Computer Simulation of Soil Compaction by Farm Equipment
C. Plouffe,
S. Tessier,*
ABSTRACT
A user-friendly computersoftwarewas developedto simulate soil compaction
causedby farmmachinery
traffic. Theprogramuses the modifiedBoussinesqequationto simulate the
pressuredistributionundertires/tracks. Pressuredistribution
undera tire or trackis calculatedfromvehicle andsoil characteristics suchas tire/track dimensions,
load andsoil conditions
thatare inputbythe user.Soil bulkdensitiesare calculatedfrom
relationshipsbasedon soil texture,organicmatterandmoisture
content,andthe pressuregivenby the Boussinesq
equation.This
softwareis not intendedto validate field measurements
since
it is basedon simplifyingassumptions
suchas homogeneity
of
the soil profile anda static uniformly
distributedload. Rather,
i! is intendedto illustrate conceptssuchas axle loadeffects,
compaction
reductionwith dual tires or tracks, andorganic
mattereffect onsoil susceptibilityto compaction.
Thisfast, userfriendlysoftwarerequiresneitherprogramming
skills norprevious knowledge
of soil mechanics.Therefore,it can be used as
a teachingor extensiontool to providea better understanding
of soil compaction
inducedby agricultural machinery.
D
URING
the past two decades, an increase in soil compaction has been observed as a result of greater
soil-tire contact pressure with larger farm equipmentand
increased agricultural machinerytraffic (Tessier et al.,
1991). Wanget al. (1985) observed that shallow root
growth and severely reduced yield were associated with
severe compaction and low hydraulic conductivity of
saturated soil in the Ottawaarea (Ontario, Canada). Yield
losses from soil compactioninduced by wheel traffic may
account for yearly revenue losses of up to $300 (US)
ha-1 (H~tkansson et al., 1988). Excessive soil compaction not only reduces crop yield but can also lead to increased soil erosion (Gupta et al., 1989).
In recent years, considerable efforts have been devoted
to the development, implementation, and promotion of
better machinery and field managementscenarios. Concepts such as controlled traffic and farm machinerywith
tracks have been proposed as meansto prevent or alleviate soil compaction. However,demonstrating the damage
associated with specific machineryon different soil types
over various field conditions becomesa major challenge.
Field experiments are resource consumingand apply only
to specific soil and climatic conditions. Conversely, soil
compaction models offer an inexpensive way to identify
which soil conditions and machinery managementpractices are conducive to harmful levels of compaction
(Gupta and Allmaras, 1987).
More than 30 yr ago, Soehne (1958) suggested the use
of the Boussinesq equation, as modified by Froelich
C. Plouffe,S. Tessier,andL. Chi,Agric.Eng.Dep.,Universit6
Laval,
Sainte-Foy,PQ,CanadaGIK7P4; and D.A.Angers,Agric. Canada
Res. Stn., 2560Hochelaga
Blvd., Sainte-Foy,PQ,CanadaGIV2J3.
Received17 Nov.1992.*Corresponding
author
Publishedin J. Nat. Resour.Life Sci. Educ.23:27-34
(1994).
D. A. Angers, and L. Chi
(1934), tO predict the pressure distribution and resulting
soil compactionof agricultural soils under a given load.
Since then, Kline and Perumpral (1984), Bowenet al.
(1984), and many others have modeled soil compaction
using this analytical equation. The modified Boussinesq
equation greatly simplifies the analysis of soil compaction problem to the case of an elastic and homogeneous
mediumcompression case. More realistic models that
describe in detail the three-dimensional response of soils
to external loading are available (Chi et al., 1993).
However, simulations of these complex representations
require powerful numerical techniques such as fine element analysis, extensive computer and programming
capabilities, and muchhigher levels of understanding soil
mechanics.
The objectives of this study were to develop an interactive, user-friendly software that could be used by
agriculture students and extension specialists to evaluate
the severity of soil compaction induced by specific vehicular traffic, and to outline the benefits obtainable from
alternative equipment configurations and management
practices. The simulation software integrates parameters
such as axle load, equipment configuration, and soil
characteristics (texture, organic matter, water content,
and initial bulk density).
THEORETICAL
CONSIDERATIONS
Pressure Calculation
Boussinesq’sstress distribution theory (Froelich, 1934)
forms the basis for the computer model developed to
predict the stress distribution resulting from a vehicle
wheel load. Simplifying assumptions are made for various stress componentsat a given point within the mediumresulting from a point load on the soil surface (Fig.
1). These assumptions bring somelimitations inherent to
the approach. Layered soils cannot be studied since the
model assumes homogeneity of the profile. Because of
this, the effects of multiple passes over the soil cannot
be simulated. The shear stress induced by wheel slippage,
which may significantly contribute to surface compaction (Raghavanet al., 1975), is also not included in the
model.
Amongthe various equations derived from Boussinesq’s theory, Eq. [1] describes the vertical stress, oz,
at a point within a semi-infinite mediumfrom the concentrated load, N (see the Appendixfor definitions of
variables).
°z
22 r R
[1]
Froelich (1934) proposed a concentration factor,/~,
account for the influence of varying initial soil strength
and water conditions. Soehne (1958) suggested # values
J. Nat. Resour.
Life Sci. Educ.,Vol. 23, no. 1, 1994* 27
~
N
\
~z
Fig. 1. Schematicrepresentation of the effects of a point load N on
the vertical pressurein the soil crz, at a depthZ.
of 4, 5, and 6 for hard (or dry), normal, and soft (or wet)
agricultural soils, respectively.
Pneumatic tires generally transmit the dynamic load
of a vehicle over a finite area rather than at a point load,
N. Hence, Eq. [1] cannot be used directly to predict the
stress distribution resulting from the loading of a vehicle
wheel. Soehne (1958) provided a methodthat can be used
to redistribute the wheel load onto a numberof point load
locations. This methodconsists of dividing the wheel contact area into 5 cm by 5 cm subareas (Fig. 2). The wheel
load is equally spread over these subareas to obtain the
subarea point load, N. The vertical stress, oz, at a given
point within the soil is computed from each individual
point load, using Eq. [1]. These stresses are summedup
to obtain the total vertical stress at a point.
Fig. 2. Schematic representation of the numerical implementationof
the point load equationto uniformlyapplied load over a finite area.
in the modeldeveloped to suit the major soils to be considered in the simulation.
Critical Bulk Density
In compactedsoils with sufficient nutrients and available water, mechanical impedance and aeration largely
control root growth. Eavis (1972) showedthat at a given
water content, these properties are largely determined by
bulk density. If root growth can be assumedto occur at
a given water content, the only variable affecting root
growth would be bulk density. Jones (1983) provided
equation describing the threshold bulk density values beyond which less than 20°7o of maximumroot growth occurs at water contents near field capacity (Eq. [3]). This
critical bulk density, Obcis closely related to the soil clay
content.
Bulk Density Prediction
Obc = 1.77 - 0.00629 (%clay)
Althoughvertical pressure distribution in the soil profile can be used to interpret soil compaction, most users
are more familiar with bulk density as an index of soil
compaction. Provided that the density-compression behavior of a given soil is known,the bulk density can be
calculated from the vertical pressure.
Larson et al. (1980) determined the uniaxial compression behavior of unsaturated soils from eight soil orders.
They proposed that the bulk density expressed on a dry
weight basis, Pb, of a soil at a given degree of saturation,
S, under an applied vertical stress, gz, can be described
ImplementCharacteristics
as:
PO = L°k + St(S - Sk)] + Cp log (ozlak)
[21
The coefficients Cp, ok, and St are calculated from
compression-density curves of agriculturall soils. Larson
et al. (1980) proposed a set of equations obtained from
soils with organic matter (OM)ranging from 0.6 to 7.4°70.
Angers (1990) presented the same coefficients describing
the compression-density curves of 20 agricultural soils,
typical of those encountered in Qu6becwith a higher OM
range (1.0-9.0%). These latter equations proved to
more sensitive to OMchanges. Either set of equations
for calculating the coefficients can be easily incorporated
28 ¯ J. Nat. Resour. Life Sci. Educ., VoL 23, no. 1, 1994
[3]
Sometire manufacturersprovide tables with either static loaded radius, hard surface contact area, or both. As
these tables are not always readily available and do not
account for wheel sinkage, an alternate approach is followed in our model to estimate surface contact area.
McKyes(1985) proposed that the length of the contact
area, L, is equal to one-half the overall wheel diameter,
d, for most tires used on agricultural equipment(Fig. 3).
The width of the contact area, w, is the nominal width
specified for the tire. Thesoftware provides a large selection of standard tire sizes. If one of these standard tire
sizes is chosen, the contact area is automatically calculated using these assumptions for the length and width
of the contact area. The tracks dimensions are not estimated, but rather requested from the user, since no standard size tables were found. Values for L and w can also
be directly entered by the user for a more precise estimate of the contact area or for other tires or tracks not
included in the selection.
The actual shape of conventional or bias construction
tires has long been approximatedas an ellipse (Barger et
al., 1963). Consequently,the contact area for these is calculated as shownin Fig. 3, along with the above rela-
RADIAL
BIAS
Area--- wL
Area = 0,78 wL
Area = wL
Fig. 3. Schematic
representation
of the soil contactareafor radialtire, biastire, andtrack,andhypotheses
underlying
its estimation.
tionships and assumptions. A rectangular contact area
shape is assumedfor radial belt tires, which accounts for
reports of tractive efficiency increases of 6 to 18%(Ellis,
1977; Taylor et al., 1976; Wulfsohnet al., 1988). The contact area of radial tire is thus approximated to that of
a rectangle with the above dimensions(Fig. 3), effectively resulting in a 22°70greater contact area with radial tires.
The axle configurations allowed by the software are
single, dual, and triple tire configurations. Whena dual
or triple tire configurationis selected, a gap betweentires
can be specified to reduce adjacent tire interaction on soil
compaction. Further, the additional tire proportional
mass is not automatically accounted for, since it is as-
sumedthat this mass is counterbalanced by a proportional
reduction of the tire ballasting.
Input/Output Interface
Three interactive windows(Fig. 4) are used to input
user information, initiate solution, display, and print
results. The input information is summarizedin a table
and can be updated at any time. The simulation produces
two output figures. The first shows the contour profile
of the bulk density profile under the wheel center. The
second figure displays the bulk density distribution under
the wheel center as well as the critical density, POc.
Boussinesq
Load
I 7000
--re’Width (cm)
,["
62 I
(cm)
Soil
Charact¢ristics
Soil characteristics
He~p
Solution
p vsZProfile
pvs Z
E:
Fig. 4. Interactiveuserinputandcontrolwindows
of the softwaredeveloped.
J. Nat. Resour.
Life Sci. Educ.,Vol. 23, no.1, 1994* 29
APPLICATIONS
AND DISCUSSION
Agreement with Other Models
and Field Measurements
Although this software is meant as an educational
rather than a predictive tool, comparisonwith other simulation data or actual soil compactiondata is used to outline its features and limitations. Outputfrom soil pressure
and bulk density subprograms are dealt with separately,
for clarity.
To discuss the numerical implementation of the Boussinesq equation, the program results are compared with
those presented by Carpenter et al. (1985). The latter
authors used the Boussinesq equation to compare pressure distribution under several conditions. A uniformly
distributed load is applied over a circular area. ThreetypiTable 1. Comparisonsof simulated soil pressures at depths with
the theoretical data reported by Carpenter et al. 11985~for different tire load, N, applied on circular contact area of diameter, d,
with concentration factor, ~.
Soil pressures
Case 2
Case1:~
Case 3
Depth Software Carpenter Software Carpenter Software Carpenter
cm
10
20
30
40
50
kPa
185
110
67
44
30
182
113
67
42
29
200
126
79
52
37
189
127
78
50
34
209
205
196
179
160
199
198
192
180
163
For Case 1, N{kN), #, and d {cm)are 10, 5, and 25; for Case 2, 10, 6, 25;
and Case 3, 150, 5, and 98, respectively.
Soft surface pressures is kept constant to 200 kPa for the three cases.
Table 2. Characteristics
of various agricultural
equipment used
for the soil compaction simulations (Germain, 1984).
Equipment
type
Model
Tire
size1"
Tractor MFI65D¶16.9x 28
Tractor JD4250 18.4 x 38
Harvester
JD882024.5 × 32
Harvester
JD8820
....
Width x Total Wheel
Axle
configuration
length:~ mass mass§
cm
-- kg -Single-bias
43 x 72 4 000 1 217
Single-bias
47 x 88 6 799 2 040
Single-bias
62 × 90 15 000 5 600
Track# 69 x 198 18 000 7 000
4 900
Single-radial
40 x 60
Widthandinside
diameter
(rim~
of thetire(ASAE,
1992}.
Widthandlength
of thesurface
contact
area{McKyes,
19851.
60%of a tractor
massand75%on a combine
harvester
massis assumed
to be applied
on themainaxle.
MF stands|or Massey-Ferguson
Co.,andJD forJohnDeereCo.
Model40 Supertrack
{Terreveh
Co.Pointe-C.laire,
PQ,Canada
HgR5P9);
eachtrackadds1400kg to theimplement
mass.
Table 3. Textural analysis and physical properties of the soil series
selected for the simulations.
Sand Clay OM Water
Water
Bulk
con-con-con-contentsaturation
density,
tent tent tent
/z
0
S
Pb
-a
Mg m
%
Chicot
loamysand1"80
1.1
5
3
29
50
5
Chicotloamysand 80
5
3
36
70
1.3
6
Ste-Rosalie
clay~ 14
1.05 6
50
3
42
70
1.05
Ste-Rosalie
clay
14 50
5
42
70
6
cal simulation runs are shownin Table l, dealing with
a variable load, N, concentration factor,/z, and contact
area of diameter, d. Except for near the surface. (10 cm),
the software soil pressure simulations are in close agreement with those of Carpenter et al. 0985). The discrepancies are under 4°70 up to a depth of 50 cm. The different
methods used to subdivide the contact area explain the
slight overestimation of pressure near the surface. This
effect vanishes quickly with depth.
The first two cases display a 15070increase with a # of
5 to 6, whichreflects the susceptibility of a soft soil (#
= 6) to transmit higher soil pressure. The first and third
cases simulate a soil surface pressure of 200 kPa and
demonstrate that increasing contact area proportionally
to the axle weight, to maintain similar surface pressure,
results in a different soil pressure profile. As shownin
Table I, the soil pressure propagation is muchdeeper with
a larger contact area despite similar surface pressures.
This phenomenonis explained by the greater number of
point loads (N), which sum up in depth to provide higher
soil pressure.
Data from Raghavan et al. 0975) are deemeddetailed
enough to allow a comparison with the simulated bulk
density distribution given from the software to illustrate
the limitations of the Boussinesq theory. These authors
report on soil compaction by a tractor model MF165D
(Table 2) over a Chicot loamy sand (fine-loamy, mixed,
frigid Typic Hapludall~ at 50°70 water saturation (Table
3). This tractor applies 1217 kg on the rear axle equipped
with 16.9 x 28 single tires. Previous field history at the
experimental site includes annual plowing to a depth of
approximately 25 cm, explaining the abrupt increase in
initial bulk density (Fig. 5) between the plow layer and
the subsoil (below 25 cm). Consequently, much of the
reported influence of actual soil compactionwas confined
to the upper 25 cm, as a result of differing soil mechanical properties betweenthe plowlayer and the subsoil (Chi
et al., 1993).
The simulated bulk density distribution (Fig. 5) outlines manyof the drawbacksarising from the constraining assumptions made to develop the Boussinesq
equation. Amongthese, the assumption of homogeneous soil properties does not hold true for the field study
considered. Consequently, simulated bulk densities below 25 cm are lower than measured and simulated bulk
densities increase from a depth of 50 cm up to the soil
z (cm)
o
Be~..........
Afar
-51)
Soil series
Canadian
classification
? Fin~loamy,
mixed,
frigid
TypicHapludalf.
:~ Very4ine,
mixed,
frigid
Typic
Humaquept.
30 ¯ J. Nat. Resour. Life Sci. Educ., Vol. 23, no. 1, 1994
Pmd~d
-100
o
1.0
,
1.2
,
1.4
,
1.6
1.8
2.0
~
3)
Density (Mg/m
Fig. 5. Observed
bulkdensityvs. depthdistribution
of a Chicotloamy
sandbeforeandafter compaction
bya MF165D
tractorreported
in
Raghaven
et al. (1975)andsimulated
bulkdensitydistribution.
surface. In addition, compaction from the front wheel
of the tractor, shear from slip, and horizontal stresses
are not considered in the model. This maypartly explain
the under-prediction observed when compared with field
data. Despite these discrepancies, the results remain very
indicative of the soil damageinduced by static compaction loads.
Typical Teaching and Extension Applications
The software can have many applications, but only
four typical cases will be discussed to demonstrateits usefulness as a soil compaction teaching or extension aid.
Note that most of these examples can be solved "by
hand" with a calculator; however, solving the simpler
problem required more than 2 h for a agricultural engineering graduate students, comparedwith 10 s with the
software.
Agronomicand Economic Justification
of Dual Tires and Tracks
Soil compactionat harvest with a JD8820six rows combine harvester with a 4.5-m cutting width on a Ste-Rosalie
clay (very-fine, mixed, frigid Typic Humaquept)at 70°-/o
water saturation is simulated to illustrate potential yield
losses and benefits from a given alternative. This example is most relevant to the case of no-till grain production where soil compaction occurring during harvest may
persist into the next cropping year and result in significant yield losses. A producer conscious of soil compaction often has the choice amongfour different vehicle
configurations to minimizesoil damageat harvest. Therefore, the object of this exercise is to determinethe optimumtire/track configuration.
Underthese conditions, the modelindicates significant
soil compaction near the surface with tires applying 126
kPa surface pressure as well as a deep propagation of the
compactionfront well below the plow layer (Fig. 6). Conversely, no significant soil compactionis predicted by the
track-mounted unit, despite the addition of approximately 2.8 t to the harvester mass. Interestingly, muchless
compaction is expected for long and narrow tracks applying a 50-kPa surface pressure than for dual tires with
o
-50
-75
-100
3)
iii
Tt~ek
,.o
Density (Mg/m
Table 4. Estimated crop yield and revenue losses resulting from
simulated average soil bulk density (0-20 cm) induced by a 15-t
combine harvester on a Ste-RosaHe clay in southern Qu6bec.
Axle type~"
Singel 24,2 × 32
Single Terra
66 × 43-25
Double 24.2 ×
32
Track model 40
Field Density,
Width:~ Pressure covered
Pb
z~Ygr~n§Losses¶
kPa
%
Mgm-a Mgha -1 $ha-i
cm
123
126
28
1.22
2.55
93
156
89
35
1.15
0.64
29
246
138
68
50
56
31
1.10
1.05
0.05
0
4
0
~"Axle configuration and tire characteristics Iwidth × inside diam[rim]}.
~:Width covered by one pass.
§ Whenthe predicted Pb remains under popt, it is concludedthat vehicular
traffic does not induce crop yield loss.
¶ Overall incomeloss at a corn grain price of $130{Canadian}Mg-1considering the area covered by wheel tracks.
a comparable 69-kPa surface pressure. In fact, as more
point loads (Fig. 2) are located further away from any
given point under the track, the summationof the predicted soil vertical pressure applied by each point load is less
than for the nearly circular shape assumedfor the duals.
This fact supports recommendations toward the use of
machinery configurations that provide a long and narrow surface contact area rather than circular and wider
ones (Carpenter et al., 1985).
To further help students, extension specialists or even
producers appreciate the impacts of soil compaction, the
software output maybe used to carry the following short
economicanalysis of the problem. A crop yield-soil density function is required as well as alternative equipment
costs. Several modelsexist to computecrop yield losses
due to soil bulk density change (Rosenberg, 1964;
H~tkanssonet al., 1988). McKyes(1985) used Rosenberg’s
parabolic yield response relationship for corn (Zea mays
L.) production. The same author suggests values of 260
for the sensitivity factor, C, for corn, and 1.08 Mgm-3
for the optimumsoil bulk density, Popt, for a clay soil.
AYtot 2= C (Pb -- Popt)
[41
This equation maybe used to estimate total dry matter
yield losses, A Ytot, of grain corn under the wheel tracks
at a given average Pb in the first 20 cm of the soil profile. The approximate grain yield losses, Aygrain,
may
then be determined as half of A Ytot, based on a 1:1
straw to grain ratio. The area of the field covered by
wheel track must be accounted for to estimate overall
crop yield losses. Considering a given corn grain price
of $130 (Canadian) -~, re venue lo sses as sociated
with each alternative to standard single tire of this combine harvester can be computedas shownin Table 4. For
example,a profit of $8900([$1osses with singles - $1osses
with duals] ha-1 x 100 ha) is forecasted for a producer
on a 100-ha corn field if duals were used instead of the
standard issue tires and axle configuration. In this case,
the return on investment could be achieved within a single
crop year.
Do~bl~
Fig.a. Simulated
hulkdensity
distribution
~nderthe~heelcenter
~or
a JD882~combine
ha~esler
eq~ipoed
withsingleanddual24.2x 32
fi~,single
~ x 43-2~
tire(Ter~),
andt~ekrondel
~ on a Sle-Rosalie
Dual Tire Spacing Effects on Soil Compaction
Whena load is applied over a finite soil surface area,
semicircular pressures profiles are encountered at depth
J. Nat. Resour. Life Sci. Educ., Vol. 23, no. 1, 1994 * 31
(Chancellor and Schmidt, 1962; Carpenter et al., 1985).
For multiple tire configuration such as double axle type,
these semicircular pressure profiles mayoverlap to increase subsoil pressures and hence soil compactionat narrow dual or triple wheel spacing.
A theoretical 4900-kg load per tire is applied over a
theoretical rectangular area 40 cm wide by 60 cm long
to average a 200-kPa surface pressure for both wheel
spacing considered. Figure 7 presents predicted bulk density profile under duals with a 0- and 35-cm gap. By increasing the gap up to 35 cm, the density distribution
shows muchless interaction between tires, thus reducing
overall soil compaction depth. Manydual or triple tire
axle configurations effectively act as rollers, since wheels
are often close to each other. In fact, increasing dual
wheel spacing up to 35 cm may be an even better alternative, in somecases, than the triple tire configuration
with a narrow spacing.
Soil Texture Influence on Compaction
Various soils are knownto display dramatically different susceptibility to soil compaction. However,this susceptibility can often be mitigated to someextent by water
content and OMlevels. Twosoils, a Chicot loamy sand
and a Ste-Rosalie clay, representing extreme cases of soil
texture (Table 3), at water contents near optimumfor
compaction (S = 70°70) are compared to illustrate this
difference. Figure 8 presents the expected density distribution for a JD4250 tractor on both soil types. The
severeness of wheel-induced soil compaction must also
be dealt with, in terms of relative impedance to root
Z (era)
o
¢.~ c~y ¢..~
-50
-75
-I00
1.0
1.2
1.4
1.6
3)
Density (Mg/m
1.8
2.0
Fig. 8. Simulated bulk density distribution underthe wheel center of
a JD4250tractor equippedwith single 18.4 x 38 tire for a Ste-Rosalie
clay (left) anda Chicot loamysand(right) at "/0070water saturation.
growth and depth of influence of the tillage implement.
This is where the concept of critical bulk density, Pbc,
becomesmost useful. Wheeltraffic by a 6.8-t tractor on
a loamy sand is not severe enough to increase bulk densities in the soil profile to values approachingPbc (Fig.
8). However, the same vehicular compaction on a clay
soil brings all bulk densities, and particularly those in the
upper soil layer, closer to Pbc. Propagation of the compaction front is also shownto progress 10 cm deeper in
a clay soil when compared with the loamy sand.
Soil Organic Matter Influence on Compaction
Soil organic matter plays a major role in increasing soil
resiliency to compaction (Ohu et al., 1985) by enhancing
resistance to applied stress (Angers, 1990) and recovery
of compacted soils (McBride and Watson, 1990). Cropping practices designed to maintain or restore soil OM
200kPa
should therefore help reduce soil sensitivity to compression (Angers et al., 1987; Soane, 1990). In Quebec,
in many other regions of North America, the shift from
=========================================================
...
forage-based crop rotations to annual cropping of high
input crops such as corn grain has depleted soil OM.
Figure 9 shows simulated bulk density distributions for
two Ste-Rosalie clay soils with different OMlevels at 70%
saturation, after compactionat harvest by a combineharvester (JD8820). Even though both density distributions
-I00
.
,
. "’’.’’" ,
.
.
remain well below Pbc, root growth maybe impaired be-!00 -75 -50 -25
0
95 50
75 too
low normal tillage depth with the moldboard plow (20
Lateral position (cm)
cm) on lower OMsoils. For both cases, assuming suitable conditions for OMdecomposition coupled with good
managementpractices, the conventional tillage practices
200 kPa
200 kPa
a)
Z (era)
Denser,(Mglm
with the moldboard plow may easily reestablish suitable
porosity near the soil surface.
.’.::::::::::::::::::::::::::
.
An interactive computer software was developed and
i:i:
-,5
used to simulate soil compaction induced by various farm
machinery. The program uses the modified Boussinesq
-50
:::::::::::::::::::::::::::::::::::::::::::
equation to model soil compaction. Pressure distribution
under a tire or track is calculated from vehicle and soil
-’/5
characteristics such as tire/track dimensions, load, and
soil conditions that are input by the user. The program’s
-tO0
,
,
,
,,
,
,
fast execution allows the user to simulate manysoil con-I00 -75 -50 -25
0
25 50
75 I00
ditions or vehicle configurations during a session. Graphic
Lateral position (cm)
output is displayed on the screen and can be printed upon
Fig. 7. Simulatedbulk density profile expectedunder dual wheels with
request.
0- and 35-cm spacing between wheels and exerting a 200-kPa surWithin the limits of the assumptions inherent to the
face pressure on a Ste-Posalie clay at 70070water saturation.
32 ¯ d. Nat. Resour. Life Sci. Educ., Vol. 23, no. 1, 1994
Z(cm)
-25 •
-50 •
-75 •
-100
•
1. 0
11
f
1.2
*/-
St-Martin and Charles Brochu, undergraduate students
who contributed to the programming effort.
3%OM
APPENDIX
Predicted
density
1 Critical
1 density
1.4
1.6
1.8
2.0
Density (Mg/m3)
Fig. 9. Simulated bulk density distribution induced by a JD8820,15-t
combine harvester equipped with single 24.5 x 32 tire for a 3 and 5%
OM content Ste-Rosalie clay soils at 70<?o water saturation.
Some Abbreviations Used in This Article
Abbrev. Definition
Unit
Sensitivity factor
t ha"'(Mgm- 3 )- 2
Compression index
Mg m 3
N
Point load on soil surface
kN
R
Distance between point load N
and a point
m
Slope of near-linear portion of curve,
S,
pfc vs. S
Mg m~3
Reference volumetric water content
Boussinesq equation, the software is most useful in
demonstrating the general concepts of agricultural soil
compaction. Results may be used to justify investments
in greater tires, duals, or tracks on the basis of expected
soil damage and consequent crop yield losses.
The software is versatile enough to simulate most soil
compaction cases and will give the user a better understanding of various factors conducive to soil compaction.
This fast, user-friendly software requires neither
programming skills nor previous knowledge of soil
mechanics. Therefore, it can be used as a teaching or extension tool to provide a better understanding of soil compaction induced by agricultural machinery.
SOFTWARE SPECIFICATIONS
Boussinesq runs on a Macintosh1 microcomputer. The
computational subprograms are written in C language
(Borenstein and Maison, 1989) and are integrated into
the HyperCard execution environment (Bond, 1988).
HyperCard allows interactive entry of the vehicle and soil
parameters, and the representation of the figures within
(50%)
Z
AJ-,0
Ar gr
M
Pb
Pbc
Pk
fopl
Ps
<>z
ok
Degree of water saturation of pore
voids, 0/(l - Pt/Pj)
Depth to a point
Decrease in total dry matter crop yield
Decrease in grain crop yield
Concentration factor
Bulk density
Critical bulk density
Bulk density at known reference
pressure, ak, and reference water
saturation, S k
Optimum soil bulk density for
maximum yield
Average particle density (2.65 Mg m" 3 )
Pressure at any depth Z in the soil
Reference pressure (100 kPa)
Volumetric soil water content
%
%
m
t ha~'
t ha" 1
Mg m~ 3
Mg m~ 3
Mg m~ 3
Mg m~ 3
Mg m~3
kPa
kPa
%
a user-friendly, mouse-driven environment. The C language routines are invoked to compute the soil pressure
distribution and to convert these into density profiles. The
package includes the shareware HyperCard 2.1, the Boussinesq stack file and a text file for basic informations on
the software utilization. It requires any model of Macintosh with system 6.5 or latest version, 1 Mbytes of RAM,
and a printer. It can run from diskette or a hard disk (requires 1 Mbytes). Boussinesq is available from the second
author upon request, at a base price of $150 (US). A network version is also available at $300 (US).
ACKNOWLEDGMENTS
The authors acknowledge the financial support of the
Centre des Ressources Pedagogiques de la Faculte des
Sciences de 1'Agriculture et de ('Alimentation of
Universite Laval, and of the FCAR scholarship and
research funds (<^Fonds pour la formation de chercheurs
et 1'aide a la recherche >) of the Quebec provincial
government, without which this project would not have
been possible. The authors express their gratitude to Luc
1
Mention of a trademark or company does not imply endorsement
of these products by the authors or the institutions they represent.
J. Nat. Resour. Life Sci. Educ., Vol. 23, no. 1, 1994 • 33
Crops as Sources of
Nutrients for Humans
ASA Special Publication Number 48
In the past, most agronomic research has focused on increasing crop yields, often overlooking increasing the nutritional quality of those
same crops. This publication reevaluates the role plants play as nutrient sources and presents an indepth review of the potential existing
for nutritional improvements through better cultural practices and/or breeding.
Major topics covered in Crops as Sources of Nutrients for Humans include: 1) processing and refining effects on crop nutritional value,
2) soil fertility and plant nutrition effects on crop quality and 3) potential for improving crop quality through breeding. Published by the
American Society of Agronomy, Crop Science Society of America and Soil Science Society of America. D.F. Bedzicek, editorial
committee chair. ASA Special Publication Number 48. Paperback, 89 pages, 1984. Price: $12.00 (members' first copy $10.00).
Please send me ________ copy(ies) of Crops as Sources of Nutrients for Humans.
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is required on all orders outside the United States. Wisconsin presidents add appropriate sales tax. Send your order to: ASA, CSSA, SSSA Headquarters
Office; Attn: Book Order Department; 677 South Segoe Road; Madison, Wisconsin 53711 USA.
34 • J. Nat. Resour. Life Scl. Educ., Vol. 23, no. 1, 1994