INTERUNIVERSITY PROGRAMME IN

PHYSICAL LAND RESOURCES

Ghent University Vrije Universiteit Brussel

Belgium

Determination and Prediction of Risk to Soil Compaction from Agricultural Operations

Promoter:

Prof. dr. ir. Wim Cornelis

Master dissertation submitted in partial fulfillment of the requirements for the degree of Master of Science in Physical Land Resources by Elmer Alosnos

2014-2015

I C E

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This is an unpublished M.Sc dissertation and is not prepared for further distribution. The author and the promoter give the permission to use this Master dissertation for consultation and to copy parts of it for personal use. Every other use is subject to the copyright laws, more specifically the source must be extensively specified when using results from this Master dissertation. Gent, The Promoter, The Author, Prof. dr. ir. Wim Cornelis Elmer Alosnos

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Acknowledgment

The author is grateful to Prof. Wim Cornelis for his guidance and constructive criticism.

The author would also like to acknowledge the help received from the technicians responsible

for laboratories used during the experiment namely, Maarten Volckaert, Jan Van der Perre,

and Filip Van Boxstael.

Grateful thanks is also due to the Flemish Inter-University Council (VLIR) for funding my

master program.

I am grateful to Jan de Pue for teaching me how to use Python program.

The moral support of Mary Rose Uy as well as the family of Bing Requieron is also very

much appreciated.

I am also indebted to my professors for sharing me their wisdom and to my classmates for

encouragement and wonderful experiences.

During my long stay in Ghent there were moments when the support of friends became of

paramount importance. I would like to thank these friends Daphne, Clod, Dan, Nina, Camar,

Dennis, Kristine, Nick, Hyzel, and Geoffrey for their moral support.

Special thanks to my ever loving family, good friends, and most of all to Almighty God.

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Table of Contents

Acknowledgment ....................................................................................................................... ii List of Figures ............................................................................................................................ v List of Tables ............................................................................................................................ vi List of Abbreviations ............................................................................................................... vii

Abstract .................................................................................................................................. viii 1. INTRODUCTION ............................................................................................................. 1

1.1 Background ...................................................................................................................... 1 1.2 Research objectives .......................................................................................................... 3 1.3 Organization of the thesis ................................................................................................ 4

2. REVIEW OF LITERATURE ............................................................................................ 5 2.1 Soil compaction processes and its implications ............................................................... 5 2.2 Methods of identifying compact soil ............................................................................... 6

2.3 Soil compression test and precompression stress ............................................................ 7 2.3.1 Methods of calculating PCS from soil compression curve ....................................... 8 2.3.2 Factors influencing soil deformation behavior during compression test ................ 10

2.4 Soil quality indicators of soil degradation by compaction ............................................. 10

2.5 Indirect methods of PCS determination by pedotransfer functions ............................... 11 2.5.1 Pedotransfer functions ............................................................................................ 11 2.5.2 Existing PTFs used for predicting PCS and other soil mechanical properties ....... 12

3. MATERIALS AND METHODS ......................................................................................... 16

3.1 Study area....................................................................................................................... 16 3.2 Field sampling and soil analysis .................................................................................... 17

3.3 Laboratory test ............................................................................................................... 17 3.3.1 Apparatus ................................................................................................................ 18 3.3.2 Preparation of soil samples ..................................................................................... 18

3.3.3 Compression tests ................................................................................................... 19

3.4 Determination of PCS .................................................................................................... 19 3.5 Development of pedotransfer functions ......................................................................... 21 3.6 Measurement of soil hydraulic properties...................................................................... 23

3.7 Determination of soil structural quality and vulnerability to compaction ..................... 23 3.8 Statistical analysis .......................................................................................................... 24

4. RESULTS AND DISCUSSIONS ........................................................................................ 25

4.1 Soil quality assessment and risk of compaction ............................................................ 25 4.2 Mechanical susceptibility of soil to compaction ............................................................ 30

4.2.1 Calculated PCS values ............................................................................................ 30 4.2.2 Influence of land use and soil depth on PCS variability ......................................... 34

4.3 Indirect estimation of PCS using pedotransfer function ................................................ 35

4.3.1 Potential predictors of PCS ..................................................................................... 35

4.3.2 Development of regression-based PTFs ................................................................. 36 4.4 Influence of compression test methods on soil deformation behavior .......................... 40

4.4.1 Stress-strain relationships ....................................................................................... 40

4.4.2 Effects of loading duration and initial water potential on PCS .............................. 40 4.4.3 Effect on pore water pressure ................................................................................. 43

5. CONCLUSION .................................................................................................................... 45 6. LITERATURE CITED ........................................................................................................ 47

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List of Figures

Figure 1. Geographical locations of the sampling fields (red crosshairs) ................................ 16 Figure 2. Standard oedometer used for uniaxial confined compression test. .......................... 18

Figure 3. Variation of clay, silt, and sand content in the dataset used to develop PTFs. ......... 22 Figure 4. Relationship between clay content and (a) dry bulk density and (b) saturated

hydraulic conductivity. Dash lines in red and blue corresponds to bulk density at PD

= 1.75 g cm-3 and PD = 1.4 g cm3, respectively. ..................................................... 26 Figure 5. Average packing density (PD) for different locations and land use. Dash lines in red

and blue indicates a threshold value for high risk (PD > 7.5 g cm-3) and low risk

(<1.4 g cm-3) in soil degradation by compaction, respectively. ............................... 28 Figure 6. Average packing density (PD) for different locations and soil depth. Dash lines in

red and blue indicates a threshold value for high risk (PD > 7.5 g cm-3) and low risk

(<1.4 g cm-3) in soil degradation by compaction, respectively. ............................... 28 Figure 7. Average air capacity (% vol.) for different locations and land use. Dash line in red

indicates a threshold value (AC < 5% vol.). ............................................................. 28

Figure 8. Average Ksat values for different locations and land use. Dash line in red indicates

a threshold value (Ksat < 10 cm/day). ...................................................................... 29

Figure 9. Stress-strain curves at (a) pF=1.8, (b) pF=2.0, and (c) pF=2.5 water potential for all

sites. Data shown are averages of six values (from cropland center, cropland

headland and grassland, at depths of 40 cm and 70 cm). .......................................... 32 Figure 10. Comparison of PCS values derived by different methods. Pearson correlation

coefficients are shown in the upper right corner and scatter plots with smoothed

lines are shown in the lower left corner. ................................................................... 33 Figure 11. Average PCS (C_DK) of soils from different locations. ........................................ 33

Figure 12. PCS (C_DK) values at different land use and soil depths. ..................................... 34

Figure 13. Average VCI values for different locations and land use....................................... 35

Figure 14. Measured versus PTF-predicted PCS (log kPa) for soil samples collected from

cropland..................................................................................................................... 39

Figure 15. Measured versus PTF-predicted PCS for soil samples collected from grassland .. 39 Figure 16. One-dimensional compression curves for soil samples pre-wetted at different

water potential (pF=1.8, 2.0, and 2.5). Vertical stress is in logarithmic scale. ......... 41

Figure 17. One-dimensional compression curves for soil samples compressed at different

loading durations (T=1, 10 and 30 min). Vertical stress is in logarithmic scale. ..... 41

Figure 18. Precompression stress values derived using different methods. Points represent

mean values of three to four replicate measurements, errors bars are 95% confidence

interval. ..................................................................................................................... 42

Figure 19. Influence of initial water potential and loading duration (values used were

determined using the C_DK method. ........................................................................ 42

Figure 20. Typical result of an oedometer test (at pF=2.0 and T=10 min) showing the loading

sequence, vertical displacement, and pore water pressure changes. ......................... 44

List of Tables

Table 1. Existing pedotransfer functions used to predict PCS and other soil mechanical

properties.......................................................................................................................... 14

Table 2. Existing pedotransfer functions used to predict PCS and other soil mechanical

properties (continued) ...................................................................................................... 15 Table 3. Classification of soil structure quality (adopted from Lebert, 2010). Maximum

threshold for packing density was modified. ................................................................... 23 Table 4. Classification of soil structure quality (adopted from Lebert, 2010). Maximum

threshold and unit for packing density was modified. ..................................................... 24 Table 5. Classification of soil structure quality (adopted from Lebert, 2010). ........................ 24 Table 6. Site characteristics and soil physical properties ........................................................ 27 Table 7. Mean values of StI (%) for different locations and land use. .................................... 29

Table 8. Soil quality index (SQI) scores for different locations and land use. ........................ 30 Table 9. Pearson’s correlation matrix between soil properties (predictors) and PCS values. . 37 Table 10. List of PTFs to predict precompression stress of arable soils with different land use.

The equations represent the best subset regression results and their mean absolute error

(MAE), root mean square error (RMSE), and adjusted R2 values. ................................. 38

`

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List of Abbreviations

BD Bulk density

kPa Kilopascal

Ksat Saturated hydraulic conductivity

OC Organic carbon

PTFs Pedotransfer functions

PCS Precompression stress

PD Packing density

PST Plate sinkage test

SCI Susceptibility to compaction index

SQI Soil quality index

VCI Vulnerability to compaction index

VCL Virgin compression line

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Abstract

Growing pressures on land resources and mismanagement often leads to deterioration of soil

quality, hence alters the functioning capacity of the soils and impairs its provisioning of

ecosystem services. Among various threats to soil quality, soil compaction due to advanced

farm mechanization may be regarded as one of the most challenging for both farm managers

and policy-makers. This study was conducted to evaluate the structural quality of the soil and

its susceptibility to deformation by mechanical loads. These information are vital in gauging

the vulnerability of arable soils against inevitable threats of compaction. These were achieved

through measurements and analysis of various soil quality indicators associated with

compaction, of which precompression stress (PCS) was considered the most important and

main focus of this research. The PCS was measured using different methods found in

literature and was used, together with other indicators (e.g. air capacity, bulk density,

saturated hydraulic conductivity), to evaluate the vulnerability of soils to degradation by

compaction. Overall, soil structural quality was found intermediate and susceptibility to

compaction was low to medium, thus the vulnerability to compaction can be considered

moderate in East Flanders, Belgium. Difficulties associated with measurement of PCS in both

laboratory and field are a reasonable cause of concern to develop an indirect method using

already available soil information. The underlying relationships between PCS and other soil

physical properties (e.g. bulk density, texture, soil organic carbon, calcium carbonate content,

water tension) were investigated using pedotransfer functions (PTFs). The PTFs developed in

this study can able to predict PCS values of cropland and grassland soils, though further

research is still needed to improve these PTFs to a reasonable degree of predictive capability.

Also, the study demonstrated that soils have different deformation behavior when subjected

to uniaxial compression at different loading duration and initial water potential. Soil

compression under short-time loading (1 to 30 minutes) simulates soil deformation which

occurs under wheel loads of short-time durations. Though the interaction effects of these two

important factors during compression test on PCS was not able to be captured well, their

main effects on stress-strain behavior of the soil were found significant as manifested in the

soil compression curves. It was found that the initial water potential has a significant

influence on PCS values. The PCS tends to be higher with decreasing water potential.

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1. INTRODUCTION

1.1 Background

Enhanced soil quality is a key component of sustainable agriculture (Lal, 1998). However,

this is compromised by growing pressures on land resources that leads to deterioration of soil

quality, hence alters the functioning capacity of the soils and impairs its provisioning of

ecosystem services. Among various threats to soil quality, soil compaction may be regarded

as one of the most challenging that may have economic and environmental consequences for

world agriculture (Soane & van Ouwerkerk, 1995). In Europe, the Commission of the

European Communities (2006) recognized soil compaction as one of the five most frequent

threats to soil quality, mainly caused by expansion of highly mechanized crop production

systems with intensive field traffic (Crescimanno et al., 2004; Toth, 2008). In contrast to

other types of soil degradation processes, soil compaction is more difficult to detect since it

occurs below the ground and its symptoms are often obscured by other site problems. As no

single universal method is currently available to identify compacted soils (Batey and

McKenzie, 2006), its geographical distribution and total area affected is only roughly known.

Globally, an estimated 68 million hectares are affected by human-induced soil compaction

where Europe and Africa contribute around 49% and 26%, respectively (Bouma and Batjes,

2000). Moreover, an estimated 36% of European subsoils were classified as highly

susceptible to compaction (Jones et al., 2003; Van Camp et al., 2004). Unlike topsoil

compaction that is partly reversible and can be loosened every cropping season, subsoil

compaction is more persistent and extremely difficult to correct since it is below the normal

tillage zone (Hakansson and Reeder, 1994). From a perspective of sustainable soil

management, a good soil quality requires the uncompromising criterion that no subsoil

compaction can be accepted (Van den Akker and Schjonning, 2004).

Since it would take several decades to restore compacted soil it is extremely important to

devise preventive measures against further compaction in order to regenerate soil functions

and avoid long-term irreversible deterioration. One of the technical approaches widely

advocated is optimization of machinery use on farms with emphasis on reducing the

overburden pressure it induced on soil by adjusting wheel loads to the soils’ carrying

capacity. Over the past years there have been an increasing amount of literatures that

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documented the mechanisms involved on how mechanical stress induced by farm machinery

tires propagates and distributes into the underlying soil layer (e.g. Van den Akker, 2004;

Keller, 2005; Keller et al, 2007; Arvidsson and Keller, 2007; Lamande and Schjonning, 2011;

Cueto et al., 2013). In contrast, it appears that only few attempts have been made to account

precisely how soil behaves during and after repeated wheeling (e.g. Keller et al., 2005) which

is equally important in evaluating soil compaction risks. Van den Akker and Schjonning

(2004) cited that one of the main constraints is the insufficient data on soil mechanical

strength.

Some researchers introduced the concept of precompression stress (PCS) as an indicator to

assess the mechanical strength of soils and eventually used PCS as a conservative criterion

for susceptibility to compaction (Horn 1981; Horn 1988; Lebert, 1989; Kirby; 1991; Veenhof

and McBride, 1996; Horn and Fleige, 2003; Imhoff et al., 2004; Rucknagel et al., 2007).

Originally based on the work of Casagandre (1936) using saturated soil medium, PCS can be

derived from a soil compression curve (or stress-strain curve) generated from laboratory

testing of intact unsaturated soils subjected to one-dimensional confined compression. PCS

denotes a transition point in the compression curve at which the compressive behavior of soil

turns from being elastic into plastic. The risk of undesirable changes in soil structure due to

compaction can be minimized by limiting the mechanically applied effective stress to below

the PCS values (Horn and Lebert, 1994; Alexandrou and Earl, 1998; Dawidowski et al.,

2001).

Despite of the potential use of PCS as a precautional value for mechanical stress in order to

avoid soil compaction, synthesizing earlier studies reveals that using PCS as an exact limit to

distinguish between elastic and plastic soil deformation is deemed controversial (Arvidsson

and Keller, 2004; Keller et al., 2004; Mosaddeghi et al., 2007). Cavalieri et al. (2008)

recently reported the performance comparison of different methods to derive PCS. As

expected, estimated PCS values of various methods tested differed significantly. Moreover,

Keller et al. (2011) argued that using a semi-logarithmic type of stress-strain diagram may

lead to misinterpretation of soil mechanical behavior. Furthermore, Keller and Lamande

(2013) found a contrasting stress-strain behavior of soils obtained in-situ during wheeling

experiments and those measured in standard laboratory tests.

While it remains unclear how these choice of calculation methods and soil testing procedures

will infer on actual PCS values, it will be interesting to pursue whether such variations in

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PCS are influenced by two key parameters during compression test – preset matric suction

and loading duration. Another question worth pursuing is whether there is a functional

relationship between PCS and other soil properties associated with soil compaction. Previous

studies showed that PCS increases with decreasing loading time and that the effect of loading

time on PCS is larger the more fine-textured the soils (Horn et al., 1994; Lebert et al., 1989).

On the other hand, Berli et al. (2003) reported that PCS increased approximately linearly with

the logarithm of negative initial soil water potential, and the dependence was stronger in the

subsoil than the topsoil. However, there is no report about the combined effects of loading

duration and initial matric suction on PCS.

Another major constraint in PCS measurements are that in general practice, very large soil

samples have been used and the conventional oedometer test is very rigorous and time-

consuming. The problem is often amplified by the lack of easily-accessible and representative

soil mechanical properties that makes the prediction of soil compaction a difficult task.

Interestingly, use of pedotransfer functions (PTF) as predictive means of estimating the soil

mechanical properties needed for soil compaction modeling have been suggested (Lebert and

Horn, 1991; Alexandrou and Earl, 1998; Imhof et al., 2004; van den Akker, 2004; Rucknagel

et al., 2007). However, one caveat in using PTFs is that they cannot be used directly to other

locations with different soil conditions than where they have been originally developed. To

augment the need for indirect estimate of soil mechanical properties with less time and effort,

development of new PTFs was carried out in this study.

1.2 Research objectives

The purpose of this study was to predict the precompression stress (PCS) as a criterion for

susceptibility to soil compaction and investigate how the variations in mechanical testing

procedures will influence its measurements. The specific objectives were to

1) gain insights on how the measurements of PCS are influenced by loading duration and

soil wetness (preset matric potential of -6 kPa, -10 kPa and -33 kPa) and how

persistent is soil wetness under uniaxial confined compression test;

2) demonstrate the dependency of PCS values on how stress-strain curve is expressed

(strain as dry bulk density or void ratio and stress as linearly or logarithmically) and

method of calculation (e.g., regression methods, fitting methods);

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3) investigate the functional relationship between PCS and other readily available soil

properties associated with soil compaction, namely texture, organic carbon content,

particle size distribution, bulk density, matric potential and land use.

Moreover, as this study is partly based on a dataset developed from soil samples taken at

various locations in Flanders that are representative for actual agricultural land use, i.e.

cropland and grassland, the actual state of soil quality in relation to soil compaction will be

evaluated.

1.3 Organization of the thesis

The manuscript has been organized in five chapters. Introduction and the objectives of the

study are covered in Chapter 1. Chapter 2 gives an overview of literature reviewed on causes

and effects of soil compaction, precompression stress, and pedotransfer functions. Chapter 3

elaborates the scope of the study as well as the materials and methods used. Chapters 4

present results and discussion, while major conclusions and recommendations were given in

Chapter 6.

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2. REVIEW OF LITERATURE

2.1 Soil compaction processes and its implications

Production risks associated with deterioration of soil structure such as soil compaction

become more prevalent nowadays and are likely to increase in the future (Batey, 2009).

Considering the important role played by several interacting factors related to machine, soil,

crop, and weather, soil compaction is now regarded as a multi-disciplinary problem with

world-wide importance (Soane and Ouwerkerk, 2013). Reduced porosity and increased shear

strength due to densification of soil structure are among the common features of a compacted

soils. Compaction may occur on the land surface, within the tilled layer, or subsoil layers and

may vary in thickness of up to 100 mm or sometimes even more (Chamen et al., 2003; Hatley

et al., 2005; Sinnett et al., 2006; Eckelman et al., 2006).

Natural processes such as weight of overlying soil, rain impact, drying and shrinking of clay,

and penetration of plant roots normally induce compaction on soil. However, soil compaction

as a result of advanced farm mechanization is the most alarming one. As world agriculture is

challenged to achieve a viable production to feed the ever-growing populations, the need for

highly efficient field operations often associated with use of heavy machinery becomes

inevitable. For instance, increased tractor power has allowed growers to cultivate in more

marginal conditions and to greater depth. Though soil compaction is prevalent in Europe and

North America, recent evidences of compaction were also reported in tropical countries.

Fortunately, exciting new opportunities recently emerged in addressing soil compaction

issues. The advent use of decision support system (DSS) in agriculture paved a way in order

to establish useful recommendations for appropriate soil management practices and site-

specific solutions to soil compaction problems (Canillas and Salokhe, 2001). Through

effective DSS, farmers can predict the potential impact of several factors such as tire

specifications, wheel load, inflation pressure, and soil properties on soil structure. For

instance, web-based decision support tools such as Terranimo (http://www.terranimo.dk)

have been introduced in some countries in Northern and Central Europe which enables to

simulate the complex dynamics when arable soil is loaded with machinery. The tool is useful

for farmers and their advisors interested in reducing compaction of their soils by simply

modifying the machinery operating conditions with the prevailing soil characteristics.

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There are a myriad of problems associated with severely compacted soils. Reduced pore

volume and connectivity will cause a significant reduction in air and water permeability

(Alakukku et al., 2003; Pitola et al., 2005; Zhang et al., 2006; Matthews et al., 2010; Whalley

et al., 2012; Chen et al., 2014), which indirectly influences other soil and plant growth

processes that mutually link on them such as soil CO2 and O2 exchange (Simojoki et al.,

1991), nutrient uptake (Arvidsson, 1999; Alakukku, 2000), and crop-water use efficiency

(Radford et al., 2001). Other indirect effects of soil compaction are increased tillage draft

requirement (Arvidsson, 1998), increased greenhouse gases emission (Ball et al., 1999),

increased soil C:N ratio (De Neve and Hofman, 2000), and increased runoff and topsoil

erosion (Fullen, 1985). Deterioration of soil structure has a negative effect on crop yields due

to restricted root growth, less biological activity, and decrease in plant available water

(Schjonning and Rasmussen, 1994; Arvidsson and Hakansson, 1996; Hansen, 1996). For

example, a series of experiments by Stalham et al. (2007) showed that soil compaction

reduced rooting density and maximum depth of rooting as well as delayed emergence of

potato. However, some studies have shown that moderate compaction might be beneficial to

some crops (Arvidsson and Hakansson, 2014). The influence of compaction on pore

geometry is quite difficult to understand since it depends on the structure and related pore

types prior to compaction, soil composition and water content, and on the intensity of

compaction.

Surprisingly, despite the voluminous pile of published papers on soil compaction and on

measures to combat the phenomenon, little is done in practice to effectively address

compaction processes.

2.2 Methods of identifying compact soil

Identification and quantification of both natural and machinery induced soil compaction are

becoming more crucial especially for sustainable soil management. Nowadays, a variety of

methods are available to detect compact soils. Among them are the visual and tactile

assessment methods (Daniells and Larsen, 1991; McKenzie, 2001; Ball and Douglas, 2003;

Spoor et al., 2003; Batey & McKenzie, 2006; Ball et al., 2007; Shephered, 2009;), predictive

methods based on computer modeling (Smith, 1987; Tranter et al., 2007), and use of field

sensors such as soil penetrometers (Sharifi et al., 2007). The Visual Evaluation of Soil

Structure (VESS, Ball et al., 2007), which is an evolution of the Peerlkamp method

(Peerlkamp, 1967), is an example of combined visual and tactile assessment of soil quality

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that can enable to pinpoint specific problems such as compaction, impeded drainage, and

restriction to roots. Field indicators used to identify compacted soil include waterlogging on

the surface layers, increased soil strength, reduction in visible porosity, changes to soil

structure, soil color and distribution of roots and soil moisture (Spoor et al., 2003; Batey and

McKenzie, 2006; Pulido Moncada et al., 2014). Soil penetrometers are useful tools that

measure the soil penetrability and can detect differences in soil strength within a soil profile.

Soil compaction is often measured by the increase in bulk density, except for swelling soils

where bulk density changes with changes in soil water content (Lal, 2006).

2.3 Soil compression test and precompression stress

In order to have a meaningful simulation of soil compaction susceptibility to mechanical

loads, knowledge on how pressure induced by machinery tires is distributed into the soil

profile and how the soil responds against mechanical stress is very essential. In principle,

since soil does not behave as a rigid body and essentially is a frictional material it tends to

deform once exposed to external stress. During field traffic, mainly three types of forces are

exerted on the soil - downward acting forces due to wheel load, shear forces imposed by

driven wheel and wheel slip, and vibration forces transmitted from the engine to wheel or

track (Alakukku, 1999). The exerted mechanical stresses are then resisted by the internal soil

strength (Fredlund and Vanapalli, 2002). If the exerted force is relatively higher than the soil

strength, considerable deformation of soil structure can be expected. Soil compaction can be

prevented by adjusting the number, size, width, and tire inflation pressure of the wheel load

to match the prevailing soil strength conditions (Lebert and Horn, 1991; Hakansson, 1994).

In the laboratory, the compressive strength of soil is usually measured using a triaxial or

uniaxial compression apparatus (oedometer). In an oedometer test, the lateral strain is fully

prevented by a cylindrical stiff ring in which the sample is enclosed, whereas in a triaxial test

both the compressive and shear behaviors of soil is measured. The triaxial cell apparatus

allows for applying an axial vertical stress and lateral confining stress by means of a piston

and hydrostatic pressure, respectively. However, since oedometer tests are less time-

consuming and easier to use than triaxial tests, they are widely used in soil mechanics. The

in-situ plate sinkage test (PST) is commonly used in the field, but it is expensive and time

consuming (Mosaddeghi et al., 2003). Also, Mosaddeghi et al. (2006) showed that PCS is

significantly lower when measured under a confined compression test than that measured by

PST. They attributed the difference in PCS values to possible distortion of the stress-strain

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curve measured in the confined compression test as a result of sampling disturbance and

boundary conditions. Considering a same density and water content, the recently disturbed

soil may have lower strength than undisturbed soil since some bonds between the particles

are broken in disturbed samples (Pytka et al., 1995).

On the other hand, Earl (1997) reported that data from PST are almost comparable to those

obtained from confined compression test at stresses lower than 300 kPa. Alexandrou and Earl

(1995) pointed out that for stresses higher than 300 kPa, the soil deformation exhibited by the

further downward movement of the plate cannot be attributed to compaction anymore but is a

result of extra lateral deformation of soil. Moreover, Dawidowski et al. (2001) and Keller et

al. (2004) measured PCS by using PST apparatus and oedometer, respectively, and they

concluded that the compression values derived from PST and oedometer did generally not

differ significantly from one another. Although laboratory tests can never mimic exactly the

mechanical stress as well as the air and water drainage situation in the soil environment of the

field, an isotropic strain state appearing during uniaxial testing on soil cores and assuming

that lateral strains are negligible in the subsoil (Koolen and Kuipers, 1983), the oedometer

test can be considered a sufficiently good approximation of the strain state in the subsoil

under a running wheel.

Results of soil compression tests are normally presented in terms of stress-strain

relationships. Change in soil volume, either expressed as void ratio or dry bulk density, are

plotted against the corresponding applied stresses. A curve is then fitted to the set of points

and the soil recompression line (RCL), virgin compression line (VCL), and point of

maximum curvature are drawn manually or by using computer programs. Finally, important

soil mechanical parameters such as compression index (Cc, slope of the VCL) and PCS are

determined.

2.3.1 Methods of calculating PCS from soil compression curve

There are several procedures presented in literature to determine PCS from the semi-

logarithmic type of soil compression curve (Dias and Peirce, 1995; Arvidsson and Keller,

2004; Baumgartl and Koch, 2004; Gregory et al., 2006; Cavalieri et al., 2008). Among these

methods, the procedure based on Casagandre (1936) that was developed empirically to

determine a preconsolidation stress (which originated from civil engineering), may be

regarded as a standard method. According to this method, if one draws two straight lines

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(tangent and parallel to the x-axis) at the point of maximum curvature in the stress-strain

curve, the PCS can be found as the point of intersection of the bisector of the angle between

these two lines and the extended virgin compression line. However, the original Casagandre

method is purely graphical and therefore the visual determination can be subjective and scale-

dependent. The method is even more problematic when the compression curve is less

distinguished or does not show a clear bend as demonstrated by gradual transition between

recompression curve and VCL (Berli, 2001). Schmertmann and Asce (1955) had similar

observation and they suggested to use Casagandre’s method for tests with a steep VCL slope

and a clearly defined point of maximum curvature. Dawidowski and Koolen (1994) found a

way to transform the graphical method of Casagandre into a computerized method by

developing a mathematical procedure to approximate PCS. They tested their method on

undisturbed samples which were subjected to uniaxial compression using universal

compression testing machine and their results showed that their mathematical method can

also be applied to compressive curves with relatively small curvature.

Several options have been suggested on fitting mathematical equations to data points in the

compressive curve such as hyperbolic (Koolen, 1974), logarithmic (Bailey et al., 1986),

fourth-grade polynomial (Arvidsson and Keller, 2004), or a continuous S-shaped function

based on the hydraulic model of van Genuchten (Baumgartl and Kock, 2004). For instance,

the fitting method of Arvidsson and Keller (2004) was carried out mathematically where the

first and second derivative of the fitted polynomial curve corresponds to the tangent of the

line and the point of maximum curvature, respectively. Baumgartl and Kock (2004) modeled

the volume change with stress using the van Genuchten equation where the parameters α and

n of the model were calculated by using the software RETC (U.S. Salinity Laboratory, 1999).

Aside from Casagandre’s method, some researchers used other methods to estimate PCS such

as the stress ordinate at the intersection of VCL and regression line fitted on either the first 2,

3, 4 or 5 points of the compression curve (Dias Junior and Pierce, 1995), intersection of VCL

and x-axis at zero strain (Dias Junior and Pierce, 1995; McBride and Joosse, 1996), and stress

at predefined strain (Arvidsson and Keller, 2004). However, different methods used to

approximate PCS will yield significantly different values and therefore they are not

interchangeable (Arvidsson and Keller, 2004; Cavalieri et al., 2008).

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2.3.2 Factors influencing soil deformation behavior during compression test

The uniaxial compression test is widely used to study the compressive behavior of arable

soils, but not in a standardized way. Several factors such as sample dimension, initial soil

conditions, compression method (initial stress, loading duration, loading increments), and

laboratory condition can influence the test results and eventually the shape of the soil

compression curve. During a compression test, the main sources of errors that can be

attributed to sample ring dimensions are ring wall-soil friction, unevenness and disturbance

of the upper and lower surface, and nonsuit of the sample at the cylinder walls (Muhs &

Kany, 1954; Leussink, 1954; Schmidbauer, 1954). Avoiding using high cores with small

diameter can minimize sample disturbance and sidewall friction. Normally, a ratio of

diameter (d) to height (h) of about d/h = 5 is functional and would yield an error within

reasonable limits.

Soil moisture content is considered the most sensitive and dominant property affecting soil

strength (Dawidowski and Lerink, 1990). In general, the strength of an unsaturated soil drops

rapidly when soil moisture content increases. As the soil dries the VCL is shifted up to the

right, indicating that more stress is needed to deform the soil.

In soil mechanics, most common oedometer testing procedure is the incremental loading

where each increment of load is applied instantaneously and is allowed to act long enough to

complete primary consolidation (Terzaghi et al, 1996). Bakker et al. (1995) suggested that it

is crucial to establish soil mechanical parameters with loading rates similar to those expected

in the field. For this purpose, different authors used a loading time of approximately 20 sec

(Etana and Comia, 1995), 60 sec (Angers, 1990), 10 min (O’Sullivan), and 30 min (Larson et

al., 1980; Keller and et al., 2011). Koolen (1974) recommended a uniaxial test with constant-

rate-of-strain. where the soil is subjected to a constant rate of axial deformation.

2.4 Soil quality indicators of soil degradation by compaction

Soil structural quality indicators serves as a yardstick of soil susceptibility to degradation by

compaction. Soil structural quality can be determined by evaluation of some soil functions

and soil physical properties that are directly affected by soil compaction. Lebert et al. (2004),

as cited by Lebert et al. (2007), suggested a list of possible indicators like air capacity, dry

bulk density, air permeability, oxygen diffusivity, available water capacity, water

conductivity, root length density, penetration resistance, and visual classification of soil

11

morphology. Some of these parameters were selected by ENVASSO project as potential

indicators for soil susceptibility to compaction, such as bulk density, air capacity, and

saturated hydraulic conductivity (Huber et al., 2008). Lebert (2010) also used these indicators

to assess the vulnerability to compaction of agricultural subsoils in Germany. In Netherlands,

van den Akker and Hoogland (2011) assessed the susceptibility of agricultural soils to

compaction as a function of texture and packing density.

2.5 Indirect methods of PCS determination by pedotransfer functions

While direct measurements are apparently the most exact determination of PCS, they are

often time-consuming and laborious, require expensive and sophisticated devices and skilled

technicians, and are impractical for wide-scale applications due to large spatio-temporal

variability of soil properties. In addition, obtaining equivalent soil mechanical measures in a

laboratory setting requires major sampling, resulting in a destructive investigation of the soil

(Carter and Gregorich, 2008). Accurate and rapid quantification of such soil property without

adding much complexity and financial burdens to the users should be taken into account

especially for developing countries. Because of these impediments, soil scientists devised an

alternative to direct approximation of soil properties that are difficult to measure - a more

simpler and rapid but less expensive indirect method known as pedotransfer functions (PTF).

2.5.1 Pedotransfer functions

The Encyclopedia of Agrophysics (2011) defined pedotransfer functions as “equations or

algorithms expressing relationship between soil properties different in difficulty of their

measurement or their availability”. The basic premise of pedotransfer functions is that since

soil properties and processes are mutually linked, there must be a specific set of soil

properties explicitly determining other properties. Although the concept of PTF has long been

used in the past to estimate soil properties that are hard to determine, the term was coined for

the first time by Bouma (1989) and since then has gained worldwide recognition as a new

field of research in soil science, water resources, agronomy, and environmental sciences

(Tranter et al., 2006).

Strong and renewed interest in empirical PTFs is mainly a result of new methods and tools

becoming available for PTF development including statistical regression, data mining

techniques, and machine learning algorithms. The advantages of using statistical regression

12

are that rigorous estimates of the statistics of the predicted values and the coefficients in the

PTF equations can be obtained. However, constructing PTFs using statistical regression

requires a lot of iteration steps, such as deciding which properties are to be used as predictors

and which regression equation to use (Pachepsky and van Genuchten, 2011). For this reason,

PTF development recently has employed data mining techniques and machine learning

algorithms wherein appropriate predictors and equations are automatically selected (Botula et

al., 2012).

2.5.2 Existing PTFs used for predicting PCS and other soil mechanical properties

Though most of the PTFs reported in literature pertain to the estimation of soil water

retention and saturated hydraulic conductivity (see review paper of Botula et al., 2014), a

mathematical function can also be used in predicting soil mechanical properties associated

with compaction. To date a handful of research works were already done to estimate the most

important mechanical parameters widely used in soil compaction models (Keller et al., 2007).

A summary of these so-called PTFs are shown in Table 1. Soil initial bulk density, water

content or tension, clay content, and OM content were seemingly used in most of these listed

PTFs as predictor for PCS and compression index (Cc), except Kirby (1991) who investigated

the dependence of PCS and Cc of Australian Vertisols on the state of the soil, of which

liquidity index and void ratio emerged as good predictor. For instance, Gupta and Larson

(1982) predicted Cc based on clay content. Lebert and Horn (1991) developed regression

equations with high degree of significance (r2 > 0.7) to predict soil strength for two water

tensions (pF 1.8 and pF 2.5, with pF being the log of tension with the latte expressed in cm)

as a function of soil bulk density, shear strength parameters (cohesion c and angle of internal

friction ), air capacity, available and non-available water capacity, saturated hydraulic

conductivity, and OM content. Apparently based on these equations, the effect of moisture

content on soil strength is clearly defined. The drier the soil is, the stronger it gets, but its

stability decreases when soil gets wetter. Over a decade later, these PTFs of Lebert and Horn

(1991) were used in Germany as an indicator for the identification of harmful subsoil

compaction (Horn and Fleige, 2003, 2009). Fritton (2008) validated and extended the use of

Lebert-Horn PTFs to estimate the PCS and maximum depth of compaction of agricultural

soils in Pennsylvania, USA. Also, Van De Vreken et al. (2009) used them to map the risk of

soil compaction in the Flemish Region of Belgium.

13

Imhoff et al. (2004) investigated the compressive behavior of a Brazilian Hapludox and

found a significant multiple correlation (r2 = 0.70) between PCS and initial bulk density,

water content, and clay content. A similar observation was also reported by Saffih-Hdadi et

al. (2009) and An et al. (2015) when they investigated the compaction susceptibility of

different soils in France and China, respectively. The findings of Imhoff et al. (2004), Saffih-

Hdadi et al. (2009), and An et al. (2015) highlighted the clear effect of initial soil water

content, initial dry bulk density, and soil texture on PCS. They showed that PCS was

positively correlated with soil bulk density and negatively correlated with initial soil water

content. A positive correlation between PCS and soil bulk density was also reported earlier

by Lebert and Horn (1991), Alexandrou and Earl (1998), Canarache et al. (2000), Silva et al.

(2000,2002), whereas a significant negative correlation between PCS and water content was

also observed by Alexandrou and Early (1998), Defossez et al. (2003), Imhoff et al. (2004)

and Mosaddeghi et al. (2006).

However, Keller et al. (2007) noted that the predictive performances of some of these

existing PTFs are not well evaluated and their reliabilities are not well known. Therefore,

those PTFs which are based on native data, provide flexibility of appending reference

databases, and with acceptable predictive performance, should be preferred than generic

PTFs (Patil et al., 2012).

14

Table 1. Existing pedotransfer functions used to predict PCS and other soil mechanical properties

Source Property Predictors Soil Class N FTP equations

R2

Gupta and

Larson

(1982)

Compression

index, Cc

Clay content, %

clay (g 100 g-1)

Expanding clay

Non-expanding

clay

54

54

Cc = 2.033 x 10-1 + 1.423 x 10-2 (%clay) – 1.447 x 10-4 (%clay)

Cc = 1.845 x 10-1 + 1.205 x 10-2 (%clay) – 1.108 x 10-4 (%clay)

0.79

0.89

Lebert

and Horn

(1991)

Precompression

stress, PCS

Internal friction, Ø

(ᴼ); cohesion, c

(kPa); bulk density,

ρb (Mg m-3); air

capacity, Lk (%,

v/v); available

water capacity, nFk

(%, v/v); non-

available water

capacity, TW (%,

v/v); saturated

hydraulic

conductivity, kf

(x103 cm s-1);

organic matter, OM

(g 100 g-1)

Sand

Sandy Loam

Silt

Clay and Clay

Loam (<35%)

Clay and Clay

Loam (>35%)

307

PCS (pF=1.8) = 438.10ρb – 0.0008(Ø1.8)3 – 3.14TW – 0.11(nFk1.8)2 – 465.60

PCS (pF=2.5) = 410.75ρb – 0.0007(Ø2.5)3 – 3.41TW – 0.35(nFk2.5)2 – 384.71

PCS (pF = 1.8) = 169.30ρb - 29.03(OM)0.5 + 6.45kf + 32.18 log(C1.8) – 9.44 Ø1.8

+ 27.25 sin(TW) + 119.74 log(nFk1.8) + 19.51

PCS (pF = 2.5) = 89.50ρb – 23.99(OM)0.5 + 2.89kf +125.76 log(C2.5) – 1.14 Ø2.5

+ 26.90 sin(TW) + 51.46 log(nFk1.8) + 77.25

PCS (pF = 1.8) =374.15ρb - 4.10OM + 3.38Lk1.8 - 1.58(kf)-0.5 + 1.79C1.8 + 1.09(TW)

- 6.37(Ø 1.8)0.67 + 0.088(nFk1.8)2 - 472.77

PCS (pF = 2.5) =460.71ρb – 20.33OM + 9.088Lk2.5 – 2.38(kf)-0.5 + 2.86C2.5 +

4.50(TW) - 20.96(Ø 2.5)0.67 + 0.304(nFk2.5)2 – 610.62

PCS (pF = 1.8) =0.843ρb – 0.544(kf)0.33 + 0.022TW + 7.03(C1.8)-1 + 0.024 Ø 1.8

– 0.015nFk1.8 + 0.725

PCS (pF = 2.5) =0.844ρb – 0.456(kf)0.33 + 0.026TW + 12.88(C2.5)-1 + 0.003 Ø 2.5

– 0.016nFk2.5 + 1.419

PCS (pF = 1.8) =4.59ρb – 1.02OM – 16.43(kf)0.33 + 0.31TW – 1.57nFk1.8 + 3.55C1,8

+ 1.18 Ø1.8 – 18.03

PCS (pF = 2.5) =70.65ρb – 0.55OM – 7.01(kf)0.33 + 1.32TW +– 1.08nFk2.5 + 1.72C2.5

+ 1.05 Ø2.5 – 100.94

0.778

0.710

0.828

0.874

0.765

0.847

0.808

0.804

0.774

0.763

15

Table 2. Existing pedotransfer functions used to predict PCS and other soil mechanical properties (continued)

Source Property Predictors Soil Class N FTP equations

R2

Kirby

(1991)

Precompression

Stress, PCS

Compression

index, Cc

Liquid index IL; void ratio at pc,

epc; saturation at epc, Sepc

Void ratio at pc, epc; saturation at

epc, Sepc

All soils

All soils

170

PCS = exp(5.856 – 4.352 IL – 1.074 epc)

Cc = 0.229 + 0.1736epc – 0.400Sepc

0.682

0.514

Imhoff et

al. (2004)

Precompression

Stress, PCS

Compression

index, Cc

Clay content, CC (g 100 g-1);

initial bulk density, ρb (Mg m-3);

water content, w (g g-1)

All soils

CC < 29.42

CC > 29.42

50

PCS = -566.764 + 442.891 ρb + 4.338CC – 773.057w

Cc = 0.248 + 0.006CC – 0.121ρb

Cc = 0.416 – 0.121ρb

0.70

0.77

0.77

Saffih

Hdadi et

al. (2009)

Precompression

Stress, PCS

Compression

Index, Cc

Initial water content, w (% g g-1);

initial bulk density, ρb (Mg m-3)

Very fine

Fine

Medium fine

Medium

Coarse

PCS = 7.71 + 112.21ρb – 2.82w

Cc = 2.37 – 1.18ρb – 0.017w

PCS = 4.19 + 202.54ρb – 10.92w

Cc = 1.85 – 0.91ρb – 0.012w

PCS = -223.71 + 347.47ρb – 7.93w

Cc = 1.36 – 0.59ρb – 0.010w

PCS = -136.87 + 155.19ρb

Cc = 1.27 – 0.628ρb – 0.006w

PCS = -220.68 + 191.45ρb – 2.77w

Cc = 1.36 – 0.77ρb – 0.005w

0.88

0.95

0.95

0.98

0.76

0.78

0.5

0.74

0.57

0.87

16

3. MATERIALS AND METHODS

3.1 Study area

The study area East Flanders is found at the Dutch-speaking northern portion of Belgium

(Figure 1). Geographically, Flanders is generally flat, agriculturally fertile, densely populated

and highly urbanized area. It shares its borders with France in the west, the Netherlands in the

north and east, and Wallonia in the south. The climate is maritime temperate with significant

precipitation in all seasons (Koppen climate classification: Cfb; average temperature is 3 °C

in January and 21 °C in July; the average precipitation is 65 mm in January and 78 mm in

July). Flanders has two main geographical regions – the coastal Yser basin plain (consists

mainly of sand dunes and clayey alluvial soils in the polders) in the north-west and a central

plain. The central plain consists of the lowermost Scheldt basin (smooth, slowly rising fertile

area irrigated by many waterways), the Campine region (having sandy soils) and the Meuse

basin.

Figure 1. Geographical locations of the sampling fields (red crosshairs)

17

3.2 Field sampling and soil analysis

In this study, two different datasets were established. For dataset one, a total of 126 intact

cores were taken for determining PCS and another 41 cores was taken to determine soil

hydraulic properties at the same location. Those cores were already taken in 2012-2013, prior

to this dissertation. Some information about sampling and soil tests will be provided in

section 3.5. For dataset two, a total of 36 cores of undisturbed soils were taken from the

center (40 to 45 cm depth) of a cropped field (spring barley) near Ghent, Belgium using a

stiff confining PVC tubes with a sharpened edge. A 2m x 2m pit was dug to a depth of 70 cm

and undisturbed samples were taken manually by pushing the sharpened side of the PVC

cylinder vertically into the horizontal soil surface using a hammer and improvised tube

holder. Plastic caps were used to seal and protect the soil samples inside the cylinders from

disturbance during transportation. Prior to the compression test, all intact/undisturbed soil

samples were placed on top of sandboxes/pressure plates and gradually subjected to either of

three preset matric potentials of -6 kPa (pF=1.8), -10 kPa (pF=2) and -33 kPa (pF=2.5).

Basic soil properties such as dry bulk density, porosity, organic matter content, clay content,

particle size distribution, and saturated hydraulic conductivity (Ksat) were determined using

standard methods used in the Laboratory of Soil Physics, Ghent University. Sand, silt and

clay content was determined using the sieve-pipette method. For OM content and bulk

density, the loss-on ignition method (temperature at 550ᴼ C) and oven-drying method (Blake

and Hartge, 1986) were used, respectively.

3.3 Laboratory test

The experiment was composed of nine treatments (three different matric potentials vs. three

different loading durations) with four replications per treatment (except when otherwise

mentioned). The uniaxial confined compression tests using a standard oedometer was done at

the Laboratory of Geotechnics, Ghent University. The oedometer test simulates vertical stress

applied to soil in the field by a tire. The soil sample (approximately 63.5 mm in diameter,

25.4 mm in height) was confined in a stiff ring and loaded in a vertical direction. The stiff

ring prevented the sample from lateral deformation. During the compression test, axial stress

was applied and the resulting displacement was measured.

18

3.3.1 Apparatus

The oedometer contained a loading device, a compression cell, and devices to record the

applied stress and the resulting displacement (Figure 2). The uniaxial stress in vertical

direction was applied to the soil specimen by dead weights. The compression cell was a

circular metal ring which enclosed the soil between two porous plates.

Figure 2. Standard oedometer used for uniaxial confined compression test.

3.3.2 Preparation of soil samples

Undisturbed soil samples conditioned to different matric potentials (-6, -10 and -33 kPa) were

cut directly from a larger soil core and inserted into the oedometer ring/cell. Excess soil was

carefully carved away. Porous stones were placed beneath and above the soil sample to

ensure free drainage. A rigid loading cap was placed on top of the upper porous stone and

then the assembly was placed into the oedometer loading frame. [The loading frame is

designed to load the specimen through a yoke assembly and one of three alternative beam

ratios. The beam is fitted with a counterbalance weight and beam support jack. The cell

platform is fitted with a central spigot to ensure accurate centering of the cell under the

loading yoke].

19

3.3.3 Compression tests

Sequential (stepwise) loading of the soil cores was carried out as described by Eriksson

(1982). A sequence of stress levels was applied to the soil, each being double the previous

value (e.g. 12, 25, 50, 100, 200, 400, and 800 kPa). Stress corresponding to the first stress

level was applied and maintained for the desired loading duration per stress level (T1 = 1 min,

T2 = 10 min, and T3 = 30 min). The step was repeated for the next stress level until the last

stress level was reached. The compression or vertical displacement (accuracy ± 0.001 mm) of

the soil sample at the end of each loading was recorded by a potentiometric displacement

transducer. Also, the pore water pressures were measured by a micro-tensiometer that was

inserted vertically into the soil sample through the hole in the lower sinter metal plate. To

avoid compacted and disturbed zones around the tensiometer tip, a hole of the size of the

tensiometer tip was bored with a hollow drill. After a maximum stress level was applied, the

soil sample was unloaded by decreasing the weights incrementally.

3.4 Determination of PCS

Initial wet mass of the samples (Ms) was determined in the oedometer ring by measuring the

mass of the ring with sample and subtracting the tare mass of the ring. Initial height (ho) of

the samples was determined to the nearest 0.025 mm (0.001 in) by taking the average of at

least four evenly spaced measurements over the top and bottom surfaces of the sample using

a dial comparator. Initial volume of the samples (V) was computed to the nearest 0.25 cm3

(0.015 in3) from the diameter of the ring and initial sample height.

Based on the results of the compression test, a stress-strain relationship for each sample was

presented graphically by plotting the logarithm of the applied stress (log-σ) and the

corresponding volumetric strain (expressed as void ratio, e) at the x-axis and y-axis,

respectively. Void ratio is calculated from the measured displacement (Δh) as

2

0( ) ( )( ) 1 1s s

s s

V h r h he h

M M

(1)

where V, r, Ms and h0 are the volume, radius, and initial height of the cylindrical soil sample,

respectively.

20

From the stress-strain curve the compression index (Cc, slope of the VCL) was calculated by

)log( ''

1

1

vav

ac

eeC

(2)

where e is the void ratio and σ the applied normal stress.

The following methods were used to determine the PCS values:

1) C_DK: Casagandre’s graphical method

2) AK_2: stress at the intersection of VCL with the x-axis at strain = 0

3) AK_3: stress at a predefined strain of 2.5%

4) AK_4: stress at the intercept of the VCL and a regression line with the first two points

of the curve

5) AK_5: stress at the intercept of the VCL and regression line with the first three points

of the curve

6) Gea_3: fitting of three-parameter Gompertz type equation to stress-strain curve

7) Gea_4: fitting of four-parameter Gompertz type equation.

The C_DK method was used by Davidowski and Koolen (1974) based on Casagandre (1930)

graphical procedure. AK_2, AK_3, AK_4, and AK_5 corresponds to the four methods used

by Arvidsson and Keller (2004). Gea_3 and Gea_4 are the modified (3 parameters) and the

original (4 parameters) Gompertz function used by Gregory et al. (2006), respectively.

For curve fitting in method 1 to 5, the fourth-order polynomial function (Eq. 3) was used

where first and second derivatives (Eq. 4 and 5) correspond to tangent of the line and point of

maximum curvature in the stress-strain curve:

4 3 2

10 10 10 10(log ) (log ) (log ) (log )e a PCS b PCS c PCS f PCS g (3)

3 2

10 10 10

10

4 (log ) 3 (log ) 2 (log )(log )

dea PCS b PCS c PCS f

d PCS (4)

22

10 102

10

12 (log ) 6 (log ) 2(log )

d ea PCS b PCS c

d PCS (5)

21

For method 6 and 7, the first (Eq. 7) and second derivatives (Eq. 8) of the Gompertz function

(Eq. 6) were calculated as follows:

10exp exp( ((log ) ))e a c b PCS m (6)

10 10

10

exp( exp( ((log ) ))) exp( ((log ) ))(log )

debc b PCS m x b PCS m

d PCS (7)

22

102

10

10 10

exp( exp( ((log ) )))(log )

exp( ((log ) )) exp( ((log ) )) 1

d eb c b PCS m x

d PCS

b PCS m x b PCS m

(8)

All the calculations were executed using Python scripts developed by Soil Physics Group,

Ghent University.

3.5 Development of pedotransfer functions

A separate dataset available at the Soil Physics Group was used to develop PTFs. The dataset

contains PCS data of 126 soil samples collected from seven sampling sites within Flanders,

Belgium with varying texture (Figure 3), bulk density, particle size distribution (expressed as

% clay, sand and silt), organic carbon content, land use, degree of compaction, and wetness.

It covers the seven major soil textural classes according to the Belgian Textural

Classification, i.e., Z (sand), S (loamy sand), P (light sandy loam), L (sandy loam), A (silt

loam), E (clay), and U (heavy clay). More details is available on this link:

http://www.labsoilscience.ugent.be/docs/pdf/soilmaplegend.pdf. At each site samples were

taken at three different locations (headland of cropland, central within cropland, grassland)

from the subsoil at depths of 40 and 70 cm. Each sample contains a set of different PCS

values which corresponds to the seven methods that have been used to derive PCS from the

stress-strain curve.

The PTFs were developed using a stepwise multiple linear regression (SMLR) which is a

combination of forward and backward selection techniques. The R software (R Foundation

for Statistical Computing, 2015) was used to develop the PTFs. The selection of the

predictors to develop the PTFs was done on a semi-hierarchical basis and the stepwise

regression procedure helped select the significant ones out of the available predictors.

22

Figure 3. Variation of clay, silt, and sand content in the dataset used to develop PTFs.

To evaluate the predictive ability of the developed PTFs, three statistical measures were used

- the mean absolute error (MAE), the root mean square error (RMSE) and coefficient of

determination (R²).

MAE = 1

1( )

t

i i

N

p m

it

PCS PCSN

(9)

RMSE = 2

1

1( )

t

i i

N

p m

it

PCS PCSN

(10)

R² =

2

1

2 2

1

( )( )

( ) ( )

t

i i i i

t

i i i i

N

p p m m

i

N

p p m m

i

PCS PCS PCS PCS

PCS PCS PCS PCS

(11)

where piPCS is the predicted precompression stress for soil sample i (kPa), miPCS is the

measured precompression stress for soil sample i (kPa), and Nt is the number of samples in

the test dataset.

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

% c

lay

% sand

clay

siltyclay

siltyclay loam

silt loam

silt

loam

sandy loam loamysand

sand

sandy clay loam

sandy

clay loam

23

3.6 Measurement of soil hydraulic properties

The Ksat was determined using the falling head (for less permeable soils) and constant head

(for highly permeable soils) permeameter tests with a closed system (Eijelkamp Agrisearch

Equipment, the Netherlands). Soil water retention curves (SWRC) was constructed using the

data from soil water contents at eight different water potentials. The sand box apparatus

(Eijelkamp Agrisearch Equipment, the Netherlands) was used to drain the soil at -10, -30, -

50, -70, and -100 cm water potentials. For water potentials of 1/3, 1, and 15 bars the pressure

plates (Soil Moisture Equipment, Santa Barbara, C.A., USA) were used based on the

procedure described by (Cornelis et al., 2005). The RETC software was used to predict Ksat

and SWRC based on the parametric model of van Genuchten (1990).

3.7 Determination of soil structural quality and vulnerability to compaction

In this study, soil quality index (SQI), susceptibility to compaction index (SCI), and

vulnerability to compaction index (VCI) were determined. For determination of SQI, a set of

soil parameters proposed by Lebert et al. (2007) were used to detect soil structure damage by

compaction which includes packing density, air capacity, and saturated hydraulic

conductivity (Table 2). A soil structure damage is possible when all these three parameters

exceed the threshold limits of a sufficient soil structure at the same time (AC < 5% vol., Ksat

< 10 cm/d and PD > 1.75 g cm-3).

Table 3. Classification of soil structure quality (adopted from Lebert, 2010). Maximum

threshold for packing density was modified.

a) Packing

density

(g cm-3)

b) Air

capacity

(% vol.)

c) Sat. water

conductivity

(cm/d)

Index

Score

Category Class of soil

structure quality

≥ 1.75 < 5 < 10 5 very low Round off from

average of classes

a), b), c) =

(a + b + c) / 3

1.7 - < 1.75 5 - < 7 10 - < 40 4 low

1.6 - < 1.7 7 - < 13 40 - < 100 3 medium

1.4 - < 1.6 13 - < 26 100 -< 300 2 high

< 1.4 ≥ 26 ≥ 300 1 very high

Classification of susceptibility to compaction based on PCS values is shown in Table 3. The

lower the PCS the higher the susceptibility to mechanical compaction. Finally, Table 4 was

used to classify the soil vulnerability to compaction based on principle that soil with the

lowest structural quality and highest susceptibility to compaction is the most vulnerable to

24

further mechanical compaction. Vulnerability to compaction was classified into very high =

5, high = 4, medium = 3, low = 2, and very low =1.

Table 4. Classification of soil structure quality (adopted from Lebert, 2010). Maximum

threshold and unit for packing density was modified.

Precompression

Stress (kPa)

Index Score

Name of class

Susceptibility to compaction

Category

< 80 5 very low very high

80 - < 120 4 low high

120 - < 160 3 medium medium

160 - < 200 2 high low

≥ 200 1 very high very low

Table 5. Classification of soil structure quality (adopted from Lebert, 2010).

Soil structural

quality (SQI)

Index Score

Susceptibility to

compaction (SCI)

Index Score

Index Score

(SQI + SCI)/2

Vulnerability to

compaction

Category

5 5 5 very high

4 4 4 high

3 3 3 medium

2 2 2 low

1 1 1 very low

3.8 Statistical analysis

R software (R Foundation for Statistical Computing, 2015) was used for data processing,

graphical presentations, and statistical analysis. Normality tests, analysis of variance

(ANOVA), and Tukey’s HSD test (at P < 0.05) were performed to analyze statistical

differences and to discriminate between means. Correlation analysis was carried out to study

the interactive effects of the levels of loading duration and water potential on PCS values.

25

4. RESULTS AND DISCUSSIONS

4.1 Soil quality assessment and risk of compaction

Basic properties of soils (dataset one) from seven locations evaluated by this study are

summarized in Table 6. A total of 126 soil samples from different land use (cropland and

grassland) and soil depths (40 and 70 cm) were brought to the lab for soil analysis. Most of

the soil samples were classified as silt loam and loamy sand according to USDA

classification. In terms of soil quality, bulk density (BD), air-filled pore volume or air

capacity (AC), and saturated hydraulic conductivity (Ksat) were used as primary indicators.

Acceptability, practicability, relevance for policy-makers, and usefulness for users were the

main criteria used for selection of these indicators (Huber et al., 2008). BD which can be

measured directly in the field is commonly used as primary indicator of soil packing density

and compaction. It is also a good indicator of soil macro-porosity. On the other hand, Ksat or

permeability integrates the importance of soil structure, oxygen diffusion capacity,

rootability, and soil biological activity (Huber et al., 2008). Carpenter et al. (1985) supposed

that the most detrimental effect of soil compaction on soil physical properties is the drastic

reduction in hydraulic conductivity that may cause excessive soil erosion and crop yield

reduction due to reduced infiltration, increase run-off and poor drainage. In addition to the

primary indicators, the structural quality index (StI) as an indicator for soil structural

degradation associated with organic carbon depletion was included (Pieri, 1992; Moncada et

al., 2013).

Based on the result of soil analysis, higher BD can be found in the upper part (40 cm) of the

subsoil in croplands, whereas in grasslands the BD is higher in the lower part (70 cm) of the

subsoil. In arable soils, the upper part of the subsoil, also called plough pan layer or

restricting layer, is the zone of most importance for identifying compaction (Huber et al.,

2008). In croplands, the higher BD in upper part of the subsoil can be caused by tractor tires

driving directly on the subsoil during ploughing or due to the pressures transmitted by heavy

wheel loads from topsoil down to the subsoil (Huber et al., 2008). However, it is important to

note that some of these soils may have inherent compacted layers such as soils that have hard

pans or cemented layers resulting from chemical precipitation of Fe, Mn, and Si.

26

In literature, a total pore volume of at least 40% is considered as a threshold for better root

penetration and oxygen supply (Hidding and van den Berg, 1961; Bakker et al., 1987; Tacket

and Pearson, 1964). This corresponds to BD of about 1.6 g cm-3 in coarse textured soils

(sands, loamy sands) with low organic matter. However, threshold values for BD also

depends on clay content and allowable soil packing density (PD). Compact soils are

characterized of having high packing density and lower air capacity. In general, a packing

density of 1.75 g cm-3 is considered as threshold value for compaction indicating detrimental

effects in the subsoil. Figure 4a shows the variability in measured BD at different clay

content where the red and green lines indicates the upper limit (PD =1.75 g cm-3) and lower

limit (PD = 1.4 g cm-3) for BD. It showed that limit for BD at any desired PD threshold tends

to decrease at higher clay content. As shown in Figure 5, most of the sites evaluated have

medium risk (1.4 > PD > 1.75) of soil compaction except those some part of Melle (CH and

GL), Wortegem (CH), and Zwalm (CC and CH). In Melle and Zwalm, the upper part of their

subsoil have higher risk (PD > 1.75 g cm3) of soil compaction (Figure 6).

Figure 4. Relationship between clay content and (a) dry bulk density and (b) saturated

hydraulic conductivity. Dash lines in red and blue corresponds to bulk density at

PD = 1.75 g cm-3 and PD = 1.4 g cm3, respectively.

Mean values of soil AC (at 6 kPa water tension) are shown in Figure 7. Lebert et al. (2007)

suggested a threshold value of < 5% for AC which can be related to soils with a very low

structural quality. All locations evaluated in this study have AC values higher than the

threshold limit and this might be due to the inherent texture of the soils. In general, loamy

sand had the largest capacity for air as they have larger pores than silt or clayey soils.

0.0

0.5

1.0

1.5

2.0

0 10 20 30

BD

(M

g/m

³)

Clay (%)

(a)

0

100

200

300

400

0 10 20 30

Ksa

t (c

m/d

ay)

Clay (%)

(b)

27

Table 6. Site characteristics and soil physical properties

Location Land

Use

Geographic

Coordinates Soil Texture

(USDA) BD

(Mg/m³)

Sand Silt Clay OC CaCO₃

(%)

Baaigem

CC 50.93° N 3.73° E Silt Loam 1.5 13.1 73.2 13.8 0.4 0.0

CH 50.93° N 3.73° E Silt Loam 1.6 12.4 74.9 12.8 0.2 0.0

GL 50.93° N 3.73° E Silt Loam 1.5 13.9 74.2 11.9 0.3 0.0

Landuit

CC 50.97° N 3.67° E Loamy Sand 1.6 81.0 12.0 7.1 0.3 0.0

CH 50.97° N 3.67° E Loamy Sand 1.6 84.5 10.5 5.0 0.6 0.1

GL 50.97° N 3.67° E Loamy Sand 1.7 86.7 8.6 4.8 0.3 0.0

Melle

CC 50.98° N 3.77° E Loam 1.6 42.0 45.3 12.7 0.2 0.1

CH 50.98° N 3.77° E Loam 1.6 38.2 46.0 15.8 0.2 0.1

GL 50.98° N 3.77° E Loam 1.7 45.5 41.4 13.2 0.2 0.0

Moortsele

CC 50.95° N 3.77° E Loamy Sand 1.6 87.3 5.1 7.7 0.1 0.0

CH 50.95° N 3.77° E Loam 1.6 62.3 23.6 14.2 0.1 0.0

GL 50.95° N 3.77° E Loamy Sand 1.5 80.6 5.9 13.5 0.1 0.1

Wortegem

CC 50.86° N 3.52° E Silt Loam 1.6 27.9 56.5 15.6 0.9 0.0

CH 50.86° N 3.52° E Silt Loam 1.7 29.6 57.4 13.1 0.6 0.0

GL 50.86° N 3.52° E Silt Loam 1.6 31.8 58.9 9.4 0.3 0.4

Zevergem

CC 50.96° N 3.68° E Silt Loam 1.5 29.2 53.5 17.3 0.4 4.3

CH 50.96° N 3.68° E Silt Loam 1.4 15.3 57.5 27.3 0.8 4.5

GL 50.96° N 3.69° E Silt Loam 1.4 23.5 61.5 15.1 0.5 3.8

Zwalm

CC 50.89° N 3.73° E Silt Loam 1.6 15.7 70.1 14.2 0.3 0.0

CH 50.88° N 3.73° E Silt Loam 1.7 25.3 61.9 12.9 0.2 7.8

GL 50.88° N 3.73° E Silt Loam 1.5 24.4 60.8 14.9 0.3 0.0 CC: cropland center, CH: cropland headland, GL: grassland, OC: soil organic carbon, CaCO3: calcium carbonate content,

BD: dry bulk density

Saturated hydraulic conductivity is a direct measure of infiltration capacity and filtering

capability of the soil. A threshold value of < 10 cm day-1 is commonly used especially for

stagnic horizons in Western European soils (Werner and Paul, 1999; Lebert et al., 2007;

Bhattacharya, 2014). Figure 8 shows the mean Ksat values of different land use types in all

locations. Most sites except Moortsele and Zwalm have recorded Ksat lower than 10 cm day-

1. Also in those areas, it seems that Ksat was always higher in the headlands than the center of

the field. This is not the case in Moortsele and Zwalm where Ksat was higher at cropland

center compared to headlands. Due to very high variance in Ksat measurement, no

generalization can be made with regards to the observed differences in Ksat values.

28

Figure 5. Average packing density (PD) for different locations and land use. Dash lines in red

and blue indicates a threshold value for high risk (PD > 1.75 g cm-3) and low risk

(<1.4 g cm-3) in soil degradation by compaction, respectively.

Figure 6. Average packing density (PD) for different locations and soil depth. Dash lines in

red and blue indicates a threshold value for high risk (PD > 1.75 g cm-3) and low

risk (<1.4 g cm-3) in soil degradation by compaction, respectively.

Figure 7. Average air capacity (% vol.) for different locations and land use. Dash line in red

indicates a threshold value (AC < 5% vol.).

1.0

1.2

1.4

1.6

1.8

2.0

Baaigem Landuit Melle Moortsele Wortegem Zevergem Zwalm

PD

(g

/cm

³)CC CH GL

1.0

1.2

1.4

1.6

1.8

2.0

Baaigem Landuit Melle Moortsele Wortegem Zevergem Zwalm

PD

(g

/cm

³)

40 cm 70 cm

0

10

20

30

40

50

Baaigem Landuit Melle Moortsele Wortegem Zevergem Zwalm

AC (%

vol.

)

CC CH GL

29

Figure 8. Average Ksat values for different locations and land use. Dash line in red indicates

a threshold value (Ksat < 10 cm/day).

In terms of StI values (Table 7), it appears that all soils except those from Landuit (cropland-

headland) were classified as structurally degraded soils (StI < 5%). It means that these soils

do not have sufficient soil organic carbon to maintain their structural stability.

Table 7. Mean values of StI (%) for different locations and land use.

Location Structural Quality Index

CC CH GL

Baaigem 0.73 ± 0.21 0.39 ± 0.26 0.58 ± 0.22

Landuit 3.13 ± 1.45 6.12 ± 0.24 4.03 ± 3.01

Melle 0.65 ± 0.04 0.44 ± 0.19 0.47 ± 0.21

Moortsele 1.18 ± 0.50 0.54 ± 0.03 1.21 ± 0.04

Wortegem 2.40 ± 2.19 1.62 ± 1.21 0.87 ± 0.38

Zevergem 1.07 ± 0.18 1.56 ± 0.35 1.21 ± 0.11

Zwalm 0.56 ± 0.01 0.40 ± 0.15 0.75 ± 0.06

(Structurally degraded soil: StI < 5%, High risk of structural degradation: 5% < StI < 7%,

Low risk of soil structural degradation: 7% < StI < 9%)

Soil quality index (SQI) was used to evaluate and classify the overall quality of the soil from

each sites. The method of classification was discussed in Section 3.7. SQI is simply the

aggregated mean scores of all indicators (AC, PD, Ksat) used in soil quality assessment

(Table 8). For instance, an SQI value of 5 corresponds to very low soil quality.

1

100

10000

Baaigem Landuit Melle Moortsele Wortegem Zevergem Zwalm

Ksa

t (

cm

/day)

CC CH GL

30

Table 8. Soil quality index (SQI) scores for different locations and land use.

(1-very high, 2-high, 3-medium, 4-low, 5-very low)

Location Land

Use

Index Scores

AC PD Ksat SQI

Baaigem

CC 2 3 5 3

CH 2 4 5 4

GL 2 2 5 3

Landuit

CC 2 4 5 4

CH 2 3 5 3

GL 2 4 4 3

Melle

CC 2 4 4 3

CH 2 5 4 4

GL 2 5 4 4

Moortsele

CC 1 3 2 2

CH 2 4 3 3

GL 1 2 2 2

Wortegem

CC 2 4 5 4

CH 2 5 4 4

GL 2 3 4 3

Zevergem

CC 2 3 5 3

CH 2 3 3 3

GL 2 2 5 3

Zwalm

CC 2 5 2 3

CH 2 5 4 4

GL 2 3 2 2

Calculated SQI values ranged from 2 to 4 indicating neither very low nor very high soil

quality in all the evaluated sites. Overall soil quality based on AC only was found high, but

tends to be lower once other indicators like PD and Ksat were included. Result of the soil

quality assessment only indicates that immediate intervention is needed in order to maintain

or safeguard the quality of these soils against further degradation by compaction.

4.2 Mechanical susceptibility of soil to compaction

4.2.1 Calculated PCS values

The likelihood or susceptibility of the soil to compact depends on several cross-cutting

influences of soil properties, climate, land use, drainage conditions, and farm management. In

practice, the most commonly used and very useful indicator of soil vulnerability to

31

compaction is the soil strength or the capacity of the soil to bear loads, and precompression

stress is the most widely used as indicator for soil strength (Lebert and Horn, 1991).

In this study, results of the confined compression test of 126 soil samples at different initial

matric suctions (-6, -10, and -33 kPa) were used to plot the soil compression curves (SCC) as

shown in Figure 9. In general, the SCC which is the graphical representation of the stress-

strain relationships of soils subjected to mechanical stress, has a sigmoidal S-shape due to

logarithmic scaling of the x-axis (log of the applied stress). The point in a curve with a

smallest radius of curvature and the straight portion of the curve (virgin compression line)

may be considered as the most crucial parts in determining two important soil mechanical

parameters – compression index (Cc) and PCS. The PCS values were determined using

different methods presented in literature, such as the one used by Davidowski and Koolen

(1974) based on Casagandre (1930) method, the four methods used by Arvidsson and Keller

(2004), and the modified (3 parameters) and original (4 parameters) Gompertz function used

by Gregory et al. (2006).

Comparison of PCS values derived from the compression curve using different methods

(C_DK, AK_2, AK_3, AK_4, AK_5, Gea_3, and Gea_4) is given in Figure 10. Highest

correlation was found between AK_4 and AK_5 (r = 0.92), whereas the lowest correlation

was found between C_DK and Gea_3 (r = 0.09). While other methods exhibited a positive

correlations with one another, only Gea_3 was inversely correlated with AK_4 (r = -0.18) and

AK_5 (r = -0.20). Highest correlation between AK_4 and AK_5 is not surprising since the

determination of PCS according to these methods is based on the first successive two or three

points of the curve, of which the variation in slope of the fitted line is not very sensitive to

smaller load increments especially if the soil had experienced considerable compaction in the

past. The significant differences in PCS values is just an indication that the method to be used

to determine PCS must be carefully chosen (Cavalieri et al., 2008).

Figure 11 shows the average PCS (C_DK) of soils sampled from seven locations. The

average PCS (C_DK) values for all soils at -6, -10, and -33 kPa water potential were 352,

344, and 419 kPa, respectively. Based on the classification of PCS proposed by Lebert and

Schafer (2005), the PCS values obtained in this study were found very high (PCS > 200 kPa)

especially for cropland soils.

32

Figure 9. Stress-strain curves at (a) pF=1.8, (b) pF=2.0, and (c) pF=2.5 water potential for all

sites. Data shown are averages of six values (from cropland center, cropland

headland and grassland, at depths of 40 cm and 70 cm).

33

Figure 10. Comparison of PCS values derived by different methods. Pearson correlation

coefficients are shown in the upper right corner and scatter plots with smoothed

lines are shown in the lower left corner.

Figure 11. Average PCS (C_DK) of soils from different locations.

C_DK

50 150

0.73 0.39

200 600

0.74 0.79

100 300

0.09

100

400

700

0.56

50

150 AK_2

0.71 0.60 0.65 0.50 0.85

AK_3

0.22 0.30 0.43

50

200

0.62

200

600 AK_4

0.92 -0.18 0.43

AK_5

-0.20

100

400

0.39

100

300

Gea_3

0.62

100 400 700 50 150 300 100 300 500 0 400 800

0400

1000

Gea_4

34

4.2.2 Influence of land use and soil depth on PCS variability

Figure 12 shows the variations in PCS values at different land use and soil depths. Among the

three land use evaluated, highest PCS was recorded in the headland at both depths. This can

be attributed to frequent passes of tractor wheels along this section of the cropland.

Headlands serves as field entrances and turning points for field machinery. In cropland center

and grassland, the upper part of the subsoil have higher PCS than the lower part. Apart from

differences on exposure of soil to field traffic, differences in pedological processes, soil

moisture conditions, and swelling-shrinking intensity (Keller et al., 2004) might be the main

reasons of the observed differences in PCS.

Figure 12. PCS (C_DK) values at different land use and soil depths.

4.2.3 Assessment of soil vulnerability to mechanical compaction

Vulnerability to compaction index (VCI) was used to assess the current capacity of the soils

to carry loads without significant damage to soil structure. Vulnerability of the soil to

compaction mainly depends on its structural quality and susceptibility to be compacted under

mechanical loading. As already discussed in Section 3.7, both results of the soil quality

assessment and PCS analysis were vital inputs in order to determine the VCI value for each

location and land use type evaluated in this study as summarized in Figure 13. All locations

have VCI values less than 3 indicating low to medium vulnerability to degradation by

mechanical compaction. This low VCI can be attributed to the present mechanical strength of

the soil which have been found very high (PCS > 200 kPa) due to previous stress history of

the soil which have preserved within the soil structure. Soils with high PCS values are

structurally stable and less susceptible to further compaction since higher stress is required in

order to distort their internal structure. D’Or and Destain also conducted a compaction risk

35

assessment in Wallonia (Belgium) based on the estimation of probability of exceeding the

PCS due to application of loads, but using the PCS values derived from pedotransfer

functions of Horn and Fleige (2003). They found a PCS values higher than 120 kPa at almost

more than half of Wallonia territory in pF between 1.8 and 2.5. Their findings also showed

that risks of subsoil compaction exist mainly in loamy forest soils.

Figure 13. Average VCI values for different locations and land use.

4.3 Indirect estimation of PCS using pedotransfer function

4.3.1 Potential predictors of PCS

Linear relationships were evaluated between PCS and other soil properties such as particle

size distribution (% of clay, silt, and sand), calcium carbonate content (CaCO3), organic

carbon content (OC), soil-water potential (PF), dry bulk density (BD), wet bulk density

(WBD), bulk density ratio (BDR) saturated hydraulic conductivity (Ksat), clay/sand ratio

(CSR), soil textural class (based on Belgian textural triangle), structural quality index (SQI),

and soil packing density (PD). A potential subset of these soil properties were then selected

through stepwise regression to construct optimal PTFs that can be used to predict PCS. The

Pearson’s correlation coefficients between predictors and PCS values are shown in Table 9.

Among soil properties, silt vs. sand has the highest negative correlation (r = -0.98) followed

by PD vs. BD (r = 0.91) and PD vs. WBD (r = 0.85). It implies that PD can be a good

substitute for BD since it also accounts the influence of clay content on soil structural quality.

Between predictor variables and PCS, highest correlation (r = 0.42) was obtained between

BDR vs. PCSAK5, followed by BD vs. PCSAK5 (r = 0.38), and DBR vs. PCSAK4 (r

= 0.35). The

1.0

2.0

3.0

4.0

5.0

Baaigem Landuit Melle Moortsele Wortegem Zevergem Zwalm

VCI

CC CH GL

36

PCSGea4 is poorly correlated with all the predictors, while only PCSGea3 showed a negative

correlation (r = -0.29) with BD. Most findings in literature (e.g. Saffih–Hdadi et al., 2009;

Imhof et al., 2004) showed that soil strength is positively correlated with BD and so the PCS.

4.3.2 Development of regression-based PTFs

A series of stepwise multiple regressions have been performed to find a reliable linear

functions that can be used to predict PCS from a combinations of a set of predictor variables.

At the outset, initial PTFs were derived using the complete dataset (N=126) of which Sn, Si,

Cl, OC, pF, DBD, and texture were used as the only predictors. Imhof et al. (2004) also used

BD and clay content to predict compression index and PCS. For practical reasons, the wet

bulk density (WBD) and saturated hydraulic conductivity (Ksat) were not included in the

analysis since in practice, measurements of these variables is not easy and time-consuming.

Results of the initial runs showed that developed PTFs had very low predictive performances

in terms of their MAE, RMSE and R2 values. The next approach then was to include other

variables such as CSR, SQI, and PD as predictors and also the dataset was splitted into two

groups based on land use (cropland, grassland) and then new PTFs were developed for each

type of land use. The developed PTFs specific for each land use type showed a better

predictability than those PTFs derived using the complete dataset. A summary of these

developed PTFs is presented in Table 10.

Reliability of new PTFs have been evaluated in terms of their accuracy in predicting PCS

using information from readily available and easy to measure soil properties. Cropland PTF

that used PCS values calculated using AK_5 method outperformed other PTFs, whereas in

grassland the one that used PCS of AK_2 showed a better predictive performance than others.

Grouping the dataset by land use and including some indices used in soil quality assessment

such as PD and SQI substantially improved the predictive performance of PTFs. By

comparing two sets of PTFs developed specifically for each type of land use, the grassland

PTFs have higher coefficient of determination (R2 > 0.40) compared to cropland PTFs. This

may indicates that the underlying causal factors of the PCS variability in cropland is

somehow more complex than grassland which cannot be deduced from soil properties alone.

37

Table 9. Pearson’s correlation matrix between soil properties (predictors) and PCS values.

Sn: sand (%), Si: silt (%), Cl: clay (%),OC: organic carbon content (%), Ca: CaCO3, PF: log of water potential, BD: dry bulk density (Mg/m³), WBD: wet bulk density

(Mg/m³), BDR: DBD/WBD ratio, Ksat: saturated hydraulic conductivity (cm day-1), CSR: clay/sand ratio, SQI: soil structural quality index, PD: packing density (g cm-3)

Sand Silt Clay OC Ca PF BD WBD BDR Ksat CSR StI PD PCSCDK PCSAK2 PCSAK3 PCSAK4 PCSAK5 PCSGea3 PCSGea4

Sand 1

Silt -0.98 1

Clay -0.6 0.45 1

OC -0.17 0.16 0.12 1

Ca -0.28 0.24 0.33 0.1 1

PF 0 0 0 0 0 1

BD 0.23 -0.14 -0.52 -0.14 -0.16 0.04 1

WBD -0.25 0.31 -0.14 0 0.07 -0.19 0.79 1

BDR 0.69 -0.63 -0.66 -0.24 -0.36 0.3 0.58 -0.04 1

Ksat 0.13 -0.15 0.03 -0.13 -0.09 -0.05 -0.19 -0.27 0.05 1

CSR -0.7 0.61 0.81 0.24 0.26 0 -0.53 -0.12 -0.7 0.01 1

StI 0.53 -0.49 -0.47 0.54 -0.13 0 0.15 -0.08 0.31 -0.11 -0.28 1

PD -0.03 0.05 -0.11 -0.11 -0.03 0.04 0.91 0.85 0.35 -0.21 -0.22 -0.06 1

PCSCDK 0.15 -0.13 -0.16 -0.04 -0.09 0.17 0.29 0.06 0.4 0.03 -0.22 -0.02 0.26 1

PCSAK2 0.1 -0.1 -0.05 -0.09 -0.11 0.19 0.15 -0.05 0.32 0.08 -0.07 -0.06 0.15 0.73 1

PCSAK3 0.07 -0.03 -0.2 -0.07 -0.1 0.05 0.25 0.16 0.21 -0.02 -0.07 0.05 0.19 0.39 0.71 1

PCSAK4 0.12 -0.09 -0.2 -0.04 -0.05 0.16 0.34 0.16 0.35 0.03 -0.23 -0.01 0.3 0.74 0.6 0.22 1

PCSAK5 0.18 -0.14 -0.29 -0.06 -0.07 0.17 0.38 0.14 0.42 0.08 -0.28 0.02 0.3 0.79 0.65 0.3 0.92 1

PCSGea3 -0.14 0.09 0.31 -0.08 -0.06 0.16 -0.29 -0.28 -0.08 0.03 0.28 -0.15 -0.19 0.09 0.5 0.43 -0.18 -0.2 1

PCSGea4 -0.15 0.14 0.12 -0.03 -0.04 0.16 0.06 0.04 0.07 0.02 0.08 -0.15 0.13 0.56 0.85 0.62 0.43 0.39 0.62 1

38

Table 10. List of PTFs to predict precompression stress of arable soils with different land use. The equations represent the best subset regression

results and their mean absolute error (MAE), root mean square error (RMSE), and adjusted R2 values.

Method LU PTF Equations MAE RMSE R² R²(adj)

C_DK CL log(PCS) = -90.27 + 0.89*Cl + 0.92*Sn + 0.92*Si + 0.24*OC + 0.19*PF - 5.24*SQI + 0.54*PD 0.15 0.21 0.35 0.29

GL log(PCS) = -142.7 - 0.10*Tx + 1.48*Cl + 1.44*Sn + 1.43*Si + 1.01*PD 0.15 0.23 0.40 0.32

AK_2

CL log(PCS) = 0.48 + 0.01*Sn + 0.01*Si + 0.17*PF - 2.27*SQI 0.14 0.18 0.18 0.14

GL log(PCS) = -191.02 -0.13*Tx +1.96*Cl +1.91*Sn +1.90*Si -0.03*Ca +0.14*PF +6.05*SQI

+1.19*PD 0.10 0.15 0.67 0.59

AK_3 CL log(PCS) = -0.47 + 0.03*Sn + 0.03*Si + 0.13*CSR 0.17 0.22 0.16 0.13

GL log(PCS) = -0.51 - 0.06*Tx + 0.01*Sn + 1.40*BD + 0.4*CSR - 3.16*SQI 0.14 0.20 0.34 0.25

AK_4 CL log(PCS) = 1.14 + 0.15*OC + 2.33*BD + 0.17*PF - 4.86*SQI - 1.69*PD 0.13 0.18 0.38 0.34

GL log(PCS) = -153.02 - 0.1*Tx 1.57*Cl + 1.54*Sn + 1.53*Si + 4.65*SQI + 1.04*PD 0.12 0.17 0.44 0.34

AK_5 CC

log(PCS) = 1.15 + 0.002*Sn + 0.21*OC + 3.75*BD + 0.16*PF + 0.14*CSR - 7.35*SQI -

3.06*PD 0.13 0.44 0.44 0.38

GL log(PCS) = -145.27 - 0.09*Tx + 1.49*Cl + 1.46*Sn + 1.46*Si + 3.12*SQI + 0.96*PD 0.10 0.15 0.48 0.39

Gea_3

CC log(PCS) = 2.85 - 0.14*Cl - 0.08*OC - 0.01*Ca - 16.67*BD + 0.07*PF + 16.25*PD 0.10 0.13 0.30 0.25

GL log(PCS) = -80.86 -0.05*Tx +0.86*Cl +0.82*Sn +0.82*Si -0.03*Ca +0.15*PF +4.89*SQI

+0.4*PD 0.08 0.11 0.58 0.48

Gea_4

CC log(PCS) = 2.03 - 0.16*Cl - 0.003*Sn - 16.77*BD + 0.13 *PF + 16.96*PD 0.18 0.23 0.13 0.08

GL log(PCS) = -259.02 -0.15*Tx +2.67*Cl +2.58*Sn + 2.58*Si +0.21*PF -0.34*CSR +10.38*SQI

+1.59*PD 0.14 0.20 0.64 0.55

LU: land use (CL - cropland, GL - grassland), log(PCS): logarithm of precompression stress (log-kPa), Sn: sand (%), Si: silt (%), Cl: clay (%), BD: dry bulk density

(Mg/m³), CSR: clay/sand ratio, OC: organic carbon content (%), Ca: CaCO3, Tx: texture (coded var.), PF: log of water potential, SQI: soil structural quality index (%),

PD: packing density (g/cm³)

39

Figure 14. Measured versus PTF-predicted PCS (log kPa) for soil samples collected from

cropland.

Figure 15. Measured versus PTF-predicted PCS for soil samples collected from grassland

.

40

4.4 Influence of compression test methods on soil deformation behavior

4.4.1 Stress-strain relationships

Effects of initial soil water potential (pF=1.8, 2.0, and 2.5) and the oedometer loading

duration (T=1, 10, and 30 min) on PCS were investigated. Results of confined compression

tests for all treatments showed that initial water potential have significant influence on the

stress-strain behavior of soil (Figure 16). Total volumetric strain tends to be higher with

decreasing water potential (more negative hence drier). It can be seen at lower water potential

that the compression range of the curve is shifted towards the right. On the other hand, higher

deformations were recorded for soils subjected to longer loading durations (Figure 17). In

general, there was no appreciable steep bend on stress-strain curves observed in all treatments

indicating that the soils may had exposed to slight compaction in the past. Substantial strain

occurred during the initial 15-20 sec of each loading stage.

4.4.2 Effects of loading duration and initial water potential on PCS

Figure 18 shows the calculated PCS using different methods for different treatments. In order

to compare the results of different treatments, the PCS values calculated using the standard

Casagandre (C_DK) method were choosen. Analysis of variance (ANOVA) showed that

there is no significant interaction effect between loading duration and initial water potential

on PCS (p=0.358). However, there is very strong evidence that the different levels of water

potential do not all have the same effects on PCS can be observed (p=0.358). It showed that

the PCS increases with decreasing (more negative) water potential irrespective of loading

duration used and this effect was highly significant (p=0.0003). The results obtained in this

study confirm the observation by other researchers that PCS increases with increasing soil

suction (Alexandrou and Earl, 1998; Defossez et al., 2003; Imhoff et al., 2004; Mosaddeghi

et al., 2006). Water influences the structural stability and strength of soil since it acts both as

a lubricant and as a binding agent of soil particulates (Carter and Gregorich, 2007). On the

other hand, despite of the noticeable change in stress-strain curve pattern when soil is

subjected to longer loading duration, it has no significant influence on PCS values (p=0.568).

Among all treatments, the lowest and highest PCS obtained were 99.5 kPa (pF=1.8, T=1 min)

and 270 kPa (pF=2.5, T=1 min), respectively.

41

Figure 16. One-dimensional compression curves for soil samples pre-wetted at different

water potential (pF=1.8, 2.0, and 2.5). Vertical stress is in logarithmic scale.

Figure 17. One-dimensional compression curves for soil samples compressed at different

loading durations (T=1, 10 and 30 min). Vertical stress is in logarithmic scale.

C_DK AK_2 AK_3

42

AK_4 AK_5 Gea_4

Figure 18. Precompression stress values derived using different methods. Points represent

mean values of three to four replicate measurements, errors bars are 95%

confidence interval.

Figure 19. Influence of initial water potential and loading duration (values used were

determined using the C_DK method.

pF=1.8 pF=2.0 pF=2.5

Initial Water Potential

Pre

co

mp

ressio

n S

tre

ss (

kP

a)

01

00

20

03

00

40

05

00

T=10min

T=1minT=30min

pF=1.8 pF=2.0 pF=2.5

Initial Water Potential

Pre

co

mp

ressio

n S

tre

ss (

kP

a)

01

00

20

03

00

40

05

00

T=10min

T=1minT=30min

pF=1.8 pF=2.0 pF=2.5

Initial Water Potential

Pre

co

mp

ressio

n S

tre

ss (

kP

a)

01

00

20

03

00

40

05

00

T=10min

T=1minT=30min

pF=1.8 pF=2.0 pF=2.5

Initial Water Potential

Pre

co

mp

ressio

n S

tre

ss (

kP

a)

01

00

20

03

00

40

05

00

T=10min

T=1minT=30min

pF=1.8 pF=2.0 pF=2.5

Initial Water Potential

Pre

co

mp

ressio

n S

tre

ss (

kP

a)

01

00

20

03

00

40

05

00

T=10min

T=1minT=30min

pF=1.8 pF=2.0 pF=2.5

Initial Water Potential

Pre

co

mp

ressio

n S

tre

ss (

kP

a)

01

00

20

03

00

40

05

00

T=10min

T=1minT=30min

43

4.4.3 Effect on pore water pressure

Changes in pore water pressure (PWP) during the whole period of compression test were

monitored using a micro-tensiometer mounted at the base of the oedometer ring. Though it

was expected during the start of the test (no applied stress) that the tensiometer will record

the PWP similar to that of the initial water potential of the soil sample, this was not achieved

as all of the initial readings were above the preset soil water potentials. The PWP response

shows an interesting phenomenon when soil compression tests were done at different loading

durations and soil water potentials. For instance, in slightly wet soils (pF=1.8 and 2.0), the

PWP did not vary much and water still remained under suction during the application of

smaller loads but PWP began to increase when applied stresses became larger until the

tensiometer registered a positive PWP indicating that the soil is at 100% saturation (Figure

20).

On the other hand, the loading duration had an influence on PWP at higher applied stresses.

For longer loading duration (30 min), PWP could reach its peak and then it declined until the

start of the next loading cycle. For short loading durations (1 and 10 min), it was quite

difficult to measure the peak PWP at every loading stage since no drop in PWP could be

observed and it continued to rise until the maximum stress was applied. Fast straining (short

loading duration) restricts the time needed for dissipation of pore water pressure. In the case

of drier soils (pF=2.5), the pore water remained under suction throughout the whole period of

compression test. In unsaturated soils the degree of saturation is less than 100% and the pore

water pressure is negative due to surface tension of pore water in soil voids causing a suction

effect on surrounding particles. Therefore, in this case little water movement is required for

compression.

44

Figure 20. Typical result of an oedometer test (at pF=2.0 and T=10 min) showing the loading

sequence, vertical displacement, and pore water pressure changes.

45

5. CONCLUSION

Both soil quality and susceptibility of the soil to structural deformation by mechanical loads

were evaluated in this study. These information are vital in gauging the vulnerability of

arable soils against inevitable threats of compaction. Different levels of exposure of these

soils to compaction were also considered in order to understand the spatial variability of

compaction risks at the farm-scale level. For instance, it was found out in this study that there

is a huge variability in measured values of soil quality indicators used in terms of varying

land use and soil depths. Overall, soil structural quality was found intermediate and

susceptibility to compaction was low to medium, thus the vulnerability to compaction can be

considered moderate in East Flanders, Belgium.

Difficulties associated with measurement of PCS in both laboratory and field are a reasonable

cause of concern to develop an indirect method using already available soil information.

There is a general consensus among soil scientists that soil properties are interrelated to each

other. In this study, the underlying relationships between PCS and other soil physical

properties were investigated using pedotransfer functions. The accuracy and applicability of

these pedotransfer functions depends on the quality of the dataset used, choice of potential

predictors, and modeling techniques which have been addressed in this study. The newly

developed PTFs can able to predict PCS values of cropland and grassland soils, however,

further research is needed to improve these PTFs to a reasonable degree of predictive

capability.

Several theoretical methods were proposed by various authors to determine PCS from the

stress-strain relationship of soil subjected to axial compression. In literature, most of the PCS

data used to develop PTFs were calculated based on Casagandre method. However, they still

differ on soil compression methods used in terms of sample dimension, initial soil wetness,

and loading duration. Since it’s difficult to conclude which method to determine PCS is the

better one, using the Casagandre method which is commonly used in saturated soil mechanics

to detect the bearing capacity of the soils is considered reasonable, though further

investigation is needed to warrant its validity especially for studying compaction properties of

agricultural soils.

Also, the study demonstrated that soils have different deformation behavior when subjected

to uniaxial compression at different loading duration and initial water potential. Soil

46

compression under short-time loading (1 to 30 minutes) simulates soil deformation which

occurs under wheel loads of short-time durations. Though the interaction effects of these two

important factors during compression test on PCS was not able to be captured well, their

main effects on stress-strain behavior of the soil were found significant as manifested in the

soil compression curves. Wetter the soil and longer the stress applications often results in

higher compaction. It was found that the initial water potential has a significant influence on

PCS values. The PCS tends to be higher with decreasing water potential. The influence of

these two factors on soil compaction behavior could also be linked to pore water pressure

dynamics during compression tests.

47

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