Chittleborough, A. McNeill Accepted with revisions in Soil Science Society of America Journal Chapter 7: ‘Characterising the chemistry of biopores using synchrotron based X-ray techniques and LA ICP-MS’ L. Jassogne, G. Hettiarachchi, D. Chittleborough, A. McNeill This paper will be submitted to the Soil Science Society of America Journal.

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Student declaration: extent of involvement of the student in the work presented in this study

The images for chapter 2 were acquired by me in case of the micro-CT and by Peter

Kolesik in case of the medical CT before the start of the PhD. The analysis of the

images of the medical CT and the micro CT was done by me at the start of my PhD.

The development of the ideas in the paper was shared between me, Ann McNeill and

David Chittleborough. I drafted the paper and Ann McNeill and David Chittleborough

commented on the paper.

The imaging method in Chapter 3 was developed by me with the help of Rob Davidson.

I tested the method and I discussed the results with mainly Rob Davidson. I drafted the

paper and Rob Davidson, Ann McNeill, Ganga Hettiarachchi and David Chittleborough

substantially commented on the paper.

The outline of the experiment in Chapter 4 and 5 was done by me. I consulted Rob

Davidson and Damien Adcock for the set-up of the experiment. The analysis of the

images was done by me. I drafted the paper in Chapter 4 and Rob Davidson, Ann

McNeill, Ganga Hettiarachchi and David Chittleborough substantially commented on

the paper.

The software used in Chapter 5 was developed by Rob Davidson. I applied the software

on the CT images and reconstructed the roots. I drafted the paper and Rob Davidson

helped writing the method section. Ann McNeill, Ganga Hettiarachchi and David

Chittleborough substantially commented on the paper.

The experiment in chapter 6 was developed by me with the help of Ganga

Hettiarachchi. She also helped me at the synchrotron. I did the analysis of the data and

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wrote the paper. Ganga Hettiarachchi helped with the development of ideas. Ann

McNeill and David Chittleborough substantially commented on the paper.

The experiment in chapter 7 was developed by me with the help of Ganga

Hettiarachchi. She also helped me at the synchrotron. I did the analysis of the data and

wrote the paper. Ganga Hettiarachchi helped with the development of ideas. Ann

McNeill and David Chittleborough substantially commented on the paper.

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CHAPTER 1

General introduction

Context

Over millions of years, salts (mainly NaCl) have accumulated in Australian soils. They

have largely been imported from the oceans via wind and rainfall, but also arisen from

weathering of rocks, in particular marine sediments (Rengasamy, 2006). Many of these

salt-affected soils are used for dryland agriculture. Prior to clearing of land for

agriculture, Australian ecosystems evolved with adaptations to the prevailing climatic

and edaphic conditions. There were deep-rooted perennials that used most of the rainfall

throughout the year and ensured that saline water tables remained deep in the soil

profile. This made it possible for shallow-rooted, annual species to also grow in these

soils. When the land was cleared for agriculture by immigrant Europeans, annual crops

were introduced in the system and most native perennials were removed. This caused

the water table to rise with associated movement of salts into the rooting zone of the

annuals. When salt concentrations in the soil solution are too high, plant performance

will decrease due to toxic and osmotic salt effects. Introducing deep-rooted perennials

into the agricultural systems can lower the water table again, and reduce the salinity

problems in Australian soils (Cox et al., 1996; Pannell and Ewing, 2006).

The accumulation of Na in the subsoil has also led to the development of soils with

sodic properties, chiefly in soils with high clay content. Due to the elevated

concentrations of Na, the clay in the soil disperses, causing the soil structure to

deteriorate and the bulk density of the soil to increase (Rengasamy et al., 2003). In

southern Australia, a large percentage of the agricultural land is situated on alkaline

duplex soils (Chittleborough, 1992). These are soils with a texture contrast in the

subsoil. Mostly, the upper soil profile has a coarse texture with neutral to slightly

alkaline pH while the subsoil is of much finer texture, more extreme alkalinity, and has

saline and sodic properties. After a significant rainfall event (>20 mm) there is a

CHAPTER1

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tendency, due to low permeability of the subsoil, for water in these soils to temporarily

accumulate or ‘pond’ in a zone immediately above the subsoil often called the E

horizon. Furthermore, when the soil dries, the subsoil and overlying E horizon can

become very hard and impenetrable. The physical and chemical characteristics

described above are suggested as key limitations to roots growing in these texture-

contrast or duplex soils (Adcock et al., 2007).

Management of duplex soils

Roots growing in the subsoil of duplex soils use channels developed by previous

vegetation as preferential pathways when they cannot penetrate or ‘drill’ through the

subsoil. These channels are also called biopores. Root proliferation in the subsoil is very

important because it facilitates access to the water that has been stored in the subsoil,

which has a higher-water holding capacity compared with the top soil (Rengasamy et

al., 2003). Access to this water is critical, especially for crops in low-rainfall areas. It

has been suggested that planting vegetation to increase the numbers of biopores in the

subsoil would improve the structure and permeability of the subsoil, and also increase

the number of preferential pathways for exploitation by roots of subsequent vegetation.

This idea of physical and chemical amelioration of hostile soil by growing roots is

known as the primer plant concept and has been suggested in previous studies. The ideal

primer plant likely evolved with the climatic conditions and soil environment, and thus

developed features to enable root growth through the hostile subsoil environment, such

as roots that are able to penetrate at high soil bulk density and modify the soil by certain

exudates. Indeed, native perennials have been described as chemical and structural

engineers of the subsoil (Yunusa and Newton, 2003; Verboom and Pate, 2006).

Root growth is influenced by soil structure and soil structure is influenced by root

growth, but the intricacies of these dynamics are still largely unknown or poorly

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CHAPTER 1

described, especially ‘in situ’ for undisturbed situations. In particular, there is a scarcity

of quantitative and qualitative data on physical and chemical characteristics related to

soil porosity in intact soil cores, and on the morphological responses of plant roots to

this porosity. The work described in this thesis broadly aimed to develop and use state-

of-the-art technologies to address this gap in our knowledge

Interactions between roots and soil

Porosity and root growth in duplex soil

Root growth is influenced by soil structure in the subsoil of duplex soils and as

suggested in the primer plant concept, roots of certain plant species could be used as

‘biological drills’ through the subsoil of those soils. Therefore, there is a need to

characterise porosity and root growth in duplex soils. Unfortunately, soil is opaque and

it is extremely difficult to examine non-destructively. Indeed, methods that characterise

porosity and root growth in soil are often destructive and, consequently, any spatial

information is lost.

Computer tomography is a good technique to characterise porosity in three dimensions

non-destructively. The only drawback is that there is a trade off between sample size

and resolution (Pierret et al., 1999). Fine resolution at µm scale can only be achieved

with small samples like soil aggregates (Peth et al., 2008). For studies needing bigger

sample sizes, the level of detail at which processes can be analysed decreases.

Therefore, certain techniques, like medical Computer Tomography (CT) that allow big

sample sizes (for example 15 cm diameter cores by 50 cm long) have been less widely

used because the resolution obtained is at mm scale (Bastardie et al., 2005; Luo et al.,

2008). However, the size of sample used in an experiment, and hence the choice of X-

ray CT equipment, will be determined by the information being sought. To provide

knowledge about root system exploration in a soil profile, large sample sizes are needed

CHAPTER1

4

that represent the soil profile. Imaging such samples allows macroporosity and root

growth in soil to be visualised, but only at a coarse resolution. This information is

sufficient if the interest concerns primary or, in some instances, secondary roots, i.e if

the study is about the exploratory behaviour of root systems in soil. Fine roots (< 0.8

mm) are responsible for nutrient and water uptake and, thus, techniques with finer

resolution may be necessary to examine these (Pierret et al., 2005). Computer

tomography cannot always resolve that level of detail and the use of 2D imaging

techniques that allow much fine resolution has been proposed (Pierret et al., 2003). It

can be concluded that root/soil interactions happen at various scales and to fully

understand root behaviour, studies at different scales have to be integrated. The choice

of whether to use three-dimensional imaging techniques or alternative two-dimensional

visualisation techniques is dependent on the scale and the nature of the process or

feature being studied.

Chemistry of micropores in duplex soil

It is well known that a root actively growing in soil affects chemical, physical and

biological properties. The volume of soil influenced by a growing root is called the

rhizosphere (Hinsinger et al., 2006). A lot less is known about what happens to the

rhizosphere when the root dies and the rhizosphere becomes remnant. This knowledge

is very important in relation to porosity in sodic subsoils as these remnant rhizospheres

are most likely to be the channels in which the roots of subsequent plants will

preferentially grow. Studying the chemistry of pores and soil in the immediate vicinity

of pores is essential for understanding where nutrients accumulate in the overall soil

structure, and how available they may be for uptake by plant roots. Such knowledge is

very important for subsoils where zones favourable for root growth may be relatively

rare within a more ubiquitous hostile matrix. Very little research has been undertaken in

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CHAPTER 1

this area because of the limitations of the technologies and lack of non-invasive

approaches. However, there are techniques now that allow a combination of chemical

analysis with spatially resolved information. Knowing the spatial distribution of

nutrients is important at fine scale as it is the detail at which nutrient uptake by fine

roots occurs. The distribution of micronutrients in relation to soil structure can finally

by studied and visualised by using synchrotron based X-ray techniques and other

surface analytical techniques (Amelung et al., 2002).

Hypothesis and thesis structure

The underlying hypothesis for this thesis was that native perennial plants are potentially

better primer plants because they have evolved to exhibit abilities to ‘drill’ through

highly impenetrable subsoil that may be sodic and alkaline, and subsequently

chemically modify the soil environment.

The aim of this work was to quantify root/soil interactions using non-invasive

techniques. Novel and emerging X-ray and imaging techniques were employed, and, if

necessary, improvements to them were suggested, in order to quantitatively and

qualitatively investigate root/soil interactions in intact soil environments and thus test

the underlying hypothesis by

1) using X-ray tomography to visualise and quantify macroporosity and root growth in

duplex soils

2) examining the distribution of elements around micro-and mesopores using novel X-

ray techniques.

A series of studies was undertaken and 6 manuscripts were prepared to present the main

findings. Four of these manuscripts deal with non-invasively visualising and measuring

porosity and root growth in undisturbed blocks of duplex soil, at two scales. The other

two describe the distribution and speciation of nutrients in and around soil pores.

CHAPTER1

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In the first study (Jassogne et al. 2007, chapter 2), the macroporosity of a sodic duplex

soil was measured in intact soil cores (diameter of 15 cm and 50 cm long) using medical

CT. Also, the mesoporosity in a 4 cm layer containing 2 cm at the base of the E-horizon

and 2 cm at the top of the B-horizon (called the interface) was characterised in three

dimensions using micro-CT. In this study the issue of using different scales to describe

aspects of porosity was investigated and the changes in porosity that roots would

encounter as they penetrate or ‘drill’ through the interface between the E and the B

boundary were analysed. The limitations of this study were that the imaging techniques

were not optimal. Therefore, the second study (Jassogne et al., in review – Geoderma,

chapter 3) describe and assess a more objective methodology for imaging and analysing

porosity in 3 dimensions based on thresholding images using indicator kriging. Using

this revised methodology a third study (Jassogne et al., to be submitted to Plant and Soil

2008, chapter 4) was completed that characterised the macroporosity of a sodic duplex

soil before and after root growth of an annual crop plant, canola (Brassica napus), a

perennial pasture plant lucerne (Medicago sativa) and a native perennial shrub saltbush

(Atriplex nummularia). In this study, changes in porosity due to soil cracking could not

entirely be delineated from changes in porosity due to root growth. Therefore, a fourth

study (Jassogne et al., in preparation for Plant and Soil, chapter 5) was undertaken

where the growing exploratory roots of an annual agricultural crop (canola), a perennial

pasture plant (lucerne) and a woody perennial saltbush were imaged using a tracing

algorithm that was especially developed to visualise growing roots in intact soil cores.

The roots of canola, lucerne and saltbush were imaged at different times during a 12

week period in order to examine the ability of these different plant root systems to

change, and in particular increase, the macroporosity of the subsoil of sodic duplex soil.

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CHAPTER 1

Complementary studies were undertaken using established and emerging techniques to

investigate the distribution of macro- and micronutrients (Ca, Cu, Zn, Mn and Fe) in

relation to soil microstructure in the E/B layers of a duplex soil. In one study (Jassogne

et al., accepted with revisions in Soil Sci Soc Am 2008; chapter 6) thin sections of

samples from the interface of a sodic duplex soil were examined. The mesostructure at

the interface of a sodic duplex soil is described using soil petrology. Synchrotron-based

X-ray fluorescence was assessed for mapping the distribution of Ca, Mn, Fe, Zn and Cu

around pores. X-ray absorption spectroscopy was used to investigate the speciation of

these elements. The results from these studies were then compared with those obtained

from scanning electron microscopy with an Energy-dispersive X-ray analyser (SEM-

EDXA). In another study (to be submitted to Soil Sci Soc Am 2008; Chapter 7) the

same synchrotron techniques are used at another beamline with finer detail of element

distribution examined and reported. The results from the two studies were compared

and the use of intact samples versus thin sections for these types of experiments is

discussed in relation to the concern that soil particles react or move when making thin

sections. Finally, nutrient distributions mapped from these synchrotron techniques are

compared with those from laser-ablation-induced couple-plasma mass spectrometry (LA

ICP-MS) measurements.

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Characterising porosity and root growth in three dimensions using

computer tomography

Chapter two entitled

3D-visualization and analysis of macro- and meso-porosity of the upper horizons of

a sodic, texture contrast soil

Appeared as a journal article in

European Journal of Soil Science (2007) 58, 589-598

Chapter three entitled

Using computer tomography to visualise and quantify macropores in intact soils,

the issue of thresholding

Submitted as a journal article to Geoderma

Chapter four entitled

Computed tomography of intact soil cores I: Spatial analysis of soil macroporosity

before and after root growth

To be submitted as a journal article to Plant and Soil

Chapter five entitled

Computed tomography of intact soil cores II: Spatial and temporal visualisations

of in situ root systems

To be submitted as a journal article to Plant and Soil

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CHAPTER 2

Short title: Porosity of a sodic soil

3D-visualisation and analysis of macro- and meso-porosity of the upper

horizons of a sodic, texture-contrast soil

L. JASSOGNE, A. MCNEILL & D. CHITTLEBOROUGH

University of Adelaide, School of Earth and Environmental Sciences, Waite Campus, PMB1, Glen Osmond SA 5064, Australia

Correspondence: L. Jassogne. E-mail [email protected]

CHAPTER 2

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Summary

The lower E and upper B horizons of sodic, texture-contrast soils are a formidable

barrier to most annual and many perennial crops. The research presented here is part of

a wider study into the nature of subsoil constraints to root exploration. The aim of this

study was to characterise in three dimensions the macro- and meso-porosity across the E

horizon – Btn horizon interface of a sodic, texture-contrast soil using X-ray computed

tomography (CT). Intact soil cores of 50cm length and 15cm diameter were scanned

with a medical CT X-ray machine. The pore volume reconstructed from these scans had

a resolution of 0.3 cm x 0.3 cm x 0.4 cm (in the x, y, z dimensions respectively). This

resolution allowed visualisation and quantification of the macroporosity of the intact

cores. Undisturbed samples of 1.5cm diameter and 4cm length were carefully excised

from the interface and scanned with micro-CT X-ray equipment. The reconstructed pore

volumes had an isotropic resolution of 19 μm that allowed analysis of the mesoporosity

just on the boundary between the E and Btn horizons. Mesoporosity decreased across

the interface and increased lower in the Btn horizon. The distribution of the pores at the

macro- and mesoscales showed the importance of the smaller pores in the A and E

horizons, whereas most of the porosity in the Btn horizon was attributed to the larger

pores. Pores in this sodic, texture-contrast soil were not distributed homogeneously at

either the macro- or meso-scale. A greater proportion of the pores in the E-B interface

were horizontal than in the upper A1, upper E and lower Btn horizons. Some ‘coiling’

of the pores was also apparent in the interface. The shape of some pores (long tubular

pores) suggested formation by roots as they drilled through the soil. The orientation of

these pores was a function of physical (and possibly) chemical impedance at the

interface.

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CHAPTER 2

Introduction

Approximately 60% of the 20 million hectares of soils used for cropping in Australia

are sodic and farming practices on these soils are mainly performed under dryland

conditions. More than 80 % of these sodic soils have dense, clay subsoils with high

sodicity (ESP >6%) and are strongly alkaline (pH > 8.5) (Rengasamy, 2002).

Worldwide there are approximately 135m hectares of such soils, formerly known as

Solonetz or Solodised Solonetz (Stace et al. 1968). In addition to their sodicity, these

soils invariably have an abrupt textural change between the A (or E) and B horizons

(Chittleborough, 1992). Textures of the A and E horizons are usually sands or loamy

sands; B horizons are clays or sandy clays. The clay horizons are very dense (bulk

densities > 2 g cm-3 are common), and present a major physical constraint for both

annual and perennial vegetation. Access by root systems to water and nutrients is

severely restricted (Clark et al., 2003). In the decades following clearing for agriculture,

these soils have been subject to either, or a combination of, waterlogging, dryland

salinity, erosion and acidification. In addition to the inherent constraints of sodicity and

texture-contrast, agricultural crops must cope with a range of supplementary chemical

and structural constraints (Cox et al., 2002; Rengasamy et al., 2003).

Previous studies have shown that few roots tend to grow in the immediate vicinity

of the E horizon – B horizon interface of these sodic soils (McNeill et al., 2004). In this

paper we will use the term E-B interface to refer to the immediate vicinity of the

boundary between the E horizon and the Btn horizon. Previous studies of relationships

between soil structure and root distribution in a texture contrast soil, using Phase

Contrast Imaging (PCI), observed that roots tended to grow more vertically than

horizontally in the topsoil and more horizontally than vertically at the interface with the

subsoil and at the top of the B horizon (Moran et al. 2000). The branching intensity also

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decreased with depth. Roots often exploit cracks, voids and large pores, or enlarge

smaller pores by displacing soil particles (Clark et al., 2003). Pores provide an easy

pathway through the soil matrix because of relatively little mechanical impedance and

because they improve contact with oxygen, water and nutrients. Furthermore, the

environment around soil macropores has been shown to be chemically and

microbiologically different from the bulk soil (Pankhurst et al., 2002). The size of the

macropore sheath, i.e. the zone around macropores in which 80% of the roots are

located (Stewart et al., 1999), may be an indication of the level of inhibition for root

growth exerted by the soil matrix. A small macropore sheath may indicate that the roots

are not evenly distributed throughout the soil matrix i.e. roots are clumping. One of the

implications of this type of root distribution, closely related to macropore distribution, is

that nominally available water in the subsoil is poorly accessible by plants. Plants

experience water stress even though measurements of soil water content indicate that

there is sufficient water in the subsoil to meet demand (Pierret et al., 1999).

These observations demonstrate the importance of quantifying soil structure and the

relationship between pores and roots. Many efforts have been made to characterise

porosity using two-dimensional (2D) image analysis. These include cross-sections from

soil reconstructed from tomography (Crestana et al., 1986; Phogat & Aylmore, 1989;

Warner et al., 1989) and soil thin sections (McBratney et al., 1992; Moran &

McBratney, 1992; Vogel, 1997; Li et al., 2004). However, the representation of a three-

dimensional (3D) object in a random 2D cross-section gives only a partial view of

reality. 3D-characterisation of macropores is the only way to define correctly the

continuity of the pores: an important property of pore structure because it largely

determines water movement through soil (Vogel, 1997). Tomography has been shown

to be a useful tool in this respect (Heijs et al., 1995; Moreau et al., 1999; Perret et al.,

1999; Clausnitzer & Hopmans, 2000). X-ray computed tomography (CT) is a non-

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invasive technique that has been used for the characterisation of pores and roots in three

dimensions in disturbed and undisturbed soils (Heeraman et al., 1997; Pierret et al.,

1999; Perret et al., 1999; Pierret et al., 2002; Gregory et al., 2003).

Few 3D studies have been undertaken at the meso-scale on intact soil cores. In this

paper, we adopt the pore-size classification of Brewer (1964) in which macroporosity

refers to pores > 75 µm in size and mesopores 30 – 75 µm. Most studies at the meso-

scale on undisturbed samples have been invasive and the analysis carried out in pseudo-

3D (e.g. Moran et al., 2000). Other 3D studies at the meso-scale were undertaken on

disturbed soil samples (Gregory et al., 2003). There is a trade-off between resolution,

large field of view and the ability to image in 3 dimensions. Images of high resolution

can only be obtained for small samples with the technology currently available. Using

this technology, we report an attempt to visualise and quantify the macro-and meso-

pore characteristics of a soil in the undisturbed state across the E-B interface that could

be of relevance for explaining root growth in those soils. We predict that these

characteristics will be major determinants of the pathway of root growth in these soils.

The objective of this study was to characterise porosity at the interface of a soil with

strong texture contrast at the macro-and meso-scale. The porosity of the A, E and upper

B-horizons of a sodic, texture-contrast soil was visualised using computed tomography

and the 3D-characteristics of pores that were assumed relevant for root growth was

calculated.

Materials and methods

Field sampling

Undisturbed soil cores (50cm depth by 15cm diameter) were taken with a drill rig from

a sodic soil with strong texture-contrast on an experimental site near Wharminda, on the

Eyre Peninsula, South Australia (33°S, 136°E). The soil is a Yellow Sodosol (Isbell,

CHAPTER 2

14

1996) or Natrixeralf (Soil Survey Staff, 1999). Relevant soil characteristics are

summarised in Table 1. The particle size was measured with the hydrometer method.

For the porosity calculations, derived from bulk density measurements, we assumed a

solid density of 2.65 g cm-3. Cores were stored at 4°C to avoid modification of the soil

structure after sampling.

CT-scanning

At the Queen Elizabeth Hospital in Adelaide, three soil cores were scanned in a medical

CT machine (Toshiba Aquilon, Tokyo, Japan) operating at 120 keV and a current of

100 mA. The pixel resolution of the reconstructed images was 0.3 mm. An image of the

cross section was taken every 0.4 mm (z-resolution) but the actual resolution was about

twice these values because of volume-averaging effects. Following the coarse CT-

scanning, three subsamples (4cm long and 1.5cm diameter) were dissected by a scalpel

from the E-B interface of one core, and scanned using a micro-CT machine (Skyscan

1072, Aartselaar, Belgium) operating at 100 keV and 98 mA. Pixel resolution of the

scan was 19 µm and the distance between two successive CT-slices was 19 µm. The

actual resolution of those images was twice the pixel resolution because of volume

averaging effects. Most biopores in soil tend to meander either horizontally or vertically

so the distance between subsequent CT-slices was taken as small as possible to reveal

detail. X-ray computed tomography of the soil provided images, i.e. maps of attenuation

coefficients, of the soil that were related to soil density.

Binary thresholding

The 2D images were segmented with a global-threshold value based on the greyscale

histogram to differentiate soil matrix and pores. The threshold value, computed using

the maximum entropy threshold algorithm in ImageJ (http://rsb.info.nih.gov/ij/), was

15

CHAPTER 2

selected at the point where the entropy (S) of the grey level histogram was maximised.

The entropy, being a measure of the uncertainty of any event, was calculated as:

S = -Σ (P x log2 (P)), (1)

where P was the probability of a pixel greyscale value in the image. When a greyscale

image was segmented, the pixels with a value smaller than the threshold value were

assigned the value 0, and were considered as pixels representing the soil matrix. These

pixels were called background. The pixels larger than the threshold value were assigned

the value 1, represented pore space and were termed foreground pixels. After

segmentation, a median filter, with a neighbourhood of two pixels, was applied to

eliminate noise (Perret et al., 1999).

A global threshold based on the greyscale histogram led to misidentification of

pixels because of finite resolution effects or noise in real 3D-images (Al-Raoush &

Willson, 2005). As a consequence, the connectivity of a number of pores in this

experiment was disrupted (Figure 1) and therefore the trends in the data will be

commented on rather than the absolute values. In further experiments, segmentation

algorithms that apply local thresholding techniques will be evaluated (Oh & Lindquist,

1999; Pierret et al., 2002).

Volumetric porosity and pore size distribution

The porosity, 3D pore-size distribution, numerical density (i.e. the number of pore

networks per volume of soil) and maximum connected volume of the three large intact

soil cores and the three subsamples were measured and calculated by loading the binary

images in the imaging software Amira 3.1 (Mercury Computer Systems, San Diego,

CA). Porosity was defined as the total number of voxels of the pore space, divided by

the total number of voxels of the sample. The 3D pore-size distribution was calculated

CHAPTER 2

16

by counting the number of disconnected volumes of pore space and separation of the

volumes according to this size (i.e. the number of voxels in the disconnected volume). A

voxel was recognised as forming part of the volume if it had adjacent voxels of the

same value. A visualisation model of the volume of the pore space was also

reconstructed using this software.

3D tortuosity and inclination

The skeleton of the pore space was reconstructed by using an algorithm run in Amira

3.0 (Kolesik et al., 2004). The algorithm was based on the 2D-derivation of the distance

map and each pixel belonging to the pore space in the binary image was replaced with a

grey value equal to that pixel's distance from the nearest background pixel. The pixels

with the largest value were connected from one cross-section to the next cross-section to

create a skeleton within which every pore was represented by a line. A segment of a

pore or a line was the length between two nodes. The nodes were the pixels on every

image that were connected to make the skeleton. The same skeleton algorithm

calculated the coordinates of every node in the pore space. Using simple geometric

theorems, the inclination and tortuosity of the pores were calculated based on the

coordinates of the nodes.

Tortuosity (τ), a dimensionless factor always >1, expresses the degree of complexity

of the sinuous pore path (Perret et al., 1999). It was defined as:

τ = Le / L, (2)

where Le was the effective average path and L the shortest distance measured along the

distance of the pore.

17

CHAPTER 2

The inclination of the pore network was calculated from the vertical. Where the

inclination from vertical was <45°, the pore was considered vertical, inclinations

between 45° and 90° were considered horizontal.

The macropore architecture in the E-horizon (of sand texture) was very complicated

because the resolution of the micro-CT scans revealed porosity that was caused by the

packing of the sand particles. McDonald et al. (1990) classified this structure as free

grain. Applied to the E horizon, the skeleton algorithm generated artefacts, the most

obvious of which were loops starting in some nodes. Therefore, the skeleton was

reconstructed only for the transition zone between E and Btn horizons and in the upper

portion of the Btn horizon. The efficacy of the skeleton, even with the artefacts, was

proven after comparing it with the volume reconstruction.

Results and discussion

In this study, the total porosity calculated on the basis of the bulk density, was greater

than the porosity measured from the CT images because measurements made from the

images were resolution-dependent. It is important to keep in mind that porosity

calculations from bulk density are also sensitive to resolution, which is defined by the

diameter of the cores and its relationship with the scale of the structure (Moran &

McBratney, 1992).

The porosity in the undisturbed large soil cores decreased drastically as a function of

depth, but slightly increased again deeper in the clay layer (Figure 1). The same trend

was found for roots growing in texture-contrast soils (McNeill et al., 2004) and

confirms that soil structure and root distribution are closely related in these soils (Moran

et al., 2000). The porosity of one of the large cores (Core 1) was more than 50 times

greater in the E-horizon than in the interface and three and four times greater in the

other two large cores (Cores 2 and 3, respectively). The greater porosity in the interface

CHAPTER 2

18

of Core 3 can be explained by further examination of the images. The CT-scans showed

relatively more root channels passing through the interface of that core. The numerical

density, mean network size and maximum network size (Table 2) showed the same

trend. The numerical density was ten times more in the sand layer than in the clay layer;

similar to the variation between samples found by Perret et al. (1999) from CT of

undisturbed soil cores. Nevertheless, although they stated that numerical density could

not be used as an indication of macroporosity, this parameter could be important for the

description of root distribution in soils because pore networks provide pathways for

roots. The variability of the same parameters between different samples, together with

the visual reconstructions (Figures 2 and 3), illustrates the patchy distribution of pores

in this soil, a feature also reported by Pierret et al. (1999) and Stewart et al. (1999) for

other soil types, and explains the clumped distribution of roots in these horizons.

The volume reconstructions and skeletons of the small intact samples from the

interface revealed that the E and Btn horizons had markedly different porosities (Figures

2 and 3) at both the macro-scale and the meso-scale. The macropores (>75 μm)

visualised with the medical CT images decreased drastically from the A and E-horizons

to the E-Btn interface. The macropores in the A and E horizon are formed by layers of

dead organic material and old root channels as revealed from destructive analysis of

some cores. The macropores in the E-Btn interface and the Btn horizon are mostly

tubular in shape and are probably old root channels. As stated above, the meso-porosity

of the E horizon, visualised at a spatial resolution of 19 µm, was largely caused by the

packing of the sand particles. Any porosity caused by roots or burrow systems that may

have been present could not be visualised in this horizon. Visually, as in the study by

Pierret et al. (2002), the pores in the Btn horizon could be separated into two groups.

The first group were biopores that were tubular and were suspected to be root channels

on the basis of their morphology. This was true for both macropores and mesopores. It

19

CHAPTER 2

was impossible to make a distinction between live or dead roots by X-ray absorption

imaging. The distinction between roots, and pores created by roots, was also not clear.

The absorption of those materials was too similar for them to be separated in X-ray

absorption images of undisturbed soil samples. The second group contained pores of no

particular shape and size. The biopores in this experiment did not have a pronounced

vertical or sub-vertical orientation (Figure 4). This orientation is mostly found in soils

that contain earthworms (Pierret et al., 2002) or in soils that do not contain physical

barriers to root proliferation. The pore networks in the A and E horizons were well

connected as would be expected in a sandy soil. This is valid at both scales studied. The

connectivity of the pores at both scales in the E-Btn interface and the Btn horizon was

very small. Increasing the number of pores by root growth would be the only realisable

option to increase pore connectivity in those layers.

Structure A in subsample 3 was suspected to be an old root channel containing a

young root. The reconstructions suggested a totally different architecture in this biopore

compared with the bulk soil. In the field, roots of live crops were observed to follow

mostly old root channels. This is consistent with other studies suggesting that roots

follow paths of least resistance or paths with different chemical characteristics than the

bulk soil (Stewart et al., 1999; Yunusa et al., 2002; Clark et al., 2003).

In subsamples 1 and 3, pores near the E horizon were more horizontal than vertical

whereas the number of horizontal pores in subsample 2 tended to be greater than the

number of vertical pores in the Btn horizon (Figures 4 and 5). Horizontal pores were

more tortuous than vertical pores in all three subsamples. These findings confirmed

studies in pseudo-three dimensions by Phase Contrast Imaging (PCI) on a similar soil

type (Moran et al., 2000). The horizontality of the pores indicated that roots could not

easily traverse the interface of this texture-contrast soil. The relatively large tortuosity

of the horizontal pores suggested that some roots were searching for paths of least

CHAPTER 2

20

resistance through the Btn-horizon, perhaps cracks caused by wetting and drying or old

root channels.

Black decaying roots can be observed down the soil profile in the field to a depth of

approximately 2m (depth of the pit). The size and shape of the tubular biopores suggests

they could not have been formed by annual vegetation. Young roots continuously re-use

those old root channels (Creswell & Kirkegaard, 1995). The measurements of

inclination and tortuosity of these biopores in texture-contrast soils indicate that roots

that penetrate the subsoils have a role in facilitating root exploration and access to

nutrients and water for subsequent species, especially those having root systems limited

in penetration ability . The environment of the pores, especially the immediate surface

of pores, is very different from the bulk soil (Pierret et al., 1999; Stewart et al., 1999;

Pankhurst et al., 2002). This suggests that old root channels are recycled in the

following growing season because the environment around these pores is different from

the hostile soil matrix.

The volumetric distribution of the small pores declined exponentially across the E-B

interface (Figure 6) but the large pores had an irregular distribution. Macro-and micro-

fractures in Vertisols showed the same pattern (Moreau et al., 1999). This pattern was

also found in the samples scanned with the micro-CT showing that this distribution

existed at different scales. The pore-size distribution in relation to pore volumes of the

macropores of the A and E horizons showed that most of the porosity was due to the

smallest and biggest pores. The distribution of the macropores in the E-Btn interface

and the Btn horizon showed that the largest pores were responsible for most of the

porosity (Figure 7). Hence, the distribution of the mesopores in the E horizon showed

that the smallest pores accounted for most of the porosity. The reason for this could be

that the packing porosity of the particles is accounted for in the porosity of the E

horizon whereas the mesopores in the Btn horizon do not represent the packing of

21

CHAPTER 2

particles (Figure 8). The length distribution in all samples did not differ for the

horizontal and the vertical pores and did not change as a function of depth (Figure 9).

Assuming that there is no overlap of the two pore types, the mean porosity of the

micro-CT images could provide an estimate of the mean mesoporosity of the medical-

CT images. The three subsamples scanned with the micro-CT were taken in the E-B

interface of Core 1. The scans of the undisturbed cores with the medical CT had to be

divided into 3 sections: the sand, the interface and the clay (Table 1). The interface was

a mixture of sand and clay because the line between the sand and the clay was not

horizontal, but depended on the domes of the Btn horizon. The macroporosity

calculated from the medical-CT binary images was 17.6% in the sand and 0.97% in the

clay. This means that the volumetric proportion of the soil matrix was 82.4% in the sand

and 99.03 % in the clay. The mesoporosity cannot be resolved with the medical-CT

images but can be with the micro-CT images. What appeared to be soil matrix in the

medical CT-images also contained mesopores resolved with the micro-CT images. So,

the soil matrix in Core 1 could be divided in a mesoporosity of 13.1% in the sand and

0.4% in the clay. Thus, the E-horizon in Core 1 has a macroporosity of 17.6% and a

mesoporosity of 13.1%, and the total porosity of 30.7% agrees with the value calculated

from the bulk density (Table 1). However, the total porosity in the clay, according to the

image analysis, was 1.37% whereas porosity calculated from the bulk density was 23%.

Thus, almost 22% of the total porosity in the clay horizon was attributed to micropores

not resolved by the micro-CT binary images. The smallest pores i.e. the pores resulting

from packing of the sand particles in the E horizon, were resolved with the micro-CT

images in accordance with the bulk density measurements.

Patterns of soil variation are the cumulative result of several physical, chemical and

biological processes acting in concert, a process at one scale having feedback to a

process at another scale (Bartoli et al., 2005). Variation is to be expected between levels

CHAPTER 2

22

of scale and resolution. This indicates the importance of quantifying porosity at all

scales. It can be argued that small samples are not representative, but there is a trade-off

between sample size and resolution.

Conclusions

The distribution of roots in a sodic, texture-contrast soil was related to pore structure.

Pores in texture-contrast soils could be separated into two groups. The first consisted of

tubular biopores, mainly caused by roots. The second group of pores did not have any

particular shape or size. There were more pores in the A and B horizons than in the E-B

interface. The size distribution of pores in all horizons was similar: exponential for

small pores and irregular for large pores. This was true at both the meso- and macro-

scale. Pores in this texture-contrast soil were not distributed homogeneously. The

distribution of macro-and meso-pores was patchy. Pores in the interface and at the top

of the B horizon were more horizontal than vertical, and horizontal pores were more

tortuous. This behaviour is a function of the impedance of the Btn horizon. Tomography

is a useful tool to analyse the architecture of pores at different scales and technological

advances in tomography will soon make it possible to quantify pore characteristics at

scales finer than those investigated here.

Acknowledgments

This work would not have been possible without the financial assistance of DEST under

the Systemic Infrastructure Initiative and the Cooperative Research Centre for Plant-

based Management of Dryland Salinity. We are grateful for the help and advice of

Damien Adcock and for laboratory assistance from Colin Rivers. We also thank Peter

Kolesik for stimulating discussions and advice.

23

CHAPTER 2

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27

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Figures

Figure 1 a) 2D view of the pores > 1mm in the upper 40 cm of a sodic texture-contrast

soil and b) 3D reconstruction of the macroporosity generated with a medical CT

machine. The vertical bars indicate the approximate position of the small samples in

Figures 2 and 3.

Figure 2 Volume reconstruction of the porosity (pores > 40µm) of the interface

subsamples by means of the micro-CT images. Arrows delineate the E-Btn interface.

Figure 3 Subsample skeleton reconstruction of the porosity (pores > 40µm) of samples

from the interface samples. Object A in subsample 3 was not taken into consideration

for the inclination, tortuosity and length measurements. The skeleton represents pores

with a single line (see text).

Figure 4 Distribution of horizontal and vertical pores as a function of depth of the

interface of subsample 1 obtained by scanning with a micro-CT machine. The 0 mm

depth does not correspond to the top of the undisturbed volume analysed but is the

location of the interface of the sample.

Figure 5 Tortuosity distribution of the “designated ” horizontal and vertical pores in

subsample 1 obtained by scanning with a micro-CT machine. This is the same volume

analysed in Figure 4.

Figure 6 Volumetric pore-size distribution (as a proportion of total number of pores) in

the A and E-horizons (0 cm – 27 cm) , E-B horizon interface (27 cm – 35 cm), and Btn

CHAPTER 2

28

horizon (35 cm – 47 cm) of the undisturbed Core 1 obtained from the medical CT

images.

Figure 7 The pore-size distribution in proportions of pore volumes of the macropores in

the A, E, interface and Btn horizons derived from the medical-CT images.

Figure 8 Pore-size distributions in relation to pore volumes of the mesopores of the E

and Btn horizon from the micro-CT images.

Figure 9 Cumulative length distribution of the horizontal and vertical pores in

subsample 1 taken from the interface of an undisturbed core scanned with the micro-CT

machine.

29

CHAPTER 2

Tables

Table1 General characteristics of an undisturbed soil core (15cm diameter, 50cm depth)

from Wharminda, South Australia.

a EC (electrical conductivity), b cation exchange capacity and c exchangeable sodium

percentage. d porosity is based on bulk density measurements ± standard error (n=3) and

solid density was assumed to be 2.65 g cm-3 for the porosity calculations.

Depth Horizon Clay Silt Sand pH(H 2 O) EC a CEC b ESP c Porosity /cm / % / % / % /dS m -1 /cmolc 100g -1 /% /% 0-10 A 0 2.5 97.5 7.5 0.12 6.4 5.0 41 ± 3 d

10-15 E 0 2.5 97.5 8.1 0.07 4.5 4.0 38 ± 2 15-20 E 0 2.5 97.5 8.7 0.07 5.8 3.8 33 ± 1 20-25 Btn 15 2.5 82.5 9.1 0.13 4.5 8.0 23 ± 3 25-30 Btn 30 2.5 67.5 9.1 0.20 10.2 6.7 22 ± 1 30-40 Btn 45 2.5 52.5 9.5 0.45 20.6 20.4 44 ± 8

CHAPTER 2

30

Table 2 Porosity, numerical density, mean network size and maximum network size of

three undisturbed soil cores calculated from the images obtained from a medical CT.

Core Horizon Depth analysed Porosity Numerical

density Mean pore

volumetric size Maximum

network size

/cm / % number of pore networks/cm -3 /mm 3 /cm 3 cm -3

1 A and E 0-16 17.6 13.9 352.5 4.49 E/Btn interface 16-26 0.68 4.2 45.6 0.05 Btn 26-43 0.97 3.2 84.1 0.12

2 A and E 0-21 17.47 10.2 473.9 5.21 E/Btn interface 21-31 4.76 4.5 296.2 0.03 Btn 31-48 4.51 5.8 214.5 0.84

3 A and E 0-20 18.69 6.1 854 4.32 E/Btn interface 20-26 0.29 1.7 48.2 0.69 Btn 26-41 4.17 8.3 140.3 0.45

31

CHAPTER 2

Table 3 Porosity, numerical density, mean network size and maximum network size of

three intact interface subsamples calculated from the images obtained from a micro CT.

Interface subsample Interface Depth Porosity Numerical density Mean pore

volumetric size Maximum

network size

/mm / % number of pore networks /cm -3 / µ m 3 / mm 3 mm -3

subsample 1 E 0-9 14.8 107.4 202.4 0.531 Btn 9-18 0.5 0.6 470.1 0.001

subsample 2 E 0-9 12.7 53.3 350.2 0.152 Btn 9-19 0.2 3.8 91.3 0.042

subsample 3 E 0-10 11.7 45.5 376.9 0.121 Btn 10-19 0.5 3.8 183.7 0.021

CHAPTER 2

32

Figures Figure 1

100 mm

A

E

Btn

a) b)

33

CHAPTER 2

Figure 2

Subsample 1 Subsample 3 Subsample 2

1 cm

CHAPTER 2

34

Figure 3

A

1 cm

Subsample 1 Subsample 2 Subsample 3

35

CHAPTER 2

Figure 4

0 5 10 15 20 25 30 35

0

1

2

3

4

5

6

7

8

9

10

Dep

th /m

m

/mm

Percentage of pores

horizontal vertical

CHAPTER 2

36

Figure 6

0

5

10

15

20

25

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35

1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8

Tortuosity

Perc

enta

ge o

f por

es

pore

s

horizontal vertical

37

CHAPTER 2

Figure 7

0.00

0.02

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0.90

1.30

1.69

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7.92

8.71

10.1

2

11.4

5

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1

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4

45.2

9

1831

.3

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ume

0

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18.8

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137.

7

volume (mm3)

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tive

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vol

ume

0.000

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0.004

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0.010

0.012

0.014

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34.7

52.0

97.8

volume (mm3)

rela

tive

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vol

ume

A and E horizon

E-Btn interface

Btn horizon

Volume /mm3

Rel

ativ

e po

re

volu

me

CHAPTER 2

38

Figure 8

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.20 7.01 13.81 20.62 27.42 34.23 41.03 47.84 54.64 61.45

Volume /μm 3 X104

Rel

ativ

e po

re v

olum

e

volu

me sand

0.000

0.005

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0.020

0.025

0.030

0.035

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0.050

0.21 1.65 3.09 4.32 5.28 6.58 8.23 10.08 18.66 35.80 84.98 Volume /µm 3 x104

Rel

ativ

e po

re v

olum

e

volu

me

39

CHAPTER 2

Figure 9

0

0.2

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0.8

1

1.2

0 1000 2000 3000 4000 5000

Length /μm

vertical horizontal

CHAPTER 3

40

Using computer tomography to visualise and quantify macropores in

intact soils, the issue of thresholding

Laurence Jassognea,c*, Rob Davidsonb, Ann McNeillc, Ganga Hettiarachchid and David

Chittleboroughc

aSchool of Plant Biology, University of Western Australia, Crawley Western Australia,

6907

bDavidson’s Agricultural Research and Development, PO Box 280, Mt Pleasant, SA,

5235 Australia

cSoil and Land Systems, School of Earth and Environmental Sciences, University of

Adelaide, Waite Campus, PMB 1 Glen Osmond, SA, 5064 Australiad

dDeparment of Agronomy, 2107 Throckmorton Plant Sciences Center, Kansas State

University, Manhattan, KS 66506 USA

CHAPTER 3

41

Abstract

Soils with strong texture contrast between surface and B horizons, called duplex soils in

Australia dominate the agricultural zone of western and southern Australia. The texture

of the A and E horizons is usually coarse and the B horizon is often of much finer

texture. These horizons are inimical to root proliferation. Native perennial vegetation

has created biopores that have been observed to extend many meters through the profile.

These macropores not only provide pathways through the soil otherwise impenetrable

by many plants but also improve exposure to preferential flows of oxygen, water and

nutrients. Understanding pore distribution is fundamental to understanding how roots

proliferate in these soils more particularly whether they are utilising a pre-existing

network or creating a network ab initio. Pore characteristics required to predict root

growth in duplex soils are pore distribution, orientation, length and connectivity.

Measurements of these pore characteristics can only be made if the three dimensional

(3D) structure of the pores is known. Analysis of CT-images from soil samples allows a

3D reconstruction from the soil pore network. In this study, a thresholding method

developed by Oh and Lindquist (1999) that utilized the spatial covariance of the image

in conjunction with indicator kriging to determine object edges, was tested to determine

macroporosity in 16 bit X-ray absorption images obtained from X-ray scans of intact

cores of duplex soils. Software created by Oh and Lindquist (1999) 3DMA-rock is a

local and adaptive technique that minimises errors in segmentation of grey scale images

in soil but the output is very sensitive to the chosen threshold values. This study shows

that the method of Oh and Lindquist (1999) is improved if the segmentation values are

derived by fitting two Gaussian curves on the each stack of images. This method will

allow visualising root exploration and its relation with macroporosity in hostile subsoils

such as those in duplex soils.

CHAPTER 3

42

Keywords: texture-contrast soils, macropores, computer tomography, thresholding,

imaging

1. Introduction


Australia, dominate the agricultural zone of western and southern Australia. The texture

of the A and E horizons is usually coarse (sand, loamy sand, sandy loam) and the B

horizon is often much finer (clay, clay loam). The E and B horizons are inimical to root

proliferation. The A and E horizons have a low water holding capacity (Tennant et al.,

1992). The B horizons (especially at the E horizon contact) are non pedal and have high

strength and bulk densities in excess of 2 g.cm-3 (Chittleborough, 1992). It is not

uncommon for the texture transition to be abrupt viz. over a distance <1cm. In the B

horizons of these soils, roots of agricultural crops and pastures, both perennials and

annuals, are often in pores not of their own making (McCallum et al., 2004.)

Macropores in these duplex soils have their origin in physico-chemical reactions,

seasonal wetting and drying gives rise to high shrink/swell forces and large cracks that

extend vertically 20-100cm from the top of the B horizon. Native perennial vegetation

e.g. Acacias, Eucalypts, Proteaceae, have created biopores that have been observed to

extend many metres through the profile. The macropores not only provide pathways

through the soil otherwise impenetrable by many plants, but also improve exposure to

preferential flows of oxygen, water and nutrients. Understanding pore distribution is

fundamental to understanding how roots proliferate in these soils more particularly

whether they are utilising a pre-existing network or creating a network ab initio.

CHAPTER 3

43

Pore characteristics required to predict root growth in duplex soils are pore distribution,

orientation, length and connectivity (Gregory and Hinsinger, 1999) and measurements

of these pore characteristics can only be made if the three dimensional (3D) structure of

the pores is known. Unfortunately, this cannot be done with conventional techniques

such as soil thin sections. Although the use of thin sections can be very valuable under

certain circumstances (Mooney et al., 1998), thin sections have significant limitations:

they are time consuming to prepare, involve impregnation of the soil with resins that

lead to perturbations in structure and the 3D measurements obtained are derived from

two dimensional information (Moreau et al., 1999). Another method used in the field

involves photographing dye traces to show water flow through pores structures, but

again, the resultant spatial information is very limited (Alaoui and Helbling, 2006).

Computer tomography (CT) analysis has proven to be a powerful tool in soil science

insofar as it enables non-invasive and non-destructive analysis of the internal structure

and texture of samples by mapping the density distribution at high resolution in three

dimensions. It has been used since the 1980s to investigate soil structure (Crestana et

al., 1986) and is currently the only technique that allows visualisation and

measurements in 3D of porosity in a manner that is non-destructive and non-invasive

(Asseng at al., 2000). Scanning soil cores results in grey scale images that can be related

to the density of soil through differences in X-ray absorption thereby facilitating a 3D

representation of the inside of the sample.

The output from a CT scan is a stack of grey scale images that can be related to the

density of the samples. Regions of low density represent pores and regions of high

density the soil matrix. There is a need to identify, reconstruct and visualise the 3D

networks of macropores. Thresholding is the step that most influences the rest of the 3D

CHAPTER 3

44

measurements from the image segmentation. During this process, binary images are

constructed by assigning the pores one value and pixels representing the background or

soil matrix another value (generally these values are 0 and 1). The thresholding

operation is complicated by various factors such as non-stationary and correlated noise,

ambient illumination, variation of grey levels within the object and its background,

inadequate contrast, and object size not commensurate with the scene. The lack of

objective measures to assess the performance of various thresholding algorithms, and

the difficulty of extensive testing in a task-oriented environment are major handicaps

(Sezgin and Sankur, 2004). Thresholding can be applied globally based on the grey

level information of the whole stack of images, or locally based on only a part of the

stack. In addition to the grey-level information, spatial information in the image can be

used to segment with a greater accuracy (Oh and Lindquist, 1999).

Pierret et al. (2002) developed an algorithm for thresholding that was iterative, local and

adaptive. In their study, it was assumed that very porous regions were likely to be part

of macropores, therefore, the tracing algorithm was initiated from the brightest voxel in

the 3D volume. Once the first object was traced, the tracing resumed from the second

brightest voxel that was not included in the previous object. This process was repeated

until the grey level of the brightest remaining voxel was less than a preset grey level

threshold (Pierret et al., 2002; Capowiez et al., 2003). The disadvantage of this method

was that the mathematical morphological measurements used needed an isotropic

dataset. The anisotropic voxels had to be transformed, which led to a loss of spatial

resolution and there was a requirement for subsampling the 16 bit dicom images to 8 bit

raw images, which again led to loss of information in the image.

CHAPTER 3

45

In this study, a thresholding method developed by Oh and Lindquist (1999) that utilized

the spatial covariance of the image in conjunction with indicator kriging to determine

object edges, was tested to determine macroporosity in 16 bit X-ray absorption images

obtained from X-ray scans of intact cores of duplex soils. The method is local and here

we test the hypothesis that it guarantees smoothness in the threshold surface.

2. Materials and methods

2.1 Background

The Lambert-Beer law is applicable for a monochromatic X-ray interacting with soil:

Ι/Ι0 = exp(-µ.h)

I is the attenuated intensity, I0 is the intensity of the incoming X-rays, µ is the

attenuation coefficient and h is the thickness of the sample. Each pixel has a grey value

that represents the X-ray attenuation (µ) in that particular spot in the soil core. The

attenuation is related with the density (ρ) of the soil at that location and the atomic

number (Z) by the relationship,

µ = ρ(a + b Z3.8 / E 3.2) (1)

for energies (E) lower than 200 kV, the X-ray interaction is by the photo-electric effect

mainly. This effect depends on the atomic number of the material. For energies higher

than 200 kV, the density of the material mainly effects µ and the interaction with the

CHAPTER 3

46

material is mainly Compton scattering. The interaction between soil and X-rays has

been described in a number of studies e.g. Mori et al. (1999).

2.2 Sampling, scanning and reconstruction

Intact soil cores (50 cm long, 15 cm diameter) were taken by pressing thin-walled PVC

tubes with an hydraulic ram from an agricultural site in southern Australia (33° 54’S,

137° 47’E) near the town of Stansbury. The soil was a Red Sodosol (Isbell 1996) or a

Typic Natrixeralf (Soil Survey Staff, 1999). Cores were scanned at the Queen Elizabeth

Hospital in Adelaide with a medical CT instrument (Toshiba Aquilon, Tokyo, Japan)

operating at 120 keV and a current of 100 mA. Reconstructed images were 16 bit

DICOM attenuation maps. The value of each pixel could be related to the density of the

soil sample in that particular location. Pixel resolution (x, y-resolution) of the

reconstructed images was 0.3 mm. An image of the cross section was taken every 0.4

mm (z-resolution).

2.3 Imaging

Because cores varied considerably in their morphology the parameters necessary for

segmentation had to be assessed for each new dataset. Each dataset was divided into

different substacks that coincided with soil horizons and each of these substacks was

treated as a separate entity for segmentation (Figure 1).

The images were prepared for segmentation using ImageJ (Rasband, W.S., ImageJ, U.

S. National Institutes of Health, Bethesda, Maryland, USA, http://rsb.info.nih.gov/ij/,

1997-2007) and the following series of processes were applied. First, the interior of the

core (in the soil without the PVC tube) was selected, the outside of the selection was

CHAPTER 3

47

cleared and a particular value assigned close to the highest absorbance. In order to

enhance the contrast and increase the number of attenuation values in the transition

between pore and matrix, the grey scale images were normalised using ImageJ with an

enhance contrast plugin and limiting the ratio of saturated pixels to 0.1 %.

Normalisation of the images was based on the histogram of the stack and not on the

histogram of each separate image. A histogram of the normalised stack was created and

two Gaussian curves were fitted on this histogram representing the distribution of the

grey values in the stack. The peak of the histogram representing the distribution of

attenuation, or grey levels, of the soil matrix and the part of the histogram that contained

the darker pixels representing pores were selected for analysis by OriginPro7.5. The

segmentation method developed by Oh and Lindquist (1999) requires two threshold

values as input based on the grey scale value of macropore and soil material. The pixels

lower than the first threshold (T1) belong to the pore phase whereas pixels higher than

the second threshold (T2) belong to the soil matrix. The first threshold was selected by

subtracting the mean of the first Gaussian curve with its width. The second threshold

was calculated by adding 3000 to the first threshold. This value was chosen by

continually comparing results for different datasets when varying the two threshold

values. The entire process from selection through to segmentation was undertaken on

every dataset.

CHAPTER 3

48

37

87

137

187

237

287

337

387

437

-100 0 100 200 300 400

absorption (HU)

dept

h (m

m)

Interface

A

E

interface

B

Figure 1: Change in absorbance with depth in relation to horizons of a sodic duplex

soil. A, E and B refer to the horizons.

CHAPTER 3

49

The sensitivity of the output (i.e. porosity) was tested by changing the value of T2 and

the width between T1 and T2. The attenuation values for clay are higher than the

attenuation values for sand. If one set of cut-off values was chosen for the entire stack

of images of the soil core, the sand matrix was selected if pores in the clay were to be

targeted or none of the pores in the clay if the pores in the sand were targeted. For this

reason, the original stacks had to be divided into several substacks the boundaries of

which were the boundaries of the soil horizons (A, E, E/B interface and B) (Figure 1).

The interface is the region of soil 2 cm at either side of the E/B boundary.

Images at the interface were of sand and clay and therefore images were segmented

twice. First, pores in the sand, and then pores in the clay, were identified. This last

segmentation meant that areas in the image containing lower densities than clay, like

sand were selected as well. These areas were removed by subtracting them from the

segmented clay interface images following which the images were added to the

interface images of sand at the interface. Binary stacks of the layers were added and

these stacks used to reconstruct and visualise porosity of the intact cores.

CHAPTER 3

50

Figure 2: Attenuation through a pore of approximately 5 pixels in diameter from the

interface region. A: a pore in clay matrix B: a pore in sand matrix

A

B

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

0 1 2 3 4 5

distance (pixels)

norm

alis

ed a

ttenu

atio

n A

B

A

B

-15000

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A

CHAPTER 3

51

3. Results

The average attenuation of the XY sectional images was markedly different between

horizons (Figure 1). The surface of the A horizon had been subjected to considerable

mechanical disturbance by tillage. Coupled with a high root density this horizon had a

lower density than deeper in the A horizon and the E horizon. Because of these

differences, A and E horizon images were separated and treated as two different stacks.

Variability within the A horizon was high and was divided in smaller substacks for

processing. Images of the E and B horizons had markedly different grey values and the

images were also divided into substacks. Images located in the interface contained

regions of sand and clay and were included in a separate stack. Clays have higher X-ray

absorbance than sands. Pores of the same diameter in the E-horizon and the B-horizon

have different attenuation values (Figure 2). If threshold values are chosen to detect

pores in clay, pixels belonging to the sandy soil matrix will be selected as well. This can

be due to different materials filling the pores as explained earlier, but the pixels in pores

of the B-horizon can have attenuation values similar to soil matrix pixels in the E-

horizon. A more likely reason for misidentification lies in the reconstruction of the cross

sections from the projection images. The solution was to undertake the reconstruction

process twice. Interface images composed of both sand and clay phases were processed

twice in order to identify pores in sand and clay matrices separately.

Sensitivity analysis showed that changing T2 from 738 HU to 1044 HU increased the

porosity (Figure 3). This was not surprising because selecting a higher T2 highlighted

smaller pores having higher attenuation and expanded the edge of bigger pores.

CHAPTER 3

52

Figure 3: Change in porosity for different values of T2 when T1 is varied.

T2

CHAPTER 3

53

Increasing the difference between T1 and T2 and keeping the same value for T2

decreased the porosity. By increasing the difference, the population of voxels attributed

to pores decreased, and the number of voxels on which indicator Kriging was applied

increased. The larger the difference between T1 and T2, the smaller the porosity.

Despite the original dicom grey scale images being 16 bit images and therefore the

image having 216 different attenuation values, values for the transition from absorption

by pores to absorption by the soil matrix were in a narrow interval of approximately

3000 grey levels for the A and E horizon and 5000 grey levels for the B horizon. The

magnitude of the interval of the attenuation values, at which this transition occurs,

seems to be dependent on soil texture. The transition in attenuation values between pore

and soil matrix in clay occurs over a longer interval than in a sand matrix (Figure 4).

The Impact of changing the threshold value on porosity is much bigger in E than B

horizon: >20-fold in E horizon qnd about 4-fold in B horizon for a 20% increase in

threshold value.

4. Discussion

In soil, the material that fills up pores is mostly air, water, organic matter and roots or a

combination of this matrices. When vertical cracks in clay formed by shrink-swell

forces are infilled with quartz of silt and sand size from E horizons these infillings have

a different X-ray attenuation. The difference allows the visualisation of soil porosity

non-destructively. However, the attenuation differences between the fillings in pores

and the matrix of the clay B horizon are not large enough to allow precise separation of

CHAPTER 3

54

these structures. Scanning the cores at a lower energy would improve the contrast

between the materials but would decrease the signal to noise ratio and the image quality

(Heeraman et al., 1997).

CHAPTER 3

55

Figure 4: Variation of porosity when a single threshold value increases for the E and B

horizons.

y = 0.0002x2 - 0.4423x + 204.1R2 = 0.9965

0

1

2

3

4

5

6

7

8

800 850 900 950 1000 1050 1100 1150

Threshold

poro

sity

(%)

y = 3E-05x2 - 0.0642x + 34.607R2 = 0.9944

0

0.5

1

1.5

2

2.5

1000 1050 1100 1150 1200 1250 1300 1350

Threshold

poro

sity

(%)

E-horizon

B-horizon

CHAPTER 3

56

In this study porosity was classified by including every material that can fill pores that

is not the surrounding soil matrix (including fresh roots drilling through soil). In the

Sodosol profiles studied here, pores in the interface may contain roots, and sand from

the E horizon transported further down the profile. Scanning samples at lower energies

could improve the contrast between objects in the sample. Scanning a soil sample at low

and high energy (dual energy scanning) can make it possible to locate water in the soil

core (Rogasik et al., 1999). However this was not possible in this study because

increasing sample size at low energy offered by medical scanners can result in beam

hardening artefacts. Beam hardening occurs when low energy X-rays are preferentially

absorbed when they traverse an object.

Image segmentation or thresholding is an important step in 3D visualisation and

measurements. It involves the separation of the pixels belonging to the pore phase from

the ones belonging to the soil matrix. This process includes converting a grey scale

image to an image with only two values (binary image). The population of pixels

belonging to the porosity phase receives value 1 and the pixels belonging to the soil

matrix phase get value 0.

Different segmentation or threshold techniques exist. The simplest one, also called

global segmentation, uses one threshold value for the entire images based on the

histogram of the stack of images (Heijs et al., 1995; Capowiez et al., 1998; Li et al.,

2004; Jassogne et al., 2007). The threshold value can be chosen visually, using simple

imaging software and by comparing the segmented images with the original image but it

is uncertain whether the value chosen is truly representative of the pore values in the

grey scale images using this technique. This issue has been addressed in some studies

by constructing an artificial pore and using the value that visualised a macropore with

the same width as the artificial macropore (Li et al., 2004). Identifying pores created by

CHAPTER 3

57

roots or burrowing animals is easier in reconstructed cores than in intact cores.

Depending on the particle size to which the soil has been sieved, reconstructed cores do

not contain pores higher than a certain resolution. Intact cores can contain pores created

by plants in earlier seasons that makes the task of choosing threshold values for porosity

in intact cores even more complex. Additionally, small pores have a lower contrast and

lower attenuation than large pores and there is a trade off between the resolution of

pores identified and the overestimation of the larger pores (Pierret et al, 1999). In

theory, it is possible to identify objects in an image that are larger than twice the voxel

resolution. If the cut-off values are chosen to isolate the small pores, the larger pores

will be overestimated. This is illustrated in Figure 5, when higher values are chosen for

the second threshold value (T3 and T5), smaller pores will be selected but more soil

matrix pixels around the bigger pixels.

Oh and Lindquist (1999) have presented a segmentation method that utilises the spatial

covariance of the image in conjunction with indicator Kriging to determine object

edges. The use of indicator Kriging makes the thresholding local and guarantees

smoothness in the threshold surface. The method requires identification a priori of the

population of some percentage of the image. In practice, this is not difficult to achieve.

It is often possible to identify the population type of much of the image with negligible

identification error (e.g. voxels clearly inside pores). The majority of the problem comes

from pore-edge voxels or small, low contrast pores. A first pass over the image

produces population assignments for a fraction of the image (non-edge voxels), leaving

a remaining population unidentified, between two grey scale values (T1 and T2).

Minimum variance estimation is then utilised on these to complete the segmentation of

the image.

CHAPTER 3

58

Figure 5: The effect on porosity of increasing the width between threshold values T1-

T5).

0

1000

2000

3000

4000

5000

6000

33400 33500 33600 33700 33800 33900 34000

attenuation values

coun

ts x

103

T1 T2

T3T4T5

T1 - T2T1 – T3 T4 – T5

0

1000

2000

3000

4000

5000

6000

33400 33500 33600 33700 33800 33900 34000

attenuation values

coun

ts x

103

T1 T2

T3T4T5

T1 - T2T1 – T3 T4 – T5

14 cm

CHAPTER 3

59

This method was efficient in including objectively pixels of pore edges and pixels

belonging to small pores (high uncertainty) to the pore phase. Nevertheless, the output

seemed to be highly sensitive to which value of T1 and T2 was chosen (Figure 5).

In addition, our images always contained a certain amount of noise. Nor was our

segmentation method perfect. Some isolated elliptical volumes were segmented that,

based on visual interpretation, were not cracks and could not have been created by plant

roots or burrowing animals. Therefore, a volume filter using Amira was used to

eliminate these volumes from the assigned pore space (Figure 6).

One possibility to obtain images of better quality would be to use tubes of a different

material (e.g. Perspex). This would decrease X-ray absorption because of the container

thereby increasing quality of the original images and thus the quality of the

segmentation. The PVC tubes used to contain soil cores during the thresholding process

absorbed a lot of X-rays.

High quality, meaning maximum contrast between a feature and background is

important for the visualisation of the object. Detectability in our system depended on

system resolution, system noise, size and composition of background (soil) and size and

composition of feature (pore or root) (Sivers and Silver, 1990).

Resolution had a significant impact on the properties of the system being characterised.

The impact was controlled by two factors: the grain size/resolution ratio and the

uniformity of the system (Al-Raoush and Willson, 2005).

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60

Figure 6: Reconstruction of the macroporosity of the E-horizon of a duplex soil before

and after filtering of volumes due to noise.

14 cm

CHAPTER 3

61

Due to partial-volume artefacts, which were caused by the averaging within a voxel

between highly contrasting materials, boundaries of small objects with high density

differences were blurred and contrast was reduced in the image. Reduced contrast may

obliterate small details of low contrast, blurred boundaries may obliterate fractures, and

artefacts degrade images in general (Heijs et al., 1995).

This study has shown that image analysis of X-ray CT images to visualise 3D volumes

of macropores can be achieved for large intact soil cores. However, thresholding is

critical in order to delineate pores from the soil matrix and has an effect on quantitative

values for total porosity. Therefore, care has to be taken when comparing results from

different tomography studies, not only because resolution and scanning systems differ,

but also because the image analysis can be different and is very sensitive.

5. Conclusions

Computer tomography is a valuable method to visualise macropores in 3D in soils.

Software created by Oh and Lindquist (1999) 3DMA-rock is a local and adaptive

technique that minimises errors in segmentation of grey scale images in soil but the

output is very sensitive to the chosen threshold values. We conclude that the method of

Oh and Lindquist (1999) is improved if the segmentation values are derived by fitting

two Gaussian curves on the grey values of each stack of images. This method should

allow visualization of macroroot systems and the relationship with macroporosity in

hostile subsoils like those in texture contrast soils.

CHAPTER 3

62

Acknowledgments

The authors are grateful to Hans Lambers for support and to the CRC for Plant based

Management of Dryland Salinity for funding. Thanks to the Australian Government and

the University of Western Australia for the International Postgraduate Research

Scholarship of Laurence Jassogne. The authors would also like to thank Geoff West and

staff at the Queen Elizabeth Hospital for scanning cores.

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a tool for non-destructive analysis of flow patterns in macroporous clay soils.

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Isbell, R.F., 1996. The Australian Soil Classification CSIRO, Melbourne.

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macro- and meso-porosity of the upper horizons of a sodic, texture-contrast soil.

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Li, D.C., Velde, B., Zhang,T.L., 2004. Observations of pores and aggregates during

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structural images from undisturbed, non-cohesive material (milled peat).

Geoderma 86, 159-168.

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Moreau, E., Velde, B., Terribile, F., 1999. Comparison of 2D and 3D images of

fractures in a Vertisol. Geoderma 92, 55-72.

Mori, Y., Iwama, K., Maruyama, T., Mitsuno, T., 1999. Discriminating the influence of

soil texture and management-induced changes in macropore flow using soft X-

rays. Soil Science 164, 467 - 482.

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transactions on patters analysis and machine intelligence 21, 590-602.

Pierret, A., Moran, C.J., Pankhurst, C.E., 1999. Differentiation of soil properties related

to the spatial association of wheat roots and soil macropores. Plant and Soil 211,

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analysis. Geoderma 106, 247-271.

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Discrimination of soil phases by dual energy X-ray tomography. Soil Science

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Soil Survey Staff, 1999. Soil taxonomy: a basic system of soil classification for making

and interpreting soil surveys, Washington.

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Computed tomography of intact soils cores I. Spatial analysis of soil

macroporosity before and after root growth

Laurence Jassognea,c*, Rob Davidsonb, Ann McNeillc, Ganga Hettiarachchid and David

Chittleboroughc

aSchool of Plant Biology, The University of Western Australia, Crawley, WA, 6009,

Australia

bDavidson’s Agricultural Research and Development, PO Box 280, Mt Pleasant, SA

5235, Australia


Adelaide, Waite Campus, PMB 1 Glen Osmond, SA 5064, Australia

d CSIRO Land and Water, Waite Campus, PMB 2 Glen Osmond, SA 5064, Australia

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Abstract

Soils with a very strong texture contrast in the profile, called duplex soils or

texture-contrast soils, are very common in southern Australia. These soils mostly have

dense subsoils that are very hard for roots to penetrate and grow through. When roots

encounter difficulty in penetrating or ‘drilling’ through the subsoil, it has been shown

that they can use pre-existing pores, such as channels created by roots of previous plants

or cracks between peds, to proliferate through the profile and gain access to water and

nutrients. It has been suggested that deep-rooted perennials have the capacity to drill

through the E B boundary of these hostile soils, especially native plants that have

evolved with the soil environment and climate. Growing plants that drill through the

subsoil will increase the porosity of the subsoil which would lead to an increase in

subsoil permeability and enable the roots of subsequent plants to exploit water and

nutrients via these old root channels. Such knowledge of root performance and

proliferation in duplex soils is fundamental to understanding the influence of old root

channels, or relict rhizospheres, on root proliferation. Since soil is an opaque medium,

the only way to fully characterise and understand root growth and porosity for

undisturbed soils representative of the field situation is by reconstruction and

visualisation of soil porosity and root growth in 3 dimensions (3D). Techniques to do

this are still being explored and developed. In this study, medical X-ray computer

tomography (CT) CT was used to characterise in 3D the macroporosity of undisturbed

columns of duplex soils and to visualise changes in that macroporosity following new

root growth by canola (Brassica napus), lucerne (Medicago sativa) and saltbush

(Atriplex nummularia). A method using indicator kriging was adapted to the resulting

output images. It was shown that macroporosity in the duplex soils was significantly

altered after root growth, although there were no observed differences between the

changes made by an annual (B. napus) a herbaceous perennial (M. sativa) and a woody

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67

perennial (A. nummularia). The method did not seem useful on its own to measure all

wanted alterations of soil structure due to root growth.

Introduction

Soils with strong texture contrast between surface and subsoil or B horizons,

called duplex soils in Australia (Isbell 1996), dominate the agricultural zone of Western

and southern Australia. The texture of the A and E horizons is usually coarse and the B

horizon is often a dense clay that is inimical to plant root proliferation, particularly for

many exotic agricultural and horticultural crops. However, the root systems of certain

types of native Australian perennial vegetation can create biopores in these subsoils that

extend many metres through the profile (Yunusa and Newton 2003). These macropores

not only provide pathways through soil otherwise impenetrable by many plants, but are

also likely to improve root exposure to preferential flows of oxygen, water and

nutrients. The roots of introduced agricultural plants have been observed actively

growing in such remnant biopores in Australia (Passioura, 2002), suggesting that these

are preferential routes for root development of annual plants. Thus it is not surprising

that studies in the USA have also shown that corn root systems in soil previously

cropped to the perennial alfalfa had root distributions similar to that of the previous

alfalfa crop (Rasse and Smucker 1998). Perennial pasture has been shown to improve

the unfavourable structure of dense subsoils in Australia (McCallum et al. 2004), an

observation that has led to the idea of bioremediation for intractable subsoil horizons

(Stirzacker et al. 1996; Yunusa and Newton 2003). Despite the tendency of many roots

to exploit existing pores and channels, particularly deeper in the profile (Goss 1991),

they will also attempt to penetrate the matrix and, via root elongation and radial

expansion, produce new channels or biopores (Watt et al. 2006). However, information

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68

on how root systems of different species exploit subsoils, especially under undisturbed

conditions, is scarce.

Roots growing in soil change their physical, biological or chemical environment,

directly and indirectly, to improve their water and nutrient uptake and create a zone

called the rhizosphere. When a root dies, the effect of the root on the surrounding soil

continues to operate because of root decomposition. This affected zone has been called

a ‘relict’ rhizosphere and clearly will influence the growth of subsequent roots.

Understanding pore distribution is fundamental to understanding how roots proliferate

in these soils, more particularly whether they are utilising a pre-existing network or

creating a network ab initio. Plants that increase porosity in hostile soils can be of

significant importance in terms of improving nutrient and water accessibility for roots

of the next generation of plants.

Soil is an opaque medium and most conventional ways to study porosity and

root growth require the destruction of the soil, which leads to loss of spatial information

of the soil structure and its relation with root architecture and growth (Smit et al., 2000).

Computer tomography has been shown to be useful to characterise porosity and root

development in soil in 3D (Gregory et al. 1999; Pierret et al. 2002). Progress in

computer tomographic technologies now allows 3D examination of soils at very fine

resolutions (Peth et al. 2008). Computer tomography has been used recently to study the

interaction between biological and physical processes in soil (Blair et al. 2007; De

Gryze et al. 2006). It has also been used to study porosity changes at µm scale due to

heavy machinery traffic (Schaffer et al. 2007). There is a trade-off however between

sample size and resolution. The finer the resolution required, the smaller the sample

size. There is a need to integrate 3D studies at fine scales such as the root-soil- microbe

interface (ex. Feeney et al. 2006) into the increasingly bigger picture of soil aggregates,

peds, cores or blocks. Scanning with medical CT allows large soil blocks to be imaged

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69

and reconstructed in 3D although analysis is limited to features at mm scale or larger

with this technology (Pierret et al. 2002). Whilst this precludes the study of fine roots

that are clearly one major component of root systems (Pierret et al. 2005), it does enable

quantitative description of the developmental architecture of coarse roots which

necessarily preface fine root formation. Furthermore it provides a clear picture of the

tortuous pathways followed by these roots in response to soil porosity, and in hostile

subsoils in particular it can be used to investigate root behaviour in response to old root

channels or relict rhizospheres. In this study, X-ray CT was used to visualise and

characterise, in 3D, macroporosity for large intact cores of duplex soils. X-ray CT was

further used to investigate and qualitatively describe how much this macroporosity

changed when a new plant was grown in the soil core for 12 weeks. Three plant species

were used to examine whether there were differences in root system exploration of

existing soil macropores, including relict rhizospheres between an annual plant canola

(Brassica napus), a perennial herbaceous species lucerne (Medicago sativa) and a

perennial woody species saltbush (Atriplex nummularia).

Materials and methods

Soil core collection and plant growth conditions

Intact soil cores (50 cm long, 15 cm diameter) were taken by pressing thin-

walled PVC tubes with a hydraulic ram into moist soil from an agricultural site in

southern Australia (33° 54’S, 137° 47’E) near the town of Stansbury. The soil was a

Red Sodosol (Isbell 1996) or a Typic Natrixeralf (Soil Survey Staff, 1999). The bulk

density of the soil varied, it was 1.5 g cm-3 in the A horizon, 1.6 g cm-3 in the E horizon,

1.9 g cm-3 in the soil layer comprising 2 cm of the base of the E horizon and the upper 2

cm of the B horizon (hereafter called the E/B interface) and 1.8 g cm-3 in the B horizon.

The bulk density was calculated using 5 cm diameter bulk density rings. Whilst salinity

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70

in the soil cores was not an issue, with the highest electrical conductivity (EC) (1:5)

value in the core found at 45-50 cm depth with a value of 0.268 dS m-1, the

exchangeable sodium percentage (ESP) in the E/B interface was 24.2 % indicating

strong sodic properties and high strength.

The cores were carefully transported to the lab to make sure that the soil was not

disturbed. In the lab, the PVC tubes were opened longitudinally with a circular saw and

carefully slid over the intact core. The cores were then coated with liquid latex to

provide a barrier for root growth at the edges of the soil. The PVC tubes were then slid

back over the intact core, closed with cable ties and the openings of the PVC tubes were

sealed with latex. Canola, lucerne and saltbush seeds were germinated and, after 3 days

for canola and lucerne and 8 days for saltbush, one seedling was planted in each of 3

cores for each species. The plants were grown in a glasshouse in which temperature was

not controlled from July until September. An amount of water equivalent to the average

rainfall for a period of the growing season in South Australia from sowing to peak

biomass (262 mm in the period May to September) was applied to the cores over a

period of 12 weeks. The season rainfall pattern was mimicked so the plants were given

more water at early growing stages and less later. Thus the cores became drier towards

the end of the study.

X-ray CT Scanning

The cores were scanned before the seeds were planted to enable analyses of

existing porosity and again 12 weeks after planting to allow the determination of

macroporosity changes due to root growth. The cores were scanned at the Queen

Elizabeth Hospital in Adelaide, South Australia, with a medical CT instrument (Toshiba

Aquilon, Tokyo, Japan) operating at 120 keV and a current of 100 mA. Reconstructed

images were 16 bit DICOM attenuation maps that could be related to the density of the

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71

soil sample in that particular location. Pixel resolution (x, y-resolution) of the

reconstructed images was 0.3 mm. An image of the cross section was taken every 0.4

mm along the core (z-resolution).

Intact soil clods (6 cm x 6 cm) were excised, as described in Jassogne et al 2007,

Chapter 2), from the E/B interface of a separate intact core from the same site and

scanned with a microCT (Skyscan, 1076, Aartselaar, Belgium). The pixel size of these

images was 18 µm isotropically and that of the 16 bit images 1024 x 1024 pixels.

Source voltage was 100 kV and the source current 80 µA.

Determination of porosity from grey scale images

The macroporosity of the cores was determined using the images obtained by

CT-scanning and the thresholding method described in Jassogne et al. (submitted to

Geoderma, Chapter 3). Briefly, the stack of images was divided into four substacks

representing the A, E, interface and B horizons of the soil core. The interior of the core

(14 cm diameter) was selected for analysis each time and an exterior annulus (0.5 cm

thick) was excluded. This is an important analytical step because artefacts from soil

compaction can be generated at the edges of the soil core close to the PVC tube as the

tube is pushed down the soil profile while sampling. Images were normalised using the

distribution of the pixel values of the whole stack.

Gaussian curves were fitted to the histogram of each substack of images and two

threshold or cut off pixel values for delineating soil matrix and pore were chosen. The

first cut off value was calculated by subtracting the width of the first curve from the

centre. The second cut off value was calculated by adding 3000 to the first threshold in

sandy soil and 5000 in clayey soil. These numbers were chosen after thresholding the

same cores several times until suitable values were decided on which were consistent

for the sand and clay in this duplex soil. The two cut off or thresholding values obtained

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72

were then inserted in the 3D analysis program 3dma_rock (Oh and Lindquist 1999) that

employs indicator kriging, among other methods, to segment images. When the images

were thresholded, the output images were binary; one value (usually 1) was assigned to

the pixels belonging to the pore phase and another value (usually 0) to the matrix phase.

These images were imported into 3D imaging software Amira (Mercury Computer

Systems, San Diego, CA) following which volumes less than 100 pixels were removed

and macropores reconstructed and visualised. This means that all disconnected

vomumes < 3,6 mm 3 were filtered out. The connected components (connected pore

networks) were measured with a floodfill algorithm using 6 neighbours. The porosity

was calculated by dividing the area of pore pixels by the area of soil matrix pixels.

The images obtained with computer tomography show density differences in the

sample and this is the reason why pores can be differentiated from soil matrix.

However, pores in intact soil cores can be filled with air, water, living roots and organic

material including dead roots. Although, in theory, these different phases should be

detectable in the image, often the X-ray absorption of these less dense materials is very

similar to each other although quite different from the distinct soil matrix comprising

dense sand or clay particles. For this reason, porosity in this study was defined as every

phase (i.e. air, water and organic matter) that can be found in pores in soil (Pierret et al.

2002).

One way analysis of variance (ANOVA) was used to test the differences

between the porosity of each replicate core before and after root growth, and the

differences between plant species. The null hypothesis of equal means was rejected at a

probability of P < 0.05.

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73

Results

Grey scale images (CT scans)

Grey scale images of the intact soil cores, reconstructed from the medical X-ray

CT scans, showed that the E/B boundary within the interface was composed of a layer

of soil much denser than that of the B-horizon below, as defined by the very bright

pixels evident at the boundary between the sandy E and clay B horizons. Also, that the

sandy section of the E/B interface was denser (had slightly brighter grey values) than

the rest of the E horizon (Figure 1).

The images also showed cracks in the E horizon. Following destructive

sampling of one spare core after scanning it was observed that some of these cracks

were filled with air and others contained layers of organic matter, possibly old root

material. Indeed, pixel analysis of the grey scale images revealed that there were many

remnant roots in the E and the B horizons (Figure 1). Destructive sampling of the intact

soil cores also showed that the characteristics of these ‘roots’ were dependent on their

age.

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74

Figure 1: Grey image obtained from the reconstruction of a CT scan (coronal view) of

an intact sodic duplex soil core prior to root growth.

crack

new root

Biopore or remnant root

interface

crack

A

E

B

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75

Some were present as coherent pieces of organic plant material; others were

small fragments of black organic material concentrated where the root was once located.

Evidence for compaction at the edges of the visible macropores due to root growth was

absent in the grey scale images of the medical CT scans although the pixel resolution of

0.3 mm may have been too low to resolve this phenomenon. However; images of the

micro-CT also did not show evidence, in the form of lighter pixels around the edge of a

pore, of any compaction around the pores (Figure 2). The resolution of the images from

the micro-CT was much higher (18 µm pixel size) than those from the medical CT and

the porosity and roots visualised were finer, but the sample size was much smaller.

3D quantification and visualisation

Volume measurements on the 3D volume reconstructions showed that the

macroporosity in the cores increased after 12 weeks of root growth, confirming that new

macropore networks were formed. This increase in macroporosity was significant (α <

0.05) for all plant species, but there was no significant difference between them (Table

1).

The number of pores before and after root growth can indicate how many pores have

been created by new roots and how many of the existing pores have been used by new

roots. If the number decreases, it can be inferred that roots have been using existing

pore networks and probably connecting previously unconnected networks. If the

number increases, roots have been creating new networks without connecting pre-

existing pore networks. In this study, there was no significant difference in the number

of macropores between plant species (Table 1).

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76

Figure 2: 2D grey scale image from a micro-CT scan of a soil clod taken from the

interface of a sodic duplex soil. The micropores do not show any compaction at the

edges.

1.07 mm

crack pore

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77

SE# SE SEporosity

0 weeks 0.73 0.13 0.60 0.07 0.98 0.1012 weeks 1.55 0.24 1.68 0.19 1.82 0.26

average size pore0 weeks 103.19 0.01 114.08 0.01 110.88 0.01

12 weeks 138.57 0.03 203.99 0.06 108.78 0.01number of pores

0 weeks 2588.33 17.56 1105.00 30.97 1902.00 40.1112 weeks 1693.00 4.23 1408.00 62.34 4613.00 40.66

%

mm 3

canola lucerne saltbush

Table 1: Macroporosity, average volume of pore and average total number of pores for

an intact duplex soil core before and after root growth of canola, lucerne or saltbush,

determined from 3D images reconstructed from X-ray CT scans.

# SE: standard error of the mean of 3 replicates

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78

In two of the three replicate cores for each species macropore number increased and in

one core per plant species there was a decrease in pore number.

The average number of pores decreased for canola after 12 weeks of root growth but

increased for lucerne and saltbush, suggesting the two latter created new pores. The

average size of macropore also increased for canola and lucerne suggesting they

increased the connectivity whilst the average pore size stayed almost the same before

and after growing saltbush (Table 1).

Visual 3D reconstructions of macroporosity for each species (Figures 3, 4 and 5)

showed that in each core, cracks appeared in the E and the B horizons after 12 weeks, as

evidenced by the increase in large areas of continuous dark pixels representing less

dense material i.e. air in cracks. Despite this, it was possible to distinguish that there

were more tubular pores in the A and E horizons following root growth for all three

species.

Discussion

Macroporosity before and after root growth

Measurements from the 3D visualisations showed significant differences (α <

0.05) in macroporosity before and after root growth indicating that the macroroots of all

three species studied were penetrating soil that previously had not been explored by

macroroots, and were thus adding to the palimpsest of old pore networks from previous

roots . The visual reconstructions also showed that after 12 weeks, there were more

tubular pores in the A and E horizons because of root growth, although it is

acknowledged that these differences have to be assessed with care.

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79

Figure 3: Three views of 3D visualisation of the macroporosity in a sodic

duplex soil (a) before and (b) after root growth of canola

14 cm

A

E

Interface

B

A

E

B

a

b

canola

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80

Figure 4: Three views of 3D visualisation of the macroporosity in a sodic duplex soil

(a) before and (b) after root growth of lucerne

14 cm

A

E

B

A

E

B

a

b

lucerne

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81

Figure 5: Three views of 3D visualisation of the macroporosity in a sodic duplex soil

(a) before and (b) after root growth of saltbush.

14 cm

A

E

Interface

B

A

E

B

a

b

saltbush

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82

Cracks have arisen during the study either through drying of the cores and/or during

transportation to the medical CT. The latter is more likely for the E-horizon because the

sand was very loose but less likely for the B-horizon where drying is likely to be the

major cause of cracking in the clay. These cracks will partly be responsible for the

increase in macroporosity after 12 weeks. An attempt was made to measure the

porosity differences only in the interface and the B horizon, but because of the

aforementioned cracks, it was not possible to separate quantitatively the changes in

porosity arising from cracks or root growth. These cracks may also have connected

many networks, a process that would cause a decrease in pore number.

The location of the E/B boundary in the interface between the E and B horizons

corresponds with dense clay which is highly sodic and dispersive, and is visualised in

the CT images as a very bright thin layer in the interface region of the B horizon (Figure

1). After a rain event, water perches on the E/B boundary immediately above the B-

horizon, because of its lower permeability compared to the horizons above. Under these

circumstances, as the profile dries out the clay in suspension concentrates through a

combination of sieving and settling (Chittleborough 1992, Chapter 6, Fig1b this thesis).

This causes the formation of a very dense ‘crust’ at the E/B boundary in the interface, as

seen in this study, with densities as high as 2 g cm-3 (Jassogne et al 2007). The CT grey

scale reconstructions also showed that in the sand region above the E/B boundary of the

interface the spaces between the sand particles were filled with clay particles, hence the

slightly lighter pixels indicating higher density, while the sand particles in the E-

horizon were more loosely packed and had darker grey values (Figure 1).

The properties of the old channels or remnant roots in the duplex soil studied

and visualised with medical CT were dependent on the decomposition state of the roots.

As these roots decompose, they, and the rhizospheres, can act as sources of nutrients

(Watt et al. 2006), especially in the leached E-horizon of the duplex soil in these studies

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83

that is very low in nutrients. Old root channels can also be used by new roots because

they act as pathways of least resistance (Creswell and Kirkegaard 1995). Again, in this

duplex soil this is likely to happen predominantly in the interface which has quite a high

strength (>1 MPa, R. Murray pers comm.). Roots growing in biopores also have a

higher contact with water and oxygen, as biopores are preferential flow pathways for

fluids. The relict rhizospheres associated with old root channels could be compared to

the macropore sheath which has been defined as the area around macropores in which

80 % of the roots are located (Stewart et al. 1999). In soils with a hostile matrix not

conducive to root growth, such as the subsoil horizons of duplex soils, new roots are

likely to remain in the vicinity of the macropore created by the previous root because

that environment would be more favourable than the soil matrix. Thus the macropore

sheath would be smaller than in a more healthy soil (Pierret et al. 1999).

As explained earlier, this study showed that there was extensive root exploration

of the E horizon by all species studied (see Figures 3b, 4b and 5b).Roots growing in the

E-horizon of a duplex soil do not experience difficulties in penetration but they often

cannot access sufficient nutrients for optimal growth, not only because of the low

nutrient content and holding capacity of the soil, but also because of insufficient contact

between sand particles and roots (Passioura 2002). Low water holding capacity means

that these E horizons dry out rapidly but decomposing roots can potentially create

‘patches’ in soil that are chemically and physically favourable to the retention of water

and nutrients and thus aid the development of organisms and root growth..

Thresholding and skeletonisation

The complexity of the grey scale patterns in the CT images of the intact soil

cores precluded the use of an algorithm that selected and traced individual pores. The

best possible approach was to segment the images based on the distribution of the grey

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84

values of the pixels. Combining this technique with indicator kriging was the most

precise and objective way to threshold the images because it was both local and

adaptive (Oh and Lindquist 1999). However, when comparing the same core before and

after root growth, it was found that here again, differences had to be assessed with care.

The cores were dried out slowly, which meant that the water content, and as a

consequence, the grey level of the soil horizons, changed slightly over time. When the

cut-off values were chosen, even after normalisation, some thinner pores with grey

values close to the matrix value could be segmented in the images after 12 weeks, but

not in the initial scans. This could lead to a measured increase in porosity after 12

weeks, but would be a function of threshold methodology as opposed to an actual

increase (Jassogne et al. submitted to Geoderma, Chapter 3).

Volume 3D measurements of soil structures in situ are not the only

measurements possible from CT images. Measurements such as pore tortuosity, angle

and length can also be derived but only if the 3D network is known and a skeleton

reconstructed (see Jassogne et al 2007, Chapter 2), which is a very computationally

intensive procedure. Reconstructing the skeleton visualised in the intact cores in this

study was not possible. Cracks present in the cores caused the program to make

numerous errors. Attempts to ‘remove’ the cracks from the network were unsuccessful

because the cracks were quite large and connected with a very large proportion of the

pores. Thus, selecting the cracks for removal led to selection of these pores as well.

Some studies have reconstructed skeletons of large pores created by roots or

earthworms (Capowiez et al. 2003) but these studies were carried out on repacked soil

monoliths so that images would have been less complicated than those from an intact

soil core, and the skeleton reconstruction would have been less computationally

intensive.

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85

Micro-CT

Field observations of the duplex soil used in this study (Adcock et al 2007)

confirm that, at the end of the growing season (October to November), the E-horizon

dries out and the only available moisture is stored in the clay B horizon. The

reconstructions and pore size distributions visualised in this study show that most of the

pores were connected and that, unsurprisingly, roots had readily found pathways into

the B horizon in spite of the high density of the interface and the hard crust of the E/B

boundary. However, the channels visualised in this study were macropores > 1mm.

Under field conditions these pores will be the first to dry out and roots growing in

macropores in the B horizon will have to find other pathways, maybe via fine root

networks, to access the water stored in meso- and micropores. Other studies have

pointed out that roots growing in macropores can become isolated or trapped due to the

high strength of the pore walls acting as a barrier to further penetration (Stirzaker et al

1996). The pores are formed as the growing roots compact the soil around them. When

a root decomposes, the legacy is a macropore with a compact surface. New roots can

follow that macropore path as a route of least resistance when there is moisture, but

when the macropores dry out the roots cannot grow further nor can they extend laterals

to access nutrients and water in the matrix because of the pore wall resistance. In the

present study evidence for compaction at the edges of the pore was absent in the images

of the medical CT scans, although the pixel resolution of 0.3 mm may have been too

low to resolve this phenomenon. However, images of the micro-CT also did not show

evidence of any compaction around the pores, in the form of lighter pixels around the

edge of a pore (Figure 2). The resolution of the images from the micro-CT is much

higher (18 µm) than those from the medical CT and so the porosity and roots visualised

will be finer although, of course, the sample size is much smaller. It could be that

compaction of the pore walls to a point where extending lateral roots cannot penetrate is

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more likely to occur at the macro level where the larger diameter root has to displace

more soil particles to expand. Additionally, the transmission of the X-rays through soil

cores in the micro-CT was very low due to the relatively lower source voltage; hence

signal to noise ratio was narrow so that some detail could have been lost in the image.

Soil is very dense and the energy and amount of photons passing through the sample

need to be high enough so that the signal to noise ratio on the detector is sufficient to

give a high quality image. Although the contrast between low density materials like

water, organic material and air will be improved with low energy X-rays these may not

have sufficient penetration power and the resolution could be improved by using a

higher energy X-ray source. The technology is improving all the time, with new X-ray

CT instruments coming onto the market and becoming more readily accessible for

research. Thus, there is an emerging capacity to combine suitable X-ray energy with

detectors that provide a wide signal to noise ratio so that good contrast can be obtained

at high resolution for imaging large soil samples.

Soil has heterogeneous characteristics from the micropore to the landscape scale.

Processes occurring at the micropore scale are linked to processes at sequentially larger

scales up to the landscape scale, and vice-versa. Thus there is a need to obtain spatial

information at a range of scales in order to gain a full understanding of the interactions

between porosity and root growth, and the impacts these have on ecosystems. Studies at

both millimetre and micrometre scale are required to understand the exploration of both

individual roots as well as whole root systems in soil. Fine roots, not visualised in this

study are important for water and nutrient uptake, hence the necessity to combine

studies at different scales (Young 2001). It has been suggested that 2D analysis

techniques are still more valuable than long processing times of 3D analysis at fine

scales (Doussan et al. 2003). However, detailed information about how roots proliferate

in 3D in soil and the tortuous pathways they follow, especially in undisturbed soils

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where root growth is influenced by previous pores, is fundamental to a comprehensive

understanding of the architectural and morphological responses of whole root systems.

Studies to investigate this intimate interaction between soil structure and roots systems

need bigger sample sizes and medical CT experiments make this possible, even if the

resolution of the information is only at mm scale. Overall, this approach enables new

insights and understanding of the influence that relict rhizospheres have on soil porosity

and root architecture and development. This is very important in relation to the

exploitation by plants of resources such as water and nutrients, particularly in harsh soil

environments or following land use changes.

A further step in this experiment involves processing the CT scans for

reconstruction of the growing macroroots alone. These can them be superimposed on

the 3D porosity visualisations obtained in this study. This work is reported in a

subsequent companion paper (Chapter 5).

Conclusions

The pore network of soils is a palimpsest exploited by root systems of

subsequent generations and these studies using CT have been able to illustrate this for

intact cores in 3D. Macroporosity was shown to change in the A, E and B horizons of a

duplex soil after 12 weeks of root growth of an annual plant, as well as an herbaceous

perennial and a woody perennial. However careful consideration has to be taken when

interpreting scans of the same samples at different times because of the effects of

changing soil moisture content on soil structure. There were no detectable differences

between the visualised macroporosity changes incurred by 12 weeks growth of canola,

lucerne or saltbush root systems. The macropores did not show any compaction at the

edges from root growth when observed at the mm scale and micropores, observed from

micro-CT images also did not show any compaction. However, further work could

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investigate micro-CT images of macropores as the compaction might be observed at

that scale.

Acknowledgments

The authors thank Peter Self at Adelaide microscopy for assistance with the

micro-CT; also, Geoff West and staff at the radiography department of the Queen

Elizabeth Hospital in Adelaide for scanning the soil cores. The CRC for plant based

management of dryland salinity funded much of the work reported here. Laurence

Jassogne also thanks the Australian government and UWA for an international

postgraduate scholarship, Professor Hans Lambers for support and Kris Broos for

valuable comments on the manuscript.

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References

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constraints to crop production on neutral and alkaline soils in south-eastern

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Agr 47:1245-1261

Bastardie F Capowiez Y de Dreuzy JR Cluzeau D (2003) X-ray tomographic and

hydraulic characterization of burrowing by three earthworm species in repacked

soil cores. Appl Soil Ecol 24:3-16

Blair JM Falconer RE Milne AC Young IM Crawford JW (2007) Modeling three-

dimensional microstructure in heterogeneous media. Soil Sci Soc Am J 71:1807-

1812

Capowiez Y Pierret A Moran CJ (2003) Characterisation of the three-dimensional

structure of earthworm burrow systems using image analysis and mathematical

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De Gryze S Jassogne L Six J Bossuyt H Wevers M Merckx R (2006) Pore structure

changes during decomposition of fresh residue: X-ray tomography analyses.

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Doussan CL Pages L Pierret A (2003) Soil exploration and resource acquisition by plant

roots: an architectural and modelling point of view. Agronomie 23:419-431.

Feeney DS Crawford JW Daniell T Hallett PD Nunan N Ritz K Rivers M Young IM

(2006) Three-dimensional microorganization of the soil-root-microbe system.

Microb Ecol 52:151-158

Goss MJ (1991) Consequences of the Activity of Roots on Soil in Atkinson D (ed).

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Growth: An Ecological Perspective. Blackwell Scientific Publications Ltd.,

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Gregory PJ Hinsinger P (1999) New approaches to studying chemical and physical

changes in the rhizosphere: an overview. Plant Soil 211:1-9.

Isbell RF (1996) The Australian Soil Classification CSIRO, Melbourne.

Jassogne L McNeill A Chittleborough D (2007) 3D-visualization and analysis of macro-

and meso-porosity of the upper horizons of a sodic, texture-contrast soil. Eur J

Soil Sci 58:589-598

McCallum MH Kirkegaard JA Green TW Creswell HP Davies SL Angus JF Peoples

MB (2004) Improved subsoil macroporosity following perennial pastures. Aust J

Exp Agr 44:299-307

Oh W Lindquist B (1999) Image thresholding by Indicator Kriging. IEEE Trans Pattern

Anal Mach Intell 21:590-602

Passioura (2002) Soil conditions and plant growth. Plant Cell Environ 25:311-318

Peth S Horn R Beckmann F Donath T Fischer J Smucker AJM (2008) Three-

dimensional quantification of intra-aggregate pore-space features using

synchrotron-radiation-based microtomography. Soil Sci Soc Am J 72:897-907.

Pierret A Moran CJ Pankhurst CE (1999) Differentiation of soil properties related to

the spatial association of wheat roots and soil macropores. Plant Soil 211:51-58

Pierret A Moran CJ Doussan C (2005) Conventional detection methodology is limiting

our ability to understand the roles and functions of the fine roots. New Phytol

166:967-980

Pierret A Capiowiez Y Belzunces L Moran CJ (2002) 3D reconstruction and


analysis. Geoderma 106:247-271

Rasse DP Smucker AJM (1998) Root recolonization of previous root channels in corn

and alfalfa rotations. Plant Soil 204:203-212

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Schaffer B Stauber M Muller R Schulin R (2007) Changes in the macro-pore structure

of restored soil caused by compaction beneath heavy agricultural machinery: a

morphometric study. Eur J Soil Sci 58:1062-1073

Smit AL Bengough AG Engels C van Noordwijk M Pellerin S van de Geijn SC (2000)

Root Methods: a handbook. Springer

Soil Survey Staff (1999) Soil taxonomy: a basic system of soil classification for making

and interpreting soil surveys, Washington

Stewart JB Moran CJ Wood JT (1999) Macropore sheath: quantification of plant root

and soil macropore association. Plant Soil 211:59-67

Stirzaker RJ Passioura JB Wilms Y (1996) Soil structure and plant growth: impact of

bulk density and biopores. Plant Soil 185:151-162

Watt M Kirkegaard JA Passioura JB (2006) Rhizosphere biology and crop productivity

- a review. Aust J Soil Res 44:299-317

Young IM Crawford JW Rappoldt C (2001) New methods and models for

characterising structural heterogeneity of soil. Soil Tillage Res 61:33-45

Yunusa IAM, Newton P.J. (2003) Plants for amelioration of subsoil constraints and

hydrological control: the primer-plant concept. Plant Soil 257:261-281

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Computed tomography of intact soil cores II. Spatial and temporal

visualisations of in situ root systems.

Laurence Jassognea,c*, Rob Davidsonb, Ann McNeillc, Ganga Hettiarachchid,e and David

Chittleboroughc

aSchool of Plant Biology, The University of Western Australia, Crawley, WA, 6009,

Australia

bDavidson’s Agricultural Research and Development, PO Box 280, Mt Pleasant, SA

5235, Australia


Adelaide, Waite Campus, PMB 1 Glen Osmond, SA 5064, Australia

dCSIRO Land and Water, Waite Campus, PMB 2 Glen Osmond, SA 5064, Australia

eDepartment of Agronomy, 2107 Throckmorton Plant Sciences Center, Kansas State

University, Manhattan, KS 66506 USA

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Abstract

Medical X-ray computer tomography (CT) image analysis was used in this study

to non-invasively image macroroots of an annual crop plant, canola (Brassica napus), a

perennial pasture plant, lucerne (Medicago sativa) and a native woody shrub, saltbush

(Atriplex nummularia) in 3 dimensions (3D) in soil. Sequential images of the same

macroroot systems, in intact cores of a sodic duplex soil or texture-contrast soil, were

produced at several times during a 12-week period. The cores were 50 cm long and 15

cm diameter, and although the roots that could be reconstructed were only at the scale of

mm due to image resolution limitations, important differences could be seen in the

performance of the exploratory macroroots in response to the macrostructure of the soil.

An algorithm was developed that could trace roots in intact soil cores to visualise the

progressive macro-root growth of the three different plant species. Canola macroroots

tended to explore the soil more horizontally via proliferation of one taproot and some

relatively large diameter lateral roots in the A horizon, but did not rapidly penetrate to

depth vertically into the E or B horizons. Primary roots of lucerne largely penetrated

vertically through the A and E horizons of the profile, and some of them drilled through

the interface between the E and B horizon. Saltbush produced the largest number of

macroroots through the E horizon, with a larger proportion of these drilling through the

interface and into the B horizon. These unique results show that CT imaging, together

with the tracing algorithm developed for this study, can be used to produce

visualisations of macroroots in large intact soil cores. These visualisations identified

differences between species in the architectural development of macro-root systems,

and clear evidence was provided that saltbush was potentially the best candidate as a

primer plant (i.e. plant that ameliorates the subsoil by drilling through the E/B

boundary) for duplex soils by penetrating deeper and faster.

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Keywords: macroroot, 3 dimensions, computer tomography, intact soil cores, non-

invasive imaging, saltbush, lucerne, canola

Introduction

The primer plant concept has been suggested as a method to increase biopores in

hostile subsoils in order to increase access to water and nutrients for following crops

(Yunusa and Newton, 2003). Indeed, several studies have shown new roots following

networks or biopores created by decaying roots of previous crops (McCallum et al.

2004; Rasse and Smucker 1998). A good primer plant will have a root system that

proliferates through the profile to depth and has evolved and adapted to any hostile

conditions. Understanding root behaviour in these hostile soils is fundamental for

assessing the potential of different plant species to be successful primers.

Unfortunately, the soil medium is opaque and studying roots in situ is difficult.

Research into root growth generally involves invasive techniques that destroy the

integrity and structure of the soil environment in which the roots grows; for example by

excavating soil to extract root information (ex. Park et al. 2007) or by washing out roots

out of soil samples (ex. Benjamin and Nielsen 2004). Some studies have attempted root

measurements in situ using minirhizotrons (ex. Pritchard et al. 2008). Minirhizotrons

are clear plastic tubes that allow repeated, non-invasive measurements of root growth.

However, insertion of the tube in the soil changes the soil environment, and roots often

grow preferentially along the tube which is an alteration of the root growth that would

occur in undisturbed conditions.

Pierret et al. (2003) developed a non-invasive X-ray technique that imaged root growth

and water uptake in thin-slab systems. The system was easy to use and inexpensive, but

the output was restricted to two dimensions and a relatively narrow block of soil.

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Understanding root exploration of soil in 3D is important because the soil is a

triphasic 3D matrix comprising irregularly shaped peds or aggregates and a network of

tortuous connected and disconnected pores filled with air, water or organic material

(Jassogne et al 2007, Chapter 2 of this thesis). Current knowledge of root behaviour, in

terms of water and nutrient uptake or how roots respond to impedances like a dense

layer of soil, is based mainly on 2D information which can be used to reconstruct 3D

models of root systems. One such method is suited to studies in the field, especially for

large root networks, and involves photographs and drawings of the arrangement of the

coarse roots after careful excavation of the soil around the root system. This information

can then be digitised and a 3D model can be reconstructed (ex. Di Ioro et al. 2008).

Nevertheless, there is always a risk that the roots will be moved or damaged when

excavating the soil.

Computer tomography has recently been shown to be a valuable technique to

non-invasively visualise roots in soil in 3D. Studies have reconstructed fine roots in 3

dimensions using micro-CT (Gregory et al. 2003; Kaestner et al. 2006; Perret et al.

2007). However, these roots were grown in very simple media like sand or sieved and

repacked soil, or were grown in very small container sizes, and thus do not necessarily

represent the response of roots to undisturbed soil features. Whilst these studies

developed algorithms to trace roots in slightly artificial soils, they were not tested for

application to the heterogeneous properties of undisturbed soil with a pre-existing

porosity. There is therefore a requirement for software to be developed that is able to

visualise roots in the complex medium of an intact soil core. This ability to reconstruct

the roots whilst monitoring intact soil structure information is crucial, because of the

continuous spatial and temporal interplay between soil structure and root development

(Clark et al. 2003).

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It has been noted that there is a trade-off between the size of sample or region of

interest that is scanned and the resolution achievable when doing CT experiments. To

achieve resolution that enables the study of fine root dynamics (Hamza et al., 2001),

microporosity or soil-microbial interactions (Young et al., 2001) in 3D require small

sample sizes and the use of micro-CT technology. Pierret et al. (2005) have recently

reviewed various limitations and possibilities for studying and imaging fine roots in

situ. Medical CT technology allows the analysis of larger soil volumes (in the order of

50 cm long and 15 to 20 cm diameter). However, the resolution is coarser and only

items in the order of mm scale can be visualised (Pierret et al. 2002). This means that

mainly primary and only some secondary roots can be visualised. However, these

macroroots are largely responsible for the exploration of the soil volume (McCully

1999) and necessarily presage the development of fine roots. To understand how

different root systems cope with nutrient scarcity or dense soil layers, the 3D

organisation of these architectural roots is as important as the chemical and physical

changes caused by fine roots at micro scale. It is elongation and penetration of these

coarser roots that enable the fine roots to proliferate in a specific environment. Clearly,

3D root studies are important at both mm and µm scale and with emerging technologies

the opportunity exists to incorporate results found at both scales (Jassogne et al. 2007).

In this study, root growth of 3 different plant species (canola, lucerne and

saltbush) in intact cores of duplex soils was compared using medical CT scanning. A

new tracing algorithm, based on multivariate analysis and allowing visualisation of root

networks in intact soil cores, was tested. Macro-root growth of the 3 plant species was

visualised at intervals of 3 weeks over a period of 12 weeks. It was hypothesised that a

native plant such as saltbush would drill through the E – B interface and explore

relatively deeper in the subsoil compared with an annual like canola or an introduced

perennial like lucerne.

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Material and methods

Soil core collection and plant growth conditions

Intact soil cores (50 cm long, 15 cm diameter) of a Red Sodosol (Isbell 1996) or

a Typic Natrixeralf (Soil Survey Staff, 1999) were taken as described in Chapter 4 of

this thesis. The bulk density of the soil ranged from 1.6 g cm-3 in the E horizon to 1.9 g

cm-3 at the interface, and 1.8 g cm-3 in the B horizon. The highest electrical conductivity

(EC) 1:5 value was 0.268 dS m-1 and the exchangeable sodium percentage (ESP) at the

interface was 24.2 % revealing strong sodic properties. Further, as described in Chapter

4, the PVC encased cores were transported to the lab, removed from the PVC, coated

with liquid latex to provide a barrier for root growth at the edges of the soil, and

replaced in the PVC tubes. Canola, lucerne and saltbush seeds were germinated and one

seedling was planted in 9 cores after 3 days for canola and lucerne and 8 days for

saltbush (3 replicates per plant, 1 plant in each core). The plants were watered during

the 12 weeks with a total amount of water (262 mm) equivalent to that received as

rainfall from April to September in an average growing season in South Australia.

Scanning, image reconstruction and root visualisation

Cores were scanned at the Queen Elizabeth Hospital in Adelaide with a medical

CT instrument (Toshiba Aquilon, Tokyo, Japan) as described in Chapter 4.

New software (RootViz) was designed that enabled roots to be traced from the grey

scale images reconstructed from the CT scans. The RootViz software was based on

multivariate discrimination applied to the grey values of the images. In the stack of

cross sections from the CT scans, more than 100 pixels belonging to each of the

possible phases were manually selected using a freely available program ImageJ. The

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phases were soil matrix, fresh root or ‘porosity’, the latter including pores filled with

air, water or organic material that was not fresh root or soil matrix. These pixels

generally have a higher attenuation (i.e. are darker grey) than the pixels belonging to the

fresh roots or the soil matrix. The Rootviz software then calculated the similarity of the

pixel of interest to that of root material, soil matrix or other. These values were then

compared within a software routine to allocate each pixel to a living root or other

category (Figure 1). The Rootviz software also includes a ‘parameter’ file consisting of

several parameters that assist in determining whether or not a pixel should be assigned

as a living root or not. One of these assisting parameters is called circle radius and this

contributes to the classification of each pixel in the centre of a circle based on

information from all the pixels belonging to the circle. Smaller values for this parameter

allows tracing of finer roots but generates more errors (i.e wrong classifications) than

larger values. Another assisting parameter is a weighting value applied to the pixel

values around each assigned root pixel in increasing circles to establish confidence in

what pixels are designated as root. A value of 1 for this assisting parameter indicates no

weight. The value for each assisting parameter differs for images from different

experiments. The assisting parameters are initialized based on characterizing the

multivariate parameter values for the three material phases from ‘known’ pixels. The

parameters are then optimized by systematic adjustment to minimise the differences

between output from the program and hand segmentation of images at three separate

locations in the image stack. The traced root pixels are then stored in an .am extension

file that can be read in Amira (http://www.amiravis.com/) and 3D visualisations from

the traced roots can be reconstructed.

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Figure 1: A plot of the grey values for porosity, root and soil in the grey scale images.

The multivariate analysis and probability cloud of the algorithm are based on these

values.

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Results

Discrimination of phases in cores – root, pore and soil matrix

A plot of standard deviation against each mean grey value for different phases of

interest in the axial slices of an intact soil core with a growing root system (Figure 1)

illustrates the difficulties faced when using CT images to describe root growth in situ in

an intact soil core. When the data points belonging to the different phases do not

overlap then discrimination between the different phases is relatively easy. This is often

the case, for example, for soil and porosity (Figure 1). However, if the data points

overlap, the discrimination becomes harder as shown for roots and soil (Figure 1).

Observations suggest that the pixels which create this overlap are the pixels selected for

small roots that tend to have a grey value similar to soil matrix pixels.

3D visualisation of root systems

The visible roots (>0.8 mm diameter) of the first lucerne replicate appeared to

grow vertically down the profile quite rapidly in 12 weeks (Figure 2). After 3 weeks of

growth, visible roots of this replicate could only be traced in the first cm of the A

horizon (Figure 2), but after a further 3 weeks the roots were visible in the middle of the

E horizon and a third root could be seen after 6 weeks which grew much faster (4 cm in

3 weeks) than the first two roots, as shown after 9 weeks by the visualisation (Figure 2).

One root penetrated the E horizon but changed orientation and grew laterally to the edge

of the core before reaching the interface. When the 3D reconstruction of the roots was

superimposed on the grey scale images of the intact soil core it could be seen that one

root was exploiting a soil crack through the E-horizon.

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Figure 2: Visualised macroroots of a lucerne plant at different growing stages (3 weeks,

6 week, 9 weeks and 12 weeks).

WEEK 3

WEEK 6

WEEK 9

WEEK 12

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102

However, the root did not follow the crack all the way to the edge of the core but

changed direction and continued growth vertically downwards in the soil profile. In a

second lucerne replicate (Figure 3B) the root system branched in the A horizon and 2 of

the roots grew laterally to the edge of the core while one root grew vertically through

the E-horizon. It was possible to trace this latter root until just above the interface with

the B horizon. The taproot of the third lucerne replicate (Figure 3C) grew diagonally

through the E horizon, penetrated through the interface and into the B horizon. Two

secondary or lateral roots of this third replicate were observed to grow through the E

horizon but found the edge of the core just above the interface. There were many cracks

present in the E horizon but none of the visualised roots in any of the lucerne replicates

appeared to follow them.

Sequential visualisations for growing canola plant roots showed that the skeletal

architecture of their root system is established at early growth. Canola subsequently

thickened roots and grew vertically down the profile (Figure 4 and 5). Canola grew

more than one taproot in the A horizon (Figure 4 and 5). Generally, the 3D

visualisations show that the canola tap roots were thicker than the lucerne roots and

grew mainly in the A horizon although some of the roots did penetrate through the

profile into the B horizon by following cracks. The root system of the first canola

replicate grew laterally to the edge of the soil core and then vertically followed the

smooth plane of the PVC to the bottom of the core. It appeared that the taproot grew

diagonally, divided in two and both of the roots gravitated to the edge of the soil core.

Two roots of the second canola replicate also went to the edge of the soil core and grew

vertically to the bottom of the core following the PVC edge.

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Figure 3: Macroroot systems of three different lucerne plants after 12 weeks. The scales

at the bottom of the core represent 14 cm. A/E and E/B show the transitions between

horizons.

A B C

A/E

E/B

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Figure 4: Visualised macroroots of a canola plant at different growing stages (3 weeks,

6 week, 9 weeks and 12 weeks).

WEEK 3

WEEK 6

WEEK 9

WEEK 12

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105

Figure 5: Macroroot systems of three different canola plants after 12 weeks. The scales

at the bottom of the core represent 14 cm. A/E and E/B illustrate the horizon transitions.

A B C

A/E

E/B

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106

The grey images showed that one root reached the interface but ‘bent’ at this point of

contact suggesting a resistance to growth was present. In the third canola replicate, one

root found the edge of the soil core and grew down the edge to the bottom of the core.

No visible root grew all the way to the interface. One root could be traced till the middle

of the E horizon (Figure 5).

The root visualisations of the second saltbush replicate showed more than 1 root

growing vertically through the E horizon, through the interface and into the B horizon

(Figure 6). Some of the secondary and even tertiary lateral roots were observed at the

edge of the core. The second saltbush replicate also showed a root that penetrated

through the A and E horizon but deflected horizontally when it met the clay of the

interface and then grew vertically straight down again when it encountered a crack filled

with sand. The root could then be traced until it hit the clay of the interface again. It

could not be traced drilling through the interface. The third saltbush replicate was

observed to have a taproot that was deflected as it grew in the A horizon but then grew

vertically through the E-horizon. It could not be traced drilling through the interface.

However, a secondary root of the same plant grew horizontally in the direction of the

edge of the core in the A horizon but suddenly changed direction and grew vertically

through the E horizon and through the interface into the B horizon.

Discussion

The unique methodology used in this study visually demonstrated, in

undisturbed soil, that species differ markedly in the development of macroroot systems.

Clear evidence was provided that saltbush was potentially the best candidate as a primer

plant for duplex soils by penetrating deeper and faster.

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Figure 6: Macroroot systems of three different saltbush plants after 12 weeks. The

scales at the bottom of the core represent 14 cm. A/E and E/B illustrate he horizon

transitions.

A B C

A/E

E/B

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Canola had the largest diameter macroroots but mainly explored the A horizon.

Canola has been observed to penetrate in a sodic B horizon (McNeill et al. 2004) but the

interface in this present experiment might have been more hostile explaining why

canola would not penetrate the B horizon. Lucerne showed it had some potential in

penetrating the B horizon; this was also suggested in a field experiment (Mc Callum et

al. 2004). The three species had different root system characteristics. The roots of

canola could not be traced drilling through the interface. In contrast, one replicate of

lucerne grew through the interface. The visualisation and grey scale images of saltbush

showed that saltbush grew more roots down the profile and more of them could grow

through the interface. This suggested that lucerne, and more so saltbush could be used

to alter the physical characteristics of the interface and the B horizon of a sodic duplex

soil. This is not surprising as saltbush is a native plant species found in natural

ecosystems on these soils. It has evolved in this environment and had more chances to

develop ways to cope with the hostile conditions that these types of soils create (Yunusa

and Newton, 2003). The soil in this study was not saline at the depth sampled but had

sodic properties. As a consequence, the interface and B horizon had a degraded

structure and low permeability. During watering events, the water would have ponded

on the interface. Saltbush is not only salt tolerant but also waterlogging tolerant, more

so than lucerne. This could further explain why more roots of saltbush grew deeper in

the profile. In this study, it seemed that saltbush would be the most appropriate plant to

create biopores in the sodic B horizon in very dry conditions (Pannell and Ewing 2006).

However the root system in a soil is not only affected by soil moisture, soil temperature,

and soil structure but also by specific aspects of the plant physiology. Thus more studies

are required to identify a range of different plant species in duplex soils, at different soil

water contents, to identify the best primer plant on these types of soils.

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Using the techniques developed in this study it is possible to examine the

interaction between roots in intimate contact with soil structures by superimposing the

visualised roots on the 3D image reconstruction of the intact soil core. The work shows

roots growing in cracks and old root channels. Although high soil strength inhibits root

growth (Bengough et al. 2006), it has also been observed that soil should be dense

enough to allow good contact between soil particles and roots (Passioura, 2002). Roots

growing in large macropores and cracks as in the case in the soil of this study, do not

have a good soil root contact. It was shown in a study done by Stirzaker et al. (1996)

that plants grow better when there was a network available of narrow biopores. The

effect of plants growing poorly when their roots grow in large pores because of poor

soil contact can be overridden when the soil is allowed to dry because then roots can

access water stored in the subsoil (Passioura 2002; Cornish 1993). When a root grows

through dense soil, it can also find pathways that have a smaller diameter than the root

tip. The root tip then pushes through that pore and enlarges the diameter of the pre-

existing pore (Clark et al. 2003). This would only be possible if the soil at the interface

contained some moisture giving it some plasticity. The exudation of mucilage could

also alter the friction forces enabling the tip op the root to push through soil.

Field experiments testing the primer plant concept showed that wheat growing

after birdsfood trefoil (Lotus corniculatus L.) and sulla (Hedysarum coronarium L.) had

a better yield than wheat growing after lucerne. It was suggested that the two first plants

created a network of small biopores while lucerne had one taproot and fewer branching

creating less suitable size biopores for exploitation by the subsequent wheat crop

(Nutall et al. 2008). The latter study also pointed out that the primer plant concept might

be more beneficial in regions with higher rainfall as the soil water deficit remaining in

the short-term after a primer phase may limit the potential benefit of additional

macroporosity within the subsoil in semi-arid regions. Moreover, using plants that take

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up saline water in the soil can lead to the increase in salt concentrations in the root zone,

which would have long term consequences on root growth (Barret-Lennard 2002).

In this study, only roots larger than 1mm could be visualised. This means that

we could only resolve less than 10 % of the root system that consists mostly of

untraceable fine roots. Nevertheless, tracing the macroroots of a root system enables an

increased understanding of root exploration of soil which can vary dramatically for one

plant species depending on the type of soil/hostilities in soil but also between plant

species for the same hostile soil characteristic (Bingham and Bengough, 2003). Using

an algorithm like the one described here, that is based on the distribution of grey values

of the roots (small and large) and the distribution of other pore (porosity) and soil grey

values, together with connectivity assumptions, allows tracing of whole root systems in

the heterogeneous environment of the soil matrix with its pre-existing porosity. The

algorithm described in this study is easy to use on fresh root networks of one or two

plants growing in soil but cannot yet be used on tracing pores. At the moment, the best

method to visualise porosity in soil is to use a segmentation method, local and adaptive,

on the grey scale images followed by a filter that removes all the irrelevant, small,

unconnected volumes (Chapter 3, Jassogne et al. submitted to Geoderma; Perret et al.

2007). Pierret et al. (2002) developed an adaptive tracing algorithm for macroporosity

that also considered connectivity parameters. However, this algorithm was

computationally intensive and the grey images had to be simplified which caused

substantial loss of information in the images.

The diameter of a growing root in soil is highly variable, therefore the average

grey scale of root material in the images changes from lower absorption values for large

roots to higher values for smaller roots. If roots were separated from soil matrix in grey

images using a threshold technique based only on the grey scale distribution of the

image (global or local thresholding), some of the smaller roots would not be segmented

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in the image. If a threshold value is chosen that segments the smaller roots, then errors

are made on the bigger roots (Chapter 3, Jassogne et al. submitted to Geoderma).

The size of the roots that can be traced in an image is dependent on the

resolution of the image. In theory, traceable roots would be larger than three times the

pixel size; in this case, roots larger than 0.9 mm. However, tracing of a root in soil

matrix in a CT image is dependent on the quality of the image; it is the amount of noise

and the contrast detectability. Noise in an image is inversely related to the amount of

photons that hit the detector. This can be regulated by changing the current and the

energy of the photons. Increasing these parameters will increase the number of photons

hitting the detector. Contrast between objects of low density in soil will be increased

when lower energies are used. However, roots become untraceable if the images have

too much noise. Detectability also becomes smaller for smaller roots. It is also

important to keep the same CT parameters for scans made in a single experiment, as

well as the same imaging software parameters because the grey values in the cross

sections of the CT images are dependent on both sets of parameters.

In this study it was relatively simple to discriminate the pixels for porosity from pixels

for roots in the A horizon, whereas porosity in the E horizon was harder to separate.

However, this does not create a problem for tracing the macroroot because the software

uses a file with the initial or starting points of the living roots which were largely in the

A horizon in this study, so that dead roots were not traced. However, intact soil is a

complex medium and constant checking of the output from the software with the

original CT images is important.

This study illustrated the enormous potential medical CT provides for research

into soil/root interactions. Most methods for analysing root-soil interplay are

complicated and destructive. The soil environment in which roots grow needs to be

destroyed in order to study root-soil relationships. It has been pointed out in the

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literature that there is a need to understand root behaviour in situ as they often exploit

pre-existing soil heterogeneities. Moreover, the architecture of roots will define the

distribution of their respective rhizospheres which has been shown to be quite different

in homogenised soil compared to undisturbed soil (Hinsinger et al. 2005). Soil

exploration and nutrient uptake by roots is also very important from the point of view of

modelling plant-soil interactions (Doussan et al. 2003). So there is a need to understand

root dynamics in relation to soil at the appropriate spatial and temporal resolution.

Although medical CT only allows visualisation of architectural roots, these are most

important in initially defining to what extent the soil volume will be occupied (McCully

1999). Soil exploration of roots in the B horizon of a sodic duplex soil is dependent on

the macrostructure of the subsoil and medical CT is the only technique that enables the

study of roots in the three dimensional organisation of the heterogeneous soil

environment. Computer tomography technology is constantly improving and machines

for larger samples that can be scanned at higher resolutions are now available, although

of course very costly to buy and maintain. Although the level of detail of the images

will increase, the imaging methods will stay the same. Increasing computational power

will allow these images to be processed more easily and rapidly in the future.

Conclusions

This study highlighted the potential of medical CT for non-invasively

visualising exploratory macroroots in intact cores of a sodic duplex soil. An algorithm

was developed, tested and used to trace macroroots of canola, lucerne and saltbush in

the heterogeneous matrix of intact soil cores. The non-invasive nature of the technique

enabled sequential imaging of macroroots over a period of 12 weeks so that visual

information on the architectural development of these roots in intimate contact with

undisturbed soil was obtained. These observations confirmed that saltbush was a good

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primer plant with many macroroots penetrating hostile subsoils. Lucerne macroroots

showed some potential for penetrating subsoil whereas canola macroroots grew much

more laterally and were not able to penetrate deeply.

Acknowledgements

The authors thank the CRC for Plant Based Management of Dryland Salinity for

funding this work and Geoff West and the medical staff at the Queen Elizabeth Hospital

for scanning the intact soil cores. The senior author thanks the Australian Government

and the University of Western Australia for an International Postgraduate Research

Scholarship and Prof. Hans Lambers for his support and encouragement.

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MB (2004) Improved subsoil macroporosity following perennial pastures. Aust J

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117

Characterising the chemistry of pores

Chapter six entitled


Accepted with revisions as a journal article in

Soil Science Society of America Journal

Chapter seven entitled

Characterising the chemistry of micropores in a sodic soil with strong texture-

contrast using synchrotron X-ray techniques and LA-ICP-MS

To be submitted to Soil Science Society of America Journal

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Laurence Jassogne1,3, Ganga Hettiarachchi2, 3, David Chittleborough3 and Ann McNeill3

1School of Plant Biology, University of Western Australia, Crawley, Western Australia,

6907 Australia

2Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA

3Soil and Land Systems, School of Earth and Environmental Sciences, University of

Adelaide, Waite Campus, PMB 1 Glen Osmond, South Australia, 5064 Australia

*corresponding author; e-mail: [email protected]

Phone: +61 8 8303 8114

Fax: +61 8 8303 6717

Acknowledgments:

We thank Dr Kris Broos for useful comments on the manuscript, the Australian

government, the University of Western Australia and Prof. Hans Lambers for the

International Postgraduate Research Scholarship of Laurence Jassogne. PNC/XOR

facilities at the Advanced Photon Source, and research at these facilities, are supported

by the US Department of Energy – Basic Energy Sciences, a major facilities access

grant from NSERC, the University of Washington, Simon Fraser University and the

Advanced Photon Source. Use of the Advanced Photon Source is also supported by the

U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under

Contract DE-AC02-06CH11357. This work was supported by the Australian

Synchrotron Research Program (ASRP) which is funded by the Commonwealth of

Australia under Major National Research Facilities Program. The CRC for plant based

management of dryland salinity also partly funded this research. The authors would also

like to thank Peter Self for help with SEM analysis.

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ABSTRACT

In Australia a class of soils known as sodic duplex soils (natrixeralfs) covers

approximately 20% of the continent. Their defining characteristic is a sharp texture

contrast between the A (or E) and B horizon. The upper B horizon at the point of

contact with the E horizon is often highly sodic and of such a high strength that root

growth and proliferation, water conductivity, aeration, water storage and water uptake

are restricted. Roots growing in these soils rely on channels created by previous roots or

cracks arising from shrink–swell forces associated with seasonal wetting and drying.

Although the characteristics of rhizospheres compared to the soil matrix are well

documented there is a paucity of knowledge about how long these changes persist after

roots decay. This knowledge is fundamental to our understanding of root growth in

duplex soils in which plants rely on pore networks formed by previous plants to

proliferate into the subsoil. In this study we investigated the heterogeneous chemistry of

micropores in situ using synchrotron-based µ-X-ray fluorescence spectroscopy (XRF),

µ-X-ray absorption near edge structure spectroscopy (XANES) and extended µ-X-ray

absorption fine structure spectroscopy (EXAFS). The distribution maps of Ca, Mn, Fe,

Cu and Zn at micrometer resolution were collected using µ-XRF. Subsequently, specific

locations with higher concentrations (hot spots) of Mn, Fe, Cu or Zn were selected and

XANES was applied to study the speciation of these elements around the micropore

compared to the soil matrix. The µ-XRF maps showed that Mn was depleted around one

studied micropore, while accumulated around another micropore. Copper and Zn

accumulated around the micropores, whereas Ca was predominantly inside the

micropores. There was no difference between matrix and micropore surface with respect

to the distribution of Fe. Mn was present in reduced form (Mn II) and Fe in its oxidized

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form (Fe III). Mn and Cu were present in the form of phosphates, Fe as Fe oxides, and

Zn as Zn phosphates and adsorbed Zn.

List of abbreviations:

XRF: x-ray fluorescence; XANES: x-ray absorption near edge structure; EXAFS:

extended x-ray absorption fine structure; SEM: scanning electron microscope; EDXA:

Energy Dispersive X-ray analyzer; APS: Advanced Photon Source; LCF: Linear

combination fitting

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INTRODUCTION

Soils with strong and abrupt textural differences between A (or E) and B horizons are

widespread in Australia. Known as duplex soils, approximately 20% of the continent is

covered by such soils (McKenzie et al., 2004). The contrast in clay content between A

and B horizons can be as high as 1:10 and can occur over a distance < 2cm. Often these

soils have high ionic strength and high pH, impoverished acid clays or bleached infertile

subsoil horizons of high bulk densities. The texture contrast in a duplex soil can be the

result of clays breaking down at the interface of the A and B horizons with the infill at

the top of the B horizon being clays either translocated from the A horizon or

synthesized in situ. As a result, pores of the subsoil are often blocked with clay

(Verboom and Pate, 2006). Duplex soils have significant restrictions to root

proliferation and water uptake in addition to low water conductivity, aeration and water

storage (Clark et al., 2003). Roots growing in these soils often follow pathways of least

resistance, such as channels previously created by roots or soil organisms, cracks or

other structural pores (Stirzaker et al., 1996). Furthermore, in these pathways of least

resistance, organic matter originating from old roots decomposes and creates nutrient

‘hotspots’ for subsequent roots to colonize the channel (Robinson, 1996).

Physical changes in soil (e.g. compaction) commence immediately when roots penetrate

whereas chemical differences between the rhizosphere and the soil matrix are created

synchronously as roots take up particular ions and release exudates. Elements such as

Ca, present as salts in high concentrations in the soil solution compared to the demand

of the plant, tend to accumulate in the rhizosphere (Hinsinger, 1998). Furthermore, the

adsorption of Ca2+, Mg2+ and Na+ from the soil solution in the rhizosphere can release K

otherwise not available for the plant (Mengel and Schubert, 1985). Springob and

Richter (1998) have also shown that non-exchangeable K release hardly occurs at bulk

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soil K-concentrations but at low K concentrations. Plants also weather silicate minerals

by exuding acids that release K, Ca, Mg and Si into the soil solution (Hinsinger and

Jaillard, 1993). The pH in the rhizosphere can be 1 to 2 units lower than the bulk soil, as

a consequence of the secretion of protons and organic molecules (exudates) by roots

into the rhizosphere to immobilize nutrients. Acidification of the rhizosphere can also

induce toxicity by mobilizing Al or Zn (Calba et al., 2004; Loosemore et al., 2004).

Chemical changes in the rhizosphere are well documented and are the subject of intense

research effort. Few studies have been conducted to understand how long these changes

persist in soil after roots decay. This knowledge is fundamental in those soils where

plants rely on former root growth and soil structure to proliferate.

The macropore sheath has been defined as the environment around a macropore (such

as an old root channel). In some Australian soils, Stewart et al., 1999, showed that roots

tended to grow preferentially in this soil domain. Studies on duplex soils have shown

that this macropore sheath has higher concentrations of organic C, total N, bicarbonate

extractable P, Ca, Cu, Fe and Mn. The macropore sheath also supports higher

populations of bacteria, fungi, actinomycetes (i.e. Pseudomonas spp., Bacillus spp.,

cellulolytic bacteria, cellulolytic fungi, nitrifying bacteria and the root pathogen

Pythium than the bulk soil (Pankhurst et al., 2002). In that study, the chemical

environment of macropores created by roots was studied by scraping a 3 mm annulus

around a biopore and comparing it with the bulk soil, operationally defined as > 10 mm

from the macropore. These techniques only give a broad idea about how the macropore

sheath differs from the bulk soil and do not provide spatially resolved information.

Moreover, useful information can be masked as a consequence of the “dilution effect”

arising from sample collection. The highly heterogeneous nature of soils, and the

surfaces of pores created by roots, requires investigative techniques capable of high

resolution and high surface sensitivity.

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Combined use of synchrotron-based micro-X-ray fluorescence spectroscopy (µ-XRF),

micro-X-ray absorption near edge spectroscopy (µ-XANES) and extended µ-X-ray

absorption fine structure spectroscopy (µ-EXAFS) provides a means to study elemental

distribution and speciation of macro- and micro-elements in the heterogeneous

environment of soil pores in a spatially resolved manner. Similar analyses have been

used to investigate the speciation of Zn in clay soils (Isaure et al., 2005; Manceau et al.,

2004) and the geochemistry of As, Se and Fe in soil developed in pyritic shale materials

(Strawn et al., 2002). Synchrotron radiation affords a superior resolution to

conventional XRF, XANES and EXAFS spectroscopy, permitting differentiation of

similar chemical species with greater certainty and superior detection limits/sensitivity.

Although other studies have shown that the environment of macropores is different

from the rest of the soil, µ-XRF, µ-XANES and µ-EXAFS should increase the detection

of elements of interest thereby facilitating an improved understanding of the distribution

and speciation of these elements around pores. Synchrotron sources currently available

allow the speciation of elements in heterogeneous systems such as soil with high spatial

resolution (Hettiarachchi et al., 2008). The X-ray beam can be focused to a size of a few

µm in diameter which is equivalent to the size of particles that are very chemically

reactive. This spatial resolution should facilitate a more accurate representation of the

soil’s inherent heterogeneity than previously achieved. It should now be possible to

study the chemistry of micropore surfaces instead of only macropores.

For the current study, we have combined optical petrology of soil thin sections with µ-

XRF µ-XANES and µ-EXAFS to investigate the distribution and the chemistry of some

selected macro and micro-nutrient elements at micropore surfaces in situ. Our objective

was to map the distribution of Ca, Mn, Fe, Cu and Zn at micrometer scale by µ-XRF. In

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specific locations with relatively high concentrations (hot spots) of Mn, Fe, Cu or Zn,

speciation was studied by µ-XANES and µ-EXAFS spectroscopy in the immediate

vicinity of the micropore and compared to the forms in the soil matrix.

MATERIALS AND METHODS

Intact soil cores (50 cm long, 15 cm diameter) were taken from an agricultural site near

Stansbury in South Australia (33° 54’S, 137° 47’E). The soil was a Red Sodosol (Isbell,

1996) or a Typic Natrixeralf (Soil Survey Staff, 1999). It consisted of moderately fertile

sand (A and E horizons) over sodic clay (B horizon). The boundary between the E and

B horizons was abrupt. Intact soil segments (10 cm × 10 cm × 10 cm) were excised

from the E–B boundary. These segments were impregnated with an epoxy resin,

sectioned to a thickness of 20 µm and size 75 mm × 50 mm. Some general

characteristics of the soil are presented in Table 1 (Brewer et al., 1983).

The total concentration of the major elements and some trace elements in the bulk soil

of the different horizons were measured by XRF (Table 2). Three of the thin sections

were carbon coated and the micropores and their surrounding environment studied using

a scanning electron microscope (SEM) with an EDAX DX4 Energy Dispersive X-ray

analyser (EDXA) (Philips XL 30 Scanning Electron Microscope).

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depth pH (H2O) EC 1:5 CECcm dS/m cmol/kg

A1 0-14 7.8 0.92 5.35E 14-42 7.2 0.3 0.97B1 42-57 8.5 1.93 18.81B2 57-100 9 2.35 21.87

Table 1: General characteristics of the Stansbury profile

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A boundary0 - 3 3 - 10 10 - 27 27 - 33 33 - 45 45 - 50

Si % 44.84 45.33 45.96 43.27 38.91 35.13Al % 0.59 0.28 0.23 1.93 4.04 5.14Mg % 0.06 0.06 0.06 0.15 0.38 0.58Fe % 0.26 < 0.08 < 0.08 0.95 2.16 2.78Ca % < 0.07 < 0.07 < 0.07 0.09 0.23 1.71Na % < 0.06 < 0.06 < 0.06 < 0.06 < 0.06 < 0.06K % 0.20 0.17 0.15 0.46 0.81 1.05Ti % 0.05 0.03 0.04 0.13 0.19 0.23P % < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01Mn % < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01S % 0.01 0.01 0.01 0.01 0.01 0.01Zn ppm < 14 < 14 < 14 < 14 16 21Cu ppm < 10 < 10 < 10 < 10 < 10 < 10Cl ppm 58 < 20 < 20 < 20 65 168

cm

E B

Table 2: Total concentrations of major and trace elements of the top 50 cm of the

Stansbury profile measured by X ray fluorescence spectroscopy.

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This allowed identification of the micro-morphological features in the thin sections.

With the aid of an optical microscope, pores with a channel morphology were selected

for study by µ-XRF, µ-XANES and µ-EXAFS at beamline PNC-CAT-20-ID (Pacific

Northwest Consortium Collaborative Access Team) at the Advanced Photon Source

(APS) at Argonne National Laboratory, Argonne, IL, USA. The distribution of Ca, Fe,

Mn, Cu and Zn and the speciation of Fe, Mn, Cu and Zn in the immediate vicinity of the

pore were analysed. The electron storage ring operated at 7 GeV with a top-up fill

status. The µ−XRF maps and µ−XANES and EXAFS data were collected at ambient

temperature in the fluorescence mode, except for the µ−XANES of the standards that

were collected in the transmission mode, with a solid state 13-element detector. The

µ−XRF microprobe at APS beamline 20-ID is capable of collecting fluorescence data

with a 2 µm beam spot size and 1 to 10 mg kg-1 sensitivity, allowing study of elements

at low concentration in complex soil samples. The distribution of Ca, Mn, Fe, Cu and

Zn were determined using µ−XRF. Because the absorption length limited the intensity

of X-ray fluorescence, the thickness of the sample was kept as small as possible (20

µm).

Thin sections were mounted on the rotation axis of an x–y–θ stepping motor stage and

fluorescence data collected for areas of 1000 µm × 600 µm (map 1), 900 µm × 960 µm

(map 2), 1100 µm × 700 µm (map 3) and 700 µm × 1600 µm (map 4). The step size was

5 µm using a solid-state energy dispersive X-ray detector that allowed simultaneous

detection of fluorescence signals from multiple elements. Aluminum foil was used to

diminish the background fluorescence from Fe. The fluorescence signal from a given

element is proportional to the integrated number of atoms of that element along the

transect of the synchrotron beam. Because the Fe content of the soil was higher than

that of the other elements, fluorescence mapping was performed at two energy levels,

11500 and 7100 eV. The map at 7100 eV provided the distribution of the elements

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below the Fe absorption edge. In this way the distribution of Mn was defined with

minimal interference from Fe fluorescence. Hotspots of the elements of interest were

chosen based on the XRF maps. Spots were randomly selected for each element (Mn,

Fe, Cu and Zn), some close to the pore surface, some further in the soil matrix. Three

spectra by µ−XAS were collected over the energy range of -200 to +1000 eV above the

K-edge (Mn: 6539 eV, Fe: 7112 eV, Cu: 8979 eV and Zn: 9659 eV) at each spot. The

XANES and EXAFS spectra of Fe, Mn, Cu and Zn standards were collected following

selection of standards based on knowledge of the likely mineralogy. The standards

selected for Mn were birnessite ((Na,Ca)0.5(Mn4+,Mn3+)2O4.1.5H2O, hureaulite (Mn,

Fe)5H2(PO4)4.4H2O, manganocalcite (Mn-CaCO3), Mn-carbonate (MnCO3), Mn-sulfate

(MnSO4), Mn2O3, MnO2, switzerite (Mn, Fe)3(PO4)2.7H2O and manganite (MnO(OH));

standards for Fe were fayalite (Fe2SiO4), Fe3O4, goethite (FeOOH), siderite (FeCO3),

vivianite (Fe3(PO4)2.8H2O), Fe2O3, ferrihydrite (Fe5O3(OH)9), for Cu azurite

(Cu3(CO3)2(OH)2), chalcosiderite (CuFe6(PO4)4(OH)8·4(H2O), cuprite (Cu2O),

libethenite (Cu2(PO4)(OH)), malachite (Cu2(CO3)(OH)2), nissonite

(Cu2Mg2(PO4)2(OH)2·5(H2O)), pseudomalachite (Cu5(PO4)2(OH)4), tenorite (CuO) and

CuSO4; for Zn were ferrihydrite adsorbed Zn, franklinite

((Zn,Mn2+,Fe2+)(Fe3+,Mn3+)2O4), hopeite (Zn3(PO4)2·4(H2O)), hydrozincite

(Zn5(CO3)2(OH)6), scholzite (CaZn2(PO4)2·2(H2O)), smithsonite (ZnCO3), willemite

(Zn2SiO4) and Zn-sulfate (ZnSO4).

The XANES and EXAFS spectra of the randomly chosen hotspots were averaged, the

edge energy was calibrated and the spectra were normalised. Linear combination fitting

(LCF) was applied using IFEFFIT software on the pre-processed XANES and EXAFS

spectra of the hotspots (Newville, 2001). For each selected hotspot, the combination

with the lowest χ2 was chosen as the most likely combination of compounds in that

hotspot. The accuracy of the fitting depended on how well the standards represent the

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data. The reduced χ2 is a parameter that estimates how well a fitting function predicts

the means of a parent distribution. A reduced χ2 smaller than 1 indicated a reliable fit.

Because of the limited number of standards, the best fit composition may not have been

the complete composition, but it gave an indication of the possible chemical

composition and could be used to understand the variation in chemistry of different

elements as affected by the distance from the micropores.

RESULTS AND DISCUSSION

Micromorphological study revealed that the E horizon was composed mainly of quartz

of sand texture and had a coarse monic distribution pattern at the base of which was a

sharp change to a thin (2–4 mm) horizon composed mainly of sand and silt grains in a

matrix of amorphous organic fine material (Figure 1a). The c/f-related distribution

pattern was mainly close porphyric but there were small patches which were chitonic.

The boundary to the B horizon was very sharp (< 2mm, Figure 1b) and the upper part of

the B horizon had a speckled B fabric. There was no obvious orientation of the clay

domains in the top portion of the B but along micro- and meso-pores there was evidence

of grey coloured coatings that indicated the deposition of organic materials (Figure 1c).

In other pores there were lighter coloured zones that appeared to be highly argillaceous

but which contained much less iron than the matrix (Figure 1d) and appeared to be

illuviation features.

The elements studied at the synchrotron facility could not provide enough information

to confirm that the features identified as pores in the XRF maps were real pores.

However, measurements with SEM–EDXA showed that the main composition of the

areas that we predicted to be pores at the synchrotron facility was carbon, originating

either from the resin or from the coating applied for this analysis or both; there was no

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Al, Si or Fe present, elements that we would expect if we were analyzing soil matrix.

Using this criterion we located and identified pores in the thin sections using optical

microscopy at the synchrotron facility. The SEM-EDXA measurements revealed

different Al–Si ratios near the pore wall compared to the bulk soil matrix, from which

we inferred that the pore walls had clays of different composition than the rest of the

soil. The detection limits for EDXA, however, were ≥ 1000 mg kg-1 and none, except

Fe, of the elements studied with synchrotron techniques could be observed.

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Figure 1: a) Thin section of Stansbury profile showing the lower portion of the E

horizon. The base of the E horizon has a thin zone high in amorphous organic matter.

b) Section of the E B horizon transition. The E horizon is composed of sand, mostly

quartz in a close porphyric distribution pattern. c) Mesopores in upper part of the B

horizon lined with a thin coating composed of organic material ~10-20 µm thick. d)

Micropore in upper part of B horizon showing argillans.

a b

c d

10 µm

quartz

Organic material clay

Organic coating

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132

For each element under study, the distribution around a micropore was different. All the

distribution maps showed a significant accumulation of Ca in the micropores (Figures 2

and 3). The concentration of Ca in the soil matrix was insignificant compared to the Ca

accumulated in the pores (Figures 2 and 3). The measurements by the SEM-EDXA

confirmed the objects recognized as pores in the thin section because the only

laboratory-based EDXA detectable element present was C from the resin and/or C

coating prior to the SEM-EDXA. The much higher resolution afforded by synchrotron

radiation revealed Ca in the pores as well. Higher concentrations of Ca have been

reported around macropores at mm scale (Pankhurst et al., 2002). The concentration of

Ca in the rhizosphere can be higher than in the atrix as a consequence of accumulation

around living roots (Hinsinger 1998) and resulting from mass-flow exceeding actual Ca

uptake by the root.

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133

Figure 2: Typical example of XRF maps of the different elements studied. The colours

are relative, so concentrations between elements cannot be compared.

Ca Mn Fe Cu Zn

Increasing concentration

600 µm

pore

Close to pore

Far from pore

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134

Figure 3: Typical example of XRF maps of the different elements studied. The colours

are relative, so concentrations between elements cannot be compared.

concentration

Ca Mn

Ca Fe Zn Cu

700 µm

700 µm

pore

CHAPTER 6

135

It is present in higher concentrations in the soil solution compared to what the root takes

up and will naturally accumulate in the rhizosphere. The occurrence of CaCO3 in the Bk

horizon leads to high concentrations of Ca in the soil solution of upper B horizons.

When the B horizon temporarily saturates after a rain event, the carbonate in solution

may rise in the soil profile by capillarity and evapotranspiration and adsorb on to the

surrounding surfaces. The much lower abundance of Ca in the matrix indicates that

there was no significant transfer of Ca from the immediate surrounds of the pore.

Zinc and Mn were always highly associated with Fe with a coefficient of determination,

R2, ranging from 0.91 to 0.99 for Zn and 0.75 to 0.98 for Mn (Figure 4). Copper was

less correlated with Fe than the previous two elements with R2 values ranging from 0.48

to 0.79. This wide variability could be statistically insignificant given the much lower

concentration of Cu in the samples compared to Fe, Zn, Mn and Ca.

Manganese and Fe are oxidation sensitive elements. The exact position of their

absorption edges (Mn around 6539 eV and Fe around 7112 eV) can vary slightly

depending on oxidation state (Figure 4). Therefore, the XANES spectra give

information on the oxidation state of the elements whereas the EXAFS area gives

information about the coordination of the element with neighbouring atoms. For Fe and

Mn, the XANES spectra, and for Cu and Zn, the EXAFS spectra, were fitted by LCF.

The XANES of the Mn-spectra revealed that Mn occurred predominantly in the reduced

form, Mn2+. The LCF suggested that a large proportion of Mn existed as Mn-phosphate-

like forms (Table 3). There was no difference between points selected close to the pore

and further from the pore.

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136

Figure 4: Typical correlation maps with R2 of Mn, Fe, Cu and Zn of one of the areas

scanned by µXRF.

R2 = 0.9127

0.00

50.00

100.00

150.00

200.00

250.00

0.00 2.00 4.00 6.00 8.00 10.00

counts of Fe (x103)

coun

ts o

f Zn

(x10

3 )

R2 = 0.789

0.00

1.00

2.00

3.00

4.00

5.00

0.00 2.00 4.00 6.00 8.00 10.00

counts of Fe (x103)

coun

ts o

f Cu

(x10

3 )R2 = 0.9755

0.001.00

2.003.004.00

5.006.00

0.00 2.00 4.00 6.00 8.00 10.00

counts of Fe (x103)

coun

ts o

f Mn

(x10

3 )

CHAPTER 6

137

Table 3: Fractions of Mn species in selected ‘Mn-hotspots’ in the area close (at the

micropore surface) and far (> 300 µm from the micropore) from a micropore.

χ2= Σ [(fit – data)/ε]2 / (Ndata – Ncomponents) is the reduced chi-squared statistic. Here ε is

the

estimated uncertainty in the normalized XANES data (taken as 0.01 for all data). The

sum

is over Ndata points and Ncomponents is the number of components in the fit. The total

percentage was constrained to be 100% in all fits. Typical uncertainties in the

percentages listed for each standard component are 5%.

Mn birnessite hureaulite Mn-sulphate manganocalcite switzerite manganite R red-c2close

point 1 0.178 0.301 0.521 0.000 0.000 0.000 0.001 0.001point 2 0.000 0.688 0.000 0.000 0.312 0.000 0.006 0.004point 3 0.000 0.305 0.000 0.063 0.287 0.345 0.002 0.002point 4 0.000 0.184 0.000 0.000 0.132 0.684 0.003 0.003

farpoint 5 0.041 0.901 0.059 0.000 0.000 0.000 0.005 0.004point 6 0.000 0.506 0.494 0.000 0.000 0.000 0.003 0.003point 7 0.000 0.274 0.000 0.090 0.456 0.181 0.002 0.037

†

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138

Some of the distribution maps show accumulation of Mn around the micropores,

whereas in other maps the distribution does not seem associated with any particular

features in the sample. Micropores will have a different history in the soil matrix and the

distribution of elements in their vicinity will be dependent on that history. A micropore

explored by a root will have a different chemistry than a micropore containing organic

matter and micro-organisms. A reduced form of Mn (viz. Mn (II) phosphate-like) was

found in the selected hotspots. Plants take up Mn2+ for photosynthesis and control of

plant hormones (Uren, 2005). The rhizosphere has been reported to be up to 1,2 units

lower than the bulk soil (Kopittke and Menzies, 2004). This facilitates the reduction of

Mn for plant uptake. Roots growing in some of the micropores may acidify their

rhizosphere to mobilize more Mn through reduction of Mn3+ and Mn4+. However,

measuring pH at the scale of this study is technically very difficult. In a study done at

mm scale, the pH around macropores was not significantly different from the bulk soil

(Pankhurst et al., 2002). Moreover roots could increase the concentration of P in their

rhizosphere (Fageria and Stone, 2006). There were no differences found in the oxidation

state or the speciation of Mn in hotspots selected immediately adjacent to and far from

the pore indicating that, although there was apparent Mn accumulation around the pore

surfaces of one studied micropore, Mn chemistry was not influenced by the

environment of the pores. Alternatively the area mapped was not large enough to

capture the differences in Mn chemistry around the pore surfaces and the bulk soil.

Therefore, if the micropore chemistry had been altered by a root or by weathering due to

drying and rewetting of the soil, the processes could not be captured at the scale and

resolution used in this experiment. Other processes could cause the accumulation of

Mn2+ in the pore environment. The samples were collected at the E–B boundary of a

sodic duplex soil. During winter, waterlogging can occur on top of the dense B horizons

and in the lower portion of the E horizons of these soils (Adcock et al., 2007). The

CHAPTER 6

139

reduced form of Mn is more mobile than its oxidized form in the soil and it is possible

that it could have been transported from other parts of the profile to where it

accumulated around the micropore. Manganese has been found in reduced form in well

aerated Mn fertilized-calcareous soils (Hettiarachchi et al., 2008). Bulk measurements

of the soil in this study showed that some carbonates were present at the E–B boundary.

The LCF also showed that Mn was present as manganese phosphates. These Mn

bindings can be fairly soluble to extremely insoluble depending on the Mn-phosphate

species and the soil conditions (Boyle and Lindsay, 1986).

At mm scale Pankhurst et al. (2002) reported that for DTPA (Diethylene triamine

pentaacetic acid) extractable Fe accumulated at pore surfaces. The maps show that there

is slight accumulation of total Fe around the micropores studied. This accumulation

could be due to transport of Fe and Fe-containing minerals from horizons above the E–

B boundary. The petrology showed coatings of organic or argillaceous nature on the

surface of the micropores. These compounds can contain high levels of Fe. Iron was

mostly present in the oxidized form. The soil was dry when collected in the field; as a

consequence, Fe could have been mostly oxidized (Figure 5). Reduced Fe will mostly

be found in partly to fully saturated soils. The much lower hydraulic conductivity of the

B horizon compared to the E results in water perching at the top of the B horizon during

significant rain events. However, these reducing conditions are intermittent (the soil has

a xeric moisture regime) and are not intense enough to reduce Fe permanently in the

interface.

CHAPTER 6

140

0

1

2

3

4

5

6

7090 7100 7110 7120 7130 7140 7150

Energy (eV)

Abs

orpt

ion

Fe2O3

Fe3O4

goethite

Fit

Fe3+

Fe2+

close 2

far 1

far2

close 1

Figure 5: Example of XANES spectrum for an Fe hotspot selected more than 300 µm

from the pore surface. The oxidation state is mostly Fe3+ as shown by the linear

combination fitting of the standard spectra and position of the absorption edge.

CHAPTER 6

141

Nevertheless conditions may be sufficient to reduce Mn, which is in accord with

thermodynamic theory. Iron is mostly in goethite-, hematite- and magnetite-like solid

phases (Table 4). Only in one point (13) the dominated form of Fe was found to be

vivianite.Goethite is at stable form of Fe in soil and non-reactive. Iron (III) in soil is

mostly present in a form slightly less reactive than amorphous Fe3+ but more reactive

than hematite, magnetite and goethite (Lindsay, 1979). The Fe3+ measured could be a

mixture of amorphous Fe3+ and these three minerals. Strategy I plants take up Fe from

the soil solution as Fe2+. In alkaline soils, roots will acidify their rhizosphere to alleviate

the possibility of Fe-deficiency. Lowering the pH weathers Fe minerals and reduces

Fe3+ to Fe2+ (Hinsinger et al., 2003).

The concentration of Cu was very low. Nevertheless, the µ-XRF maps showed that

there was an accumulation of Cu in the soil surrounding the micropore. The same

distribution pattern was found for Zn, but the total concentration of Zn was higher than

Cu. This is in agreement with findings at mm scale (Pankhurst et al., 2002). Zinc and

Cu are micronutrients and plants induce different rhizosphere processes to mobilize

them. First, they can lower the soil pH or decrease the redox potential of the

rhizosphere. They also exude organic ligands to complex metals in soil solution and

they can take up the elements from the soil exchange complex (Fageria and Stone,

2006). Thus, living roots may accumulate Zn and Cu in the rhizosphere (Hinsinger et

al., 2006) where they remain when the root dies and the channel becomes a micropore.

Alternatively, the micropore could have been used as a pathway for fine roots. Zinc is

often added to phosphate fertilizers. From the soil solution Zn can adsorb to soil pore

surfaces.

CHAPTER 6

142

Fe hematite fayalite goethite magnetite siderite vivianite R red -c2

closepoint 8 0.183 0.131 0.686 0.000 0.000 0.000 0.001 0.001point 9 0.078 0.000 0.710 0.212 0.000 0.000 0.002 0.002

point 10 0.156 0.000 0.472 0.372 0.000 0.000 0.002 0.001

farpoint 11 0.000 0.000 0.906 0.094 0.000 0.000 0.003 0.003point 12 0.302 0.075 0.343 0.280 0.000 0.000 0.001 0.001point 13 0.000 0.000 0.000 0.000 0.091 0.909 0.079 0.038point 14 0.234 0.000 0.571 0.194 0.000 0.000 0.002 0.001

Table 4: Fractions of Fe species in selected ‘Fe-hotspots’ in the area close (at the

micropore surface) and far (> 300 µm from the micropore) from a micropore.

χ2= Σ [(fit – data)/ε]2 / (Ndata – Ncomponents) is the reduced chi-square statistic. Here ε is

the estimated uncertainty in the normalized XANES data (taken as 0.01 for all data).

The sum is over Ndata points and Ncomponents is the number of components in the fit. The

total percentage was constrained to be 100% in all fits. Typical uncertainties in the

percentages listed for each standard component are 5%.

†

†

CHAPTER 6

143

The concentrations of Zn and Cu were very low in the studied soil. The LCF of two Cu

hot spots show that Cu was mostly present as Cu oxide- or Cu phosphate-like minerals

(Table 5). Three points selected on the maps showed that Zn existed mostly as Zn

phosphate-like, Zn carbonate-like minerals or Fe oxide adsorbed-Zn (Table 6). Thus,

not all Zn was associated with Fe (Figure 6), an observation reinforced by the Fe–Zn

correlation maps. There were some outliers in the graph that might be Zn-bindings not

associated with Fe. Copper was even less associated with Fe in the correlation maps.

The LCFs indicated that again, showing Cu bindings mostly associated with PO4 and

CO3 bindings.

In general, there was evidence in the thin sections that roots follow these existing

micropores (Figure 7). Root development significantly alters the physical, chemical and

biological properties of the surrounding soil (Hinsinger et al., 2006). The secretion of

organic compounds and other plant induced chemical changes results in an environment

in the rhizosphere that is biologically different from the bulk soil. Furthermore, the

rhizosphere is spatially and temporally dynamic. For example, the moisture content of

the soil, and thus the rhizosphere, will change according to rainfall. Spatial variations

arise because roots of different sizes have different functions. Exudates of different

types and amounts transform the physico-chemical characteristics of the soil

immediately surrounding a channel created by a root (Hinsinger et al., 2005). These

differences represent the combined rhizosphere effect of the root that created the

pathway and subsequent roots. It is also important to remember that roots/ root

proportions have different ages. When a crack or other pore type is used by a root, there

will be an effect due to that root but also due to the rapid transfer of fluids in

macropores. Moreover, the characteristics of the soil surfacing a channel can also be

different to the bulk soil because the entire surface area is in direct and immediate

contact with the soil solution and soil atmosphere.

CHAPTER 6

144

Table 5: Fractions of Cu species in selected ‘Cu-hotspots’ in the area close (at the

micropore surface) and far (> 300 µm from the micropore) from a micropore

determined by linear combination fitting of EXAFS spectra.

χ2= Σ [(fit – data)/ε]2 / (Ndata – Ncomponents) is the reduced chi-square statistic. The sum is

over Ndata points (161 data points between 2 to 8 k space), and Ncomponents is the number

of components in the fit (either 3 or 4 as indicated in the Table). The total percentage

was constrained to be 100% in all fits. Typical uncertainties in the percentages listed for

each standard component are 5%.

†

Cu calcosiderite malachite nissonite pseudomalachite cuprite libethenite R red-c2

Closepoint 1 0.133 0.247 0.472 0.147 0 0 0.414 2.03point 2 0 0.237 0.485 0 0.145 0.133 0.257 1.14

†

CHAPTER 6

145

Table 6: Fractions of Zn species in selected ‘Zn-hotspots’ in the area close (at the

micropore surface) and far (> 300 µm from the micropore) from the soil micropore

determined by linear combination fitting of EXAFS spectra.

χ2= Σ [(fit – data)/ε]2 / (Ndata – Ncomponents) is the chi-square statistic. The sum is over

Ndata points (161 data points between 2 to 8 k space), and Ncomponents is the number of

components in the fit (either 3 or 4 as indicated in the Table). The total percentage was

constrained to be 100% in all fits. Typical uncertainties in the percentages listed for

each standard component are 5%.

†

† Zn hydrozincite scholzite smithsonite hopeite R red-c2

Closepoint 1 0.564 0.436 0 0 0.583 20.4point 2 0.621 0 0.469 0.395 0.616 26.2

farpoint 3 0.454 0.546 0 0 0.352 6.13

0,136

CHAPTER 6

146

Figure 6: The raw Zn k3-weighted EXAFS spectra (solid lines) for the Zn K-edge bulk

XAS data. Red lines indicate the linear combination fits using all the standard

compounds.

close

close

far

-20

-10

0

10

20

30

40

2 4 6 8

k (A-1)

k3 X(k)

CHAPTER 6

147

Figure 7: Evidence that fine roots follow micropores in the interface of sodic duplex

soils.

root

0.3 mm

CHAPTER 6

148

It should be noted that the interpretation of the results in this experiment was difficult

because soils contain a variety of minerals, water, air, organic matter and soil organisms

and these phases occur at different scales larger than the samples studied here or at

scales even finer, at a resolution not detectable with the techniques used here. The

elemental correlations show how well the studied elemental distributions were

correlated with each other (Figure 5). Also, the results of this study is limited by

combination of standards for obtaining the spectra. In later experiments, standards with

organically bound Cu and highly weathered Fe based minerals should be used.

CONCLUSIONS

Micropores at the top of the B horizon of this sodic soil with strong texture contrast

between the E and B horizons contain debris of roots but we cannot be certain that the

micropores were created by a root or just colonized by one. Of the elements studied Ca

was concentrated in the micropores. Apart from a few locations, Fe concentrations in

the immediate vicinity of the pore and the matrix were similar. Manganese, Zn and Cu

accumulate around micropores. The XANES suggested that Mn was present in reduced

form (Mn II) and Fe in its oxidized form (Fe III). Mn was in phosphate forms, Fe as Fe

oxides, Cu as Cu phosphates and Zn as Zn phosphates and adsorbed Zn. There was no

difference in chemistry of these elements at the pore surface and > 300 µm from it. We

conclude that the influence of the micropore is to concentrate Zn, Mn, Cu within and in

the immediate vicinity of it but that there is no significant influence of the micropore on

the chemical form of these elements. The chemical form of these 3 elements is similar at

the pore surface and in the matrix. A larger area around the micropore should be studied

to see whether the influence on the chemical form of these elements varies at a larger

scale.

CHAPTER 6

149

There are some major limitations for this technique. To have a clear picture, the

standards used need t match fully with the actual cominant forms of elements in the

sample. The sensitivity of the elements is limited, and thus, one tends to study spots

where elements are concentrated. The study is also only limited at the selected regions.

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Characterising the chemistry of micropores in a sodic soil with strong

texture-contrast using synchrotron X-ray techniques and LA-ICP-MS

Laurence Jassogne1,3, Ganga Hettiarachchi2,3, Ann McNeill3, David Chittleborough3

1School of Plant Biology, University of Western Australia, Crawley Western Australia,

6907

2Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA

3Soil and Land Systems, School of Earth and Environmental Sciences, University of

Adelaide, Waite Campus, PMB 1, Glen Osmond, SA, 5064 Australia

*corresponding author; e-mail: [email protected]

Phone: +61 8 8303 8114

Fax: +61 8 8303 6717

Acknowledgments:

We thank the Australian government for the International Postgraduate Research

Scholarship of Laurence Jassogne. We would also like to thank Prof. Hans Lambers for

support. This work was performed at GeoSoilEnviroCARS (Sector 13), Advanced

Photon Source (APS), Argonne National Laboratory. GeoSoilEnviroCARS is supported

by the National Science Foundation - Earth Sciences (EAR-0622171) and Department

of Energy - Geosciences (DE-FG02-94ER14466). We especially want to thank Matt

Newville for the valuable suggestions for sample setup and support for XRF/XAS data

collection. Use of the Advanced Photon Source was supported by the U. S. Department

of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-

AC02-06CH11357.

This work was supported by the Australian Synchrotron Research Program (ASRP)

which is funded by the commonwealth of Australia under Major National Research

Facilities Program. The CRC for plant based management of dryland salinity also partly

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154

funded this research. The authors would also like to thank Angus Netting for the help

with LA-ICP-MS analysis at Adelaide Microscopy.

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155

ABSTRACT

Duplex soils dominate the agricultural zone of western and southern Australia. The

texture of the A and E horizons in these soils is usually coarse. The low organic matter

content of these horizons together with their texture makes them have a low nutrient

content and water holding capacity. The B horizon is often of much finer texture and

can have a bulk density as high as 2 g cm-3. When this subsoil is dry, most roots cannot

drill through it. The only way some of the roots have to proliferate in the subsoil is by

using pores that extend many meters through the profile. In a root system, the finer roots

are responsible for water and nutrient uptake. These roots will explore preferably meso-

and micropores. The highly heterogeneous nature of soils and especially pores made or

used by finer roots require investigative techniques capable of high resolution and high

sensitivity. Synchrotron radiation affords a superior resolution to conventional lab based

instruments, permitting differentiation of similar chemical species with better certainty.

In this study, synchrotron based µ-X-ray fluorescence spectroscopy (µ-XRF) was used

to investigate the distribution of calcium (Ca), manganese (Mn), iron (Fe), zinc (Zn) and

copper (Cu) and synchrotron based X-ray absorption near edge structure (XANES)

spectroscopy to investigate the speciation of Fe, Mn Zn and Cu around a mesopore.

Intact samples and thin sections were used and overall it was found that measurements

from thin section were more useful due to the smaller thickness that can be obtained

with them. The distribution maps showed that Ca was present in the pores while none of

the other elements were. The concentration of Cu and Zn mostly increased around the

mesopore. Manganese was always well correlated with Fe.

Manganese was found in reduced form and associated with phosphate bindings while Fe

in oxidised form and mostly associated with oxides. Zinc was mostly associated in

bindings with carbonates (CO3), sulfates (SO4) and silicates (SiO4). The results were

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156

then compared with measurements done with Laser Ablation Inductively Coupled

Plasma Mass Spectrometry (LA-ICP-MS) and it was found that using both methods Cu

was enriched around the micropore. All the mesopores studied with the synchrotron

were enriched with Ca while only some of the one studied with LA-ICP-MS were.

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157

INTRODUCTION


Australia, dominate the agricultural zone of western and southern Australia. The texture

of the A and E horizons is usually coarse and of low nutrient content and water holding

capacity. The B horizon has a much finer texture than the surface horizons and can have

a bulk density as high as 2 g cm-3 (Chittleborough, 1992). High soil strength causes

mechanical resistance to root penetration. Furthermore, root growth can be impeded

because of seasonal waterlogging caused by a perched watertable on the dense, high-

strength sodic B horizon (Adcock et al., 2007). Roots growing through the B-horizon of

texture-contrast soils can use pores that extend many meters through the profile

(Yunusa et al., 2002). These biopores, presumably created by native perennial

vegetation, not only provide pathways through the soil otherwise impenetrable by many

plants but also improve exposure to preferential flows of oxygen, water and nutrients

(Bouma, 1992; Eldridge and Freudenberger, 2005).

Roots change the chemical, physical and biological properties of the soil in which they

grow and the zone of soil in which these changes occur is called the rhizosphere

(Hinsinger et al., 2006). These effects can be direct, for example the exudation of

protons lowers the pH of the soil thereby facilitating access to nutrients, or indirect, for

example, the exudation of organic molecules that can be used as substrate by soil

microbes. In a root system, fine roots (< 0.8 mm) and root hairs are predominantly

responsible for water and nutrient uptake (McCully, 1999) because of their high specific

surface area. These roots will be located in the meso- and micropores of a structured

soil. Micropores also called matrix pores occur between individual mineral grains and

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158

soil particles and are not generally created by soil biota (Eldridge and Freudenberger,

2005).

The rhizosphere develops, matures and senesces in parallel with developmental changes

in adjacent regions of the subtending root and remains as a relic after root death, often

as a biopore which, in hard soils, is occupied by roots of subsequent crops (Jones et al.,

2004; McCully, 2005). Stewart et al. (1999) defined the macropore sheath as the zone

around a macropore in which 80 % of the roots in the soil are located. In hostile soils,

the macropore sheath is small and the roots are concentrated in the immediate vicinity

of the macropore. “Hostile” is a descriptor that has been used to convey the difficulty of

many introduced crop and pasture plants to cope with duplex soils having high strength

B horizons. In less hostile soils, the influence of the macropore sheath extends further

into the soil matrix and roots are more evenly distributed in the soil. Few studies have

looked at the chemistry of remnant rhizospheres in soil. Most studies have been carried

out at mm scale and on soils in which the natural structure has been destroyed. In

Stewart’s study a 3 mm annulus around the macropore was scraped and separated from

the matrix and analysed for several elements and microbiological activity. It has thereby

been shown that the environment around such a macropore has higher organic C, total

N, bicarbonate extractable P, Ca, Cu, Fe and Mn and supported higher populations of

bacteria, fungi, actinomycetes (i.e. Pseudomonas spp., Bacillus spp., cellulolytic

bacteria, cellulolytic fungi, nitrifying bacteria and the root pathogen Pythium) than the

bulk soil (Pierret et al., 1999; Pankhurst et al., 2002).

Because root growth in hostile subsoils is dependent on pore character, there is a need

to understand the distribution of nutrients in relation to pore surfaces. The distribution

of micronutrients, the highly heterogeneous nature of soils and especially their pore

surfaces, require techniques capable of high resolution and high surface sensitivity. In a

previous study Jassogne et al. (accepted with revisions in Soil Sci Soc Am J, Chapter 6)

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159

employed synchrotron based X-ray techniques to produce high resolution maps of the

distribution of Ca, Mn, Fe, Zn and Cu. Synchrotron radiation allowed differentiation of

these elements with greater certainty than normal X-ray techniques of lower resolution.

There is a paucity of information at the nano- and micro-scale of the effect of roots on

the chemistry of the pore surface and to what extent, synchrotron based X-ray

Fluorescence (XRF) and X-ray absorption near edge spectroscopy (XANES) can aid in

these investigations. The X-ray beam can be focused to a spot size of amplitude of a

couple of µm, depending on the beamline, using a combined harmonic rejection/vertical

mirror. The spot size is the average size of soil particles that are chemically reactive. A

study by Voegelin et al. (2007) used these techniques to investigate the distribution and

speciation of arsenic (As) around roots in thin sections of riparian soils. The analysis of

soil thin sections by µ-XRF and XANES has also been employed to investigate the

speciation of Zn in clay soils (Isaure et al., 2005; Manceau et al., 2004) and the

geochemistry of As, Se and Fe in soil developed in pyritic shale materials (Strawn et al.,

2002). The benefit of using thin sections is that the surface is polished and flat. Thin

sections are also easier to handle than intact samples. Nevertheless, impregnating a soil

sample with a resin is invasive and the chemistry and structure of the sample could be

altered. Drying the sample too quickly with acetone can make roots shrink and can give

a misrepresentation of the soil/root contact phase for example. However, thin sections

can be prepared in such a manner that the interface between soil and root is only

minimally perturbed (Van Noordwijk et al., 1992).

In this study we analyse aggregates of soil in which the original structure has been

maintained and thin sections of undisturbed soil at µm-scale in order to resolve the

distribution of Ca, Fe, Mn, Zn and Cu around mesopores by µ-XRF and their speciation

by XANES. The locus of our study was the E horizon-B horizon boundary, the site in

the profile of abrupt texture contrast. Because of the novelty of this study, it was

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160

important to investigate other techniques that could confirm our findings. In a previous

study by Jassogne et al. (accepted with revisions in Soil Sci Soc Am J, Chapter 6) some

observations by X-ray absorption spectroscopy were confirmed by scanning electron

microscopy fitted with an energy-dispersive X-ray analyser (SEM-EDXA) but the

instrument was not sensitive enough to study all the elements of interest. Because LA-

ICP-MS can provide spatially-resolved information at ppm detection limits for many

elements (Jimenez et al., 2007) this technique was employed. In this paper, LA-ICP-MS

was used to determine the distribution of Ca, Mn, Fe, Zn and Cu along a transect

crossing a mesopore.

MATERIALS AND METHODS

Intact soil cores (50 cm long, 15 cm diameter) were taken from an agricultural site in

southern Australia (33° 54’S, 137° 47’E). The soil was a Red Sodosol in the Australian

Soil Classification (Isbell 1996,) or a Typic Natrixeralf (Soil Survey Staff, 1999). It

consisted of A and E horizons of sand texture overlying a sodic B horizon of clay

texture at approximately 35 cm. The general characteristics of this soil are presented in

another paper (Chapter 6 or Jassogne et al., accepted in Soil Sci Soc Am J with

revisions). Intact soil segments (10 cm x 10 cm x 10 cm) were excised from the zone

around the E-B boundary (hereafter called the interface). These segments were

impregnated with an epoxy resin and sections of thickness 20 µm prepared.

Furthermore, soil clods (approx. 1.5 cm x 1.5 cm x 0.7 cm) were isolated from the

interface. A criterion for selection of the clods for analysis was that they had

distinguishable root channels on their outer surfaces. Mesopores were selected on two

of the clods. The channels selected for analysis in thin section were those containing a

decaying root identifiable by the black organic material in the channel. The pores

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161

selected in the thin sections did not always contain organic matter, but often had organic

coatings (Figure 1). The distribution of Fe, Mn, Cu, Zn and Ca around the selected

pores was mapped by synchrotron-based µ−XRF, the speciation of Mn, Fe and Zn by µ-

XANES and that of Cu by µ-X-ray absorption fine structure spectroscopy (µ-XAFS).

The µ−XRF, µ−XANES and µ−XAFS data were collected at beamline 13-BM-

GSECARS (GeoSoilEnviroConsortium of Advanced Radiation Sources) at the

Advanced Photon Source (APS) at Argonne National Laboratory, Argonne, IL. The

electron storage ring operated at 7 GeV with a top-up fill status. This bending magnet

beamline is specialised for earth and environmental science research. The µ−XRF maps

and µ−XANES spectra were collected at ambient temperature in fluorescence mode

except for the µ−XANES spectra of the standards that were collected in transmission

mode. The µ−XRF microprobe at APS beamline 13-BM is capable of collecting

fluorescence data with a 10-30 µm beam spot size range and 10 to 50 mg kg-1

sensitivity, allowing the study of elements at low concentration in complex soil

samples.

The XRF maps were taken at two energies. The high energy map was taken at 10500

keV and showed the distribution of Fe, Zn and Cu. The low energy map was taken at

energy of 7050 eV. This is below the absorption edge of Fe to avoid interference from

background Fe fluorescence for elements (in our study, Mn) with an absorption edge

less than that of Fe and located close to the Fe absorption edge.

The intact samples and the thin section were mounted on the rotation axis of an x-y-

θ stepping motor stage. Fluorescence data were collected for a 10,000 µm by 200 µm

area on the first intact sample, a 10,000 µm by 950 µm on the second intact sample and

two 2,400 µm by 1000 µm areas on the thin section. The step size was 50 µm for the

intact sample and 25 µm for the thin sections using a solid-state energy dispersive X-ray

detector that allowed simultaneous detection of fluorescence signals from multiple

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162

elements. Aluminium foil was used to diminish the background fluorescence from Fe.

The fluorescence signal from a given element is proportional to the integrated number

of atoms of that element along the transect of the synchrotron beam.

‘Hotspots’(zones of relatively high concentration) of the elements of interest were

chosen based on the XRF maps. Selecting these points allowed collection of XAFS

spectra, especially for elements present in very low concentrations. Hotspots were

randomly selected for each element (Mn, Fe, Cu and Zn), some close to the pore

surface, some further into the soil matrix. A similar procedure was adopted for the thin

section analysis. Three µ−XANES spectra were collected over the energy range of -200

to + 600 eV above the K-edge. The XANES and EXAFS spectra were collected around

the absoption edges of the elements of interest: Mn; 6539 eV, Fe: 7112 eV, Cu: 8979

eV and Zn: 9659 eV. Additionally, the XANES and EXAFS spectra of Fe, Mn, Cu and

Zn standards were collected. Standards were chosen carefully according to the

knowledge of the type of soil. For example, the soil had a strong red colour which

indicated that it potentially contained much oxidised Fe. Therefore, for Fe, goethite,

magnetite and hematite were selected as standards for fitting the spectra to the data. The

standards selected for Mn were birnessite ((Na,Ca)0.5(Mn4+,Mn3+)2O4.1.5H2O),

hureaulite ((Mn, Fe)5H2(PO4)4.4H2O), manganocalcite (Mn-CaCO3), Mn-carbonate

(MnCO3), Mn-sulfate (MnSO4), bixbyite (Mn2O3), pyrolusite (MnO2) and switzrite

((Mn, Fe)3(PO4)2.7H2O). The standards selected for Fe were fayalite (Fe2SiO4),

magnetite (Fe3O4), goethite (FeOOH), siderite (FeCO3), vivianite (Fe3(PO4).8H2O),

hematite (Fe2O3), greenrust-Cl ((Fe, Mg2+)6(Fe3+)2(OH)18.4(H2O)18Cl), greenrust-

((Fe,Mg2+)6(Fe3+)2(OH)18.4(H2O)18SO4). The standards selected for Cu were azurite

(Cu3(CO3)2(OH)2), calcosiderite (Cu,Fe6(PO4)4(OH)8·4(H2O)), cuprite (Cu2O),

libethenite (Cu2(PO4)(OH)), malachite (Cu2(CO3)(OH)2), nissonite

(Cu2Mg2(PO4)2(OH)2·5(H2O)), pseudomalachite (Cu5(PO4)2(OH)4), tenorite (CuO) and

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163

CuSO4. The standards selected for Zn were ferrihydrite adsorbed Zn (Zn-Fe5O3(OH)9),

franklenite (Zn,Mn2+,Fe2+)(Fe3+,Mn3+)2O4), hopeite (Zn3(PO4)2·4(H2O)), hydrozincite

((Zn5(CO3)2(OH)6), scholzite (CaZn2(PO4)2·2(H2O)), smithsonite (ZnCO3), willemite

(Zn2SiO4) and zn-sulfate (ZnSO4).

The XANES spectra of the randomly chosen hotspots were averaged, the edge energy

calibrated and the spectrum normalised. Linear combination fitting (LCF) was applied

using IFEFFIT software on the pre-processed XANES spectra of the hotspots

(Newville, 2001). For each selected hotspot, the combination with the lowest χ2 was

chosen as the most likely combination of compounds in that hotspot. The accuracy of

the fitting depends on how well the standards represent the data. A reduced χ2 smaller

than 1 indicated a reliable fit. Owing to the limited number of standards, the best fit

composition may not give the true composition, although it can provide an indication of

the chemical information of the element of interest and describe the chemical

differences among the selected hotspots in a spatially-resolved manner.

Subsequently, impregnated soil samples were chemically analysed with an Agilent

7500cs ICP MS. The regions of interest were ablated using a high performance New

Wave Nd Yag 213 UV laser. An optical microscope was used to find pores in the

impregnated samples with a thickness of approximately 0.5 cm and a length of 3cm.

The pores did not always obviously contain organic matter. With the laser, the samples

were ablated across the micropores over a length of 2 mm. The laser ablated at a speed

of 10 µm s-1 and the spotsize was 30 µm. The sensitivity was 4.7 mg l-1 for Ca; 280 ng l-

1 for Mn; 86 µg l-1 for Fe; 1.9 µg l-1 for Zn and 290 ng l-1 for Cu. Measurements were

qualitative and only gave a representation of the depletion or accumulation of elements

along the micropore. For quantitative measurements, calibration is necessary. This

could be done with homogeneous samples. However, this would defeat the purpose of

the study insofar as the objective of our study was to characterise the heterogeneity of

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164

the elements in the immediate vicinity of micropores in which we were interested.

Another reason why quantitative measures were not possible was that the depth to

which the laser ablated, and hence the volume of soil nebulised, was not always

constant (Weis et al., 2005).

RESULTS AND DISCUSSION

An intact sample containing a black decaying root was scanned over an area of 1cm by

0.2 cm. The XRF images showed that Ca was concentrated in the channel containing

the root (Figure 1). The pore selected contained organic matter from a decaying root.

Therefore a source of Ca may have been from a decomposing root given that roots can

accumulate Ca (Singh and Jacobson, 1979). Another source may be Ca from the soil

solution adsorbed onto the organic matter. Pores in the thin sections did not always

contain decaying organic matter but pores selected always had coatings of organic

matter (Figure 2). As shown on the distribution maps, Ca was also concentrated in the

pores (Figure 3). In this case, Ca could have adsorbed from the soil solution onto the

pore surface because there was no organic matter inside the pore. The correlation

graphs originating from the XRF distribution maps showed that Mn and Zn were always

strongly correlated with Fe in the intact samples and the thin sections (R2 for Fe and Mn

= 0.92, Figure 2). The correlations of Mn and Fe were based on the low energy maps

taken below the absorption edge of Fe. The only elements not well correlated with Fe in

the thin sections were Ca and, to a lesser extent, Cu. Ca was mainly accumulated in

pores whereas Fe, Mn, Cu and Zn were mainly accumulated in soil. Calcium, Mn, Zn

and Cu were always more correlated with Fe in the intact samples than in the thin

sections, a result that has its explanation in the difference in x-ray penetrating depth of

the two sample types. The pores selected were always those exposed on the surface of

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165

the samples. X-rays used in these experiments penetrate to a maximum depth of 50 µm.

Given that intact samples were approximately 10 mm thick spectral information will

have been gathered, not only from the pore surface but also the soil matrix. Because the

soil contains a total Fe concentration of approximately 4 %, a considerable contribution

to the Fe spectral signatures will have come from the matrix. The thickness of the thin

sections was only 20 µm; therefore, the influence of matrix Fe would have been less

significant. Data from hotspots suggested that most of the Mn existed in reduced form

(Table 1). More than 50 % of Mn was associated with phosphate-like bindings that can

also contain reduced Fe (hureaulite and switzerite).The Mn hotspots selected in the

intact samples always had a small fraction bound in oxide-like bindings.

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166

Figure 1: Distribution maps of Ca, Mn, Fe, Zn and Cu around a pore obtained by µ-

XRF in an intact sample and the correlation between these elements.

Fe

Ca Mn

Zn Cu

R=0.97 P < 0.0001

R=0.82 P < 0.0001

R=0.93 P < 0.0001

R=0.77 P < 0.0001

1 cm Decaying root

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167

Figure 2: Optical photomicrograph of the pore scanned by µ-XRF on the thin section

represented in Figure 3.

pore

Organic matter coating

Quartz particle

1mm

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168

Figure 3: Distribution maps of Ca, Mn, Fe, Zn and Cu around a pore by µ-XRF in a

thin section and the correlation of these elements.

Ca

Mn

Fe

Zn

Cu

R=0.32 P<0.0001

R=0.92 P<0.0001

R=0.91 P<0.0001

R=0.73 P<0.0001

2.4 mm

pore

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169

birnessite hureaulite switzrite Mn2O3 MnO2 red-c2

close 1 0.00 0.00 0.76 0.24 0.00 0.011

far2 0.11 0.66 0.25 0.00 0.00 0.0183 0.056 0.00 0.58 0.00 0.36 <0.010

close_ts

4 0.00 0.36 0.64 0.00 0.00 <0.010

Table 1: Fractions of Mn species in selected ‘Mn-hotspots’ in the area close and far

from the soil micropore in an intact soil sample and in a thin section.

χ2= Σ [(fit – data)/ε]2 / (Ndata – Ncomponents) is the chi-square statistic. Here ε is the


sum is over Ndata points and Ncomponents is the number of components in the fit. The total

fractions were constrained to be 100% in all fits. Typical uncertainties in the fractions

listed for each standard component are 5%.

ts: thin section

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170

This small fraction was the only difference found between the thin sections and the

intact samples for Mn. Furthermore, the measurements close to the pore surface did not

differ from the ones further in the soil matrix (Figure 4).

Only a small proportion of Fe in soil is available for plants because of the form in which

the Fe is present in these duplex soils. The three chemical processes affecting Fe

availability to plants are acidification, reduction and chelation (McFarlane, 1999). The

distribution maps showed that there was no enrichment of Fe around the selected pores

(Figure 1 and 3). Iron was distributed randomly throughout the areas chosen for

analysis. The XANES spectra suggested that Fe was mostly present in oxidised form

(Table 2). Oxide-like bindings such as those of goethite and hematite were found in the

hotspots selected in the intact samples and the thin sections. Some spots in the intact

samples seemed to contain greenrust-Cl like and greenrust-sulfate like bindings but

these forms were not found in soil thin sections. In contrast magnetite was always

found in the thin sections (except for one hotspot) but never in the intact samples.

Greenrust ((Fe, Mg2+)6(Fe3+)2(OH)18.4(H2O)18) and magnetite (Fe3O4) both are oxides

with a mixture of oxidised and reduced Fe. The only conclusion that could be made was

that in the selected hotspots, a mixture of oxidised and reduced Fe was present. The

hotspots selected in the thin sections always had a higher proportion of mixed oxidation

forms of Fe compared with hotspots selected on the intact samples. Therefore, it could

not be concluded that the Fe speciation in the intact sample was different from the ones

in the thin sections. The amount of standard used in this type of study is limited. Given

that soil is highly heterogeneous we could not state that the bindings in the hotspots

were exactly the same as the bindings of the standards.

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Figure 4: Some spectra and respective linear combination fittings of hotspots of Mn

selected in the immediate vicinity of the pore (close) and in the soil matrix > 5 mm from

the pore surface in the intact samples and > 1mm from the pore surface in the thin

sections (far).

fit

spectrum

Far from pore

0

1

2

3

4

5

6

6520 6540 6560 6580 6600 6620

Energy (eV)

Abs

orpt

ion

Close to pore

Switzerite

Hureaulite

Mn2O3

MnO2

Birnessite

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172

franklenite hydrozincite zn-sulfate ferrihydrite adsorbed zn willemite smithsonite scholzite red-c2

close1 0.00 0.66 0.00 0.00 0.34 0.00 0.00 0.0352 0.00 0.23 0.00 0.00 0.00 0.77 0.00 0.15

far3 0.00 0.18 0.60 0.00 0.23 0.00 0.00 0.013

close_ts

4 0.00 0.49 0.12 0.00 0.39 0.00 0.00 0.005 0.00 0.34 0.22 0.00 0.44 0.00 0.00 0.006 0.29 0.39 0.17 0.15 0.00 0.00 0.00 0.007 0.00 0.00 0.38 0.10 0.52 0.00 0.00 0.008 0.29 0.00 0.40 0.00 0.00 0.31 0.00 0.00

far_ts9 0.00 0.00 0.51 0.087 0.40 0.00 0.00 0.00

10 0.00 0.49 0.00 0.00 0.16 0.00 0.35 0.01611 0.00 0.00 0.00 0.30 0.70 0.00 0.00 0.064

Table 3: Fractions of Zn species in selected ‘Zn-hotspots’ in the area close and far from

the soil micropore in an intact soil sample and in a thin section.






ts: thin section

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173

We concluded that, in all these hotspots, Fe-O like bindings were present and that these

were a mixture of Fe in oxidised and reduced form similar to the ones found in the

standards (Figure 5). In only one hotspot in the thin section was Fe found in a phosphate

binding (vivianite).

Zinc sometimes accumulated around the mesopores but was also present in higher

concentrations away from the pore (Figures 1 and 3). Linear combination fitting of the

XANES and EXAFS region of the absorption spectra showed that the speciation of Zn

for the hotspot at the edge of the pore and further in the soil matrix chosen in the intact

samples were very similar and in forms resembling hydrozincite, Zn-sulphate and

willemite. The same was found for the hotspots selected on the thin sections: zinc was

always found associated with sulphates at the pore surface. Franklenite-like forms were

found at the pore edge whereas Zn adsorbed on ferrihydrite was found in the soil

matrix. Only one instance of smithsonite-like bindings was found and this at the pore

surface. This could be due to the higher CO2 levels inside soil pores that favourise the

formation of carbonates. The one occurrence of scholzite was in the soil matrix (Figure

6). Copper was only present in small amounts in the soil (< 10 ppm in the whole soil

profile). The XRF maps of the thin sections showed that Cu was enriched at the edges

of the areas where Ca was located or in the same areas (Figure 3). These areas of

enrichment were coincident with organic matter coatings. Previous studies have found

that Cu is associated with organic matter (Jacobson et al., 2007). In this study, only two

hotspots in the thin sections could be analysed because of the low concentration of Cu

in the soil. The components resulting from the linear combination fitting were different

for both hotspots. However, both were composed of approximately 70 % carbonate and

30 % phosphate (Table 4).

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174

Figure 5: Some spectra and respective linear combination fittings of hotspots of Fe



sections (far).

fit

spectrum

0

1

2

3

4

5

6

7080 7100 7120 7140 7160 7180 7200

Energy (eV)

Abs

orpt

ion

Close intact

Close thin section

Far intact

Far thin section

Fe3O4

Goethite

Greenrust-Cl

Greenrust-S

Fe2O4

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175

Figure 6: Some spectra and respective linear combination fittings of hotspots of Zn



sections (far).

Fit

Spectrum

0

1

2

3

4

5

6

7

9650 9660 9670 9680 9690 9700

Energy (eV)

Abs

orpt

ion

Close intact

Close thin section

Far intact

Far thin section

Ferrihydrite adsorbed Zn

Franklenite

Hydrozincite

Smithsonite

Willemite

Zn-sulfate

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176

malachite nissonite azurite calcosiderite red-c2

close_ts1 0.69 0.31 0.00 0.00 19.552 0.00 0.00 0.70 0.30 121.63

Table 4: Fractions of Cu species in selected ‘Cu-hotspots’ in the area close and far from

the soil micropore in a thin section.






ts: thin section

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177

Again, this could be due to higher levels of CO2 in and in the vicinity of soil pores,

favouring the formation of carbonates.

The distribution of elements of interest across a section of a micropore in impregnated

samples were measured by LA-ICP-MS and compared with XANES and EXAFS data

of the same section. By looking at the ablating point on the sample on the screen of the

microscope and comparing it with the counts of the elements detected, it was

established that the decrease in counts of siliceum (Si) was a sensitive measure of the

location of the micropore. The point with the lowest counts was the middle of the pore.

In all of the µ-XRF maps Ca was concentrated in the micropores but Ca measured by

LA-ICP-MS was detected in only some of the micropores. The graphs also show that

wherever there was an accumulation of Ca, there was also an accumulation of Fe and

Mn (Figures 7, 8). This is in contradiction with the distribution maps by µ-XRF. The

differences are probably a result of the different volumes of soil material sampled

during measurement: sampling depth for µ-XRF was 50 µm whereas that for LA-ICP-

MS was higher. Because Fe is relatively depleted at the very surface of the micropore,

LA-ICP-MS will detect a greater proportion of Fe than µ-XRF. There was always an

accumulation of Zn and Cu in proximity of the micropore. Measurements from 0 s to 10

s at the beginning of the X-axis could not be accounted as the instrument always needed

period to adjust. Care has to be taken when interpreting results obtained by LA-ICP-

MS. Elemental fractionation depends on characteristics of the sample such as optical

absorption behaviour. In an heterogeneous medium such as soil, this will vary between

samples and therefore, overcoming this problem for matrix independent quantification

becomes a problem (Weis et al., 2005).

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178

0

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100

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06

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coun

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02

020406080

100120140160

0 20 40 60 80 100

Time (s)

coun

ts *1

02

pore pore

Si Si

Ca

Cu

Mn Zn

Fe

Figure 7: Relative distribution of Si, Ca, Mn, Fe, Cu and Zn across a micropore using

LA-ICP-MS. Ca is enriched in the pore.

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179

Figure 8: Relative distribution of Si, Ca, Mn, Fe, Cu and Zn along a line across a

micropore using LA-ICP-MS. Ca is impoverished in the pore.

0

20

40

60

80

100

0 20 40 60 80 100

Time (s)

coun

ts *1

06

01020304050607080

0 20 40 60 80 100

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unts

*102

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100150200250300350400450

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coun

ts *1

02

pore pore

Si

Ca

Fe

Mn

Si

Cu

Zn

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180

The high degree of heterogeneity of the elements in the samples and their

inhomogeneous distribution makes it impossible to have precise and accurate results

that allow quantification (Jimenez et al., 2007).

Each micropore created by roots and used by subsequent roots has a different history.

The inhomogeneity in elemental concentration and spatial distribution will be greater at

smaller scale than at larger scale. Rhizosphere chemistry will depend on the type of root

(e.g. root hair, mature root), state of decomposition, extent and diversity of occupancy

of pore, and types of plants. Further complexity arises from transport of particles in

suspension and solutions, a process dependent on a range of factors such as pore size

and pore continuity. Surface analytical techniques such as those employed here have a

significant role to play in refining our understanding of nutrient form, concentration and

availability and how plant roots affect these in space and time.

In this investigation we attempted to study microstructure, in as undisturbed condition

as possible, by using intact soil aggregates and thin sections prepared following vacuum

impregnation with resin. However, many surface sensitive techniques require a flat

surface. Because this chemical analysis is surface sensitive, intact samples cannot be

polished, and when surfaces are flattened, smearing occurs which alters the organisation

of soil particles that could lead to problems when the chemistry of the surface is studied.

Soils of low coherence fragment readily. We attempted to study the chemical nature of

the rhizosphere across the E horizon-B horizon boundary but the samples fragmented

and our study was confined to the upper B horizon.

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CONCLUSION

Calcium was always concentrated in the pores selected for analysis. There was no

enrichment or depletion of Fe and Mn around the pore surfaces. Iron was mostly

oxidised and in oxide like bindings while Mn was mostly reduced and in phosphate

bindings. Zinc was concentrated around the selected pores but was also found in the soil

matrix in the same amounts. Zinc was mostly associated in bindings with carbonate,

sulphate and silicate. Copper was always found in vicinity of highly concentrated

patches of Ca, probably in association with organic matter.

The difference in micro-spatial chemistry between the thin sections and the intact

samples was attributed to the thickness of the sample analysed. This resulted in Mn, Zn

and Cu having stronger correlations with Fe in the thin sections compared with the

intact samples.

As mentioned in the previous chapter, there are some limitations with the synchrotron

method. The LA-ICP-MS results which do not quite corroborate the other set of data,

addresses this limitation again.

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Chittleborough D.J. 1992. Formation and pedology of duplex soils. Aust. J. Exp. Agr.

32:15-25.

Eldridge, D.J., and D. Freudenberger. 2005. Ecosystem wicks: Woodland trees enhance

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Hinsinger, P., C. Plassard, and B. Jaillard. 2006. Rhizosphere: A new frontier for soil

biogeochemistry. J. Geochem. Explor. 88:210-213.

Isaure, M.P., A. Manceau, N. Geoffroy, A. Laboudigue, N. Tamura, and M.A. Marcus.

2005. Zinc mobility and speciation in soil covered by contaminated dredged

sediment using micrometer-scale and bulk-averaging X-ray fluorescence,

absorption and diffraction techniques. Geochim. Cosmochim. Acta 69:1173-

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Manceau, A., M.A. Marcus, N. Tamura, O. Proux, N. Geoffroy, and B. Lanson. 2004.

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McCully, M. 2005. The rhizosphere: the key functional unit in plant/soil/microbial

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McCully, M.E. 1999. Roots in soil: unearthing the complexities of roots and their

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Pankhurst, C.E., A. Pierret, B. Hawke, and J.M. Kirby. 2002. Microbiological and


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Stewart, J.B., C.J. Moran, and J.T. Wood. 1999. Macropore sheath: quantification of

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Strawn, D., H. Doner, M. Zavarin, and S. McHugo. 2002. Microscale investigation into

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Vannoordwijk, M., M.J. Kooistra, F.R. Boone, B.W. Veen, and D. Schoonderbeek.

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Voegelin, A., F.A. Weber, and R. Kretzschmar. 2007. Distribution and speciation of

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Weis, P., H.P. Beck, and D. Gunther. 2005. Characterizing ablation and aerosol

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Yunusa, I.A.M., P.M. Mele, M.A. Rab, C.R. Schefe, and C.R. Beverly. 2002. Priming

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crops, and a permanent pasture. Aust. J. Soil Res. 40:207-219.

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Summary and General Discussion

Soils with an abrupt contrast in texture between A (and/orE) horizons and B horizons),

known as duplex soils in Australia, present a challenge to ecologists and agronomists

alike. Ecologically, the challenge is to understand the mechanisms that native plants

have evolved in order to penetrate and grow in the hostile environment of this soil type.

Duplex soils also create a challenge from an agronomic point of view because many

exotic species in particular, such as imported annual crops, lack the adaptations that

native plants might have and do not grow as well on these soils compared to other

places in the world with the same climate but more favourable edaphic conditions

(Rengasamy, 2003). Thus, deeper understanding of the dynamics between root growth

and the soil environment for both native and introduced species will be useful,

particularly for these duplex soils.

Until recently very little was known about the reciprocal influence that root growth and

soil structure have on each other, and about the distribution of nutrients in relation to

soil structure (Hinsinger et al., 2005). Furthermore, most of the information has been

obtained from disturbed situations, caused by invasive and destructive methodologies,

and is therefore not necessarily completely representative of the system. The aim of the

work described by this thesis was to develop and assess mainly non-invasive novel

techniques as a means of studying root-soil interactions, particularly for duplex soils,

and to analyse the information they provide. This information would be additional to

measurements possible by other, more destructive techniques.

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Use of novel techniques to study root-soil interactions

Non-invasive 3D imaging

First, the use of computer tomography (CT) to characterise porosity in soils in 3D was

explored. Soil is an opaque medium, and therefore, it is difficult to obtain characteristics

of undisturbed or intact pores. However, knowledge of the 3D orientation and

connectivity of pores is crucial in terms of water flow through soil and root growth

(Gregory et al., 2003; Luo et al., 2008; Mooney and Morris, 2008). Recent studies have

illustrated the beneficial use of computer tomography to understand certain processes in

soil that require 3D information (ex. Udawatta et al., 2008; Mooney and Morris, 2008;

Taina et al., 2008) but there is a trade off between the sample size that can be examined

and the resolution obtained (Pierret et al., 2002). For this PhD study, two types of CT

were available, a medical-CT and a micro-CT. The medical-CT was successfully used

to visualise and characterise the macroporosity of large intact cores of duplex soils (50

cm long, 15 cm diameter). These large samples only allowed images to be obtained in

which pores > 1 mm could be resolved. Although this resolution was quite coarse, it

enabled the study of porosity caused by cracks and root growth in unperturbed samples

at a large scale (Chapter 2). Micro-CT allows much finer resolution albeit for much

smaller samples. It has been used by others to investigate soil-microbe processes or

aggregate porosity (Nunan et al., 2006; Blair et al., 2007). In this PhD study micro-CT

was used to visualise and quantify the mesoporosity in a layer of 4 cm across the E/B

boundary of a sodic duplex soil, here called the interface (Chapter 2).

Studies that use CT often highlight the importance of the image methodology when

visualising and quantifying features in 3D, especially the image threshold step, on

which all the other image measurements are dependent (Jassogne et al., 2007; Elliott

and Heck, 2007). In the first component of this thesis (Chapter 2), a global threshold

was used. The limitations of this approach were explained, as was the importance of

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developing a threshold methodology that was both local and adaptive. A global

threshold can assign pixels to the wrong ‘phase’ when the grey value is in the

overlapping region between pore pixels and soil pixels in the grey scale histogram of

the image stack. Therefore, the next step in this PhD study was to test a threshold

method developed by Oh and Lindquist (1999) on the output images from the CT scans

(Chapter 3). This method employed indicator kriging to assign pixels in the overlap

region to the pore phase or the soil phase. The main problem with CT images is that

there will always be an overlap between the pixels belonging to the pore space and the

ones belonging to the soil phase. Indicator kriging is a method that allows the best-

educated guess using spatial statistics to assign pixels to pores or soil based on the

histogram of the grey values of the pixels of the scan images. The complexity of image

thresholding for visualising pores or roots in intact soil cores lies in the fact that CT

images of a very heterogeneous medium such as soil need to be segmented into images

that only contain two phases: porosity and soil matrix.

The threshold methodology developed in the work reported by this thesis (Chapter 3)

was then used in a study in which the objective was to visualise and quantify porosity

changes caused by root growth (Chapter 4). The hypothesis was that root growth of

canola (Brassica napus), lucerne (Medicago sativum) and saltbush (Atriplex

nummularia) in large intact soil cores of duplex soils would change the macroporosity

in the hostile horizons of a duplex soil and that saltbush, as a native plant, would create

the biggest changes. This hypothesis was based on the ‘primer’ plant concept (Yunusa

and Newton, 2003). A primer plant will ameliorate subsoil constraints, by increasing the

macroporosity and so improve water and nutrient uptake of following plants. Although

significant differences in soil porosity before and after root growth were found, no

significant differences were detected between plant species. One reason was that many

cracks were formed in the clay subsoil when the soil cores dried out towards the end of

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the experiment. This was a function of the water regime adopted for the experiment that

mimicked the growing season. Therefore, another study was undertaken to reconstruct

the living roots alone in the intact soil cores (Chapter 5). An algorithm based on

multivariate analysis was developed and used to trace the roots of each plant in the

intact soil core. Analysis revealed that saltbush had the greatest potential to grow roots

down the profile. Lucerne also had a relatively strong penetration but to a lesser extent

than saltbush. Roots of these plants species appeared to exhibit more geotropic features

with more longitudinally extending roots than canola which seemed to grow more

laterals. The objective of this study was to visualise and characterise the roots and the

soil environment in which roots grow. In the past, roots reconstructed in 3D studies

have largely been grown in repacked soil columns (Perret et al., 2007). Reconstructing

roots in an artificially homogeneous environment is relatively simple compared with

reconstructing roots in intact samples where soil structure is much more heterogeneous.

A major aim of the work was the study of root systems in their undisturbed environment

and this is only possible if intact soil cores are used.

Further research on non-invasive 3D imaging

The next step in this research is to improve the skeletonisation algorithm used in the

first study for a more efficaceous 3D quantification of porosity. The 3D reconstructions

of the porosity in the fourth study were too complicated and the skeleton generated

many errors and was not very apposite to the 3D quantification. The structure of roots is

less complicated than porosity. Reconstruction of the root skeleton to measure root

length and angles of branching will be easier for the skeleton algorithm.

This study makes extensive use of medical CT. The reason for this was the ability to

scan large sample sizes (15 cm diameter and 50 cm long). In later experiments, these

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results should be compared with measurements done on smaller samples using micro-

CT. This will enable comparison at two different scales and two different resolutions.

In this thesis, visualisation and quantification of porosity before and after root growth

has been demonstrated. Further analysis should be undertaken in which the results of

the third and fourth papers are compared. This could be achieved by aligning and

registering the images from the scans before and after root growth and visualising and

quantifying changes at macro scale caused by the growing root.

Other threshold techniques have been developed by other groups (ex. Pierret et al.,

2002). Comparing results by scanning and analysing the images with the different

methods will help understand the variation between studies and help specify the best

imaging method for intact soil samples.

Current root growth models are not based on root behaviour in intact soil cores. The

output from the type of study reported in this PhD will help develop root models based

on a more realistic approach (Dunbabin et al., 2002).

Chemistry of pores

Roots use pre-existing pores as pathways to grow through. These pores can be created

by roots from previous vegetation which means that there is a ‘remnant’ rhizosphere

when the roots decompose. There is a need to understand the distribution of nutrients in

relation with soil structure. In the studies (Chapter 6 and 7) reported in this thesis,

synchrotron-based X-ray techniques were used to study the distribution of Ca, Mn, Fe,

Zn and Cu at fine scale in the vicinity of pores relative to the soil matrix. The elements

are micro-nutrients and differences in distribution could be found for Zn, Cu and Mn to

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a lesser extent. Iron was present in higher concentrations in the soil and the sample size

might have been too small in both experiments to see any distribution changes as

rhizosphere effects can vary from µm to mm for different nutrients. Mn was depleted at

the edge of the pore in some samples, in others Mn concentration increased. It is well

known that roots induce chemical changes in the soil environment in which they grow.

Furthermore, pore surfaces are also in contact with water and air, which can change the

chemistry as well. The ‘history’ of each pore, from inception to its current form, will be

unique. Differences between two pores in respect of their nutrient profile will be greater

if one pore has been used by a root in the past and the other has not. Furthermore, when

the results were compared with measurements obtained with LA-ICP-MS in the second

experiment (Chapter 7), the distribution of Zn and Cu coincided with what was found

with XRF but the distribution of Ca, Mn and Fe was different. The distribution of Ca

and Fe was always similar along a transect through a pore whereas the opposite was true

for element profiles by XRF. High resolution techniques such as those referred to here

are only useful for the study of micronutrients in which the distribution varies at

microscale. These techniques cannot be used when studying distribution changes of

macronutrients such as N which, in the case of rhizosphere effects, can vary at a scale of

cm (Hinsinger et al., 2006).

Further research on surface chemistry of pores

Measurements undertaken at the synchrotron are time consuming. If satisfactory

representation of the distribution of the elements of interest is to be achieved, more

pores than were studied in this investigation should be analysed. Furthermore, results

should be compared with other surface techniques such as X-ray photoelectron

spectroscopy (XPS), secondary ion mass spectroscopy (SIMS) and LA-ICP-MS.

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Conclusion

This work has clearly shown the efficacy of medical-CT in understanding the

exploratory behaviour of roots in soil, particularly duplex soils which have strong

texture contrast and dense, high strength subsoils. Medical-CT combined with micro-

CT allows multi-scale root dynamic studies insofar as the exploratory behaviour of both

fine and macro-roots can be investigated.

Using medical CT to compare the ability of macroroots of an annual (canola), grassy

perennial (lucerne) and woody perennial (saltbush) to ‘drill’ through the high strength

subsoil of a duplex soil the studies reported in this thesis have shown that saltbush

penetrated deeper and faster. Medical-CT can also be used to quantify macroporosity

but reliable quantification is dependent on more method development. Using high

resolution analytical techniques such as synchrotron- based X-ray methods and LA-

ICP-MS, the distribution and speciation of Ca, Mn, Fe, Cu and Zn were quantified. It

was found that Ca was concentrated in the pores. Manganese was variably depleted or

concentrated in the immediate vicinity of the pores compared to the matrix. Mn

distribution was strongly correlated with that of Fe. Cupper and Zn were accumulated

around the micropores relative to the matrix. The application of high resolution

techniques such as X ray absorption spectroscopy and surface analytical techniques

such as X ray photon spectroscopy to study the impact of root growth on soil and soil on

root growth is in its infancy. Studies such as reported here need to be expanded to

include more elements and the combination of analytical techniques. Only then will the

dynamics between root growth, soil porosity, ‘relict’ rhizospheres and nutrient

distribution be understood.

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