SIDASS project: Part 3. The optimum and the range of water content for tillage – further developments

SIDASS project

Part 3. The optimum and the range of water content

for tillage – further developments

A.R. Dexter a,*, E.A. Czyz a, M. Birkas b, E. Diaz-Pereira c, E. Dumitru d, R. Enache d,H. Fleige e, R. Horn e, K. Rajkaj f, D. de la Rosa c, C. Simota d

a Institute of Soil Science and Plant Cultivation (IUNG), ul. Czartoryskich 8, 24-100 Pulawy, Polandb Szent Istvan University, PaterKaroly u.1, 2130 Godollo, Hungary

c Institute of Natural Resources and Agrobiology of Seville, CSIC, Avda. Reina Mercedes 10, 41012 Seville, Spaind Research Institute for Soil Science and Agrochemistry, Bd. Marasti 61, 71331 Bucarest, Romania

e Christian-Albrechts-University, Olshausenstrasse 40, D-24118 Kiel, Germanyf Research Institute of Soil Science and Agrochemistry, Herman Otto ut. 15, H-1022 Budapest, Hungary

Abstract

The SIDASS project ‘‘A spatially distributed simulation model predicting the dynamics of agro-physical soil state within

Eastern and Western Europe countries for the selection of management practices to prevent soil erosion based on sustainable

soil–water interactions’’ required a method for estimating the dates (or soil water conditions) under which soil tillage operations

could be performed. For this purpose, methods were developed for estimating the optimum and the range of soil water contents

for tillage. These methods are based on the soil water retention curve. In this paper, we further develop the method in two ways.

First, we take account of the fact that the soil properties: clay content, organic matter content and bulk density are not

independent. This is done through the use of simple pedo-transfer functions which are based on measurements on many soils.

Second, we present a simplified and more rapid method for estimating the lower (dry) limit for tillage. This enables this lower

limit to be calculated using a computer spreadsheet instead of through tedious iterative calculations which were previously

obtained with a special computer program. Examples are given for the tillage limits which take account of the interdependencies

between the contents of clay, the content of organic matter and the bulk density. Estimated typical values of the tillage limits are

presented for all the soil texture classes in the FAO/USDA classification system. Additionally, it is shown that the range of water

contents for tillage is expected to decrease with decreasing soil physical quality as measured by S.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Pedo-transfer functions; Soil physical quality; Water retention curve; Van Genuchten equation

www.elsevier.com/locate/still

Soil & Tillage Research 82 (2005) 29–37

* Corresponding author. Tel.: +48 81 886 3421;

fax: +48 81 886 4547.

E-mail address: [email protected] (A.R. Dexter).

0167-1987/$ – see front matter # 2005 Elsevier B.V. All rights reserved

doi:10.1016/j.still.2005.01.005

1. Introduction

Part of the SIDASS project required estimation of

conditions under which tillage operations could be

.

A.R. Dexter et al. / Soil & Tillage Research 82 (2005) 29–3730

performed without damage to the soil structure as

would occur if the soil is too wet, and without

excessive use of energy as would occur if the soil is too

dry. Accordingly, a simple theory was developed and

published which enabled the optimum and the range of

water contents for tillage to be determined in terms of

the water retention curve of the soil (Dexter and Bird,

2001). This is especially useful because the para-

meters of the van Genuchten equation for water

retention are available as pedo-transfer functions. In

the original work, factors in the pedo-transfer

functions (e.g. clay and organic matter contents and

soil bulk density) were considered to be independent

variables, and their effects on the optimum water

content for tillage and the tillage limits were

investigated and reported separately. However, it

was evident that bulk density was the factor most

affecting the predicted soil water contents for tillage. It

seemed likely that the effects of other factors such as

clay content and organic matter content were mainly

indirect through their effects on the bulk density.

In this paper, we now develop and incorporate a

simple pedo-transfer function for the soil bulk density

so that we can look more realistically at the typical

effects of soil composition on the optimum water

content for tillage and on the trends in the tillage limits

that might be expected to occur in the field. Whereas

the pedo-transfer functions enabled the effects of

different factors to be separated, some of the factors

are now recombined in a special way to take account

of their interdependence in the field.

We also develop and propose a simplified method

for estimating the lower (dry) tillage limit that enables

this to be calculated more easily. Additionally, we

illustrate the effect of soil physical quality, as

described by Dexter (2004a,b,c), on the range of

water contents for soil tillage.

2. Theory

The theory was given in full by Dexter and Bird

(2001), and will only be summarized here. It is based

on the van Genuchten (1980) equation for soil water

content, u, as a function of applied water potential (or

‘‘suction’’), h:

u ¼ ðuSAT � uRESÞ½1 þ ðahÞn��m þ uRES (1)

Here, uSAT and uRES are the water content at saturation

and the residual water content, respectively, a is a

scaling factor for the water potential, and m and n are

parameters which govern the shape of the curve. In

this paper, all water contents are gravimetric. Eq. (1)

fits many soils well, although there are some excep-

tions that include soils which have a bi-modal pore

structure. In this paper, we assume that soil water

retention is described by Eq. (1).

It is useful to note that Eq. (1), when plotted as ln(h)

against u, has only one characteristic point. This is the

inflection point where the curve has zero curvature.

The curve at its inflection point has two character-

istics: its position and its slope.

As shown by Dexter and Bird (2001), the opti-

mum water content for tillage, uOPT, may be identified

with the water content at the inflection point of the

water retention curve, that is its position. In terms of

the van Genuchten equation, this is given in general

by

uINFL ¼ ðuSAT � uRESÞ 1 þ 1

m

� ��m

þ uRES (2)

The modulus of the optimum water matric potential

for tillage (i.e. the potential at the inflection point) is

given by

hINFL ¼ 1

a

1

m

� �1=n

(3)

Eqs. (2) and (3) give estimates of the status of the soil

water at the optimum conditions for tillage.

Dexter and Bird (2001) suggested that the upper

(wet) limit for tillage could be estimated as fixed

proportion (they chose 0.4) of the distance between the

optimum water content and the water content at

saturation. In terms of the parameters of the water

retention curve using the equation

uUTL ¼ uINFL þ 0:4ðuSAT � uINFLÞ (4)

The lower (dry) limit for tillage was defined arbitrarily

by Dexter and Bird (2001) as the water content at

which the soil strength was twice its value at the

optimum water content for tillage. They estimated

this from a simplified form of effective stress theory

as used by Greacen (1960) and Mullins and Panayio-

topoulos (1984). The dominant role of water in con-

trolling the strength of agricultural soils has been

shown using effective stress theory by Giarola et al.

A.R. Dexter et al. / Soil & Tillage Research 82 (2005) 29–37 31

(2003) and Vepraskas (1984). To a first approximation,

we may write that

tOPT ¼ kxOPThOPT (5)

and

tLTL ¼ kxLTLhLTL ¼ 2tOPT (6)

where the x-values are the degrees of saturation = u/

uSAT. The coefficient, k, is assumed to be a constant the

value of which depends on the type of strength mea-

surement. In this paper, the interest is only in relative

strength values, and so the value of k need not be

considered. The value of hLTL, of course, has a corre-

sponding value of water content, uLTL, at the lower

tillage limit.

More generally, account also needs to be taken of

the contribution to soil strength due to surface tension

forces in the soil water menisci between soil particles

in unsaturated soil (e.g. Towner and Childs, 1972).

This effect starts to become significant when soil dries

below about x = 0.4 and becomes dominant when soil

is drier than about x = 0.3 (Vepraskas, 1984). There-

fore, this refinement is probably not necessary for the

situations considered here where the soils are usually

only slightly drier than optimum.

Additionally, we shall consider the slope, S = du/

d(ln h), of the retention curve at the inflection point. If

we use Eq. (1), then we obtain the analytical solution

S ¼ �nðuSAT � uRESÞ 1 þ 1

m

� ��ð1þmÞ(7)

where S has been shown to be a useful measure of soil

physical quality (Dexter, 2004a,b,c) that is positively

correlated with soil friability (Dexter, 2004b), and

negatively correlated with the amount of clods pro-

duced when tillage is done at the optimum water

content (Dexter and Birkas, 2004).

Fig. 1. Clod production during tillage as a function of the gravi-

metric water content at the time of tillage. The left-hand graph shows

the measured points and the optimum water content for tillage, uOPT.

The right-hand graph shows the fitted quadratic equation with the

upper and lower tillage limits, uUTL and uLTL, and the range of water

contents for tillage, R.

3. Methods

Tillage experiments were done in Hungary using a

mouldboard plough on a soil with contents of clay and

silt of 40 and 28 kg (100 kg)�1, respectively. Soil

water content varied naturally in the field and was

measured twice every day. Tillage was done over a

range of water contents in approximate steps of 2 kg

(100 kg)�1. The resulting tilled soil was sieved (6–10

replicates of 20 kg) to determine the amount of clods

>50 mm.

Measurements of the bulk density of 93 soil horizons

of Polish agricultural soils were made by sampling in

100 mL stainless steel cylinders. Additionally, the

particle size distributions were determined by sieving

and sedimentation using standard methods. The organic

matter content was measured by wet oxidation.

The correlation between organic matter and clay

content was investigated using results from samples

from the tilled layers of 210 Polish soils.

As in the earlier paper (Dexter and Bird, 2001), the

van Genuchten equation for the water retention

characteristic was fitted using the Mualem (1976)

restriction:

m ¼ 1 � 1

n(8)

4. Results and discussion

4.1. Tillage results

The amount of clods larger than 50 mm produced,

expressed as a percentage of the total tilled soil, is

shown as a function of gravimetric water content at the

time of tillage in Fig. 1 (left). It can be seen that there is a

distinct minimum in the amount of clods produced at a


water content of about 21.5 kg (100 kg)�1. The water

content at this minimum is defined as the optimum

water content for tillage, uOPT. This optimum is well

illustrated by the data shown in Fig. 1 (left). Fig. 1

(right) shows the fitted quadratic equation. This is used

for clarity to show the estimated upper and lower tillage

limits, and also the range of water contents for tillage, R.

It is interesting to note that, for this soil, the range of

water contents for tillage is not limited by the amounts

of clods produced, but by soil strength (at the lower

tillage limit) and by potential soil damage by plastic

deformation (at the upper tillage limit). The amounts of

clods produced are governed by a different factor as

discussed by Dexter and Birkas (2004).

4.2. Effects of soil composition on bulk density

The values of bulk density, D, for the Polish soils

were regressed against the values of clay content, C,

and organic matter content, OM. The equation used

was

1

D¼ a þ bC þ cOM (9)

We feel that it is more logical to use the reciprocal of

the bulk density (i.e. the specific volume or volume per

unit mass of solids) because it is better to have the

mass (which is constant) in the denominator. Unit

mass of soil is also in the denominators of the C and

OM terms, and this therefore gives a common basis for

all the terms in Eq. (9). The resulting equation is

1

D¼ 0:598ð�0:020Þ þ 0:0203ð�0:0049ÞOM;

r2 ¼ 0:16; p< 0:001

(10)

Here D is in Mg m�3 and OM is in % or kg (100 kg)�1.

Eq. (10) shows the strong effect of OM on soil-specific

volume (or bulk density). There was no significant

effect of clay content in these soils of low clay content

(mean clay content = 7.5%).

This result can be compared with a similar equation

obtained for 91 Dutch clay soils (clay content > 8%)

by Dr. J.H.M. Wosten (Alterra, Wageningen, personal

communication):

1

D¼ 0:581 þ 0:00325C þ 0:0303OM;

r2 ¼ 0:78

(11)

In this case, there is a significant effect of clay content,

C (% or kg (100 kg)�1), but this is much smaller than

that of organic matter content, OM. We do not know

the precise reason for the apparent differences in the

results between Polish and the Dutch soils which are

given by Eqs. (10) and (11). Simply for the purposes of

illustration of the possible effects of soil composition,

we use the mean of the above two Eqs. (10) and (11) in

the next section:

1

D¼ 0:590 þ 0:00163C þ 0:0253OM (12)

It must be stressed that for prediction of the properties

of particular soils in a region, regression equations

(pedo-transfer functions) should be used that are

appropriate for that particular region.

Additionally, there is the result from analysis of

210 Polish soils from the tilled layer, that organic

matter content is dependent on soil clay content. The

results of regression show that

OM ¼ 1:59ð�0:07Þ þ 0:048ð�0:007ÞC;

r2 ¼ 0:19; p< 0:001(13)

Although this equation does not account for much of

the variance, it does at least demonstrate a statistically

significant trend. Eq. (13) is similar in form, but with

significantly different coefficients, to those for UK

soils with different management (Eqs. (6) and (7) in

Dexter, 2004a). This again illustrates that, for predic-

tion purposes, it is important to use equations which

are appropriate for the soils being considered. How-

ever, to illustrate trends, Eq. (13) is used in this paper

where necessary.

4.3. Effects of soil composition on water content

for tillage

The parameters of the van Genuchten equation for

water retention were estimated using the pedo-transfer

functions of Wosten et al. (1999). However, in every

place in these pedo-transfer functions where the bulk

density, D, appeared, we have used a value of D

estimated from Eq. (12). In this way, we obtained

estimates of the effects of soil clay content and organic

matter content on the optimum water content for

tillage and on the upper and lower tillage limits which

take into account typical effects of these factors on the

soil bulk density.


Fig. 2. Estimated values for the optimum water content for tillage

and the upper and lower tillage limits as functions of soil clay

content at a constant organic matter content. The other assumptions

are described in the text.


and the upper and lower tillage limits as functions of soil clay

content. Here, organic matter content is assumed to be positively

correlated with clay content. Values are shown for the lower tillage

limit for two different strength criteria. The other assumptions are

described in the text.
In Fig. 2 we show the predicted effects of clay
content on the tillage limits. These values were

calculated assuming a constant silt content of 35% and

a constant organic matter content of 1.95%. The bulk

densities were estimated using Eq. (12). The figure

shows how the optimum water content for tillage and

both the tillage limits are predicted to increase with

increasing clay content of the soil.

In Fig. 3, we show the predicted effects of organic

matter content on the tillage limits. These values were

calculated assuming a constant clay content of 15%

and a constant silt content of 35%. The bulk densities


and the upper and lower tillage limits as functions of soil organic

matter content at a constant clay content. The other assumptions are

described in the text.

were estimated using Eq. (12). The figure shows how

the optimum water content for tillage and both the

tillage limits are predicted to increase with increasing

content of organic matter in the soil.

In Fig. 4, we show predictions of the combined

effects of soil clay and organic matter contents on the

tillage limits. These were calculated using Eq. (13),

and then the values of bulk density were calculated

using Eq. (12). The resulting values were then used in

the pedo-transfer functions of Wosten et al. (1999).

Silt content was assumed to be constant at 35%.

Values for the lower (dry) tillage limit are shown for

two different criteria (the strength being twice its value

at the optimum water content and being three times its

value at the optimum water content). It can be seen

that changing the criterion for the strength at the lower

tillage limit has only a small effect on the predicted

values of the water content at this limit. This is

because the soil strength increases rapidly with

decreasing water content.

In Table 1, we present values of some important

quantities for the different soil texture classes as used

in the FAO and USDA classification systems. The

values for clay and silt content were read from the

centre of the area for each texture class on the standard

FAO/USDA texture triangle. The extreme edges and

corners of the texture triangle have not been

considered, as these represent rather rare soils. The

values of organic matter content were estimated using


Table 1

Mean values for the particle size distribution (expressed in terms of the contents of clay and silt) for the 12 USDA/FAO soil texture classes

FAO/USDA texture class Clay (%) Silt (%) OM (%) D (Mg m�3) usat (kg kg�1) a (h Pa)�1 n

cl 60 20 4.47 1.249 0.395 0.0217 1.103

sa cl 42 7 3.61 1.334 0.335 0.0616 1.139

si cl 47 47 3.85 1.309 0.362 0.0220 1.104

cl l 34 34 3.22 1.376 0.324 0.0400 1.127

si cl l 34 56 3.22 1.376 0.325 0.0226 1.129

sa cl l 27 13 2.89 1.414 0.299 0.0727 1.169

l 17 41 2.41 1.474 0.278 0.0314 1.208

si l 14 66 2.26 1.492 0.269 0.0134 1.245

si 5 87 1.83 1.552 0.243 0.0045 1.392

sa l 10 28 2.07 1.518 0.258 0.0400 1.278

l sa 4 13 1.78 1.559 0.239 0.0534 1.406

sa 3 3 1.73 1.566 0.226 0.0671 1.581

Notes: sa = sand, si = silt, l = loam, cl = clay. Values of organic matter content (OM) were estimated using Eq. (13) and then values of bulk

density (D) were estimated using Eq. (12). The values of the parameters usat, a and n of Eq. (1) were calculated using the values for clay, silt, OM

and D in the pedo-transfer functions of Wosten et al. (1999).

Eq. (13), and then the values of bulk density were

calculated using Eq. (12). The resulting values were

then used in the pedo-transfer functions of Wosten

et al. (1999).

Table 2 shows the optimum and the upper and lower

tillage limits for the different FAO/USDA soil texture

classes in terms of the values of the water content, u, as

calculated using Eqs. (2)–(6) with the values given in

Table 1. These values were calculated on the

assumption that the residual water content, uRES, is

zero and using the Mualem restriction in Eq. (8).

Comment must be made about the small values of

water potential (not shown) which are predicted for the

upper (wet) tillage limit especially for sand and sandy

Table 2

Values for the optimum water content for tillage and the upper and lower t

quantified in Table 1

FAO/USDA texture class uLTL (kg kg�1)a uLTL (

cl 0.292 0.291

sa cl 0.231 0.230

si cl 0.267 0.266

cl l 0.229 0.228

si cl l 0.228 0.227

sa cl l 0.195 0.194

l 0.170 0.169

si l 0.155 0.154

si 0.111 0.111

sa l 0.141 0.140

l sa 0.106 0.106

sa 0.068 0.077

a Values were calculated using the exact iterative procedure based on Eb Values were calculated using the new method with Eq. (18).

loam. In the field, water potentials smaller than

h = 100 h Pa would usually not be found during tillage

because such wet, coarse-textured soils would drain

rapidly under gravity to the ‘‘field capacity’’ which

corresponds to approximately h = 100 h Pa.

A prediction from results which is not shown is

that all four of the silty soils will present difficulties in

tillage because even after they have drained to ‘‘field

capacity’’, they will still be too wet (u > uUTL) and will

be outside the range of water contents for tillage

(uLTL < u < uUTL). This situation was described by

Boekel (1959, 1965). Such soils can usually be dried

sufficiently only by transpiration of plants because the

process of evaporation from the soil surface is too slow.

illage limits calculated for the 12 USDA/FAO soil texture classes as

kg kg�1)b uOPT (kg kg�1) uUTL (kg kg�1)

0.314 0.347

0.256 0.287

0.287 0.317

0.251 0.280

0.250 0.286

0.221 0.252

0.199 0.231

0.188 0.220

0.159 0.192

0.177 0.209

0.155 0.188

0.140 0.174

qs. (5) and (6).


It can also be seen that the mean value of the ratio

uOPT/uUTL is close to 0.9, which corresponds with field

observations as discussed by Dexter and Bird (2001).

4.4. Improved method for estimation of the lower

(dry) tillage limit

In the original paper (Dexter and Bird, 2001), the

lower tillage limit was defined as the water content at

which the soil strength has twice the value that it has at

the optimum water content for tillage. This was

estimated through the use of an iterative procedure

involving Eqs. (5) and (6). The rationale for this was

that a farmer will normally use a tillage implement of

such a width that his tractor will pull it efficiently

when the soil conditions are optimum. If the soil

strength is double, then his tractor will not pull the

implement easily, and this effectively sets the dry limit

for tillage of that soil-implement combination.

The reader should remember, however, that there is

no real lower tillage limit because soil can be tilled

even when very dry without damage to its structure.

The only consideration is how much time and energy a

farmer is prepared to use for tillage. The lower tillage

limit as defined above is, therefore, only an arbitrary,

working definition based on practical soil manage-

ment considerations.

We now propose a method for estimating the lower

tillage limit as defined above that does not require

iterative calculations. It is based on the observation that

the when soil is drier than a water potential of 1/a, then

the shape of the water retention curve depends primarily

on the parameter n. In order to investigate this, we used

the values of the van Genuchten parameters given in

Table 1 and also some for soils having larger values of n

(in the range 1.8 < n < 2.5) which we have measured

on some natural soils in Poland. It should be noted,

however, that the majority of Polish soils fall in the

range of 1.2 < n < 1.6. Values of log hOPT were calcu-

lated using Eq. (3) whereas values of log hLTL were

calculated by the iterative procedure described pre-

viously. The differences, D(log h), were obtained from

Dðlog hÞ ¼ log hLTL � log hOPT (14)

It should be noted that all logarithms here are to the

base 10. The resulting values of D(log h) were

regressed against the corresponding values of log n

using the program MinitabTM. However, we can also

take advantage of the fact that as n ! 1, then the soil

remains saturated and from the theory of effective

stress, the strength will increase proportionally with

the suction, h. Therefore, the intercept of the regres-

sions will be equal to the logarithm of the strength

ratio required. The resulting regressions are presented

in Eqs. (15) and (16) below.

For strength to be twice as large:

Dðlog hÞ ¼ log 2 þ 1:10 log nð�0:05Þ

; p< 0:001 (15)

It is possible to produce similar equations for other

strength ratios. For example, for strength to be three

times as large:

Dðlog hÞ ¼ log 3 þ 1:32 log nð�0:09Þ

; p< 0:001 (16)

The above equations were developed using values of n

in the range 1.09 < n < 2.5, and are sufficiently accu-

rate for practical purposes over this range. It should be

noted that this range of values of n covers all the FAO/

USDA soil texture classes discussed in the previous

section, representative values for which are given in

Table 1.

The procedure for estimating the lower tillage limit

is therefore as follows:

(i) c
alculate the optimum water potential for tillage
using Eq. (2),

(ii) t
ake the logarithm of this (to base 10),
(iii) c
alculate D(log h) using Eq. (15),
(iv) a
dd (iii) to (ii) to get log(hLTL),
(v) t
ake the antilogarithm of (iv) to get hLTL.
These calculation steps can be done easily on a

simple spreadsheet without the need for any iterative

procedures or special programs.

Alternatively, it is possible to arrive at step (iv)

directly using:

log hLTL log1

a

1

m

� �1=n

þ log 2 þ 1:1 log n (17)

or at step (v) directly using

hLTL 2

a

1

m

� �1=n

n1:1 (18)

The water content at the lower tillage limit, uLTL, can

then be estimated using the value of hLTL from Eq. (18)

in Eq. (1).


Fig. 5. Estimated value for the range, R, of water contents for tillage

as a function of soil physical quality, S, for five Hungarian soils.

Eqs. (17) or (18) can be calculated on a computer

spreadsheet, and provide an easier method for

estimating the position of the lower (dry) tillage limit

than the cumbersome iterative procedure described

previously. Some comparisons of values of the water

content at the lower (dry) tillage limit calculated by

the two methods are presented in Table 2.

4.5. The range of water contents for tillage

and soil physical quality, S

The range, R, of water contents for tillage was

calculated using

R ¼ ðuUTL � uLTLÞ (19)

where uUTL and uLTL were calculated as described

above. Additionally, S was calculated using Eq. (7).

A graph of R against S for five different Hungarian

soils is presented in Fig. 5. This shows clearly how the

range of water contents for tillage deceases as soil

physical quality, S, decreases. It should be remem-

bered, however, that both R and S were calculated from

the same water retention curves and are therefore not

independent. Nevertheless, the correlation between the

two calculated quantities is of interest.

5. Conclusions

For soils for which the water retention character-

istics have been measured, the optimum water content

for tillage and the upper (wet) and lower (dry) limits

may be estimated directly using Eqs. (2), (4) and (18).

For soils for which water retention data are not

available, the water retention characteristics may first

be estimated from basic soil data using pedo-transfer

functions. Of course, measured values will always be

more accurate than estimated values.

The pedo-transfer functions of, for example,

Wosten et al. (1999) enable the effects of different

factors such as clay content, organic matter content

and bulk density to be invstigated separately. How-

ever, in most cases, these factors are not independent

but are strongly correlated. An example is the inverse

correlation between organic matter content and bulk

density. The exception is bulk density, the effects of

which can considered alone because soil can be

compacted without any change in composition. For

the other factors, we have produced regression

equations (Eqs. (9)–(13)) which recombine them in

realistic ways. This enables the effects of the factors,

in combinations which may be expected to occur in the

field, on the tillage limits to be estimated.

The new method which has been presented in

Eq. (18) enables the lower (dry) tillage limit of soils to

be estimated much more easily than by the previous

iterative method which involved the use of a special

computer program. This new method enables the

values to be obtained easily on a standard computer

spreadsheet. A comparison of the results obtained by

the two methods as given in Table 2 shows that the

results from Eq. (18) are very close to those obtained

by the exact method of calculation.

Pedo-transfer functions are very useful for showing

trends in soil behaviour, however they must be treated

with great caution when used for prediction purposes

for particular soils. For example, the use of the clay

content alone has severe limitations and different clay

minerals such as montmorillonite, illite and kaolin

would give different responses. Similarly, the suite of

exchangeable cations associated with the clay influ-

ences the soil–water interactions. Nevertheless, the

results which are presented above can be considered to

show trends of behaviour which may be expected to be

representative of typical European agricultural soils.

The prediction that the range of water contents over

which tillage may be done decreases with decreasing

soil physical quality (i.e. with physical degradation) is

consistent with the observations of Hoogmoed (1985)

and with the observations of Australian farmers as


heard by the first author. The implication is that

management practices which increase the value of the

soil physical quality, S, will also increase the range of

water contents for tillage. Further observations in the

field are needed to test these predictions.

Acknowledgements

The authors would like to thank the European

Commission for their support of the SIDASS project

under grant number ERBIC15-CT98-0106. They

would also like to thank Dr. J.H.M. Wosten of

Alterra, Wageningen, for providing Eq. (11) and for

useful discussions.

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Documents

SIDASS project: Part 3. The optimum and the range of water content for tillage – further developments