Mohammad Omar Faruk Murad - University of Sydney

ACQUIRING IN-SITU HIGH-RESOLUTION SOIL INFORMATION USING COST-EFFECTIVE TECHNOLOGY

Mohammad Omar Faruk Murad

A thesis submitted to fulfil requirements for the degree of

Doctor of Philosophy

2021

School of Life and Environmental Science

Faculty of Science

The University of Sydney

New South Wales

Australia

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STATEMENT OF ORIGINALITY

This is to certify that to the best of my knowledge, the content of this thesis is my own work.

This thesis has not been submitted for any degree or other purposes.

I certify that the intellectual content of this thesis is the product of my own work and that all

the assistance received in preparing this thesis and sources have been acknowledged.

Mohammad Omar Faruk Murad

November 2020

SUMMARY

Soil is an essential natural resource for our agriculture-based food production systems. Soil

plays the most important role in Earth's ecosystem by filtering the rainwater, regulating the

discharge of excess rainwater, buffering against pollutants, and storing large amounts of

organic carbon. The production of the crop and ecosystem services are greatly influenced by

the physical properties (such as soil moisture, organic carbon, particle size distribution, bulk

density etc.) of soils.

To optimize the precious natural resources, soil conditions need to be monitored precisely and

accurately regularly. Conventional methods for soil physical properties are laborious, time-

consuming, expensive, and destructive. Also, soil properties widely vary both horizontally and

vertically within the field. To monitor soil variability, we need to develop new in-situ

technologies and techniques that can accurately measure soil physical properties rapidly, cost-

effectively and non-invasively across the cropping fields. The cost-effective and quick

measuring techniques allow greater representation of the spatial distribution of soil physical

properties. This thesis aims to provide new technologies that enable soil investigation at finer

spatial and temporal resolution. In particular, this thesis contributes to developing new methods

for measuring soil moisture dynamics and organic carbon in situ, and soil density and particle

size analysis in the laboratory with high accuracy.

To understand drought tolerance mechanisms and phenotypic water use traits in crop breeding

programs, a plastic buggy system based on the EMI surveys was developed (Chapter 2) for

monitoring crop water use at the plot level. Soil ECa of different plots calibrated with soil

volumetric water content and used to determine the total water use by various chickpea

genotypes. The ECa data recorded from the EMI surveys were used for the depth-specific

temporal analyses of soil water use by different genotypes (Chapter 3). The system can detect

water extraction at different depths, and thus the chickpea genotypes can be grouped based on

their root activities. The chickpea genotypes from rainfed plots extracted more water compared

to the genotypes grown in irrigated plots. Depth profiles of available soil moisture at various

growth stages showed most of the soil water was extracted from the depth below 1 m.

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For mitigating global warming by reducing atmospheric CO2 and increasing crop production a

rapid in-situ soil organic carbon (SOC) measurement technique using a VisNIR penetrometer

system from high-resolution in-situ spectra was introduced (Chapter 4) in this thesis. It was

observed that penetrometer external parameter orthogonalisation (EPO) transformation

matrices successfully removed the effects of soil moisture from the in-situ VisNIR spectra. The

validation statistics between the predicted SOC and the SOC measured in the laboratory

indicate the potential of VisNIR penetrometer system as viable tool for in-situ SOC

measurement.

Conventional particle size analysis in the laboratory using hydrometer method is tedious and

practically unable to produce a continuous particle size distribution curve. In this thesis, an

automated hydrometer method using a ToF distance and a digital temperature sensor was

introduced (Chapter 5). The proposed automated hydrometer method was used to automatically

record hydrometer reading with suspension temperature and produce a continuous particle-size

distribution curve (2-40 μm range).

Soil compaction is a growing problem that can lead to impermeable layers within the soil that

restrict water and nutrient cycles. Bulk density and soil stiffness moduli are related to soil

compaction, porosity etc. and required to be monitored spatially. In this thesis, a novel

technique based on shear wave velocity using piezoelectric sensors was introduced for

measuring bulk density and soil stiffness moduli in the laboratory (Chapter 6). This technique

was a rapid, cost-effective and accurate method for predicting bulk density, shear, and young

moduli, which can be used directly in the field in the future.

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PUBLISHED CHAPTERS OF THIS THESIS Chapter 5 of this thesis is published as: Murad, M. O. F., Jones, E. J., & Minasny, B. Automated soil particle size analysis using time of flight

distance ranging sensor. Soil Science Society of America Journal. I designed the study with the co-authors, collected and analysed the data and wrote the drafts of the manuscript. Jones helped me with the laboratory experiments and analysing the data.

Chapter 6 of this thesis is published as: Murad, M. O. F., Minasny, B., Malone, B., & Crossing, K. (2020). Measuring soil bulk density from shear

wave velocity using piezoelectric sensors. Soil Research. I designed the study with the co-authors, collected and analysed the data and wrote the drafts of the manuscript. Crossing helped me with the hardware design of the closed system.

Publications in preparation from this thesis

Chapter 2 Murad, M. O. F., Minasny, B., McBratney, A.B., Bramley, C. & Bramley, H., Monitoring Soil Water Use at

the Plot Level Using Electromagnetic Induction Surveys.

Chapter 3 Murad, M. O. F., Minasny, B., McBratney, A.B., Bramley, C. & Bramley, H., Temporal Analysis of Soil

Water Extraction by Different Genotypes of Chickpeas Using EMI Surveys.

Chapter 4 Murad, M. O. F., Jones, E. J., Minasny, B. & McBratney, A.B., VisNIR Penetrometer System for Predicting

Soil Carbon.

In addition to the statements above, in cases where I am not the corresponding author of a published item, permission to include the published material has been granted by the corresponding author.

Mohammad Omar Faruk Murad 27 November 2020

As supervisor for the candidature upon which this thesis is based, I can confirm that the authorship

attribution statements above are correct.

Budiman Minasny 27 November 2020

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OTHER PRESENTATIONS AND POSTERS MADE FROM THIS THESIS

Presentations

Murad, M. O. F., Minasny, B., Bramley, H. & McBratney, A.B., Monitoring Soil Water Use at the Plot Level Using Electromagnetic Induction Surveys. Conference on science, technology, engineering and economics for Digital Agriculture (steeDA), 2019.

Murad, M. O. F., Minasny, B. & Jones, E. J., Hydrometer Test Automation Using Time of Flight Distance and Digital Temperature Sensor, EGU General Assembly, 2019.

Murad, M. O. F., Minasny, B., Malone, B.P. & Crossing, K., A novel technique for measuring soil density from shear wave velocity using piezoelectric sensors, National Soil Science Conference, 2018.

Posters Murad, M. O. F., Minasny, B., Malone, B.P. & Crossing, K., A novel technique for measuring soil density

from shear wave velocity using piezoelectric sensors, National Soil Conference, 2018, Australia.

Murad, M. O. F., Minasny, B., Bramley, H. & McBratney, A.B., Monitoring Soil Water Use at the Plot Level Using Electromagnetic Induction Surveys, SIA HDR Showcase, 2019, The University of Sydney.

Awards Best poster presentation - SIA HDR Showcase, 2019, The University of Sydney

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ACKNOWLEDGMENTS First of all, I would like to thank almighty Allah for giving me the faith, health and patience to finish

my PhD studies.

I would like to thank my supervisor, Budiman Minasny, for his directions, advice, patience and for the

freedom he gave me in pursuing this research. Coming from a different area of study, initially it was

hard for me to understand the main focuses of soil science in agriculture. Thank you, Budi for tolerating

me over the duration of this study and correcting my simplest mistakes with a smile on your face. I

could not imagine having had a better supervisor.

I would like to give sincere gratitude to Brendan Malone for helping me with my manuscript and

introducing me to the soil carbon sequestration industry in Australia. Also, Helen Bramley for providing

me with all the logistical support required to conduct field experiments at PBI, Narrabri.

Alex McBratney for the new ideas and advice for widening my several research topics from various

perspectives. Thanks Alex, for engaging me with the cutting-edge research on soil moisture and

introducing me with the NSW Smart Sensing Network (NSSN).

Edward Jones for engaging me with several research projects and helping me with everything from

understanding theoretical concepts, fieldwork and data analysis to publishing the key findings. Thank

you, Ed for all the support and guidance.

I would like to thank Philip Hughes for helping me settle into Sydney when I first arrived. Also, for his

continuous support, encouragement, suggestions and introducing me to R.

Thomas Bishop, Floris Van Ogtrop, Liana Pozza, Balwant Singh, David Airey and Guien Miao for

allowing me to work as a demonstrator in the faculty of Science and Engineering.

Kipling Crossing for teaching me electronics, hardware design and helping me with field operations.

Jingyi Huang for helping me with the analysis of EMI survey data and for showing me how to use

EM4Soil.

Mario Fajardo for helping me with R and Ian for helping me with Python and QGIS. Tom O’Donoghue

for proofreading my manuscripts, thesis chapters and for all the advice on improving my writings.

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Yuxin, Wartini, James, Ignacio, José and Dhahi for assisting me to overcome my issues with R. Patrick,

Vanessa, Kate, Niranjan, Stacey, Niranjan, Kanika, PeiPei, Yumi, Alex, Brett, Stephen and Uta for

supporting me over the last few years.

For technical support, I would like to thank Glen, Ivan, and Michael. A special thanks to Iona for taking

the care of my dietary requirement during field trips.

And to everyone that I have forgotten to mention above.

I would also like to thank my father (Salek Makbul), brother (Omar Sharif), sister-in-law (Mou Sharif),

in-laws (Shamsun Nahar, Abdul Mannan, Sumaiya, Sabrina) close relatives for the support and

encouragement. A special thanks to my close friends (Zenat, Galib, Nabil and Preety and other

childhood friends) for continuously inspiring me to achieve my goals and taking good care of my

parents while I am away.

My beloved wife, Sadia Mitu. I could not have finished my PhD studies without her unconditional love,

support, inspirations, patience and understanding. Thank you for taking good care of me during my hard

time and busy days.

Finally, I would like to express my deepest appreciation to my mum (Momtaz Jahan), the most

important person in my world. I will not be here without her. Thank you for taking care of everything

from the day I was born. Thank you for unconditional love and fulfilling my each and every need and

tolerating my faults. I could not have achieved anything without your prayers. Love you mum.

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DEDICATION

To my lovely mum for her endless support, inspiration and encouragement.

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CONTENTS STATEMENT OF ORIGINALITY............................................................................................ i

SUMMARY ............................................................................................................................... ii

PUBLICATIONS AND PRESENTATIONS MADE FROM THIS THESIS. ......................... iv

OTHER PRESENTATIONS AND POSTERS MADE FROM THIS THESIS......................... v

ACKNOWLEDGEMENTS ....................................................................................................... vi

DEDICATION............................................................................................................................ viii

CONTENTS ............................................................................................................................... ix

LIST OF TABLES ...................................................................................................................... xiii

LIST OF FIGURES ..................................................................................................................... xiv

LIST OF ABBREVIATIONS ..................................................................................................... xviii

CHAPTER 1 GENERAL INTRODUCTION………………………………………………….. 1

1.1 Background.................................................................................................................... 2

1.2 Visualisation of research topics on soil properties in agriculture……………………… 4

1.3 Statement of Problem..................................................................................................... 6

1.3.1 Soil Moisture Analysis.......................................................................................... 7

1.3.2 Soil organic carbon analysis…………………………………………………….. 8

1.3.3 Using Cheap sensors to analyse soil physical properties………………………… 9

1.4 Thesis Objectives........................................................................................................... 11

1.5 References...................................................................................................................... 14

CHAPTER 2 DEVELOPMENT OF A CROP WATER USE MONITORING SYSTEM AT

THE PLOT LEVEL………………………………………………………………………….. 20

2.1 SUMMARY……………………………………………………………………………. 21

2.2 Introduction…………………………………………………………………………….. 21

2.3 Materials and Methods…………………………………………………………………. 26

2.3.1 EMI system testing………………………………………………………………. 26

2.3.2 The buggy system……………………………………………………………….. 27

2.3.3 Collection of EMI data…………………………………………………………... 28

2.3.4 Inversion of the EMI data………………………………………………………... 29

2.3.5 Field trial on water use of chickpeas……………………………………………. 30

2.4 Results and Discussion…………………………………………………………………. 33

2.4.1 Environmental effects on EM measurements…………………………………… 33

2.4.1.1 Stability check of EM38-MK2…………………………………………… 33

2.4.1.2 Temperature effect on EM38-MK2………………………………………. 34

2.4.2 Calibration of the ECa data to soil moisture content……………………………. 37

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2.4.3 Field trial on water use efficiency of chickpea genotypes……………………… 39

2.4.3.1Total water use of different chickpea genotypes…………………………. 39

2.4.4 Comparisons of ECa data with the Neutron probe results………………………. 40

2.5 Conclusions…………………………………………………………………………….. 41

2.6 References……………………………………………………………………………… 43

CHAPTER 3 TEMPORAL ANALYSIS OF SOIL WATER EXTRACTION BY

DIFFERENT GENOTYPES OF CHICKPEAS USING EMI SURVEYS……………………. 50

3.1 Summary……………..……………..……………..……………..……………..……… 51

3.2 Introduction…………………………………………………………..………………… 52

3.3 Materials and Methods……………………………………..……………..…………….. 55

3.3.1 EMI Measurement System…………………………..……………..…………… 55

3.3.2 Field location……………..……………..……………..……………..…………. 56

3.3.2.1 Chickpea variety 2018 trial……………..……………..…………………... 57

3.3.2.2 Chickpea variety 2019 trial……………..……………..…………………… 58

3.3.3 Inversion of the EMI data……………..……………..……………..…………… 60

3.3.4 Calibration of ECa data……………..……………..……………..………………. 61

3.3.5 Water use calculation……………..……………..……………..………………... 61

3.3.6 Data Analysis……………..……………..……………..……………..………… 62

3.3.6.1 Soil moisture time series and depth profile of cumulative RAW analyses

from 2018 EMI survey……………..……………..……………..………………… 62

3.3.6.2 Depth profile of cumulative RAW, ∆S, and temporal analyses at different

growth stages from 2019 EMI surveys……………..……………..……………….. 63

3.4 Results and discussion……………..……………..……………..……………..……….. 64

3.4.1 Depth specific temporal analyses for the EMI surveys from 2018 (I5A) ………. 64

3.4.1.1 Time series soil moisture analysis at different layers……………..……….. 64

3.4.1.2 Depth profile of cumulative RAW at different growth stages……………… 72

3.4.2 Depth specific temporal analyses for the EMI surveys from 2019 field trial

(Campey-1) ……………..……………..……………..……………..…………….. 75

3.4.2.1 Depth profile of ∆S of various clusters from EMI surveys at different

growth stages for Campey-1……………..……………..……………..…………… 75

3.4.2.2 Variations in the total water use by different clusters of chickpea genotypes 76

3.4.2.3 Depth profile of cumulative RAW at different growth stages…………….. 77

3.4.2.4 Temporal analysis of ∆S for high and low water-efficient genotypes at

different soil layers at the mature stage……………..……………..………………. 79

3.5 Conclusions……………..……………..……………..……………..……………..…… 80

3.6 References……………..……………..……………..……………..……………..…….. 83

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CHAPTER 4 VISNIR PENETROMETER SYSTEM FOR PREDICTING SOIL CARBON…. 87

4.1 Summary……………..……………..……………..……………..……………..…..….. 88

4.2 Introduction……………..……………..……………..……………..……………..…… 89

4.3 Materials and Methods……………..……………..……………..……………..…..…… 93

4.3.1 VisNIR penetrometer system……………..……………..……………..…..……. 93

4.3.2 Field experiments to collect VisNIR spectra using the penetrometer system…… 96

4.3.2.1 Study site……………..……………..……………..……………..………... 96

4.3.2.2 Penetrometer operation and field sampling……………..……………..…… 100

4.3.3 Laboratory processing……………..……………..……………..……………..… 102

4.3.4 VisNIR processing and estimation of SOC……………..……………..…..……. 103

4.3.5 Calculation of SOC stock……………..……………..……………..……………. 106

4.3.6 Validation and other statistics……………..……………..……………..…..…… 106

4.4 Results and Discussion……………..……………..……………..…..…..…..…..…..…. 107

4.4.1 Performance of VisNIR penetrometer system……………..……………..…..….. 107

4.4.2 EPO transformation matrices……………..……………..……………..…..…… 109

4.4.3 Calibration and Validation Between Soil VisNIR Spectra and SOC…………….. 111

4.4.3.1 Dry cores……………..……………..……………..……………..…..…..… 112

4.4.3.2 Moist Cores……………..……………..……………..……………..…..….. 114

4.4.3.3 VisNIR penetrometer system……………..……………..………………… 116

4.4.4 Estimation of SOC stock from VisNIR penetrometer system…………………… 127

4.5. Conclusions……………..……………..……………..……………..…………………. 128

4.6 References……………..……………..……………..……………..…………………… 130

CHAPTER 5 AUTOMATED SOIL PARTICLE SIZE ANALYSIS USING TIME OF

FLIGHT DISTANCE RANGING SENSOR……………..……………..…………………….. 138

5.1 Summary……………..……………..……………..……………..……………………. 139

5.2 Introduction……………..……………..……………..……………..…………………. 140

5.3 Material and Methods……………..……………..……………..……………………… 144

5.3.1 Basic Principles of Gravitational Sedimentation……………..………………… 144

5.3.2 Instrumental Setup……………..……………..……………..………………….. 145

5.3.3 Principle of ToF Distance Sensor……………..……………..…………………. 146

5.3.4 Stability Check of Vl6180x ToF Sensor……………..……………..…………… 148

5.3.5 Experimental Procedure……………..……………..……………..……………... 148

5.4 Results and Discussions……………..……………..……………..……………………. 153

5.4.1 Repeatability……………..……………..……………..……………..…..….. 156

5.5 Conclusions……………..……………..……………..……………..……………..…… 158

5.6 References……………..……………..……………..……………..……………..……. 160

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CHAPTER 6 MEASURING SOIL BULK DENSITY FROM SHEAR WAVE VELOCITY

USING PIEZO-ELECTRIC SENSORS……………..……………..……………..…………… 165

6.1 Summary……………..……………..……………..……………..……………..…..….. 166

6.2 Introduction……………..……………..……………..……………..……………..…… 167

6.3 Background study and theory……………..……………..……………..………………. 168

6.4 Materials and method……………..……………..……………..……………..……….. 174

6.5 Results and discussion……………..……………..……………..……………..…..…… 184

6.5.1 Shear wave velocity at different compactions……………..……………..…..….. 184

6.5.2 Predicting bulk density……………..……………..……………..……………… 185

6.5.3 Shear modulus and VS……………..……………..……………..……………… 189

6.5.4 Young modulus and VS……………..……………..……………..…………….. 190

6.6 Limitations……………..……………..……………..……………..……………..…..… 191

6.7 Conclusions……………..……………..……………..……………..……………..…… 192

6.8 References……………..……………..……………..……………..……………..…….. 193

CHAPTER 7 CONCLUDING REMARKS AND FUTURE WORKS……………..…..…..….. 199

7.1 Overview……………..……………..……………..……………..……………..…..….. 200

7.1.1 Plot level soil moisture monitoring using EMI surveys……………..………. 201

7.1.2 Depth-Specific Temporal Analyses of Soil Water Extraction…………….. 201

7.1.3 VisNIR penetrometer system for predicting SOC……………..…..…..…..…. 203

7.1.4 Automated hydrometer method for soil particle size analysis……………..… 204

7.1.5 Bulk Density and Soil Stiffness Moduli from Shear Wave Velocity………… 205

7.2 General conclusions and future directions……………..……………..……………..….. 206

7.3 Closing statement……………..……………..……………..……………..……………. 209

7.4 References……………..……………..……………..……………..……………..…..… 210

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LIST OF TABLES

Table 2.1 Summary of comparisons of different methods for monitoring soil moisture…………….23

Table 2.2 Standard deviation and the Coefficient of Variation of the ECa at different depths below the ground surface……………………………………………………………..............................35

Table 4.1. A summary of the types of soil samples analysed different sites………………………….99

Table 4.2. Calibration and validation statistics for dry cores……………….......................................112

Table 4.3. Calibration and validation statistics for moist cores………………...................................115

Table 4.4. Calibration statistics from VisNIR penetrometer (without EPO) ……………….............117

Table 4.5. Validation statistics for Penetrometer insertion (without EPO) ………………................118

Table 4.6. Calibration statistics from VisNIR penetrometer (with local EPO) ………………..........120

Table 4.7. Validation statistics for Penetrometer insertion (with local EPO) ………………..............121

Table 4.8. Calibration statistics from VisNIR penetrometer (with penetrometer EPO)……………123

Table 4.9. Validation statistics for Penetrometer insertion (with penetrometer EPO)……………..124

Table 5.1. Summary of comparisons of different methods of soil particle size analysis (data derived from: Coates & Hulse, 1985; Beuselinck et al., 1998; Jacob et al., 2002; Arriaga et al., 2006)..………..……………..……………..……………..……………..……………..........142

Table 5.2. Characteristics of soil samples used for testing the automated hydrometer method ……………………………………………………………………………………………...150

Table 6.1. Characteristics of soil samples used for the tests……………………………….. ……...182

Table 6.2. Statistical comparisons between the density equations obtained from Piezoelectric sensor and previous studies……………………………………………………………………......189

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LIST OF FIGURES

Figure 1.1 Spatial variability of soil across the cropping field in Lansdowne Farm, Sydney, New South Wales. ………………..……………..…………………………………………………………3

Figure 1.2 Density visualisation of keywords from word clustering relevant research documents using VOSviewer (v1.6.15) (Van Eck & Waltman 2010) …………………………………………..5

Figure 2.1 Customized plastic buggy for measuring soil ECa at 0, 20, 40, 60, and 80 cm above the ground surface………………………………………………………………………………..28

Figure 2.2 Attachment of EM38-MK2 with customized plastic buggy……………………………...29

Figure 2.3 Trial area divided into Rainfed and irrigated blocks………………………………………30

Figure 2.4 Soil ECa vs. time for different depth below the ground surface…………………………..34

Figure 2.5 Soil EMI vs. ambient temperature for different depth of soil………………………….....37

Figure 2.6 Calibration between VMC and ECa of soil………………………………………………..38

Figure 2.7 Total water uses in mm by different chickpea genotype………………………………….39

Figure 2.8 Comparisons of total water use from EMI surveys and NMM measurements…...............41

Figure 3.1 Plastic chain system for plastic buggy…………………………………………………….56

Figure 3.2 Trial areas (I5A and Campey-1) for chickpea mapping population……………………….57

Figure 3.3 EMI surveys at 0, 20, 40, 60, and 80 cm heights at I5A (2018) and 0, 20, and 80 cm heights at Campey-1 (2019)……………………………………………………….………………….60

Figure 3.4. Cluster means based on the weekly change in soil moisture content (ΔS) over the whole season within 0-30 cm depth. Each line represents a chickpea genotype grouped into clusters of similar patterns. The red line represents the mean value of the cluster…………………...66

Figure 3.5. Cluster means based on the weekly change in soil moisture content (ΔS) over the whole season within 30-60 cm depth. Each line represents a chickpea genotype grouped into clusters of similar patterns. The red line represents the mean value of the cluster.…………………..68

Figure 3.6. Cluster means based on the weekly change in soil moisture content (ΔS) over the whole season within the depth of 60-100 cm. Each line represents a chickpea genotype grouped into clusters of similar patterns. The red line represents the mean value of the cluster……………70

Figure 3.7. ΔS depth profile for all clusters at three different stages from EMI surveys in 2018…...72

Figure 3.8 Soil RAW at different stages (pre-podding, post-podding, and maturity) of plant growth in the irrigated plots for chickpea genotypes with (a) minimum and (b) maximum water extraction throughout the whole season………………………………………………………………....73

Figure 3.9 Soil RAW at different stages (pre-podding, post-podding, and maturity) of plant growth in the rainfed plots for chickpea genotypes with (a) minimum and (b) maximum water extraction throughout the whole season………………………………………………………………....74

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Figure 3.10. ΔS depth profile for all clusters at three different stages from EMI surveys in 2019…………………………………………………………………………………………..75

Figure 3.11. Total water uses of various clusters…………………………………………………….76

Figure 3.12. Soil RAW at different stages (pre-podding, post-podding, and maturity) of plant growth chickpea genotypes with (a) minimum and (b) maximum water extraction all over the growing season………………………………………………………………………………………...78

Figure 3.13. Available soil VMC of different layers against the days after sowing for least and most water-efficient chickpea genotypes………………………………………………………......80

Figure 4.1 VisNIR penetrometer with ultrasonic depth sensor…………..…………….……………..95

Figure 4.2 Instrumentation box including ASD spectroradiometer, circuit board and 12-volt battery………………………………………………………………………………………...96

Figure 4.3 Locations of sampling sites on Lansdowne Farm, within Australia and in relation to the city of Sydney, New South Wales.…………………………………………………………...…...97

Figure 4.4. Operation of VisNIR penetrometer system in the field………………………………….101

Figure 4.5 Sampling Scheme of all three sites at different days……………………………………..102

Figure 4.6 Methodology scheme of complete study from NIR scans to validations of predicted soil organic carbon (SOC). ……………………………………………………………………..105

Figure 4.7 Soil VisNIR spectra collected from (a) dry and the moist cores in the laboratory and (b) using VisNIR penetrometer system in the field. Mean spectra of dry and moist cores were plotted for laboratory measurements. ………………………………………………………108

Figure 4.8. Wilks’ Λ for a different number of principal components from NIR spectra from sampling sites (local EPO) and combined NIR spectra from sampling sites (penetrometer EPO) …………………………………………………………………………………………..…..110

Figure 4.9. A comparison of EPO projection matrices developed using VisNIR spectra of a) moist cores (local) and b) the penetrometer system. The reference state in both cases were VisNIR spectra of split, air-dried (40°C) soil cores…………………………………………………..111

Figure 4.10. Depth profile of observed and predicted SOC from a single dry core extracted on three different days for all three sites. For spectroscopic measurement, predicted SOC was plotted for every 2 cm. For lab measurements, SOC of 10 cm homogenised depth samples and an additional 0-2 cm topsoil samples were plotted against depths…………………………….114

Figure 4.11. Depth profile of observed and predicted SOC from moist (with local EPO) cores at different days for all three sites. For spectroscopic measurement, predicted SOC was plotted for every 2 cm. For lab measurements, SOC of 10 cm homogenised depth samples and an additional 0-2 cm topsoil samples were plotted against depths………………………….…116

Figure 4.12. Depth profile of observed and predicted SOC from raw spectra on different days for all three sites. For spectroscopic measurement, predicted SOC was plotted for every 2 cm. For lab measurements, SOC of 10 cm homogenised depth samples and an additional 0-2 cm topsoil samples were plotted against depths…………………………………………………..……119

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Figure 4.13. Depth profile of observed and predicted SOC from EPO (local) transformed VisNIR spectra on different days for all three sites. For spectroscopic measurement, predicted SOC was plotted for every 2 cm. For lab measurements, SOC of 10 cm homogenised depth samples and an additional 0-2 cm topsoil samples were plotted against depths………………………122

Figure 4.14. Depth profile of observed and predicted SOC from EPO (penetrometer) transformed VisNIR spectra on different days for all three sites. For spectroscopic measurement, predicted SOC was plotted for every 2 cm. For lab measurements, SOC of 10 cm homogenised depth samples and an additional 0-2 cm topsoil samples were plotted against depths…………….125

Figure 4.15. Residuals of estimated soil organic carbon (SOC) content for different wetness of the soil ……………………………………………………………………………………………...126

Figure 4.16. Comparisons of predicted SOC stocks from the penetrometer and actual SOC stocks from laboratory measurements. Dotted black line and the blue line indicate the 1:1 line and the actual trend in the model, respectively……………………………………………………………..128

Figure 5.1 (a) Schematic diagram; and (b) Actual setup of Adafruit VL6180X ToF and DS18B20 temperature sensor coupled with Arduino Uno R3 that includes a liquid-crystal display to track the progress of the measurements…………………………………………………………..146

Figure 5.2 Adafruit VL6180X ToF Distance sensor………………………………………………..147

Figure 5.3 Working mechanism of ToF distance sensor…………………………………………….147

Figure 5.4 (a) Hydrometer with styrofoam cap; and (b) Stability check of Adafruit VL6180X Time of Flight Distance (ToF) sensor……………………………………………………………….148

Figure 5.5. The setup of automated hydrometer including Adafruit VL6180X (ToF) and DS18B20 temperature sensor with a hydrometer and measuring cylinder…………………………….151

Figure 5.6 Graph of hydrometer reading vs. time obtained from the automated hydrometer method for a clay loam………………………………………………………………………………….154

Figure 5.7 Percent mass finer and the diameter of soil samples obtained from the automated hydrometer method………………………………………………………………………....155

Figure 5.8 Comparison of % finer from Pipette vs. Automated Hydrometer Method Trial-1 and Trial-2 for the particle diameters of 2, 5, 10, 20 and 40 μm………………………………….…..156

Figure 5.9. Automated Hydrometer method Trial-1 vs. Trial-2 for the particle diameters of 2, 5, 10, 20 & 40 μm…………………………………………………………………………………….157

Figure 6.1. Propagations of P and S waveform through a 3D grid (after E. Onajite, 2014)…………172

Figure 6.2. Standard quick-mount Piezo Bender (Q220-A4-303YB) sensor that transmits shear wave velocity through the soil surface…………………………………………………………….175

Figure 6.3. Schematic diagram of the setup of the piezoelectric sensor probe system embedded in the soil in a glass beaker……………………………………………………………………..…175

Figure 6.4. Mechanism of wave propagation of 2-Layer Piezo-electric Bender and Extender elements between the transmitter and the receiver…………………………………………………….176

xvii

Figure 6.5. The closed system of the piezoelectric sensor…………………………………………..177

Figure 6.6. Arrival times calculated the distance between the trigger, and the first point when the sine wave starts to form for P wave and the intersection between the sine waves obtained from different polarities for S-wave………………………………………………………………178

Figure 6.7. Amplitude vs. travel time of output signal without applying low pass filter……………179

Figure 6.8. Optimized design parameters of low pass filter used in the closed system……………..180

Figure 6.9. Schematic diagram of low pass filter used in the closed system with piezoelectric sensor ……………………………………………………………………………………………...180

Figure 6.10. Improved output signal after applying low pass filter indicating the amplitude of S-wave against the travel time……………………………………………………………………….181

Figure 6.11. Comparison in the S-wave travel time between no vibratory compaction and vibratory compaction for 20 minutes………………………………………………………………….184

Figure 6.12. An empirical relationship between Shear wave (VS) travel time and bulk density of soil. ……………………………………………………………………………………………...186

Figure 6.13. An empirical relationship between Shear wave velocity (VS) and bulk density of soil...187

Figure 6.14. Comparisons between the actual and predicted bulk density of soil…………………..188

Figure 6.15. Empirical relationship between Shear wave velocity (VS) and Shear modulus (Gmax) of soil………………………………………………………………………………………..…190

Figure 6.16. Empirical relationship between Shear wave velocity (VS) and the Young modulus (E) of soil…………………………………………………………………………………………..191

xviii

LIST OF ABBREVIATIONS

3-D Three-Dimensional SOC Soil Organic Content

OC Organic Content

EMI Electromagnetic Induction

ECa Apparent Electrical Conductivity

VisNIR Visible and Near Infrared

PSD Particle Size Distribution

ToF Time of flight

NMM Neutron Moisture Meter

TDR Time Domain Reflectometry

GPR Ground-Penetrating Radar

PVC Polyvinyl Chloride

2-D Two-Dimensional VMC Volumetric Moisture Content

1-D One-Dimensional

RAW Readily Available Water

EMCIs EM Conductivity Images

ERT Electrical Resistance Tomography

PBI Plant Breeding Institute

EPO External Parameter Orthogonalisation

CEC Cation Exchange Capacity

RMSE Root Mean Square Error RMSEc Corrected Root Mean Square Error

SVM Support Vector Machine

PLSR Partial Least Squares Regression

LCCC Lin's Concordance Correlation Coefficient

USDA United States Department of Agriculture

FAO Food and Agriculture Organization

PTF Pedotransfer Function

ISO International Organization for Standardization

IR Infra-Red

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IEC International Electrotechnical Commission

1

CHAPTER 1

GENERAL INTRODUCTION

Chapter 1: General Introduction

2

1.1 Background

The nature of the agroecosystem vastly depends on soils, and soils are home to over one-fourth

of all living species on earth (Turbé et al., 2010). Soil acts as a water filter, a growing medium,

and provides essential nutrients to our forests and crops (Arkhipova et al., 2007; Truu, Nurk,

Juhanson, & Mander, 2005). Soil is the main source of food, fiber, and fuel (Lal, 2009, 2010).

Nevertheless, with the increasing population, it becomes challenging to ensure soil security

(Koch et al., 2013; A. McBratney, Field, & Koch, 2014). The biggest challenge is to manage

exponentially decreasing cultivable land and to produce more feed, fiber, food, and fuel with

less water, less energy, and fewer nutrient inputs (Powlson et al., 2011; Ramankutty et al.,

2018). To ensure soil can be used sustainably, we must develop new technologies and

techniques that can monitor soil conditions so we can optimise our non-renewable resources to

produce food for this world's entire population.


3

Figure 1.1 Spatial variability of soil across the cropping field in Lansdowne Farm, Sydney,

New South Wales.

However soil is not uniform everywhere, the state or condition of soil changes rapidly with

space and time, making it challenging to manage soil resources effectively for agricultural

purposes (Stenberg, Rossel, Mouazen, & Wetterlind, 2010). Figure 1.1 shows the soil at three

locations marked 1, 2, and 3 in a field in Landsdowne, near Sydney. The distance between site

2 and 3 is 170 m and 560 m from site 1. Within this small distance, extraordinary horizontal

and vertical soil variability was observed. To manage soil in precision, we need to know how

soil changes not only along the horizontal axis but also vertically, along the soil profile.

The variability of soil physical properties along the depths has a significant contribution to the

soil resources management and environmental ecosystem (Ovalles & Collins 1986). While


4

digital soil mapping of soil in 3-D has been achieved at a regional and continental scale, the

fine-scale variation in a field cannot be captured. We need to find better solutions to map the

fine-scale variation of soil.

Among important soil properties that need to be monitored and measured accurately are soil

moisture content and soil carbon content. Monitoring soil moisture at different depths will

allow us to understand soil–water–plant system so that irrigation water can be applied more

efficiently (Mubarak et al. 2009). Also, measuring soil organic content (SOC) stock at various

depth with enable us to calculate carbon stock accurately (Post & Kwon, 2000). So, it is

necessary to regularly monitor temporal soil variability, which requires analysing soil samples

to measure various soil properties with sufficient precision within a specific time interval.

1.2 Visualisation of research topics on soil properties in agriculture

A keyword density visualisation analysis was done among the recent scientific literature on

different agricultural soil properties essential for optimising natural resources and maximising

crop yield. This analysis was done by word clustering of 1772 relevant articles, conference

papers, and book chapters published over the past five years. The abstract, author keywords

and index keywords were exported from the SCOPUS database and used for this word

clustering.


5

Figure 1.2 Density visualisation of keywords from word clustering relevant research

documents using VOSviewer (v1.6.15) (Van Eck & Waltman, 2010)

VOSviewer (v1.6.15) was used to cluster the occurrence of the various words within the

abstracts, keywords, and co-occurrence between the words for density visualisation, as shown

in Figure 1.2 (Van Eck & Waltman, 2010, 2011). This analysis showed that soil moisture and

organic carbon (OC) appeared most of the time among all other soil properties in agricultural

soil research articles. It indicates that these two soil properties significantly contribute to

optimal natural soil resource management and crop production.

Primary keywords such as land-use change, enzyme activity, saturated hydraulic conductivity,

clay content, etc. relevant soil properties can be observed around the OC keyword. SOC has a

direct influence on these properties. Soil moisture is the second most researched topic in

agriculture after OC. Soil moisture is the most crucial resource that needs the most attention


6

for semi-arid countries like Australia (Noble, 1998). Figure 1.1 shows the nearby soil moisture

keywords are irrigation, rain, runoff, etc., which have a direct relationship with soil moisture.

In the middle of the density visualisation, crop production can be found, which indicates it is

related to both OC and soil moisture. This keyword analysis shows the importance of these

two topics for sustaining agricultural production.

1.3 Statement of problem

Conventional methods for determining physical soil properties in the laboratory are time-

consuming, expensive, and tedious. Field methods for measuring these soil properties are

destructive in nature, and often the sphere of influence (measurement volume) is relatively

small. In precision agriculture, the necessity of high-resolution soil information with spatial

variability that can be obtained rapidly and conveniently is highly needed (Behrens & Scholten

2006; Camera et al. 2017; McBratney et al. 2003). Lateral and vertical soil data with high

resolution is essential for plant breeding (Niazian & Niedbała, 2020), phenotyping (Stahl,

Wittkop, & Snowdon, 2020), crop and yield modelling (Liu et al. 2007; Semenov 2004;

Viscarra Rossel et al. 2010; Zhao et al. 2020).

This thesis aims to develop novel, cost-effective systems to provide high-resolution soil data,

in particular:

- Field measurement of 3-D soil water dynamics at the plot level,

- Field measurement of soil carbon with depth at a very fine interval,

- Developing a new sensor to measure soil bulk density,

- Developing an efficient way to automate the measurement of particle-size distribution.


7

1.3.1 Soil moisture analysis

In recent years, drought has become one of the major environmental stresses in many

continents, and it is an extremely crucial abiotic factor that limits plant growth and crop

production. (Fathi & Tari, 2016; Hasanuzzaman, Nahar, Gill, & Fujita, 2013). It is more

surprising that only 10 percent of the world's cultivable land is free from environmental stresses

(Fathi & Tari, 2016). To reduce the effect of drought stress, it is essential to understand drought

tolerance and the dynamics of how water is extracted from soil extraction by plants.

Conventional methods for measuring soil moisture are limited to point measurements (i.e., soil

moisture sensing probes), which have a considerably low sphere of influence. These probes

can measure soil moisture up to a certain depth, but it is impractical, tedious, and expensive if

the sensors need to be installed across the whole cropping field. The ability to measure soil

properties at the plot level on a meaningful scale is limited (Reynolds & Tuberosa 2008). So,

it is not easy for plant-breeders to conduct drought-tolerant and effective water use related

research using point-scale observing methods (Blum 2005). Thus, it is crucial to monitor soil

water change accurately at the plot scale with the high vertical resolution, to choose the

genotypes of different crops that can extract water from more in-depth soil profiles.

Phenotyping is a vital bottleneck for genotyping discovery and molecular markers'

development (Roitsch et al., 2019). Within the past few decades, the research on sensor

technologies' applications in the phenotyping methods has increased. The sensor-based plant

phenotyping approach is important for genotype x environment x management interactions in

germplasm screening and precision agriculture (Antille, Lobsey, McCarthy, Thomasson, &

Baillie, 2018; Roitsch et al., 2019). So, it is essential to introduce a new technique to phenotype

plant roots in the field. The current direct method involving the excavation of roots is time-

consuming and tedious (Whalley et al., 2017). Alternatively, monitoring plant roots' activities

can help to phenotype plant roots directly in the field (Blouin et al., 2005).


8

This thesis will develop a measurement system based on Electromagnetic Induction (EMI)

surveys to survey the whole field rapidly and cost-effectively with high lateral and vertical

resolution. The survey aims to record ECa at different heights above ground so they can be used

to produce high-resolution 3-dimensional soil moisture maps for the whole cropping field.

These high-resolution data over lateral and vertical dimensions can serve as a tool for plant

phenotyping.

1.3.2 Soil organic carbon analysis

The chemical composition and biological productivity, fertility, and nutrient holding capacity

of a soil depend on organic carbon (Bhogal, Nicholson, & Chambers, 2009). So, plant growth

and overall agricultural productivity are highly influenced by SOC. Moreover, SOC plays a

major role in the global carbon cycle. Soil carbon sequestration can potentially slow down

global warming (Fornara et al., 2011).

Soil carbon is highly variable spatially (both horizontal and vertical) across the cropping field.

In particular, soil carbon concentration drops rapidly with depth. Taking bulk soil samples (e.g.,

0-30 cm) would miss the detail on how soil carbon varies significantly within the topsoil.

Conventionally soil cores are collected from the field and tested in the laboratory for measuring

SOC using combustion methods or the Walkley-Black method. Recently, visual and Near-

infrared (VisNIR) spectroscopy was used to predict soil organic carbon with high precision

(Morellos et al., 2016; Nocita et al., 2014; Stevens, Nocita, Tóth, Montanarella, & van

Wesemael, 2013; Stockmann et al., 2015). This technique does not involve time-consuming

and tedious laboratory analysis, but soil samples need to be scanned using the NIR spectrometer

to predict soil. For this reason, there are many ongoing pieces of research related to on-the-go

in-situ VisNIR measurements for predicting soil properties (Ben-Dor et al. 2008; Rossel et al.

2009; Chang et al. 2011; Kodaira & Shibusawa 2013; Poggio et al. 2015; Ackerson et al. 2017).


9

However, most field analysis involves manually digging or coring the soils, obtaining the soil

sample, and then scanning the soils with VisNIR. Such manual measurement is time-

consuming and taking soil cores out from the soil will disturb the soil.

In this thesis, a VisNIR penetrometer system that is capable of recording soil VisNIR spectra

with the depth of measurements was introduced. This system is developed as a solution to

replacing conventional laboratory tests for measuring SOC. It can be used as a rapid, robust

and cost-effective system for in-situ high-resolution SOC in three dimensions that can

potentially benefit disciplines such as precision agriculture, carbon accounting, digital soil

mapping, etc.

1.3.3 Using Cheap sensors to analyse soil physical properties

Precision agriculture requires measuring soil physical properties at small scales throughout the

cropping field. Often it is challenging to evaluate soil physical properties at a reasonable scale

using conventional methods because most of those methods are expensive, time-consuming,

and invasive. Electromagnetic induction (EMI) surveys can be a potential solution for

determining soil variability across the field cost-effectively. Studies such as Castrignano et al.

(2012) & Benedetto et al. (2010, 2012) showed that EMI data can be calibrated to produce

maps of clay or sand content. Thus, actual soil analysis is still necessary to confirm or calibrate

specific soil characteristics such as particle size distribution (PSD), bulk density, stiffness

moduli, etc.

Soil physical properties such as particle size distribution, stability, etc. also belong to the

popular research topics according to the density visualisation (Figure 1.2). For precsion

agriculture, soil PSD and bulk density are often required as inputs in crop modelling.


10

Particle size distribution analysis

Particle size distribution is one of the key parameters used for evaluating the quality of soil in

precision agriculture. Conventional laboratory methods, i.e., hydrometer and pipette methods

for measuring soil particle size distribution, are time-consuming, tedious, and involve a high

possibility of human error. Also, these methods are not capable of generating continuous

particle size distribution. Continuous particle size distribution indicates pore spaces within soil

particles, hydraulic conductivity, water retention & absorption capacity, etc. (Shiozawa and

Campbell, 1991; Hajnos et al., 2006; Slawinski et al., 2006; Ryżak and Bieganowski, 2011;

Yang et al., 2019). X-ray, γ-ray attenuation, and laser diffraction techniques can produce

continuous particle size distribution, but these are very expensive and comparatively

complicated to operate (Ferro and Mirabile, 2009). So, it becomes necessary to develop a

technique that can measure soil particle size and produce continuous particle size distribution

with sufficient precision. In this study, an automated hydrometer testing method using the time

of flight (ToF) distance sensor and digital temperature sensor was introduced, which is easy to

operate, cost-effective, and capable of producing soil continuous particle size distribution.

Bulk density and stiffness moduli analysis

Compaction, porosity, penetration resistance, structural integrity, and water storage capacity of

the soil are closely related to the bulk density and stiffness moduli (Kramer & Boyer, 1995;

Arshad & Martin, 2002; Adhikari et al., 2014). These strength properties also provide

information about seed germination and root penetrability through soil layers (Bengough et al.,

2005). Bulk density is required in soil carbon accounting, converting the gravimetric

measurement into soil carbon stock.


11

Traditional methods for measuring bulk density is time-consuming, tedious, overall expensive,

and, most importantly, destructive in nature. In this study, soil bulk density was evaluated from

shear wave velocity measurements using the piezoelectric extender and bender elements.

1.4 Thesis objectives

This thesis's main objective is to develop cost-effective measurement systems for measuring

3-dimensional high-resolution soil physical properties with good accuracy. The aims of this

thesis are given below,

• To develop technologies to monitor soil variability and changes in space and time for

application in precision agriculture.

• To measure soil properties rapidly, cost-efficiently, and accurately using non-

destructive techniques in the field.

• To establish robust measuring techniques for increasing the measurements'

repeatability and reproducibility.

• To develop measurement techniques using cheap sensors to predict soil physical

properties more conveniently with high accuracy in the laboratory.

Specifically, Chapter 2 looks at developing a crop water use monitoring system at the plot level

using a plastic buggy system based on the EMI surveys. The proposed system was used to

monitor soil moisture at different depths to quantify total water uses by 36 chickpea genotypes

using fortnightly EMI surveys throughout the growing season. The total water uses by different

chickpea genotypes were validated against the total water uses obtained from Neutron Moisture

Meter (NMM) measurements.


12

Chapter 3 includes the depth-specific temporal analyses of soil water extraction by different

genotypes of chickpeas using EMI surveys for chickpea variety trails in 2018 and 2019. This

chapter explains the potential use of the proposed soil water moisture measuring technique

using EMI surveys for grouping most and least water use efficient chickpea genotypes at

various depths and different growth stages. This technique also analyses the depth profile of

cumulative water uses by the most and least water-efficient genotype for different growth

stages. Overall, this chapter focuses on the possible use of this EMI based plastic buggy system

to analyse soil water extraction by the roots of chickpeas at various depth and growth stages.

Chapter 4 includes a VisNIR penetrometer system with an ultrasonic depth sensor for

measuring SOC using high-resolution in-situ spectra. The effectiveness of the penetrometer

system to record soil spectra with depths from three different sites for predicting the SOC was

tested. The predicted SOC was validated against the SOC measured in the laboratory and

compared with the SOC measured from soil cores using contact probes. The potential of

VisNIR penetrometer system as an alternative to the conventional method for predicting SOC

is discussed in this chapter.

Chapter 5 contains a proposed automated hydrometer method for soil particle size analysis

using Adafruit VL6180X ToF distance sensor and DS18B20 1-Wire digital temperature sensor.

This chapter's main objective is automatically recording hydrometer readings and suspension

temperature every 5 seconds that allows calculating percentages of sands, silt, and clays with

a continuous particle-size distribution curve conveniently and cost-effectively with higher

accuracy.

Chapter 6 focuses on measuring bulk density and soil stiffness moduli from shear wave velocity

using piezoelectric benders and extenders. The efficiency of piezoelectric sensors to produce

shear wave velocity in the soil particles were examined. The study tested the possibility of


13

using shear wave velocity of soil to measure bulk density, Shear, and Young moduli rapidly

and cost-effectively.


14

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Hawkesford, M. J. (2017). Methods to estimate changes in soil water for phenotyping

root activity in the field. Plant and Soil, 415(1–2), 407–422.

Zhao, Y., Potgieter, A. B., Zhang, M., Wu, B., & Hammer, G. L. (2020). Predicting wheat yield

at the field scale by combining high-resolution Sentinel-2 satellite imagery and crop

modelling. Remote Sensing, 12(6), 1024.

20

CHAPTER 2

DEVELOPMENT OF A CROP WATER USE MONITORING

SYSTEM AT THE PLOT LEVEL

Chapter 2: Development of a Crop Water Use Monitoring System at the Plot Level

21

2.1 Summary

Monitoring soil water use at the plot level is essential for studying the plant’s response to

drought or irrigation. Soil electromagnetic induction (EMI) surveys can be a rapid, convenient,

and non-invasive solution for investigating soil moisture at the plot level. In this study, a crop

water use monitoring system for the plot level was developed using an EMI instrument that

collects measurements at 0, 20, 40, 60, and 80 cm above the ground surface on a plastic buggy.

The method was tested on a field trial of 36 chickpea genotypes maintained under

supplementary irrigation and rainfed conditions on a vertosol soil. After inverting the EMI data

to bulk soil electrical conductivity (ECa), the measurements were calibrated to soil moisture

contents using volumetric moisture contents of soil cores to the depth of 1.5 m. The R2 was

found to be 0.86. Approximately ±1 mS m-1 average drift within the ECa measurements was

detected. Standard deviation and coefficient of variation vary between 0.84 to 3.45 mS m-1 and

1.54 % to 9.26 % within a change in the daily temperature from 25.5 °C to 34 °C. This plastic

buggy system was used to measure the water use efficiency of chickpea genotypes over the

whole season. Total water use obtained from EMI surveys is highly correlated with values

obtained from Neutron Moisture Meter (NMM) (R2 = 0.73). In conclusion, the EMI survey

using the plastic buggy system is an efficient and rapid soil moisture measurement technique

at the plot level.

2.2 Introduction

Drought is one of the most critical environmental stresses for plants in many parts of the world,

especially in warm and dry areas (Pourdad & Beg, 2003). Crop production is severely

constrained by drought with yields of major cereal and grain legume crops dependent upon

water availability (Blessing et al., 2018; Sadras and Angus, 2006). In addition, up to 50%

reduction in yield can occur under drought stress due to low humidity and high


22

evapotranspiration (Alghabari, Ihsan, Hussain, Aishia, & Daur, 2015; Sehgal et al., 2017). To

identify, understand drought tolerance mechanisms in plants and phenotype water use traits in

crop breeding programs, it is essential to monitor soil water content at different depths and their

dynamics. Moreover, in experimental plots, water use efficiency, a trait typically selected for

improvement in breeding programs for yield under water-limited conditions (Bramley et al.,

2013) has traditionally been determined by monitoring the soil water content in pots and

lysimeter-type systems (Meissner, Rupp, & Haselow, 2020) or from limited measurements

using probes at different soil depths and gravimetrically (Siddique et al., 2001; Pascual-Seva

et al. 2018). To avoid confounding pot effects and planting density, measurements should be

conducted in the field where roots penetrate undisturbed soil profiles. However, this type of

soil moisture monitoring was considered impractical by plant breeders (Blum, 2005; Reynolds

& Tuberosa, 2008). This is because conventional methods for measuring volumetric water

content such as Time Domain Reflectometry (TDR) probes (Roth, Schulin, Flühler, & Attinger,

1990; Topp, Davis, & Annan, 1980), Ground-Penetrating Radar (GPR) (Huisman et al. 2003;

Klotzsche et al. 2018), capacitance probes (Kelleners, Robinson, Shouse, Ayars, & Skaggs,

2005; Kizito et al., 2008), Neutron Moisture Meter (NMM) (W. Gardner & Kirkham, 1952),

and soil electrical resistivity tomography (Kemna, Vanderborght, Kulessa, & Vereecken, 2002;

Loke, Chambers, Rucker, Kuras, & Wilkinson, 2013; Samouëlian, Cousin, Tabbagh, Bruand,

& Richard, 2005) are labour intensive or cost-prohibitive. In addition, not all of the methods

are suitable for plot-scale moisture monitoring (Long, Wraith, & Kegel, 2002; Lunt, Hubbard,

& Rubin, 2005; Petropoulos, Griffiths, Dorigo, Xaver, & Gruber, 2013) (Table 2.1).


23

Table 2.1 Summary of comparisons of different methods for monitoring soil moisture Po

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24

Table 2.1 shows a comparison of commonly used methods for soil moisture measurement

techniques. Most of the methods are invasive in nature. The TDR and capacitance probes have

a small effective measurement volume (few mm) (Dettmann & Bechtold, 2018) would not

represent an area where water is used by plants, depending on the application. While smaller

sensors such as TDR and FDR can be placed near roots, the small volume of measurement

means that many sensors are required at a plot scale to monitor crop water use (Kirkham 2005;

Sparks 2012).

Instead of relying on soil core sampling or various invasive measuring probes, measurement

of soil apparent electrical conductivity (ECa) using Electromagnetic Induction (EMI) surveys,

can be a potential solution as ECa is a function of soil moisture, clay content, salinity, cation

exchange capacity, etc. (Aragüés et al. 2010; Coppola et al. 2016; Li et al. 2013; Sudduth et al.

2003; Triantafilis & Lesch 2005). Using this technique, soil apparent electrical conductivity

(ECa) of a bulk volume of soil at different depths can be measured, which provides a cost-

effective and non-invasive solution for predicting and mapping soil water content.

EMI surveys have been used to predict soil moisture across ecosystem and catchment levels of

scale (Altdorff et al., 2017; Mallet, Carrière, Chalikakis, & Marc, 2018) and agricultural fields

comprising clay loams, medium clays, and homogenous sands (Huang et al. 2016; Huang et al.

2017). Using time-lapsed EMI surveys, changes in soil moisture conditions have been

estimated with reasonable accuracy. For example, Huang et al. (2016) developed a

spatiotemporal inversion algorithm, which accounted for the temporal continuity of ECa and

proved that the algorithm was more accurate and less biased as compared with non-

spatiotemporal techniques. This algorithm was used in a case study where time-lapse ECa was

collected on a 350 m transect for consecutive days.


25

In contrast, the use of EMI as a monitoring tool for measuring soil water use at the plot scale

has received less attention. This is surprising given the urgent need to improve yields and yield

stability of many crop species to meet future demand under more extreme conditions and the

history of “water productivity” research in understanding crop adaptations to water-limited

environments. While pot and lysimeter studies have proven useful in identifying genetic

variation for various traits associated with or dependent on water use (Nicoară et al., 2014;

Octura, Gadiaware, & Octura, 2020; Tharanya et al., 2018), their translation to breeding

programs and new varieties has been limited by application to field conditions.

Albeit, predicting soil moisture from ECa measurements has not always been successful

(Altdorff, Galagedara, Nadeem, Cheema, & Unc, 2018; Robinson, Abdu, Lebron, & Jones,

2012) due to several factors that vary across fields and with environmental conditions. In this

study, the potential of EMI as a monitoring tool for soil water use has been demonstrated for

wheat (Blanchy et al., 2020; Shanahan et al., 2015) and chickpea (Foley & Boulton, 2015;

Subba et al., 2013).

Huang et al. (2018) measured plot scale soil moisture dynamics across a chickpea field using

time-lapsed ECa data from an EM38 sensor. Soil moisture dynamics and movements were

successfully predicted with good correlation with measured values (R2 of 0.87 and RMSE of

0.037 m3 m-3) and variation in water extraction at different soil depths were observed after a

rainfall event. However, that pilot study was conducted with manual observations using EMI

at several heights, which were very time-consuming. In that study, data were captured from

point measurements in the centre of each plot, which was not only tedious but does not

incorporate spatial variation. Such setup is not suitable for automation. In addition, soil water

contents were not determined directly for the calibration with ECa but were obtained using

neutron probe measurements on a subset of plots. It is possible that the aluminium access tube


26

for the neutron probe could have interfered with the EM38 sensor during measurements and

therefore, the method needs confirming with actual soil water contents.

This chapter developed a new data acquisition method for plot scale soil water monitoring

system using the EMI instrument. The system uses a plastic buggy that allows the EM38-MK2

instrument to take measurements at five different heights to measure soil water use up to 1 m

in the soil. This chapter will present the measurement system and examine the ECa

measurement's stability as affected by temperature. The system was used to evaluate the water

use of 36 chickpea genotypes grown on 288 plots over 10 weeks.

2.3 Materials and Methods

2.3.1 EMI system testing

Bulk soil electrical conductivity (ECa) measurements were captured using a ground

conductivity meter (EM38-MK2, Geonics Limited, Ontario, Canada). The EM38-MK2 uses

electromagnetic induction to determine ECa where the electromagnetic field generated by a

transmitting coil induces eddy currents when there are charged particles in the soil generating

their own electromagnetic field. A receiver coil measures the amplitude and phase of this

secondary electromagnetic field. The EM38-MK2 has two receiver coils, distanced 0.5 m and

1 m from the transmitter, enabling data capture for effective depths of 0.75 m and 1.5 m,

respectively.

After turning on the device and allowing it to warm up for 5 minutes, the battery level was

checked, and the device was calibrated according to the user manual. EMI measurements were

captured with both receiver coils at five different heights above the ground surface, 0, 20, 40,

60, and 80 cm. EMI measurements of these five different heights were coupled with an

inversion algorithm to provide ECa measurements 15, 35, 55, 75, 90, 110, 130, and 150 cm


27

below the ground surface. These depths encompass most of the physiological and biological

activities of plant roots and plant-soil interactions.

To test the stability and consistency of ECa measurements, the device was placed in the field

at the heights of 0, 20, 40, 60 and 80 cm above the ground and ECa readings were continuously

recorded for 6 minutes.

Although the EM38-MK2 includes temperature compensation circuitry, the effect of ambient

air temperature on the ECa measurements was tested to determine whether data capture would

be affected by temperature drift during the course of a day. The instrument was placed in the

field from 10:00 until 18:00, and ECa measurements were taken at 10:30, 12:00, 13:30, 15:00,

16:30 and 18:00 at heights of 0, 20, 40, 60 and 80 cm above the ground surface of 20 plots.

During this experiment, the ambient temperature varied from 25.5°C to 34°C.

2.3.2 The buggy system

To increase the throughput and record continuous ECa measurements across the entire field of

plots for each height above the ground, a customized buggy was constructed using inexpensive

off-the-shelf components available at any hardware store (Figure 2.1). All components of the

buggy were made of materials so that both quad-phase (conductivity) and in-phase (magnetic

susceptibility) measurements would not be affected.


28

Figure 2.1 Customized plastic buggy for measuring soil ECa at 0, 20, 40, 60, and 80 cm above

the ground surface.

The buggy frame was made from 40 mm diameter PVC pressure pipe cut to relevant lengths

to span the plot (2 m between wheel tracks) and provide rigidity to the frame. The pipe was

joined together using PVC elbows and tee-pieces. Eight plastic-centre wheels with solid rubber

tyres (378 mm diameter, 50 mm wide) were connected to the frame, two wheels per leg that

were joined by an axle formed of plastic hose pipe and held in place by plastic ties. The EM

device was clamped to a piece of Perspex using plastic ties, and plastic chains were used to

adjust the device’s height above the ground surface as well as the position in the plot depending

on the crop’s row spacing. The plastic conduit prevented the EM device from swinging as it

moved through the plots. Plastic handles constructed of the same PVC pipe and fittings were

attached to the back of the buggy. The buggy rolled along the wheel tracks between plots,

pushed by one or two operators depending on the experiment.

2.3.3 Collection of EMI data

Measurements of the entire field were captured for a specific height above the soil surface,

starting at ground level. After starting the recording of the EMI data at the first plot, the buggy

system was pushed along the wheel tracks of each row of plots with the EM-device positioned

so that it passed between the central two plant rows of each plot. At the end of a row of plots,

the buggy was turned in the buffer region and then pushed back along the next row of plots.


29

This procedure was repeated, collecting measurements of the entire field. The buggy was then

brought back to the first plot, the EM-device raised to the next height above the soil surface

and the procedure repeated until EMI data was collected for all five heights above the soil

surface. Soil ECa for each plot was recorded along with the internal device temperature and the

geographical coordinates.

Figure 2.2 Attachment of EM38-MK2 with customized plastic buggy

The raw EMI-data were recorded in a binary log (*.N38) format in a data logger (DAS70-

MESA2, Geonics Limited, Canada) connected to the EM-device via an RS-232 serial cable.

These data were then transferred to a computer and converted into DAT38 to extract the ECa

measurements with the coordinates using an open-source R package “em38” (EMTOMO,

2014).

2.3.4 Inversion of the EMI data

The EC data collected by the EM38 instrument was converted to ECa using the software

EM4Soil version 3.04 (Triantafilis & Santos, 2013) and using the S2 algorithm (Sasaki 2001).

The inversion was done to calculate ECa data to a depth of 1.5 m. A multi-layered 2-D inversion

technique was used to consider all the depths in the inversion analysis. For the 2-D inversion,


30

several input parameters are required, such as damping factor (λ), number of iterations, and

types of algorithms. Different λ were trialled to obtain minimum RMSE of the inversion results.

The optimum number of iterations was considered to be 20.

The inversion process produced estimates of soil ECa at 0.08, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65,

0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55 and 1.65 m depth below ground surface. Soil

ECa was converted to volumetric moisture content (VMC) from the empirical correlation

between ECa data from EMI surveys and moisture content measured from soil cores.

2.3.5 Field trial on water use of chickpeas

A field trial was conducted to test the system during the 2018 growing season. The experiments

were done on an experimental farm of the Plant Breeding Institute of The University of Sydney

(30.3324° S, 149.7812° E), as shown in Figure 2.3.

Figure 2.3 Trial area divided into Rainfed and irrigated blocks

The trial area (180 m × 32 m) was divided equally into two blocks, each consisting of 144 plots

(1.6 m x 6 m) arranged in 12 runs and ranges. Each block was surrounded by two buffer rows.


31

The southwest block (termed “irrigated”) was maintained with higher soil moisture content

throughout the season by supplementary irrigation, while the northwest block (termed

“rainfed”) only received supplementary irrigation until the crop’s reproductive stage when

terminal drought conditions developed due to low rainfall. Irrigation was applied

homogeneously to the field using a lateral move irrigator. In summary, the irrigated block

received 396 mm of water within the season and the rainfed block 251 mm.

A total of 36 different chickpea (Cicer arietinum L.) breeding lines and commercial cultivars

were sown in a randomised block design with four replicate plots per genotype and treatment

(irrigated and rainfed) on 1st June 2018, which falls within the recommended optimal sowing

window for northern New South Wales. The cultivar PBA Seamer was sown in the buffer plots.

Soil ECa was measured fortnightly, starting two weeks after sowing until maturity. ECa

measurements were taken for all 288 plots at the heights of 0, 20, 40, 60, and 80 cm above the

surface. In total 11 surveys were conducted on 14th June, 5th July, 23rd July, 1st August, 15th

August, 5th September, 13th September, 12th October, 26th October, 1st November and 9th

November 2018.

Water use by the plant was expressed as Evapotranspiration (ET), calculated using the plant

root zone water balance model as below,

ET = P + I –∆S (2.1)

Where,

ET = Evapotranspiration or water use by plants

∆S = Change in root zone soil water storage over the time period

P = Precipitation

I = Irrigation


32

The calculation was done from fortnightly data. Precipitation (P) was obtained from local

weather data, and irrigation (I) is based on the volume of water applied by the irrigator. The

change in soil moisture (ΔS) was calculated by successive subtraction of water content of

depths up to 100 cm (in m) at each fortnight with the initial soil moisture until the last fortnight

of measurement. Finally, total plant water use was calculated by summing all ET values from

planting to harvest (11 fortnights from 14th June to 9th November 2018).

To validate the water use obtained from the EMI surveys, total water use obtained from the

EMI surveys were compared with the total water use calculated using the measurement from

Neutron Moisture Meter (NMM) (CPN® 503DR Hydroprobe, CPN International, Concord,

CA, USA) on 40 plots within the study area. The plots represent 10 genotypes of chickpeas,

each with four replicates under rainfed and irrigated blocks. Aluminium access tubes installed

in these plots facilitated the collection of NMM readings at 0.1, 0.2. 0.3, 0.4, 0.5, 0.6, 0.8, 1.0

1.2 and 1.4 m depths. NMM measurements were recorded fortnightly, within a week after every

EMI survey. The universal calibration formula was used to convert the neutron counts into soil

volumetric water content (𝜃𝜃) (Carneiro & De Jong, 1985),

𝜃𝜃 = 𝑎𝑎 + 𝑏𝑏 .𝐶𝐶𝐶𝐶 (2.2)

Where,

𝜃𝜃 = Volumetric water content

a = Constant

b = Slope = (Neutron counts -7863)

CR = Count ratio =1/182.9


33

2.4 Results and Discussion

2.4.1 Environmental effects on EM measurements

The stability of EM38-MK2 and the effect of the ambient temperature on the device were

analysed. All the experiments were conducted in the same trial area at Plant Breeding Institute

(PBI), Narrabri.

2.4.1.1 Stability check of EM38-MK2

ECa of 0, 20, 40, 60 and 80 cm heights were measured for six minutes in the automatic

continuous mode. The plastic buggy was used to maintain the constant height above the ground

surface. From the two receiver coils, soil ECa at a depth of 15, 35, 55, 75, 90, 110, 130, and

150 cm was calculated and plotted against the time to observe the drifting of ECa within this

time. From Figure 2.4, it can be seen that, except at a depth of 35 cm, there was no considerable

drift in the measurements and the average drift is within ±1 mS m-1.


34

Figure 2.4 Soil ECa vs. time for different depth below the ground surface

2.4.1.2 Temperature effect on EM38-MK2

Geonics Limited stated that EM38-MK2 has a coil technology that includes temperature

compensation circuitry. This technology was designed to improve on the drift related

temperature as compared with the previous versions of EM38. To check the efficiency of the

compensation circuitry, a temperature test was conducted in the same chickpea field at PBI

Narrabri. In the test, EMI surveys were done for 0, 20, 40, 60, and 80 cm heights were recorded

using the plastic buggy to measure the ECa of 15, 35, 55, 75, 90, 110, 130, and 150 cm below


35

the ground surface. At each of the heights, the continuous measurements of soil ECa were

recorded for 5 minutes using EM38-MK2. The device was kept switched on under the direct

sunlight for the period of testing.

Standard deviation and the Coefficient of Variation of ECa were measured for different depths

below the ground surface are shown in Table 2.2. Within the duration of the test, the ambient

temperature varied between 25.5°C to 34°C.

Table 2.2 Standard deviation and the Coefficient of Variation of the ECa at different depths

below the ground surface.

Depth (cm) 15 35 55 75 90 110 130 150

Standard Deviation (mS m-1) 3.45 2.45 2.14 1.39 0.90 0.84 1.29 2.10

Coefficient of Variation (%) 9.26 8.76 9.26 6.54 1.54 1.74 3.09 6.51

The standard deviation of the ECa of all the depths varied between 3.45 mS m-1 to 0.84 mS m-

1. The highest standard deviation was for 55 cm, and the lowest was observed for the depth of

110 cm below the ground surface. The coefficient of variation varied between 9.26 % to 1.54

% within the depth of 15 cm to 150 cm below the surface. Both standard deviation and

coefficient of variation are higher within the top layer of soil surface because of the far-field

effect of the electromagnetic waves (McNeill, 1992). The least standard deviation and the

coefficient of variation are measured at a depth of 90 cm, 110 cm, and 130 cm. At a depth of

150 cm, both standard deviation and the coefficient of variation is high due to the near field

effect of the EM38-MK2 (Heil & Schmidhalter, 2017; Owino, 2012)


36

Figure 2.5 shows the boxplots of all the ECa measurements recorded at different depths

throughout the day of the experiment. The recorded temperature at 10:30 am, 12:00 am, 1:30

pm, 3:00 pm, 4:30 pm and 6:00 pm were 25.5 °C, 26.4 °C, 28.3 °C, 29.0 °C, 34.0 °C and 32.3

°C respectively. Boxplots indicate soil ECa for sequential changes in the temperature

throughout the day. For most of the depths, a slight jump can be observed when the ambient

temperature decreased to 32.3°C after the peak temperature of 34°C. For the change in the

ambient temperature of the whole day (8.5 °C), the drift in the ECa measurements was 2-3 %

within different depths below the surface. This study indicates that a negligible amount of drift

in soil ECa can be observed due to the highest temperature of 34 °C. So, the temperature

compensation circuitry embedded in the EM38MK-2 works fine, at least up to a temperature

of 34 °C.


37

Figure 2.5 Soil EMI vs. ambient temperature for different depth of soil

2.4.2 Calibration of the ECa data to soil moisture content

Soil ECa measurements were converted into soil moisture content using the calibration between

the soil ECa and VMC recorded from 15 plots in the field trial. For calibration, 1.5 m soil cores

were collected using a hydraulic core drill, and soil ECa data were recorded at 0, 20, 40, 60,

and 80 cm heights above the ground using the plastic buggy from the same plots. Soil VMC

was measured for every 10 cm segment of each soil core and compared with the inverted soil

ECa. A 1-D inversion algorithm was employed for the inversion of the ECa data measured at

different heights to generate the depth-specific electrical conductivity by using the cumulative

function of the electromagnetic field. This 1-D inversion algorithm was embedded in the


38

EM4Soil Version 3.04 (Santos, 2004). An initial model of 16 layers was applied with the depths

of 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150 cm, and the last layer at

infinity. The calculated ECa data were compared with the volumetric moisture content

measured from the soil cores. A linear regression model was developed to convert the ECa

value to volumetric moisture content (VMC). Figure 2.6 shows the relationship between VMC

and ECa of soil from the linear regression model.

Figure 2.6 Calibration between VMC and ECa of soil

The following empirical equation was found from the regression analysis between ECa and

volumetric moisture content,

𝑉𝑉𝑉𝑉𝐶𝐶 = 0.2874𝐸𝐸𝐶𝐶𝑎𝑎 + 12.112 (2.3)


39

2.4.3 Field trial on water use efficiency of chickpea genotypes

Water use efficiency of all 36 chickpea genotypes was evaluated for the 288 plots of the field

trial. Total water use by different chickpea genotypes over the whole season was calculated

using the EMI survey data, and the results were compared with the water use from NMM.

2.4.3.1 Total water use of different chickpea genotypes

Total water use by different genotypes for both irrigated and rainfed plots over the whole

season (22 weeks), as calculated by the ECa survey, is shown in Figure 2.7. As thirty-six

genotypes of chickpeas were planted in four replicates on each treatment, each data represents

each genotype's mean water use under irrigated and rainfed conditions.

Figure 2.7 Total water uses in mm by different chickpea genotype

Figure 2.7 shows that the minimum and maximum water use by the chickpea genotypes planted

in the irrigated plots were 320.32 and 457.46 mm, respectively. Water uptake of the chickpea

genotypes in rainfed plots was lower, with a minimum and maximum water uptake of 256.86

and 386.09 mm respectively.


40

Differences in the total water use by different chickpea genotypes were identified from this

technique. This method is sensitive enough to characterise small variations in soil moisture

content. With the help of plant physiological measurements, EMI surveys can be used to

calculate the root depths based on water uptake at different depths and drought tolerance of

different genotypes of chickpeas. This will allow plant breeders to evaluate effective water use

in breeding programs.

2.4.4 Comparisons of ECa data with the Neutron probe results

To validate the water use obtained from the EMI surveys, total water use obtained from the

EMI surveys were compared with the total water use of 40 trial plots of chickpeas measured

using Neutron Moisture Meter (NMM).

Total water use using EMI surveys was plotted against the water uptake obtained from NMM

in Figure 2.8, and the relationship shows an R2 of 0.73. There was not a perfect relationship

between the total water uptake from EMI surveys and NMM as there was a time difference

(approximately 7 days) between the NMM measurements and EMI surveys. Also, NMM

measures soil moisture at a specific location with a considerably smaller sphere of influence,

while EMI surveys cover a larger measurement volume. Nevertheless, this correlation indicates

a compelling relationship between water use obtained by NMM and EMI survey.


41

Figure 2.8 Comparisons of total water use from EMI surveys and NMM measurements

Overall, multi-layer EMI surveys using plastic buggy system was successfully used to monitor

soil water uptake by 36 genotypes of chickpeas at plot level. Total water use by 20 chickpea

genotypes (in 40 plots) measured by NMM has a significant correlation with the total uses

measured from EMI surveys.

2.5 Conclusions

Soil water monitoring at the plot level is essential with relevance to drought tolerance and the

plants' water use efficiency (Blum 2005; Huang et al. 2018). EMI surveys can potentially be a

less labour-intensive, less expensive, and non-invasive solution for measuring soil water

content at the plot scale. In this study, calibration was done by comparing the soil ECa and soil

volumetric water content with an excellent R2 of 0.86. The instrument was relatively stable and

not much affected by temperature variation. The stability of the EM38-MK2 was ±1 mS m-1.

The coefficient of variation of soil ECa varies between 1.54 % to 9.26 % for a daily change in

the ambient temperature from 25.5 °C to 34.0 °C.


42

EMI surveys using the plastic buggy system were successfully used to quantify all 36 chickpea

genotypes' total water use. Within the 288 plots of chickpea genotypes, the minimum and

maximum water use were 320.62 - 457.46 mm for irrigated plots and 256.86 - 386.09 mm for

rainfed plots.

The proposed technique is rapid, efficient, and convenient compared to conventional methods.

Also, the resolution of the measurement area and sphere of influence is much larger than the

conventional soil moisture probes. Thus, it can measure soil moisture in the root zone more

accurately. In the future, this method can be potentially be used over the entire season to

estimate the soil moisture with the genotypic and growth-dependent variation in total soil water

uptake by the roots at different depths.


43

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50

CHAPTER 3

TEMPORAL ANALYSIS OF SOIL WATER EXTRACTION BY

DIFFERENT GENOTYPES OF CHICKPEAS USING EMI

SURVEYS

Chapter 3: Temporal Analysis of Soil Water Extraction by Different Genotypes of Chickpeas Using EMI Surveys

51

3.1 Summary

The growth and productivity of plants are negatively affected by the water deficit that causes

drought stress. Phenotyping of plants is important for explaining important traits' genetic basis

and their performance in resource-limited environments. The cost of genotyping and

phenotyping using conventional methods limits the number of research projects required for

gene discovery and molecular marker development. Monitoring the soil moisture cost-

effectively at the plot scale will allow breeders to conduct drought tolerance and phenotyping

research and select water-efficient cultivars. EMI surveys can be an efficient, cost-effective

and rapid solution for these types of studies. In this chapter, depth-specific temporal analyses

of chickpea plants over two growing seasons (summer 2018 and 2019) were studied using

plastic buggy based EMI surveys. In 2018, I5A chickpea cropping field surveys were

conducted every fortnight at five different heights (0, 20, 40, 60, and 80 cm) in static recording

mode. K-means cluster analysis was used to group chickpea genotypes of three soil layers (0-

30 cm, 30-60 cm, and 60-100 cm) based on the change in soil moisture. The cumulative RAW

depth profile at pre-podding, post-podding, and mature stages were analysed along 1.5 m

depths using soil ECa data. In 2019, soil ECa was recorded at three different heights (0, 20,

and 80 cm) in an automatic recording mode from Campey-1 cropping field at various growth

stages. These survey data were used to group 192 genotypes of chickpeas based on soil

moisture extraction along various depths. Boxplots of total water uses of these chickpea

genotypes were analysed. Overall, the analyses revealed crops’ different water extraction

pattern. Water-efficient chickpea plants tend to extract more water from deeper soil profiles.

Temporal analysis of the soil moisture extraction at different growth stages indicates that the

least water-efficient genotypes extracted more water until podding but comparatively less water

at the maturity stage. This chapter illustrated the buggy-based EMI survey system's ability to


52

conduct the depth-specific temporal analysis of soil moisture at a plot scale that can be

potentially used for genotypic rankings based on root phenotypes.

3.2 Introduction

Temporal analysis of soil moisture extraction by plants at various depths in the soil is very

important for studying plants' response to drought and irrigation (Huang et al. 2018) and

phenotyping root activities (Whalley et al., 2017). In precision agriculture, soil moisture

sensing probes are commonly used to monitor soil moisture activity in particular sections of a

field that enable farmers to react quickly to changes in the land and crops (Zhang, Wang, &

Wang, 2002). It also helps farmers to make rapid and accurate decisions related to crop

productions and land managements (Girard & Hubert, 1999; Rinaldi & He, 2014; Rossi, Caffi,

& Salinari, 2012). These sensors allow farmers to observe the big picture of the agricultural

fields by combining soil sensors, weather, historical crop data, and other physiological

measurements by plants (Araus & Cairns, 2014; Denmead & Shaw, 1962).

Most soil moisture sensors are capable of depth-specific measurement of soil moisture of a

point location as the sphere of influence is low compared to proximal geophysical sensors

(Corwin & Lesch, 2003; Paraskevas, Georgiou, Ilias, Panoras, & Babajimopoulos, 2012).

However, the type of soil is never consistent across the agricultural field (Guber et al., 2008).

This problem can be tackled by installing multiple moisture sensors across a field. The

increased numbers of moisture sensors improve the measurements' accuracy and actively

monitor the moisture changes. These permit farmers to take necessary action in critical field

conditions such as low water levels that produce a stress condition for plants. But it is not cost-

effective to install and maintain many soil moisture probes in a field as these sensors are

expensive, and the maintenance cost is also high (González-Teruel et al., 2019; Gutiérrez,

Villa-Medina, Nieto-Garibay, & Porta-Gándara, 2013).


53

Monitoring soil water uptake by plants is becoming an important tool for plant phenotyping.

The ability to monitor soil water change accurately at the plot scale or individual plant-scale is

crucial to select the best cultivars that can extract water more efficiently from deeper soil

profiles (Whalley et al., 2017). Efficient methods are required to monitor plant water extraction

at different depths to infer root water uptake efficiency.

Over recent years, many studies have been conducted on predicting soil moisture using EMI

surveys (Sheets & Hendrickx 1995; Hanson & Kaita 1997; Reedy & Scanlon 2003). But only

a few studies were conducted on the depth-specific temporal analysis of water uptake by plants

using soil ECa. A novel approach was used for estimating relative variation in soil moisture by

David A Robinson et al. (2012). In their study, bulk soil electrical conductivity (ECa) of the

driest seasonal soil map was subtracted from the ECa collected during subsequent wetting. This

approach permits us to remove the effects of spatially distributed mineral and other factors and

allows the estimation of water content.

Huang et al. (2016) developed a spatiotemporal inversion algorithm, which accounted for the

temporal measurement of ECa, and demonstrated that the approach was more accurate and less

biased compared with the non-spatiotemporal inversion techniques. This algorithm was used

for a case study where time-lapse ECa was collected on a 350 m transect for seven consecutive

days. The spatiotemporal inversion algorithm allows reducing the number of ECa surveys over

the long transacts. A similar type of study was conducted where DUALEM-421 was used for

the EMI surveys over 12 days after the irrigation events. EM4Soil was used to generate EM

conductivity images (EMCIs) (Huang et al. 2017).

Moghadas et al. (2017) monitored the spatiotemporal soil moisture profile of an irrigated field

using the probabilistic inversion of time-lapse EMI data. In this study, time-lapse EMI surveys

were done along a 10 m transect of a maize field over six days using a CMD mini-Explorer


54

sensor. A probabilistic sampling approach, DREAM (ZS) was applied to derive a time-lapsed

depth-specific subsurface electrical conductivity (σ) tomography (Rhoades, Raats, & Prather,

1976). The petrophysical relationship was used to convert the soil σ to moisture content. The

temporal root zone soil moisture variations and the dynamics of soil moisture or infiltration

processes were also studied.

Martini et al. (2017) conducted a temporal repeated EMI survey for soil moisture mapping and

validated the wireless soil moisture monitoring network using EM38-DD. It was found that

soil ECa changes slightly with the change in soil moisture from dry conditions to almost

saturation. Also, the relation between soil ECa and moisture content varied with time. One of

the key findings of that study was that it is challenging to monitor change in the soil moisture

with the soil with low clay content. Depth specific temporal variation of the soil ECa was

analysed in this study.

Huang et al. (2018) measured plot scale soil moisture across a chickpea field using time-lapsed

ECa data from EM38 meter. Soil ECa data of 20 plots were recorded for both irrigated and

rainfed blocks in vertical and horizontal mode at five different heights (i.e., 0 m, 0.2 m, 0.4 m,

0.6 m, and 0.8 m). The ECa data were converted to soil moisture from an empirical multiple

linear regression model of soil moisture measured by neutron probe and depth-specific

electrical conductivity generated by a 1-D EM inversion algorithm. Then EM38 surveys were

done for all 288 plots at the same heights on four different dates within two weeks. In that

study, soil moisture dynamics were successfully determined with a coefficient of determination

(R2) of 0.87 and a root-mean-square-error (RMSE) of 0.037 m3 m-3.

Whalley et al. (2017) estimated change in soil moisture to monitor the activity of phenotyping

roots in the field using electrical resistance tomography (ERT), EMI surveys, and penetrometer

measurements. A comparative study between these techniques shows that EMI surveys are


55

more sensitive compared to the penetrometer or ERT surveys but less effective in drier soils.

The potential of EMI surveys to investigate the root activity quickly for a large area has not

been realised.

In this study, depth-specific temporal analyses of chickpea plants over two growing seasons

were studied to monitor water extraction and phenotyping root activity of chickpea genotypes

along the soil's depth profile.

The study was conducted in two different fields, I5A in 2018 and Campey-1 in 2019, at the

Plant Breeding Institute (PBI) of The University of Sydney, Narrabri. In 2018, the EMI

measurement method was being developed, including measuring EMI at each plot manually at

five heights above ground. Based on these results, in 2019, the survey was made more efficient

by using the EMI on-the-go mode at only three heights above ground. The EMI data allows the

calculation of the depth profile of water use and the distribution of available soil moisture in

different layers of soil for different genotypes.


In this chapter, EMI surveys with the plastic buggy system described in Chapter 2, were used

to collect all ECa measurements in two different chickpea cropping fields. The aim is to conduct

depth specific temporal analysis of soil moisture at the plot scale. This information will be used

to identify more efficient chickpea varieties.

3.3.1 EMI Measurement System

EMI surveys were done using a customized buggy system wholly made of plastic. The plastic

buggy consists of four wheels fixed at the four corners of the buggy to allow it to easily follow

the 2 m wheel track of the tractors between the plots. The EM38MK-2 was suspended from

two sides using two plastic chains, as shown in Figure 3.1. These chains were used to set the


56

device at different heights above the ground surface. There was provision for changing the

position of the EM38-MK2 along the width of the plots as well to keep the device in between

the rows of the plants, especially for the measurements of 0 and 20 cm above the ground

surface.

Figure 3.1 Plastic chain system for plastic buggy

3.3.2 Field location

The study area was located in Narrabri, NSW, Australia. The experiments were done on an

experimental farm of the Plant Breeding Institute of The University of Sydney (30.3324° S,

149.7812° E) (Figure 3.2).


57

Figure 3.2 Trial areas (I5A and Campey-1) for chickpea mapping population

3.3.2.1 Chickpea variety 2018 trial

A total of 36 different chickpea (Cicer arietinum) genotypes were sown on 1st June 2018 in

I5A field. Two ranges of buffer plots were planted with the cultivar PBA Seamer on both sides

and in the middle. Two buffer rows were also sown at the start and end of the trial plots. The

trial area was 0.6 ha (180 m × 32 m) and divided into irrigated and rainfed blocks (Figure 3).


58

About 201 mm of rainfall was recorded within the whole season. Irrigated and rainfed blocks

received around 190 mm and 110 mm of irrigation water, respectively. A total of 288 plots (1.6

m x 6 m) were planted in the field, which is surrounded by two rows of buffer plots. EMI

surveys were done fortnightly, starting from 14th June 2018 to 9th November 2018.

Static ECa measurements were recorded in I5A chickpea field fortnightly. The buggy was

pushed in the middle of each plot, and a trigger was pressed to record the ECa at five different

heights (0, 20, 40, 60, and 80 cm) to obtain the ECa of 15, 35, 55, 75, 90, 110, 130 and 150 cm

below the ground surface (Figure 3).

3.3.2.2 Chickpea variety 2019 trial

In Campey-1, 192 different chickpea (Cicer arietinum) genotypes were sown on 3rd June 2019

in 384 plots. One buffer plot was planted with the Desi chickpea genotype on all the field sides

and three ranges of buffers in the middle. The trial area was 0.72 ha (180 m × 40 m) and divided

equally into two blocks, based on two replications of all 192 chickpea genotypes. The trial has

received around 70 mm of rainfall water within the whole season. It also received about 110

mm of irrigation water.

Soil ECa was measured during the critical plant growth stages starting from 6th June 2019 to 9th

November 2019. This total time frame includes the early stage of seed germination to the

harvesting of the chickpeas. In total, four measurements were taken on 6th June, 25th September,

3rd October, and 9th November 2019. Continuous ECa measurements were taken for all 384

plots at the heights of 0, 20, and 80 cm above the surface, which provides the ECa of 55, 75,

130, and 150 cm depth below the soil surface.


59

The continuous ECa data were interpolated for the whole area using kriging to produce ECa

maps, and then the zonal statistics were extracted for each plot. Interpolated soil ECa's were

proven to be similar to the soil ECa's collected from static measurements (Chapter 2).

A study was done to optimize the number of EMI surveys at different heights and found that

ECa measurements at 0, 20, and 80 cm measurements can be used for the multi-layer inversion

to obtain the inverted ECa up to 1.5 m as explained in Chapter 2.

These heights were implemented in 2019 for surveying Campey -1 chickpea field. Continuous

soil ECa was measured in auto mode at 0, 20, and 80 cm above the ground to record soil ECa

of 55, 75, 130, and 150 cm below the ground surface (Figure 3.3). Only four EMI surveys were

conducted within the whole growing season at different growth stages (post-sowing, pre-

podding, post-podding and after harvesting). The measurement mode was set for five

measurements per second and saved in the data logger continuously for the whole field. The

recorded measurements were saved with extension N38 as binary raw data files.


60

(a) (b)

Figure 3.3 EMI surveys at 0, 20, 40, 60, and 80 cm heights at I5A (2018) and 0, 20, and 80 cm

heights at Campey-1 (2019).

3.3.3 Inversion of the EMI data

The inversion of the EMI data was done to interpolate measurements up to the depth of 1.5m.

A multi-layered 2-D inversion technique was used to consider all the depths in the inversion

analysis using EM4Soil. The inversion was done for the initials layers of 0-0.3 m, 0.3-0.6 m,

0.6-0.9 m, 0.9-1.2 m, and 1.2-1.5 m. Optimised damping factor (λ) and the number of iterations

were used to obtain minimum RMSE from the inversion results. In this study, the S2 model

(Sasaki 2001) was used. After the inversion, results were exported as a text file that includes

the co-ordinates, topology, distance, depth below the surface, electrical conductivity, and

electrical resistivity. Same as EMI surveys with five heights, soil ECa of every 10 cm intervals

were obtained after the inversion.


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3.3.4 Calibration of ECa data

Soil cores of 1.5 m were collected from 15 plots, and EMI surveys were done at 0, 20, 40, 60,

and 80 cm heights above the ground. Volumetric moisture contents were measured for every

10 cm of the soil cores. From the linear regression analysis between VMC and ECa, R2 was

found to be 0.86. The following empirical equation was found from the regression analysis,

which was used to convert soil ECa to VMC,

𝑉𝑉𝑉𝑉𝐶𝐶 = 0.2874𝐸𝐸𝐶𝐶𝑎𝑎 + 12.112 (3.1)

3.3.5 Water use calculation

Water use by the plant was expressed as Evapotranspiration (ET), calculated using the plant

root zone water balance model as below,

ET = P + I –∆S (3.2)

Where,

ET = Evapotranspiration or water use by plants

∆S = Change in root zone soil water storage over the time period

P = Precipitation

I = Irrigation


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3.3.6 Data Analysis

3.3.6.1 Soil moisture time series and depth profile of cumulative RAW analyses from 2018

EMI survey

Time series soil moisture analysis was done to understand the pattern of soil moisture

extraction by different genotypes. A k-means cluster analysis was used to group the change in

soil moisture over time into clusters with a similar trend of three different soil layers (0-30 cm,

30-60 cm, and 60-100 cm). The change in soil moisture ΔS of each fortnight from week 4 to

11 was calculated based on the EMI surveys up to the depth of 100 cm. The cumulative soil

water change was then calculated as the sum of ΔS from week 2 to successive weeks. The

cluster analysis was conducted for combined measurements of soil moisture change in three

layers. JMP Pro was used for this cluster analysis. Based on the Cubic Clustering Criterion

(SAS-Sarle, 1983), the optimum number of clusters was six.

The depth profile of ∆S at different stages (pre-podding, post-podding, and mature stage) was

calculated for six different chickpeas clusters. At first, the available soil VMC of every 20 cm

of soil layers was calculated. Then change in soil moisture (∆S) for three different stages was

determined by subtracting the soil VMC of the first measurement from the mean soil VMC of

the end of the pre-podding stage, post-podding stage, and mature stage. Then k-means cluster

analysis was conducted for all the measurements to divide all 36 genotypes into six clusters

based on a similar pattern on soil moisture extraction. Finally, the ∆S of all clusters for three

different stages were plotted against the depth of soil.

In this study, the depth profile of soil readily available water (RAW) was investigated for

different growth stages of chickpea plants. RAW was measured for the genotypes with

maximum and minimum soil water use in different treatments (irrigated and rainfed) at various

growth stages. Cumulative RAW at pre-podding and post-podding stages were determined by


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adding all soil VMC measurements from sowing until pre-podding and after podding until

harvesting, respectively. The total was calculated by adding the RAW obtained from all the

EMI surveys from sowing until the harvesting.

3.3.6.2 Depth profile of cumulative RAW, ∆S, and temporal analyses at different growth

stages from 2019 EMI surveys

In 2019, four EMI surveys were conducted for 192 genotypes of chickpeas that were sown in

384 plots. Similarly, like 2018, k-means cluster analyses of ∆S were done for data of every 20

cm, and all genotypes of chickpeas were grouped into six different clusters. After that, ∆S depth

profile for pre-podding, post-podding, and mature stages was plotted against depth.

The boxplots of the total water use for all six clusters were plotted to analyse the distribution

and the median of the total water used for different clusters (Figure 3.11). Total water uses for

all chickpea genotypes were evaluated as evapotranspiration from equation (3.2).

Cumulative RAW measured from the EMI surveys at various growth stages was plotted against

the depths to analyse soil water uptake in chickpea plants' root zones (Figure 3.12). Cumulative

RAW of pre-podding stages was calculated by adding available soil moisture obtained from

the EMI surveys of 6th June and 25th September after converting soil ECa to soil VMC using

equation (3.1). Post-podding cumulative RAW was calculated by adding available water from

the 6th June, 25th September, and 3rd October surveys. Finally, the cumulative RAW of the

mature stages was calculated from the EMI surveys of all measurements (6th June, 25th

September, 3rd October, and 9th November) in 2019.

Temporal analysis of soil ∆S at different soil layers was also calculated. The most and the least

water-efficient genotypes were selected based on total water uptake. In this analysis, a total

measured depth of 150 cm was divided into four layers (20 – 30 cm, 40 – 60 cm, 70- 80 cm,


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and 90 – 120 cm) based on the similar pattern of the existing soil VMC's. Soil ∆S for 114, 122,

and 159 days after sowing the seeds were calculated by subtracting the available soil moisture

measured on 25th September, 3rd October, and 9th November from the initial soil moisture

recorded from the EMI surveys on 6th June 2019.

3.4 Results and discussion

The results and discussions of the analyses from the EMI surveys conducted in I5A (2018) and

Campey -1 (2019) chickpea fields, were divided into two separate sections.

3.4.1 Depth specific temporal analyses for the EMI surveys from 2018 (I5A)

3.4.1.1 Time series soil moisture analysis at different layers

Soil VMC of three soil layers (0-30 cm, 30-60 cm, and 60-100 cm) evaluated by the EMI

survey was used in a time series analysis. A k-means cluster analysis was used to group the

change in soil moisture over time into clusters with a similar trend. The cluster analysis was

conducted for combined measurements of change in soil moisture in three layers. ΔS of all

three layers, as grouped by the k means analysis, were plotted separately in Figures 3.4, 3.5,

and 3.6. This analysis shows the variation of water use of different chickpea genotypes at 0-30

cm, 30-60 cm, and 60-100 cm over the whole season.

Figure 3.4 shows the soil moisture uptake trend for various chickpea genotypes within 0-30 cm

depth. Chickpea genotypes extract soil water slowly until the week of 17. A slight increase in

ΔS was observed until week 20 due to rainfall water.

Cluster 1 and 2 contain chickpea genotypes grown in the irrigated plots. Both clusters show a

slight extraction of soil moisture throughout the whole season. Genotypes from rainfed plots

constituted clusters 3 and 4. A slight increase in existing soil moisture was observed after week

17 until week 20. Chickpea genotypes of clusters 5 and 6 were grown in both treatment


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(irrigated and rainfed). Like other clusters, a similar downward trend can be observed until

week 15 before increasing the soil until week 20.


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Figure 3.4. Cluster means based on the weekly change in soil moisture content (ΔS) over the

whole season within 0-30 cm depth. Each line represents a chickpea genotype grouped into

clusters of similar patterns. The red line represents the mean value of the cluster.


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A higher proportion of soil moisture has been extracted from the soil layer of 30-60 cm

compared to the previous layer. On average, ΔS were varied between 70 mm to 110 mm within

week 1 to week 16. Chickpea genotypes from rainfed plots (clusters 3 & 4) extracted more soil

water compared to irrigated plots.


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whole season within 30-60 cm depth. Each line represents a chickpea genotype grouped into

clusters of similar patterns. The red line represents the mean value of the cluster.


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During the maturity stage, soil moisture was predominantly extracted from 0 – 60 cm. Due to

the high rainfall and irrigation water, soil moisture accumulates at a depth of 60 – 100 cm from

week 20 to week 22.

Cluster 1 and 2 were grown under the irrigated condition. Water use by the genotypes in cluster

1 was relatively steady throughout the whole period except for a few increases due to the

irrigation and rainfall water. The average ΔS for Cluster 1 is 26.19 mm, and cluster 2 is 28.77

mm.

Clusters 3 and 4 comprise a higher proportion of chickpea genotypes from the rainfed plots.

These 2 clusters show a decrease in ΔS until week 16 and then another water uptake at week

20 during maturity. Cluster 3 has an average ΔS of 35.01 mm, and cluster 4 has 40.26 mm.

A similar proportion of genotypes from irrigated and rainfed plots were found in clusters 5 and

6. These genotypes appear to have a decreasing trend of ΔS over time. Cluster 5 has an average

ΔS of 34.56 mm, and cluster 6 has 29.31 mm. Overall, clusters 3 & 4 extracted more water

compared to other clusters at the maturity stage.


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whole season within the depth of 60-100 cm. Each line represents a chickpea genotype grouped

into clusters of similar patterns. The red line represents the mean value of the cluster.


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Genotypes from Rainfed plots extracted most of the water from all three soil layers (0-30 cm,

30-60 cm, and 60-100 cm) below the ground surface. This analysis shows that the EMI

fortnightly surveys are able to detect changes in soil moisture due to irrigation, rainfall, and

plant uptake. The time series ΔS can reveal the pattern of water uptake of different genotypes.

Under the rainfed condition, genotypes in cluster 3 were more water-efficient, and under

irrigation, genotypes in cluster 1 were the most water-efficient. Genotypes in cluster 5 and 6

tend to behave less water-efficient genotypes. This system can potentially be used for depth

specific temporal analysis to allow plant breeders to study the soil moisture dynamics and

plant's response to drought and irrigation.

As a summary, the change in soil moisture represented as the mean of each cluster was plotted

against the depth for three different growth stages (pre-podding, post-podding, and mature

stages). Figure 3.7 shows water uptake behaviour of plant roots at different depths below the

ground surface.

Most genotypes behave similarly at pre-podding stages, extracted most water at 60-80 cm

except for cluster 3 (rainfed) that extracted more at 60-100 cm. At the post-podding stage, most

genotypes still used water in the 60-80 cm depth, except for cluster 4 (rainfed) that extracted

more water below 1 m. At the maturity stage, clusters 3 and 4 stood out to be using most water

below 1 m, but these clusters also extracted more water from 0-50 cm depth compared to other

clusters. The dynamic behaviour of water extraction from this analysis indicates that the EMI

survey can detect water extraction pattern for different genotypes.


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Figure 3.7. ΔS depth profile for all clusters at three different stages from EMI surveys in

2018

3.4.1.2 Depth profile of cumulative RAW at different growth stages

After converting soil ECa into soil VMC, cumulative RAW in each plot was evaluated. RAW

was measured for each genotype based on its maximum and minimum soil water content at

different treatments (irrigated and rainfed). Soil RAW for three different stages was plotted

against the depth of soil up to the depth of 1.5 m to study the water extraction behaviour of

maximum and minimum water use genotypes in both irrigated and rainfed plots.

Irrigated plots

Figure 3.8 shows the soil RAW depth profile for the most water-efficient chickpea genotypes

in the irrigated plots. Soil moisture was extracted mostly from 60 to 110 cm until the pre-

podding stage. After the podding, soil moisture was extracted from 110 – 140 cm. Because of

irrigation and rainfall water, RAW was higher up to the depth of 30 cm from the surface. The

RAW depth profile at the mature stage is almost similar to the RAW pattern at the pre-podding

stage. Chickpeas with maximum water use extracted less water up to the depth of 60 cm. After


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that, soil moisture extraction by plant roots starts to increase gradually. Most of the water

uptake was noticed within 60 – 100 cm depth, but it continued until 140 cm. After the podding

stage, soil moisture extraction of the least water use efficient chickpea genotypes is almost

similar to the most water-efficient chickpea plant.

(a) (b)

Figure 3.8 Soil RAW at different stages (pre-podding, post-podding, and maturity) of plant

growth in the irrigated plots for chickpea genotypes with (a) minimum and (b) maximum water

extraction throughout the whole season.

Rainfed plots

The RAW depth profile for the most (Figure 3.9a) and least water-efficient chickpea genotypes

(Figure 3.9b) is illustrated for different plant growth stages. The most water extracted by the

most water-efficient genotype is from the depth of 50 – 80 cm. However, for the least water-

efficient genotype, maximum soil moisture was extracted from 60 – 90 cm, and steady water

uptake can be observed from 90 -110 cm below the ground surface.


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(a) (b)

Figure 3.9 Soil RAW at different stages (pre-podding, post-podding, and maturity) of plant

growth in the rainfed plots for chickpea genotypes with (a) minimum and (b) maximum water

extraction throughout the whole season.

Overall, it can be observed that the most water-efficient genotypes extract more water from

shallow depths compared to the least water-efficient genotypes for both treatments. Still, at

higher depths, the least efficient chickpea genotypes extract more moisture than most efficient

genotypes. The pattern of soil moisture extraction at the mature stage is similar to the pre-

podding stage pattern. The least water-efficient genotypes extract more water from the depth

of 90 – 120 cm compared to the most water-efficient chickpea genotypes.

These analyses (time series soil moisture analysis and cumulative RAW at different growth

stages and the depth profile) were able to monitor soil water extraction dynamics at different

depths of soil. It shows that the fortnightly conducted EMI surveys can be potentially used to

quantify genotypic differences in chickpea plants' root activity. In the next sub-section, water

extraction of most and least water-efficient chickpea genotypes was analysed to investigate


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genotypic differences in water uptake with the depth profiles. The optimized number of EMI

surveys (four different stages) were conducted at 0, 20, and 80 cm above the ground to collect

continuous ECa measurements.

3.4.2 Depth specific temporal analyses for the EMI surveys from 2019 field trial

(Campey-1)

3.4.2.1 Depth profile of ∆S of various clusters from EMI surveys at different growth stages

for Campey-1

K-means cluster analysis of ∆S was conducted for 192 genotypes from Campey-1, similar to

the analysis done for I5A (2018). All the genotypes were grouped into six different clusters

based on the water extraction time series data. The mean ∆S was plotted against soil depth for

three different stages, as shown in Figure 3.10.

Figure 3.10. ΔS depth profile for all clusters at three different stages from EMI surveys in 2019


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Figure 3.10 shows that almost all genotypes of chickpeas extracted most water at a depth deeper

than 1 m. Clusters 5 and 6 extracted less water in the surface but more water at a lower depth.

Due to the higher amount of water availability at the podding stage due to irrigation, the effect

of soil moisture extraction is less dominant at the post-podding stage. All the clusters extracted

more water at the maturity stage compared to the pre-podding and post-podding stage. Clusters

5,6, and 3 appear to extract more at 100-140 cm compared to other genotypes.

3.4.2.2 Variations in the total water use by different clusters of chickpea genotypes

Boxplots of total water use of all six clusters were plotted, as shown in Figure 11. From the

depth profile of ∆S at different growth stages, soil water extraction pattern at various depths

by different clusters was investigated. In this section, total water uses for different clusters were

compared to find the relationship between water extraction of different clusters and total water

use for the whole season.

Figure 3.11. Total water uses of various clusters


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Chickpea genotypes in clusters 5 and 6 (median of 132.21 and 135.14 mm) extracted the largest

amount of soil moisture compared to the other clusters. The depth profile of ∆S (Figure 3.10)

shows these clusters extracted more water from 60 cm to 140 cm and comparatively less water

from the surface. Cluster 1 and 4 used less water (median of 105.1 and 100.12 mm) within the

whole season. The water uptake by these two clusters was high within the topsoil but

comparatively low at higher depths. The total water uptake by clusters 3 and 4 was moderate,

with a median of 116.29 mm and 125.73 mm, respectively.

3.4.2.3 Depth profile of cumulative RAW at different growth stages

Cumulative RAW of pre-podding, post-podding, and mature stages along the depth profile for

the most and least water-efficient chickpea genotypes were analysed in this section. Cumulative

RAW of different growth stages was plotted against the depth profile of 1.5 m to monitor the

soil dynamics of the soil moisture extractions by chickpea genotypes with high and low water

use.


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Figure 3.12. Soil RAW at different stages (pre-podding, post-podding, and maturity) of plant

growth chickpea genotypes with (a) minimum and (b) maximum water extraction all over the

growing season.

Cumulative RAW at the pre-podding stage indicates that most of the soil moisture was

extracted within 90 cm depth from the surface, as shown in Figure 3.12. Since the RAW of the

pre-podding stage was calculated by adding the available soil moisture from the EMI surveys

until the podding stage, some details of soil moisture extraction activities up to the pre-podding

stage were missing unlike in the cumulative RAW depth profile for I5A field in 2018. During

the plants' post-podding phase, a higher amount of soil moisture was extracted within 40 cm

from the surface by the least water-efficient genotype compared to the most water-efficient

genotype. But more soil moisture was extracted by the most water use genotype within 100 -

120 cm at the post-podding stage. From the cumulative RAW depth profile of the mature stage,

it can be observed that the most water-efficient genotype of chickpea extracts less water at

shallow depths.


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3.4.2.4 Temporal analysis of ∆S for high and low water-efficient genotypes at different

soil layers at the mature stage

Temporal analysis of ∆S of four different layers (20 – 30 cm, 40 – 60 cm, 70- 80 cm, and 90 –

120 cm) was done by plotting ∆S of 108, 118, and 155 days after sowing against the days after

sowing the seeds as shown in Figure 3.13. The water uptake behaviour at three growth stages

was studied in the earlier sections. This section will examine the water extraction dynamics of

most and least water-efficient genotypes at different days of EMI surveys.

The least water-efficient genotype extracted nearly 5 mm of water within 108 to 118 days after

sowing. On the other hand, the most water-efficient genotype extracted about 7 mm of water

within these days. So, the most water-efficient genotype extracted more water during the

podding of chickpeas compared to the least water-efficient genotype. A combined effect of

high rainfall and irrigation water caused a significant rise in the available soil moisture in 118

to 155 days after sowing. At this stage, the least water-efficient chickpea genotype extracted

more water than most water-efficient genotypes at all soil layers. Considerable difference in

water extraction was noticed in the top layer (20 - 30 cm) between these genotypes. of soil

between the most, and the least water-efficient genotypes was observed.


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Figure 3.13. Available soil VMC of different layers against the days after sowing for least and

most water-efficient chickpea genotypes

Overall, the least water-efficient genotype of chickpea extracted more water during the podding

but slightly less water after the podding stage compared to more water-efficient chickpea

genotypes. Most of the soil moisture was extracted within the depth of 50-100 cm from the

surface.

3.5 Conclusions

The EMI surveys using the plastic buggy system have the potential to analyse the temporal and

depth analysis of water extraction by various genotypes of chickpeas. This technique can

monitor the soil moisture so that quick measures can be taken in response to the drought stress

condition of plants more conveniently. EMI surveys were done in 2018 (I5A field) at 0, 20, 40,


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60, and 80 cm heights in static recording mode and in 2019 (Campey-1) at 0, 20, and 80 cm

heights in automatic recording mode with EM38MK-2. The calibration equation between soil

ECa and VMC with R2 of 0.86 was used to measure soil moisture at the plot level.

K-means cluster analysis of soil moisture extraction at different depths successfully

differentiated chickpea genotypes into clusters based on their treatments (rainfed and irrigated).

This analysis shows the dynamics of ΔS for different weeks after sowing the seeds in the field

(I5A) and genotypes' water efficiency in various clusters. Overall, chickpeas on rainfed plots

(clusters 3 & 4) used most water, and comparatively, most water use efficient chickpea

genotypes were found in cluster 1.

The cumulative RAW depth profile illustrated the total soil moisture extraction at pre-podding

and post-podding stages within 1.5 m depths from the surface. The major variations in the

cumulative RAW were observed between irrigated and rainfed after 50-130 cm. Water

extraction at pre-podding stages varies within the depth in the irrigated and rainfed plots.

Similar cluster analyses were conducted for just four EMI surveys in 2019 throughout the

whole season at three heights in the on-the-go mode. The ΔS depth profile shows that clusters

5 and 6 exhibited the least soil moisture efficiency. Most soil moisture were extracted from the

depth of 100 cm in all three stages.

From the distribution of total water uses of chickpea genotypes in the various clusters, clusters

6 and 4 were the most and least water-efficient. So, using this analysis, a general idea of total

water use of the clusters of different chickpea genotypes was successfully determined from

optimized EMI surveys in auto mode.

Depth profile analysis for the cumulative RAW was done using the EMI surveys conducted in

just four different growth stages of chickpea plants in 2019. These RAW depth profiles do not


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provide a detailed illustration of root zone water uptake within initial ECa measurements and

podding phase like the cumulative RAW depth profiles from the EMI surveys conducted in

2018. Nevertheless, these were sufficient to shows soil extraction dynamics by the chickpea

plants at major growth stages.

Temporal analysis of ∆S explained the change in available soil moisture for several soil layers

within various days after sowing the seeds. With the growth of the chickpea plants, more soil

water was extracted from the depth of 40 to 120 cm for both most and least water-efficient

genotypes. In the top layer (20 – 30 cm) of soil, the least water-efficient chickpea genotype

extracts less water within pre-podding to the maturity stage than the most water-efficient ones.

This study describes soil EMI surveys' ability to conduct the depth-specific temporal analysis

of soil moisture over the whole season from sowing till harvesting. This technique can be

applied at the plot level. In the future, this method will be beneficial for plant physiologists and

breeders to investigate the plant root zone behavior and the dynamics of water extraction by

plants. This technique can also be used for genotypic rankings based on root phenotypes.


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CHAPTER 4

VISNIR PENETROMETER SYSTEM FOR PREDICTING SOIL

CARBON

Chapter 4: VisNIR Penetrometer System for Predicting Soil Carbon

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4.1 Summary

Soils are one of the main carbon sources and contain more carbon than the combined total in

the atmosphere and terrestrial vegetation. Protecting and monitoring soil organic carbon (SOC)

stocks at national and global levels can help to preserve biodiversity and ecosystem function

while boosting soil productivity and increasing resilience to floods and drought. The

sequestration of atmospheric C as SOC can play a vital role in mitigating global warming by

reducing atmospheric carbon dioxide. In this study, a visible and near-infrared (VisNIR)

penetrometer system was developed to automatically measure in-situ soil VisNIR reflectance

spectra and depth of insertion along with soil profile. This VisNIR penetrometer system rapidly

measures soil properties at fine-depth-resolution without collecting soil cores from the field

and preparing soil samples in the laboratory. In this study, a spectral library with 1,826 SOC

observations and corresponding VisNIR spectra of soil in air-dry and ground condition was

used to calibrate Cubist regression models for both field-moist and dry spectra. Two external

parameter orthogonalisation (EPO) projection matrices were constructed from the dried soil

VisNIR spectra with moist core (local) and field VisNIR spectra (penetrometer) based on the

optimum numbers of components from Wilks’ Λ test and applied to the in-situ spectra to

remove the effects of soil moisture and other in-situ effects from the spectra. This penetrometer

system was used to estimate SOC at three sites on three different days, each chosen based on

rainfall incidences to predict SOC content under variable soil moisture conditions at

Lansdowne Farm, New South Wales, Australia. External validation was performed using SOC

contents measured at 10 cm depth intervals and also an additional 0-2 cm depth observation

due to the very high SOC observed at this depth. The penetrometer EPO performed better

compared to the local EPO constructed from moist cores. The predicted SOC contents were

validated against the SOC content of 33 samples measured in the laboratory using the Walkley-

Black method with mean R2, RMSE, and bias of 0.88, 0.32 and 0.15, respectively. It is


89

concluded that the in-situ VisNIR penetrometer can potentially be used to measure SOC

content rapidly and cost-effectively in situ with a high vertical resolution.

4.2 Introduction

Soil organic carbon (SOC) content is an important indicator of soil condition (Killham &

Staddon, 2002). Biodiversity, nutrient, and waste cycling ecosystem processes will be

compromised if SOC drops below critical levels (Power, 2010). SOC is vital for microflora as

different organisms use it as a source of energy and nutrition (Lescure et al., 2016). These

organic materials are cycled through the soil and used by organisms as sources of energy and

nutrients. Increased SOC content increases energy supply for all microorganisms (microbes,

macrofauna, earthworms, etc.) and nutrient supply to plants (Metcalfe & Bui, 2017). SOC also

helps aggregate soil particles and increase soil structure stability, which improves the water

storage capacity (Carter, 2004). SOC also stabilises the thermal properties of soil and maintains

pH buffering (Metcalfe & Bui, 2017). Unfortunately, land-clearing and production on the

world’s arable soils have resulted in an estimated release of 50 Gt C (186 Gt of CO2) to the

Earth’s atmosphere (Smith et al., 2015). The current world population is expected to reach 8

billion by the year 2025 (Scherbov, Lutz, & Sanderson, 2011). Crop yields must be increased

to support this population growth while minimising ecological damage.

Soil carbon sequestration can be a potential solution to reduce levels of atmospheric CO2 while

also providing benefits for soil formation, soil fertility, and increases yield potential

(Körschens, 2002). Soil carbon sequestration reduces greenhouse gas from the atmosphere by

storing carbon root, stem, and leaf material of pasture grasses and crops, which may mitigate

global warming (Irving 2015; Fornara et al. 2011). Carbon farming benefits landowners by

increasing agricultural production, and profitability (Gerrand, Keenan, Kanowski, & Stanton,

2003).


90

Paying landholders to sequester additional SOC on their land has been promoted as a mutually

beneficial solution for the climate, crop production, and ecosystem services (Brown, Miller,

Ordonez, & Baylis, 2018; K. M. A. Chan, Anderson, Chapman, Jespersen, & Olmsted, 2017;

Heal & Small, 2002; Nelson, 2009). The cost of SOC auditing has been a factor limiting further

uptake, and new methods must be developed to make the process more cost-effective to

promote wider adoption (J J de Gruijter, Wheeler, & Malone, 2019; Jaap J de Gruijter et al.,

2018; Smith et al., 2020). Conventional methods to quantify SOC in the laboratory are time-

consuming, expensive, and destructive in nature. As soils vary vertically as well as laterally,

soil information must be collected at distinct locations and at different depths below the soil

surface (Mubarak, Mailhol, Angulo-Jaramillo, Ruelle, et al., 2009; Ovalles & Collins, 1986).

An ideal method to calculate SOC stock a rapid and accurate soil, organic measurement

technique is required. Research into the acquisition of high-resolution soil data with accurate

information of spatial variability that can be obtained in a timely manner is increasing in

various disciplines of precision agriculture, digital soil mapping, crop and yield modelling

(Behrens & Scholten, 2006; Camera et al., 2017; J. Liu et al., 2007; A. B. McBratney et al.,

2003; Semenov, 2004; Viscarra Rossel et al., 2010; Zhao et al., 2020).

Visible and near-infrared (VisNIR) soil spectra have been investigated as a rapid and cost-

effective method to accurately predict many soil properties simultaneously, including SOC,

clay content, moisture, total N, and CEC (Chang, Laird, & Hurburgh Jr, 2005; Chang, Laird,

Mausbach, & Hurburgh, 2001; Kusumo et al., 2008; Rossel et al., 2009; Rossel, Walvoort,

McBratney, Janik, & Skjemstad, 2006). As a replacement of laboratory measurement of SOC,

VisNIR spectroscopy can be successfully used to predict SOC more cost-effectively and

efficiently (R2 = 0.96, RPIQ = 5.83) (Guo et al. 2019).

While most studies using VisNIR are mainly conducted in the laboratory by scanning air-dried

soil under a controlled environment, recent studies have used VisNIR spectroscopy in-situ and


91

as rapid on-the-go measurements of soil properties (Angelopoulou, Balafoutis, Zalidis, &

Bochtis, 2020; Hutengs, Seidel, Oertel, Ludwig, & Vohland, 2019; Pei, Sudduth, Veum, & Li,

2019). In particular, NIR spectroscopy has become very popular for predicting soil organic

carbon (SOC) for soil carbon sequestration and quality assessment for agricultural purposes

(Morellos et al., 2016; Nocita et al., 2014; Stevens et al., 2013; Stockmann et al., 2015). This

technique can provide cheap and rapid estimation in the field. Ben-Dor et al. (2008) developed

a device that can be inserted into the pre-excavated holes to collect soil VisNIR spectra in-situ.

Chang et al. (2011) modified this technique to predict soil SOC using a new micro-optical

sensor probe. The optical probe consists of two microfabricated source chips that include a

side-viewing function that enables recording of in-field VisNIR spectra. Rossel et al. (2009)

attempted to scan VisNIR spectra on soil profiles exposed during sampling pit-excavation to

predict clay content and found slightly better accuracy compared to soil spectra collected under

laboratory conditions. Though this method provides higher accuracy, it still involves

excavating the soil during the field tests.

Challenges to in-situ use include differences in the ambient temperature of the atmosphere and

pedosphere, soil heterogeneity, macro-aggregation, and soil moisture variability as sensors

moving through the surface of the soil experience inconsistency in the soil presentation and

smearing (Christy, 2008; Morgan, Waiser, Brown, & Hallmark, 2009; Waiser, Morgan, Brown,

& Hallmark, 2007). A study was conducted by Mouazen et al. (2005) to develop a fiber- type

VisNIR spectrometer attached to the back of the chisel tine that was able to scan soil surface

as it was dragged through the topsoil layer, and a significant correlation was found with soil

carbon, pH, and P using a cross-validation technique (Mouazen et al. 2007). Bricklemyer &

Brown (2010) found that soil SOC and clay content predicted from the in-situ VisNIR spectra

are less accurate than VisNIR from laboratory tests. They used a commercially available on-

the-go VisNIR sensor (Veris Technologies Inc., Salinas, Kansas, USA) which was attached to


92

an agricultural shank pulled by a tractor. Other studies indicate that SOC, pH, electrical

conductivity, moisture content, CEC, TC, and other properties can be predicted using a real-

time VisNIR spectrometer attached to a tractor and use for mapping purposes (Kodaira &

Shibusawa, 2013).

In the field, many external environmental parameters, such as ambient temperature, soil

moisture, and soil surface conditions (aggregate size or roughness) may produce unwanted

effects on the quality of VisNIR reflectance spectra. Soil moisture has a significant impact on

the VisNIR spectra, and SOC predicted on the same soil, but at different moisture content can

vary by up to 1% (Minasny et al., 2011). For this reason, the variability of field soil moisture

can decrease the accuracy of SOC estimation from VisNIR spectra of field samples (Minasny

et al., 2011; Morgan et al., 2009). Few studies have been conducted where a wide range of

water content was included in the calibration set to predict soil SOC. However, the accuracy

of the SOC prediction was not satisfactory. To estimate soil SOC from moisture-affected soil

spectra, external parameter orthogonalisation (EPO) technique was introduced by Minasny et

al. (2011). The EPO algorithm, originally developed by Roger et al. (2003), projects VisNIR

spectra into a new space orthogonal to the undesirable variation introduced by variable soil

moisture. The use of EPO has been demonstrated to reduce the effect of variable moisture on

the prediction of SOC, clay,and other soil properties (Abdul Munnaf, Nawar, & Mouazen,

2019; Ackerson, Demattê, & Morgan, 2015; Minasny et al., 2011; Roudier, Hedley, Lobsey,

Rossel, & Leroux, 2017).

Ackerson et al. (2017) attempted to predict soil clay content using a custom-made VisNIR

penetrometer. The study was conducted on 20 locations from the terrace, seven from

floodplains, and six from upland locations in Burleson and Brazos counties, Texas, USA. In

the study, a Texas soil spectra library was used together with EPO transformation to reduce the

moisture effect and allows the correlation models calibrated on dry ground samples to predict


93

using spectra taken from moist samples in situ (Ge, Morgan, & Ackerson, 2014; Minasny et

al., 2011). PLSR models were used to predict clay content from EPO transformed in-situ

spectra with the accuracy of RMSE, bias, and R2 of 88 g kg−1, −15 g kg−1, and 0.76. In a

continuation of this research work, the penetrometer was modified by adding a load cell to

measure driven force, a GPS module, and an ultrasonic depth sensor for real-time depth

measurement (Wijewardane, Hetrick, Ackerson, Morgan, & Ge, 2020). Using this system, the

predicted R2 and RMSE were found to be 0.50 and 0.79% for total carbon. R2 and RMSE were

0.62 and 0.06% for total nitrogen and 0.80 and 0.12 g cm−3 for bulk density.

The use of VisNIR penetrometers to provide high vertical resolution soil information is

promising, but the technology has not been thoroughly tested for Australian soil types and

variable moisture conditions. In this study, a VisNIR penetrometer system was developed and

tested for in-situ, high-resolution vertical soil sensing and estimation of SOC levels using

calibrations developed from an existing spectral library. To be suitable for field applications,

the system must be robust enough to provide accurate estimations of SOC content independent

of soil type and variability of in-field moisture content at the investigation site.


4.3.1 VisNIR penetrometer system

This study investigated the in-situ estimation of SOC using a truck-bed skid-mounted VisNIR

penetrometer system. The VisNIR penetrometer consisted of a penetrometer head housing a

halogen lamp and a fibre-optic cable to collect and transmit reflectance photons from below

the soil surface through the penetrometer shaft to an above-ground spectrometer. It also had an

ultrasonic depth sensor to record insertion depth. The penetrometer head was constructed of

stainless steel with an external diameter of ⌀ 32.5 mm and length of 900 mm, including a

removable tip in the shape of a cone to reduce penetration resistance, as shown in Figure 1.


94

The penetrometer head housed an optical module containing a broad-spectrum halogen light

source (MR4-188, International Light Technologies, Peabody, MA, USA) and parabolic mirror

(MPD019-G01, Thorlabs Inc, Newton, NJ, USA) to direct light from the lamp through a

sapphire window into the pedosphere. Diffuse reflectance spectra from the soil immediately

external to the sapphire window returned through the sapphire window. A fibre-optic cable

mounted inside the optical module at a 45° angle to the illuminated soil surface captured and

returned some of this light to the surface through a 3 m long ruggedized fiber optic cable to an

above-ground ASD AgriSpec spectroradiometer (ASD, Malvern Panalytical, Longmont, CO,

USA).

The fibre-optic bundle and power wires for the halogen lamp were enclosed within a connector

rod machined from high carbon steel hollow bar that connected the penetrometer head to the

mandrel and the hydraulic system. The external diameter of the bar was 30 mm, and the internal

diameter was 13 mm. This bar was specially designed to withstand the typically dry soil

condition of the south-west part of Sydney, New South Wales. The penetrometer had a

maximum insertion depth of 107 cm, and as the sapphire window was recessed from the tip of

the penetrometer head, the maximum reading depth was 90 mm. An ultrasonic distance sensor

(ToughSonic14, Senix Corp., Hinesburg, VT, USA) mounted on a bracket attached

perpendicular to the penetrometer shaft and directed towards the soil surface provided a

reading of the height of the sensor above the ground as the penetrometer was inserted into the

soil.


95

Figure 4.1. VisNIR penetrometer with ultrasonic depth sensor

Additional hardware required included a laptop computer, a data logger, a voltage regulator, a

voltage converter, and a 12 V deep-cycle battery. A voltage regulator provided a stable 12 V

power supply from the battery to ancillary power circuits. A DC-DC voltage converter was

used to step up the power to control the 24 V ultrasonic depth sensor. A variable resistor was

used to control the light source's voltage to adjust the intensity of the halogen light from the

main circuit board, and a switch was used to cut the power supply to the light source completely

when taking a dark reference reading. A LabJack U6 (LabJack Corporation, Lakewood, CO

80227 USA) multifunctional data acquisition device was used to receive and log concurrent

spectra and depth measurements. A program was written in LABVIEW (National Instruments,

11500, Austin, USA) to control and monitor data acquisition. The advantage of using the

LABVIEW program is the user-friendly graphical interface that enables to control the VisNIR

and ultrasonic distance sensor with the display of real-time measurements, including in-situ

soil spectra and insertion depth.


96

The ASD spectroradiometer, main circuit, and 12 V battery were stored inside a protective box,

as shown in Figure 4.2. Ventilation of hot air was provided by two cooling fans that pushed hot

air out of exhaust vents to maintain a reasonable working temperature inside the box to reduce

effects on the measurement of the spectra.

Figure 4.2. Instrumentation box including ASD spectroradiometer, circuit board and 12-volt

battery.

4.3.2 Field experiments to collect VisNIR spectra using the penetrometer system

4.3.2.1 Study site

Field experiments were conducted to test the system at three sites located on Lansdowne Farm,

Cobbitty, NSW, Australia (34°01’24” S, 150°39’48” E) (Figure 4.3). The three sites identified

were known to encompass a range of soil types, textures, and organic carbon contents (Table

4.1). The three sites occupied distinct landscape positions. Site 1 was located on low-rise, and

10.2% clay measured in the Ap1 horizon was the lowest observed at all sites. The site had

previously undergone cultivation but had been under pasture cover for five years prior to

sampling. Site 2 was situated in a floodplain depression giving it a fine-grained alluvium parent

material and the highest observed clay content of 38.9% in the Bt2 horizon. Site 3 was located

on an undisturbed levee bank and subsequently had the highest SOC content of all sites.


97

Figure 4.3 Locations of sampling sites on Lansdowne Farm, within Australia and in relation to

New South Wales.


98

Field experiments were conducted on three separate days over a 29 day period, namely

23/01/2020, 31/01/2020, and 18/02/2020. Sampling days were chosen reactively to rainfall

incidences to test the robustness of predictions under variable soil moisture conditions. Soil

moisture has unfavorable effects on the accuracy of predictions with VisNIR spectra and is one

of the main limitations to field use.


99

Table 4.1. A summary of the types of soil samples analysed different sites.

*CL, clay loam; CS, clayey sand; LMC, light-medium clay; SCL, sandy clay loam; SL, sandyloam.**silt-sand threshold of 20 µm.

Site

ID

Hor

izon

Upp

er

dept

h

(cm

)

Low

er

dept

h

(cm

)

Tex

ture

* C

lay

(g 1

00 g

-1)

Silt**

(g 1

00 g

-1)

Sand

**

(g 1

00 g

-1)

pHc

EC

(dS

m-1

) O

C

(g 1

00 g

-1)

1

Bro

wn

Kan

doso

l

Ap1

0

12

CS

10.2

3.

6 86

.3

5.58

0.

07

0.52

Ap2

12

30

C

S 10

.7

4.8

84.5

5.

76

0.13

0.

32

Bw

30

50

SL

14

.4

3.7

81.9

5.

68

0.03

0.

20

Bc

50

100

SL

16.6

4.

5 78

.9

5.95

0.

03

0.18

2

Red

Der

mos

ol

Ap

0 23

C

L 28

.2

17.8

54

.0

5.41

0.

06

0.79

Bt1

23

45

LM

C

37.2

18

.6

44.2

6.

03

0.03

0.

43

Bt2

45

10

0 LM

C

38.9

18

.0

43.1

6.

19

0.04

<0

.15

3

Red

Ten

osol

A

0 16

SC

L 18

.4

12.5

69

.1

5.08

0.

10

2.97

Bw

1 16

31

C

L 21

.1

11.5

67

.4

4.74

0.

08

0.90

Bw

2 31

48

C

L 22

.8

7.8

69.4

5.

17

0.06

0.

24

2Bw

1 48

73

SC

L 18

.4

5.5

76.1

5.

24

0.05

<0

.15

2Bw

2 73

10

0 SL

12

.4

4.4

83.2

5.

38

0.06

<0

.15


100

4.3.2.2 Penetrometer operation and field sampling

The penetrometer was attached via a modified mandrel drive with the Atlas hydraulic system

skid-mounted in the bed of a truck. The percussive hammer function was disabled to ensure

the penetrometer was inserted smoothly into the soil and reduce the potential for hardware

failure. Insertion force was provided by a hydraulic system skid mounted on the bed of a 2.5-

tonne truck (Figure 4.4). The direct location of insertion was cleared of plants, litter, and debris

to expose the very top of the A horizon. An area directly below the ultrasonic distance sensor

was also cleared of tall plants to prevent movement induced by wind interfering with insertion

depth calculations. Immediately before insertion, the penetrometer was calibrated utilising a

Spectralon (Labsphere, North Sutton, NH 03260 US) tile held flush on the sapphire window

for the baseline reading, and a dark reference reading was also obtained with the halogen lamp

switched off and the sapphire window covered. The penetrometer was inserted into the soil

until the sapphire window was just below the soil surface, giving an initial reading depth

centered at ~5 mm depth. Once in position, the ultrasonic distance sensor depth was zeroed and

recording of VisNIR spectra was initiated. The penetrometer was inserted in by ~2 cm

increments in the first 30 cm depth and then by ~5 cm increments to the maximum reading

depth of 85 cm. A pause of 10 seconds between insertion events facilitated the capture of

multiple, high-quality spectra over the 350-2,500 nm wavelength range at each depth interval

while the penetrometer system was stationary. The penetrometer's final insertion depth was

also recorded for each location and compared with the insertion depth recorded from the

distance sensor. The second reading of the Spectralon tile was taken immediately following

sampling to assess calibration stability.


101

Figure 4.4. Operation of VisNIR penetrometer system in the field

Three penetrometer insertions were made at each sampling site on each sampling day. The

insertion points were spaced linearly 50 cm apart. Two 50.8 mm diameter soil cores were

collected to a depth of 1 m from the middle of the insertion points, as shown in Figure 4.5.

Measurements taken on subsequent days were spaced 50 cm away from the first day to limit

the effects of soil variability on observed carbon values. Extracted soil cores were immediately

wrapped in polypropylene tubing to conserve field moist conditions while transporting samples

back to the laboratory. An additional 0 to 2 cm sample was obtained as SOC often follows an

exponential decay function with depth. If taken as a bulk value, contents at lower depths may

dilute the high SOC contents of the upper topsoil.


102

Figure 4.5 Sampling Scheme of all three sites at different days

4.3.3 Laboratory processing

Extracted soil cores were processed back in the laboratory each day following sampling. To

provide a point of comparison with the penetrometer system, one of the soil cores was cut

lengthways and VisNIR spectra were acquired on the core at 2 cm increments using a contact

probe with an inbuilt halogen lamp attached via fibre-optic bundle. This laboratory-based scan

used the same ASD spectroradiometer as the penetrometer system. The second core was

divided into 10 cm depth segments and used to calculate soil wetness at air-dry (40°C) and

oven-dry (105°C) conditions. The split soil core on which VisNIR spectra were obtained was

then dried at 40°C, and VisNIR spectra were again acquired at 2 cm increments in an air-dried

state. Once soil cores from the third day had been processed, a composite sample from the three

air-dried cores at each site was obtained at 10 cm depth segments to 1 m. The 0 to 2 cm depth

samples and the ten composite samples per site were ground and passed through a 2 mm sieve.

Triplicate VisNIR readings were acquired on the 33 samples in the dry-ground state, and


103

organic carbon was quantified for each sample using the Walkley-Black method (Walkley and

Black, 1934).

4.3.4 VisNIR processing and estimation of SOC

All VisNIR processing, model calibration, and validation were performed in R (R Core Team,

2019). Organic carbon estimates were produced under the four different soil conditions for

comparison, namely: penetrometer collected, field-moist core, air-dry core, and dry-ground

composite sample. Each condition required a unique combination of pre-processing, external

parameter orthogonalisation (EPO) for field-moist condition, application of predictive models,

and depth aggregation prior to validation with observed SOC values (Figure 4.6). An existing

spectral library with 1,826 SOC observations and matching VisNIR spectra in air-dry and

ground condition was used to calibrate predictive models for both field-moist and dry spectra

in the form of Cubist regression models (Kuhn and Quinlan, 2020). The spectra in the library

underwent distinct pre-processing and transformation for each condition to match the pre-

processing of acquired spectra, as described below. SOC was log-transformed prior to

modeling, and estimations were back-transformed prior to the calculation of validation

statistics.

Field penetrometer spectra

Spectra acquired with the penetrometer first had to be screened for abnormal spectra associated

with plant roots and debris that are present in, but separate from, the soil and not represented

in the spectral library. This matches standard laboratory procedures to remove visible plant

roots during the drying and grinding process that would otherwise artificially increase observed

SOC carbon levels. Spectra were converted to absorbance, A = log(1/R), then smoothed with

a Savitzky-Golay filter comprising of window size of 11 and a second-order polynomial. Low

signal:noise ratio areas of the spectrum were removed to leave the 500 to 2450 nm wavelength


104

range, which was resampled at 2 nm resolution. A baseline correction was performed utilising

a standard normal variate transformation. An EPO transformation matrix was used to project

spectra into a space orthogonal to the effect of soil moisture before resampling at 10 nm

intervals (Roger et al. 2003; Minasny et al. 2011; Wijewardane, Ge, & Morgan, 2016; S Veum,

A Parker, A Sudduth, & H Holan, 2018). Pausing during insertion to acquire spectra while the

penetrometer was stationary meant that there was no need to smooth spectra between sequential

readings. Predictive models were applied to estimate SOC content for each reading, and as

readings were not taken on strict depth intervals, local polynomial regression was used to

smooth readings for each insertion. The fitted smoothing line was sampled at 1 cm intervals

and averaged at the corresponding depth segment of observed SOC values. Aggregated values

were averaged between insertions and days for comparison.

Moist core spectra

Moist core spectra were also converted to absorbance and smoothed with a Savitzky-Golay

filter (window size of 11 and a second-order polynomial). Then the spectra were trimmed 500

to 2450 nm wavelength to get rid of low signal:noise ratio areas. Then the spectrum was

resampled at 10 nm resolution before applying a baseline correction. After that local EPO

transformation matrix was applied for the analysis where EPO matrix was used to reduce the

moisture effect from the NIR spectrum. SOC was also predicted without applying EPO matrix

in the NIR spectrum, where this step of applying EPO was skipped.

Air-dried core spectra

Air-dried core spectra were converted to absorbance, A = log(1/R), then smoothed with a

Savitzky-Golay filter comprising of window size of 11 and a second-order polynomial. Low

signal:noise ratio areas of the spectrum were removed to leave the 500 to 2450 nm wavelength

range, which was resampled at 10 nm resolution. A baseline correction was performed utilising


105

a standard normal variate transformation. Predictive models were applied, and as readings were

taken on strict 2 cm intervals, the predicted SOC contents were aggregated by directly

averaging all samples within a given depth segment before validation with observed SOC

values.

Air-dried ground spectra

Soil cores obtained from each site at various day of insertions were divided into 10 cm segments

and grounded before passing through 2 mm sieve. The ground samples were scanned using the

contact probe in the laboratory. Spectra obtained on ground composite samples were processed

in a similar manner to air-dry core spectra except averaging triplicate spectra as the first step

in pre-processing, and SOC estimations did not have to be aggregated as readings were taken

directly on the ground samples used for SOC quantification.

Figure 4.6 Methodology scheme of complete study from NIR scans to validations of

predicted soil organic carbon (SOC).


106

4.3.5 Calculation of SOC stock

SOC stock was calculated from the SOC predicted using VisNIR spectra collect from

penetrometer insertions. SOC stock to a given depth was calculated by summation of SOC for

contributing depth increments using the following equation, bulk density was provided at 10

cm, and no coarse fragments were present in the soils investigated,

𝑆𝑆𝑆𝑆𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (𝑠𝑠 ℎ𝑎𝑎−1) = ∑ 𝑆𝑆𝐶𝐶𝑖𝑖 × 𝐵𝐵𝐵𝐵𝑖𝑖 × 𝑠𝑠𝑖𝑖𝑑𝑑𝑖𝑖=1 × 10000 (1)

Where,

𝑑𝑑 = number of depth increments

𝑆𝑆𝐶𝐶𝑖𝑖 = organic content (g g-1) of depth increment i

𝐵𝐵𝐵𝐵𝑖𝑖 = bulk density (Mg m-3) of depth increment i

𝑠𝑠𝑖𝑖 = thickness of depth increment i (m)

4.3.6 Validation and other statistics

In this study, the coefficient of determination (R2), root-mean-square error (RMSE), bias, and

corrected RMSE (RMSEc) were determined for the calibration between soil dry core spectra

and SOC. These statistics were also used for validation of SOC predicted from moist cores and

VisNIR penetrometer system. The coefficient of determination (R2) is the proportion of the

variance in the SOC that is predictable from soil spectra. Root-mean-square error (RMSE) is a

measure of the differences between SOC predicted by a cubist regression model and the

observed SOC. Bias is a measure of the expected value of the SOC differs from the estimated

SOC. RMSEc measures the goodness of fit between predicted and actual SOC. These statistics

were also used to validate the calibration models by comparing the predicted SOC with the

observed SOC measured from the 33 samples in the laboratory.


107

4.4 Results and Discussion

4.4.1 Performance of VisNIR penetrometer system

The VisNIR penetrometer system was used in three different locations of Lansdowne Farm,

Cobbitty, NSW. The penetrometer system was tested at the same sites on three separate days

selected based on rainfall incidences to maximise the variability in soil moisture. The

maximum depth of insertions varied between 70 cm and 90 cm depending on soil moisture

conditions. The comparisons between dry and moist core spectra are shown in Figure 4.7a. The

mean dry soil spectra have higher reflectance than mean spectra from moist cores, but similar

features were observed between the dry and moist core spectra (i.e. broad absorbance features

between 1300–1500 nm, 1800–2000 nm, and 2150-2300 nm). The soil spectra collected using

VisNIR penetrometer (Figure 4.7b) have a higher variation compared to the soil spectra

collected in the laboratory. A number of distinct VisNIR reflectance patterns with higher

reflectance, particularly in the visible range, were recorded using the penetrometer system.

These spectra were not observed in the moist and dry core samples of Figure 4.7a, and believed

to be due to living plant roots, and fresh plant debris.


108

(a)

(b)

Figure 4.7 Soil VisNIR spectra collected from (a) dry and the moist cores in the laboratory and

(b) using VisNIR penetrometer system in the field. Mean spectra of dry and moist cores were

plotted for laboratory measurements.


109

4.4.2 EPO transformation matrices

A number of EPO projection matrices (local and penetrometer) were constructed to test

different scenarios based on the optimum numbers of components from Wilks’ Λ test. The

effects of these EPO matrices on the improvement of calibration and validation statistics were

analysed.

The local EPO transformation matrix was constructed using VisNIR spectra from the moist

and dried soil cores collected from the Lansdowne farm, Cobbitty, NSW. After the collection,

soil cores were transported to the laboratory, and VisNIR scans of the half-cut cores were

scanned with the handheld ASD spectroradiometer. Then these cores were air-dried at 40°C

for 48 hours before scanning the dry cores.

The penetrometer EPO transformation matrix was built using VisNIR spectra recorded in the

field using the penetrometer and dried soil cores collected from the Lansdowne farm, Cobbitty,

NSW. Soil cores were transported in the laboratory and air-dried at 40°C for 48 hours. Half-

cut dry core VisNIR scans were recorded using a contact probe in the laboratory.

The number of components used in the construction of EPO transformation matrices was

selected based on the similarity of the transformed spectra of two or more moisture contents

with the sample spectra known as the Wilks’ Λ (Roger et al., 2003). It is a cluster separation

metric calculated by the ratio between the inter-group variance and the total variance (Webster,

1971). In this study, two (local and penetrometer) EPO transformation matrices were used for

removing the effect of the soil moisture from the NIR spectra.


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Figure 4.8. Wilks’ Λ for a different number of principal components from NIR spectra from

sampling sites (local EPO) and combined NIR spectra from sampling sites (penetrometer EPO)

For both EPO transformation matrices, the first maximum Wilks’ Λ were found for two

principal components, as shown in Figures 4.8. So, EPO transformation matrices were

constructed using two principal components.

Ideally, an EPO would not be constructed for each study, and it is believed an EPO projection

matrix could be generalised with increased observations. However, it was found that using the

EPO constructed from moist and dry soil cores did not work adequately for the spectra collected

in the field using the penetrometer system. It was hypothesised that the unique effects of

moisture content are produced when using a penetrometer system as the pressure induced by

the insertion of the penetrometer forces additional moisture out of the pore space. In addition,

the pushed penetrometer can produce a smearing effect on soil surfaces.

The projection matrices displayed in Figure 4.9 shows the elements where the spectra were

transformed in the wet scans from the moist split cores and the penetrometer system. High


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positive values were found within 500 -600 nm and 1900- 2000 nm in the EPO transformation

matrix from moist cores. EPO transformation matrix from the penetrometer system shows a

moderate value (0.4-0.5) within 600 -800 nm and a high value within 1850 -2100 nm. Moist

core (local) EPO transformation matrix was highlighted by large positive values, indicating

more spectra regions were transformed compared to the penetrometer transformation matrix.

After the transformation, the deleterious effect of variable soil moisture was reduced and the

calibrated SOC models could be applied to the transformed spectra .

(a) (b)

Figure 4.9. A comparison of EPO projection matrices developed using VisNIR spectra of a)

moist cores (local) and b) the penetrometer system. The reference state in both cases were

VisNIR spectra of split, air-dried (40°C) soil cores.

4.4.3 Calibration and validation between soil VisNIR spectra and SOC

Cubist regression models were constructed to estimate SOC from 1826 observations of SOC

and corresponding VisNIR spectra in dry-ground conditions for both field-moist and dry

spectra. The predicted SOC were aggregated at the matching depth segment of observed OC


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values. Then these aggregated values were averaged between the number of insertions and days

of measurements for comparison. Validation of predicted SOC was done by comparing with

the SOC of 33 samples measured in the laboratory. Estimated SOC values were aggregated by

depth to the corresponding laboratory-based observations where appropriate.

4.4.3.1 Dry cores

The calibration statistics are shown in Table 4.2. Reasonably good R2 (0.81) and low RMSE

(0.20 %) were observed from this model. The corrected RMSE was exactly the same as RMSE

as no bias was observed in this analysis.

Table 4.2. Calibration and validation statistics for dry cores

Spectra source R2 LCCC RMSE (%) Bias (%) RMSEc (%)

Spectral library (calibration) 0.81 0.89 0.20 -0.02 0.20

Dry-ground (validation) 0.93 0.80 0.36 0.01 0.36

Dry core (average 3 cores) (validation) 0.94 0.85 0.32 0.13 0.29

Validation was done with both the soil VisNIR spectra collected from the dry ground samples

and dry cores. High R2 (0.94) was found for both the dry ground samples and the dry cores. An

improvement in the RMSE was found for the dry core compared to the ground samples.

Slightly less accurate prediction of the two high SOC samples caused higher RMSE for dry-

ground samples, which were modelled better on the dry cores. Samples with SOC <2% were

modelled more accurately with the dry-ground samples, which may be attributable to the

greater representation of such samples in the calibration library. After the corrections for bias,


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RMSEc becomes 0.29 % for dry cores, but for ground samples, RMSEc remains the same

(0.36).

Ogrič et al. (2019) used dry-ground 173 samples and different approaches based on the

calibration subset, validation subset, and selection method. Mean R2 for calibration and

validation was found to be 0.90 and 0.84 from Partial Least Squares Regression (PLSR) model,

respectively. The R2 for the calibration model from the spectral library (0.81) was slightly

lower than the mean R2 found from the study by Ogrič. But better R2 was found for the

validation model from dry cores compared to the mean R2 calculated in this study.

Jia et al. (2017) used 24 soil cores of 1 m and recorded field-moist intact VisNIR and air-dried

ground VisNIR for every 5 cm interval to predict SOC using PLSR and a Support Vector

Machine (SVM). Both validation R2 from SVM (0.86) and PLSR (0.72) was lower than the R2

found from the dry core and dry-ground samples.

For both observed and predicted SOC depth profiles of various days and three sites are shown

in Figure 4.10. Site 3 has the highest SOC (about 0.15% - 3.08%) and the lowest SOC (0.15%

- 1.88 %) was found for site 1. A higher bias was observed between the predicted and observed

SOC for all three days on site 1, and the least bias was observed in site 3.


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Figure 4.10. Depth profile of observed and predicted SOC from a single dry core extracted on

three different days for all three sites. For spectroscopic measurement, predicted SOC was

plotted for every 2 cm. For lab measurements, SOC of 10 cm homogenised depth samples and

an additional 0-2 cm topsoil samples were plotted against depths.

4.4.3.2 Moist Cores

VisNIR scans from moist soil cores collected from the insertion sites merged with scans from

the spectral library were used to construct soil moist core calibration models using cubist

regression analysis. R2 and RMSE were found to be 0.80 and 0.21%, respectively, with a

considerably low bias of -0.02%.

The calibration model was used to predict SOC and validated with the actual SOC measured

in the laboratory. Using field spectra without EPO transformation, the determination coefficient

reduced (0.82) and a comparatively high RMSE (0.59%) was observed, as found from the

validation statistics between predicted and observed SOC. Another validation was done with


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the scans from moist cores when the local EPO transformation matrix from moist to dry cores

was used to minimise the effect of soil moisture from the moist soil scans. After the application

of local EPO, improved R2, and reduced RMSE of 0.88 and 0.54% were observed. The bias

was -0.07%, and the corrected RMSE was reduced by 0.05%.

Jia et al. (2017) used field-moist intact VisNIR to predict SOC, and reported validation R2 from

SVM, and PLSR to be 0.81 and 0.67, respectively. The validation R2 for the moist core without

applying the EPO transformation matrix was 0.82, which is almost similar to Jia's study. But

validation statistics (R2 = 0.88) were much improved after applying the local EPO matrix. Since

EPO transformation was not applied with the field-moist intact VisNIR, the validation statistics

were not as promising as this study.

Liu et al. (2020) used spectra angle of EPO (EPO-SA) to study whether other inherent soil

properties such as clay content, organic matter etc get removed by the EPO transformation

process by investigating the shape of the external part of the spectra. It was found that, the

external spectra were not related to any other soil properties but, soil moisture was quantitavely

predicted with R2 = 0.77 and RMSE = 0.09 g kg-1.

Table 4.3. Calibration and validation statistics for moist cores


Spectral library EPO transformed (calibration)

0.80 0.88 0.21 -0.02 0.21

Moist core (No EPO) (validation) 0.82 0.41 0.59 0.06 0.59

Moist core (local EPO) (validation) 0.88 0.51 0.54 -0.07 0.54


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Figure 4.11 shows the observed and predicted SOC depth profile for high, medium, and low

soil wetness values, which correspond to the collection of soil cores on three different days

from all the sites. For site 3, high SOC was found within 0-10 cm in the laboratory, but from

the prediction, the maximum SOC at that depth was around 2%. Comparatively lower SOC

was found for site 1 than site 2.

Figure 4.11. Depth profile of observed and predicted SOC from moist (with local EPO) cores

at different days for all three sites. For spectroscopic measurement, predicted SOC was plotted

for every 2 cm. For lab measurements, SOC of 10 cm homogenised depth samples and an

additional 0-2 cm topsoil samples were plotted against depths.

4.4.3.3 VisNIR penetrometer system

Without EPO transformation

VisNIR penetrometer was used to acquire in-situ soil spectra from all three sites on three

different days. Field spectra (without EPO transformation) collected using the penetrometer


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was used to calibrate against SOC. The R2 and RMSE of the calibration model were found to

be 0.88 and 0.24%, respectively (Table 4.4).

Table 4.4. Calibration statistics from VisNIR penetrometer (without EPO)


Spectral library (Without EPO transformation) 0.88 0.94 0.24 0.01 0.24

From the validation statistics obtained with the calibration model with raw spectra, the mean

R2 with all sites and days was 0.67, even though the mean R2 of different days for each site

varies within 0.86 – 0.93. For separate sites, RMSE varied between 0.39-0.88%, but overall,

RMSE was 0.58%. Comparatively, low bias was observed for sandy clay loams at site 3, and

high bias was found on site 2, which comprises mostly medium clay soils from 0.5 m – 1 m

(Table 4.5).


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Table 4.5. Validation statistics for Penetrometer insertion (without EPO)

Sites R2 LCCC

RMSE (%) Bias (%) RMSEc

(%)

Mean (all sites and days) 0.67 0.65 0.58 0.36 0.45

Site 1 (mean) 0.93 0.58 0.60 0.52 0.29

Site 1(day 1) 0.94 0.68 0.39 0.37 0.12

Site 1 (day 2) 0.93 0.49 0.88 0.62 0.63

Site 1 (day 3) 0.89 0.53 0.61 0.59 0.18

Site 2 (mean) 0.86 0.42 0.56 0.48 0.28

Site 2 (day 1) 0.73 0.55 0.43 0.30 0.31

Site 2 (day 2) 0.76 0.41 0.60 0.52 0.30

Site 2 (day 3) 0.90 0.43 0.53 0.44 0.29

Site 3 (mean) 0.94 0.69 0.58 0.08 0.57

Site 3 (day 1) 0.94 0.49 0.78 0.10 0.77

Site 3 (day 2) 0.90 0.66 0.58 -0.18 0.55

Site 3 (day 3) 0.93 0.78 0.46 0.03 0.46

Depth profiles of all sites show how predicted, and observed SOC varies to the maximum

reading depth 0.9 m, as shown in Figure 4.12. High SOC (up to 3.5 – 4 %) was predicted at

sites 1 and 2 within 10 cm below the surface due to the high proportion of grassroots and debris.

But laboratory SOC measurements were done for the dry ground soil samples sieved through

the 2 mm sieve. So, relatively reasonable SOC was observed in the laboratory data.


119

Figure 4.12. Depth profile of observed and predicted SOC from raw spectra on different days

for all three sites. For spectroscopic measurement, predicted SOC was plotted for every 2 cm.

For lab measurements, SOC of 10 cm homogenised depth samples and an additional 0-2 cm

topsoil samples were plotted against depths.

Using moist to dry EPO

An EPO transformation matrix was locally constructed from the soil VisNIR spectra from half-

cut moist and air-dried (40°C for 48 hours) soil cores transported in the laboratory. The

calibration statistics when developing predictive models on the spectral library after the

application of EPO transformation matrix were comparable to the statistics without EPO

transformation. The R2 was 0.88, and the RMSE was 0.25. The corrected RMSE for the bias

was found to be 0.25%, as shown in Table 4.6.


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Table 4.6. Calibration statistics from VisNIR penetrometer (with local EPO)

Spectra source (with local EPO) R2 LCCC RMSE (%) Bias (%) RMSEc (%)

Spectral library EPO transformed 0.88 0.93 0.25 0.01 0.25

In situ estimation of SOC using the penetrometer system were improved after applying the

local EPO transformation matrix compared to the statistics with no EPO as shown in Table 4.7.

Overall, the observed R2 increased to 0.86 and the RMSE decreased to 0.36%. The bias of the

validation statistics reduced by half (0.18%). The corrected RMSE for all sites and days of

insertions decreased to 0.32%. Among all sites, site 2 had the lowest mean R2 (0.92), and site

3 had highest mean RMSE (0.42%) compared to other sites.


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Table 4.7. Validation statistics for Penetrometer insertion (with local EPO)

Sites R2 LCCC RMSE (%) Bias (%) RMSEc (%)

Mean (all sites and days) 0.86 0.84 0.36 0.18 0.32

Site 1 (mean) 0.94 0.78 0.29 0.22 0.19

Site 1(day 1) 0.95 0.80 0.26 0.17 0.20

Site 1 (day 2) 0.93 0.77 0.30 0.20 0.23

Site 1 (day 3) 0.93 0.75 0.31 0.27 0.15

Site 2 (mean) 0.92 0.67 0.38 0.33 0.18

Site 2 (day 1) 0.84 0.71 0.31 0.23 0.21

Site 2 (day 2) 0.85 0.57 0.47 0.41 0.23

Site 2 (day 3) 0.94 0.71 0.33 0.30 0.16

Site 3 (mean) 0.96 0.81 0.42 -0.02 0.42

Site 3 (day 1) 0.95 0.71 0.55 0.02 0.55

Site 3 (day 2) 0.89 0.79 0.35 -0.08 0.34

Site 3 (day 3) 0.93 0.75 0.52 -0.12 0.50

The penetrometer system over-predicted SOC content within the 5 cm depth for site 1, due to

the high proportion of plan roots in the topsoil (Figure 4.13). Similar to the depth profiles from

without EPO, site 2 has a higher bias which reflects in the over-prediction of SOC content

thorough the whole profile. Comparatively higher OC was observed in site 3 from both

laboratory tests and penetrometer system within 0 -20 cm depth.


122

Figure 4.13. Depth profile of observed and predicted SOC from EPO (local) transformed

VisNIR spectra on different days for all three sites. For spectroscopic measurement, predicted

SOC was plotted for every 2 cm. For lab measurements, SOC of 10 cm homogenised depth

samples and an additional 0-2 cm topsoil samples were plotted against depths.

Using penetrometer EPO

The penetrometer EPO transformation matrix was constructed from the in-situ soil VisNIR

spectra collected using the penetrometer and the scans from the air-dried soil samples (Table

4.8). There were no differences observed in the calibration statistics when developing

predictive models on the spectral library after local EPO and penetrometer EPO

transformations except the bias of the statistics. The bias is slightly lower (0.008%) for the

penetrometer EPO, but it did not improve the RMSE.


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Table 4.8. Calibration statistics from VisNIR penetrometer (with penetrometer EPO)

Spectra source (with

penetrometer EPO) R2 LCCC

RMSE

(%)

Bias

(%) RMSEc (%)

Spectral library EPO transformed 0.88 0.93 0.25 0.008 0.25

In situ estimation of SOC using the penetrometer system with penetrometer EPO

transformation were improved compared to those obtained using the local EPO. After removing

the effect of field soil moisture, R2 raised to 0.88, and RMSE dropped to 0.32%. Bias was also

reduced to 0.15%, so the corrected RMSE was found to be 0.28% (Table 4.9).


124

Table 4.9. Validation statistics for Penetrometer insertion (with penetrometer EPO)

Sites R2 LCCC RMSE (%) Bias (%) RMSEc (%)

Mean (all sites and

days) 0.88 0.87 0.32 0.15 0.28

Site 1 (mean) 0.94 0.78 0.29 0.21 0.19

Site 1(day 1) 0.94 0.78 0.29 0.18 0.23

Site 1 (day 2) 0.94 0.74 0.37 0.24 0.28

Site 1 (day 3) 0.91 0.78 0.25 0.22 0.13

Site 2 (mean) 0.92 0.70 0.33 0.27 0.19

Site 2 (day 1) 0.82 0.70 0.28 0.11 0.25

Site 2 (day 2) 0.83 0.64 0.41 0.35 0.22

Site 2 (day 3) 0.95 0.74 0.30 0.25 0.17

Site 3 (mean) 0.95 0.84 0.35 -0.02 0.35

Site 3 (day 1) 0.95 0.66 0.61 -0.06 0.61

Site 3 (day 2) 0.90 0.79 0.35 -0.13 0.32

Site 3 (day 3) 0.95 0.87 0.25 -0.02 0.25

The depth profile of observed and predicted SOC from the VisNIR penetrometer insertion for

all the sites is shown in Figure 4.14. For site 1, high SOC was found within 0 – 10 cm from the

prediction from VisNIR penetrometer scans. For site 2, higher SOC was predicted from 20 –

90 cm from the NIR scans compared to laboratory measurements.


125

Figure 4.14. Depth profile of observed and predicted SOC from EPO (penetrometer)

transformed VisNIR spectra on different days for all three sites. For spectroscopic

measurement, predicted SOC was plotted for every 2 cm. For lab measurements, SOC of 10

cm homogenised depth samples and an additional 0-2 cm topsoil samples were plotted against

depths.


126

Figure 4.15. Residuals of estimated soil organic carbon (SOC) content for different wetness of

the soil

OC residuals plotted against soil wetness across the three sampling days demonstrate that

prediction obtained using the penetrometer system has a slight bias (0.15%). However, SOC

residuals were not correlated with soil wetness (Figure 4.15). Most of the residuals are within

0 to -0.5 %, which indicates the VisNIR penetrometer, to some extent overestimating SOC.

Overall, the predicted SOC content from the penetrometer system shows a high correlation

with the SOC content measure in the laboratory using the Walkley-Black method. The

penetrometer EPO transformation matrix was able to remove the effect of the soil moisture

from the VisNIR spectra to predict SOC content precisely. Penetrometer EPO performed better

compared to the local EPO from moist cores as VisNIR spectra collected from the insertion of

the penetrometer compress soil and the effect of soil moisture. The results suggest that we do


127

not require to collect additional information on soil moisture and models calibrated using

VisNIR spectra from air-dried and ground soils can be applied to in-situ data. Due to the

excessive amount of plant roots, penetrometer system predicts high SOC content compared to

SOC content measured in the laboratory within the 0 -10 cm depth.

4.4.4 Estimation of SOC stock from VisNIR penetrometer system

SOC stocks for every 10 cm depth were calculated at the three different sites using the

estimated SOC from VisNIR penetrometer system and the laboratory measurements from

homogenised samples of all three days using equation 1. Figure 4.16 shows that estimated SOC

stocks measured using VisNIR penetrometer system are higher than the actual SOC stocks

from laboratory measurements using Walkley-Black method. Only estimated SOC stocks for

the top 20 cm of site 3 were lower than the actual SOC stocks. Standard deviations of between

the estimated and actual were calculated for all three sites. Standard deviation seems to

decrease with depth except for site 3 and varies from 0.31 t ha-1 to 4.59 t ha-1 within all sites.

The average standard deviation is highest in site 3 (2.52 t ha-1) and lowest in site 1 (1.31 t ha-

1).


128

Figure 4.16. Comparisons of predicted SOC stocks from the penetrometer and actual SOC

stocks from laboratory measurements. Dotted black line and the blue line indicate the 1:1 line

and the actual trend in the model, respectively.

4.5. Conclusions

In this study, a VisNIR penetrometer system was successfully used to estimate SOC from

spectra collected in situ. The external soil spectral library constructed from different locations

around NSW, Australia, was successfully used for calibration purposes, and the EPO

transformation matrices were used to correct for the spectral difference between the in-situ and

dry spectra acquired by the VisNIR penetrometer system. SOC was predicted from four

different conditions (air- dry core, ground composite samples, field moist core, and field

conditions using penetrometer insertion). Predicted SOC was then validated from 33 samples

collected from three different sites, and SOC was measured in the laboratory using the

Walkley-Black method (Walkley and Black, 1934). Good R2 and RMSE (0.88 and 0.32%,

respectively) were found between the observed SOC and predicted SOC from VisNIR


129

penetrometer insertions at three different locations after the correction using the penetrometer

EPO transformation matrix. VisNIR penetrometer system seems to overpredict SOC stocks

compared to the laboratory measurements. To test the effectiveness of this penetrometer

system, different locations with various mineralogy and textures of soils need to be analysed

in the future. This technique substitutes the conventional methods for measuring SOC content

where soil cores are extracted and transported to the laboratory for analysis. This system can

be potentially used to record the high-resolution vertical distribution of SOC to characterise its

profile rapidly and cost-effectively compared to the conventional methods that would benefit

the soil science and agriculture communities.


130

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138

CHAPTER 5

AUTOMATED SOIL PARTICLE SIZE ANALYSIS USING

TIME OF FLIGHT DISTANCE RANGING SENSOR

This chapter is published as:

Murad, M. O. F., Jones, E. J., & Minasny, B. Automated soil particle size analysis using time

of flight distance ranging sensor. Soil Science Society of America Journal.

Chapter 5: Automated Soil Particle Size Analysis Using Time of Flight Distance Ranging Sensor

139

5.1 Summary

Particle size distribution influences various physical, chemical, and biological properties of

soil. The hydrometer test is the most commonly used method for soil particle size analysis

because of its inexpensive instrumental set up and simple use. But manual observations are

required to obtain high-resolution particle size distributions. X-ray, γ-ray attenuation and laser

diffraction techniques can measure particle size distribution automatically and faster than the

sedimentation methods. However, these techniques have very high equipment costs. The

objective of this study is to develop an automated method for measuring a wide range of soil

particle-size distribution based on the hydrometer method. Adafruit VL6180X Time of Flight

(ToF) distance Ranging Sensor was used to measure the distance between the sensor and the

tip of the hydrometer over an eight-hour time interval as particles settled. An initial distance

between the sensor and tip of the hydrometer was recorded and used to convert the distance

with time to particle size and concentration. The temperature of the suspension was measured

at the same time using a waterproof DS18B20 1-Wire digital temperature sensor. The Arduino

Uno R3 was used to program the ToF sensor to record both the distance and the temperature at

5-second intervals. The recording of the distance with time and solution temperature was

converted to particle diameter and concentration based on Stokes’ law. The whole particle size

distribution, from 40 μm to 2 μm, was obtained automatically within eight hours. A total of 10

soil samples with different soil texture were used to test the

system. The measurement from the automated system was checked against the standard pipette

method. Results show that measurement of the percentages finer than 2, 5, 10, 20, and 40 μm

obtained by the two methods are highly correlated, with R2 values between 0.857 to 0.896. The

hardware to automate the system costs less than $70 (USD) and provides an easy, efficient, and

cost-effective way of measuring soil particle size distribution.


140

5.2 Introduction

The particle size distribution of soil, commonly represented as a percent mass at defined ranges

of particle size, is a fundamental property of soil. The particle size distribution affects soil

physical properties such as water retention, hydraulic conductivity, leaching and erosion

potential, as well as plant nutrient storage and organic matter dynamics, including carbon-

sequestration potential (Kettler et al., 2001). Soil particle size distribution is a key parameter

used for evaluating the quality of soil in terms of agricultural management practices (Kettler et

al., 2001).

A major difficulty when comparing or sharing soil textural information internationally is

different equivalent diameters used to define the silt-sand threshold. The silt-sized fraction is

defined as particles with an equivalent diameter of 2-50 μm by the USDA/FAO system, but

Australia, Belgium, France, Italy, Netherlands, etc. adopted the International system where the

particles of 2-20 μm are considered as silt-sized. Other systems have also utilized 2-60 μm or

2-63 μm. This leads to difficulties in comparing soil textural classes and also incompatibilities

for PTFs generated under one texture classification system to be applied to data measured in

another system. Several models have been proposed to convert measurements between silt and

sand fraction boundary from 20 to 50 μm via PTF (Marshall, 1947; Shirazi et al., 1988; Buchan,

1989; Rousseva, 1997; Minasny and McBratney, 2001; Padarian et al., 2012). Even though we

can use these functions to relate the fractions in one system to the fractions in the other, it

would be more accurate to measure both values directly. Also, because of the different range

of particle size used in the different texture classification system, it would be advantageous to

measure a full particle-size distribution, rather than percentages of sand, silt, and clay (Nemes

et al., 1999). Thus, the ability to measure a complete particle distribution will circumvent the

need to convert from one system to the other. A continuous particle size distribution also

provides richer information of essential soil properties such and pore space distribution, water


141

retention capacity, hydraulic conductivity, absorption and thermal properties of soil (Shiozawa

and Campbell, 1991; Hajnos et al., 2006; Slawinski et al., 2006; Ryżak and Bieganowski, 2011;

Yang et al., 2019).

Table 5.1 summarizes the comparisons of commonly used techniques for particle size analysis

of soil. The hydrometer and pipette methods are commonly used methods based on

sedimentation of soil particles through a fluid under a constant gravitational field. The

hydrometer methods rely on measuring the density of the soil particle suspension at a specific

depth and time. In contrast, the pipette method measures the concentration of soil suspension

at a specific depth and time. These methods are well-established for soil particle size analysis

(Loveland et al., 2000; Gee and Or, 2002; Kroetsch and Wang, 2008; ISO 11277, 2009),

although the results obtained from these methods sometimes slightly vary from each other

(Miller, Radcliffe, and Miller, 1988; ISO 11277, 2009). The two methods also have some

unavoidable disadvantages. These methods are time-consuming and require a relatively large

amount of soil sample compared to laser diffraction and Sedigraph methods. Moreover, soil

particles having a size of less than 1 μm cannot be measured precisely because of Brownian

motion on the sedimentation rate (Ferro and Mirabile, 2009). The results obtained from these

methods depend on the expertise of the operators (Coates & Hulse, 1985). Moreover, the

pipette method requires manual sampling of the soil suspension, and the hydrometer method

requires manual data reading for different soil particle sizes. Therefore, it is time-consuming

to obtain detailed continuous particle size distributions from these methods (Naime et al.,

2001). Moreover, hydrometer or pipette methods require manual readings for each particle size

threshold, often leading to a low-resolution PSD. On the other hand, more precise techniques

such as the laser diffraction analysis and sedigraph technique require specialized equipment

which involves a high initial cost.


142

Table 5.1. Summary of comparisons of different methods of soil particle size analysis (data

derived from: Coates & Hulse, 1985; Beuselinck et al., 1998; Jacob et al., 2002; Arriaga et al.,

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143

2006).

There have been many attempts to automate the gravitational sedimentation procedure for soil

particle analysis, as summarized in Table 5.1. Zhang and Tumay suggested a method based on

the relation between the specific weight of soil and solution pressure measured in two depths

and calculated by linear analysis of the pressure difference between two points (Zhang &

Tumay, 1995). By analyzing the force exerted by floating soil particles in the suspension,

Nemes proposed an automated hydrometer method (Nemes et al., 2002). In 2004, Kovacs used

another liquid of known density to determine the soil density using an electronic measurement

and test control system (Kovács et al., 2004). High-resolution measurements used in this

method allows for finer measurements of particle size distribution. Another method based on

the integral suspension pressure was introduced to take into account all the soil particles above

a certain measuring depth (Durner et al., 2017). This technique can determine particle size

distribution with very small variance and absolute error in the clay and silt fractions but require

a specialized system for measuring suspension pressure. Larger sampling zone compared to

pipette and hydrometer test makes this technique more reliable than other techniques (Durner

et al., 2017).

X-ray attenuation (sedigraph), γ-ray attenuation, dielectric, and laser diffraction methods are

rapid, repeatable and less operator-dependent than hydrometer and pipette methods. The major

advantage of these methods is that these provide continuous particle size distribution over a

large range of particle diameters, but they are also expensive, as shown in Table 5.1. The

hydrometer test is the most commonly used sedimentation method for soil particle analysis

because of its inexpensive instrumental set up and simple use. While it is possible to take

continuous manual readings during the settling time to provide more points on the particle size

distribution, it would be very labour-intensive. For these reasons, it becomes essential to


144

automate the hydrometer testing procedure to obtain an accurate, less laborious and cost-

effective continuous particle size distribution.

The objective of this study was to develop an automated, cheap, and simple method for

evaluating soil particle-size distribution. This method will provide a continuous particle size

distribution curve of soil particles with the continuous measurement of suspension temperature.

Suspension temperature will allow us to calculate the viscosity more precisely and improve the

soil particle size assessments.

5.3 Material and Methods

5.3.1 Basic Principles of Gravitational Sedimentation

The terminal velocity of particles settling in a fluid under a constant gravitational field is

defined by Stokes’ law,

𝑣𝑣 = 𝑔𝑔(𝜌𝜌𝑠𝑠−𝜌𝜌𝑓𝑓)18𝜂𝜂

𝐵𝐵2 (5.1)

where,

𝑣𝑣 = terminal sedimentation velocity (cm s-1)

𝑔𝑔 = acceleration due to gravity (cm s-2)

𝜌𝜌𝑠𝑠 = sediment density (g cm-3)

𝜌𝜌𝑠𝑠 = fluid density (g cm-3)

𝜂𝜂 = viscosity (g cm-1 s)

𝐵𝐵 = particle diameter (cm)

In this procedure, soil particles were chemically and physically dispersed over 48 hours and

then transferred in a measuring cylinder. Stirring or shaking of the cylinder is performed to

fully distribute suspended particles are allowed to settle under the force of gravity. The


145

gravitational sedimentation methods are based on Stokes’ law (Stokes, 1851) with the

following assumptions:

• Particles are settling in laminar flow

• The particles are perfect spheres

• Particle surfaces are smooth

• Particles do not interfere with each other during sedimentation

• The terminal velocities of the particles are reached immediately

• Despite different particle sizes, particle density, is constant (commonly given as 2.65 g

cm-3, i.e., the density of quartz)

It was demonstrated that soil particles almost immediately reach terminal velocity (Gee and

Or, 2002), but the rest of the assumptions are not always true.

With the rising temperature, the atomic distance of particles in the fluid increases. So, it causes

the expansion of the fluid and its volume increases. As particle volume has an inversely

proportional relationship with density, fluid density will decrease with the increasing

temperature. Also, with the increment of atomic distances, the attraction forces between the

atoms decreases which allows it to flow past each other. Thus, with a higher temperature, the

viscosity of fluid increases. As the change of temperature can affect the measurement of

particle size diameter, the proposed method measured and recorded suspension temperature

using a waterproof DS18B20 1-Wire digital temperature sensor. These temperatures were

considered during the calculation of the particle diameters.

5.3.2 Instrumental Setup


146

This study utilized the established hydrometer method but automated the reading of specific

gravity by measuring hydrometer displacement over time with a ToF sensor. The Adafruit

VL6180X ToF Distance Ranging Sensor was used to measure the distance between the sensor

and the tip of the hydrometer. A waterproof DS18B20 1-Wire digital temperature sensor was

used to record the temperature. Arduino Uno R3 microcontroller was used to program and

control both sensors and record the measurements at 5-second intervals for 8 hours (Figure

5.1). The measurements can be analyzed instantly after the test on the computer attached to the

Arduino. Also, the data can be saved in an SD card and analyzed later.

(a) (b)

Figure 5.1 (a) Schematic diagram; and (b) Actual setup of Adafruit VL6180X ToF and

DS18B20 temperature sensor coupled with Arduino Uno R3 that includes a liquid-crystal

display to track the progress of the measurements.

5.3.3 Principle of ToF Distance Sensor

The Adafruit VL6180X ToF distance sensor is a compact module that contains a laser emitter

and single photon avalanche diode light receiver to measure the time required by the laser light

to bounce back to the sensor. This type of sensor uses a very narrow light source that can

Digital Temperature Sensor

Time of Flight (ToF) distance

Sensor

Arduino Uno R3


147

measure the distance of the object directly in front of it. Adafruit VL6180X can be used with

any 3-5 V power or logic microcontroller and measure distances between 20 to 200 mm (Figure

5.2).

Figure 5.2. Adafruit VL6180X ToF Distance sensor

Generally, distance or proximity sensors use IR (Infra-Red) technology that measures the

strength of the signal. But the strength of a signal can be easily affected by the reflectance

coefficient of the surface. Also, this type of sensor has linearity or 'double imaging' problems

where it cannot differentiate whether an object is too close or too far. To overcome this issue,

ToF Distance sensor was introduced to measure the time required for emitted photons to be

reflected in the source (Figure 5.3). The distance of an object can be accurately measured

without being affected by the surface characteristics using ToF distance sensors. The main

advantages of the ToF sensors are precise and fast distance measurement in an energy-efficient

and cost-effective way.


148

Figure 5.3. Working mechanism of ToF distance sensor

5.3.4 Stability Check of Vl6180x ToF Sensor

To ensure there is enough reflectance of the laser to bounce back to the source, a white

Styrofoam cap was used at the top of the hydrometer (Figure 5.4a). To confirm the accuracy

of the ToF sensor, a stability experiment was performed with the Adafruit VL6180X Time of

Flight Distance (ToF) sensor. In this experiment, a styrofoam block was placed at a distance of

10 cm for 24 hours in a temperature-controlled laboratory (Figure 5.4b). Approximately ±2

mm drift in the distance was found in this period, which was quite satisfactory at this stage of

the study. The distance was converted to hydrometer reading, where 1 mm is equal to 0.6 unit

of the hydrometer reading (g L-1).


149

(a) (b)

Figure 5.4 (a) Hydrometer with styrofoam cap; and (b) Stability check of Adafruit VL6180X

Time of Flight Distance (ToF) sensor

5.3.5 Experimental Procedure

Ten soil samples that were collected from across New South Wales, Australia, with a range of

texture were used to test the automated method. The characteristics of the soil samples are

described in Table 5.2. The soil samples were passed through a mechanical crusher with

apertures of 2 mm to obtain the soil sample <2 mm. Since all soil samples had a small amount

of organic matter (<1%), oxidization of organic matter was not performed. Then, 100 g for

sandy and 30 g for clayey samples was transferred to a 600 mL bottle to which 50 ml of sodium

hexametaphosphate [HMP, (NaP03)] and 500 mL of deionized water were added. Samples

were then placed in a rotating wheel for 48 hours of end-over-end shaking. After the agitation,

the soil solution was transferred to a 1000 mL measuring cylinder before adding deionized

water up to 1000 mL. Then the suspension was agitated using a stirrer to distribute soil particles

throughout the solution, and the time recording was started. After that, the Adafruit VL6180X

Time of Flight Distance (ToF) and DS18B20 1-Wire digital temperature sensor was set up at

the top of the cylinder, as shown in (Figure 5.5). A small tube of a diameter larger than the

styrofoam cap was added on the top of the suspension cylinder to inhibit movement of the

hydrometer outside of the reading zone of the ToF sensor. The ToF sensor was placed at the

top of the tube to maintain a constant distance above the surface of the suspension. The initial

distance between the ToF sensor and the styrofoam cap on top of the hydrometer, and the

hydrometer reading were recorded and adjusted for a time delay due to the setup of the

instrument. Temperature and distance data were recorded every 5 seconds for 8 hours, and the

data were stored in an SD card. Then the distance between the ToF sensor and the tip of the

hydrometer was converted to the hydrometer reading (R). Two trials were conducted with the


150

same soil samples to assess the repeatability of the proposed method. The second trial of

automated hydrometer test was performed 24 hours after the first trial.

Table 5.2. Characteristics of soil samples used for testing the automated hydrometer method

Site Soil type Clay Silt Fine Sand

Coarse Sand

Texture class

(< 2 µm) (2-20 µm) (20-200 µm)

(200-2000 µm)

%

Westwood 1 Kurosol 24.5 14.3 15.6 45.6 Clay Loam

Westwood 2 Kurosol 28.8 17.9 19.1 34.2 Clay Loam

Narrabri 2 Sodosol 33.4 15.3 12.6 38.7 Clay Loam

Nowley 1 Dermosol 43.8 9.8 11.2 35.2 Clay

Narrabri 1 Sodosol 53.6 13 13.6 19.8 Clay

Nowley 2 Dermosol 57.5 19.7 4.3 18.5 Clay

Landsdowne 1

Kurosol 62.1 5.4 5.3 27.2

Clay

Rawson Dermosol 64.9 15 3.1 17.1 Clay

Landsdowne 2

Dermosol 65.3 13.3 3.3 18.1 Clay

Nowley 3 Vertosol 73.8 7.5 5.1 13.6 Clay


151

Figure 5.5. The setup of automated hydrometer including Adafruit VL6180X (ToF) and

DS18B20 temperature sensor with a hydrometer and measuring cylinder.

For determining the percentage finer of soil particles from the automated hydrometer test, the

following equations were applied,

Percent mass finer = (𝐴𝐴 ∗ 𝐶𝐶𝐶𝐶𝐶𝐶 ∗ 100) / 𝑊𝑊𝑠𝑠 (5.2)

where,

𝐴𝐴 = correction for specific gravity (as hydrometer is calibrated for 𝐺𝐺𝑠𝑠 = 2.65)

= 1.65 * 𝐺𝐺𝑠𝑠 / ((𝐺𝐺𝑠𝑠 – 1) * 2.65)

𝐶𝐶𝐶𝐶𝐶𝐶 = corrected hydrometer reading

= 𝐶𝐶 + 𝐹𝐹𝑡𝑡 + 𝐹𝐹𝑧𝑧

𝐶𝐶 = Hydrometer reading

𝐹𝐹𝑡𝑡 = temperature correction

For 152H hydrometers,

𝐹𝐹𝑡𝑡 = −12.35952257 + T ∗ (1.51062059 + T ∗ (−0.06923056 + T ∗ 0.00122483))

For 151H hydrometers,


152

𝐹𝐹𝑡𝑡= −7.6338851 + T ∗ (0.93361976 + T ∗ (−0.04284159 + T ∗ 0.000758977))

where,

T = fluid temperature, in degrees Celsius

𝐹𝐹𝑧𝑧 = zero Correction

𝑊𝑊𝑠𝑠 = oven dried weight of soil (g)

The corresponding soil particle diameter (mm) was calculated from:

𝐵𝐵 = 𝐴𝐴 �𝐿𝐿 𝑠𝑠� (5.3)

where,

𝐿𝐿 = effective length (cm)

= −0.1642 ∗ 𝐶𝐶𝐶𝐶𝐶𝐶 + 16.31

𝑠𝑠 = settling time (min)

Even though the pipette method is fairly similar to the hydrometer method as both the methods

are based on gravitational sedimentation, it is more accurate than the hydrometer method

(Elfaki et al., 2016). This is due to the hydrometer method underestimating the clay content

during soil particle size analysis (Beretta et al., 2014) and also having a larger sampling zone

than the pipette method (Durner et al., 2017). For these reasons, the pipette test was conducted

to validate the percent finer values obtained from the proposed automated hydrometer method.

Stokes’ law was used to determine the settling time of soil particles of equivalent diameters of

2, 5, 10, 20 and 40 μm at a depth of 10 cm. In this calculation, soil density and viscosity at 22ºC

was, assumed to be 2.65 g cm-3 and 0.0098 g cm-1 s, respectively. A 10 mL pipette was used to

collect soil suspension and transferred in a steel container for oven drying. After the final

reading, the suspended soil was carefully poured off, and the remaining sediment washed

repeatedly to obtain the sand fraction.

Percentage finer from the pipette method was obtained by using the following equation,


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Percent finer = �𝑚𝑚𝑝𝑝𝑉𝑉𝑝𝑝� −

𝑚𝑚(𝑁𝑁𝑎𝑎𝐶𝐶𝑂𝑂3)6𝑉𝑉𝑝𝑝(𝑁𝑁𝑎𝑎𝐶𝐶𝑂𝑂3)6� � �𝑚𝑚𝑠𝑠

𝑉𝑉𝑠𝑠� �� (5.4)

where,

𝑚𝑚𝑝𝑝 = mass of soil in Pipette

𝑉𝑉𝑝𝑝 = volume of soil in Pipette

𝑚𝑚𝑠𝑠 = oven-dried soil mass

𝑉𝑉𝑠𝑠 = volume of solution in the cylinder

𝑚𝑚(𝑁𝑁𝑎𝑎𝐶𝐶𝑂𝑂3)6= mass of sodium hexametaphosphate

𝑉𝑉𝑝𝑝(𝑁𝑁𝑎𝑎𝐶𝐶𝑂𝑂3)6 = volume of sodium hexametaphosphate

5.4 Results and Discussions

The converted hydrometer reading for every 5 seconds for a clay loam sample is plotted against

time in Figure 5.6. The hydrometer reading drop very quickly until about 40 minutes, and the

dropping rate reduces after the bulk proportion of silt settles below the zone of influence of the

hydrometer. After 6 hours and 30 minutes, the slope flattens as most of the soil sediments

passed the effective length of the hydrometer, and only soil colloids remain.


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Figure 5.6 Graph of hydrometer reading vs. time obtained from the automated hydrometer

method for a clay loam.

Percent finer and diameter of the soil particles were calculated for every 5 seconds and plotted

against each other. The continuous particle distribution for five clayey and sandy representative

soil samples are illustrated in Figure 5.7. Percent of particles finer from 2 μm to 40 μm diameter

were shown for five soil samples. Among these soil samples, Westwood 1, Westwood 2 and

Narrabri 2 are clay loams, and the rest are clays according to the Australian soil classification

system. The percent finer varies only between 40.1% to 48.5% for Nowley 1, and 46.5% to

53.7% for Nowley 2 as clay contents are 33.4% and 57.5%, respectively. In contrast, percent

finer varies 66.26% to 83.46% for Nowley 3 soil sample as clay content is about 73.8%. Some

irregularities can be observed for the particle sizes <5 μm for Nowley 1 and Nowley 2 soil

samples.


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Figure 5.7 Percent mass finer and the diameter of soil samples obtained from the automated

hydrometer method.

The percentage of mass finer than the defined particle diameter obtained from the pipette

method shows a strong correlation with the results obtained from the automated hydrometer

method for Nowley 1, Nowley 2, Lansdowne 1, Rawson and Nowley 3 soil sample (Figure

5.8). The tests were done in a temperature-controlled room, so variation in temperature was <

2ºC.


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Figure 5.8 Comparison of % finer from Pipette vs. Automated Hydrometer Method Trial-1 and

Trial-2 for the particle diameters of 2, 5, 10, 20 and 40 μm.

From the comparison of the percentages of finer obtained from these two methods, R2 values

vary from 0.857 to 0.896.

5.4.1 Repeatability

The establishment of a measurement method requires measures of comparability with the

conventional method, the amount of accidental and systematic errors, and the repeatability of

similar results (Papuga et al., 2018). In addition, the uncertainty in the measurement is also a

requirement for soil testing laboratories seeking to retain ISO/IEC 17025 accreditation. In this

study, the repeatability of the proposed automated hydrometer method was tested. The same

test was repeated 24 hours after the first test. All experimental conditions such as temperature,

lighting condition, etc. and suspension concentration were held constant for both tests.


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From the repeatability analysis test, it is clear that this method can produce similar results under

similar environmental conditions. Percent finer for all five particle sizes (2, 5, 10, 20 & 40 μm)

obtained from two trials is plotted in Figure 5.9. From the graph, it is found that the percent

finer obtained from these two trials are similar to each other with an R2 of 0.93 and a root-

mean-square error of 3.13%.

Figure 5.9. Automated Hydrometer method Trial-1 vs. Trial-2 for the particle diameters of 2,

5, 10, 20 & 40 μm.

One of the unique features of the proposed automated hydrometer method is that the

temperature of the solution can be measured for every 5 seconds. Variation of the temperature

can affect the viscosity of the fluid, which plays a vital role in Stokes’ law. Generally, the

variation in the temperature in a controlled laboratory is negligible and does not affect the

particle size calculation. For example, in the Nowley 2 soil sample, assuming a constant

suspension temperature of 20 ºC, resulted in mean clay content of 57.21% as compared to

57.45% when accounting for temperature in the calculation. Monitoring suspension


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temperature is beneficial when a significant variation in the suspension temperature is

observed. For example, a 2ºC suspension temperature variation could cause a change in

particle-size of 1%.

On the other hand, this method has some limitations as well. Setting up of the ToF and

temperature sensors requires about 50-65 seconds. Generally, hydrometer takes about 30-40

seconds to provide a stable reading, and we cannot rely on the first 30-40 seconds reading in

the conventional hydrometer method. The automated hydrometer system takes an additional

20-25 seconds to set up, so only soil particles less than 40 μm in diameter can be measured

accurately. But the setting time can be minimized to less than 40 seconds by reducing the set-

up time of ToF and temperature sensors. This will help to obtain more reliable results for larger

particles of soil.

5.5 Conclusions

In this chapter, an automated hydrometer testing method using Adafruit VL6180X ToF

distance sensor and DS18B20 1-Wire digital temperature sensor is introduced. Using this

technique, hydrometer reading, and suspension temperature were recorded every 5 seconds

using an Arduino Uno R3 device for 8 hours. A stability check was performed on the ToF

sensor with ± 2 mm drift observed over a 24 hour period. Ten different soil samples were tested

using the pipette and automated hydrometer method, and similar percent finer was found from

both methods. Also, the repeatability of this proposed method was tested by repeating the same

test after 24 hours of the first trial and was found to produce a similar percent finer for the same

soil samples. Overall R2 values of the pipette method and the automated hydrometer method

were 0.857 to 0.896, respectively. A particle-size distribution curve can be generated instantly

after the test covering the 2-40 μm range. Previous attempts for automating the sedimentation

methods are complex, expensive and less accurate for silt and clay fractions (Zhang & Tumay


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1995; Nemes et al. 2002; Kovács et al. 2004). This proposed automated technique is easy,

cheaper and provides similar accuracy compared to conventional methods of soil particle size

analysis.

In the future, these experiments should be performed for a wider range of soil textures. Also,

the whole system should be placed in a customized stand for raising and lowering the ToF

sensor smoothly. The hardware cost to convert a traditional hydrometer into an automated

system including the Adafruit VL6180X Time of Flight Distance (ToF), DS18B20 1-Wire

digital temperature sensor and Arduino Uno R3 is approximately $70 USD which is one of the

main advantages of this procedure. This method has the potential to replace the manual

hydrometer reading and provide an automated continuous and precise method of soil particle

size analysis.


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CHAPTER 6

MEASURING SOIL BULK DENSITY FROM SHEAR WAVE

VELOCITY USING PIEZO-ELECTRIC SENSORS

This chapter is published as:

Murad, M. O. F., Minasny, B., Malone, B., & Crossing, K. (2020). Measuring soil bulk density

from shear wave velocity using piezoelectric sensors. Soil Research.

Chapter 6: Measuring Soil Bulk Density from Shear Wave Velocity Using Piezo-Electric Sensors

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6.1 Summary

Bulk density and soil stiffness moduli are vital physical parameters related to soil compaction,

porosity, moisture storage capacity, soil penetration resistance, and structural integrity. These

parameters can be used as indicators of root penetrability and seed germination. Conventional

methods for measuring soil density are destructive, time-consuming, and often require skilled

operators to conduct the tests. Measuring stiffness moduli using conventional techniques are

complex, expensive, and often difficult to measure within a wide range of strain level. A new

soil density and stiffness moduli measurement technique that can evaluate soil density and

stiffness moduli more rapidly, efficiently and precisely, at a low cost is introduced here. This

study evaluated the use of shear wave velocity measurements using the piezo-electric extender

and bender elements as a viable alternative to soil density and stiffness moduli of soil. To test

this idea, soda-lime glass beads less than 0.002, 0.04 - 0.07 mm and 1.00 -1.30 mm diameter

were used to develop the empirical relationship between the shear wave velocity and the bulk

density of soil in a laboratory condition. These empirical equations were then tested on sands

and clayey soils to validate the empirical relationships. In this analysis, bulk density can be

well predicted with shear wave velocity at a frequency of 20 Hz. The accuracy in terms of co-

efficient of determination (R2) and root mean squared error (RMSE) from the current and

existing studies and frequencies varied

between 0.91 to 0.93 and 0.073 to 0.177g cm-3, respectively. Both Shear and Young modulus

were compared with the shear wave velocity of soil with R2 and RMSE between 0.96-0.97 and

0.48-3.5MPa, respectively. The major advantage of this technique is the input and output signal

data can be stored in a computer that can be used to calculate soil density and stiffness moduli

automatically. In the future, a robust probe system can be designed to implement this technique

so that it can be used in the field under variable moisture conditions. This technique can play a

vital role in improving crop yield and soil management practices.


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6.2 Introduction

Bulk density is one of the major soil physical parameters that indicate the soil’s capability to

support its structural integrity, and the movement of air, water, heat, and solutes (Kramer &

Boyer, 1995). Bulk density can be an indication of soil compaction, which determines the rate

of seed germination and root penetrability (Bengough et al., 2005). In addition, soil physical,

chemical and biological measurements require density measurement in order to convert mass

basis to volumetric basis for soil quality assessment, such as soil carbon stocks (Arshad &

Martin, 2002; Adhikari et al., 2014).

Bulk density of soil is usually measured gravimetrically or indirectly using a gamma-ray

absorption instrument. Core, clod, and excavation methods are commonly used in direct

techniques based on the gravimetric analysis. However, there are problems and shortcomings

with the gravimetric basis method. The accountability of laboratory tests depends mostly on

the ability to recreate the conditions found in the field. As this test requires soil sampling, there

is always a possibility that the samples will be disturbed in the sampling process, which could

result in inaccurate testing. Another possible source of error in sampling is the disturbance in

the soil by compression during the insertion of the ring sampler.

While gamma radiation is highly accurate, its radiation source limits its practical application

(Lobsey & Viscarra Rossel, 2016; Pires, 2018; Sun, Wang, & Wang, 2019). All types of nuclear

gauges are potential health hazards, and strict regulations must be maintained during the

measurements (Lobsey & Viscarra Rossel, 2016). Like the methods based on the gravimetric

analysis, a small amount of disturbance could take place during this operation, where the access

hole is required. The presence of stones in the soil causes difficulties either by preventing the

insertion of access holes to the full depths or affecting the source/detector separation. In

general, the bulk density of soil in the presence of stone may be overestimated. Also, the initial


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cost gamma-ray gauges are considerably high compared to the methods based on the

gravimetric analysis (Al-Shammary et al., 2018).

Strain level is the measure of deformation resulting from the application of an external stress

(Puttlitz et al., 2019). Soils generally behave as a nonlinear and plastic material, but at the strain

levels below 10-3 %, it is usually considered to be elastic and linear in nature (Haeri & Fathi,

2018; Lourenço, dos Santos, & Pinto, 2017; Mucciacciaro & Sica, 2018; Raja & Maheshwari,

2016). Soil exhibits a quasi-elastic behaviour at the strain range of about 10-6 to 10-5 (Danne &

Hettler, 2017; Mandolini, 2018). Both the Young and Shear modulus behave independently

with the strain amplitude, related to a maximum limit value, and are known as initial shear (G0)

and young (E0) moduli. G0 and E0 are essential parameters for evaluating the shear modulus

and young modulus of soil. These shear moduli are related to soil bulk density.

There is a need to have an efficient and rapid soil density evaluation, at a low cost, which also

must be balanced with the appropriate precision and accuracy. This study will evaluate the use

of shear wave velocity measurements using the piezo-electric sensor as a viable alternative to

soil density and stiffness moduli estimation. Few studies have been conducted where piezo-

electric sensors were used to generate shear wave velocity for engineering purposes (Munoz &

Caicedo, 2013; Park et al., 2018; Yang et al., 2018; Zeng & Hlasko, 2005). In this study, a low-

cost system using BitScope Micro Oscilloscope & Analyzer was used to calculate soil density

and stiffness moduli automatically.

6.3 Background Study and Theory

The strain is the deformation in the direction of the applied force divided by the initial length

of the material, as shown in equation 6.1 (Gilmore, 2014). When the deformation in the material

is too small, the material will return to its original state after withdrawing the stress applied to

it, which is known as Elastic deformation.


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𝑆𝑆𝑠𝑠𝑆𝑆𝑎𝑎𝑆𝑆𝑆𝑆 = 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑎𝑎𝑡𝑡𝑖𝑖𝐷𝐷𝐷𝐷 𝑂𝑂𝐷𝐷𝑖𝑖𝑔𝑔𝑖𝑖𝐷𝐷𝑎𝑎𝑂𝑂 𝐶𝐶𝐷𝐷𝐷𝐷𝑔𝑔𝑡𝑡ℎ

(6.1)

ince agricultural soil is generally subjected to low stresses compared to those that carry the

foundation of a structure, a small amount of strain is produced. Thus, it is useful to study the

stiffness moduli of soil at a low strain level. The stiffness moduli of soil (Young and Shear

modulus) are mechanical properties of soil that have a strong relationship with water content

and bulk density of soil (Kézdi, 1980). Bravo et al. (2012) studied three clay soil samples

collected from a sugarcane field at different depths. In that study, it was found that Young

modulus declines gradually when the water content is larger than 25% and increases rapidly

under dry conditions. Also, the state of plastic deformation was achieved by the loose soil at

an average Young modulus of 40 MPa. Bravo et al. (2012) also found a non-linear relationship

between water content and the shear strength of a clayey soil at different depths of a sugarcane

field. The shear strength of the soil decreases rapidly with the increasing water content. After

20% of water content, the void spaces between the soil particles are mostly filled with water,

and the strength of soil mainly depends on pore water pressure.

In the laboratory, the triaxial test has been used to measure the stiffness modulus of soils

(Kokusho, 1980). But this instrument is limited in its measurable response of strain range and

accuracy restrictions. Wetting deformation of coarse-grained soil varies typically with the cell

pressure and wetting stress level, which is challenging to maintain in the Triaxial test (Song &

Jun-Gao, 2007). Additionally, the principal stress on the soil specimen in the triaxial tests does

not continuously rotate, and the two loading conditions (compression and extension) are

applied alternatively during each leading cycle. Moreover, the parts of the triaxial apparatus

that are subjected to continuous movement are affected by mechanical friction and can become

highly sensitive. As a result of the internal energy loss, it becomes unreliable for measuring

soil modulus at a small strain level (Kokusho, 1980).


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Many studies have been done to overcome these issues. Shear-wave velocity (VS) is one of the

essential parameters for determining the dynamic properties of soils. Like other types of body

waves, travel through the inner layers of the earth surface (Voigt et al., 2005). Shear wave

velocity of granular soils at strain levels less than 10−3% have been widely used to evaluate

different moduli of soil (Choi & Stewart, 2005; Gilmore, 2014; Lee et al., 2014; Patel, Bartake,

& Singh, 2008; Burland, 1989; Jardine & Sparks, 1984; Moore et al., 2003; Richart, Hall, &

Woods, 1970; Shibuya et al., 1992; Munoz & Caicedo, 2013; Park et al., 2018; Yang et al.,

2018). Also, the shear wave velocity can be used as a primary function of the bulk density of

soil (Gardner et al., 1974; Potter & Stewart, 1998; Keceli, 2012)

A piezoelectric sensor is a type of sensor that exploits the piezoelectric effect. It monitors the

variations of several physical properties such as pressure, acceleration, temperature, strain, or

force by converting the received signals into an electrical charge. Pierre Curie first noticed the

piezoelectric effect in 1880. But the industrial application of these sensors did not start for more

than a hundred years later in the 1950s (Katzir, 2003). The major component of a piezoelectric

sensor is a piezoceramic material. When a high voltage passes through the piezoelectric sensor,

its dimensions change due to the expansion of piezo material. There are different piezoelectric

materials that can be used for producing acoustic wave sensors. Among these, the most

common materials are quartz (SiO) and lithium tantalate (LiTaO) (Drafts, 2001). Commonly,

piezoceramics are used as actuators, and polymer piezo films are used as sensing materials. For

self-sensing actuators, piezoceramics can be used for both sensing and actuation (Dosch,

Inman, & Garcia, 1992).

(Walter & Lawrence, 1965) demonstrated one of the first applications of piezoelectric

transducers in shear wave testing in sand and clay. For generating and receiving signals, he

used shear-plate transducers. That was the first time he used shear wave, rather than the

longitudinal wave, to study the possibility of stress wave propagation through the soil.


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In 1978, shear wave velocity and attenuation in kaolinite clay sediments were measured using

the ceramic bender transducer. For measuring the shear wave velocity, a single-cycle pulse

with a peak-to-peak amplitude of 600 V and a frequency of 338 Hz were used. The received

signal from the transducer was amplified by 40 dB before displaying it into the oscilloscope.

Also, a bandpass filter between 200-1500-Hz was used to get rid of the electrical and

environmental noises. A unique type of transducer was employed, consisting of two transverse-

expansion mode piezoelectric crystals that can generate and receive signals from shear waves.

Those bender transducers have been preferred to shear-plate transducers in many other research

works (Alba, et al., 1984; Dyvik & Madshus, 1985; Horn, 1980; Schultheiss, 1981).

Based on the earlier development in the field of piezoelectric transducers, Horn studied

different dynamic properties of unconsolidated sediments in the laboratory (Horn, 1980). In

that study, saturated sand sediments were subjected to shear wave propagation in a specially

designed sedimentation chamber using a pair of piezoelectric ceramic transducers.

The working principle of all piezoelectric transducers is based on two different types of body

waves (P or compressional wave and S or shear wave). The wave that generates when energy

is applied at right angles to a medium is known as P-wave. A particle moves in the direction of

propagation of the wave, as shown in Figure 6.1. So, the soil particles are subjected to

alternative compression and tension or pulled apart as the waves propagate. These are the

fastest among the body waves. S-wave or shear wave is another type of body wave. It generally

forms when energy is applied in a direction parallel to the surface of a medium, as shown in

Figure 6.1. S-wave does not propagate through fluids.


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Figure 6.1. Propagations of P and S waveform through a 3-D grid (after E. Onajite, 2014)

Gardner et al. (1974) found that there is a strong correlation between S-wave velocity (𝑉𝑉𝑆𝑆) and

bulk density (𝜌𝜌) and established the following empirical relationship,

𝜌𝜌 = 𝛼𝛼 𝑉𝑉𝑠𝑠 𝛽𝛽 (6.2)

Potter & Stewart (1998) estimated coefficients 𝛼𝛼 and 𝛽𝛽on a shale-filled and a porous sand-

filled channel in Alberta, Canada and obtained 𝛼𝛼 = 0.37 and 𝛽𝛽 = 0.22.

Asten & Boore (2005) analyzed data from previous studies and summarised two equations for

a different range of shear-wave and compressional-wave velocity for determining the bulk

density of soil.


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When P- wave velocity,

𝑉𝑉𝐶𝐶 < 1.50 𝑠𝑠𝑚𝑚 𝑠𝑠−1,

𝜌𝜌 = 1.93 𝑔𝑔 𝑠𝑠𝑚𝑚−3


1.50 𝑠𝑠𝑚𝑚 𝑠𝑠−1 ≤ 𝑉𝑉𝐶𝐶 < 6.0 𝑠𝑠𝑚𝑚 𝑠𝑠−1,

𝜌𝜌 = 1.74𝑉𝑉𝐶𝐶 0.25𝑔𝑔 𝑠𝑠𝑚𝑚−3

(6.3)


𝑉𝑉𝐶𝐶 ≥ 6.0 𝑠𝑠𝑚𝑚 𝑠𝑠−1,

𝜌𝜌 = 1.6612𝑉𝑉𝐶𝐶 − 0.4721𝑉𝑉𝐶𝐶2 + 0.0671𝑉𝑉𝐶𝐶3 − 0.0043𝑉𝑉𝐶𝐶4 + 0.000106𝑉𝑉𝐶𝐶5

(6.4)

For shear wave velocity, the same equations of 𝑉𝑉𝐶𝐶 can be used to determine the bulk density of

soil.

For S- wave velocity,

0.30 𝑠𝑠𝑚𝑚 𝑠𝑠−1 ≤ 𝑉𝑉𝑆𝑆 < 3.55 𝑠𝑠𝑚𝑚 𝑠𝑠−1,

𝜌𝜌 = 1.74𝑉𝑉𝐶𝐶0.25𝑔𝑔 𝑠𝑠𝑚𝑚−3

For S- wave velocity,

(6.5)

3.55 𝑠𝑠𝑚𝑚 𝑠𝑠−1 ≤ 𝑉𝑉𝑆𝑆

𝜌𝜌 = 1.6612𝑉𝑉𝐶𝐶 − 0.4721𝑉𝑉𝐶𝐶2 + 0.0671𝑉𝑉𝐶𝐶3 − 0.0043𝑉𝑉𝐶𝐶4 + 0.000106𝑉𝑉𝐶𝐶5

𝑉𝑉𝐶𝐶 ( 𝑠𝑠𝑚𝑚 𝑠𝑠−1) = 0.9409 + 2.094𝑉𝑉𝑆𝑆 − 0.8206𝑉𝑉𝑆𝑆2 + 0.9409 + 2.094𝑉𝑉𝑆𝑆− 0.8206𝑉𝑉𝑆𝑆2 + 0.2683𝑉𝑉𝑆𝑆3 − 0.0251𝑉𝑉𝑆𝑆4

(6.6)

Keceli (2012) modified Gardner’s equation using sedimentary rocks, which can be expressed,

as shown in equation 6.7.

𝜌𝜌 = 0.44 𝑉𝑉𝑆𝑆 0.25

(6.7)


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Most studies for measuring soil density using piezo-electric sensors have been performed on

sands for engineering purposes (Park et al., 2018; Yang et al., 2018). It is notable that this

technique has not been utilized for measuring soil density for agricultural contexts.

Both time and frequency domain signal analysis techniques are used to evaluate Shear and

Young modulus of soil (Da Fonseca, Ferreira, & Fahey, 2008; Greening & Nash, 2004;

Viggiani & Atkinson, 1995). Bender elements that were used in these studies are made of

piezo-ceramic materials, and the maximum strain generated by a piezoelectric sensor is 10-3 %,

which lies within the elastic range of soils (Dyvik & Madshus, 1985). Many other studies were

conducted on the measurement of small strain stiffness modulus by the velocities of elastic

waves propagating through the soil particle (Burland, 1989; Jardine & Sparks, 1984; Moore et

al., 2003; Richart et al., 1970; Shibuya et al., 1992). The advantage of stiffness modulus using

elastic waves for the determination of stiffness modulus, and shear waves, is that the tests can

be performed both in laboratory and field conditions in a non-destructive manner.

6.4 Materials and Method

Piezo bender and extender elements are the main components of the Piezo-electric technique

for soil density measurement. These sensors were used as a transmitter for input signals and

receiver for output signals. A standard quick-mount piezo bender (Q220-A4-303YB) (Figure

6.2) and a piezo extender (Q220-A4-303XE) were used to produce and transmit the shear wave

through the soil and to receive the shear wave and then convert it to a digital signal.


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Figure 6.2. Standard quick-mount Piezo Bender (Q220-A4-303YB) sensor that transmits shear

wave velocity through the soil surface

The transmitter and receiver of the piezo bender and extender sensors were attached to two

steel probes. The sensors were separated by a distance of 60 mm. There was no joint between

the probes to avoid the transmission of the waves through the joints, as shown in Figure 6.3. In

the future, the probes will be attached together using a steel connector with rubber vibration

isolation joints to maintain constant clearance between the sensors.

Figure 6.3. Schematic diagram of the setup of the piezoelectric sensor probe system embedded

in the soil in a glass beaker.


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After setting up the bender and extender elements at a separation distance of 60 mm, BitScope

software was used to trigger the input signal from the transmitter. Square waves were produced

as input signals using a BitScope Micro Oscilloscope & Analyzer. The elastic stress waves

released when activated by an alternating electric charge from the square waves and of the one

layer of piezo sensor expands and the other contracts causing the sensor to flex, as shown in

Figure 6.4.

Figure 6.4. Mechanism of wave propagation of 2-Layer Piezo-electric Bender and Extender

elements between the transmitter and the receiver

The transmitted signal was then received by the receiver to measure the strength and frequency

of the waves. A linear amplifier was used to amplify the voltage before sending the signal to

the receiver. The BitScope Micro Oscilloscope & Analyzer was used to collect the digital signal

and produce a time-domain signal. The output signals were received and analyzed as sine

waves. A low pass filter was used to filter the input and output signal. Both transmitting and


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receiving signals were amplified by a preamplifier before displaying the signals in the BitScope

Micro Oscilloscope & Analyzer as shown in Figure 6.5.

Figure 6.5. The closed system of the piezoelectric sensor

The polarity of the input signal was changed to obtain the sine wave in the opposite direction.

Theoretically, shear wave velocity can be measured by dividing the distance between the

sensors with the time required by the shear wave to travel this distance, as shown in equation

8 (Dyvik & Madshus, 1985).

𝑉𝑉𝑆𝑆 = 𝐿𝐿𝑆𝑆/𝑠𝑠𝑆𝑆 (6.8)

where,

𝑉𝑉𝑆𝑆 = Shear wave velocity

𝐿𝐿𝑆𝑆 = The distance between the tip of the transmitter and the tip of the receiver

𝑠𝑠𝑆𝑆 = The travel time of the shear wave through the distance L.

The S-wave arrival time can be found by measuring the time required by the wave to travel

from the trigger and the first intersection between the sine waves obtained from different


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polarities. Figure 6.6 shows the square waves in blue and red lines that were used as input

signals. A trigger point was considered when the amplitude of the input signal starts declining

from the peak of the square wave.

Figure 6.6. Arrival times calculated the distance between the trigger, and the first point when

the sine wave starts to form for P wave and the intersection between the sine waves obtained

from different polarities for S-wave

The P-wave arrival time is considered to be the time required by the wave to travel from the

trigger, and the first point when the sine wave starts to form. P- wave arrival time also helps to


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identify the S-wave arrival time when it is difficult to trace the first intersection point between

the sine waves obtained from different polarity due to the noise.

𝑉𝑉𝐶𝐶 = 𝐿𝐿𝐶𝐶/𝑠𝑠𝐶𝐶 (6.9)

where,

𝑉𝑉𝐶𝐶 = P- wave velocity

𝐿𝐿𝐶𝐶 = The distance between the tip of the transmitter and the tip of the receiver

𝑠𝑠𝐶𝐶 = The travel time of the shear wave through the distance L

A low pass filter was used to remove all the unnecessary noises from the output signal. Without

the filter, it was a challenge to distinguish between the actual signal and noise. Figure 6.7 shows

the output signal without the low pass filter. To obtain the travel time of both P and S waves,

it is very important to identify the exact position P and S-wave arrival.

Figure 6.7. Amplitude vs. travel time of output signal without applying low pass filter


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For designing the low pass filter, the Analog Filter Wizard (www.analog.com) was used. The

low pass filter has been designed for the passband of gain 100V/V with -1dB and 100Hz, as

shown in Figure 6.8. Also, the stopband was selected as -40 dB and 700Hz.

Figure 6.8. Optimized design parameters of low pass filter used in the closed system

The 2nd order band-pass filter components (Figure 6.9) from the Analog Filter Wizard were

applied to filter out unwanted noises and to obtain smooth sine waves as output signals.

Figure 6.9. Schematic diagram of low pass filter used in the closed system with piezoelectric

sensor


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After the application of the low pass filter, a considerable reduction of the noise in the output

signals was observed, as shown in Figure 6.10. The gain was optimized in such a way that the

peak amplitude does not exceed the cut-off frequency.

Figure 6.10. Improved output signal after applying low pass filter indicating the amplitude of

S-wave against the travel time

There was also an issue of transmitting the electric signal through the soil medium when the

sample was wet. Water particles in the soil sample can conduct electrical signals from the

receiver to the transmitter. To counteract this problem, the piezoelectric sensors were required

to be insulated electrically, but at the same time, the insulating material should not affect the

generation of the mechanical waves. A liquid electrical tape was used as an insulation material.

In this study, glass beads were first used to develop empirical relationships between the shear

wave velocity and the bulk density of soil in an ideal laboratory condition. Three different sizes

of glass beads were used. Soda-lime glass beads less than 0.002, 0.04 - 0.07 mm, and 1.00 -

1.30 mm diameter was considered as clays, silts, and sands, respectively. The experiments were

done in the physical lab for soil at Biomedical building, Australian Technology Park, Sydney.


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A small vibratory soil compaction table was used to compact the different-sized glass beads

for 10, 20, and 30 minutes to create different densities. The weight of the glass beads and dry

soil samples was measured in the laboratory with a weighing scale. The volume and bulk

density of the soil was calculated from equation (10), respectively.

𝜌𝜌 = 𝑉𝑉 𝑉𝑉� (6.10)

Where,

𝜌𝜌 = Density of soil

𝑉𝑉 = Weight of soil

Afterward, sands and two types of clayey soils were used to validate the empirical equations

that were derived from the glass beads. These soil samples were dried and crushed before

sieving through a 2 mm sieve. The characteristics of these soils are described in Table 6.1.

Similar to the glass beads, a vibratory table was used to compact the sandy and clayey soils for

10, 20 and 30 minutes.

Table 6.1. Characteristics of soil samples used for the tests

Site Soil type Clay Silt Fine Sand Coarse Sand Texture class

%

Sodosol Dermosol 57.5 19.7 4.3 18.5 Clay

Narrabri Vertosol 73.8 7.5 5.1 13.6 Clay

The shear wave velocities obtained from the tests with the glass beads and soils were used to

measure the stiffness moduli using the empirical relationships between density and shear wave

velocity of soils. Shear modulus of the soil was calculated using the following equation,


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𝐺𝐺 = 𝜌𝜌𝑉𝑉𝑆𝑆2 (6.11)

Where,

𝐺𝐺 = Shear modulus of soil

𝑉𝑉𝑆𝑆 = Shear wave velocity

Both Shear modulus and the Poisson’s ratio are the functions of Young modulus. Poisson’s

ratio was calculated from the relationship with Shear and Bulk modulus of soil, as shown in

equation 14. The Young modulus and Bulk modulus of soil were calculated using the following

equation,

𝐸𝐸 = 2𝐺𝐺(1 + 𝜗𝜗) (6.12)

Where,

𝐸𝐸 = Young modulus of soil

𝐾𝐾 = Bulk modulus of soil

= 𝜌𝜌(𝑉𝑉𝑝𝑝2 −43𝑉𝑉𝑠𝑠2) (6.13)

𝜗𝜗 = Poisson’s ratio

= (3𝐾𝐾 − 2𝐺𝐺)(6𝐾𝐾 − 2𝐺𝐺)� (6.14)

Significant correlations between both Shear and Young modulus with shear wave velocity were

observed.


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6.5 Results and discussion

6.5.1 Shear wave velocity at different compactions

Since soils act as elastic material at strain level less than 10−3%, the soil sample will return to

its original shape after a shear force is applied. Adjacent layers of soil will undergo shear and

cause the propagation of the shear wave (Simic, Ivanac, Pustahija, & Brkljacic, 2012).

Figure 6.11 shows a comparison of S-wave arrival time between 0 and 20 minutes of vibratory

compaction of glass beads. The shear wave travelling time decreases with the increasing

compaction applied on the glass beads using a vibratory feeder. Due to the compaction, the

particles of the glass bead and soil particles get closer to each other, which allows the transfer

of the body waves from the transmitter to the receiver faster than a less compacted soil. Δt

indicates the differences between the sand particles with 20 minutes of vibratory compaction

and no vibratory compaction (Figure 6.11).

https://www.merriam-webster.com/dictionary/adjacent


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Figure 6.11. Comparison in the S-wave travel time between no vibratory compaction and

vibratory compaction for 20 minutes.

6.5.2 Predicting bulk density

Once we have established a method for determining shear wave velocity, glass beads were

compacted for 10, 20, and 30 minutes using a vibratory feeder to achieve the bulk density

within the range of 0.59-1.48 g cm-3. Shear wave velocity was measured using piezoelectric

sensors to compare with the corresponding density of soil. In this study, three different

frequencies (10Hz, 20Hz, and 50Hz) of the input signal were used to find the optimum

frequency for measuring shear wave velocity. Non-linear regression analysis was used to fit a

non-linear model that satisfies the relationship between the shear wave travel time and the

density of soil.


186

Figure 6.12. An empirical relationship between Shear wave (𝑉𝑉𝑆𝑆) travel time and bulk density

of soil.

Figure 6.12 shows there is a clear relationship between travel time and the bulk density of soil.

The shear wave velocity at 10 Hz provides a better correlation between the 𝑉𝑉𝑆𝑆 and the density

with an R2 of 0.92. R2 values from 20 Hz and 50 Hz were 0.89 and 0.85, respectively. This

relationship indicates that the travel time for the shear wave velocity decreases with increasing

density as the shear wave requires more time to travel through less dense soil.

The travel time of the shear wave from the transmitter to the receiver was then converted to the

shear wave velocity (Equation 8). Empirical relationships between shear wave velocity and

density were derived, as shown in Figure 6.13. Relationships were developed for three different

frequencies of 10, 20 and 50Hz. Bulk density changes abruptly with 𝑉𝑉𝑆𝑆 until around 1 g cm-3.

After that, the slope of the trend line decreases with the increasing 𝑉𝑉𝑆𝑆 and bulk density.


187

Figure 6.13. An empirical relationship between Shear wave velocity (𝑉𝑉𝑆𝑆) and bulk density of

soil.

Empirical equations 6.15 to 6.17, showed the correlation between the shear wave velocity and

density:

𝜌𝜌 = 0.38𝑉𝑉𝑆𝑆0.27 (10 𝐻𝐻𝐻𝐻) (6.15)

𝜌𝜌 = 0.48𝑉𝑉𝑆𝑆0.21 (20 𝐻𝐻𝐻𝐻) (6.16)

𝜌𝜌 = 0.41𝑉𝑉𝑆𝑆0.25 (50 𝐻𝐻𝐻𝐻) (6.17)

Maximum R2 and minimum root mean squared error (RMSE) for bulk density were found to

be 0.86 and 0.09 g cm-3, respectively, at 10 Hz.

The accuracy of these relationships to predict soil bulk density was tested on real soil

compacted at different densities. In addition, the empirical relationships developed by Potter

& Stewart (1998) and Keceli (2012) (Equation 2) were also tested. The relationships between

the prediction by different models with the actual bulk density are given in Figure 6.14.


188

Figure 6.14. Comparisons between the actual and predicted bulk density of soil

R2 for 20 Hz was found to be maximum for the density equations obtained from the glass beads

and other studies, but the difference with other frequencies is small, as shown in Figure 6.14.

Table 6.2 shows the statistical comparisons of the density equations obtained from the tests

with Piezoelectric sensors and models from previous studies. All models show a high linear

correlation between the prediction using 𝑉𝑉𝑆𝑆 and bulk density (R2 > 0.9). However, the model

from Potter & Stewart (1998) under-predicted the bulk density. R2 and RMSE between the

different studies and frequencies varied between 0.91 to 0.93 and 0.073 to 0.177 g cm-3.

Empirical equations obtained from the piezoelectric sensor in this study (Equation 2 & 17)

provides minimum RMSE compared to other studies (RMSE = 0.073).


189

Table 6.2. Statistical comparisons between the density equations obtained from Piezoelectric

sensor and previous studies

Researcher Equation for density Frequency (Hz) R2 RMSE (g cm-3)

Piezoelectric sensor

0.38𝑉𝑉𝑆𝑆0.26 10 0.91 0.096

0.48𝑉𝑉𝑆𝑆0.21 20 0.93 0.073

0.41𝑉𝑉𝑆𝑆0.25 50 0.91 0.119

Potter & Stewart (1998)

0.37𝑉𝑉𝑆𝑆 0.22

10 0.91 0.141

20 0.93 0.162

50 0.91 0.147

Keceli (2012) 0.44 𝑉𝑉𝑆𝑆 0.25

10 0.91 0.177

20 0.93 0.139

50 0.91 0.172

6.5.3 Shear modulus and 𝑽𝑽𝑺𝑺

The shear wave velocity is a function of Shear modulus. The shear modulus of soil was

calculated using equation 12 and plotted against the shear wave velocity in Figure 6.15. Similar

to the bulk density of soil, a power regression analysis was done between bulk density and

shear wave velocity. Shear modulus also increases with the progressive shear wave velocities.

A linear increment can be observed until 5 M Pa, but the slope of the trend line rises

dramatically after this stage. R2 and the RMSE of all three frequencies are very close, but the

maximum R2 and minimum RMSE can be observed at the frequency of 50 Hz.


190

Figure 6.15. Empirical relationship between Shear wave velocity (𝑉𝑉𝑆𝑆) and Shear modulus

(𝐺𝐺𝐷𝐷𝑎𝑎𝑚𝑚) of soil.

6.5.4 Young modulus and 𝑽𝑽𝑺𝑺

The bulk modulus of soil was calculated from shear and P-wave velocity using equation 14.

Equation 15 was used to compute Poisson’s ratio from shear and bulk density of soil. Young

modulus of soil was evaluated from equation 13 using Shear modulus and Poisson’s ratio.

Almost similar types of relationships can be observed between the shear wave velocities and

young modulus of soil as shear modulus, and poisons ratio are direct functions of this modulus,

as shown in Figure 6.16. Similar to shear modulus, maximum R2 and minimum RMSE was

found to be 0.97 and 1.4 M Pa for 50 Hz.


191

Figure 6.16. Empirical relationship between Shear wave velocity (𝑉𝑉𝑆𝑆) and the Young

modulus (𝐸𝐸) of soil.

6.6 Limitations

There are still a few limitations to this technique. The application of this technique in this study

is limited to the laboratory. This prototype was evaluated using a limited number of crushed

dried soil samples. The elastic wave propagation is affected by soil moisture, which has not

been investigated in the paper. More tests need to be done with different types of soil with

various physical, and chemical variances to check the dependency of this technique on these

properties of soil. Also, the Shear and Young moduli obtained from this technique need to be

validated with the conventional methods. To apply this technique in the field, some

modifications will be necessary for the design of the probe to withstand the pressure that will

be exerted in the sensor while pushing these probes into the grounds. This technique will

require the field moisture content for converting the field density to the bulk density of soil.

TDR or FRD sensors can be used to evaluate field moisture to correct the field density of soil.

In addition, a small number of ambient noises in the operational environment, such as


192

vibrations from nearby machinery can affect the measurement of the shear wave velocity with

this technique. This technique is non-destructive but invasive. Soil cores are not required for

this technique, but the probes need to be driven into the soil that may alter the physical integrity

of the soil.

6.7 Conclusions

Soil density and stiffness moduli are important parameters for soil management. From the

results, we can observe that the shear wave velocity has a strong relationship with soil density

and stiffness moduli. Piezoelectric sensors appear to be a rapid, cost-effective, convenient tool

for investigating bulk density. In addition, these sensors can also be used for measuring the

stiffness of soil at a small strain level. It is an inexpensive method that can produce accurate

data. One of the key features of this technique is, it can store the input and output signal data

in the computer and calculate soil density and stiffness moduli automatically. In the future, a

robust soil probe system can be implemented based on this technique so that it can be used in

the field to measure soil density and stiffness moduli. It can provide essential information

regarding soil physical properties that are invaluable in agricultural contexts and for assessing

soil function in general.


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CHAPTER 7

CONCLUDING REMARKS AND FUTURE WORKS

Chapter 7: Concluding Remarks and Future Works

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7.1 Overview

This thesis proposes sensor-based soil physical measurement techniques to obtain fine-scale

soil information rapidly and cost-effectively. In particular, this thesis looks at field sensing and

laboratory sensing methods. The field sensing methods are designed to obtain high-resolution

three-dimensional soil data, with a horizontal spatial scale between 1-5 m, and vertical

resolution of 1-5 cm.

The contents of this thesis can be divided into a field system and a lab system.

The field system includes:

• A crop water use monitoring system at the plot level using a plastic buggy system for

the EMI survey.

• Depth-specific temporal analyses of soil water extraction by different chickpeas

genotypes using data collected from EMI surveys.

• VisNIR penetrometer system for measuring SOC using high-resolution in-situ spectra.

The laboratory sensing system includes:

• Automated hydrometer method for soil particle size analysis using ToF distance sensor

and digital temperature sensor.

• Measuring bulk density and soil stiffness moduli from shear wave velocity using piezo-

electric sensors.

The summary and future recommendations on sensor-based soil physical properties are

outlined below.


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7.1.1 Plot level soil moisture monitoring using EMI surveys

It is essential to measure soil moisture at the plot scale for plant breeders to monitor extensive

roots under moisture stress and phenotyping (Hurd, 1974; Roitsch et al., 2019). Lack of

moisture is the main limiting factor in increasing crop production in the future (Ali & Talukder,

2008; Richards, 1991). Plant phenotyping can help understand the plant function or target traits

that affect the crop yield, yield stability, efficiency of storing and using resources, quality of

the crop, resistance to various stresses, etc. (Roitsch et al., 2019). However, one of the single

most important limitations is how to monitor crop water uptake in the field. Establishing soil

moisture probes at every experimental plot in a field is not feasible.

To solve this issue of finding the best drought tolerance genotype and phenotyping, an EMI

survey plastic buggy system for soil moisture monitoring at the plot scale was introduced in

Chapter 2. The proposed technique was successfully implemented to quantify soil content at a

plot from multi-layer ECa measurements of 0, 20, 40, 60, and 80 cm above the ground surface.

The stability and temperature effect on EM38-MK2 were checked, and were shown that it was

reasonably stable and capable of recording consistent ECa measurements at a higher field

temperature. Total water uses by different genotypes of chickpeas measured using EMI

surveys, and traditional neutron moisture meter was compared, and a good correlation was

found. This chapter explains the potentiality of using plastic buggy based EMI surveys as a

rapid, efficient, and convenient soil moisture monitoring technique at the plot scale, which can

be beneficial for drought tolerance and plant phenotyping researches.

7.1.2 Depth-Specific Temporal Analyses of Soil Water Extraction

Due to a lack of technologies that allows field phenotyping, our capability to explore the

genetics of traits related to drought, heat, and nutrient efficiency tolerance are limited (Araus

& Cairns, 2014). Effective field-based high-throughput phenotyping platforms (HTPPs) have


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become necessary for plant breeders (Araus, Kefauver, Zaman-Allah, Olsen, & Cairns, 2018;

Cabrera‐Bosquet, Crossa, von Zitzewitz, Serret, & Luis Araus, 2012). High-performance

computing and sensors-based technology have the potential to establish HTPPs. Time-lapse

EMI surveys system developed in Chapter 2 can be used for field phenotyping of various plants'

root water uptake rapidly and cost-effectively.

The plastic buggy system based on EMI surveys was successfully applied to monitor water

extraction by various chickpea genotypes in Chapter 3. In this chapter, soil ECa was collected

at 0, 20, 40, 60, and 80 cm heights in static recording mode in 2018 (I5A field) and at 0, 20,

and 80 cm heights in automatic recording mode in 2019 (Campey-1) using the plastic buggy

system equipped with EM38MK-2 to measure soil water extraction dynamics at different depth

of soil.

Based on the treatments applied to the plots, k-means cluster analysis of soil moisture

extraction time series at different depths effectively separated 36 chickpea genotypes into

various groups. The analysis allows monitoring the dynamics of soil water extraction behaviour

of different genotypes. Water extraction at pre-podding stages varies within the irrigated and

rainfed plots. From these analyses, it was observed that chickpea genotypes from the rainfed

plots had the least water use efficiency compared to the genotypes in the irrigated plots. The

system can efficiently monitor water extraction, as seen by changes in soil moisture within 60

– 80 cm depth. The least efficient water-use chickpea genotypes extract more water from the

surface soil during the maturity stage, which agrees with the assumption by Hurd (1974) and

Tanner (1983).

In Campey-1 (2019) cropping field, 192 chickpea genotypes were clustered using optimised

heights of measurements (0, 20, and 80 cm) in continuous recording mode. ΔS depth profile

indicates most soil waters were extracted at a depth deeper than 1 m. The distribution of total


203

water uses of different groups of genotypes was analysed to find the most and least water-

efficient clusters. Temporal analysis of ∆S showed that at the mature stage, more soil water

was extracted from the bottom three layers (0-30 cm, 30-60 cm, and 60-100 cm) for both most

and least water-efficient genotypes of chickpeas. Overall, continuous ECa measurements at

three different heights using a plastic buggy system was efficiently used to monitor chickpea

plants' soil water extraction dynamics at various depths.

7.1.3 VisNIR penetrometer system for predicting SOC

SOC directly influences soil physical, chemical, and biological properties (Killham & Staddon

2002; Kibblewhite, Ritz, & Swift, 2008; Power, 2010). Plants captured the CO2 from the

atmosphere during photosynthesis processes and cycled through the soil organic matter.

Different organisms use these organic matters to source energy and nutrients (Lescure et al.,

2016). Higher SOC increases energy supply for all microorganisms (microbes, macrofauna,

earthworms, etc.) and nutrient supply to the plants (Metcalfe & Bui, 2017). It also helps

aggregate soil particles and increases soil structure stability, which improves the water storage

capacity (Carter, 2004). SOC stabilises the thermal properties of soil and maintains pH

buffering (Metcalfe & Bui, 2017).

In addition, soil carbon sequestration takes carbons from roots, stems, and leaf materials of

pasture grasses and crops and store them in the soil (Irving, 2015). Carbon farming benefits

landowners by maximising profit while increasing crop yield (Gerrand et al., 2003). The

Australian government has recently placed soil carbon as one of the low emission technology.

However, a significant hindrance to soil carbon farming payment development is the high cost

of soil carbon measurement in the field.

Conventional methods for soil organic carbon measurement in the laboratory are time-

consuming, expensive, and destructive in nature. A rapid and accurate SOC measurement


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technique with high-resolution becomes essential to calculate soil organic carbon stock.

VisNIR spectroscopy has been found to be a rapid, efficient, and cost-effective method for

measuring SOC. However, its use for measurement in the field down to 1 m of the soil profile

is still limited. In this study, a VisNIR penetrometer system was introduced and tested for

estimating SOC levels using an existing spectral library. The system works by inserting VisNIR

penetrometer ~2 cm increments in the first 30 cm depth and then by ~5 cm increments to the

maximum reading depth of 85 cm using a hydraulic system. There was 10 seconds pause

between each insertion events to capture high-quality spectra over the 350-2,500 nm

wavelength. To minimise the effect of soil moisture from the NIR signal, a method called the

EPO (external parameter orthogonolisation) was used to project the field measured NIR signal

to spectra that are insensitive to soil water content changes yet still contain information useful

for SOC prediction. The predicted OC contents from penetrometer using EPO transformed

VisNIR spectra were validated against the 33 homogenised samples of OC tested in the

laboratory. A slight over-prediction can be observed in the OC contents predicted from the

penetrometer system along with the total depth profile. The site with higher clay content (site

2) seems to have the most standard deviations at various depths between the predicted

(penetrometer system) and actual (laboratory measurement) OC contents. The higher

proportion of plant roots within the top 10 cm of the soil layer causes significant over-

prediction in SOC stocks in most experimental sites.

7.1.4 Automated hydrometer method for soil particle size analysis

The particle size distribution of soil is a function of soil physical properties such as water

retention, hydraulic conductivity, erosion potential, etc. (Kettler et al., 2001). It is also one of

the key parameters for evaluating agricultural management practices, plant nutrient storage

capacity, and carbon-sequestration potential (Chen et al., 2018; Eynard, Schumacher,

Lindstrom, & Malo, 2005; C. Li et al., 2017; Sarker et al., 2018).


205

The hydrometer method for soil particle size analysis is one of the most commonly used

techniques for soil particle analysis, but it is tedious and involves a greater chance of human

errors. Also, it is practically difficult to obtain continuous particle size distribution graphs using

a hydrometer. Continuous particle size distribution is essential for comparing or sharing soil

textural information globally as the silt-sand thresholds are different in soil classification

systems in different countries. To minimise the conventional hydrometer method's issues, a

new automated, cheap, and simple method for evaluating soil particle-size distribution was

introduced in Chapter 5. A ToF distance sensor and digital temperature sensor was used to

record hydrometer reading and suspension temperature every 5 seconds. This automated

hydrometer technique will produce a continuous particle size distribution curve with

continuous measurements. In addition, the suspension temperature was also monitored to

calculate the viscosity more precisely. This system was tested with ten different soil samples

and compared the particle size analysis using the pipette method. A particle-size distribution

curve covering the 2-40 μm range was generated within 8 hours by observing soil particle

sedimentation using the automated hydrometer method. An additional cost of only $70 USD

potentially upgraded the conventional hydrometer technique for automatic continuous particle

size distribution more efficiently with higher accuracy.

7.1.5 Bulk Density and Soil Stiffness Moduli from Shear Wave Velocity

Bulk density of soil directly relates to soil physical quality and indicates the degree of soil

compaction (Letey, 1958; McNabb, Startsev, & Nguyen, 2001). Soil compaction negatively

affects the rate of seed germination and root penetrability (Lipiec et al., 2003; Nosalewicz &

Lipiec, 2014).

Conventional gravimetric methods such as core, clod, and excavation techniques are the direct

methods for measuring soil density. Due to the destructive nature of the tests, it is hard to


206

collect an undisturbed soil sample from the field. The compression during the soil sampling

can cause an error in the measurements of bulk density. Gamma-ray absorption techniques are

more accurate, but these gauges are expensive and a potential health hazard. Measuring soil

stiffness moduli in the laboratory using conventional techniques are costly, and it is hard to

measure within the strain level below 10-3 %.

So, there is a necessity to develop a new technique for evaluating soil density and stiffness with

appropriate precision and accuracy. Chapter 6 explains the potential of shear wave velocity

(𝑉𝑉𝑆𝑆) measurements using the piezo-electric sensor for estimating soil density and stiffness

moduli cost-effectively. A low-cost sensing system that includes piezo bender and extender

elements with BitScope Micro Oscilloscope was used to automatically generate, receive, and

analyse shear wave velocity to calculate soil density and stiffness moduli. 𝑉𝑉𝑆𝑆 produced and

analysed using the proposed piezo-electric sensors, found to have a strong relationship with

soil density (R2 = 0.77 to 0.86) and stiffness moduli (R2 = 0.96 to 0.97). The piezo-electric

sensor system can potentially measure soil density and stiffness moduli rapidly, cost-

effectively, and conveniently.

7.2 General conclusions and future directions

All of the research chapters in the thesis have demonstrated different applications of cost-

effective sensing technology for predicting the physical properties of soil rapidly and

conveniently. These proposed systems need to experiment with various soils and conditions

from other locations around Australia. However, there is room for further studies on these

measurement techniques in the future, as described here:

EMI survey-based soil moisture monitoring

In Chapter 2, an EMI survey-based plastic buggy system was introduced for monitoring soil

moisture at the plot scale using a multi-layer inversion technique. In the future, the EMI surveys


207

can be conducted in automatic mode to record continuous ECa measurements instead of point

measurements. Also, EMI surveys at 0, 20, and 80 cm heights can be used instead of five

different heights for the multi-layer inversion to minimise the time and labour required for

recording ECa measurements across the whole cropping field. This technique has the potential

to completely automate the EMI survey procedure using autonomous tractors. Aerial drones

can also be used for this purpose, but more researches are required to mitigate the

electromagnetic effect from the rotors and other metals on ECa measurements. This experiment

was only conducted on chickpea cropping trials, so in the future, this system needs to be tested

for other crops such as wheat, cotton, canola trials, etc.

Temporal depth-specific crop water analysis

Chapter 3 demonstrated the potential use of the proposed EMI survey technique introduced in

Chapter 2 for depth-specific temporal analysis of soil moisture at the plot level. It is essential

to validate this technique for different soil types and conditions in the future to check the

robustness of the proposed in-situ method for monitoring soil moisture. Plant physiologists and

breeders can use this system to conduct further studies to investigate the plant root zone

activities by various plant genotypes. Detailed soil moisture monitoring at multiple depths

using this EMI survey system, drought and heat tolerance by plants can be studied more

conveniently and precisely using this technique. Also, the potential of this technique can be

tested for root phenotyping to rank genotypes.

VisNIR penetrometer system

Chapter 4 presents a new VisNIR penetrometer system with an ultrasonic depth sensor for

predicting SOC using high-resolution EPO transformed in-situ spectra. In the future, the

proposed VisNIR probe system can be tested in various locations around Australia for other

types of soils. It was still difficult to push into the dry clay soils such as the vertosols. To resolve


208

this issue, a smaller diameter soil core than the diameter of the penetrometer can be extracted

from the sites before the penetrometer's insertion. It will allow pushing the penetrometer in

easily at the desired depth. More studies need to be conducted to reduce plant roots' effect in

the VisNIR spectra. The reason for OC content over-prediction using this penetrometer system

need to investigate, and further studies will be required to fix this issue. A spectral library based

on the proposed penetrometer system can be developed to check the possibility of increasing

OC prediction accuracy using VisNIR penetrometer spectral library-based calibration. A force

transducer can also be incorporated in the penetrometer to allow readings of mechanical

strength, which can be used to estimate soil bulk density.

Automated hydrometer method

Chapter 5 of this thesis includes an automated hydrometer method for soil particle size analysis

using a ToF distance sensor and a digital temperature sensor to record the hydrometer readings

with temperature automatically. This automated hydrometer system can be tested with other

types of soil from different locations around Australia to check the efficiency and the

robustness of the proposed approach. To transfer the recorded data wirelessly, a Bluetooth or

Wifi based hardware can be added to the system. This automated hydrometer system has the

potential to program for calculating the percentages of various soil components such as sands,

silts, and clays with the continuous particle-size distribution curve covering the 2-40 μm range.

In the future, an android or IOS app can be developed to operate the system and to obtain the

particle sizes and continuous particle-size distribution curve directly on mobile devices. Also,

the hydrometer readings with the solution temperature at every five seconds can be stored on a

cloud server so that the data can be remotely accessed for further analysis.

Piezoelectric sensors-based shear wave velocity


209

In Chapter 6, a new technique was introduced for measuring bulk density and soil stiffness

moduli from shear wave velocity using piezo-electric sensors. This system needs to be

experimented with various types of soils to predict bulk densities and soil moduli in the

laboratory. A probe system can be developed based on the piezo-electric sensors in the future

to measure the shear wave velocity of soil directly in the field. This system can also be added

with the penetrometer system, such as the VisNIR penetrometer, to collect additional soil

information from a single insertion. It can be a potential solution for determining many other

strength properties and soil moduli in situ. It can be applied to measure different soil moduli.

The overall cost of the hardware required for this experiment can be minimised by replacing

with cheaper locally produced hardware. Furthermore, Bluetooth or Wifi adapters can be added

to the hardware system for obtaining the recorded data wirelessly. This adaptation will also

allow to store and analyse the shear wave velocity data in the cloud server.

7.3 Closing statement

To conclude, cost-effective sensors have a significant potential to measure soil physical

properties rapidly, accurately using non-destructive techniques in the field. These technologies

can effectively monitor horizontal and vertical variability in space and time. Various

experiments with each technique show reproducibility and repeatability of these methods for

predicting soil moisture, OC content in the field, particle size distribution, bulk density, and

strength moduli in the laboratory. These cost-effective and robust measurement systems for

measuring 3-dimensional high-resolution soil physical properties can be used in precision

agriculture to optimise the resources for maximum crop yields.


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