Aust. J. Soil Res., 1984, 22, 413-31
Identification of Dispersive Behaviour
and the Management of
Red-brown Earths
P. RengasamyA, R. S. B. GreeneB, G. W. Fordc, and A . H. MehanniB
State Rivers and Water Supply Commission, Irrigation Research Institute, Tatura, Vic. 3616.
Irrigation Research Institute, Tatura, Vic. 3616.
CVictorian Crops Research Institute, Private Bag 260, Horsham, Vic. 3400.
A
Abstract
A scheme is proposed, together with a procedure suitable for routine laboratory use, for the prediction
of dispersive behaviour of surface layers of red-brown earths and their classification into one of six
classes. Each class is defined on the basis of predictive relationships established between dispersion
(spontaneous and mechanical), sodium adsorption ratio (SAR) and total cation concentration (TCC).
These relationships were established experimentally using 138 samples representing both surface and
subsurface layers from 69 red-brown earth profiles. Preliminary studies including samples from red clay
and black earth profiles indicated that the proposed scheme is not suitable for these soils. Neither can
it be used for soils containing free lime.
The procedure proposed enables the prediction of the probable dispersive behaviour of the surface
layer of red-brown earths, including exposed subsoils. It provides a rational basis for the formulation
of appropriate management strategies for the manipulation of the surface structure of individual redbrown earths used for dryland or irrigated agriculture. Application of the proposed scheme to the
estimation of the minimum level of residual gypsum required to maintain aggregate stability via the
electrolyte effect is discussed, with special reference to low-sodic soils (i.e. with a SAR below 3, e.g.
Classes 2a and 3c).
Introduction
Many soils currently used for irrigated or dryland agriculture are difficult to
manage owing to their tendency to develop unsatisfactory structure, particularly in
their surface and near-surface layers. Breakdown of soil aggregates leads to
problems such as surface crusting, reduced water infiltration and restricted plant
establishment and growth. Breakdown occurs due to slaking, and also in many soils
due to dispersion of aggregates when wet by irrigation or rain. It is well known that
aggregates susceptible to breakdown by these processes are sensitive to mechanical
stress, particularly when moist. Thus, raindrop impact, cultivation or trampling by
stock may aggravate the development of compacted layers in the upper portion of
the soil profile, whilst the presence of a surface mulch or the use of reduced tillage
techniques may minimize such problems. Although dispersion in surface aggregates
is emphasized in this paper, it is pertinent to note that dispersion in the subsoil may
impede water flow through preferential pathways, and may be important when
subsoil becomes exposed through landforming.
Dispersion of clay in soils is influenced by both the nature of exchangeable
cations and the amount of electrolyte present (Quirk and Schofield 1955; Shainberg
et al. 1981). A simple dispersion test developed by Emerson (1967), and modified
414
P. Rengasamy et al.
by Loveday and Pyle (1973), is widely used to assess soil stability. These tests use
only a few 3-5 mm air-dried aggregates, and no quantitative measurement is made
of the exchangeable sodium levels and the electrolyte concentration. Another
difficulty encountered with these tests is the variability in dispersion which arises
due to differences in cation and electrolyte composition between individual
aggregates. Non-uniform distribution of cations within aggregates may result from
the phenomenon of salt sieving where salt is trapped within the microfabric of the
aggregates (Blackmore 1976).
In the above tests, the measured degree of dispersion cannot be related
unambiguously to exchangeable sodium or electrolyte effects. However, in the
management of dispersive soils, it may be necessary to distinguish between these
two factors. For example, Loveday (1981) emphasized the importance of the
electrolyte effect when using gypsum in ameliorating hard-setting soils. These
problems may lead to incorrect conclusions relating to the gypsum requirement of
soils.
The probable dispersive behaviour of soils under different conditions may be
estimated in various ways. Thus, the measurement of spontaneous dispersion in the
absence of any imposed external force will reflect the behaviour of surface soils
during the rainfall events when the soil surface is effectively protected by plant
material such as stubble, pasture or established field crops. The extent to which soil
particles disperse after gentle shaking may be used as a qualitative indication of
their field behaviour when bare soil is subjected to raindrop impact. The more
severe remoulding test as used by Emerson and others may simulate the effect of
mechanical shearing during cultivation or trampling by stock when wet. The
erodibility of dispersive clay soils (e.g. evaluation of the risk of pipe erosion of clay
dams) may be estimated by inducing high flow rates through recompacted soil
materials as in the pinhole test (Sherard et a[. 1976).
The aims of the work reported here are:
1. To develop a general scheme for classifying the dispersive behaviour of redbrown earths based on a laboratory procedure suitable for routine use.
2. To define the soil solution parameters associated with the degree of dispersion
in red-brown earths and to relate these results to management options.
Materials and Methods
Soils Used
A representative range of red-brown earth profiles (Oades et al. 1981) from various locations in
south-eastern Australia was used in the development of a dispersion test. In all, a total of 138 soil
samples representing surface (0-15 cm) and subsurface (15-45 cm) layers from 69 red-brown earth
profiles was used. Selected physical and chemical characteristics of some of the typical soils are given
in Table 1.
Thirty-two surface samples of red-brown earths from 16 sites in northern Victoria were used to
evaluate the predicability of field response to gypsum by the models developed in our study.
Dispersion Test
Spontaneous dispersion
Soil samples collected from representative locations were air dried, passed through a 2 mm sieve,
and thoroughly mixed. Twenty grams were weighed into a 120 ml transparent jar (10 cm high), and 100
ml distilled water slowly added down the sides of the jar, care being taken to avoid disturbance of the
soil sample. The mixture was left undisturbed for 12 h (overnight). The dispersed clay adjacent to the
soil was then uniformly mixed, care again being taken to avoid disturbing the soil layer. This was
Dispersive Behaviour of Red-brown Earths
achieved by placing a mechanical stirrer (Dynamax Laboratory Stirrer) mid-way into the suspension and
then stirring at a speed of 0.16 rev s-I for 30 s. After an appropriate sedimentation time (Loveday
1974a), the dispersed clay was estimated by pipetting 10 ml of the suspension from a depth of 5 cm.
The clay was measured either gravimetrically or spectrophotometrically. The percentage of dispersed
clay was expressed on an oven-dried soil basis. In gravimetric estimations, corrections were made for
the weight contributed by any dissolved salts.
Table 1. Selected chemical and physical properties of some surface (0-15 em) and subsurface (15-45
cm) soils used in this study
-
Depth
(cm)
pHA
-
OrganicB
CaC0,B carbon
ECA
(70) ( 7 0 ) (dS m-I)
TCCA
(me I-])
SARA
ESP
TotalB
clay
(70)
Mechanically
dispersed clay
(70)
Lemnos loam (Kyabram)
0-15
15-45
nil
0.01
1.9
0.4
0.08
0.16
0.72
1.73
0.8
3.0
2.3
6.5
Shepparton jine sandy loam (Tatura)
0-15
15-45
nil
0.02
2.6
0.4
0.07
0.31
0.81
3.48
0.9
3.0
2.8
5.4
Goulburn clay (Tongala)
0-15
15-45
nil
0.02
2.1
0.2
0.52
0.75
4.56
6.98
3.7
6.8
7.9
13.3
Stat~sticsfor the surface (68) and subsurface (40) soils
0-15 range
mean
s.e.
15-45 range
mean
s.e.
-
-
-
-
0.06-1.4
0.48
0.32
0.1-1.3
0.69
0.41
0.3-12.5
3.56
0.36
0.9-12.0
4.86
0.38
0.1-12.4
1.0-21.6
3.1
0.38
3.0-12.4
6.5
0.43
0.42
5.4-22.2
13.8
0.51
6.7
* p H and EC determined in 1:5 soil water suspensions, and SAR and TCC in 1: 5 soil water extracts
obtained after shaking.
Determined using procedures described by Loveday (1974~).
Standard error of the mean.
Mechanical dispersion
A duplicate sample prepared and treated as above was shaken for 1 h in an end-over-end shaker (0.5
rev s-I). After an appropriate sedimentation time, the dispersed clay was estimated as described above.
Analysis of the soil solution composition
After measurement of pH and electrical conductivity, the equilibrium solutions from the two
preceding dispersions were separated from the soil suspensions. About 25 ml of the suspension was
centrifuged for 10 min at 85 rev s-I. The cations Ca2+, Mg2+,K+ and Na+ in the supernatant were
estimated by atomic absorption spectrophotometry. The sodium adsorption ratio (SAR) was calculated
as ~ a / d { ( ~ Mg)/2)
a+
(all cation concentrations in m.e. 1-1). TCC, the total cation concentration, was
calculated by summing the concentrations (m.e. 1-I) of the cations N a + , K + , Ca2+ and Mg2+.
Exchangeable Cation Analysis
Exchangeable cations and cation exchange capacity of the soils were estimated by the method of
Tucker (1974). Exchangeable cation percentages were based on the sum of four cations Nat , K + , Ca2+
and Mg2+,as these values were found to be better correlated with the soil physical properties than were
the percentages based on CEC (Martin et al. 1964).
Saturated Hydraulic Conductivity
Measurements of the hydraulic conductivity of saturated soils were made using the falling head
method of Klute (1965), with minor modifications as described by Mehanni (1973).
P . Rengasamy et al.
Residual Gypsum
The amount of gypsum present was estimated from determining sulfate concentrations of aqueous
1 M NH,C1 extracts (Greene and Ford 1985).
Flocculation Value
Twenty g of soil were placed in a 120 ml transparent jar and 100 ml of CaCI, solution (from 0 to
6 m.e. I-') added. The mixture was shaken for 1 h in an end-over-end shaker (0.5 rev s-') and then
allowed to stand for 6 h. The minimum concentration of CaCl, solution in which the suspension
remained clear was then taken as the best estimate of the flocculation value or CCC (critical coagulation
concentration) (Rengasamy and Oades 1977).
Statistical Analysis
The data were analysed using simple linear regression analysis and multiple linear stepwise regression
analysis. Partial correlation coefficients and p-coefficients (standardized regression coefficients) were
calculated as described by Nie et a[. (1975). Mitscherlich functions were fitted to the data relating to
the residual gypsum, hydraulic conductivity and clay dispersion using the programme GENSTAT V
(Rothamsted Experimental Station, U.K.).
Results and Discussion
During the development of our dispersion test, data on spontaneously dispersed
clay, mechanically dispersed clay, SAR and TCC in 1 : 5 soil-water extracts were
obtained for a range of soil profiles including 69 red-brown earths, 19 red clays and
7 black earths. The amount of dispersed clay in these samples was not significantly
correlated with either SAR or TCC. However, when only data from the 108 redbrown earth samples which dispersed were considered, the amount of mechanically
dispersed clay was found to be significantly correlated with both SAR and TCC
(Table 2).
As both the proportion of the soil clay fraction which disperses in water, and
the rate at which dispersion occurs, vary with the clay mineralogy and the SAR and
TCC levels in the soil-water suspension (Velasco-Molina et al. 1971), we decided
to restrict our test, and the derivation of quantitative inter-relationships between
the various soil parameters studied, to groups of soils having similar mineralogical
composition. Red-brown earths are known to have a similar mineralogy in that
their clay fraction is predominantly illite (Oades et al. 1981), and so are susceptible
to dispersion even when weak mechanical forces are applied (Emerson 1983;
Rengasamy 1983). Thus, we restricted subsequent statistical analysis of the results
reported in this paper to samples of red-brown earths.
Mechanical Dispersion
Of the 138 samples from red-brown earth profiles, 30 samples showed no
dispersion after mechanical shaking, while the remaining 108 samples dispersed.
The difference in the dispersive behaviour between surface and subsoils is clearly
brought out by the improvement in the various correlation coefficients obtained
when the soils were separated into surface (0-15 cm) and subsurface (15-45 cm)
samples (Table 2). The correlation coefficient was increased from r = 0 - 6 9 to
R = 0.79 by including TCC with SAR. This was confirmed by stepwise regression
analysis; the partial correlation coefficients (Table 2) indicate that SAR has a
positive, and TCC a negative, influence on dispersion. The /3-coefficients
(standardized regression coefficients) indicate that in surface soils SAR is 1.3 times
more important than TCC in influencing dispersion, while in subsoils SAR is 3.6
times more important (equations 2.3 and 2.2, Table 2).
417
Dispersive Behaviour of Red-brown Earths
For the 138 red-brown earth samples investigated, the SAR of the 1 : 5 extract
obtained after shaking and the ESP of the soils were related as follows:
ESP = 1.95SAR + 1.8
(R2=0.82).
(1)
This indicates that ESP is approximately twice the numerical value of the SAR (1 : 5
extract).
Table 2.
Correlation of SAR and TCC with per cent mechanically dispersed clay in some
red-brown earths
Data analysed by stepwise multiple linear regression using the model Y = a x , + bX, + c,
where Y = % mechanically dispersed clay, and X I = SAR and X, = TCC of 1:5 w/v extract
after shaking for 1 hour
Multiple correlation
coefficient
Simple correlation
coefficients
'5
'XI
RFXlX2
Regression equation with partial
correlation coefficients [ ]
and p-coefficients ( )
2.1 All soils pooled (108 samples)
Y
4
0.69**
0.37**
0.79**
0.85**
2.2 Subsoils, 15-45 cm (40 samples)
Y
0.81**
0.47**
4
0.83**
0.74**
2.3 Surface soils, 0-15 cm (68 samples)
Y
0.63**
0.31**
0.92**
xi
0.92**
2.4 Surface soils with a SAR > 3 (28 samples)
Y
0.45*
- 0.02n.s.
0.95**
x~
0.87**
2.5 Surface soils with a SAR
Y
0.35*
-0.46**
4
< 3 (40 samples)
0.76**
0.44**
** P 6 0.01. * P < 0.05.
n.s., not significant.
The critical value for ESP above which a soil is sodic is controversial. For
example, Northcote and Skene (1972) defined a soil as sodic if the ESP is 6 or more.
However, this critical value may vary with soil type and with the basis on which
the ESP is calculated, largely because of confusion relating to the use of CEC or
the sum of the four major exchangeable cations (i.e. Na+ + K + + Ca2++ Mg2+)as
the divisor. Moreover, because of the considerable experimental problems involved
in an accurate determination of exchangeable cations, SAR of the soil extract is
generally preferred as an index for sodic soils (Rhoades 1982). In our study, the
surface soils with a SAR of less than 3 deviated from the general trend that SAR
influences clay dispersion more than TCC (Table 2). The importance of TCC in
controlling the dispersive behaviour of these soils is indicated by the higher simple
correlation coefficient for TCC obtained when the surface soils with a SAR of less
418
P. Rengasarny et a[.
than 3 were considered separately (equation 2.5, Table 2). The 0-coefficients show
only a slightly greater influence of TCC. The importance of electrolyte
concentration in soils with low levels of exchangeable sodium (i.e. ESP less than
6) in controlling dispersion, crusting and infiltration has been reported by many
workers (e.g. Oster and Schroer 1979; Kazman et al. 1983). Further, it is known
that in the absence of electrolytes, even calcium-saturated red-brown earths
disperse due to weak mechanical forces (Rengasamy 1983).
In our study, surface soils with a SAR > 3 would disperse spontaneously,
whereas those with a SAR < 3 dispersed only after mechanical shaking. Therefore
it seems appropriate to regard soils with SAR < 3 different from the soils with SAR
> 3. Because sodium levels in soils with SAR c 3 can still influence dispersion, these
soils can be considered as low-sodic instead of non-sodic. For surface soils with a
SAR above 3, TCC is not significantly correlated with dispersed clay (Table 2).
However, multiple linear regression analysis indicated that both SAR and TCC
influence dispersion in these soils almost equally, as is shown by the @-coefficients
of equation 2.4 (Table 2).
Table 3. Correlation of SAR and TCC with per cent spontaneously dispersed clay in some
red-brown earths
Data analysed by stepwise multiple linear regression using the model Y = a x , + bX2 + c,
where Y = % spontaneously dispersed clay, and X I = SAR of 1:5 w/v extract after shaking
for 1 h and X 2 = TCC of undisturbed 1:5 w/v equilibrum solution
Simple correlation
coefficients
rxl
Multiple correlation
coefficient
'xz
-
RY.XIx2
-
-
Regression equation with partial
correlation coefficients [ ]
and 6-coefficients ( )
-
3.1 AN soils pooled (28 samples)
Y
0.43*
- 0.14 n s .
4
0.82**
0.97**
3.2 Subsoils, 15-45 cm (18 samples)
Y
0.49*
- 0.16 n.s.
4
0.78**
0.99**
3.3 Surface soils, 0-15 cm (I0samples)
Y
0.66**
0.39 n s .
0.90**
XI
0.93 n s .
** P < 0.01. * P < 0.05.
n.s., not significant.
Spontaneous Dispersion
Only 28 of the 138 soils tested dispersed spontaneously in the absence of
mechanical stress. These soils had a SAR above 3. Multiple regression analysis of
data from soils which spontaneously dispersed gave the results presented in Table
3. Simple correlation coefficients indicated that the percentage of spontaneously
dispersed clay was not significantly correlated with either the SAR or the TCC of
the undisturbed equilibrium solution. However, the percentage of spontaneously
dispersed clay was significantly (r = 0.43, P < 0.05) correlated with the SAR of the
extract obtained after shaking for 1 h.
419
Dispersive Behaviour of Red-brown Earths
Multiple linear regression analysls showed that the correlation between
percentage of clay and the SAR and TCC values measured in the extract obtained
after 1 h shaking was low (R2 = 0.43, P < 0.05). However, when the SAR values
measured after 1 h shaking were used, together with the TCC values measured in
the undisturbed solution, the multiple correlation coefficient was higher (R2 = 0.94,
P < 0.01).
In our study the TCC of the undisturbed solution was very low compared to the
TCC in solutions obtained after shaking. Probably the reason for this difference is
the rate of diffusion of salts from the soil aggregates to the soil solution. The partial
correlation coefficients and the &coefficients (equation 3.1, Table 3) suggest that the
SAR (measured after 1 h shaking) and the TCC (measured in the undisturbed
equilibrium solution) influence spontaneous dispersion to a similar extent. As these
SAR values are correlated with the ESP of the soil (equation I), spontaneous
dispersion may be affected by both exchangeable sodium levels and the rate of
diffusion of salts from soil aggregates to the soil solution. The slow diffusion of
salts would contribute to the marked variability in dispersion observed when large
aggregates are used in the Emerson dispersion test.
In contrast to soils which dispersed when subjected to mechanical stress, there
was no further improvement in the prediction of dispersion by separating the
spontaneously dispersed soils into surface v. subsoils (Table 3).
The Influence of SAR and TCC on the State of Flocculation
Using regression equation 3.1 (Table 3) obtained for spontaneously dispersed
soils, i.e.
Clay % = 0.49SAR-3.12TCC
+
0.44
(2)
and solving for nil dispersion, the relation between SAR and TCC which defines
the state of flocculation can be expressed as
TCC=O.l6SAR
+
0.14,
(3)
where TCC is measured in undisturbed solutions and SAR is measured after
shaking.
In a similar way, the state of flocculation for the mechanically dispersed soils can
be defined by:
TCC = 1.46SAR + 1.44,
(4)
where both TCC and SAR are measured in solutions obtained after shaking.
Equation 3 indicates that spontaneous dispersion occurs only when the diffused
electrolyte level is below (O.16SAR + 0.14) m.e. I-'. This level is much lower than
the minimum amount of electrolyte required to prevent mechanical dispersion (see
equation 4). This supports the observation made by Rowel1 et al. (1969) that the
type of treatment used to estimate the extent of clay dispersion could affect the
apparent relationship between SAR (or ESP) and TCC corresponding to the state
of flocculation or dispersion. They concluded that the application of mechanical
stress to a clay could affect the electrolyte concentration at which dispersion
occurred.
By plotting the values of SAR and TCC for the 138 soils (Fig. I), measured in
extracts obtained after shaking, we found that the line representing the equation
(4) separated flocculated from dispersed soils. Only three of the 138 soils deviated
P. Rengasamy et al.
Dispersed Samples
Total cation concentration (me. 1-l)
Fig. 1. Dispersive behaviour of soil samples in relation to SAR and TCC measured in
1:5 soil-water extracts.
( O s t o r ot a l 1 9 8 0 )
Y
P e r m o a b , l ~ t y( R h o a d e s 1 9 8 2 )
dlspersfon ( a u t h o r s
M Spontaneous
,
0
10
20
30
Total cation concentration ( m e . 1-1)
.
data)
,
,
I
40
Fig. 2. Relationships between ESP and TCC defining a stable condition obtained in
various studies.
from this relationship. However, improved relationships could be obtained by
separating the soils into different groups on the basis of their multiple linear
correlation coefficients. The following relationships were obtained for different
groups:
TCC = 3.19SAR - 1 . 7 (subsoils)
(5)
TCC = 1.21SAR + 3.3 (surface soils)
(6)
Dispersive Behaviour of Red-brown Earths
TCC = 1 18SAR
TCC
+
1-20SAR
+
+
42 1
3.1 (surface soils with an SAR > 3)
(7)
3.9 (surface soils with an SAR < 3).
(8)
Equations 7 and 8 are not found to be significantly different from equation 6.
Hence equation 6 can be used for all surface soils.
The data obtained in soil permeability experiments, dispersion or coagulation
studies on pure clays, and soil samples by different workers (Rowel1 et al. 1969;
Quirk 1971; Arora and Coleman 1979; Oster et al. 1980; Rhoades 1982) were used
to obtain the relationship between ESP and TCC which will define a stable soil
condition, viz. unaffected permeability or a flocculated state. These lines are drawn
in Figure 2, together with the lines obtained in our study for surface and subsurface
soils. The SAR data of the 1: 5 extracts obtained after shaking were converted to
ESP values using equation 1. Comparison on these different lines indicates that the
relationship between ESP and TCC varies with the type of clay mineral, the amount
of total clay, organic matter and the mechanical forces.
Many other factors, not encountered in our study, may influence the degree of
dispersion, e.g. iron and aluminium oxides, exchangeable aluminium or pH,
presence of CaCO,, possibly exchangeable Ca:Mg ratios (Emerson 1983), organic
anions acting as peptizing agents (Shanmuganathan and Oades 1983), absence of
clay domain formation (Hardcastle and Mitchell 1976), the strength of edge-to-face
attractions (Greene ef al. 1978), and the severity of drying (Collis-George and
Smiles 1963).
Practical Signi3cance of Spontaneous and Mechanical Dispersion
The importance of mechanical stress in influencing dispersion has been clearly
brought out in this study. As discussed earlier, red-brown earths in Australia are
dominantly illitic, and so are susceptible to dispersion even when weak mechanical
forces are applied. The practical significance of the equations obtained for
spontaneous and mechanical dispersion is that they probably represent the two
extreme types of disturbance experienced by a soil in the field, viz. zero tillage v.
intensive cultivation. They may also represent the effects of raindrop impact
when the soil surface is completely covered by plant material compared to that on
bare soil.
Some indication of the effects of these factors in the field is provided by
observations made during a rainfall event (2.5 cm in 1 h) on 6 May, 1983 at the
Irrigation Research Institute, Tatura. Pasture and bare cultivated plots, previously
irrigated with saline waters of different total soluble salts, had different levels of
ponded water and different turbidity of the undrained water.
The pasture plots, with complete plant cover of the soil surface, were mainly well
drained, although a few sodic plots had slightly turbid ponded water. In contrast,
the water remained undrained on all the bare plots cultivated for cropping. In these
plots, the amount of ponded water, and its turbidity, varied with the level of
exchangeable sodium and the electrical conductivity of the soils. The water samples
from these plots were collected and analysed for the amount of dispersed clay, SAR
and TCC. It was found that the SAR-TCC relationship for the turbid samples from
the bare cultivated plots correspond to the line derived for mechanical dispersion,
and that the corresponding relationship for the samples from pasture plots
corresponded to the line for spontaneous dispersion (Fig. 3). These results
P. Rengasamy et al.
422
support the conclusion that the mechanical dispersion line represents the effect of
raindrop impact on a bare soil, and the spontaneous dispersion line represents the
effect of minimum mechanical disturbance, e.g. when the soil surface is protected
by plants.
The mechanical dispersion test described in this paper probably identifies the
effect of weak to moderate mechanical stress, such as that caused by the flow of
water across irrigation bays or rainfall of the intensity commonly experienced in
south-eastern Australia. However, our test may not be suitable for identifying the
effect of strong mechanical forces on aggregate stability, e.g. during cultivation of
wet soil or during hydrodynamic flow in tunnel erosion. Probably tests such as
severe remoulding when wet (Emerson 1967), or the pin-hole test (Sherard et al.
1976), may be more appropriate for such purposes.
0
I
a /
T u r b ~ dw a t e r
j=
Pasture plots
0 - Bare
cult~vated
Total cation concentration (m.e. 1-')
Fig. 3. The effect of rainfall (2.5 cm in 1 h) on the stability of surface soils from
pasture and bare cultivated plots at Tatura as related to the SAR and TCC of the
ponded water.
ClassiJication of Soils on the Basis of Dispersion and Soil Solution Composition
The various relationships between SAR and TCC defining the susceptibility of
soils to spontaneous and/or mechanical dispersion can be used to predict the
probable behaviour of the surface layers of red-brown earths. The following
classes, based upon the relationships derived in this study, characterize the surface
soils which may disperse or flocculate under different electrolyte environments. It
is important to note that the relationship derived for subsoils is applicable only
when they are subjected to mechanical stress under a water regime similar to that
of surface layers, e.g. when exposed after landforming. The key relationships for
surface soils are diagramatically shown in Fig. 4. Details of the characteristics of
each class are discussed below.
Class I - dispersive soils
Soils which disperse spontaneously and which have a TCC < (O.16SAR + 0 - 14)
in the equilibrium solution obtained without shaking, will have severe problems
Dispersive Behaviour of Red-bra-~n Earths
associated with crusting, reduced porosity etc., even when subjected to minimum
mechanical stress, e.g. under zero tillage. Such soils under pasture or with crop
cover are highly dispersive. Red-brown earths with a SAR below 3 were not found
to disperse spontaneously, as the cation concentrations in their equilibrium
solutions were always above 0.6 m.e. 1- I. Soils which spontaneously disperse under
field conditions will be non-saline but sodic. The stability of such soils is largely
controlled by exchangeable sodium. Hence, amelioration with calcium compounds
should aim to reduce sodium levels in the exchange complex of the soil, and also
to maintain enough electrolyte to keep the clay flocculated.
Class 3a
Class 3b
0
2
4
6
8
10
12
14
16
Total cation concentration (m.e. 1-')
Fig. 4. A classification scheme for the prediction of dispersive behaviour of A-horizon
of red-brown earths.
Class 2 - potentially dispersive soils
Soils which disperse after mechanical shaking are potentially dispersive. Such
soils are unstable if mechanically disturbed, e.g. by intensive cultivation or
raindrop impact. The electrolyte concentration required to keep these soils
flocculated varies with their SAR and their original profile position, i.e. A or
B-horizon. Thus, three categories are possible.
Class 2a
Soils from the A-horizon of red-brown earths with a SAR of less than 3 and
which mechanically disperse, require an electrolyte concentration of (1.21SAR +
3.3) m.e. 1- for structural stability. The small difference in correlation coefficients
(equation 2.5, Table 2) may not warrant any separation of the soils with SAR less
than 3 into a distinct class. However, in terms of management, reducing the
exchangeable sodium levels to zero in these low-sodic soils will be inefficient
(Loveday 1981), and probably impractical. The buffering effects due to mineral
weathering and hydrolysis make it impossible to obtain a zero sodic soil
(Rengasamy 1983).
P. Rengasamy et al.
In our study, even the samples with a SAR as low as 0.01 have been found to
be potentially dispersive. Hence it appears that in these low-sodic soils, maintaining
adequate electrolyte levels is the most practical management option. Problems of
surface crusting and/or reduced surface permeability are likely to occur when
rainfall leaches the soluble salts below the threshold electrolyte level, or when lowsalt waters are used for irrigation. Periodic surface application of an amendment,
such as gypsum, should be used in such circumstances to avoid this potential
problem (Rhoades 1982). Field studies of non-irrigated soils of this type in northcentral Victoria showed that the applied gypsum is leached at the rate of 1 t ha-l
per 125 mm to 360 mm of rainfall, depending on the rate of gypsum application
(Greene and Ford 1985). Hence, a periodic application of gypsum is necessary to
maintain surface structure.
Class 2a soils will have few structural problems if managed using minimum
tillage techniques or if maintained under continuous pasture growth. Many (40) of
the red-brown earths used in our study fall into this category. Despite their low
sodium content, these soils when cultivated often crust following exposure to
rainstorms or to flood irrigation. Similar soils are common throughout the
agricultural areas of south-eastern Australia, and treatment with gypsum at the rate
of 1 to 5 t ha-l has been reported as being beneficial (Sims and Rooney 1965;
Matheson 1969).
Class 2b
Surface (A-horizon) soils with a SAR above 3 require an electrolyte level similar
to class 2a soils in order to maintain flocculation. However, the addition of gypsum
should aim to reduce exchangeable sodium levels and also to provide sufficient
electrolyte. Reduction of exchangeable sodium from higher levels of sodium would
be both efficient and practical. Once the exchangeable sodium is reduced to a
practical limit (e.g. an ESP of 6 or less), then the management of these soils would
be aimed at maintenance of a minimum level of electrolyte. Unlike class 2a soils,
these soils become spontaneously dispersive (class 1) when leached without the
addition of calcium compounds, and if there is no generation of electrolytes in the
soils due to mineral weathering (Shainberg et al. 1981).
Class 2c
Subsoil (B-horizon) samples of red-brown earths, with similar SAR values (i.e.
above 3), require higher electrolyte levels to prevent dispersion (not shown in Fig.
4). The amount required is equivalent to (3.19 SAR- 1.7) m.e. 1-l. It has been
observed that the exposed subsoils in northern Victoria, where landforming for
irrigation is widely practised, have more severe surface structure problems than
corresponding areas where the topsoil is retained.
Class 3 - jlocculated soils
When soils have more than the minimum required electrolyte levels (as defined
by equations 3 to 6), they remain flocculated when subjected to rainfall, irrigation
or mechanical stress. However, it is important to recognize that excessive levels of
soluble salts in soil water may reduce its availability to plants. Under conditions of
excessive salinity, selection of a tolerant crop or leaching of salts from appropriate
layers may become necessary. There are three possible categories in this class,
differing in the management procedures required for their reclamation.
Dispersive Behaviour of Red-brown Earths
Class 3a
If the SAR of a soil is above 3 and its TCC exceeds the flocculation value, then
it is saline and sodic. Leaching with good quality water may change a saline-sodic
soil to class 2b (e.g. Quirk 1971), or on extreme leaching to class 1. The soil may
consequently disperse and cause severe crusting. Addition of calcium compounds
in calculated amounts together with gradual leaching are essential for the
reclamation of such soils (Quirk 1971). Many of the irrigated red-brown earths
surveyed by Mehanni and Repsys (1978) in northern Victoria, which have been
subjected to fluctuating water tables, fall into classes 2b and 3a.
Class 3b
When the SAR is less than 3 and the TCC is above the flocculation value, the
soils are saline and dominated by non-sodium salts. Typically, such soils have a
TCC of 7 m.e. 1-I or more (i.e. a saturation extract EC of approximately 4 dS m-I),
and so would be classified as saline (Richards 1954). These soils have no physical
problems, and their leaching requirements depend on the salt tolerance of the crops
to be grown.
Class 3c
When the SAR is less than 3 and the TCC is ideally similar to the flocculation
level, there are no dispersion or salinity problems. This situation should be the
long-term aim of management strategies for the surface layer of red-brown earths.
Validation of Dispersion Test
Prediction of field response to gypsum
The sensitivity of our dispersion test was assessed using samples from the control
(nil gypsum) and gypsum (7.5 t ha-') treated plots from 16 sites on red-brown
earths in northern Victoria (Ford 1978). Samples of the cultivated layer were
collected after two fallow-wheat cycles. The results of the mechanical dispersion
test on samples from control plots (Table 4) indicated that all soils were potentially
dispersive and low-sodic (class 2a), and so would be expected to respond to gypsum
in terms of maintaining electrolyte levels. Field observations indicated that on 12
sites the gypsum treatment improved the surface structure of the soils (Ford 1978).
As this experiment was conducted during seasons in which there was little or no
rainfall between sowing and crop emergence, there was no effect of gypsum on the
establishment of either wheat crop. Any effect of gypsum on improving the surface
structure of such potentially dispersive soils would probably operate via improved
aggregate stability to raindrop impact.
Loveday (1974b), in a study of laboratory methods for prediction of the
probable response of a soil to application of gypsum, found that hydraulic
conductivity was the most sensitive predictor. Analyses of samples from these 16
sites showed that treatment with gypsum had increased hydraulic conductivity (Fig.
5). The improvement, as indicated by the relative hydraulic conductivity (KJK,)
values, was closely related to the amount of residual gypsum in the samples. The
amount of dispersed clay in samples from gypsum-treated plots showed a reciprocal
relationship with the amount of residual gypsum. The strong negative linear
relationship between dispersed clay and relative hydraulic conductivity (r = 0.86,
P < 0.05) suggests that clay dispersion may be the major mechanism controlling
water movement in these soils.
-
P. Rengasamy et al.
Table 4. Dispersion test results and flocculation values of surface soils from 16 red-brown
earths (Ford 1978)
Site
No.
Nil gypsum plots
SAR
TCC Dispersed
(me. 1
clay
(VQ)
Gypsum treated plotsA
SAR
TCC Dispersed
(me. 1
clay
(70)
Flocculation
valuesB
Predicted Actual
(m.e. I-]) (m.e. 1-I)
AGypsum was applied to the surface soil at the rate of 7.5 t ha-l of CaSO4.2H,O. Soil
samples (0-10 cm) were collected from all plots after two fallow-wheat cycles.
BFlocculation values either calculated from equation 6 or as experimentally determined.
10
20
30
40
50
60
Residual gypsum (m.e. kg- ')
Fig. 5. Relationships between relative hydraulic conductivity (Y,, closed circles), dispersed
clay % (Y,, open circles) and the residual gypsum ( X ) in 16 sites on red-brown earths (Ford
1978).
The relative hydraulic conductivity was found to be related to residual gypsum
by a function of the form (dy/dx) = k(A-Y), where A is the maximum possible
value for the relative hydraulic conductivity (Y), X is the amount of residual
gypsum and k is a constant (Fig. 5). At levels exceeding 20 m.e. gypsum kg-' soil
there was no further increase in the relative hydraulic conductivity. By using an
427
Dispersive Behaviour of Red-brown Earths
approach similar to that used in determining threshold concentration values (Quirk
and Schofield 1955), the concentration of gypsum in soils that maintained 85% of
the maximum possible relative hydraulic conductvity was 12 m.e. kg-' or
approximately 1.5 t ha-l of gypsum per 10 cm depth. At this concentration of
gypsum there was less than 1% of dispersed clay.
The flocculation values of samples from these 16 sites were calculated using
equation 6. The values varied from 4.2 to 5.6 m.e. I-', and were closely related
to the flocculation values (critical coagulation concentrations) experimentally
determined for these soils (Table 4). This indicates the ability of our dispersion test
to predict gypsum response, and also supports our conclusion that potentially
dispersive soils with a low sodium content (i.e. Class 2a) respond to gypsum via the
electrolyte effect.
Prediction of dispersive behaviour of red-brown earths
The efficiency of equations 3-6 in predicting the dispersive behaviour of surface
samples of red-brown earths was assessed as follows. Analytical data were obtained
for 100 samples from our study, and for 250 samples analysed by the State
Chemistry Laboratories, Melbourne (Mr. A. Brown, personal communication).
The samples were classified using the appropriate equation and the measured values
of SAR and TCC, and the numbers of samples in each class deviating from the
predicted dispersive behaviour noted. The results are presented in Table 5, together
with data for soil pH and organic carbon content.
Table 5. Evaluation of equations 2-8 for prediction of dispersive behaviour of 350 samples
of red-brown earths based on analytical data from various laboratories
Soil
class
Total No. of
soils/class
No. of
aberrant
soils/classA
Soil propertyB
pH (1:s w/v)
Organic carbon (70)
Range
Mean (s.e.)
Range
Mean (s.e.)
Aberrant samples were those with dispersive behaviour differing from that predicted by the
appropriate equation, i.e. those either not dispersing (although members of classes 1, 2a, 2b
or 2c) or dispersing (although members of classes 3a, 3b or 3c).
BData tabulated refer to all samples in a given class. Values given are for range, mean, and
(in parenthesis) the standard error of mean.
Corresponding mean values for aberrant samples are:
Class 1: pH 5 SO, 2.9% organic carbon;
Class 2a: pH 5.2, 1.8% organic carbon.
Cn.d., not determined.
A
A chi-square test was used to assess the predictive accuracy of our dispersion
test. The value of chi-square was 3.69 (6 d.f.), indicating no significant difference
between the observed and predicted dispersive behaviour of these samples. It is
however relevant to note that those samples in classes 1 and 2a which did not
disperse as expected were characterized by having a lower pH and/or higher
Pipette 10 ml from a depth of 5 cm
and determine To clay
I
Pipette 10 mi from a depth of 5 cm
and determine % clay
Fig. 6 . Flow sheet of recommended procedure for classification of red-brown earths
Centrifuge 25 ml of supernatant.
Determine Na*, K + , Ca2+and MgZ'
in supernatant. Calculate SAR,
TCC.
Measure pH and EC in supernatant
without further stirring
Centrifuge 25 ml of suspension.
Determine Na+, K * , Ca2+and Mg2+
in supernatant. Calculate SAR,
TCC.
1
Measure pH and EC in the
suspension
Shake for 1 h in an end-over-end shaker (0.5 rev s-I). Allow appropriate
sedimentation time (e.g. 4 h at 20°C)
Mix the dispersed clay without disturbing the soil using a stirrer at 0 - 16 rev
s-' for 30 s. Allow appropriate sedimentation time (e.g. 4 h at 20°C)
I
No dispersion
I
4,
Spontaneous dispersion
I
Weigh 20 g air-dried soil (< 2 mm) into a transparent jar (in duplicate). Add 100 ml
distilled water without disturbing the soil. Stand for 12 hours (overnight). Observe soil
surface for zone of dispersed clay
Dispersive Behaviour of Red-brown Earths
429
organic matter content than other members of these classes. Their failure to
disperse would thus be consistent with the known effects of these factors on
aggregate stability (Emerson 1983).
Comments on Recommended Procedure
The recommended experimental procedure for testing red-brown earth samples
is summarized in Fig. 6. Further details of the various measurements are described
in the Materials and Methods section of this paper.
Our test can probably be simplified for routine use by using an infra-red
nephelometer to measure turbidity (i.e. 70 clay), a conductivity meter to measure
EC, and sodium-ion electrode to measure sodium ion concentrations in the
equilibrium suspensions. We suggest, on the basis of the data obtained in this
study, that TCC may be reliably estimated using the following relationship:
TCC = 9.62EC + 0.14,
(9)
where TCC is in m.e. 1-I and EC in dS m-I (R2=0.97). Assuming that soluble K +
is constant and negligible, SAR may then be estimated from the measured Na+ as
follows:
SAR = ~ a/ ~+( T C C- Na+).
(10)
The TCC required for flocculation may then be calculated using equations 3-6.
Fig. 4 may be used to predict the probable dispersive behaviour of surface soils,
and a decision can be made on gypsum requirement.
Where the observed dispersive behaviour of a sample differs from that predicted
from its SAR and TCC values, we recommend that the pH and organic carbon
content also be determined.
Our scheme will enable reasonably accurate prediction of the probable stability
of red-brown earth aggregates in the field, particularly those in the surface soil.
However, it should only be regarded as a guide to field behaviour, as many factors
will influence the actual extent of dispersion of clay from aggregates. Further, the
electrolyte environment of surface aggregates will be continuously changing owing
to leaching by rainfall or irrigation, so that frequent testing of the soil is required
when estimating gypsum application rates.
The effects of exchangeable sodium (SAR) and electrolytes on soils in subsurface
layers will probably be governed by swelling reactions rather than dispersion.
Further work is needed to establish useful relationships between SAR, TCC and
soil physical problems in subsurface soils.
It should be noted that equations 3-6 cannot be applied to samples containing
free lime, as Emerson (1983) has noted that the presence of lime affects the
dispersive behaviour of soil aggregates. Thus our test probably cannot be applied
to most samples from the deeper subsoil of red-brown earths, unless lime is known
to be absent. Williams (1981) has summarized information illustrating the
variability in lime content both within and between red-brown earth profiles.
Conclusions
1. A scheme is proposed which enables the prediction of the probable dispersive
behaviour of the surface layer of red-brown earths, including exposed subsoils.
Evidence is presented for the ability of this scheme to adequately explain the
observed field behaviour of both dryland and irrigated soils.
430
P. Rengasamy et a[.
2. A procedure for the routine laboratory testing of samples is described. Using
this procedure, surface layers of red-brown earth may be classified into one of six
classes based on relationships established between dispersion, SAR and TCC. The
implications of these relationships for the development of strategies for the
successful management of soils in each class are discussed.
3. Using the criterion of a SAR of 3 or less, a group of low-sodic soils was
identified in which dispersive behaviour could be practically controlled by the
electrolyte effect. Some of the implications for the prediction of the gypsum
requirement of low-sodic soils are discussed.
4. Spontaneous dispersion occurred in 20% of the samples tested. These were
found to have a SAR above 3, and it is suggested that in such soils spontaneous
dispersion is controlled to a similar degree by both the level of exchangeable sodium
and the rate of diffusion of salts from soil aggregates to the soil solution.
5. Most (nearly 80%) of the red-brown earth samples tested dispersed after
mechanical shaking. A greater proportion of the clay fraction dispersed from
subsoil than from surface aggregates, probably due to their higher clay content and
lower organic matter levels. Surface soils released from 1 to 14% clay; a level of
1070 mechanically dispersed clay is proposed as desirable for minimum aggregate
breakdown in the field. The application of this concept to the estimation of the
minimum levels of residual gypsum required for the maintenance of satisfactory
surface structure is discussed.
Acknowledgments
We acknowledge the statistical advice by Dr G . Robinson and the technical
assistance by Mrs F. Robertson, Ms M. L. Mann and Mr 0. Dunne.
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Manuscript received 22 February 1984, accepted 25 May 1984