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Water and nutrient movement in two tropical cropping systems

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Title:
Water and nutrient movement in two tropical cropping systems
Creator:
Seyfried, Mark S., 1954-
Publication Date:
Language:
English
Physical Description:
vi, 170 leaves : ill. ; 28 cm.

Subjects

Subjects / Keywords:
Cropping systems ( jstor )
Crops ( jstor )
Incubation ( jstor )
Leaching ( jstor )
Nutrients ( jstor )
Parametric models ( jstor )
Soil water ( jstor )
Soils ( jstor )
Solutes ( jstor )
Water tables ( jstor )
Cropping systems -- Costa Rica ( lcsh )
Dissertations, Academic -- Soil Science -- UF
Soil Science thesis Ph. D
Soils -- Costa Rica ( lcsh )
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Florida, 1986.
Bibliography:
Bibliography: leaves 157-168.
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Mark S. Seyfried.

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Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Resource Identifier:
029600229 ( ALEPH )
15096901 ( OCLC )
AEH7576 ( NOTIS )
AA00004870_00001 ( sobekcm )

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Full Text


WATER AND NUTRIENT MOVEMENT IN TWO TROPICAL
CROPPING SYSTEMS
By
MARK S. SEYFRIED
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1986


ACKNOWLEDGEMENTS
In working on this project I have been extremely
fortunate to have had a great deal of assistance. At the
outset, the grant money and connections supplied by Bob Volk
enabled me to get my "foot in the door" in Costa Rica.
While I was in Costa Rica, Carlos Burgos of CATIE looked
after my interests in every conceivable way. He not only
contributed experimental plots, but helped me obtain fund
ing, allowed me access to transportation and provided the
services of his assistant, Carlos Arya, who was very help
ful .
I owe a debt of gratitude to several others at CATIE.
Roberto Diaz gave me the run of his laboratory and saw to it
that his assistants, Taco, Flaco, and Eduardo, were able to
help me when it was needed. Louis Alpizar provided help
with background data. Gustavo Enriquez provided land for
plot work, and Raul Moreno helped me obtain employment
there.
While in Costa Rica I also received help from a number
of Floridians. In particular, Bob Mansell saved the project
by supplying a much needed neutron probe. Jack Ewel re
lieved me from the daily routine long enough to take a short
vacation and shared data with me. And Chris McVoy assisted
with interest, discussion and companionship.
11


In Florida, Suresh Rao "adopted" me as his student,
which is probably the best thing that could have happened.
His input into all phases of the research, even in his
absence, has been invaluable. Ron Jessup has also taught
and helped a great deal. I am especially grateful to these
two men.
My remaining committee members, Don Graetz, Nick
Comerford, and Jerry Bennet have provided valuable feedback
and definitely improved the end product. In addition, I
must thank Peter Nkedi-Kizza and Linda Lee.
Finally, last and most, I thank my wife, Helen Fisher,
who not only put up with a great deal, but helped me most
when I most needed help.
1X1


TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS
ABSTRACT V
CHAPTERS
IINTRODUCTION
Leaching in Humid Tropical Regions 3
Objectives 6
Presentation 8
IIPATTERNS OF SOLUTE MOVEMENT
Introduction 9
Materials and Methods 14
Results and Discussion 21
Conclusions 49
IIILEACHING LOSSES FROM TWO CONTRASTING CROPPING SYSTEMS
Introduction 51
Materials and Methods 62
Results 74
Discussion 95
Summary 105
IVMATHEMATICAL DESCRIPTION OF NITROGEN MINERALIZATION
DURING INCUBATION
Introduction 108
Materials and Methods 115
Results 118
Discussion 133
Conclusions 143
VOVERALL SUMMARY
Water Movement and Nutrient Leaching 146
Mineralization and Nutrient Cycling 150
Overall Assessment of Cropping Systems 152
Future Work 154
REFERENCES 157
BIOGRAPHICAL SKETCH 169
IV


Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
WATER AND NUTRIENT MOVEMENT IN TWO TROPICAL
CROPPING SYSTEMS
By
Mark S. Seyfried
August 1986
Chairman: P.S.C. Rao
Major Department: Soil Science Department
Soil-water and nutrient movement in two contrasting
cropping systems was compared to investigate the effect of
cropping system management on leaching losses under humid
tropical conditions. One cropping system, the monocropped
annual, consisted of maize (Zea mays L.) alone; the other,
mixed perennial system, consisted of cacao (Theobroma
cacao), laurel (Cordia alliodora), and plantain (Musa
paradisiaca) grown together. A model was developed to
calculate nutrient leaching losses given measured soil
solution concentrations. Displacement through undisturbed
soil columns was used to select a modeling approach. The
contribution of mineralized soil N was studied in incubation
chambers.
Miscible displacement studies indicated that nutrient
movement was well described by the convective-dispersive
model except when the soil was at or very near saturation.
Saturated hydraulic conductivity values measured in the
v


field were considerably higher than rainfall intensities,
suggesting that preferential flow conditions were very rare.
A zero-order kinetic model, with consideration made for
pretreatment effects, was shown to describe the measured N
mineralization rates well. These results appear to apply to
a variety of soils.
The simulation model used was based on soil-water
capacity parameters. Piston displacement of solutes and
drainage to field capacity were assumed.
Simulated profile soil-water contents closely approxi
mated those measured. Calculated net leaching losses of N,
2 + 2+
K Ca and Mg were greater in the monocropped annual
system due mainly to differences in soil solution concentra
tions, which were constant over time. The calculated loss
of N from the monocropped plot was 56 kg ha ^ over 260 days.
Losses from the mixed perennial plot were negligible.
The solute residence time within the crop root zone was
proposed as an index of cropping system sensitivity to
leaching loss. The residence time in the mixed perennial
system was about 1.4 times larger in the monocropped system.
The low leaching losses from the mixed perennial plot, in
spite of substantial annual inputs of nutrients, indicates
that that system is conservative in terms of nutrient use.
This was attributed to the relatively long residence time in
the rooting zone of the mixed perennial plot.
vi


CHAPTER I
INTRODUCTION
The humid tropics, as defined by Sanchez et al. (1982),
comprises about 10% of the earth's land surface. This
region is currently undergoing very rapid ecologic, economic
and social changes. These changes frequently require more
intensive use of the land to produce food and fiber. This,
in turn, requires changes in extant farming methods.
The traditional system of farming for much of the
region, which is still used extensively, is commonly re
ferred to as slash and burn agriculture or shifting cultiva
tion. Although the details of operation are extremely
variable, the basic pattern is fairly consistent throughout
the world. First, the forest is felled and burned. Crops
are then planted in the ash and 2 to 4 harvests follow. The
land is then abandoned and crop production is shifted to a
new site. Shifting is necessitated by the marked, rapid
decline in crop production that almost invariably accompa
nies cropping. The abandoned land is left in fallow for 4
to 20 years after which time the process is repeated.
The success of the system is based on the restoration
of soil productivity during the fallow period. If that
period is sufficiently long, the system works in an ecologic
sense (Sanchez, 1982). In much of the region, increasing
populations, coupled with governmental encouragement, is
1


2
causing influxes of peasant farmers and increased demand for
food. This, along with the desire to preserve some of this
unique habitat, is forcing reduced fallow periods and
consequent land degradation (Trenbath, 1984). These changes
are forcing the development and adoption of agricultural
techniques that are productive and can sustain continuous
cultivation.
Several factors have been attributed to the observed
yield declines with cropping. These include, declines in
soil fertility, increased weed infestation, deterioration of
soil physical properties, increased insect and disease
attacks, and social customs (Sanchez, 1976).
Whatever the cause of yield declines may be, it is
clear that, if sustained soil productivity is to be
achieved, soil fertility must be maintained or enhanced.
One way to achieve this is to add nutrients and other
required inputs in sufficient quantities to overcome yield
limitations. This approach has recently been adopted by
industrialized (mostly temperate zone) nations. Sanchez et
al. (1982) have shown that this approach can work in both an
agronomic and economic sense in a humid tropical setting.
The problem is that it requires a fairly well developed
infrastructure that provides farmers with access to a wide
variety of soil amendments, seed varieties and pesticides,
sufficient capital and credit to purchase them, sufficient
knowledge to properly manage them, and a well developed
marketing and transportation system.


3
An alternative approach that has received considerable
attention in recent years is based on the somewhat paradoxi
cal observation that the land that supports some of the
world's most productive natural ecosystems (tropical
rainforest), supports some of the world's least productive
agro-ecosystems. If cropping systems were developed that
are more like the natural ecosystem, it is reasoned, they
should be more productive. This reasoning is supported by
the observation that most indigenous systems (excluding
paddy rice) that are of semi-permanent nature, are somewhat
like natural systems in that they are characterized by a
variety of different crops, including tree species, grown
together. The potential for designing such systems was
demonstrated by Hart (1980), who carefully planted crops to
mimic natural succession and obtained good, economic yields.
In practice it appears that few farmers are purists in
terms of the approach they adopt. A wide variety of crop
ping systems and input levels are in use. In all cases, an
important consideration is that plant nutrients in the soil
be conserved. Low input systems will "run down" and high
input systems will be prohibitively expensive if nutrient
losses are high. One process that can cause nutrient losses
is leaching.
Leaching in Humid Tropical Regions
The observed soil productivity decline under shifting
agriculture has been attributed to leaching losses (Sanchez,
1976). Leaching is also considered to be a major factor


4
that limits the effectiveness of fertilizers in the region
(Engelstad and Russel, 1975).
At the outset it should be pointed out that the pro
cesses involved in nutrient leaching in the tropical regions
are the same as those in temperate regions. A given nutri
ent will be leached below the crop rooting zone when it
moves through the soil faster than the crop can absorb it.
This is dependent on the climate, soil and crop. Thus, the
amount of a nutrient entering the soil solution and the rate
at which it moves are important. Management practices, as
well as the soil, climate, and crop, effect this. Since all
of these factors are generally different in humid tropical
regions, the magnitude of the problem is potentially differ
ent there than in temperate regions.
Rainfall is particularly noteworthy in this respect.
In many humid tropical regions the mean annual rainfall
exceeds 200 cm and, even though the potential
evapotranspiration is also high, it is often exceeded
considerably by rainfall. At Turrialba, Costa Rica, for
example, rainfall exceeds the potential evapotranspiration
by approximately 100 cm yr-1, which is roughly equal to the
average annual precipitation in most temperate agricultural
regions.
Although generalizations about soils for such a large
region are dangerous, there are some differences between
soils of temperate and tropical regions that are common. In
particular, soils in humid tropical regions frequently


5
exhibit infiltration rates much greater than those expected
in temperate soils of similar clay content (Sanchez, 1976).
This is generally attributed to the relatively high degree
of aggregation commonly found in those soils. Since this
characteristic obviously affects water movement, it also
effects solute (nutrient) movement.
Another difference between soils is that the clay
fraction of the soils in the humid tropics is commonly
dominated by variable charged, low activity clays. This
effects the retention of both cations and anions.
Finally, the differences in individual crop species and
their management is frequently different from temperate
crops and management. All of these factors combine to make
transfer of agricultural experience and research in temper
ate regions to the humid tropics tenuous. This is certainly
true of nutrient leaching studies.
Despite widespread concern that leaching may be an
important constraint to soil productivity in the humid
tropics, very little quantitative data have been reported in
the literature (Greenland, 1977; Keeney, 1982; Omoti et
al., 1983). This is partly because it is a difficult
parameter to measure and partly because much of the research
in the region is result-oriented (crop yield) as opposed to
process-oriented.
Measurements that have been made range considerably.
Amounts of N estimated to have been lost in a year, for
example, range from 37 kg ha-1 (Omoti et al., 1983) under


6
oil palm, to 204 kg ha-1 from a pasture in Colombia
(Sanchez, 1976). A recent study of leaching from maize in
Nigeria calculated N losses to be about 80 kg ha-1 for a
single fertilizer application of 150 kg ha ^ and approxi
mately half that for a split application (Arora et al.,
+ ?+
1982). Similar ranges of losses for K and Mg and even
2+
greater ranges for Ca have been reported (Sanchez, 1976).
Objectives
At present it seems clear that there is a high poten
tial for nutrient leaching losses in humid tropical regions.
It is also clear that research into management systems that
minimize such losses is an important priority. It has been
proposed that cropping systems can be manipulated in such a
way that such losses can be minimized. The principal objec
tive of this study was to establish if nutrient losses from
two different cropping systems, which are strongly contrast
ed in terms of species composition and diversity, are
different when levels of management are similar.
The fact that such wide ranges in leaching losses are
reported in the literature indicates that the processes
responsible for leaching must vary greatly in the region. A
second, related objective, was to quantify the processes
responsible for leaching in such a way that the sensitivity
of cropping systems can be characterized.
Setting
The two cropping systems chosen for comparison were:
(1) a mixed cropping system composed of laurel (Cordia


7
alliodora), cacao (Theobroma cacao) and pltano (Musa
paradisiaca), and (2), a monocropping system composed of
maize (Zea mays L.). These two cropping systems will be
referred to as the MP, for mixed perennial, and MA, for
monocropped annual, in the remainder of the dissertation.
The two cropping systems were separated by approximate
ly 100 m and were located in the "la Montana" section of the
experimental plots at CATIE (Centro Agronmico de
Investigaciones y Enseanzas) near Turrialba, Costa Rica.
The soil series for both systems is Instituto clay loam
which is classified as a Typic Dystropept, fine, mixed,
isohyperthermic (Aguirre, 1971).
The management level of both systems was designed to
promote high yields and profitability. The MA plot is
located in a larger study area used by Dr. Carlos Burgos to
study the effects of tillage and residue management on maize
yields. It was initiated in November of 1976. Since then
40,000 plants ha ^ of a 120-day maize variety have received
approximately 400, 55, and 40 kg ha ^ of N, P, and K,
respectively, each year in four applications. Two crops
were grown each year. The dimensions of the plot discussed
in this study were 32 by 20 m.
The MP plot was part of a larger study of intercropping
with perennials directed by Dr. Gustavo Enriquez. It was
initiated in 1977. The planting densities were 1,111; 432;
and 123 plants per hectare for cacao, laurel, and platano,
respectively. The annual fertilizer regime was 140 kg ha-1


8
N, 30 kg ha-1 P, and 20 kg ha-1 K in four applications. The
dimensions of the MP plot were 18 by 18 m.
Presentation
Three lines of inquiry, connected to the basic theme of
nutrient movement under the two cropping systems will be
presented in three separate chapters. First, investigations
of the nature of solute flow through the soil at the site
will be presented. This work was performed to establish the
appropriate model to be used in estimating field-scale water
and solute movement at the site.
The second line of inquiry is devoted to the develop
ment and implementation of a soil-water and nutrient move
ment model that was used to estimate leaching losses and
examine critical parameters associated with those losses.
The third line of inquiry is related to the supply of
nutrients to the systems via mineralization of soil organic
matter. In this section a laboratory method for estimation
of N mineralization is examined in terms of three models.
Implications of the results to field application of incuba
tion results is also discussed.
The results presented in the three chapters are summa
rized and discussed in terms of implications to the func
tioning of the two cropping systems in the final chapter.


CHAPTER II
PATTERNS OF SOLUTE MOVEMENT
Introduction
This study is part of a larger project investigating
nutrient movement under different cropping systems in Costa
Rica. Throughout the humid tropics there is a high poten
tial for loss of plant nutrients by leaching due to the
considerable excess of precipitation over evaporative and
transpirational demand. Near Turrialba, Costa Rica, where
this study was performed, for example, the mean annual
precipitation of 264 cm exceeds mean annual pan evaporation
by 130 cm. Since improved crop yields are largely dependent
upon increasing the nutrient status of soils in the region,
quantification of leaching loss is important.
Quantitative description of solute (e.g., nutrient)
transport through packed soil columns has been accomplished
using the convective-dispersive (CD) model of solute flow
(Kirkham and Powers, 1972). Verification of this model has
come through numerous studies of miscible displacement
through uniformly packed, sieved, water saturated columns of
soil and other porous media (Nielsen and Biggar, 1961,
1962). According to the CD model, the transport of a
non-adsorped, conservative solute under steady water flow
conditions is described by
9c/3t = Dd 2C/d Z2 vq3 C/3 z (2-1)
9


10
. _3
where C is the solute concentration (mg cm ), z is distance
(cm), t is time (hr), D is the "effective" dispersion
2 -1
coefficient (cm hr ) and vq is the average pore water
velocity (cm hr-1). The average pore-water velocity is
calculated by dividing the Darcy-flux (q) by the soil water
content 0. This implies that all soil water participates in
the convective transport of the solute.
In working with packed soil columns under steady water
flow conditions, two experimental conditions in which the CD
model has failed are (1) when aggregated media are used
(Nielsen and Biggar, 1961; Green et al., 1972), and (2) when
displacement was conducted under unsaturated conditions
(Biggar and Nielsen, 1962; Gupta et al., 1973; Gaudet et
al., 1977). In both cases this failure may be ascribed to a
failure of the assumption that all soil-water participates
in convective transport. When aggregated media are used,
intra-aggregate water is essentially stagnant so that virtu
ally all convective transport occurs in the mobile water
located in the inter-aggregate portion of the soil. Simi
larly, the application of tension to the soil causes the
entrance of gas which may result in the isolation of stag
nant regions. Again, convective flux is restricted to the
mobile, nonstagnant-region water and Eq. 2-1 is not applica
ble.
Application of miscible displacement techniques to
"undisturbed" soil columns taken from the field have shown
that inhomogeneities in field soils can strongly affect the


11
nature of water and solute flow (McMahon and Thomas, 1974;
Cassel et al., 1975). The presence of macropores is often
associated with solute flow behavior that is inconsistent
with the CD model (Kanchanasut et al., 1978). At present no
precise definition of macropores is widely accepted (Bouma,
1981; Luxmoore, 1981), but the term is generally used to
describe large, continuous pores that can conduct water and
solutes much more rapidly than the surrounding soil matrix
(Bevin and German, 1982; White, 1985). The process of flow
along macropores has been variously described as "channel
ing", "bypassing", "short circuiting", "preferential flow"
or "partial displacement" (Scotter, 1978; Bouma, 1981; Beven
and German, 1982; Thomas and Phillips, 1979). The term
bypassing will be used in this paper.
The presence of relatively few macropores can greatly
increase the soil hydraulic conductivity (Bouma and Ander
son, 1973) and cause very rapid transport of solutes through
the soil (Thomas and Phillips, 1979; Bouma et al., 1982)
that is inconsistent with the CD model (Kanchanasut et al.,
1978). The average pore water velocity (vq) is not an
appropriate descriptor of convective transport when there is
bypassing because flux is effectively limited to a small
portion of the total soil-water (i.e., that within
macropores).
Application of Eq. 2-1 on a field-plot scale has shown
that the parameters vq and D are extremely variable and
log-normally distributed (Biggar and Nielsen, 1976), which


12
makes their estimation very difficult. Application of
numerical models, which have been developed for more realis
tic, transient conditions that exist in the field, are
subject to similar limitations.
As an alternative, management level models that use
less variable, capacity-type parameters (e.g., 0) have
proven successful in many cases (Rao et al., 1981; Rose et
al., 1982). These models are largely empirical in nature
but utilize the concept of convective transport, as defined
by v0* Failure of these simplified management level models
has been attributed to flow along macropores (Thomas and
Phillips, 1979; Barry et al., 1985).
The three situations mentioned above in which the
inapplicability of Eg. 2-1 have been documented; unsaturated
flow; flow around aggregates; and flow along macropores,
have in common the fact that a portion of the soil-water,
called mobile water (@m) in this paper, moves much more
rapidly through the soil than the remaining, immobile water
(im)- Surface applied solutes can be transported much more
rapidly in the mobile region and thereby bypass solutes
residing in immobile regions.
An alternative model, known as the mobile-immobile
water model (MIM) has been developed to explicitly account
for this situation (Passioura, 1971; Van Genuchten and
Wierenga, 1976). In this model two soil-water phases are
explicitly differentiated, with all convective transport
assumed to occur in the mobile phase and transport in the


13
immobile phase restricted to diffusion. Since no specific
pore geometry is assumed, the diffusive transfer between the
two soil-water phases is described as being proportional to
the concentration difference between them. The equation for
transport of a nonadsorped solute under steady water flow
conditions that is consistent with the MIM model is
3c/3t + 0,3C. /3t= D32C /9z2 0 v 3C/3z (2-2)
m m m m mm
with interphase transfer described as
0. 3c. /3t = a(C -C. ) (2-3)
m inr v m im' v '
where the subscripts "m" and "im" refer to mobile and
immobile phases, respectively. The parameter a is an
empirical constant called the mass transfer coefficient
(hr *). The parameter v differs from v in that it is
m o
calculated from q/@m. Simplified, capacity-parameter based
management level models, analogous to those based on the CD
model, have been developed for the MIM model (Addiscott,
1977). Use of this model requires the fraction of mobile
water 4> (m/0), as an input.
Our primary objective in this study was to select a
modeling approach to describe water and solute movement
under field conditions. From the above discussion it is
clear that the nature of the conducting pore network, or the
hydrologically effective pore geometry, greatly affects the
applicability of a given model. Ideally, it would be
possible to infer which model (if either) is appropriate
from standard soil descriptions. Although the impact of
soil structure on the hydrologically effective pore geometry


14
is well documented (Elrick and French, 1966; Cassel et al.,
1974; McMahon and Thomas, 1974), and the effects of soil
texture are widely recognized (Thomas and Phillips, 1979;
Addiscott and Wagenet, 1985), the information is of a
qualitative nature (Bouma, 1981). More detailed morphologi
cal descriptions are generally not well suited to this
purpose because they do not yield information on pore
continuity or the degree of pore interconnections (Bouma,
1981). For these reasons, study of the hydrologically
effective pore geometry has usually focused on measurements
of soil hydraulic properties (Bouma and Anderson, 1973) and
movement of tracers (Bouma, 1981; White, 1985).
Our secondary objective was to identify soil structural
or pore characteristics that characterize the hydrologically
effective pore geometry. This information is potentially
useful in using field-level observations to infer the nature
of flow through soils. Dyes have been used extensively for
this purpose (Bouma and Dekker, 1978; Omoti and Wild, 1979).
Materials and Methods
Soil Characteristics and Management
The soil studied is an Instituto clay loam (fine-loamy,
mixed, isohyperthermic, Typic Dystropept) located at CATIE
(Centro Agricola Tropical de Investigaciones y Enseanzas)
near Turrialba, Costa Rica. It is derived from alluvium
deposited from the surrounding mountains which are primarily
of volcanic origin. Very little soil profile development


15
was evident except for an accumulation of organic matter at
the surface. Textures are clay loam throughout. Aguirre
(1971) described the structure as weak, subangular blocky
with peds ranging from 0.5 to 2.0 cm in diameter near the
surface and becoming increasingly less pronounced with
depth. The site is nearly level and the soil is considered
to be moderately well drained (Aguirre, 1971).
Soil columns were taken from two experimental plots
located about 100 m apart. One plot had been planted to
maize for six years prior to column removal. The crops on
the other plot were a mixture of cacao, laurel, and plantain
and had been under continuous management for five years
prior to column removal. No tillage was performed on either
plot during that time and no machinery entered either plot.
Columns numbered 1, 2, and 3 (Table 2-1) were taken from the
first plot and those numbered 4 and 5 were taken from the
second.
Column Experiments
Five undisturbed soil columns were collected at two
depths (0-30 cm and 75-105 cm). The procedure used to
collect the samples was as follows: (1) dig a pit approxi-
2
mately 1.0 by 1.5 m leaving a pedestal approximately 0.3 m
in the middle, (2) cut a circle about 15 cm in diameter in
the middle of the pedestal with a sharp knife, (3) force the
12 cm diameter PVC tube that is beveled at the end and lined
with petroleum jelly into the area bounded by the circle,
(4) shave the surrounding soil from the edges of the pipe


16
and cut another circle about 1 cm deep, (5) repeat the last
two steps until the 30 cm long column is filled, and (6)
separate the column from the soil at the bottom. The upper
12 to 15 cm of each column were used in the displacement
experiments.
Fritted glass endplates were fitted to both the inlet
and outlet ends of the columns. A constant hydraulic
potential was maintained at both column ends during dis
placement experiments. Tension was applied at the inlet end
via a Marriot devise and at the outlet end by a hanging
water column. All displacement experiments were conducted
under a unit hydraulic head gradient except the two dis
placements performed in Column 1 (Table 2-1) under saturated
3 -1
conditions. The influent H activity was about 7 nCi ml
in a 0.01 M CaCl2 solution. The 3H activity in effluent
fractions was assayed using liquid scintillation techniques.
Rhodamine B dye displacements were performed using the
3
same columns after the H20 displacements. The influent dye
concentration was 2 g L The amount of dye displaced
ranged from 0.05 to 0.32 pore volumes (Table 2-1). Dye
patterns in cross-sections of the columns were photographed
at 1 cm intervals after displacement. Selected
cross-sections were then traced with pen and ink.
The experimental conditions under which all displace
ments were performed are summarized in Table 2-1.


17
Table 2-1. Experimental conditions for displacement.
Column
Number
Column
Length
Depth
Tension of
JH Displ.
Tension of
Dye Displ.
Pore Vol.
Displ.*
1
cm
12.5
surface
kPa-
0.0,0.1,0.5,
1.0
0.32
2
15.0
subsoil
1.0,2.0
0.0,1.0
0.0
0.05
3
12.5
surface
0.0,1.0
0.0
0.13
4
12.0
subsoil
0.0,1.0
1.0
0.19
5
12.5
surface
0.0,1.0

* The number of pore volumes displaced during the dye
application.
Field Techniques
The depth to the water table was measured in perfo
rated, 2.54 cm diameter PVC pipe. Measurements were made
several times each week. Rainfall was measured daily at the
site. Infiltration measurements, carried out under the
direction of Dr. Carlos Burgos, were made with a double-ring
infiltrometer at 24 locations on an adjacent plot. The
dimensions of the inner and outer rings were 31 and 60 cm,
respectively.
Adsorption Experiments
3
Adsorption of i^O was measured using two different
batch techniques under two slightly different conditions.
The first method was that described by Dao and Lavy (1978)
in which the soil-solution ratio was 2 to 1 (g g ^). The
soil used was taken from column 3 after the displacement
experiments had been performed and the soil was oven dried
for pore volume determination.
In the second method, air-dried soil from column 3 but
3
not used in the displacement experiment, was added to
solution in a soil-solution ratio of 1 to 2 and placed in a


18
. 3
shaker overnight. Concentrations of H20 ranged from 7 to
0.7 nCi ml *" in both experiments.
Parameter Estimation and Model Evaluation
In Eqs. 2-1 through 2-3 no adsorption was assumed,
which is unrealistic for most tracers. Expansion of Eq. 2-1
to include adsorption results in the following expression:
3C/3t + (BD/9) (3S/3t) = D^C/gz2 v^C/gz (2-4)
where BD is the bulk density (g/cm^), S is the adsorbed
phase concentration (mg g and the other parameters have
been previously defined.
Adsorption can frequently be described by a linear (or
linearized) equation of the form
s = KDCe (2-5)
_3
where Cg is the equilibrium solution concentration (mg cm )
_3
and Kd is an empirical distribution coefficient (g cm ).
If the adsorption process is assumed to be instantaneous and
reversible, Eq. 2-5 can be substituted into Eq. 2-4 to give
RF 3C/3t =D32C/3z2 -Vq3C/3z (2-6)
where RF, the retardation factor, is defined by
RF = 1 + BDKd/0. (2-7)
Analysis and interpretation of parameter values is
facilitated by the use of dimensionless variables. Equation
2-6 can be described in terms of the following dimensionless
variables:
T = vQt/L, (2-8)
x = z/L, (2-9)
P = vqL/D, and
(2-10)


19
C = Cb/CQ/ (2-11)
where v t, z and D have been defined previously, L is the
column length, P is the Peclet number, T is the number of
pore volumes, x the dimensionless distance, and C the ratio
of effluent (C^) to influent (CQ) concentration. (Note that
the initial soil solution concentration is assumed to be 0).
Substitution of these variables into Eq. 2-6 results in the
following expression:
RFOC/3T) = (1/P) (32C/3X2) 9C/9x. (2-12)
In order to incorporate adsorption into Eqs. 2-2 and
2-3, S is partitioned between mobile and immobile phases
such that
S = £Sm + (2-13)
where S,,, and S. are the adsorbed concentrations in the
m m
mobile and immobile regions, respectively, and f is the
fraction of adsorption sites in the mobile region. Assuming
that the same linear relationship expressed in Eq. 2-5
applies to both the mobile and immobile regions, Eqs. 2-2
and 2-3 can be written
len+BDfKD)3Cm/3t + [9im + (1-f)BDKD]3Cim/8t =
(9 D)32C/3x2 (8v )3C/3x
m m m m m
(2-14)
and
[eim+(l-f)R0KD]3Cim/3t = a(Cm-Cim). (2-15)
As with the CD model, the MIM model can be described in
terms of dimensionless variables. The variables used are
3 = (0m + fBDKD)/(0 + BDKd), (2-16)
w = aL/q,
(2-17)


20
C1 Cm/Co' and
c2 = Cin/Co'
(2-18)
(2-19)
where c and C. refer to soil solution concentrations in
m m
the mobile and immobile regions. All other parameters have
been defined previously. The MIM model for steady-state
water flow conditions can now be described by
PRF(3c1/9T) + (l-(3)RF3c1/3T =(1/P) (32c1/9x2) 3^/sx (2-20)
and
(1-P)RF3c2/3T = w(c1-c2). (2-21)
The result of these transformations is that the CD
model is described by Eq. 2-12 and the MIM model by Eqs.
2-20 and 2-21. Measured solute breakthrough curves (BTC's)
were fit to both the CD and MIM models. The program CFITIM
(van Genuchten, 1981), which uses a nonlinear, least sum of
squares criteria for goodness-of-fit was used. In every
case a first type, constant concentration, influent end
boundary condition was assumed. The other boundary condi
tion was that of a semi-infinite column.
Results and Discussion
Hydraulic Parameters
-1 3
The Darcy flux (q, cm hr ), soil-water content (0, cm
_3
cm ), and soil-water tension (h, kPa) under which the
experiments were performed are shown in Figs. 2-1 through
2-5. The hydraulic conductivity (K, cm hr-1) value is
included for the two BTC's in which K was not equal to q.


21
Hydraulic conductivity values were extremely high
considering that the soil has a clay content of greater than
30%. The combination of high clay content and high saturat
ed hydraulic conductivity (Ksat) is frequently indicative of
flow along macropores (Bouma and Anderson, 1973; McKeague et
al., 1982). Saturated 0 values are also fairly high.
A dramatic decrease (two orders of magnitude) in K was
observed as h increased from 0 to 2 kPa (Fig. 2-1). This
was accompanied by a relatively modest decrease (6.5%) in 0.
Similar trends in K and 0 were observed in the other columns
(Figs. 2-2 to 2-5). This indicates either that: (1) few
discrete, large (>0.5 mm radius) pores conduct large volumes
of water rapidly under saturated conditions, or (2) water
held in large pores serves to "connect" a number of pores
that conduct water rapidly.
The series of tensions in Fig. 2-1 illustrates the
transition between saturated and unsaturated conditions for
the surface soil. Preliminary investigations with subsoil
showed a qualitatively similar pattern. This similarity
between subsoil and topsoil was confirmed in subsequent
comparisons between saturated and unsaturated conditions
(Figs. 2-2 through 2-5).
Qualitative Evaluation of Breakthrough Curves
In general, BTC shapes changed dramatically as soil
water tension was increased. BTC's obtained under saturated
conditions were highly skewed, characterized by very early


22
C/C
Figure 2-1, a-f. The effect of soil-water tension (h, kPa),
soil-water content (0, cnr cm j), and Darcy flux (q, cm
hr i) on the elution of tritiated water in Column 1. The
continuous line represents the best fit of the MIM model in
a, b, and c, and of the CD model in d, e, and f.


23
I i 1 1
1.0 2.0 3.0
PORE VOLUME
Figure 2-2, a and b. Effect of soil-water tension (h, kPa),
soil-water content (, citi cm J), and Darcy flux (cm hr 1)
on elution of tritiated water in Column 2. The solid lines
in a and b represent the best fit of the MIM and CD models,
respectively.
PORE VOLUME PORE VOLUME
Figure 2-3, a and b. The effect of soil-water tension (h,
kPa),_|oil-water content (0, cmJ cm J), and Darcy flux (q,
cm hr ) on elution of tritiated water in Column 3. The
solid lines in a and b represent the best fit of the MIM and
CD models, respectively.


24
Figure 2-4, a and b. The effect of soil-water tension (h,
kPa), soil-water content (0, cm cm J), and Darcy flux (q,
cm hr 1) on elution of tritiated water in Column 4. The
solid lines represent the best fit of the MIM and CD models
in a and b, respectively.
Figure 2-5, a and b. The effect of soil-water tension (h,
kPa), soil-water content (0, cmJ cm J), and Darcy flux (q,
cm hr i) on the elution of tritiated water in Column 5. The
solid lines represent the best fit of the MIM and CD models
in a and b, respectively.


25
appearance of tracer in the effluent and a slow approach of
effluent concentration towards 1.0 (also called "tailing").
This behavior has been observed in undisturbed columns
(Anderson and Bouma, 1977; White et al., 1984), and has been
inferred from field studies of solute movement (Wild and
Babiker, 1976). Such skewed BTC's indicate that solute was
conducted relatively rapidly through the columns by a small
fraction of the total soil-water. The rapidly conducting
fraction of the soil-water has been related to
inter-aggregate regions (Nkedi-Kizza et al., 1982),
inter-ped regions (Anderson and Bouma, 1977), and discrete
macropores (Kanchanasut et al., 1978) in other soils.
Unsaturated BTC's are markedly more symmetric, with a
later arrival of tracer and less tailing. This trend was
not reversed as tension was increased to 2 kPa. These
results contradict other studies that have shown that
tailing in BTC's resulted from increases in soil water
tension (Nielsen and Biggar, 1961; Gaudet et al., 1977).
Apparently the pore network in this soil is sufficiently
interconnected that drainage of large pores does not result
in the isolation of stagnant regions in the column. It
should be noted that both of the studies referenced above
were performed on sands in packed columns at relatively low
soil-water contents. Lower soil-water contents enhance the
possibility of the creation of isolated, stagnant regions in
the soil. One study by Elrick and French (1966) that
compared saturated and unsaturated flow in an undisturbed


26
column found that dispersion decreased with application of
tension, although marked asymmetry during saturated flow was
not observed.
The observed change in BTC shape with increasing soil
water tension can be explained by the concomitant decrease
in q and/or by changes in the effective pore geometry. In
general, bypassing is enhanced by greater fluxes at a given
0 because because there is less time for diffusive transfer
into stagnant regions. At the same time, increasing tension
changes the effective pore geometry by draining larger pores
that may be responsible for the observed bypassing.
Both effects were operative in this study but the
effect on the effective pore geometry was dominant. This is
illustrated in Fig. 2-1. When flow rates were reduced and
the soil remained saturated (Figs. 2-la and b), the BTC
shape was only slightly altered. However, when displacement
on the same column was performed under unsaturated condi
tions at approximately the same q (Figs. 2-lb versus 2-ld
and e), there was a considerable change in BTC shape. These
trends were reflected in the models and parameters used to
describe the BTC's.
Quantitative Evaluation of Breakthrough Curves
All BTC's were fit to both the MIM model and the CD
models. In general, curve fits fell into two groups, satu
rated and unsaturated, with the 0.1 kPa run (Fig. 2-lc)
intermediate. The parameter values obtained will be dis
cussed by these groups.


27
Unsaturated Breakthrough Curves
Two dimensionless parameters are required in the CD
model, P and RF. The best least sum of squares fit was
obtained allowing both parameters to vary. The solid line
in all the unsaturated BTC's except the 0.1 kPa run repre
sent the calculated best fit using the CD model (Figs. 2-1
through 2-5). In general, the agreement was excellent. The
resultant parameter values and associated 95% confidence
intervals shown in Table 2-2 appear to be independent of the
depth from which the columns were sampled. Peclet numbers
(P) ranged from 3 to 12 and RF values from 1.12 to 1.17.
Dispersion coefficients calculated from those P values were
high relative to those measured in sieved, packed columns,
but this is expected in undisturbed columns (McMahon and
Thomas, 1974; Cassel et al., 1975). It indicates that there
was a relatively wide range of pore water velocities within
the column.
The RF values obtained were high considering that
tritium is frequently assumed to be nonadsorped (RF=1.0).
Tritium sorption has been noted by several workers (Mansell
et al., 1973; Wierenga et al., 1975; Van de Pol et al.,
1977; Nkedi-Kizza et al., 1982) and has been associated with
hydroxyl exchange with clay lattice hydroxyls (Stewart,
1973) .
As a check of the accuracy of RF values determined by
curve-fitting, the area above the measured BTC was calculat
ed for several of the unsaturated BTC's (Pandey and Gupta,


28
1984). The areas measured agreed closely with the RF values
obtained by curve-fitting. Since the fit between calculated
and measured curves was excellent in every case, this result
confirms the finding of van Genuchten and Parker (1984) that
mass balance is preserved with the solution and boundary
conditions used.
Table 2-2. Convective-dispersive model parameter values.
Col
No.
h
P
RF
kd
D
kPa
ml g ^
cm2 hr ^
1
0.1
0.90
1.08
0.031
492
(0.02)*
(0.01)
(0.006)
(11.7)
1
0.5
2.28
1.10
0.043
60.9
(0.06)
(0.01)
(0.006)
(0.02)
1
1.0
4.45
1.12
0.053
6.44
(0.31)
(0.02)
(0.008)
(0.17)
1
2.0
6.62
1.17
0.069
0.58
(0.17)
(0.01)
(0.002)
(0.01)
2
1.0
13.5
1.12
0.062
2.52
(0.76)
(0.01)
(0.005)
(0.13)
3
1.0
13.1
1.18
0.080
3.78
(0.49)
(0.01)
(0.002)
(0.14)
4
1.0
12.2
1.16
0.071
1.65
(0.57)
(0.01)
(0.003)
(0.07)
5
1.0
7.01
1.18
0.079
5.81
(0.36)
(0.01)
(0.005)
(0.18)
* Numbers in parenthesis are the 95% confidence intervals
associated with the estimated value.
An independent check of RF can be obtained from mea
surement of adsorption in batch isotherms. The batch
isotherms obtained using both methods described in the
previous section were essentially identical. Both isotherms
were linear with r2 values of 0.994 and 0.997 and KD values,
with 95% confidence intervals of 0.134 + 0.0045 and 0.132 +
0.0047 mg g-1. These values are significantly larger than
those obtained from values derived from fitted parameters
(Table 2-2).


29
Discrepancies between batch and column-measured adsorp
tion parameters have been noted by others (Nkedi-Kizza et
al., 1982). The value of RF calculated using the
batch-derived value (including a coarse fragment content
of 3.9% in the column) is 1.26, which is slightly higher
than that obtained from curve fitting (1.17). This discrep
ancy is likely due to differences in the condition of the
soil when the experiments were performed. The batch iso
therms were conducted on air-dried or oven-dried soil while
the columns were never air-dried.
When all unsaturated BTC's except the 0.1 kPa run were
fit to the MIM model either extremely high w values (>35) or
(3 values of 1 were obtained. From inspection, it is clear
that Eqs. 2-20 and 2-21 are indistinguishable from Eq. 2-12
when (3=1, and use of the MIM model is not justified. The
effect of high w values is less clear but implies extremely
rapid transfer between mobile and immobile regions which
effectively eliminates the need to make a distinction
between them. This will be discussed in greater detail in
the next section.
Saturated Breakthrough Curves
The 0.1 kPa run (Fig. 2-lc) will be included in this
part of the discussion because it more closely resembles the
saturated BTC's than the other unsaturated BTC's. When the
CD model was applied to the saturated BTC's generally poor
fits resulted. This was due to the very rapid rise in C/CQ
and obvious leftward shifting. Use of the MIM model


30
requires specification of 4 dimensionless variables; P, RF,
3, and w. Although it is possible to allow all variables to
vary simultaneously, the resultant parameter estimation is
relatively imprecise. One value, RF, should be consistent
with those derived from the unsaturated (CD model) BTC's.
This value was accordingly taken from the unsaturated curves
and fixed during parameter estimation for the saturated
curves.
In every case, very close agreement between measured
and calculated BTC's was obtained. In general, the parame
ter values in Table 2-2 indicate the following trends; very
small P values corresponding to extremely large D values; (3
values on the order of 0.23 to 0.45; and w values ranging
from 0.33 to 3.84.
Table 2-3. Mobile-immobile water model parameter values.
Col.
P
RF
P
w
D
ESR
$
No.
cm2/hr
cm
1 (fast)
0.29
1.12
0.76
1.02
2515
0.18
0.81
(0.01)*
(0.03)
(0.39)
1 (slow)
0.86
1.12
0.43
2.46
150
0.61
0.47
(0.05)
(0.04)
(0.45)
1 (lkPa)
1.11
1.12
0.84
0.08
437
0.51
0.89
(0.03)
(0.04)
(0.03)
2
0.43
1.13
0.34
3.84
1643
0.18
0.37
(0.03)
(0.03)
(0.48)
3
0.55
1.17
0.34
2.99
2758
0.19
0.38
(0.03)
(0.03)
(0.48)
4
0.76
1.15
0.23
0.16
3497
0.86
0.26
(0.30)
(0.05)
(0.03)
5
0.14
1.17
0.45
3.89
7525
0.15
0.51
(0.02)
(0.11)
(2.53)
* Numbers
in parenthesis
are the
95% confidence
: intervals
associated with the estimated value.


31
The very low P values are indicative of an extremely
broad range in pore-water velocities in the mobile water
region. This indicates that the compartmentalization of
soil-water into only two phases assumed in the MIM model was
insufficient to account for the range of pore-water veloci
ties encountered. It is possible that the logic of the MIM
model be extended to consider gradations of soil-water
mobility (e.g., "very rapidly mobile water", "somewhat
rapidly mobile water", etc.). Skopp et al. (1981) have
applied this approach but to only two soil-water phases.
Recent work applying the transfer function model developed
for soil applications by Jury (1982) to the analysis of
BTC's similar to the saturated BTC's presented here (White
et al., 1986) have described the distribution of pore-water
velocity as a continuous, albeit skewed, function. Viewed
in this perspective the MIM model may be considered to
represent an extreme, bimodal pore-water velocity distribu
tion.
Calculation of D from fitted P values is somewhat
questionable when P values are so low. There are two sets
of boundary conditions that approximate the experimental
conditions and the solutions to those boundary conditions
diverge when P is less than 4 or 5 (see van Genuchten and
Parker, 1984; Parlange et al., 1985).
The parameter (3 means little on its own, but can be
related to the mobile water fraction, 4> (recall that $=0^/0)
with the expression 3>= (RF(3) -f (RF1). Note that, if RF=1,


32
=3- As a first approximation, (3 can be considered to be a
measure of $ in these experiments because RF is close to 1.
A more refined estimate of $> is obtained if some assumptions
concerning f are made. Recall that f was defined as the
fraction of sorption sites in the mobile region. If the
distribution of sorption sites is independent of location in
soil-water regions, then f=S> when the soil is saturated and
S>=3. However, it seems reasonable to expect that propor
tionately more sorption sites will be found in immobile than
mobile regions because the pores in immobile regions should
be smaller and therefore have more exposed surface area.
This reasoning has been used to justify the assumption that
f=0 (Nkedi-Kizza et al., 1982) which leads to $=RF(3. Thus,
in soils with positive adsorption, $> values of greater than
3 are expected. The $ values in Table 2-3 were calculated
assuming that f=$>/2, which is an intermediate estimate.
The results in Table 2-3 indicate that values of 0.25
to 0.50 are generally consistent with the parameters fitted
with the MIM model. These values are surprisingly high in
light of the large changes in K that resulted from relative
ly small changes in 0. An independent estimate of $ can be
obtained by assuming that water held at field capacity is
immobile (Addiscott et al., 1977). In this case, such an
approach yields estimates of $ of approximately 0.156, which
is distinctly lower than those obtained from curve-fitting.
When this value was fixed along with RF the resultant P
values were increased, w values were decreased and the


33
goodness-of-fit was substantially reduced. These fitted
curves were too angular, displaying a more rapid rise in
effluent concentration with greater tailing than the mea
sured BTC's. The difficulty of obtaining independent
estimates of $ has been noted by others (Addiscott et al.,
1978) .
The parameter w is somewhat more difficult to interpret
as it is not directly related to any specific soil charac
teristic or property. However, work by Rao et al. (1980a)
has shown that w can be used to successfully calculate
inter-aggregate concentrations during diffusion into spheri
cal aggregates of known volume. In subsequent work (Rao et
al., 1982) it was demonstrated that media composed of
different sizes and shapes of aggregates could be approxi
mated by a single "equivalent" spherical aggregate size on a
volume-weighted basis. This work has recently been extended
to miscible displacement studies by van Genuchten (1985).
Using analytical solutions for flow through media
composed of immobile regions of known geometry van Genuchten
(1985) was able to express w in terms of an average sphere
or other aggregate shape. This technique was applied to the
saturated BTC's to determine size of effective spherical
radius (ESR) consistent with the fitted parameter values.
The parameters shown in Table 2-3 indicate that the soil may
be considered to be composed of spherical aggregates of of
0.15 to 0.6 cm radius.


34
Another way of considering the effect of aggregate size
on the effective pore geometry was presented by Rao et al.
(1980b). They showed that, when the aggregate size is small
enough relative to the pore-water velocity, a condition of
near-equilibrium will be established and the CD model should
be appropriate. For spherical aggregates, the condition of
near-equilibrium is valid when
DeL(l->)/(a2vo20.3) > 1 (2-22)
where is the diffusion coefficient and L is the column
e
length (Rao et al., 1980b). Taking ESR as 0.5 leads to a
critical vq of 1.6 cm/hr, which is generally less than the
vq values of the unsaturated runs (vQ=2q in these columns).
Thus spherical regions of immobile water could exist in the
columns but their effect on solute transport would be
"masked" as high dispersion.
Taken as a whole, some inferences concerning the nature
of the effective pore geometry of the soil can be made. The
high Kgat values measured in conjunction with highly skewed
BTC's indicate that water was conducted relatively rapidly
through some portion of the soil when saturated. The large
reduction in K and BTC skewedness that resulted from appli
cation of 0.1 to 0.5 kPa of tension suggests that soil water
was rapidly conducted via macropores (Luxmoore, 1981;
Germann and Bevin, 1981). However, the extremely low P
values and values of 0.25 to 0.50 under saturated condi
tions suggest that there were several such regions of
varying conductivities. The model parameter values obtained


35
are consistent with fairly large regions of immobile water.
If immobile regions are assumed, for example, the immobile
regions would have radii of 0.15 to 0.6 cm.
Although immobile regions were described in terms of
spherical aggregates, the MIM model specifies no pore
geometry and numerous other possibilities exist. Rhodamine
B dye was used to better determine the actual effective pore
geometry.
Dye Experiment
Dyes have frequently been used to visually investigate
the nature of flow paths through the soil (Bouma and Dekker,
1978; Omoti and Wild, 1979; McVoy, 1985). The approach
provides the opportunity to observe the conducting pathways
and thereby relate water and solute flow to observable
structural features or biochannels. The basic assumption
made in interpreting dye patterns is that the more solution
that passes a given point, the more darkly stained that
point will be. Thus, stained regions are interpreted as
being regions of relatively fast flow, unstained regions to
be of relatively slow flow.
It is important that this fairly simple-minded approach
not be extrapolated far in terms of correlation of dye
patterns with BTC's. In the first place, Rhodamine B dye is
sorped to the soil much more strongly than tritium (McVoy,
1985) so that dispersion is apparently reduced. Secondly,
the dye is not instantaneously and reversibly desorped as
tritium is assumed to be. And thirdly, visual evaluation of


36
color is qualitative, so that quantification of the amount
of dye at a location is not possible.
Given these difficulties, four observations of note can
be made from the dye patterns illustrated in Figs. 2-6
through 2-9. First, no significant staining of the column
edges was observed in Columns 1, 2, and 3 and the staining
on the edge of Column 4 was not as intense as in the inter
nal portions of the column. This is a critical question
that must be considered when undisturbed columns are used.
From these observations we do not believe that observed
BTC's were strongly affected by the presence of the column
boundary.
Second, while specific stained regions that must have
been responsible for the very early appearance of tracer in
the effluent were easily identified, with one exception,
they were not obviously associated with discrete biochannels
or structural features. Even with the segmented column at
hand, it was very difficult to determine exactly which pores
were conducting, as, in every case many visible pores were
stained. In addition, it was difficult to trace individual
pore sequences up the column because they meandered consid
erably across the column. Omoti and Wild (1979) and McVoy
(1985) have made a similar observations.
Third, where structural units were relatively strong as
in Column 1, there was preferential flow around them. The
structural units isolated in Column 1 ranged in radius from
about 0.3 to 1.2 cm, which is in rough agreement with that


37
Figure 2-6. Rhodamine B dye staining pattern in Column 1
resulting from displacement under a soil-water tension of 1
kPa.


38
CM
Figure 2-7. Rhodamine B dye staining pattern in Column 2
resulting from displacement under saturated conditions.


39
6
9
12
CM
CM
Figure 2-8. Rhodamine B dye staining pattern in Column 3
resulting from displacement under saturated conditions.


40
Figure 2-9. Rhodamine B dye staining pattern in Column 4
resulting from displacement under a soil-water tension of 1
kPa.


41
calculated from the curve-fit parameters. The fact that
these units were identified during unsaturated flow which
was well described by the CD model indicates that movement
into and out of those units was sufficiently rapid that
bypassing was not indicated in the BTC. This observation
probably accounts for the generally greater dispersion
observed in undisturbed columns and field studies. That is
bypassing is "masked" in the dispersion term.
Fourth, the nature of the stained regions does not
appear much different in the saturated and unsaturated
columns. This is evidence that, rather than drain a few,
discrete large pores, the application of tension drains
regions of the soil that serve to "connect" pore-sequences.
The main difference between saturated and unsaturated
dye patterns is that the "solute front" is more compressed
in the unsaturated columns. Note that the number of pore
volumes of dye solution applied to all columns was approxi
mately the same (Table 2-1), but the extent of staining in
the unsaturated runs was more strongly weighted toward the
inlet end of the column.
These observations compliment the results of BTC
analysis and hydraulic property measurement in the previous
section. It appears that the highly skewed BTC's and very
high Kgat measured under saturated conditions were due to
very rapid transport in a very restricted region of the
columns. These regions are better characterized as conduct
ing pore sequences than discrete macropores and their


42
identification in the field would be very difficult. There
appears to be a number of such pore sequences that range
widely in conductivity. Application of tension "discon
nects" the largest effective pore sequences and therefore
results in reduced skewing and K. Immobile regions were
generally characterized as regions between conducting pore
sequences as opposed to easily identifiable, physically
controlled regions. When displacement occurred under
tension, flow in the conducting regions was slow enough, and
those regions were close enough, that observed heterogeneous
flow was described as dispersion with the CD model.
Field Application
One of the stated objectives of this study was to use
information from column experiments as a basis for selecting
a model to describe movement of nutrients and water under
field conditions. The basic distinction between the two
models considered is whether or not all soil-water effec
tively participates in convective transport (i.e., whether
or not vQ applies). It is clear from the dye patterns in
Figs. 2-6 through 2-9 that there was bypassing in the sense
of heterogeneous flow under both saturated and unsaturated
conditions. In terms of model selection, however, bypassing
was significant only when the soil-water content was at or
very near saturation. Hence, "significant" bypassing is
expected in the field only when the soil is near saturation.
The necessary precondition for saturation of the soil
surface under field conditions is that the water input rate


43
(rainfall intensity) exceed the infiltrability of the soil
(Hillel, 1971). If no surface disturbance occurs (none was
observed) the minimum infiltrability of a uniform soil
profile is K Since no differentiation of horizons in
sat
terms of hydraulic properties was observed, the rainfall
intensity must at least exceed Kgat if saturation and hence
"significant" bypassing are to occur.
The K values measured in undisturbed columns were
sat
between 20 and 40 cm hr Given the well known high
spatial variability of Kgat (Warrick and Nielsen, 1980),
these values probably should not be used in this context.
We obtained estimates of Kgat from 24 double ring infiltra
tion measurements. The results are shown in Fig. 2-10. The
values obtained are considerably lower than those measured
in the columns. Aside from spatial variability, two
explanations for this difference are the fact that the
columns were saturated from below under tension and were
therefore closer to true saturation and the fact that
macropore continuity is enhanced in shortened columns
(Edwards et al., 1979)
When the frequency distributions of K and rainfall
intensity are compared (Fig. 2-10), it is evident that
rainfall intensity sufficient to cause ponding is rare.
Based on this information it appears that saturated flow and
the attendant bypassing are not expected to occur at the
site. This is consistent with the observation at the site
that ponding did not occur.


60
Rainfall Intensity (cm/hr) K$at (cm/hr)
Figure 2-10. Relative frequency of rainfall intensity and saturated
hydraulic conductivity estimated with a double ring infiltreireter.
4^
4^


45
Application of BTC's measured in columns to field
conditions involves a considerable extrapolation in scale.
This requires that the representative elementary volume
(Bear, 1972) for bypassing be considerably less that the
volume of the column. Several studies have shown that
measured bypassing at a field scale was expressed in undis
turbed soil columns (Bouma and Wosten, 1979; Omoti and Wild,
1979; White 1985). This, however, need not be the case.
If significant bypassing at a scale larger than column
dimension is to occur, there must be some means by which
soil water movement is concentrated. This may be the result
of soil properties or the distribution of incoming water.
Large soil structural units or biochannels have been shown
to be responsible for bypassing (Bouma and Dekker, 1978;
Bouma et al., 1982). These effects are enhanced by a very
slow matrix K . Other soil characteristics that could
sat
serve to concentrate flow are coarse fragments, steep
slopes, and strongly contrasting horizons (Bevin and
Germann, 1982).
The soils in this study exhibited none of the above
characteristics. Soil structural units were generally less
than 2 cm in diameter and poorly expressed. From the column
studies it is clear that the matrix conductivity is rela
tively high (>1 cm hr-'*'). Other characteristics such as
steep slopes, contrasting horizons and coarse fragments were
not evident. Soil animals that could potentially make large
channels (e.g., leaf cutter ants and armadillos) were


46
carefully excluded from the site.
The input of water may have been concentrated in two
ways. First, the reported intensities are averages over the
entire event so that much higher intensities would prevail
for short time periods. Second, vegetation causes a spatial
redistribution of incoming rain such that small areas of
much higher intensity than the average are expected. On the
other hand, it should be considered that Ksat is the minimum
infiltrability and that lateral movement away from local
high intensity spots is likely.
Rapid changes in water table depth and/or stream
discharge have been cited as evidence for bypassing (Thomas
and Phillips, 1979; Beven and Germann, 1982). Thomas and
Phillips (1979) noted that water in macropores can flow into
or below the rooting zone in a matter of minutes and de
scribed flow from a spring 30 minutes after cessation of a
large (4.57 cm) rainfall.
From 1 September to 24 July, 1982-1983, the water table
depth was monitored about 5 times each week. At the same
time, a daily record of precipitation was maintained.
Measured water table depths ranged from 80 to 200 cm. The
length of time between rainfall events and rise in water
table depth could be estimated only to within 24 hours
because measurements of both rainfall and water table depth
were made at approximately the same time (7:00 AM). There
fore, if a rise in water table depth and rainfall were
recorded on the same morning, the water table response could


47
have occurred between 0 and 24 hours after the rainfall
event. On the other hand, if no change in water table depth
was recorded the following day, either the incoming water
was absorbed in the soil above the water table or the
response took longer than 24 hours.
During the 11 month measurement period there were 27
rainfall events of less than one cm had no measurable effect
on water table depth. Among the rainfall events greater
than 1 cm, there were 27 for which the water table depth was
recorded both the day of the event and the day following and
that were not immediately preceded by large (>1.0 cm)
events. Of these there were 23 for which no response was
recorded the following day. The remaining 4 events occurred
when the soil was relatively moist (there had been between
3.2 and 6.6 cm of rain during the preceding 5 days) and the
water table was between 128 and 143 cm deep. If conditions
of field capacity and moderate (0.5 cm hr ^) rainfall
intensity are assumed, infiltration as calculated by the
Green-Ampt method (see Hillel, 1971) to a depth of 100 cm or
greater within 6 hrs is expected. Given this estimation,
rapid arrival of water at the water table is expected.
Thus, no evidence in support of macropore flow could be
found.
From this information it appears that macropore flow
was not common and that it likely did not occur during the
period of measurement. This is consistent with other find
ings in the field and laboratory. From this we conclude


48
that the CD model, or other simplified models based on the
concept that all soil-water participates in convective
transport of solutes, should be appropriate for describing
solute flow in this soil.
It is important to note that, at this time, we do not
have criteria for simple determination of bypassing in the
field. Recent work by Russel and Ewel (1985) performed near
our experimental site reported considerable amounts of flow
through selected channels. Many of the soil characteristics
described above as being related to bypassing on a large
scale were present at that site. These included, steep
slopes, the presence of large coarse fragments, a mixed
canopy, and the presence of native soil fauna. It may also
be noted that their observations of bypassing were restrict
to two large events and relatively small portion of the
total surface area. While the effects may be significant on
a hydrologic scale, they may not be in terms of calculating
nutrient losses by leaching.
CONCLUSIONS
Solute breakthrough curves resulting from miscible
3
displacement of H20 in undisturbed soil columns under a
range of soil-water tensions were evaluated in terms of the
mobile-immobile water model and the convective-dispersive
model. Model parameters derived from curve fitting indicat
ed that the convective-dispersive model accurately described
breakthrough curves performed under tensions greater than


49
0.1 kPa while the mobile-immobile model better described
breakthrough curves performed under soil-water tensions less
than or equal to 0.1 kPa. Thus, in terms of model selec
tion, bypassing was significant only when soil-water con
tents were at or very near saturation.
Dye patterns obtained under saturated conditions showed
that soil-water flow (and thus convective transport) was
confined to small regions within the columns. However, no
easily identifiable, discrete channels were observed in
these regions. It appears that flow was conducted via a
series of relatively large pores, or continuous
pore-sequences. The net effect of the pore sequences on
soil hydraulic conductivity was identical to discrete
channels, but the identification of the channels responsible
is virtually impossible.
Application of tension appears to have disconnected the
most rapidly conducting pore-sequences, thus reducing the
skewedness of breakthrough curves. Under unsaturated
conditions the conducting pore-sequences were slow enough,
and well enough interconnected with the rest of the soil,
that the convective-dispersive model was applicable. Even
so, dye patterns showed that flow was very heterogeneous, as
was also evidenced by high dispersion coefficients.
Comparison of the frequency distributions of
field-measured Kgat values and measured rainfall intensities
indicated that saturated conditions, and hence significant
bypassing, are not expected to occur in this soil.


50
Observation of water table response to rainfall events
supports this. Based on these experiments, we conclude that
field-scale models based on the convective-dispersive model
for solute movement should be applicable this soil.


CHAPTER III
LEACHING LOSSES FROM TWO CONTRASTING CROPPING SYSTEMS
Introduction
There is a large and increasing demand for increased
food production from humid tropical regions. The climate of
these regions is characterized by very high annual rainfall
that is considerably in excess of potential
evapotranspiration. Under these conditions there is a large
potential for loss of nutrients by leaching. This has been
cited as a major limitation to the transfer of fertilizer
technologies developed in temperate zones to humid tropical
regions (Engelstad and Russel, 1975). It is especially
important, therefore, that loss of nutrients by leaching be
considered in the development of cropping systems in the
region. One approach that has been proposed is to manipu
late cropping systems so that land is continuously planted
to a variety of crops (Cox and Atkins, 1979). Reduction of
leaching loss is among the many benefits ascribed to such
systems.
Measurements of nutrient loss from agricultural fields
in the tropics are rare (Greenland, 1977; Keeney, 1982;
Omoti et al., 1983). Those results that have been published
indicate a very wide range in leaching losses (e.g.,
Sanchez, 1976). The lack of leaching studies is due, at
least in part, to the numerous difficulties associated with
51


52
such measurements. The fundamental difficulty is that there
is no known means of directly measuring solute leaching
losses. Indirect methods must be employed.
One method that is commonly employed is to use a mass
balance based on the equation:
AF = Fa Fu Fi' (3-1)
where F (kg ha refers to the change in storage of any
given nutrient and the subscripts "a", "u", and "1", refer
to the amounts of nutrient applied, taken up by the crop,
and lost by leaching, respectively. From an agronomic point
of view, this is reasonable since the amount taken up by the
crop, which is really the object of nutrient application, is
evaluated. The chief limitation to this approach is that
Eq. 3-1 is incomplete. In addition to plant uptake and
leaching, nutrient dynamics are governed by other processes,
e.g., immobilization and denitrification of N and fixation
of K. Another problem is that the amount of nutrient stored
in the root mass is usually poorly known.
Another method used to calculate nutrient leaching is
to calculate the convective solute flux from the the rooting
zone with the equation:
J = q C (3-2)
-2 -1
where J is the solute flux (mg cm day ), q is the
soil-water flux (cm day-1), and C is the soil solution
concentration (mg cm-3). This provides a direct estimation
of leaching loss that is independent of other processes that
effect the fate of an applied nutrient. The method requires


53
knowledge of q and C. The value of q can be calculated
using Darcy's law, but the spatial variability of the soil
hydraulic conductivity is so large that this approach is
generally impractical for field-level research (Warrick and
Nielsen, 1980). An alternative is to calculate q from water
mass balance. This method provides an areal estimate of the
net water movement. Estimation of C can be based on mea
surements of the soil solution at specific locations in the
field, which can be made with quantifiable precision.
This approach can be extended to trace the movement of
a solute through the crop rooting zone. This provides
information concerning the length of time a solute (e.g., an
applied nutrient) can be expected to reside in the crop
rooting zone under known soil, plant and weather conditions.
Water Balance and Nutrient Leaching
If the system of interest is considered to be delineat
ed by a unit surface area of soil extending to the depth of
the crop rooting zone, then, assuming that the mass of water
is conserved,AW = W. W., where W is the mass of water
m out
and the subscripts "in" and "out" refer to water entering
and leaving the system. This expression can be decomposed
into a number of components such that:
AS + ARW = (P+I+UP+RON) (E+T+APA+ROF+DP), (3-3)
where S = soil profile water content
RW = water held in the roots,
P = precipitation,
I = irrigation,


54
RON = runon of water from adjacent soil,
UP = upward percolation,
E = evaporation,
T = transpiration,
PA = water stored in the above-ground biomass,
ROF = runoff of water to adjacent soil, and
DP = deep percolation,
with all components expressed in units of cm (the density of
water is assumed to be 1 g cm-3). This general equation can
usually be simplified considerably when adapted for a
specific location and set of objectives. Based on observa
tions in the field, I, RON, and ROF equal 0 in this study.
In addition, the assumption was made that, relative to the
magnitudes of the other components, ARW can be ignored and
APA can be lumped with transpiration. For practical rea
sons, E and T were lumped into the single term,
evapotranspiration (ET). Thus,
AS = (P+UP) (ET+DP). (3-4)
If the net deep percolation (NDP) is defined as DP-UP and
substituted into Eq. 3-4, the result is as follows:
NDP = P ET AS. (3-5)
In general, S and P can be measured directly, ET can be
estimated from measured parameters, and NDP can then be
calculated as the difference. It is apparent that the
accuracy with which NDP can be determined in this way is
dependent on the accuracy with which the other three parame
ters are determined.


55
This fairly straightforward approach is complicated by
the fact that ET is a function of S (or soil-water poten
tial) when conditions of soil-water stress are encountered,
which requires either that S be determined frequently, or
that S be estimated between measurements. In addition, it
provides no information about water movement between S
measurements or within the system.
Models based on the use of Eqs. 3-2 and 3-5 that
incorporate soil and crop parameters have been developed to
overcome these limitations (Rao et al., 1981; Rose et al.,
1982a). They apply this approach incrementally to layers
within the system and can thereby provide more information
about water and solute movement over depth within the system
and out of the system over time.
Model Development-Concepts
The model used in this study fits the general classifi
cation of capacity-parameter based models (Addiscott and
Wagenet, 1985). Water movement is calculated from differ
ences in amounts of water stored. Specific processes
governing the rates of movement are explicitly ignored.
The advantages of this approach over other, rate models, are
that the required parameters are generally easier to measure
and exhibit considerably less spatial variability (Warrick
and Nielsen, 1980). The disadvantage of capacity models is
that, because they are empirical representations of process
es, they lack the flexibility of more process oriented
models. For this reason it is important to carefully


56
consider the relationship between the processes of interest
and the empirical representation of them.
There are two basic outputs desired from the model:
(1) NDP, which is to be used to calculate movement of
nutrients beyond the rooting zone using Eq. 3-2 with q =
NDP, and (2) the time of solute residence in the rooting
zone.
The following two assumptions are considered fundamen
tal to the approach: (1) the position of a solute front of
a nonadsorbed, conserved solute is dependent only on convec
tive transport (i.e., dispersion and diffusion are not
considered), and (2) drainage of soil water proceeds to a
consistent soil-water content called the field capacity
<9fC>.
Examination of solutions to the convective-dispersive
equation indicate that, except where vqL/D (discussed in the
previous chapter) is very low (i.e., when dispersion is very
high), dispersion has relatively little effect on the
position of a solute front. That is to say that solutes
move in roughly symmetric pulses through the soil. Even
under field conditions, where D is expected to be quite
high, the effect of dispersion on the position of a solute
front is small compared with other errors and the accuracy
that is usually considered acceptable in field studies (Rose
et al., 1982b).
From the first assumption, the traditional
convective-dispersion equation is simplified to


57
0 3c / 91 = -qsc/az, (3-6)
3 -3
where 0 is the soil-water content (cm cm ). If one is
interested only in tracking the depth of the solute front
(DSF), the equation becomes;
[3DSF/3t] = (q/0) = VQ, (3-7)
where DSF is expressed in cm, and vq is the average pore
water velocity (cm day-1). The importance of vq as dis
cussed in the previous chapter is apparent. Studies using
packed soil columns have shown that the above principles are
applicable to a wide range of textures and initial condi
tions (e.g., Dahiya et al., 1984). In general, it is
expected that the approach will be valid for most
field-scale applications if vq is an appropriate descriptor
of soil water movement. It is clearly inappropriate when
water flows preferentially through large macropores or
around large soil peds or aggregates.
Several studies have demonstrated the validity of the
approach under a wide range of field conditions (Rao et al.,
1976; Cameron and Wild, 1982; Barry et al., 1985). Such
success, however, is by no means universal. Smith et al.
(1984), working with a variety of soils over a three year
period, found that agreement between measured solute front
depths and those calculated with Eq. 3-7 ranged from excel
lent to poor, varying both with soil and year. Still other
studies have shown the approach to be completely inappropri
ate (Addiscott et al., 1978; Bouma and Dekker, 1978; Thomas
and Phillips, 1979; White, 1985). In these studies


58
preferential flow through macropores causing bypassing was
cited as the reason for failure of agreement.
This work demonstrates that the applicability of the
model to the specific conditions of interest should be
tested. In the previous chapter it was demonstrated that,
at the scale of 12.5 cm diameter undisturbed soil columns,
such bypassing did not occur under flow regimes encountered
in the field. Further evidence was cited that such behavior
did not occur at the field level.
The second assumption (concerning fc) has seen a long
history of controversy and it is clear that its application
should depend on the objectives of the study and specifi
cally measured values. It is based on the general observa
tion that, upon cessation of infiltration, soils drain with
decreasing rapidity over time. For uniform, well drained
soils, drainage is commonly described with the following
__ T_
expression: S = at where a and b are arbitrary constants
(Hillel, 1980). Thus, drainage never actually ceases, but
becomes increasingly slow in finite time. The choice of 0^
and the length of time required to reach that value are
arbitrary and depend on the soil in question and the objec
tives of the study. In addition, it is important to recog
nize that the relationship between soil water tension and
soil-water content at field capacity depends on the soil
(Ratliff et al., 1983).


59
When the second assumption is made, the eventual depth
of leaching from a given infiltration event (I), if ET
during redistribution is negligable, is
DSF = I/0fc, (3-8)
where I is the amount of water entering the soil during an
infiltration event (Rao et al., 1976). The significance of
the assumption of a field capacity is that it allows the use
of the amount of the event, which is relatively easy to
measure and use in computations, as opposed to using rates
and time intervals. Equation 3-8 has been modified ro
account for the effects of evapotranspiration and movement
of the solute front within the soil profile (Rao, et al.,
1976; Davidson et al., 1978; Rose et al, 1982b).
Water Table Effects
Application of the modeling approach described above
has been restricted to conditions in which a water table was
not present. The presence of a fluctuating water table in
close proximity to the rooting zone renders the second
assumption invalid because the soil-water does not drain to
field capacity as it is generally defined but to some
greater value that depends on the location of the water
table. In the extreme, at the water table interface, that
value is the saturated water content of the soil (0 ). At
s
increasing distances above the water table that value
decreases, until at some point, it equals the "normal" field
capacity.


60
The soil profile can therefore be divided into two
regions; that above the influence of the water table, and
that within the influence of the water table. The propor
tion of the soil profile in either region is variable,
depending on the depth of the water table and the height of
water table influence (HWTI). In the region above the HWTI,
drainage is assumed to proceed as if no water table were
present. Drainage in the region below the HWTI is assumed
to proceed to a different, greater value. This value is
considered to be a "temporary" field capacity, 0fc*, that is
dependent on the position of the water table.
The relationship between field capacity, the HWTI, and
the water table depth are illustrated in Fig. 3-1. Note
that the soil-water content at the HWTI is equal to 0^c and
that the water table represents effective saturation. Under
the quasi-equilibrium conditions to which the soil is
assumed to drain, the soil-water tension between the HWTI
and the water table (measured in cm ^O) is numerically
equal to the height above the water table (expressed in cm).
This is illustrated as a linear function in Fig. 3-1, but
may be quite different, depending on the soil moisture
characteristic of the soil. This relationship should be
independent of the direction of water table movement if
hysteresis effects are ignored.


Depth (cm)
61
0 (cm3cm"3)
Figure 3-1. Schematic representation of the relationship
between water table depth, the height of water table
influence (HWTI), 0f and 0f .


62
Materials and Methods
Cropping Systems and Soils
The two cropping systems chosen for comparison were:
(1) a mixed cropping system composed of laurel (Cordia
alliodora), cacao (Theobroma cacao) and pltano (Musa
paradisiaca), and (2) a monocropping system composed of
maize (Zea mays L.). These two cropping systems will be
referred to as the MP, for mixed perennial, and the MA, for
monocropped annual, cropping systems.
The sites of the two cropping systems were separated by
approximately 100 m and were located in the "la Montana"
section of the experimental plots at CATIE (Centro Agricola
de Investigaciones y Enseanzas) near Turrialba, Costa Rica.
Both cropping systems were planted on Instituto clay loam,
which is classified as a Typic Dystropept, fine, mixed,
isohyperthermic (Aguirre, 1971).
The management level of both systems was designed to
promote high yields and profitability. The MA plot is
located in a larger study area used by Dr. Carlos Burgos to
study the effects of tillage and residue management on maize
yields. It was initiated in November of 1976. A 120-day
variety was planted at a density of 40,000 plants ha ^. Two
crops were planted each year, one in late May, and the other
in early November. Approximately 240, 55, and 40 kg ha 1 of
N, P, and K, respectively, were applied each year. Applica
tions were made on the planting date and approximately 30


63
days after planting, resulting in 4 applications each year.
The plot dimensions were 32 by 20 m.
The MP plot was part of a larger study of intercropping
with perennial crops directed by Dr. Gustavo Enriquez. It
was initiated in 1977. The planting densities were 1,111;
432; and 123 plants ha-1 for cacao, laurel, and platano,
respectively. The annual fertilizer regime was 140 kg ha
-1
N, 30 kg ha-1 P, and 20 kg ha"1 K in four applications. The
dimensions of the MP plot were 18 by 18 m.
Model Developement-Construction
The model used in this study was based on the model,
NITROSIM, developed by Rao et al. (1981). Water movement
was divided into three phases; infiltration, redistribution,
and static. Calculation of soil-water content (01) and flux
(q1) for depth increments (i=l,2,...n) of thickness of Az cm
within the soil profile was an iterative process carried out
at discrete time increments (At).
Infiltration was assumed to proceed as a "square pulse"
(i.e. Green-Ampt infiltration) within one time increment at
an infiltration soil-water content (0. £) where 0^ <0. ^<0 .
inf fc inf s
Assumed ET during infiltration was 0. The depth of the
wetting front (dwf) resulting from a rainfall event of I cm
was calculated such that
I = E (0 fx 01) dwf, i=l, 2, . ,n. (3-9)
The change in DSF (ADSF) resulting an infiltration event, I,
was calculated as


64
ADSF =0 I < AW (3-10)
ADSF = (I AW)/inf I > AW (3-11)
where
AW = 2(inf1 1)Az, i = 1,2,...,m (3-12)
and m is the depth increment in which DSF resided prior to
the event.
Redistribution calculations were based on the following
expression:
0 = 0fc + (0inf 0fc) exP(ct)' (3-13)
where TRD is the length of time required for the soil to
drain to field capacity. In this way 0 decreases "exponen
tially" to a value within 1% of 0fc after TRD days of
drainage.
In the algorythm used to update 0, q, and DSF described
i i
below, 0 is used to denote 0 at t = t + At. Calculations
proceeded from the upper depth increment (i=l) down as
follows:
q1 = 0 (3-14)
O1 = 0fc + (01 0f X) exp(cAt) (3-15)
q2 = (01 01) Az/At (3-16)
and, for i > 1 and 01 > fc
01' = 0fc1 01 0fc) exp(cAt), (3-17)
q1+1 = (01' ') Az/At + q1, (3-18)
or, for 01 < 0£C,
01 = q1 Az/At + 01 01 < 0fc. (3-19)
For depth increments within the crop rooting zone ET is
included so that


65
01' = 01' U1 At (3-20)
where U1 is the rate of ET from the i-th depth increment.
Changes in DSF (ADSF) were calculated as
ADSF = DSF + q1 (At/Az1) (3-21)
when DSF was in the i + 1 depth increment at time t.
Some modifications were required to incorporate water
table effects. The water table depth was an input to the
model taken from measured and interpolated values. The HWTI
*
and 0£c (Fig. 1) were calculated on a daily basis from the
water table depth. All water entering depth increments
within the HWTI in excess of that required to account for
measured changes in water table depth was considered to be
NDP. Redistribution within the HWTI proceeded as described
above with 0^c replaced by Upward movement of water
was not explicitly considered.
The static phase commenced after field capacity was
achieved. During that phase changes in soil-water content
occurred only as the result of ET. Note that redistribution
could result from either infiltration events of a lowering
of the water table.
The soil profile was divided into 22, 5 cm increments
for numerical computations in the simulation model. Thus
the total depth of interest of 110 cm. The time step used
for calculation of water and solute movement was 0.1 day.
Days were considered to begin at 7:00 AM, when measurements
of water table depth, rainfall, and soil-water content were
made. Rainfall events were assumed to take place at 4:00


66
PM, which is roughly the most common time for such events.
All programming was done on a VAX-11 minicomputer in
FORTRAN-77.
Determination of Inputs
Use of the model requires as input the rainfall,
potential evapotranspiration, water table depth, initial
soil water content, and the soil and plant parameters.
Rainfall was measured daily with gauges located adjacent to
each plot. The water table depth to 220 cm was measured
about 5 times each week in perforated PVC tubes located in
the center of each plot that served as piezometers. Model
outputs were generated for two different time periods, the
simulation period and the calibration/validation period,
which are described subsequently. The initial soil-water
content for the simulation periods was determined
gravimetrically and for the calibration/validation period
with a neutron probe.
Soil samples for soil-water content determination were
taken at 10 cm intervals from 6 auger holes in the MP plot
and 9 holes in the MA plot. Soil-water content was deter
mined by weight loss after drying at 110 C to a constant
weight. This was converted to a volumetric basis by multi
plication with the soil bulk density.
Two methods were used to calibrate the neutron probe.
First, counts were made simultaneously with gravimetric
sampling approximately 1 meter from the access tube.
Second, a large tub was filled with soil taken from an


67
adjacent plot. The soil was then mixed and adjusted to
three different soil-water contents, packed into the tub,
and measured. Counts measured in the tub were adjusted for
bulk density as suggested by Greacen et al. (1981). The
resultant calibration curve, including field and
tub-measured values are shown in Fig. 3-2. There were five
access holes in each plot and counts were made at 15-cm
intervals from depths of 20 to 110 cm.
Evaporation from a class A US Weather Bureau pan
located approximately one km from the site was the basic
input used for calculating the potential evapotranspiration
(PET). The measured pan evaporation (E ) was converted to
pcin
PET using the equation PET = k Epan where k is the pan con
stant, taken from Doorenbos and Pruitt (1974) as 0.8.
Soil Parameter Estimation
Soil profiles were assumed to be uniform over the
depths of interest with respect to the required soil hydrau
lic parameters because only small trends with depth were
observed in the plots. There were some differences in soil
properties between the two plots used in the study even
though they were separated by only 100 m and mapped as the
same soil series. The main difference was that soils in the
MP plot had about 5% more clay, on the average, than the MA
plot, which corresponded with noticeably higher soil-water
contents. For this reason slightly different parameter
values were used for the two plots.


68
Figure 3-2. The relationship between neutron probe count
ratio and soil-water content used to calibrate the probe.
The regression equation was; count ratio = 2.69 + 0.65,
with r = 0.993.


69
The soil parameters required to calculate infiltration,
drainage, and movement of the solute front in depths above
the HWTI are @^n^, fc' an<^ TRD* Sixteen tensiometers,
placed at 15 cm intervals from 10 to 115 cm deep, 2
tensiometers at each depth, were installed in a 3x3 m
subplot immediately adjacent to the MA plot. Water was
ponded until tensiometers indicated that approximate steady-
state flow conditions had been achieved. At that point the
subplot was covered with straw mulch and plastic to elimi
nate evaporation, and changes in soil-water content were
monitored with a neutron probe. The value of TRD was
estimated as the length of time required for the rate of
change of soil-water content to become negligible.
A similar test was attempted in the MP plot but had to
be discontinued after large rainfall events caused a rise in
water table. The value of 0^ was estimated as the soil
water content at the soil-water tension (cm) equal to the
HWTI (also in cm). The soil water characteristic curve
under desaturating conditions in two 5.4 cm diameter cores
of undisturbed soil from the MP plot was measured for this
purpose. The value of TRD was assumed to be the same for
both plots. Estimation of was based on observation of
the soil water content over time in both plots.
Two additional soil parameters are required for calcu
lations within the zone of influence of the water table; the
*
height of water table influence (HWTI), and 0^ Average
measured soil-water tensions at two different depths, 75 and


70
105 cm, were compared with the water table depth. The
soil-water tensions were measured with tensiometers at the
two plots. In the MP plot there were 10 tensiometers at 75
cm and 5 at 105 cm. There were 9 tensiometers at both
depths in the MA plot. The HWTI was taken as the maximum
elevation difference for which approximate equilibrium
between soil-water tension and the water table could be
*
expected. Values of 8^ were assumed to vary linearly with
the height above the water table as shown in Fig. 1.
Plant Parameter Estimation
Measurements of root weights in the MP system shown in
Table 3-1 (Alpizar, personal communication) were used to fit
the following exponential relationship of root concentration
with depth:
Root weight = 25.179 exp (-0.067z), (3-22)
where z is depth (cm). The maximum rooting depth was taken
as 70 cm. This rooting depth was assumed to be constant
over time.
*
Table 3-1. Root distribution in the MP plot .
Depth
Mean Root^Weight
Standard Deviation
cm
g cm
0-15
14.0
8.7
15-30
6.4
3.3
30-45
0.4
0.4
*Alpizar (Personal Communication)
No measurements of root concentrations were made in the
maize plot. Root distribution with depth over time was
calculated from relationships observed by NaNagara et al.
(1976) as applied by Davidson et al. (1978). The rooting
depth during fallow periods was assumed to be 35 cm.


71
Estimated PET values were converted to estimates of
actual evapotranspiration (AET) using a cropping factor (CF)
in the following equation: AET = (CF)(PET). A constant CF
value of 1 was used for the MP plot. This consistent with
estimates for cacao grown alone (Doorenbos and Pruitt, 1974)
and the fact that canopy coverage on the plot was complete.
The CF value for the MA plot varied over time as the
maize crop was planted and matured. Estimates of CF during
the time that the maize crop was present were taken from
Doorenbos and Pruitt (1974), who divided the cropping season
into 4 stages with respect to water use. Estimation of CF
in the MA plot during fallow periods, which had considerable
weed growth, was made by calibration as will be described in
the next section.
The amount of water extracted from each layer was
calculated using the approach of Molz and Remson (1970).
Estimation of soil-water stress was based on the following
relationship which is similar to that used by Davidson et
al., (1978):
AET = AET .(- )/(*. -0 ), 0 < 0 (3-23)
cal pwp stress pwp stress'
where AETca^ is the transpiration calculated in the absence
of water stress and 0 and 0 are the soil water
stress pwp
contents of stress initiation and permanent wilting, respec
tively.
Calibration/Validation
Soil-water measurements were made periodically with the
neutron probe during the calibration/validation period.


72
There were 5 neutron probe access tubes in each plot. The
model output used for these purposes was the profile water
content, S. This is defined as
S = /0dz (3-24)
where z is depth and the lower boundary is at 110 cm. This
was calculated from measured 0 values as
S = EiAz, n=l,7 (3-25)
where i represents different depth increments, and Az the
increment thickness. As a check on the averaging procedure
used, S values determined by gravimetric sampling and
neutron probe measurements 18 July were compared.
For logistic reasons, the calibration/validation period
was initiated at different dates at the two plots. It
started on 14 April in the MA plot and 18 April in the MP
plot. These data were used for two purposes, calibration
and validation.
The first 18 days of the calibration/validation period
were used for model calibration. There was no rain during
that time, which was preceded by 3 weeks in which only 4.6
cm of rain were recorded. Thus, DP and P during those 18
days were 0.
Soil-water tensions of greater than 60 kPa by 23 April
and changes in S that were less than that expected indicated
that the crops on the MP plot probably experienced water
stress during the period. The parameters stress and 0pWp
were adjusted to account for this, assuming that changes in
S were due to AET. Soil-water tensions on the MA plot were


73
much lower at that time (about 22 kPa) due to the reduced
transpiration of the fallow vegetation. Measured changes in
S during this time were used to determine the cropping
factor for the fallow vegetation, again assuming that all
changes in S were the result of AET. The water stress
parameters for the MA plot were taken from characterization
data of Aguirre (1971) but were not critical because dry
periods did not occur while the maize crop was actively
transpiring.
After 2 May there was consistent rain and the condi
tions of P and DP equal to 0 did not hold. From this point
on the calibration ended and measured S values were used to
evaluate the accuracy of model calculations.
Simulation
Simulation of water and solute front movement began on
4 Nov. 1982 for the MA plot and 24 Nov., 1982 for the MP
plot and ended for both plots on 18 July 1983. Initial
soil-water contents were determined gravimetrically. The
starting date for the MA plot corresponded to the maize
planting date. A second planting of maize in the MA plot
took place on 30 May, 1983. All parameter values used in
the simulation were determined as described above.
Net Leaching Loss
Net leaching losses over the simulation period were
calculated using Eq. 3-2 with the NDP used in place of q and
C representing measured soil solution concentrations. Soil
+ 2+ 2+ +
solution concentrations of NC>3 NH^ Ca Mg and K


74
were measured in extractions from 7.6 cm diameter ceramic
porous cup samplers located at a depth of 90 cm. The cups
were pretreated in 0.1N HCL prior to installation. There
were 9 samplers in the MA plot and 8 in the MP plot.
Collections were made at approximately biweekly intervals
starting 30 Nov. in the MA plot and 11 Dec. in the MP plot.
Samples were collected by applying 40 kPa tension overnight.
No collection was made when the soil-water tension was
greater than 40 kPa. Concentrations of NO^ and NH^+ were
measured using a steam distillation technique (Bremner,
1965) and the cations were measured by atomic absorption.
Depth of Solute Front
The depth of the solute front movement resulting from
application of a nonadsorbed nutrient (e.g., NO^ ) was
simulated for 3 application dates in the MA field plot and 2
in the MP plot. The 3 application dates chosen for the MA
plot, 2 Nov., 30 Nov., and 30 May, were actual fertilization
dates on that plot. Calculations for the MP plot at the two
latter dates were included for purposes of comparison.
Results
Model Inputs
The depth to the water table and measured rainfall
amounts over the simulation period are shown in Fig. 3-3.
The water table was consistently deeper under the MA plot
than under the MP plot. A slight elevation difference
between the two plots (the observed water table differences


Watertable Depth (cm) Rainfall (cm)
75
Figure 3-3. Precipitation and water table depth at both
plots over the simulation time period (2 Nov., 1982 to 18
July, 1983 ) .


76
were about 20 cm) and differences in the drainage system at
the two plots, explain this. Open ditch drains of 100 to
150 cm depth drained both plots, but the drains around the
MA plot had a greater gradient and removed water more
quickly to a greater depth. After that depth was achieved,
both plots behaved similarly.
The rainfall distribution was fairly uniform during the
early portion of the simulation period and was then charac
terized by two relatively severe droughts. Shortly after
day 180 rainfall was consistently high.
Measured pan evaporation varied slightly during the
simulation period. It ranged from 0.26 cm day-1 in Dec. to
0.48 cm day-1 in April. The average monthly pan evaporation
over the simulation period was 0.35 cm day"1.
Parameter Estimation
A summary of parameter values obtained is shown in
Table 3-2. The change in soil-water tension with depth over
time during the determination of 0fc and TRD is shown in
Fig. 3-4. It indicates that the profile is roughly uniform
with depth with respect to soil hydraulic properties. The
corresponding changes in S are shown in Fig. 3-5. The
overall course of drainage is similar to that reported
elsewhere in the literature (Hillel, 1980) in that In S is
proportional to In t. This relationship was approximated
using the following parameter values: nf = 0.49, 0fc =
0.46, and TRD = 5.0 days. The curve calculated using the
above parameters is also shown in Fig. 3-5. The calculated


TENSION (K Pa)
77
Figure 3-4. Soil-water tension as a function of depth and
time after ponding. Time after ponding, in days, is
indicated below points representing the upper depth.


(ujo)s
78
TIME (days)
Figure 3-5. Profile soil-water content after cessation of
ponding. Fitted values were calculated with model
parameters in Table 3-2.


79
values are close to those measured for times up to 7 days.
This is considered to be sufficient because drainage rates
for longer time periods are expected to be considerably
different due to changes in gradient that result from plant
uptake of water and evaporation.
Table 3-2. Summary of model parameter values.
Cropping System
Parameter
MA
MP
0.
f
rnf
0sat
0.46 (cm
-3) 0.52 (cm cm-3,
cm )
0.49
0.55
0.55
0.57
0.29
0.38
pwp
0.42 "
0.48 "
Ti£ress
5 (day)
5 (day)
Max. Root
100 (cm)
70 (cm)
Depth
CF
variable
1.0
tension of 5.5 kPa (or 55 cm of B.^0), 0.52, was chosen.
sat and 0inf are shown in Table 3-2
The measured soil water characteristic curves upon
which the estimation of 0^c in the MP plot was based are
shown in Fig. 3-6. The value corresponding to a soil-water
The
tension of 55 cm is numerically equal to the value of
HWTI chosen (see discussion below). The estimated values of
The TRD was assumed
to be equal for both plots.
The average measured soil-water tension and distance
from the tensiometers to the water table in both plots are
plotted in Figs. 3-7 to 3-10. These plots were used to
estimate the HWTI. Each plot is somewhat distinctive, which
is due, in part, to the differing conditions encountered at
the different depths in the two plots. Recalling Fig. 3-1,
the HWTI should be the maximum distance between the water


h(kPa)
Figure 3-6. Volumetric soil-water content as a function of soil-water
tension in two cores taken from the HP plot. The soil-water content at
55 kPa (or 55 cm H~0) tension is approximately 0.52.
oo
o


AVERAGE TENSION CK Pa)
ELEVATION DIFFERENCE (cm)
Figure 3-8. The relationship between the average soil-water tension measured
with 9 tensiometers at 105 cm depth on the fiA plot and the depth to the water
table. Elevation difference refers to the distance between the tensiometers and
the water tabele. The straight line represents equilibrium.
oo
to


AVERAGE TENSION CK Pa)
ELEVATION DIFFERENCE (cm)
Figure 3-9. The relationship between the average soil-water tension measured
with 10 tensiometers at 75 cm depth on the MP plot and the depth to the water
table. Elevation difference refers to the distance between the tensioir.eters and the
water table. The straight line represents equilibrium.
oo
U>


AVERAGE TENSION CK Pa)
ELEVATION DIFFERENCE (cm)
Figure 3-10. The relationship between the average soil-water tension measured
with 5 tensiometers at 105 cm depth in the. yiP plot and the depth to the water
table. Elevation difference refers to the distance between the tensiometers and
the water table. The straight line represents equilibrium.
00


85
table and tensiometers at which equilibrium can be assumed.
When that distance is greater than the HWTI, soil-water
tension could either be greater than equilibrium due to ET,
or it could be less, due to the attainment of field capacity
or infiltration. Of course, equilibrium values could also
be observed.
Considering the errors that could be introduced by
varying elevations within the plots and the accuracy with
which measurements were made, reasonable estimates of HWTI
range from 45 to 65 cm. A value of 55 cm was chosen for
HWTI for modeling purposes, which should be approximately
correct in all cases. This value agrees reasonably well
with the tension measured at the plots after 5 days drainage
(Fig. 3-4). Discrepancies on the order of 1 to 2 kPa are
not considered important because they have little effect on
estimated 0 values (Fig. 3-5). Although the tension at
field capacity is somewhat lower than commonly assumed, it
is similar to values measured in other studies (Wierenga,
1985) .
Calibration/Validation
As a check on the overall averaging procedures used to
calculate S in the field from measured 0 values, S calculat
ed from 0 measured gravimetrically was compared to S calcu
lated from 0 measured with the neutron probe. The results
shown in Table 3-3 indicate good agreement.


86
Table 3-3. Comparison of profile water contents measured
gravimetrically and with the neutron probe.
MA Plot
MP Plot
Method
Ave. S
S.D.
o

<

n
Ave. S
S.D.
C.V.
n
Grav.
cm
50.99
2.30
0.045
9
cm
55.48
2.59
0.047
5
Probe
50.79
0.73
0.014
4
56.62
1.76
0.031
4
The value of CF on the MA plot during the fallow period
obtained by calibration was 0.5. This value was used in the
remainder of the validation run and in the subsequent
simulation run for fallow periods. The other values ob
tained by calibration were 0. and 9 for the MP plot
stress pwp
(Table 3-2).
Measured values of S over time in both plots are shown
in Figs. 3-11 and 3-12 along with simulated results. There
was close agreement between simulated and measured values on
both plots. The variability of S estimation as indicated by
the 80 and 90% confidence intervals (vertical bars in Figs.
3-11 and 3-12) in the MA and MP plots, respectively, was
rather high. This was not due to high variability per se
(the coefficient of variation ranged from 1 to 5%), but to
the limited sample numbers. Efforts to improve that condi
tion were hampered by defective aluminum tubing.
Soil Solution Concentrations
Measured soil solution concentrations over time under
the two cropping systems are shown in Figs. 3-13 and 3-14.
In general, the concentrations under the MA system are at
the lower end of the range reported by Barber (1984) for
concentrations in the surface horizons of Midwestern soils.
By that standard, the concentrations in the MP plot were


Figure 3-11. Comparison of profile soil-water contents in the FA plot measured
over the validation period commencing in April and those computed from initial
conditions in November.
CO


Time (day)
Figure 3-12. Comparison of profile soil-water contents in the MP plot
over the validation period commencing in April and those computed from
initial conditions in November.
CO
CO


K (mM) N CmM)
Figure 3-13. Changes in soil-solution concentration of N and K+ during the
simulation period for both plots. '¡.lie vertical bars indicate the 90% con
fidence interval.


Mg CmM) Ca CmM)
Figure 3-14. Changes in soil-solution concentrations of Mg and Ca during
the simulation period for both plots. lie vertical bars indicate the 90%
confidence interval.


91
quite low for all ions except K+. Concentrations of
NH4+were relatively low under both systems and were not
included because the concentrations measured were generally
at or below the minimum concentration required for accurate
determination for the methods of analysis used.
With the exception of K+, soil solution concentrations
under the MA plot were clearly greater than those under the
MP plot. Even in the case of K+, the soil solution concen
trations in the MP plot were consistently lower than those
in the MA plot.
Examination of the concentrations and the associated
90% confidence intervals, suggests that the concentrations
were fairly constant over time. Analysis of variance
indicated that there was no significant difference
(a=0.9-0.99) between the mean concentrations measured on
each plot for all ions measured except NO^- on the MP plot.
This indicates that the means can be pooled. The overall
means and pooled standard deviations are shown in Table 3-4.
Comparison of the means of the ions in the different plots
. + 2 +
were significantly different (a=0.99) for K Ca and
2+
Mg The concentrations of NO^ in the MP plot were more
than one order of magnitude less than in the MA and fre
quently below the accurate detection level of 0.011 mM.


92
Table 3-4. Average soil solution concentrations.
Cropping System
Nutrient MA MP
ion Ave. S. D. Ave. S. D.
mM
K$3
ca3+
Mg2+
0.62
0.349
*
0.11
0.074
0.043
0.028
0.16
0.104
0.016
0.010
0.13
0.057
0.024
0.006
*Could not be pooled.
Simulation
Values of S calculated for the simulation period are
also shown in Figs. 3-11 and 3-12. After approximately 150
days of simulation, calculated values agreed well with those
measured. The relatively low S values calculated for the
simulation run were probably due to one of two causes:
either the AET in the dry period prior to the calibra
tion/validation period was overestimated due to the fact
that the laurel trees shed their leaves during the dry
season, or there was unaccounted upward movement of water
prior to that time.
The greater AET from the MP plot (3-5) was due to the
fact that transpiration from that plot was continuous. This
resulted in simulated differences in NDP (Table 3-5).
Net Leaching Loss
The net leaching loss (NLL) of the four elements
measured were calculated by multiplying the NDP and the
average soil solution concentration. The results are shown
in Table 3-5. These results are for a time period somewhat
less than one year, which makes direct comparison with most
other measured results difficult. The range of values


93
reported, however, is so great that these values easily fall
within it (Sanchez, 1976). From an agronomic viewpoint, the
loss of N03~-N in the MA plot is clearly important.
Table 3-5. NDP and Net Leaching Loss Values.
Calculated
Value
Croppinq
System
MA
MP
AET
TR*
NDP
NLL
N+
45 (cm)
111 (cm)
66 (cm)
57 (kg/ha)
55 (cm)
111 (cm)
57 (cm)
1 (kg/ha)
K 2+
*2+
Caz
3 (kg/ha)
21 (kg/ha)
43 (kg/ha)
1 (kg/ha)
3 (kg/ha)
3 (kq/ha)
*TR is the total rainfall during the time of
comparison.
Depth of the Solute Front
The simulated movement of a nonadsorbed solute front is
shown in Fig. 3-15. The rate of movement from the third
application is considerably greater than that from the
first. Movement is clearly related to patterns of rainfall.
Note that the DSF from the first two applications tend
to converge with time. Also note that little residual
effect of fertilizer can be expected between the two appli
cations as the front has moved well beyond 110 cm depth
before the second planting. The difference between the MP
and MA plot is ascribed to the greater AET from the MP plot.


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PAGE 184

+ eSV 6 %Q rO n ZDWHUQXWULHQWPRY22VH\I



WATER AND NUTRIENT MOVEMENT IN TWO TROPICAL
CROPPING SYSTEMS
By
MARK S. SEYFRIED
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1986

ACKNOWLEDGEMENTS
In working on this project I have been extremely
fortunate to have had a great deal of assistance. At the
outset, the grant money and connections supplied by Bob Volk
enabled me to get my "foot in the door" in Costa Rica.
While I was in Costa Rica, Carlos Burgos of CATIE looked
after my interests in every conceivable way. He not only
contributed experimental plots, but helped me obtain fund¬
ing, allowed me access to transportation and provided the
services of his assistant, Carlos Arya, who was very help¬
ful .
I owe a debt of gratitude to several others at CATIE.
Roberto Diaz gave me the run of his laboratory and saw to it
that his assistants, Taco, Flaco, and Eduardo, were able to
help me when it was needed. Louis Alpizar provided help
with background data. Gustavo Enriquez provided land for
plot work, and Raul Moreno helped me obtain employment
there.
While in Costa Rica I also received help from a number
of Floridians. In particular, Bob Mansell saved the project
by supplying a much needed neutron probe. Jack Ewel re¬
lieved me from the daily routine long enough to take a short
vacation and shared data with me. And Chris McVoy assisted
with interest, discussion and companionship.
11

In Florida, Suresh Rao "adopted" me as his student,
which is probably the best thing that could have happened.
His input into all phases of the research, even in his
absence, has been invaluable. Ron Jessup has also taught
and helped a great deal. I am especially grateful to these
two men.
My remaining committee members, Don Graetz, Nick
Comerford, and Jerry Bennet have provided valuable feedback
and definitely improved the end product. In addition, I
must thank Peter Nkedi-Kizza and Linda Lee.
Finally, last and most, I thank my wife, Helen Fisher,
who not only put up with a great deal, but helped me most
when I most needed help.
1X1

TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS Ü
ABSTRACT V
CHAPTERS
IINTRODUCTION
Leaching in Humid Tropical Regions 3
Objectives 6
Presentation 8
IIPATTERNS OF SOLUTE MOVEMENT
Introduction 9
Materials and Methods 14
Results and Discussion 21
Conclusions 49
IIILEACHING LOSSES FROM TWO CONTRASTING CROPPING SYSTEMS
Introduction 51
Materials and Methods 62
Results 74
Discussion 95
Summary 105
IVMATHEMATICAL DESCRIPTION OF NITROGEN MINERALIZATION
DURING INCUBATION
Introduction 108
Materials and Methods 115
Results 118
Discussion 133
Conclusions 143
VOVERALL SUMMARY
Water Movement and Nutrient Leaching 146
Mineralization and Nutrient Cycling 150
Overall Assessment of Cropping Systems 152
Future Work 154
REFERENCES 157
BIOGRAPHICAL SKETCH 169
IV

Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
WATER AND NUTRIENT MOVEMENT IN TWO TROPICAL
CROPPING SYSTEMS
By
Mark S. Seyfried
August 1986
Chairman: P.S.C. Rao
Major Department: Soil Science Department
Soil-water and nutrient movement in two contrasting
cropping systems was compared to investigate the effect of
cropping system management on leaching losses under humid
tropical conditions. One cropping system, the monocropped
annual, consisted of maize (Zea mays L.) alone; the other,
mixed perennial system, consisted of cacao (Theobroma
cacao), laurel (Cordia alliodora), and plantain (Musa
paradisiaca) grown together. A model was developed to
calculate nutrient leaching losses given measured soil
solution concentrations. Displacement through undisturbed
soil columns was used to select a modeling approach. The
contribution of mineralized soil N was studied in incubation
chambers.
Miscible displacement studies indicated that nutrient
movement was well described by the convective-dispersive
model except when the soil was at or very near saturation.
Saturated hydraulic conductivity values measured in the
v

field were considerably higher than rainfall intensities,
suggesting that preferential flow conditions were very rare.
A zero-order kinetic model, with consideration made for
pretreatment effects, was shown to describe the measured N
mineralization rates well. These results appear to apply to
a variety of soils.
The simulation model used was based on soil-water
capacity parameters. Piston displacement of solutes and
drainage to field capacity were assumed.
Simulated profile soil-water contents closely approxi¬
mated those measured. Calculated net leaching losses of N,
2 + 2+ •
K , Ca and Mg were greater in the monocropped annual
system due mainly to differences in soil solution concentra¬
tions, which were constant over time. The calculated loss
of N from the monocropped plot was 56 kg ha ^ over 260 days.
Losses from the mixed perennial plot were negligible.
The solute residence time within the crop root zone was
proposed as an index of cropping system sensitivity to
leaching loss. The residence time in the mixed perennial
system was about 1.4 times larger in the monocropped system.
The low leaching losses from the mixed perennial plot, in
spite of substantial annual inputs of nutrients, indicates
that that system is conservative in terms of nutrient use.
This was attributed to the relatively long residence time in
the rooting zone of the mixed perennial plot.
vi

CHAPTER I
INTRODUCTION
The humid tropics, as defined by Sanchez et al. (1982),
comprises about 10% of the earth's land surface. This
region is currently undergoing very rapid ecologic, economic
and social changes. These changes frequently require more
intensive use of the land to produce food and fiber. This,
in turn, requires changes in extant farming methods.
The traditional system of farming for much of the
region, which is still used extensively, is commonly re¬
ferred to as slash and burn agriculture or shifting cultiva¬
tion. Although the details of operation are extremely
variable, the basic pattern is fairly consistent throughout
the world. First, the forest is felled and burned. Crops
are then planted in the ash and 2 to 4 harvests follow. The
land is then abandoned and crop production is shifted to a
new site. Shifting is necessitated by the marked, rapid
decline in crop production that almost invariably accompa¬
nies cropping. The abandoned land is left in fallow for 4
to 20 years after which time the process is repeated.
The success of the system is based on the restoration
of soil productivity during the fallow period. If that
period is sufficiently long, the system works in an ecologic
sense (Sanchez, 1982). In much of the region, increasing
populations, coupled with governmental encouragement, is
1

2
causing influxes of peasant farmers and increased demand for
food. This, along with the desire to preserve some of this
unique habitat, is forcing reduced fallow periods and
consequent land degradation (Trenbath, 1984). These changes
are forcing the development and adoption of agricultural
techniques that are productive and can sustain continuous
cultivation.
Several factors have been attributed to the observed
yield declines with cropping. These include, declines in
soil fertility, increased weed infestation, deterioration of
soil physical properties, increased insect and disease
attacks, and social customs (Sanchez, 1976).
Whatever the cause of yield declines may be, it is
clear that, if sustained soil productivity is to be
achieved, soil fertility must be maintained or enhanced.
One way to achieve this is to add nutrients and other
required inputs in sufficient quantities to overcome yield
limitations. This approach has recently been adopted by
industrialized (mostly temperate zone) nations. Sanchez et
al. (1982) have shown that this approach can work in both an
agronomic and economic sense in a humid tropical setting.
The problem is that it requires a fairly well developed
infrastructure that provides farmers with access to a wide
variety of soil amendments, seed varieties and pesticides,
sufficient capital and credit to purchase them, sufficient
knowledge to properly manage them, and a well developed
marketing and transportation system.

3
An alternative approach that has received considerable
attention in recent years is based on the somewhat paradoxi¬
cal observation that the land that supports some of the
world's most productive natural ecosystems (tropical
rainforest), supports some of the world's least productive
agro-ecosystems. If cropping systems were developed that
are more like the natural ecosystem, it is reasoned, they
should be more productive. This reasoning is supported by
the observation that most indigenous systems (excluding
paddy rice) that are of semi-permanent nature, are somewhat
like natural systems in that they are characterized by a
variety of different crops, including tree species, grown
together. The potential for designing such systems was
demonstrated by Hart (1980), who carefully planted crops to
mimic natural succession and obtained good, economic yields.
In practice it appears that few farmers are purists in
terms of the approach they adopt. A wide variety of crop¬
ping systems and input levels are in use. In all cases, an
important consideration is that plant nutrients in the soil
be conserved. Low input systems will "run down" and high
input systems will be prohibitively expensive if nutrient
losses are high. One process that can cause nutrient losses
is leaching.
Leaching in Humid Tropical Regions
The observed soil productivity decline under shifting
agriculture has been attributed to leaching losses (Sanchez,
1976). Leaching is also considered to be a major factor

4
that limits the effectiveness of fertilizers in the region
(Engelstad and Russel, 1975).
At the outset it should be pointed out that the pro¬
cesses involved in nutrient leaching in the tropical regions
are the same as those in temperate regions. A given nutri¬
ent will be leached below the crop rooting zone when it
moves through the soil faster than the crop can absorb it.
This is dependent on the climate, soil and crop. Thus, the
amount of a nutrient entering the soil solution and the rate
at which it moves are important. Management practices, as
well as the soil, climate, and crop, effect this. Since all
of these factors are generally different in humid tropical
regions, the magnitude of the problem is potentially differ¬
ent there than in temperate regions.
Rainfall is particularly noteworthy in this respect.
In many humid tropical regions the mean annual rainfall
exceeds 200 cm and, even though the potential
evapotranspiration is also high, it is often exceeded
considerably by rainfall. At Turrialba, Costa Rica, for
example, rainfall exceeds the potential evapotranspiration
by approximately 100 cm yr-1, which is roughly equal to the
average annual precipitation in most temperate agricultural
regions.
Although generalizations about soils for such a large
region are dangerous, there are some differences between
soils of temperate and tropical regions that are common. In
particular, soils in humid tropical regions frequently

5
exhibit infiltration rates much greater than those expected
in temperate soils of similar clay content (Sanchez, 1976).
This is generally attributed to the relatively high degree
of aggregation commonly found in those soils. Since this
characteristic obviously affects water movement, it also
effects solute (nutrient) movement.
Another difference between soils is that the clay
fraction of the soils in the humid tropics is commonly
dominated by variable charged, low activity clays. This
effects the retention of both cations and anions.
Finally, the differences in individual crop species and
their management is frequently different from temperate
crops and management. All of these factors combine to make
transfer of agricultural experience and research in temper¬
ate regions to the humid tropics tenuous. This is certainly
true of nutrient leaching studies.
Despite widespread concern that leaching may be an
important constraint to soil productivity in the humid
tropics, very little quantitative data have been reported in
the literature (Greenland, 1977; Keeney, 1982; Omoti et
al., 1983). This is partly because it is a difficult
parameter to measure and partly because much of the research
in the region is result-oriented (crop yield) as opposed to
process-oriented.
Measurements that have been made range considerably.
Amounts of N estimated to have been lost in a year, for
example, range from 37 kg ha-1 (Omoti et al., 1983) under

6
oil palm, to 204 kg ha-1 from a pasture in Colombia
(Sanchez, 1976). A recent study of leaching from maize in
Nigeria calculated N losses to be about 80 kg ha-1 for a
single fertilizer application of 150 kg ha ^ and approxi¬
mately half that for a split application (Arora et al.,
+ â– ?+
1982). Similar ranges of losses for K and Mg“ , and even
2+
greater ranges for Ca , have been reported (Sanchez, 1976).
Objectives
At present it seems clear that there is a high poten¬
tial for nutrient leaching losses in humid tropical regions.
It is also clear that research into management systems that
minimize such losses is an important priority. It has been
proposed that cropping systems can be manipulated in such a
way that such losses can be minimized. The principal objec¬
tive of this study was to establish if nutrient losses from
two different cropping systems, which are strongly contrast¬
ed in terms of species composition and diversity, are
different when levels of management are similar.
The fact that such wide ranges in leaching losses are
reported in the literature indicates that the processes
responsible for leaching must vary greatly in the region. A
second, related objective, was to quantify the processes
responsible for leaching in such a way that the sensitivity
of cropping systems can be characterized.
Setting
The two cropping systems chosen for comparison were:
(1) a mixed cropping system composed of laurel (Cordia

7
alliodora), cacao (Theobroma cacao) and plátano (Musa
paradisiaca), and (2), a monocropping system composed of
maize (Zea mays L.). These two cropping systems will be
referred to as the MP, for mixed perennial, and MA, for
monocropped annual, in the remainder of the dissertation.
The two cropping systems were separated by approximate¬
ly 100 m and were located in the "la Montana" section of the
experimental plots at CATIE (Centro Agronómico de
Investigaciones y Enseñanzas) near Turrialba, Costa Rica.
The soil series for both systems is Instituto clay loam
which is classified as a Typic Dystropept, fine, mixed,
isohyperthermic (Aguirre, 1971).
The management level of both systems was designed to
promote high yields and profitability. The MA plot is
located in a larger study area used by Dr. Carlos Burgos to
study the effects of tillage and residue management on maize
yields. It was initiated in November of 1976. Since then
40,000 plants ha ^ of a 120-day maize variety have received
approximately 400, 55, and 40 kg ha ^ of N, P, and K,
respectively, each year in four applications. Two crops
were grown each year. The dimensions of the plot discussed
in this study were 32 by 20 m.
The MP plot was part of a larger study of intercropping
with perennials directed by Dr. Gustavo Enriquez. It was
initiated in 1977. The planting densities were 1,111; 432;
and 123 plants per hectare for cacao, laurel, and platano,
respectively. The annual fertilizer regime was 140 kg ha-1

8
N, 30 kg ha-1 P, and 20 kg ha-1 K in four applications. The
dimensions of the MP plot were 18 by 18 m.
Presentation
Three lines of inquiry, connected to the basic theme of
nutrient movement under the two cropping systems will be
presented in three separate chapters. First, investigations
of the nature of solute flow through the soil at the site
will be presented. This work was performed to establish the
appropriate model to be used in estimating field-scale water
and solute movement at the site.
The second line of inquiry is devoted to the develop¬
ment and implementation of a soil-water and nutrient move¬
ment model that was used to estimate leaching losses and
examine critical parameters associated with those losses.
The third line of inquiry is related to the supply of
nutrients to the systems via mineralization of soil organic
matter. In this section a laboratory method for estimation
of N mineralization is examined in terms of three models.
Implications of the results to field application of incuba¬
tion results is also discussed.
The results presented in the three chapters are summa¬
rized and discussed in terms of implications to the func¬
tioning of the two cropping systems in the final chapter.

CHAPTER II
PATTERNS OF SOLUTE MOVEMENT
Introduction
This study is part of a larger project investigating
nutrient movement under different cropping systems in Costa
Rica. Throughout the humid tropics there is a high poten¬
tial for loss of plant nutrients by leaching due to the
considerable excess of precipitation over evaporative and
transpirational demand. Near Turrialba, Costa Rica, where
this study was performed, for example, the mean annual
precipitation of 264 cm exceeds mean annual pan evaporation
by 130 cm. Since improved crop yields are largely dependent
upon increasing the nutrient status of soils in the region,
quantification of leaching loss is important.
Quantitative description of solute (e.g., nutrient)
transport through packed soil columns has been accomplished
using the convective-dispersive (CD) model of solute flow
(Kirkham and Powers, 1972). Verification of this model has
come through numerous studies of miscible displacement
through uniformly packed, sieved, water saturated columns of
soil and other porous media (Nielsen and Biggar, 1961,
1962). According to the CD model, the transport of a
non-adsorped, conservative solute under steady water flow
conditions is described by
9c/3t = Dd 2C/d Z2 - vq3 C/3 z (2-1)
9

10
. _3
where C is the solute concentration (mg cm ), z is distance
(cm), t is time (hr), D is the "effective" dispersion
2 -1
coefficient (cm hr ) and vq is the average pore water
velocity (cm hr-1). The average pore-water velocity is
calculated by dividing the Darcy-flux (q) by the soil water
content 0. This implies that all soil water participates in
the convective transport of the solute.
In working with packed soil columns under steady water
flow conditions, two experimental conditions in which the CD
model has failed are (1) when aggregated media are used
(Nielsen and Biggar, 1961; Green et al., 1972), and (2) when
displacement was conducted under unsaturated conditions
(Biggar and Nielsen, 1962; Gupta et al., 1973; Gaudet et
al., 1977). In both cases this failure may be ascribed to a
failure of the assumption that all soil-water participates
in convective transport. When aggregated media are used,
intra-aggregate water is essentially stagnant so that virtu¬
ally all convective transport occurs in the mobile water
located in the inter-aggregate portion of the soil. Simi¬
larly, the application of tension to the soil causes the
entrance of gas which may result in the isolation of stag¬
nant regions. Again, convective flux is restricted to the
mobile, nonstagnant-region water and Eq. 2-1 is not applica¬
ble.
Application of miscible displacement techniques to
"undisturbed" soil columns taken from the field have shown
that inhomogeneities in field soils can strongly affect the

11
nature of water and solute flow (McMahon and Thomas, 1974;
Cassel et al., 1975). The presence of macropores is often
associated with solute flow behavior that is inconsistent
with the CD model (Kanchanasut et al., 1978). At present no
precise definition of macropores is widely accepted (Bouma,
1981; Luxmoore, 1981), but the term is generally used to
describe large, continuous pores that can conduct water and
solutes much more rapidly than the surrounding soil matrix
(Bevin and German, 1982; White, 1985). The process of flow
along macropores has been variously described as "channel¬
ing", "bypassing", "short circuiting", "preferential flow"
or "partial displacement" (Scotter, 1978; Bouma, 1981; Beven
and German, 1982; Thomas and Phillips, 1979). The term
bypassing will be used in this paper.
The presence of relatively few macropores can greatly
increase the soil hydraulic conductivity (Bouma and Ander¬
son, 1973) and cause very rapid transport of solutes through
the soil (Thomas and Phillips, 1979; Bouma et al., 1982)
that is inconsistent with the CD model (Kanchanasut et al.,
1978). The average pore water velocity (vq) is not an
appropriate descriptor of convective transport when there is
bypassing because flux is effectively limited to a small
portion of the total soil-water (i.e., that within
macropores).
Application of Eq. 2-1 on a field-plot scale has shown
that the parameters vq and D are extremely variable and
log-normally distributed (Biggar and Nielsen, 1976), which

12
makes their estimation very difficult. Application of
numerical models, which have been developed for more realis¬
tic, transient conditions that exist in the field, are
subject to similar limitations.
As an alternative, management level models that use
less variable, capacity-type parameters (e.g., 0) have
proven successful in many cases (Rao et al., 1981; Rose et
al., 1982). These models are largely empirical in nature
but utilize the concept of convective transport, as defined
by v0* Failure of these simplified management level models
has been attributed to flow along macropores (Thomas and
Phillips, 1979; Barry et al., 1985).
The three situations mentioned above in which the
inapplicability of Eg. 2-1 have been documented; unsaturated
flow; flow around aggregates; and flow along macropores,
have in common the fact that a portion of the soil-water,
called mobile water (@m) in this paper, moves much more
rapidly through the soil than the remaining, immobile water
(©im)- Surface applied solutes can be transported much more
rapidly in the mobile region and thereby bypass solutes
residing in immobile regions.
An alternative model, known as the mobile-immobile
water model (MIM) has been developed to explicitly account
for this situation (Passioura, 1971; Van Genuchten and
Wierenga, 1976). In this model two soil-water phases are
explicitly differentiated, with all convective transport
assumed to occur in the mobile phase and transport in the

13
immobile phase restricted to diffusion. Since no specific
pore geometry is assumed, the diffusive transfer between the
two soil-water phases is described as being proportional to
the concentration difference between them. The equation for
transport of a nonadsorped solute under steady water flow
conditions that is consistent with the MIM model is
© 3c/3t + 0,3C. /3t= D32C /3z2 - 0 v 3C/3z (2-2)
m ím ím m mm
with interphase transfer described as
0. 3c. /3t = a(C -C. ) (2-3)
ím inr v m im' v '
where the subscripts "m" and "im" refer to mobile and
immobile phases, respectively. The parameter a is an
empirical constant called the mass transfer coefficient
(hr â– *â– ). The parameter v differs from v in that it is
m o
calculated from q/@m. Simplified, capacity-parameter based
management level models, analogous to those based on the CD
model, have been developed for the MIM model (Addiscott,
1977). Use of this model requires the fraction of mobile
water $ (0m/0), as an input.
Our primary objective in this study was to select a
modeling approach to describe water and solute movement
under field conditions. From the above discussion it is
clear that the nature of the conducting pore network, or the
hydrologically effective pore geometry, greatly affects the
applicability of a given model. Ideally, it would be
possible to infer which model (if either) is appropriate
from standard soil descriptions. Although the impact of
soil structure on the hydrologically effective pore geometry

14
is well documented (Elrick and French, 1966; Cassel et al.,
1974; McMahon and Thomas, 1974), and the effects of soil
texture are widely recognized (Thomas and Phillips, 1979;
Addiscott and Wagenet, 1985), the information is of a
qualitative nature (Bouma, 1981). More detailed morphologi¬
cal descriptions are generally not well suited to this
purpose because they do not yield information on pore
continuity or the degree of pore interconnections (Bouma,
1981). For these reasons, study of the hydrologically
effective pore geometry has usually focused on measurements
of soil hydraulic properties (Bouma and Anderson, 1973) and
movement of tracers (Bouma, 1981; White, 1985).
Our secondary objective was to identify soil structural
or pore characteristics that characterize the hydrologically
effective pore geometry. This information is potentially
useful in using field-level observations to infer the nature
of flow through soils. Dyes have been used extensively for
this purpose (Bouma and Dekker, 1978; Omoti and Wild, 1979).
Materials and Methods
Soil Characteristics and Management
The soil studied is an Instituto clay loam (fine-loamy,
mixed, isohyperthermic, Typic Dystropept) located at CATIE
(Centro Agricola Tropical de Investigaciones y Enseñanzas)
near Turrialba, Costa Rica. It is derived from alluvium
deposited from the surrounding mountains which are primarily
of volcanic origin. Very little soil profile development

15
was evident except for an accumulation of organic matter at
the surface. Textures are clay loam throughout. Aguirre
(1971) described the structure as weak, subangular blocky
with peds ranging from 0.5 to 2.0 cm in diameter near the
surface and becoming increasingly less pronounced with
depth. The site is nearly level and the soil is considered
to be moderately well drained (Aguirre, 1971).
Soil columns were taken from two experimental plots
located about 100 m apart. One plot had been planted to
maize for six years prior to column removal. The crops on
the other plot were a mixture of cacao, laurel, and plantain
and had been under continuous management for five years
prior to column removal. No tillage was performed on either
plot during that time and no machinery entered either plot.
Columns numbered 1, 2, and 3 (Table 2-1) were taken from the
first plot and those numbered 4 and 5 were taken from the
second.
Column Experiments
Five undisturbed soil columns were collected at two
depths (0-30 cm and 75-105 cm). The procedure used to
collect the samples was as follows: (1) dig a pit approxi-
2
mately 1.0 by 1.5 m leaving a pedestal approximately 0.3 m
in the middle, (2) cut a circle about 15 cm in diameter in
the middle of the pedestal with a sharp knife, (3) force the
12 cm diameter PVC tube that is beveled at the end and lined
with petroleum jelly into the area bounded by the circle,
(4) shave the surrounding soil from the edges of the pipe

16
and cut another circle about 1 cm deep, (5) repeat the last
two steps until the 30 cm long column is filled, and (6)
separate the column from the soil at the bottom. The upper
12 to 15 cm of each column were used in the displacement
experiments.
Fritted glass endplates were fitted to both the inlet
and outlet ends of the columns. A constant hydraulic
potential was maintained at both column ends during dis¬
placement experiments. Tension was applied at the inlet end
via a Marriot devise and at the outlet end by a hanging
water column. All displacement experiments were conducted
under a unit hydraulic head gradient except the two dis¬
placements performed in Column 1 (Table 2-1) under saturated
3 -1
conditions. The influent H activity was about 7 nCi ml
in a 0.01 M CaCl2 solution. The 3H activity in effluent
fractions was assayed using liquid scintillation techniques.
Rhodamine B dye displacements were performed using the
3
same columns after the H20 displacements. The influent dye
concentration was 2 g L The amount of dye displaced
ranged from 0.05 to 0.32 pore volumes (Table 2-1). Dye
patterns in cross-sections of the columns were photographed
at 1 cm intervals after displacement. Selected
cross-sections were then traced with pen and ink.
The experimental conditions under which all displace¬
ments were performed are summarized in Table 2-1.

17
Table 2-1. Experimental conditions for displacement.
Column
Number
Column
Length
Depth
Tension of
JH Displ.
Tension of
Dye Displ.
Pore Vol.
Displ.*
1
cm
12.5
surface
kPa-
0.0,0.1,0.5,
1.0
0.32
2
15.0
subsoil
1.0,2.0
0.0,1.0
0.0
0.05
3
12.5
surface
0.0,1.0
0.0
0.13
4
12.0
subsoil
0.0,1.0
1.0
0.19
5
12.5
surface
0.0,1.0
—
* The number of pore volumes displaced during the dye
application.
Field Techniques
The depth to the water table was measured in perfo¬
rated, 2.54 cm diameter PVC pipe. Measurements were made
several times each week. Rainfall was measured daily at the
site. Infiltration measurements, carried out under the
direction of Dr. Carlos Burgos, were made with a double-ring
infiltrometer at 24 locations on an adjacent plot. The
dimensions of the inner and outer rings were 31 and 60 cm,
respectively.
Adsorption Experiments
3
Adsorption of i^O was measured using two different
batch techniques under two slightly different conditions.
The first method was that described by Dao and Lavy (1978)
in which the soil-solution ratio was 2 to 1 (g g ^). The
soil used was taken from column 3 after the displacement
experiments had been performed and the soil was oven dried
for pore volume determination.
In the second method, air-dried soil from column 3 but
3
not used in the displacement experiment, was added to
solution in a soil-solution ratio of 1 to 2 and placed in a

18
. 3
shaker overnight. Concentrations of H20 ranged from 7 to
0.7 nCi ml â– *" in both experiments.
Parameter Estimation and Model Evaluation
In Eqs. 2-1 through 2-3 no adsorption was assumed,
which is unrealistic for most tracers. Expansion of Eq. 2-1
to include adsorption results in the following expression:
3C/3t + (BD/9) (9S/3t) = D32C/9Z2 - v^C/gz (2-4)
where BD is the bulk density (g/cm^), S is the adsorbed
phase concentration (mg g and the other parameters have
been previously defined.
Adsorption can frequently be described by a linear (or
linearized) equation of the form
s = KDCe (2-5)
_3
where Ce is the equilibrium solution concentration (mg cm )
_3
and Kd is an empirical distribution coefficient (g cm ).
If the adsorption process is assumed to be instantaneous and
reversible, Eq. 2-5 can be substituted into Eq. 2-4 to give
RF3C/3t =D32C/3z2 -Vq3C/3z (2-6)
where RF, the retardation factor, is defined by
RF = 1 + BDKd/0. (2-7)
Analysis and interpretation of parameter values is
facilitated by the use of dimensionless variables. Equation
2-6 can be described in terms of the following dimensionless
variables:
T = vQt/L, (2-8)
x = z/L, (2-9)
P = vqL/D, and
(2-10)

19
C = Cb/CQ/ (2-11)
where v , t, z and D have been defined previously, L is the
column length, P is the Peclet number, T is the number of
pore volumes, x the dimensionless distance, and C the ratio
of effluent (C^) to influent (CQ) concentration. (Note that
the initial soil solution concentration is assumed to be 0).
Substitution of these variables into Eq. 2-6 results in the
following expression:
RFOC/3T) = (1/P) (32C/3X2) - 9C/9X. (2-12)
In order to incorporate adsorption into Eqs. 2-2 and
2-3, S is partitioned between mobile and immobile phases
such that
S = fSm + (2-13)
where S,,, and S. are the adsorbed concentrations in the
m ím
mobile and immobile regions, respectively, and f is the
fraction of adsorption sites in the mobile region. Assuming
that the same linear relationship expressed in Eq. 2-5
applies to both the mobile and immobile regions, Eqs. 2-2
and 2-3 can be written
lem+BDfV3Cm/3t + [9im + U'f >BDK„]3Cim/3t =
(0 D)32C_/3X2 - (8v )3C„/3x
m m m m m
(2-14)
and
[eim+(l-f)R0KD]3Cim/3t = a(Cm-Cim). (2-15)
As with the CD model, the MIM model can be described in
terms of dimensionless variables. The variables used are
3 = (0m + fBDKD)/(0 + BDKd), (2-16)
w = aL/q,
(2-17)

20
C1 Cm/Co' and
c2 = Cin/Co'
(2-18)
(2-19)
where c and C. refer to soil solution concentrations in
m ím
the mobile and immobile regions. All other parameters have
been defined previously. The MIM model for steady-state
water flow conditions can now be described by
3RF(3c1/9T) + (l-(3)RF9c1/3T =(1/P) (32c1/9x2) - 3^/ax (2-20)
and
(1-P)RF3c2/3T = w(c1-c2). (2-21)
The result of these transformations is that the CD
model is described by Eq. 2-12 and the MIM model by Eqs.
2-20 and 2-21. Measured solute breakthrough curves (BTC's)
were fit to both the CD and MIM models. The program CFITIM
(van Genuchten, 1981), which uses a nonlinear, least sum of
squares criteria for goodness-of-fit was used. In every
case a first type, constant concentration, influent end
boundary condition was assumed. The other boundary condi¬
tion was that of a semi-infinite column.
Results and Discussion
Hydraulic Parameters
-1 3
The Darcy flux (q, cm hr ), soil-water content (0, cm
_3
cm ), and soil-water tension (h, kPa) under which the
experiments were performed are shown in Figs. 2-1 through
2-5. The hydraulic conductivity (K, cm hr-1) value is
included for the two BTC's in which K was not equal to q.

21
Hydraulic conductivity values were extremely high
considering that the soil has a clay content of greater than
30%. The combination of high clay content and high saturat¬
ed hydraulic conductivity (Ksat) is frequently indicative of
flow along macropores (Bouma and Anderson, 1973; McKeague et
al., 1982). Saturated 0 values are also fairly high.
A dramatic decrease (two orders of magnitude) in K was
observed as h increased from 0 to 2 kPa (Fig. 2-1). This
was accompanied by a relatively modest decrease (6.5%) in 0.
Similar trends in K and 0 were observed in the other columns
(Figs. 2-2 to 2-5). This indicates either that: (1) few
discrete, large (>0.5 mm radius) pores conduct large volumes
of water rapidly under saturated conditions, or (2) water
held in large pores serves to "connect" a number of pores
that conduct water rapidly.
The series of tensions in Fig. 2-1 illustrates the
transition between saturated and unsaturated conditions for
the surface soil. Preliminary investigations with subsoil
showed a qualitatively similar pattern. This similarity
between subsoil and topsoil was confirmed in subsequent
comparisons between saturated and unsaturated conditions
(Figs. 2-2 through 2-5).
Qualitative Evaluation of Breakthrough Curves
In general, BTC shapes changed dramatically as soil
water tension was increased. BTC's obtained under saturated
conditions were highly skewed, characterized by very early

22
C/C
Figure 2-1, a-f. The effect of soil-water tension (h, kPa),
soil-water content (0, cnr cm j), and Darcy flux (q, cm
hr i) on the elution of tritiated water in Column 1. The
continuous line represents the best fit of the MIM model in
a, b, and c, and of the CD model in d, e, and f.

23
Figure 2-2, a and b. Effect of soil-water tension (h, kPa),
soil-water content (©, citi cm J), and Darcy flux (cm hr 1)
on elution of tritiated water in Column 2. The solid lines
in a and b represent the best fit of the MIM and CD models,
respectively.
PORE VOLUME PORE VOLUME
Figure 2-3, a and b. The effect of soil-water tension (h,
kPa),_|oil-water content (0, cmJ cm J), and Darcy flux (q,
cm hr ) on elution of tritiated water in Column 3. The
solid lines in a and b represent the best fit of the MIM and
CD models, respectively.

24
Figure 2-4, a and b. The effect of soil-water tension (h,
kPa), soil-water content (0, cm cm J), and Darcy flux (q,
cm hr 1) on elution of tritiated water in Column 4. The
solid lines represent the best fit of the MIM and CD models
in a and b, respectively.
Figure 2-5, a and b. The effect of soil-water tension (h,
kPa), soil-water content (0, cmJ cm J), and Darcy flux (q,
cm hr i) on the elution of tritiated water in Column 5. The
solid lines represent the best fit of the MIM and CD models
in a and b, respectively.

25
appearance of tracer in the effluent and a slow approach of
effluent concentration towards 1.0 (also called "tailing").
This behavior has been observed in undisturbed columns
(Anderson and Bouma, 1977; White et al., 1984), and has been
inferred from field studies of solute movement (Wild and
Babiker, 1976). Such skewed BTC's indicate that solute was
conducted relatively rapidly through the columns by a small
fraction of the total soil-water. The rapidly conducting
fraction of the soil-water has been related to
inter-aggregate regions (Nkedi-Kizza et al., 1982),
inter-ped regions (Anderson and Bouma, 1977), and discrete
macropores (Kanchanasut et al., 1978) in other soils.
Unsaturated BTC's are markedly more symmetric, with a
later arrival of tracer and less tailing. This trend was
not reversed as tension was increased to 2 kPa. These
results contradict other studies that have shown that
tailing in BTC's resulted from increases in soil water
tension (Nielsen and Biggar, 1961; Gaudet et al., 1977).
Apparently the pore network in this soil is sufficiently
interconnected that drainage of large pores does not result
in the isolation of stagnant regions in the column. It
should be noted that both of the studies referenced above
were performed on sands in packed columns at relatively low
soil-water contents. Lower soil-water contents enhance the
possibility of the creation of isolated, stagnant regions in
the soil. One study by Elrick and French (1966) that
compared saturated and unsaturated flow in an undisturbed

26
column found that dispersion decreased with application of
tension, although marked asymmetry during saturated flow was
not observed.
The observed change in BTC shape with increasing soil
water tension can be explained by the concomitant decrease
in q and/or by changes in the effective pore geometry. In
general, bypassing is enhanced by greater fluxes at a given
0 because because there is less time for diffusive transfer
into stagnant regions. At the same time, increasing tension
changes the effective pore geometry by draining larger pores
that may be responsible for the observed bypassing.
Both effects were operative in this study but the
effect on the effective pore geometry was dominant. This is
illustrated in Fig. 2-1. When flow rates were reduced and
the soil remained saturated (Figs. 2-la and b), the BTC
shape was only slightly altered. However, when displacement
on the same column was performed under unsaturated condi¬
tions at approximately the same q (Figs. 2-lb versus 2-ld
and e), there was a considerable change in BTC shape. These
trends were reflected in the models and parameters used to
describe the BTC's.
Quantitative Evaluation of Breakthrough Curves
All BTC's were fit to both the MIM model and the CD
models. In general, curve fits fell into two groups, satu¬
rated and unsaturated, with the 0.1 kPa run (Fig. 2-lc)
intermediate. The parameter values obtained will be dis¬
cussed by these groups.

27
Unsaturated Breakthrough Curves
Two dimensionless parameters are required in the CD
model, P and RF. The best least sum of squares fit was
obtained allowing both parameters to vary. The solid line
in all the unsaturated BTC's except the 0.1 kPa run repre¬
sent the calculated best fit using the CD model (Figs. 2-1
through 2-5). In general, the agreement was excellent. The
resultant parameter values and associated 95% confidence
intervals shown in Table 2-2 appear to be independent of the
depth from which the columns were sampled. Peclet numbers
(P) ranged from 3 to 12 and RF values from 1.12 to 1.17.
Dispersion coefficients calculated from those P values were
high relative to those measured in sieved, packed columns,
but this is expected in undisturbed columns (McMahon and
Thomas, 1974; Cassel et al., 1975). It indicates that there
was a relatively wide range of pore water velocities within
the column.
The RF values obtained were high considering that
tritium is frequently assumed to be nonadsorped (RF=1.0).
Tritium sorption has been noted by several workers (Mansell
et al., 1973; Wierenga et al., 1975; Van de Pol et al.,
1977; Nkedi-Kizza et al., 1982) and has been associated with
hydroxyl exchange with clay lattice hydroxyls (Stewart,
1973) .
As a check of the accuracy of RF values determined by
curve-fitting, the area above the measured BTC was calculat¬
ed for several of the unsaturated BTC's (Pandey and Gupta,

28
1984). The areas measured agreed closely with the RF values
obtained by curve-fitting. Since the fit between calculated
and measured curves was excellent in every case, this result
confirms the finding of van Genuchten and Parker (1984) that
mass balance is preserved with the solution and boundary
conditions used.
Table 2-2. Convective-dispersive model parameter values.
Col
No.
h
P
RF
kd
D
kPa
ml g ^
cm2 hr ^
1
0.1
0.90
1.08
0.031
492
(0.02)*
(0.01)
(0.006)
(11.7)
1
0.5
2.28
1.10
0.043
60.9
(0.06)
(0.01)
(0.006)
(0.02)
1
1.0
4.45
1.12
0.053
6.44
(0.31)
(0.02)
(0.008)
(0.17)
1
2.0
6.62
1.17
0.069
0.58
(0.17)
(0.01)
(0.002)
(0.01)
2
1.0
13.5
1.12
0.062
2.52
(0.76)
(0.01)
(0.005)
(0.13)
3
1.0
13.1
1.18
0.080
3.78
(0.49)
(0.01)
(0.002)
(0.14)
4
1.0
12.2
1.16
0.071
1.65
(0.57)
(0.01)
(0.003)
(0.07)
5
1.0
7.01
1.18
0.079
5.81
(0.36)
(0.01)
(0.005)
(0.18)
* Numbers in parenthesis are the 95% confidence intervals
associated with the estimated value.
An independent check of RF can be obtained from mea¬
surement of adsorption in batch isotherms. The batch
isotherms obtained using both methods described in the
previous section were essentially identical. Both isotherms
were linear with r2 values of 0.994 and 0.997 and KD values,
with 95% confidence intervals of 0.134 + 0.0045 and 0.132 +
0.0047 mg g-^. These values are significantly larger than
those obtained from values derived from fitted parameters
(Table 2-2).

29
Discrepancies between batch and column-measured adsorp¬
tion parameters have been noted by others (Nkedi-Kizza et
al., 1982). The value of RF calculated using the
batch-derived value (including a coarse fragment content
of 3.9% in the column) is 1.26, which is slightly higher
than that obtained from curve fitting (1.17). This discrep¬
ancy is likely due to differences in the condition of the
soil when the experiments were performed. The batch iso¬
therms were conducted on air-dried or oven-dried soil while
the columns were never air-dried.
When all unsaturated BTC's except the 0.1 kPa run were
fit to the MIM model either extremely high w values (>35) or
(3 values of 1 were obtained. From inspection, it is clear
that Eqs. 2-20 and 2-21 are indistinguishable from Eq. 2-12
when (3=1, and use of the MIM model is not justified. The
effect of high w values is less clear but implies extremely
rapid transfer between mobile and immobile regions which
effectively eliminates the need to make a distinction
between them. This will be discussed in greater detail in
the next section.
Saturated Breakthrough Curves
The 0.1 kPa run (Fig. 2-lc) will be included in this
part of the discussion because it more closely resembles the
saturated BTC's than the other unsaturated BTC's. When the
CD model was applied to the saturated BTC's generally poor
fits resulted. This was due to the very rapid rise in C/CQ
and obvious leftward shifting. Use of the MIM model

30
requires specification of 4 dimensionless variables; P, RF,
3, and w. Although it is possible to allow all variables to
vary simultaneously, the resultant parameter estimation is
relatively imprecise. One value, RF, should be consistent
with those derived from the unsaturated (CD model) BTC's.
This value was accordingly taken from the unsaturated curves
and fixed during parameter estimation for the saturated
curves.
In every case, very close agreement between measured
and calculated BTC's was obtained. In general, the parame¬
ter values in Table 2-2 indicate the following trends; very
small P values corresponding to extremely large D values; (3
values on the order of 0.23 to 0.45; and w values ranging
from 0.33 to 3.84.
Table 2-3. Mobile-immobile water model parameter values.
Col.
P
RF
P
w
D
ESR
$
No.
cm2/hr
cm
1 (fast)
0.29
1.12
0.76
1.02
2515
0.18
0.81
(0.01)*
(0.03)
(0.39)
1 (slow)
0.86
1.12
0.43
2.46
150
0.61
0.47
(0.05)
(0.04)
(0.45)
1 (lkPa)
1.11
1.12
0.84
0.08
437
0.51
0.89
(0.03)
(0.04)
(0.03)
2
0.43
1.13
0.34
3.84
1643
0.18
0.37
(0.03)
(0.03)
(0.48)
3
0.55
1.17
0.34
2.99
2758
0.19
0.38
(0.03)
(0.03)
(0.48)
4
0.76
1.15
0.23
0.16
3497
0.86
0.26
(0.30)
(0.05)
(0.03)
5
0.14
1.17
0.45
3.89
7525
0.15
0.51
(0.02)
(0.11)
(2.53)
* Numbers
in parenthesis
are the
95% confidence
: intervals
associated with the estimated value.

31
The very low P values are indicative of an extremely
broad range in pore-water velocities in the mobile water
region. This indicates that the compartmentalization of
soil-water into only two phases assumed in the MIM model was
insufficient to account for the range of pore-water veloci¬
ties encountered. It is possible that the logic of the MIM
model be extended to consider gradations of soil-water
mobility (e.g., "very rapidly mobile water", "somewhat
rapidly mobile water", etc.). Skopp et al. (1981) have
applied this approach but to only two soil-water phases.
Recent work applying the transfer function model developed
for soil applications by Jury (1982) to the analysis of
BTC's similar to the saturated BTC's presented here (White
et al., 1986) have described the distribution of pore-water
velocity as a continuous, albeit skewed, function. Viewed
in this perspective the MIM model may be considered to
represent an extreme, bimodal pore-water velocity distribu¬
tion.
Calculation of D from fitted P values is somewhat
questionable when P values are so low. There are two sets
of boundary conditions that approximate the experimental
conditions and the solutions to those boundary conditions
diverge when P is less than 4 or 5 (see van Genuchten and
Parker, 1984; Parlange et al., 1985).
The parameter 0 means little on its own, but can be
related to the mobile water fraction, $ (recall that $=0^/0)
with the expression 3>= (RF(3) -f (RF1). Note that, if RF=1,

32
=(3. As a first approximation, (3 can be considered to be a
measure of $ in these experiments because RF is close to 1.
A more refined estimate of $ is obtained if some assumptions
concerning f are made. Recall that f was defined as the
fraction of sorption sites in the mobile region. If the
distribution of sorption sites is independent of location in
soil-water regions, then f=S> when the soil is saturated and
$=3. However, it seems reasonable to expect that propor¬
tionately more sorption sites will be found in immobile than
mobile regions because the pores in immobile regions should
be smaller and therefore have more exposed surface area.
This reasoning has been used to justify the assumption that
f=0 (Nkedi-Kizza et al., 1982) which leads to $=RF(3. Thus,
in soils with positive adsorption, $> values of greater than
3 are expected. The $ values in Table 2-3 were calculated
assuming that f=$>/2, which is an intermediate estimate.
The results in Table 2-3 indicate that values of 0.25
to 0.50 are generally consistent with the parameters fitted
with the MIM model. These values are surprisingly high in
light of the large changes in K that resulted from relative¬
ly small changes in 0. An independent estimate of $ can be
obtained by assuming that water held at field capacity is
immobile (Addiscott et al., 1977). In this case, such an
approach yields estimates of $ of approximately 0.156, which
is distinctly lower than those obtained from curve-fitting.
When this value was fixed along with RF the resultant P
values were increased, w values were decreased and the

33
goodness-of-fit was substantially reduced. These fitted
curves were too angular, displaying a more rapid rise in
effluent concentration with greater tailing than the mea¬
sured BTC's. The difficulty of obtaining independent
estimates of $ has been noted by others (Addiscott et al.,
1978) .
The parameter w is somewhat more difficult to interpret
as it is not directly related to any specific soil charac¬
teristic or property. However, work by Rao et al. (1980a)
has shown that w can be used to successfully calculate
inter-aggregate concentrations during diffusion into spheri¬
cal aggregates of known volume. In subsequent work (Rao et
al., 1982) it was demonstrated that media composed of
different sizes and shapes of aggregates could be approxi¬
mated by a single "equivalent" spherical aggregate size on a
volume-weighted basis. This work has recently been extended
to miscible displacement studies by van Genuchten (1985).
Using analytical solutions for flow through media
composed of immobile regions of known geometry van Genuchten
(1985) was able to express w in terms of an average sphere
or other aggregate shape. This technique was applied to the
saturated BTC's to determine size of effective spherical
radius (ESR) consistent with the fitted parameter values.
The parameters shown in Table 2-3 indicate that the soil may
be considered to be composed of spherical aggregates of of
0.15 to 0.6 cm radius.

34
Another way of considering the effect of aggregate size
on the effective pore geometry was presented by Rao et al.
(1980b). They showed that, when the aggregate size is small
enough relative to the pore-water velocity, a condition of
near-equilibrium will be established and the CD model should
be appropriate. For spherical aggregates, the condition of
near-equilibrium is valid when
DeL(l-í>)/(a2vo20.3) > 1 (2-22)
where is the diffusion coefficient and L is the column
e
length (Rao et al., 1980b). Taking ESR as 0.5 leads to a
critical vq of 1.6 cm/hr, which is generally less than the
vq values of the unsaturated runs (vQ=2q in these columns).
Thus spherical regions of immobile water could exist in the
columns but their effect on solute transport would be
"masked" as high dispersion.
Taken as a whole, some inferences concerning the nature
of the effective pore geometry of the soil can be made. The
high values measured in conjunction with highly skewed
BTC's indicate that water was conducted relatively rapidly
through some portion of the soil when saturated. The large
reduction in K and BTC skewedness that resulted from appli¬
cation of 0.1 to 0.5 kPa of tension suggests that soil water
was rapidly conducted via macropores (Luxmoore, 1981;
Germann and Bevin, 1981). However, the extremely low P
values and values of 0.25 to 0.50 under saturated condi¬
tions suggest that there were several such regions of
varying conductivities. The model parameter values obtained

35
are consistent with fairly large regions of immobile water.
If immobile regions are assumed, for example, the immobile
regions would have radii of 0.15 to 0.6 cm.
Although immobile regions were described in terms of
spherical aggregates, the MIM model specifies no pore
geometry and numerous other possibilities exist. Rhodamine
B dye was used to better determine the actual effective pore
geometry.
Dye Experiment
Dyes have frequently been used to visually investigate
the nature of flow paths through the soil (Bouma and Dekker,
1978; Omoti and Wild, 1979; McVoy, 1985). The approach
provides the opportunity to observe the conducting pathways
and thereby relate water and solute flow to observable
structural features or biochannels. The basic assumption
made in interpreting dye patterns is that the more solution
that passes a given point, the more darkly stained that
point will be. Thus, stained regions are interpreted as
being regions of relatively fast flow, unstained regions to
be of relatively slow flow.
It is important that this fairly simple-minded approach
not be extrapolated far in terms of correlation of dye
patterns with BTC's. In the first place, Rhodamine B dye is
sorped to the soil much more strongly than tritium (McVoy,
1985) so that dispersion is apparently reduced. Secondly,
the dye is not instantaneously and reversibly desorped as
tritium is assumed to be. And thirdly, visual evaluation of

36
color is qualitative, so that quantification of the amount
of dye at a location is not possible.
Given these difficulties, four observations of note can
be made from the dye patterns illustrated in Figs. 2-6
through 2-9. First, no significant staining of the column
edges was observed in Columns 1, 2, and 3 and the staining
on the edge of Column 4 was not as intense as in the inter¬
nal portions of the column. This is a critical question
that must be considered when undisturbed columns are used.
From these observations we do not believe that observed
BTC's were strongly affected by the presence of the column
boundary.
Second, while specific stained regions that must have
been responsible for the very early appearance of tracer in
the effluent were easily identified, with one exception,
they were not obviously associated with discrete biochannels
or structural features. Even with the segmented column at
hand, it was very difficult to determine exactly which pores
were conducting, as, in every case many visible pores were
stained. In addition, it was difficult to trace individual
pore sequences up the column because they meandered consid¬
erably across the column. Omoti and Wild (1979) and McVoy
(1985) have made a similar observations.
Third, where structural units were relatively strong as
in Column 1, there was preferential flow around them. The
structural units isolated in Column 1 ranged in radius from
about 0.3 to 1.2 cm, which is in rough agreement with that

37
Figure 2-6. Rhodamine B dye staining pattern in Column 1
resulting from displacement under a soil-water tension of 1
kPa.

38
CM
Figure 2-7. Rhodamine B dye staining pattern in Column 2
resulting from displacement under saturated conditions.

39
6
9
12
CM
CM
Figure 2-8. Rhodamine B dye staining pattern in Column 3
resulting from displacement under saturated conditions.

40
Figure 2-9. Rhodamine B dye staining pattern in Column 4
resulting from displacement under a soil-water tension of 1
kPa.

41
calculated from the curve-fit parameters. The fact that
these units were identified during unsaturated flow which
was well described by the CD model indicates that movement
into and out of those units was sufficiently rapid that
bypassing was not indicated in the BTC. This observation
probably accounts for the generally greater dispersion
observed in undisturbed columns and field studies. That is
bypassing is "masked" in the dispersion term.
Fourth, the nature of the stained regions does not
appear much different in the saturated and unsaturated
columns. This is evidence that, rather than drain a few,
discrete large pores, the application of tension drains
regions of the soil that serve to "connect" pore-sequences.
The main difference between saturated and unsaturated
dye patterns is that the "solute front" is more compressed
in the unsaturated columns. Note that the number of pore
volumes of dye solution applied to all columns was approxi¬
mately the same (Table 2-1), but the extent of staining in
the unsaturated runs was more strongly weighted toward the
inlet end of the column.
These observations compliment the results of BTC
analysis and hydraulic property measurement in the previous
section. It appears that the highly skewed BTC's and very
high Kgat measured under saturated conditions were due to
very rapid transport in a very restricted region of the
columns. These regions are better characterized as conduct
ing pore sequences than discrete macropores and their

42
identification in the field would be very difficult. There
appears to be a number of such pore sequences that range
widely in conductivity. Application of tension "discon¬
nects" the largest effective pore sequences and therefore
results in reduced skewing and K. Immobile regions were
generally characterized as regions between conducting pore
sequences as opposed to easily identifiable, physically
controlled regions. When displacement occurred under
tension, flow in the conducting regions was slow enough, and
those regions were close enough, that observed heterogeneous
flow was described as dispersion with the CD model.
Field Application
One of the stated objectives of this study was to use
information from column experiments as a basis for selecting
a model to describe movement of nutrients and water under
field conditions. The basic distinction between the two
models considered is whether or not all soil-water effec¬
tively participates in convective transport (i.e., whether
or not vQ applies). It is clear from the dye patterns in
Figs. 2-6 through 2-9 that there was bypassing in the sense
of heterogeneous flow under both saturated and unsaturated
conditions. In terms of model selection, however, bypassing
was significant only when the soil-water content was at or
very near saturation. Hence, "significant" bypassing is
expected in the field only when the soil is near saturation.
The necessary precondition for saturation of the soil
surface under field conditions is that the water input rate

43
(rainfall intensity) exceed the infiltrability of the soil
(Hillel, 1971). If no surface disturbance occurs (none was
observed) the minimum infiltrability of a uniform soil
profile is K . Since no differentiation of horizons in
sat
terms of hydraulic properties was observed, the rainfall
intensity must at least exceed Kgat if saturation and hence
"significant" bypassing are to occur.
The K . values measured in undisturbed columns were
sat
between 20 and 40 cm hr Given the well known high
spatial variability of Ksat (Warrick and Nielsen, 1980),
these values probably should not be used in this context.
We obtained estimates of Kgat from 24 double ring infiltra¬
tion measurements. The results are shown in Fig. 2-10. The
values obtained are considerably lower than those measured
in the columns. Aside from spatial variability, two
explanations for this difference are the fact that the
columns were saturated from below under tension and were
therefore closer to true saturation and the fact that
macropore continuity is enhanced in shortened columns
(Edwards et al., 1979)
When the frequency distributions of K and rainfall
intensity are compared (Fig. 2-10), it is evident that
rainfall intensity sufficient to cause ponding is rare.
Based on this information it appears that saturated flow and
the attendant bypassing are not expected to occur at the
site. This is consistent with the observation at the site
that ponding did not occur.

60
Rainfall Intensity (cm/hr) K$at (cm/hr)
Figure 2-10. Relative frequency of rainfall intensity and saturated
hydraulic conductivity estimated with a double ring infiltrcireter.
4^
4^

45
Application of BTC's measured in columns to field
conditions involves a considerable extrapolation in scale.
This requires that the representative elementary volume
(Bear, 1972) for bypassing be considerably less that the
volume of the column. Several studies have shown that
measured bypassing at a field scale was expressed in undis¬
turbed soil columns (Bouma and Wosten, 1979; Omoti and Wild,
1979; White 1985). This, however, need not be the case.
If significant bypassing at a scale larger than column
dimension is to occur, there must be some means by which
soil water movement is concentrated. This may be the result
of soil properties or the distribution of incoming water.
Large soil structural units or biochannels have been shown
to be responsible for bypassing (Bouma and Dekker, 1978;
Bouma et al., 1982). These effects are enhanced by a very
slow matrix K . . Other soil characteristics that could
sat
serve to concentrate flow are coarse fragments, steep
slopes, and strongly contrasting horizons (Bevin and
Germann, 1982).
The soils in this study exhibited none of the above
characteristics. Soil structural units were generally less
than 2 cm in diameter and poorly expressed. From the column
studies it is clear that the matrix conductivity is rela¬
tively high (>1 cm hr-^). Other characteristics such as
steep slopes, contrasting horizons and coarse fragments were
not evident. Soil animals that could potentially make large
channels (e.g., leaf cutter ants and armadillos) were

46
carefully excluded from the site.
The input of water may have been concentrated in two
ways. First, the reported intensities are averages over the
entire event so that much higher intensities would prevail
for short time periods. Second, vegetation causes a spatial
redistribution of incoming rain such that small areas of
much higher intensity than the average are expected. On the
other hand, it should be considered that Ksat is the minimum
infiltrability and that lateral movement away from local
high intensity spots is likely.
Rapid changes in water table depth and/or stream
discharge have been cited as evidence for bypassing (Thomas
and Phillips, 1979; Beven and Germann, 1982). Thomas and
Phillips (1979) noted that water in macropores can flow into
or below the rooting zone in a matter of minutes and de¬
scribed flow from a spring 30 minutes after cessation of a
large (4.57 cm) rainfall.
From 1 September to 24 July, 1982-1983, the water table
depth was monitored about 5 times each week. At the same
time, a daily record of precipitation was maintained.
Measured water table depths ranged from 80 to 200 cm. The
length of time between rainfall events and rise in water
table depth could be estimated only to within 24 hours
because measurements of both rainfall and water table depth
were made at approximately the same time (7:00 AM). There¬
fore, if a rise in water table depth and rainfall were
recorded on the same morning, the water table response could

47
have occurred between 0 and 24 hours after the rainfall
event. On the other hand, if no change in water table depth
was recorded the following day, either the incoming water
was absorbed in the soil above the water table or the
response took longer than 24 hours.
During the 11 month measurement period there were 27
rainfall events of less than one cm had no measurable effect
on water table depth. Among the rainfall events greater
than 1 cm, there were 27 for which the water table depth was
recorded both the day of the event and the day following and
that were not immediately preceded by large (>1.0 cm)
events. Of these there were 23 for which no response was
recorded the following day. The remaining 4 events occurred
when the soil was relatively moist (there had been between
3.2 and 6.6 cm of rain during the preceding 5 days) and the
water table was between 128 and 143 cm deep. If conditions
of field capacity and moderate (0.5 cm hr ^) rainfall
intensity are assumed, infiltration as calculated by the
Green-Ampt method (see Hillel, 1971) to a depth of 100 cm or
greater within 6 hrs is expected. Given this estimation,
rapid arrival of water at the water table is expected.
Thus, no evidence in support of macropore flow could be
found.
From this information it appears that macropore flow
was not common and that it likely did not occur during the
period of measurement. This is consistent with other find¬
ings in the field and laboratory. From this we conclude

48
that the CD model, or other simplified models based on the
concept that all soil-water participates in convective
transport of solutes, should be appropriate for describing
solute flow in this soil.
It is important to note that, at this time, we do not
have criteria for simple determination of bypassing in the
field. Recent work by Russel and Ewel (1985) performed near
our experimental site reported considerable amounts of flow
through selected channels. Many of the soil characteristics
described above as being related to bypassing on a large
scale were present at that site. These included, steep
slopes, the presence of large coarse fragments, a mixed
canopy, and the presence of native soil fauna. It may also
be noted that their observations of bypassing were restrict
to two large events and relatively small portion of the
total surface area. While the effects may be significant on
a hydrologic scale, they may not be in terms of calculating
nutrient losses by leaching.
CONCLUSIONS
Solute breakthrough curves resulting from miscible
3
displacement of H20 in undisturbed soil columns under a
range of soil-water tensions were evaluated in terms of the
mobile-immobile water model and the convective-dispersive
model. Model parameters derived from curve fitting indicat¬
ed that the convective-dispersive model accurately described
breakthrough curves performed under tensions greater than

49
0.1 kPa while the mobile-immobile model better described
breakthrough curves performed under soil-water tensions less
than or equal to 0.1 kPa. Thus, in terms of model selec¬
tion, bypassing was significant only when soil-water con¬
tents were at or very near saturation.
Dye patterns obtained under saturated conditions showed
that soil-water flow (and thus convective transport) was
confined to small regions within the columns. However, no
easily identifiable, discrete channels were observed in
these regions. It appears that flow was conducted via a
series of relatively large pores, or continuous
pore-sequences. The net effect of the pore sequences on
soil hydraulic conductivity was identical to discrete
channels, but the identification of the channels responsible
is virtually impossible.
Application of tension appears to have disconnected the
most rapidly conducting pore-sequences, thus reducing the
skewedness of breakthrough curves. Under unsaturated
conditions the conducting pore-sequences were slow enough,
and well enough interconnected with the rest of the soil,
that the convective-dispersive model was applicable. Even
so, dye patterns showed that flow was very heterogeneous, as
was also evidenced by high dispersion coefficients.
Comparison of the frequency distributions of
field-measured Kgat values and measured rainfall intensities
indicated that saturated conditions, and hence significant
bypassing, are not expected to occur in this soil.

50
Observation of water table response to rainfall events
supports this. Based on these experiments, we conclude that
field-scale models based on the convective-dispersive model
for solute movement should be applicable this soil.

CHAPTER III
LEACHING LOSSES FROM TWO CONTRASTING CROPPING SYSTEMS
Introduction
There is a large and increasing demand for increased
food production from humid tropical regions. The climate of
these regions is characterized by very high annual rainfall
that is considerably in excess of potential
evapotranspiration. Under these conditions there is a large
potential for loss of nutrients by leaching. This has been
cited as a major limitation to the transfer of fertilizer
technologies developed in temperate zones to humid tropical
regions (Engelstad and Russel, 1975). It is especially
important, therefore, that loss of nutrients by leaching be
considered in the development of cropping systems in the
region. One approach that has been proposed is to manipu¬
late cropping systems so that land is continuously planted
to a variety of crops (Cox and Atkins, 1979). Reduction of
leaching loss is among the many benefits ascribed to such
systems.
Measurements of nutrient loss from agricultural fields
in the tropics are rare (Greenland, 1977; Keeney, 1982;
Omoti et al., 1983). Those results that have been published
indicate a very wide range in leaching losses (e.g.,
Sanchez, 1976). The lack of leaching studies is due, at
least in part, to the numerous difficulties associated with
51

52
such measurements. The fundamental difficulty is that there
is no known means of directly measuring solute leaching
losses. Indirect methods must be employed.
One method that is commonly employed is to use a mass
balance based on the equation:
AF = Fa - Fu " Fi' (3-1)
where F (kg ha refers to the change in storage of any
given nutrient and the subscripts "a", "u", and "1", refer
to the amounts of nutrient applied, taken up by the crop,
and lost by leaching, respectively. From an agronomic point
of view, this is reasonable since the amount taken up by the
crop, which is really the object of nutrient application, is
evaluated. The chief limitation to this approach is that
Eq. 3-1 is incomplete. In addition to plant uptake and
leaching, nutrient dynamics are governed by other processes,
e.g., immobilization and denitrification of N and fixation
of K. Another problem is that the amount of nutrient stored
in the root mass is usually poorly known.
Another method used to calculate nutrient leaching is
to calculate the convective solute flux from the the rooting
zone with the equation:
J = q C (3-2)
-2 -1
where J is the solute flux (mg cm day ), q is the
soil-water flux (cm day-1), and C is the soil solution
concentration (mg cm-3). This provides a direct estimation
of leaching loss that is independent of other processes that
effect the fate of an applied nutrient. The method requires

53
knowledge of q and C. The value of q can be calculated
using Darcy's law, but the spatial variability of the soil
hydraulic conductivity is so large that this approach is
generally impractical for field-level research (Warrick and
Nielsen, 1980). An alternative is to calculate q from water
mass balance. This method provides an areal estimate of the
net water movement. Estimation of C can be based on mea¬
surements of the soil solution at specific locations in the
field, which can be made with quantifiable precision.
This approach can be extended to trace the movement of
a solute through the crop rooting zone. This provides
information concerning the length of time a solute (e.g., an
applied nutrient) can be expected to reside in the crop
rooting zone under known soil, plant and weather conditions.
Water Balance and Nutrient Leaching
If the system of interest is considered to be delineat¬
ed by a unit surface area of soil extending to the depth of
the crop rooting zone, then, assuming that the mass of water
is conserved,AW = W. - w„„. , where W is the mass of water
m out
and the subscripts "in" and "out" refer to water entering
and leaving the system. This expression can be decomposed
into a number of components such that:
AS + ARW = (P+I+UP+RON) - (E+T+APA+ROF+DP), (3-3)
where S = soil profile water content
RW = water held in the roots,
P = precipitation,
I = irrigation,

54
RON = runon of water from adjacent soil,
UP = upward percolation,
E = evaporation,
T = transpiration,
PA = water stored in the above-ground biomass,
ROF = runoff of water to adjacent soil, and
DP = deep percolation,
with all components expressed in units of cm (the density of
water is assumed to be 1 g cm-3). This general equation can
usually be simplified considerably when adapted for a
specific location and set of objectives. Based on observa¬
tions in the field, I, RON, and ROF equal 0 in this study.
In addition, the assumption was made that, relative to the
magnitudes of the other components, ARW can be ignored and
APA can be lumped with transpiration. For practical rea¬
sons, E and T were lumped into the single term,
evapotranspiration (ET). Thus,
AS = (P+UP) - (ET+DP). (3-4)
If the net deep percolation (NDP) is defined as DP-UP and
substituted into Eq. 3-4, the result is as follows:
NDP = P - ET - AS. (3-5)
In general, S and P can be measured directly, ET can be
estimated from measured parameters, and NDP can then be
calculated as the difference. It is apparent that the
accuracy with which NDP can be determined in this way is
dependent on the accuracy with which the other three parame¬
ters are determined.

55
This fairly straightforward approach is complicated by
the fact that ET is a function of S (or soil-water poten¬
tial) when conditions of soil-water stress are encountered,
which requires either that S be determined frequently, or
that S be estimated between measurements. In addition, it
provides no information about water movement between S
measurements or within the system.
Models based on the use of Eqs. 3-2 and 3-5 that
incorporate soil and crop parameters have been developed to
overcome these limitations (Rao et al., 1981; Rose et al.,
1982a). They apply this approach incrementally to layers
within the system and can thereby provide more information
about water and solute movement over depth within the system
and out of the system over time.
Model Development-Concepts
The model used in this study fits the general classifi¬
cation of capacity-parameter based models (Addiscott and
Wagenet, 1985). Water movement is calculated from differ¬
ences in amounts of water stored. Specific processes
governing the rates of movement are explicitly ignored.
The advantages of this approach over other, rate models, are
that the required parameters are generally easier to measure
and exhibit considerably less spatial variability (Warrick
and Nielsen, 1980). The disadvantage of capacity models is
that, because they are empirical representations of process¬
es, they lack the flexibility of more process oriented
models. For this reason it is important to carefully

56
consider the relationship between the processes of interest
and the empirical representation of them.
There are two basic outputs desired from the model:
(1) NDP, which is to be used to calculate movement of
nutrients beyond the rooting zone using Eq. 3-2 with q =
NDP, and (2) the time of solute residence in the rooting
zone.
The following two assumptions are considered fundamen¬
tal to the approach: (1) the position of a solute front of
a nonadsorbed, conserved solute is dependent only on convec¬
tive transport (i.e., dispersion and diffusion are not
considered), and (2) drainage of soil water proceeds to a
consistent soil-water content called the field capacity
(0£C>.
Examination of solutions to the convective-dispersive
equation indicate that, except where vqL/D (discussed in the
previous chapter) is very low (i.e., when dispersion is very
high), dispersion has relatively little effect on the
position of a solute front. That is to say that solutes
move in roughly symmetric pulses through the soil. Even
under field conditions, where D is expected to be quite
high, the effect of dispersion on the position of a solute
front is small compared with other errors and the accuracy
that is usually considered acceptable in field studies (Rose
et al., 1982b).
From the first assumption, the traditional
convective-dispersion equation is simplified to

57
0 3c / 31 = -qsc/az, (3-6)
3 -3
where 0 is the soil-water content (cm cm ). If one is
interested only in tracking the depth of the solute front
(DSF), the equation becomes;
[3DSF/3t] = (q/0) = VQ, (3-7)
where DSF is expressed in cm, and vq is the average pore
water velocity (cm day-1). The importance of vq as dis¬
cussed in the previous chapter is apparent. Studies using
packed soil columns have shown that the above principles are
applicable to a wide range of textures and initial condi¬
tions (e.g., Dahiya et al., 1984). In general, it is
expected that the approach will be valid for most
field-scale applications if vq is an appropriate descriptor
of soil water movement. It is clearly inappropriate when
water flows preferentially through large macropores or
around large soil peds or aggregates.
Several studies have demonstrated the validity of the
approach under a wide range of field conditions (Rao et al.,
1976; Cameron and Wild, 1982; Barry et al., 1985). Such
success, however, is by no means universal. Smith et al.
(1984), working with a variety of soils over a three year
period, found that agreement between measured solute front
depths and those calculated with Eq. 3-7 ranged from excel¬
lent to poor, varying both with soil and year. Still other
studies have shown the approach to be completely inappropri¬
ate (Addiscott et al., 1978; Bouma and Dekker, 1978; Thomas
and Phillips, 1979; White, 1985). In these studies

58
preferential flow through macropores causing bypassing was
cited as the reason for failure of agreement.
This work demonstrates that the applicability of the
model to the specific conditions of interest should be
tested. In the previous chapter it was demonstrated that,
at the scale of 12.5 cm diameter undisturbed soil columns,
such bypassing did not occur under flow regimes encountered
in the field. Further evidence was cited that such behavior
did not occur at the field level.
The second assumption (concerning ©fc) has seen a long
history of controversy and it is clear that its application
should depend on the objectives of the study and specifi¬
cally measured values. It is based on the general observa¬
tion that, upon cessation of infiltration, soils drain with
decreasing rapidity over time. For uniform, well drained
soils, drainage is commonly described with the following
__ T_
expression: S = at , where a and b are arbitrary constants
(Hillel, 1980). Thus, drainage never actually ceases, but
becomes increasingly slow in finite time. The choice of 0^
and the length of time required to reach that value are
arbitrary and depend on the soil in question and the objec¬
tives of the study. In addition, it is important to recog¬
nize that the relationship between soil water tension and
soil-water content at field capacity depends on the soil
(Ratliff et al., 1983).

59
When the second assumption is made, the eventual depth
of leaching from a given infiltration event (I), if ET
during redistribution is negligable, is
DSF = I/0fc, (3-8)
where I is the amount of water entering the soil during an
infiltration event (Rao et al., 1976). The significance of
the assumption of a field capacity is that it allows the use
of the amount of the event, which is relatively easy to
measure and use in computations, as opposed to using rates
and time intervals. Equation 3-8 has been modified ro
account for the effects of evapotranspiration and movement
of the solute front within the soil profile (Rao, et al.,
1976; Davidson et al., 1978; Rose et al, 1982b).
Water Table Effects
Application of the modeling approach described above
has been restricted to conditions in which a water table was
not present. The presence of a fluctuating water table in
close proximity to the rooting zone renders the second
assumption invalid because the soil-water does not drain to
field capacity as it is generally defined but to some
greater value that depends on the location of the water
table. In the extreme, at the water table interface, that
value is the saturated water content of the soil (0 ). At
s
increasing distances above the water table that value
decreases, until at some point, it equals the "normal" field
capacity.

60
The soil profile can therefore be divided into two
regions; that above the influence of the water table, and
that within the influence of the water table. The propor¬
tion of the soil profile in either region is variable,
depending on the depth of the water table and the height of
water table influence (HWTI). In the region above the HWTI,
drainage is assumed to proceed as if no water table were
present. Drainage in the region below the HWTI is assumed
to proceed to a different, greater value. This value is
considered to be a "temporary" field capacity, ©fc*, that is
dependent on the position of the water table.
The relationship between field capacity, the HWTI, and
the water table depth are illustrated in Fig. 3-1. Note
that the soil-water content at the HWTI is equal to 0^c and
that the water table represents effective saturation. Under
the quasi-equilibrium conditions to which the soil is
assumed to drain, the soil-water tension between the HWTI
and the water table (measured in cm H2O) is numerically
equal to the height above the water table (expressed in cm).
This is illustrated as a linear function in Fig. 3-1, but
may be quite different, depending on the soil moisture
characteristic of the soil. This relationship should be
independent of the direction of water table movement if
hysteresis effects are ignored.

Depth (cm)
61
0 (cm3cm"3)
Figure 3-1. Schematic representation of the relationship
between water table depth, the height of water table
influence (HWTI), 0f * , and 0f .

62
Materials and Methods
Cropping Systems and Soils
The two cropping systems chosen for comparison were:
(1) a mixed cropping system composed of laurel (Cordia
alliodora), cacao (Theobroma cacao) and plátano (Musa
paradisiaca), and (2) a monocropping system composed of
maize (Zea mays L.). These two cropping systems will be
referred to as the MP, for mixed perennial, and the MA, for
monocropped annual, cropping systems.
The sites of the two cropping systems were separated by
approximately 100 m and were located in the "la Montana"
section of the experimental plots at CATIE (Centro Agricola
de Investigaciones y Enseñanzas) near Turrialba, Costa Rica.
Both cropping systems were planted on Instituto clay loam,
which is classified as a Typic Dystropept, fine, mixed,
isohyperthermic (Aguirre, 1971).
The management level of both systems was designed to
promote high yields and profitability. The MA plot is
located in a larger study area used by Dr. Carlos Burgos to
study the effects of tillage and residue management on maize
yields. It was initiated in November of 1976. A 120-day
variety was planted at a density of 40,000 plants ha ^. Two
crops were planted each year, one in late May, and the other
in early November. Approximately 240, 55, and 40 kg ha 1 of
N, P, and K, respectively, were applied each year. Applica¬
tions were made on the planting date and approximately 30

63
days after planting, resulting in 4 applications each year.
The plot dimensions were 32 by 20 m.
The MP plot was part of a larger study of intercropping
with perennial crops directed by Dr. Gustavo Enriquez. It
was initiated in 1977. The planting densities were 1,111;
432; and 123 plants ha-1 for cacao, laurel, and platano,
respectively. The annual fertilizer regime was 140 kg ha
-1
N, 30 kg ha-1 P, and 20 kg ha"1 K in four applications. The
dimensions of the MP plot were 18 by 18 m.
Model Developement-Construction
The model used in this study was based on the model,
NITROSIM, developed by Rao et al. (1981). Water movement
was divided into three phases; infiltration, redistribution,
and static. Calculation of soil-water content (01) and flux
(q1) for depth increments (i=l,2,...n) of thickness of Az cm
within the soil profile was an iterative process carried out
at discrete time increments (At).
Infiltration was assumed to proceed as a "square pulse"
(i.e. Green-Ampt infiltration) within one time increment at
an infiltration soil-water content (0. £) where 0^ <0. ^<0 .
inf fc inf s
Assumed ET during infiltration was 0. The depth of the
wetting front (dwf) resulting from a rainfall event of I cm
was calculated such that
I = E (0¿ fx - 01) dwf, i=l, 2, . . . ,n. (3-9)
The change in DSF (ADSF) resulting an infiltration event, I,
was calculated as

64
ADSF =0 I < AW (3-10)
ADSF = (I - AW)/©inf I > AW (3-11)
where
AW = 2(©inf1 " ©1)Az, i = 1,2,...,m (3-12)
and m is the depth increment in which DSF resided prior to
the event.
Redistribution calculations were based on the following
expression:
0 = 0fc + (0inf " 0fc) exP(ct)' (3-13)
where TRD is the length of time required for the soil to
drain to field capacity. In this way 0 decreases "exponen¬
tially" to a value within 1% of 0fc after TRD days of
drainage.
In the algorythm used to update 0, q, and DSF described
i * i
below, 0 is used to denote 0 at t = t + At. Calculations
proceeded from the upper depth increment (i=l) down as
follows:
q1 = 0 (3-14)
O1 ' = 0fc + (01 - 0f X) exp(cAt) (3-15)
q2 = (01 ' - 01) Az/At (3-16)
and, for i > 1 and 01 > ®fc»
01' = 0fc1 - 01 - 0fc) exp(cAt), (3-17)
q1+1 = (01' - ©') Az/At + q1, (3-18)
or, for 01 < 0£C,
01 = q1 Az/At + 01 01 < 0fc. (3-19)
For depth increments within the crop rooting zone ET is
included so that

65
01' = 01' - U1 At (3-20)
where U1 is the rate of ET from the i-th depth increment.
Changes in DSF (ADSF) were calculated as
ADSF = DSF + q1 (At/Az©1) (3-21)
when DSF was in the i + 1 depth increment at time t.
Some modifications were required to incorporate water
table effects. The water table depth was an input to the
model taken from measured and interpolated values. The HWTI
*
and 0£c (Fig. 1) were calculated on a daily basis from the
water table depth. All water entering depth increments
within the HWTI in excess of that required to account for
measured changes in water table depth was considered to be
NDP. Redistribution within the HWTI proceeded as described
above with 0^c replaced by Upward movement of water
was not explicitly considered.
The static phase commenced after field capacity was
achieved. During that phase changes in soil-water content
occurred only as the result of ET. Note that redistribution
could result from either infiltration events of a lowering
of the water table.
The soil profile was divided into 22, 5 cm increments
for numerical computations in the simulation model. Thus
the total depth of interest of 110 cm. The time step used
for calculation of water and solute movement was 0.1 day.
Days were considered to begin at 7:00 AM, when measurements
of water table depth, rainfall, and soil-water content were
made. Rainfall events were assumed to take place at 4:00

66
PM, which is roughly the most common time for such events.
All programming was done on a VAX-11 minicomputer in
FORTRAN-77.
Determination of Inputs
Use of the model requires as input the rainfall,
potential evapotranspiration, water table depth, initial
soil water content, and the soil and plant parameters.
Rainfall was measured daily with gauges located adjacent to
each plot. The water table depth to 220 cm was measured
about 5 times each week in perforated PVC tubes located in
the center of each plot that served as piezometers. Model
outputs were generated for two different time periods, the
simulation period and the calibration/validation period,
which are described subsequently. The initial soil-water
content for the simulation periods was determined
gravimetrically and for the calibration/validation period
with a neutron probe.
Soil samples for soil-water content determination were
taken at 10 cm intervals from 6 auger holes in the MP plot
and 9 holes in the MA plot. Soil-water content was deter¬
mined by weight loss after drying at 110° C to a constant
weight. This was converted to a volumetric basis by multi¬
plication with the soil bulk density.
Two methods were used to calibrate the neutron probe.
First, counts were made simultaneously with gravimetric
sampling approximately 1 meter from the access tube.
Second, a large tub was filled with soil taken from an

67
adjacent plot. The soil was then mixed and adjusted to
three different soil-water contents, packed into the tub,
and measured. Counts measured in the tub were adjusted for
bulk density as suggested by Greacen et al. (1981). The
resultant calibration curve, including field and
tub-measured values are shown in Fig. 3-2. There were five
access holes in each plot and counts were made at 15-cm
intervals from depths of 20 to 110 cm.
Evaporation from a class A US Weather Bureau pan
located approximately one km from the site was the basic
input used for calculating the potential evapotranspiration
(PET). The measured pan evaporation (E ) was converted to
pcin
PET using the equation PET = k Epan where k is the pan con¬
stant, taken from Doorenbos and Pruitt (1974) as 0.8.
Soil Parameter Estimation
Soil profiles were assumed to be uniform over the
depths of interest with respect to the required soil hydrau¬
lic parameters because only small trends with depth were
observed in the plots. There were some differences in soil
properties between the two plots used in the study even
though they were separated by only 100 m and mapped as the
same soil series. The main difference was that soils in the
MP plot had about 5% more clay, on the average, than the MA
plot, which corresponded with noticeably higher soil-water
contents. For this reason slightly different parameter
values were used for the two plots.

68
Figure 3-2. The relationship between neutron probe count
ratio and soil-water content used to calibrate the probe.
The regression equation was; count ratio = 2.69 © + 0.65,
with r = 0.993.

69
The soil parameters required to calculate infiltration,
drainage, and movement of the solute front in depths above
the HWTI are ®fc' an<^ TRD* Sixteen tensiometers,
placed at 15 cm intervals from 10 to 115 cm deep, 2
tensiometers at each depth, were installed in a 3x3 m
subplot immediately adjacent to the MA plot. Water was
ponded until tensiometers indicated that approximate steady-
state flow conditions had been achieved. At that point the
subplot was covered with straw mulch and plastic to elimi¬
nate evaporation, and changes in soil-water content were
monitored with a neutron probe. The value of TRD was
estimated as the length of time required for the rate of
change of soil-water content to become negligible.
A similar test was attempted in the MP plot but had to
be discontinued after large rainfall events caused a rise in
water table. The value of 0^ was estimated as the soil
water content at the soil-water tension (cm) equal to the
HWTI (also in cm). The soil water characteristic curve
under desaturating conditions in two 5.4 cm diameter cores
of undisturbed soil from the MP plot was measured for this
purpose. The value of TRD was assumed to be the same for
both plots. Estimation of was based on observation of
the soil water content over time in both plots.
Two additional soil parameters are required for calcu¬
lations within the zone of influence of the water table; the
*
height of water table influence (HWTI), and 0^ . Average
measured soil-water tensions at two different depths, 75 and

70
105 cm, were compared with the water table depth. The
soil-water tensions were measured with tensiometers at the
two plots. In the MP plot there were 10 tensiometers at 75
cm and 5 at 105 cm. There were 9 tensiometers at both
depths in the MA plot. The HWTI was taken as the maximum
elevation difference for which approximate equilibrium
between soil-water tension and the water table could be
*
expected. Values of 8^ were assumed to vary linearly with
the height above the water table as shown in Fig. 1.
Plant Parameter Estimation
Measurements of root weights in the MP system shown in
Table 3-1 (Alpizar, personal communication) were used to fit
the following exponential relationship of root concentration
with depth:
Root weight = 25.179 exp (-0.067z), (3-22)
where z is depth (cm). The maximum rooting depth was taken
as 70 cm. This rooting depth was assumed to be constant
over time.
*
Table 3-1. Root distribution in the MP plot .
Depth
Mean Root^Weight
Standard Deviation
cm
g cm
0-15
14.0
8.7
15-30
6.4
3.3
30-45
0.4
0.4
*Alpizar (Personal Communication)
No measurements of root concentrations were made in the
maize plot. Root distribution with depth over time was
calculated from relationships observed by NaNagara et al.
(1976) as applied by Davidson et al. (1978). The rooting
depth during fallow periods was assumed to be 35 cm.

71
Estimated PET values were converted to estimates of
actual evapotranspiration (AET) using a cropping factor (CF)
in the following equation: AET = (CF)(PET). A constant CF
value of 1 was used for the MP plot. This consistent with
estimates for cacao grown alone (Doorenbos and Pruitt, 1974)
and the fact that canopy coverage on the plot was complete.
The CF value for the MA plot varied over time as the
maize crop was planted and matured. Estimates of CF during
the time that the maize crop was present were taken from
Doorenbos and Pruitt (1974), who divided the cropping season
into 4 stages with respect to water use. Estimation of CF
in the MA plot during fallow periods, which had considerable
weed growth, was made by calibration as will be described in
the next section.
The amount of water extracted from each layer was
calculated using the approach of Molz and Remson (1970).
Estimation of soil-water stress was based on the following
relationship which is similar to that used by Davidson et
al., (1978):
AET = AET .(©-© )/(©*. -0 ), 0 < 0 . , (3-23)
cal pwp stress pwp ' stress'
where AETca^ is the transpiration calculated in the absence
of water stress and 0 . „ and 0 are the soil water
stress pwp
contents of stress initiation and permanent wilting, respec¬
tively.
Calibration/Validation
Soil-water measurements were made periodically with the
neutron probe during the calibration/validation period.

72
There were 5 neutron probe access tubes in each plot. The
model output used for these purposes was the profile water
content, S. This is defined as
S = /0dz (3-24)
where z is depth and the lower boundary is at 110 cm. This
was calculated from measured 0 values as
S = E©iAz, n=l,7 (3-25)
where i represents different depth increments, and Az the
increment thickness. As a check on the averaging procedure
used, S values determined by gravimetric sampling and
neutron probe measurements 18 July were compared.
For logistic reasons, the calibration/validation period
was initiated at different dates at the two plots. It
started on 14 April in the MA plot and 18 April in the MP
plot. These data were used for two purposes, calibration
and validation.
The first 18 days of the calibration/validation period
were used for model calibration. There was no rain during
that time, which was preceded by 3 weeks in which only 4.6
cm of rain were recorded. Thus, DP and P during those 18
days were 0.
Soil-water tensions of greater than 60 kPa by 23 April
and changes in S that were less than that expected indicated
that the crops on the MP plot probably experienced water
stress during the period. The parameters ®stress and 0pWp
were adjusted to account for this, assuming that changes in
S were due to AET. Soil-water tensions on the MA plot were

73
much lower at that time (about 22 kPa) due to the reduced
transpiration of the fallow vegetation. Measured changes in
S during this time were used to determine the cropping
factor for the fallow vegetation, again assuming that all
changes in S were the result of AET. The water stress
parameters for the MA plot were taken from characterization
data of Aguirre (1971) but were not critical because dry
periods did not occur while the maize crop was actively
transpiring.
After 2 May there was consistent rain and the condi¬
tions of P and DP equal to 0 did not hold. From this point
on the calibration ended and measured S values were used to
evaluate the accuracy of model calculations.
Simulation
Simulation of water and solute front movement began on
4 Nov. 1982 for the MA plot and 24 Nov., 1982 for the MP
plot and ended for both plots on 18 July 1983. Initial
soil-water contents were determined gravimetrically. The
starting date for the MA plot corresponded to the maize
planting date. A second planting of maize in the MA plot
took place on 30 May, 1983. All parameter values used in
the simulation were determined as described above.
Net Leaching Loss
Net leaching losses over the simulation period were
calculated using Eq. 3-2 with the NDP used in place of q and
C representing measured soil solution concentrations. Soil
— + 2+ 2+ +
solution concentrations of NC>3 , NH^ , Ca , Mg , and K

74
were measured in extractions from 7.6 cm diameter ceramic
porous cup samplers located at a depth of 90 cm. The cups
were pretreated in 0.1N HCL prior to installation. There
were 9 samplers in the MA plot and 8 in the MP plot.
Collections were made at approximately biweekly intervals
starting 30 Nov. in the MA plot and 11 Dec. in the MP plot.
Samples were collected by applying 40 kPa tension overnight.
No collection was made when the soil-water tension was
greater than 40 kPa. Concentrations of NO^ and NH^+ were
measured using a steam distillation technique (Bremner,
1965) and the cations were measured by atomic absorption.
Depth of Solute Front
The depth of the solute front movement resulting from
application of a nonadsorbed nutrient (e.g., NO^ ) was
simulated for 3 application dates in the MA field plot and 2
in the MP plot. The 3 application dates chosen for the MA
plot, 2 Nov., 30 Nov., and 30 May, were actual fertilization
dates on that plot. Calculations for the MP plot at the two
latter dates were included for purposes of comparison.
Results
Model Inputs
The depth to the water table and measured rainfall
amounts over the simulation period are shown in Fig. 3-3.
The water table was consistently deeper under the MA plot
than under the MP plot. A slight elevation difference
between the two plots (the observed water table differences

Watertable Depth (cm) Rainfall (cm)
75
Figure 3-3. Precipitation and water table depth at both
plots over the simulation time period (2 Nov., 1982 to 18
July, 1983 ) .

76
were about 20 cm) and differences in the drainage system at
the two plots, explain this. Open ditch drains of 100 to
150 cm depth drained both plots, but the drains around the
MA plot had a greater gradient and removed water more
quickly to a greater depth. After that depth was achieved,
both plots behaved similarly.
The rainfall distribution was fairly uniform during the
early portion of the simulation period and was then charac¬
terized by two relatively severe droughts. Shortly after
day 180 rainfall was consistently high.
Measured pan evaporation varied slightly during the
simulation period. It ranged from 0.26 cm day-1 in Dec. to
0.48 cm day-1 in April. The average monthly pan evaporation
over the simulation period was 0.35 cm day"1.
Parameter Estimation
A summary of parameter values obtained is shown in
Table 3-2. The change in soil-water tension with depth over
time during the determination of 0fc and TRD is shown in
Fig. 3-4. It indicates that the profile is roughly uniform
with depth with respect to soil hydraulic properties. The
corresponding changes in S are shown in Fig. 3-5. The
overall course of drainage is similar to that reported
elsewhere in the literature (Hillel, 1980) in that In S is
proportional to In t. This relationship was approximated
using the following parameter values: ©¿nf = 0.49, 0fc =
0.46, and TRD = 5.0 days. The curve calculated using the
above parameters is also shown in Fig. 3-5. The calculated

TENSION (K Pa)
77
Figure 3-4. Soil-water tension as a function of depth and
time after ponding. Time after ponding, in days, is
indicated below points representing the upper depth.

(ujo)s
78
TIME (days)
Figure 3-5. Profile soil-water content after cessation of
ponding. Fitted values were calculated with model
parameters in Table 3-2.

79
values are close to those measured for times up to 7 days.
This is considered to be sufficient because drainage rates
for longer time periods are expected to be considerably
different due to changes in gradient that result from plant
uptake of water and evaporation.
Table 3-2. Summary of model parameter values.
Cropping System
Parameter
MA
MP
© f
0lnf
9sat
0.46 (cm
„ -3) 0.52 (cm cm-3,
cm )
0.49
0.55
0.55
0.57
0.29
0.38
0pwp
0.42 "
0.48 "
Ti£ress
5 (day)
5 (day)
Max. Root
100 (cm)
70 (cm)
Depth
CF
variable
1.0
tension of 5.5 kPa (or 55 cm of B.^0), 0.52, was chosen.
©sat and 0j_n£ are shown in Table 3-2
The measured soil water characteristic curves upon
which the estimation of 0^c in the MP plot was based are
shown in Fig. 3-6. The value corresponding to a soil-water
The
tension of 55 cm is numerically equal to the value of
HWTI chosen (see discussion below). The estimated values of
The TRD was assumed
to be equal for both plots.
The average measured soil-water tension and distance
from the tensiometers to the water table in both plots are
plotted in Figs. 3-7 to 3-10. These plots were used to
estimate the HWTI. Each plot is somewhat distinctive, which
is due, in part, to the differing conditions encountered at
the different depths in the two plots. Recalling Fig. 3-1,
the HWTI should be the maximum distance between the water

h(kPa)
Figure 3-6. Volumetric soil-water content as a function of soil-water
tension in two cores taken from the HP plot. The soil-water content at
55 kPa (or 55 cm H~0) tension is approximately 0.52.
oo
o

AVERAGE TENSION (K Pa)
ELEVATION DIFFERENCE (cm)
Figure 3-7. The relationship between the average soil-water tension measured
with 9 tensiometers at 75 cm depth on the f' A plot and the depth to the water
table. Elevation difference refers to the distance between the tensiometers and
the water table. The straight line represents equilibrium.
oo

AVERAGE TENSION CK Pa)
ELEVATION DIFFERENCE (cm)
Figure 3-8. The relationship between the average soil-water tension measured
with 9 tensiometers at 105 cm depth on the fiA plot and the depth to the water
table. Elevation difference refers to the distance between the tensiometers and
the water tabele. The straight line represents equilibrium.
oo
to

AVERAGE TENSION CK Pa)
ELEVATION DIFFERENCE (cm)
Figure 3-9. The relationship between the average soil-water tension measured
with 10 tensiometers at 75 cm depth on the MP plot and the depth to the water
table. Elevation difference refers to the distance between the tensioir.eters and the
water table. The straight line represents equilibrium.
oo
U>

AVERAGE TENSION (K Pa)
25.0
20.0
ELEVATION DIFFERENCE (cm)
Figure 3-10. The relationship between the average soil-water tension measured
with 5 tensiometers at 105 cm depth in the tylP plot and the depth to the water
table. Elevation difference refers to the distance between the tensiometers and
the water table. The straight line represents equilibrium.
oo

85
table and tensiometers at which equilibrium can be assumed.
When that distance is greater than the HWTI, soil-water
tension could either be greater than equilibrium due to ET,
or it could be less, due to the attainment of field capacity
or infiltration. Of course, equilibrium values could also
be observed.
Considering the errors that could be introduced by
varying elevations within the plots and the accuracy with
which measurements were made, reasonable estimates of HWTI
range from 45 to 65 cm. A value of 55 cm was chosen for
HWTI for modeling purposes, which should be approximately
correct in all cases. This value agrees reasonably well
with the tension measured at the plots after 5 days drainage
(Fig. 3-4). Discrepancies on the order of 1 to 2 kPa are
not considered important because they have little effect on
estimated 0 values (Fig. 3-5). Although the tension at
field capacity is somewhat lower than commonly assumed, it
is similar to values measured in other studies (Wierenga,
1985) .
Calibration/Validation
As a check on the overall averaging procedures used to
calculate S in the field from measured 0 values, S calculat¬
ed from 0 measured gravimetrically was compared to S calcu¬
lated from 0 measured with the neutron probe. The results
shown in Table 3-3 indicate good agreement.

86
Table 3-3. Comparison of profile water contents measured
gravimetrically and with the neutron probe.
MA Plot
MP Plot
Method
Ave. S
S.D.
o
•
<
•
n
Ave. S
S.D.
C.V.
n
Grav.
cm
50.99
2.30
0.045
9
cm
55.48
2.59
0.047
5
Probe
50.79
0.73
0.014
4
56.62
1.76
0.031
4
The value of CF on the MA plot during the fallow period
obtained by calibration was 0.5. This value was used in the
remainder of the validation run and in the subsequent
simulation run for fallow periods. The other values ob¬
tained by calibration were 0„. and 9 _ for the MP plot
stress pwp
(Table 3-2).
Measured values of S over time in both plots are shown
in Figs. 3-11 and 3-12 along with simulated results. There
was close agreement between simulated and measured values on
both plots. The variability of S estimation as indicated by
the 80 and 90% confidence intervals (vertical bars in Figs.
3-11 and 3-12) in the MA and MP plots, respectively, was
rather high. This was not due to high variability per se
(the coefficient of variation ranged from 1 to 5%), but to
the limited sample numbers. Efforts to improve that condi¬
tion were hampered by defective aluminum tubing.
Soil Solution Concentrations
Measured soil solution concentrations over time under
the two cropping systems are shown in Figs. 3-13 and 3-14.
In general, the concentrations under the MA system are at
the lower end of the range reported by Barber (1984) for
concentrations in the surface horizons of Midwestern soils.
By that standard, the concentrations in the MP plot were

Figure 3-11. Comparison of profile soil-water contents in the FA plot measured
over the validation period commencing in April and those computed from .initial
conditions in November.
CO

S (cm)
Time (day)
Figure 3-12. Comparison of profile soil-water contents in the MP plot
over the validation period commencing in April and those computed from
initial conditions in November.
CO
CO

K (mM) N CmM)
Figure 3-13. Changes in soil-solution concentration of N and K+ during the
simulation period for both plots. 'Ihe vertical bars indicate the 90% con¬
fidence interval.

Mg CmM) Ca CmM)
Figure 3-14. Changes in soil-solution concentrations of Mg and Ca during
the simulation period for both plots. lie vertical bars indicate the 90%
confidence interval.

91
quite low for all ions except K+. Concentrations of
NH4+were relatively low under both systems and were not
included because the concentrations measured were generally
at or below the minimum concentration required for accurate
determination for the methods of analysis used.
With the exception of K+, soil solution concentrations
under the MA plot were clearly greater than those under the
MP plot. Even in the case of K+, the soil solution concen¬
trations in the MP plot were consistently lower than those
in the MA plot.
Examination of the concentrations and the associated
90% confidence intervals, suggests that the concentrations
were fairly constant over time. Analysis of variance
indicated that there was no significant difference
(a=0.9-0.99) between the mean concentrations measured on
each plot for all ions measured except NO^- on the MP plot.
This indicates that the means can be pooled. The overall
means and pooled standard deviations are shown in Table 3-4.
Comparison of the means of the ions in the different plots
. . + 2 +
were significantly different (a=0.99) for K , Ca , and
2+
Mg . The concentrations of NO^ in the MP plot were more
than one order of magnitude less than in the MA and fre¬
quently below the accurate detection level of 0.011 mM.

92
Table 3-4. Average soil solution concentrations.
Cropping System
Nutrient MA MP
ion Ave. S. D. Ave. S. D.
raM
K$3
ca3+
Mg2+
0.62
0.349
*
0.11
0.074
0.043
0.028
0.16
0.104
0.016
0.010
0.13
0.057
0.024
0.006
*Could not be pooled.
Simulation
Values of S calculated for the simulation period are
also shown in Figs. 3-11 and 3-12. After approximately 150
days of simulation, calculated values agreed well with those
measured. The relatively low S values calculated for the
simulation run were probably due to one of two causes:
either the AET in the dry period prior to the calibra¬
tion/validation period was overestimated due to the fact
that the laurel trees shed their leaves during the dry
season, or there was unaccounted upward movement of water
prior to that time.
The greater AET from the MP plot (3-5) was due to the
fact that transpiration from that plot was continuous. This
resulted in simulated differences in NDP (Table 3-5).
Net Leaching Loss
The net leaching loss (NLL) of the four elements
measured were calculated by multiplying the NDP and the
average soil solution concentration. The results are shown
in Table 3-5. These results are for a time period somewhat
less than one year, which makes direct comparison with most
other measured results difficult. The range of values

93
reported, however, is so great that these values easily fall
within it (Sanchez, 1976). From an agronomic viewpoint, the
loss of N03~-N in the MA plot is clearly important.
Table 3-5. NDP and Net Leaching Loss Values.
Calculated
Value
Croppinq
System
MA
MP
AET
TR*
NDP
NLL
N+
45 (cm)
111 (cm)
66 (cm)
57 (kg/ha)
55 (cm)
111 (cm)
57 (cm)
1 (kg/ha)
K 2+
M Caz
3 (kg/ha)
21 (kg/ha)
43 (kg/ha)
1 (kg/ha)
3 (kg/ha)
3 (kq/ha)
*TR is the total rainfall during the time of
comparison.
Depth of the Solute Front
The simulated movement of a nonadsorbed solute front is
shown in Fig. 3-15. The rate of movement from the third
application is considerably greater than that from the
first. Movement is clearly related to patterns of rainfall.
Note that the DSF from the first two applications tend
to converge with time. Also note that little residual
effect of fertilizer can be expected between the two appli¬
cations as the front has moved well beyond 110 cm depth
before the second planting. The difference between the MP
and MA plot is ascribed to the greater AET from the MP plot.

Depth of Solute Front (cm) Rainfall (cm)
94
Figure 3-15. Depth of solute front movement resulting from
application of a nonadsorbed solute on days 1, 30, and 207
in the MA plot and days 30 and 201 in the MP plot.

95
Discussion
Parameter Estimation
Two aspects of the methods of parameter estimation bear
discussion. The first is that, with the exception of
parameters related to fallow periods in the MA plot and
water stress in the MP plot, all parameter values were
estimated prior to simulation. In other words, the values
used are not "best fit" parameters. The goodness-of-fit
obtained by such values is encouraging.
The second aspect is that the chosen parameter values
have essentially no statistical validity and were, in most
cases, made subjectively. This is a common practice in
studies employing capacity parameter-based models (Rose et
al., 1982a; Barry et al., 1985). It carries with it the two
assumptions that: (1) the parameters do not vary greatly in
the field, and (2) the desired model output is not very
sensitive to parameter selection. In fact, the ultimate
utility of this kind of approach is dependent on the above
conditions.
One of the principal advantages of using capacity
parameters is that, in concept, a field or plot can be
unambiguously represented by a single value. The value of S
at a given time, for example, is simply the integral of 0
over the volume of soil in the system of interest. The
significance of this is that the value of parameters can be
bracketed. It is clear, for example, that 0^c must be less
than 0 at saturation. Similarly, it is not difficult to

96
establish minimum values from examination of 0 in the field
drainage patterns and moisture release curves. This reason¬
ing also applies to the other soil parameters. Thus,
although information on the precision and accuracy of
parameter estimations is lacking in this study, it is
possible to have confidence in the bounds of those values.
The accuracy of the parameter values used can be
evaluated on the basis of the sensitivity of desired outputs
to variations of parameter values. There are two outputs
desired from the model in this study. The first is the NDP,
which was used to calculate net leaching losses. The second
is the DSF, which is included to illustrate the movement of
solutes through the two systems.
Calculation of NDP
The fact that NDP (=DP-UP), rather than DP, was calcu¬
lated should be recognized. This is because parameters
related to water stress and transpiration during fallow
periods were obtained by calibration. The calibration was
performed during an extensive dry period. At that time, P
and DP were 0. Under this condition DS=UP-AET. Since UP
was not considered in model calculations, AET values cali¬
brated under these dry conditions, when UP should be great¬
est, was actually a lumped term that implicitly included UP.
The drop of the water table in both plots after major
rainfall events was generally rapid (Fig. 3-3) so that it
was below about 170 cm by the time the upper portion of the
soil profile had dried enough to create an upward gradient

97
for soil-water flow. This suggests that UP was not large.
Clearly, it was small relative to DP, since rainfall was
much greater than PET during the simulation period (Table
3-5). In terms of net leaching loss calculation, NDP should
be the preferred parameter.
Given the approach to water movement used, the value of
NDP is ultimately calculated from Eq. 3-5 (note that ET in
Eq. 3-5 is replaced by AET). From this equation it is clear
that the accuracy of NDP calculation is dependent on the
accuracy of P, AET, and DS. The calculation of S depends
on several parameter values, including HWTI, 9fC/ and TRD.
The possible values of S are bracketed, however, by 0^c and
© . The difference between S at those two soil-water
pwp
contents represents the maximum contribution to NDP possible
from the DS term. Since neither extreme value was encoun¬
tered in the field, the contribution was much less than
that. This value is fixed and, whatever its magnitude, must
eventually become small relative to P and AET, both of which
accumulate over time. Thus, for long time periods, the DS
term, and therefore the parameters used to calculate it,
become increasingly unimportant to the calculation of NDP.
Precipitation was measured at the site and is generally
taken as a given. Some error is introduced in this way when
a plant canopy is present because it retains some of the
incoming rain thereby reducing the actual amount of rain
entering the soil. This effect is balanced somewhat by the
reduced AET that results.

98
Estimations of AET were based on measured evaporation
from a class A USWB pan. The accuracy of this method is
somewhat dependent on the climate and quality of pan moni¬
toring (Jensen, 1973). Climates characterized by low
radiation winters and frequent hot dry winds are least
conducive to the use of the pan (Jensen, 1973). On the
other hand, the method has been very successful in humid
tropical regions (Chang et al., 1963). According to Jensen
(1973), the class A pan should be accurate within + 10% with
proper management, which is of an accuracy comparable to
that of other estimation methods. This is important from a
practical standpoint because further improvement (e.g., by
use of lysimeters) requires considerable expense and effort.
Table 3-6 illustrates the relative effects of varying
selected parameter values as compared to a + 10% change in
pan factor (k) used to calculate PET for the 98 day valida¬
tion simulation for the MA plot. Over this time period the
+ 10% variation in pan factor had a greater impact on NDP
calculations than alterations of the other parameters within
the the likely range of values. This effect is expected to
be greater over longer simulation periods, as explained
above. Also note that the alteration of the HWTI had no
effect on S at the end of the simulation period or on NDP.
This will be true of all simulations with the model in which
either the final water table depth is equal to the initial
depth, or the the water table depth at both times is below
the depth of interest by HWTI cm. The impact of water table

99
depth in the model is to elevate S calculations when the
water table is high and to temporarily slow the rate of
solute movement through the system.
Table 3-6. Sensitivity of Model Output to Variations in
Critical Parameters.
Parameter
Altered Value AET S*NDP
none
k
k
0fc
H&Í
0.9
0.7
0.475
(cm3 cm-
â– 3>
16.1
18.0
14.2
16.4
cm—
50.7
50.6
50.8
51.8
46.4
44.6
48.2
44.9
0.445
!»
16.1
49.7
47.4
0.70
(cm)
16.1
50.7
46.4
HWTI
0.45
I»
16.1
50.7
46.4
TRD
4
(days)
16.1
50.5
46.5
TRD
6
If
16.1
50.8
46.2
*The S
value at he
end of
the
calibration/validation
period.
Depth of Solute Movement
The calculation of DSF is dependent on fluxes within
the soil profile and the value of 0^ . Both the amount of
input to soil layers during redistribution (Eq. 3-9) and the
change in solute front depth from a given input (Eq. 3-8)
are largely dependent on the value of 0^ . From Table 3-5
it is clear that the calculation of S is relatively sensi¬
tive to 0. The good agreement between measured and
calculated S values indicates that 0^c is reasonably well
estimated.
The solute front is conceived, in an ideal sense, as
the leading edge of the solute "pulse" that would result
from soluble fertilizer application. From column and field
experiments it is clear that hydrodynamic dispersion and
diffusion cause a spreading of the idealized pulse over

100
depth. As the pulse travels through the profile this
spreading will increase and the original, sharp pulse,
becomes increasingly less well defined. This effect is
accentuated by plant uptake, which reduces the height of the
pulse above backround levels. When the DSF crosses a
particular depth, therefore, a large portion (>50%) is
expected to be above that location and the remainder below.
Even so, it appears that little of the applied fertilizer is
expected to be carried over to subsequent crops in the MA
plot.
Also note that, as time (or DSF) increases, the posi¬
tion of the DSF from different application times tends to
converge. Thus, the pulses from different application times
tend to become indistinguishable.
The patterns of solute movement over time in the two
plots are quite distinct. Solutes move through the MP
profile more slowly than through the MA plot. Two factors
are responsible for this difference; the greater AET and 0^
in the MP plot. This slower movement reduces the likelihood
of leaching because the crop has a greater opportunity to
use the nutrient.
Soil Solution Concentration
Two aspects of the soil solution measurements were
noted previously: (1), the relative uniformity of solution
concentrations over time and (2), the difference in concen¬
tration between cropping systems. Both have important
implications to the estimation of leaching loss. Three

101
factors that contribute to the uniformity of soil solution
concentrations over time were described above. They are the
spreading of solute pulses by dispersion, the reduction of
peak heights by plant uptake, and the merging of pulses with
depth as the DSF increases. To these may be added the
effect of relatively slow release of nutrients by mineral¬
ization, which will tend to increase the backround "noise"
levels.
The relative uniformity of solution concentrations is
important because it permits the interpolation of concentra¬
tion values between measurements with some confidence.
Net Leaching Loss
The errors associated with both the estimation of soil
solution concentration and NDP have been discussed. It
should be recognized that movement of a solute below 100 cm
does not necessarily mean that it is lost from the crop.
There is, undoubtedly, some uptake of water and nutrients
from below that depth. This quantity does provide a good
comparative index that is a good approximation, however.
Penetration of roots to that depth must be short lived
on the MA plot, because both the maize crop and the fallow
vegetation are short lived. The root systems of cacao,
which is the dominant crop in the MP plot, are not expected
to feed from deep in the soil profile because cacao plants
are mainly lateral feeders and are intolerant of flooded
conditions (Smythe, 1966).

102
System Leaching Loss Sensitivity
Based on the above discussion it is possible to isolate
environmental and cropping system properties that may be
used to characterize the potential sensitivity of a cropping
system to leaching loss. The properties considered here
will be restricted to those directly related to water
movement.
In these terms, the most important crop property is
AET. Although different crops have different propensities
for transpiration, from a cropping system standpoint, the
longer an actively transpiring crop is in the field, the
greater AET and the less the potential nutrient loss by
leaching.
The AET must be combined with rainfall to be of use in
determining the sensitivity to leaching loss. The net water
input to the system is the difference between P and AET.
The resultant parameter, the effective precipitation (EP),
can be used to approximate the movement of a solute front
using the equation
DSF = EP/©f . (3-26)
Calculation of DSF using Eq. 3-26 is accurate only if
AET = 0 and the initial soil-water content is 9fc- When AET
> 0, DSF calculated using Eq. 3-26 will be inaccurate for
two reasons: (1) water loss by ET is distributed over the
entire rooting zone whereas the use of Eq. 3-26 presumes
that all ET occurs above the DSF, and (2) the actual effec¬
tive input from a given precipitation event is somewhat less

103
than the amount of that event because there is ET during
redistribution, which is not accounted for in Eq. (3-26).
The two effects tend to counter each other, but the net
effect is generally for Eq. 3-26 to slightly underestimate
DSF. If this small discrepancy is acceptable, then it is
possible to estimate DSF with climatic data.
The other important component of the system is the
soil. Recall that the DSF was calculated using ©fc* If the
entire rooting depth is considered, it is possible to
calculate a leaching depth (LD) that is analogous in concept
to the pore volume described in the previous chapter. Thus,
it is considered to be the volume of water per unit area
required to displace a nonadsorbed solute through the crop
rooting zone. The leaching depth is calculated as:
LD = D 0f (3-27)
where D is the depth of interest (cm), or 110 cm, and LD is
in cm. Small changes in 9fc, such as those between the MA
and the MP plots, have relatively little effect on the
leaching volume (50.6 vs. 57.2 cm, respectively). The
effect of soils can be dramatic when strongly contrasting
soils are compared, however. A sand textured soil with a
0fc of 0.1, for example, has a leaching volume of only 11
cm.
At this point it is possible to combine soil, climate
and crop characteristics into a single parameter that
characterizes the sensitivity of the system to leaching

loss. This parameter, the solute residence time (SRT) in
days, is defined by
104
SRT = LD/EP. (3-28)
Using mean monthly values of rainfall and pan evapora¬
tion provided by Forsythe (1975) it is possible to compare
the average residence times for the two plots. Comparisons
were made assuming that fertilizer applications were made on
1 Nov. and 1 June, which is usual for the MA plot. The
residence times for the MP plot for the two dates of 83 and
97 days are greater than those for the MA plot of 60 and 74
days. Thus, a nonadsorbed solute, such as NO^ , is expected
to reside in the MP plot about 23 days longer that in the MA
plot. The MA plot can therefore be considered to be more
sensitive to leaching loss than the MA plot under current
management. This partially explains the observed differenc¬
es in net leaching losses.
The difference in solute residence time is, however,
only partly due to the cropping system itself. Part of the
reason for it is the greater 0fc value of the MA plot.
When the 0^c value of the MA plot is used to calculate the
SRT in the MP plot, values of 63 and 84 days are obtained,
which indicate a much smaller difference.
The reasons that a greater difference was not observed
are that: (1) AET in the MP plot was less than the maximum
due to reduced transpiration during dry periods, and (2) the
transpiration from the MA plot is increased by the weeds in

105
the fallow periods. It appears that other factors are
responsible for the lower leaching losses from the MP plot.
It should emphasized that the SRT as defined above is
intended only as a means of characterizing
soil-climate-cropping system combinations, not as a means of
predicting the actual residence time of a given solute over
a specific time period. That requires information concern¬
ing the chemical interactions of that solute with the soil
and specific weather patterns. The SRT is intended as an
index in much the same way that mean annual rainfall is a
climatic index.
Summary
There is a relatively high potential for nutrient loss
by leaching in humid tropical regions. One way that has
been proposed to reduce such losses is to maintain a con¬
stant crop cover that will take up water and nutrients
continuously. In this study the leaching losses from two
contrasting cropping systems managed at approximately the
same level were compared. The major objectives were to
determine if: (1) leaching losses were different from the
two cropping systems and (2) factors responsible for differ¬
ences could be expressed in terms of simple parameters that
could be used to evaluate the sensitivity of cropping
systems to nutrient loss by leaching.
A capacity-parameter based model, similar to NITROSIM
(Rao et al., 1981), was developed. Additional

loss. This parameter, the solute residence time (SRT) in
days, is defined by
SRT = LD/EP. (3-28)
Using mean monthly values of rainfall and pan evapora¬
tion provided by Forsythe (1975) it is possible to compare
the average residence times for the two plots. Comparisons
were made assuming that fertilizer applications were made on
1 Nov. and 1 June, which is usual for the MA plot. The
residence times for the MP plot for the two dates of 83 and
97 days are greater than those for the MA plot of 60 and 74
days. Thus, a nonadsorbed solute, such as NO^ , is expected
to reside in the MP plot about 23 days longer that in the MA
plot. The MA plot can therefore be considered to be more
sensitive to leaching loss than the MA plot under current
management. This partially explains the observed differenc¬
es in net leaching losses.
The difference in solute residence time is, however,
only partly due to the cropping system itself. Part of the
reason for it is the greater ©fc value of the MA plot.
When the 0fc value of the MA plot is used to calculate the
SRT in the MP plot, values of 63 and 84 days are obtained,
which indicate a much smaller difference.
The reasons that a greater difference was not observed
are that: (1) AET in the MP plot was less than the maximum
due to reduced transpiration during dry periods, and (2) the
transpiration from the MA plot is increased by the weeds in

105
the fallow periods. It appears that other factors are
responsible for the lower leaching losses from the MP plot.
It should emphasized that the SRT as defined above is
intended only as a means of characterizing
soil-climate-cropping system combinations, not as a means of
predicting the actual residence time of a given solute over
a specific time period. That requires information concern¬
ing the chemical interactions of that solute with the soil
and specific weather patterns. The SRT is intended as an
index in much the same way that mean annual rainfall is a
climatic index.
Summary
There is a relatively high potential for nutrient loss
by leaching in humid tropical regions. One way that has
been proposed to reduce such losses is to maintain a con¬
stant crop cover that will take up water and nutrients
continuously. In this study the leaching losses from two
contrasting cropping systems managed at approximately the
same level were compared. The major objectives were to
determine if: (1) leaching losses were different from the
two cropping systems and (2) factors responsible for differ¬
ences could be expressed in terms of simple parameters that
could be used to evaluate the sensitivity of cropping
systems to nutrient loss by leaching.
A capacity-parameter based model, similar to NITROSIM
(Rao et al., 1981), was developed. Additional

106
considerations were made to include the effects of a fluctu¬
ating water table.
Estimation of net leaching loss was based on the net
deep percolation calculated with the model and measured soil
solution concentration values. Calculated soil profile
water contents over a 98 day period closely approximated
measured values.
Sensitivity analysis of selected soil parameter values
and actual evapotranspiration indicated that the + 10%
accuracy in estimates of actual evapotranspiration was the
main limitation to the accuracy of net deep percolation
estimation.
. . . - +4- 2 +
Soil solution concentrations of NO^ , NH4 , K , Ca ,
2+ . 4-
and Mg were monitored. The concentration of NH^ was too
low in both cropping systems to contribute to leaching loss.
This was also true of NO^ in the mixed perennial cropping
system. Soil solution concentrations of the other ions were
significantly greater, by a factor of 2 to 15 times, in the
monocropped annual system. The estimated loss of NO^'-N
over a 250 day period was 56 kg/ha.
The movement of applied nutrients was illustrated for
the case of a nonadsorbed solute like NO^ by calculating
the movement of the solute front. These calculations
indicated that, for the conditions encountered, little
residual fertilizer effect could be expected in the
monocropped annual system. Movement of ions in the mixed

107
perennial system was somewhat slower, due to both greater
transpiration and higher field capacity values.
The two systems were characterized in terms of sensi¬
tivity to leaching using the residence time of a nonadsorbed
solute as an index. Slightly greater residence times were
calculated for the MP system, indicating that it is less
sensitive to leaching loss than the MA system. This differ¬
ence is largely due to differences in 0fc between the two
plots. On this basis it was concluded that differences in
soil solution concentrations were due to additional factors,
such as the amounts of nutrients applied and the crop
demand, as well as water use patterns.

CHAPTER IV
MATHEMATICAL DESCRIPTION OF NITROGEN
MINERALIZATION DURING INCUBATION
Introduction
This study is part of a larger project investigating
the movement of nutrients in different cropping systems
found in Costa Rica. In many of those systems the mineral¬
ization of soil organic N contributes a major portion of
crop needs. The movement of nutrients through the rooting
zone is partially dependent on the timing of their entry
into the soil solution. It is important, therefore, to
establish some means of estimating amounts and rates of
mineralization.
Despite the long recognized importance of N mineraliza¬
tion to crop performance and waste disposal, a widely
accepted measurement method has not been developed (Keeney,
1982; Stanford, 1982). Problems of measurement stem partly
from the fact that both the N source (soil organic N; plant
and animal residues) and the microbes that mineralize N are
poorly characterized. This situation is exacerbated by the
fact that a number of soil and environmental conditions
control the rates and products of mineralization.
Field methods, which attempt to use the ambient soil
and environmental conditions and microbial populations have
been used but require a certain degree of disruption of the
soil and are lengthy and labor intensive. Most laboratory
108

109
methods use either chemical (extraction) or biological
(incubation) methods to isolate a fraction of soil organic N
which is then compared and indexed to some measure of N
mineralization. For these laboratory approaches to succeed
three conditions must be met. First, there must be some
fraction of the total soil organic N that is significantly
more mineralizable than the rest of the soil organic N.
Second, the extraction procedure must consistently isolate a
fixed portion of this N. Third, the kinetics of mineral¬
ization, which are sensitive to soil and environmental
conditions, must be accounted for. The first condition is
assumed and is fundamental to the approach. The second and
third conditions are usually approached empirically through
some type of calibration procedure (Stanford, 1982).
Models of N Mineralization
First Order Kinetic Model. Stanford and Smith (1972)
proposed a laboratory method that generated a great deal of
interest in the assessment of soil organic N availability.
The method calls for aerobic incubation of soil under
optimal conditions with periodic leaching and measurement of
inorganic N in the leachate. While this method has received
widespread use, its primary advantage is in the way the data
generated are analyzed. Specifically, two assumptions were
made which enable the calculation of the actual amount of N
mineralized over time thereby bypassing the need for cali¬
bration which has proved problematic. This combination of

110
assumptions and mathematical analysis constitutes a simple
model for the mineralization of N.
The two basic assumptions made by Stanford and Smith
(1972) were that: (1) there is a more or less distinct,
homogeneous pool of soil organic N that mineralizes with
sufficient rapidity that other sources may be ignored; and
(2) that mineralization of this pool can be described by
first order kinetics. Given these assumptions, the amount
of N mineralized (Nm, mg kg 1) at a given time (t, days),
can be calculated from the expression,
Nm = PMN [l-exp(-kt)] (4-1)
where PMN (mg kg 1) is the amount of potentially mineraliz-
able N corresponding to the N pool in the first assumption,
and k (day ^) is a rate constant. Potentially mineralizable
N is viewed as the amount of N that could potentially be
made available to plants by mineralization. Values of PMN
and k can be estimated by fitting Eq. 4-1 to measured values
of N •
m
Stanford and coworkers (Stanford et al., 1973; Stanford
and Epstein, 1974) went on to experimentally determine the
functional relationship between k and temperature and
between k and soil-water content. This makes it possible to
calculate amounts of N mineralized under field conditions
when fluctuations in temperature and soil-water content are
known.
Alternative Models. Although Stanford and Smith's (1972)
approach has been used widely, several authors (e.g., Molina

Ill
et al., 1980; Juma and Paul, 1981; van Veen and Frissel,
1981; Deans et al., 1986) have proposed that more than one
pool of soil organic N may be directly mineralized and
should be explicitly considered in N mineralization models.
Transfer from each of these pools is commonly described with
first-order kinetics (Tanji, 1982).
In general, the multiple N-pool approach can be de¬
scribed mathematically as follows:
Nm = S NQi [l-exp(kit)]; i=l,2,...,n (4-2)
where the subscript i represents the specific N pool, n is
the total number of pools, is the amount of N (mg kg â– *â– )
in the i-th pool at t=0, and k^ is the rate constant (day ^)
for the ith pool. Note that the total mineralizable organic
N (equivalent to PMN) is equal to E Nq^. The simplest
example of this approach is when n=2, as proposed by Nuske
and Richter (1981). They described the two pools as "fresh"
and "old" organic matter.
Another approach used to describe net mineralization is
with zero-order kinetics (Tabatbai and Al-Khafaji, 1980;
Addiscott, 1983). This model can be described mathematical¬
ly as:
Nm = Kt (4-3)
where K (mg kg 1 day 1) is a zero-order rate constant. With
this approach, no assumption is made as to the number of N
pools mineralizing. The implication is that the N pool(s)
is (are) sufficiently large that they are not significantly

112
All of the above equations have been used to describe
net N mineralization in laboratory incubation studies. For
reasons that will be discussed in another section, a slight
modification of Eq. 4-3 is presented in this chapter. The
modification proposed is that a relatively small pool of
highly mineralizadle N, which is considered to be the result
of experimental conditions, is included. This pool is
assumed to mineralize by first-order kinetics, so that
= A[1-exp(kft)] + Kt (4-4)
with A (mg kg 1) and k^ representing the amount of N in that
pool at the initiation of the incubation and the rate
constant (day ^), respectively, and other terms are as
defined previously.
The mineralization models above are summarized in Table
4-1. In subsequent discussion they will be referred to as
the FO model for the first-order kinetic model; DFO for the
two-pool first-order kinetic model; ZO for the zero-order
kinetic model; and FOZ for the combined first-order,
zero-order kinetic model.
Table 4-1. Summary of Models of N Mineralization Discussed.
Eq. #
Model
# N
Pools
Reference
Equation
1
FO
1
Stanford and
Smith (1972)
N
m
= PNM [l-exp(kt)]
2
DFO
2
Nuske and Rich¬
ter (1981)
N
m
= NQl [l-exp(k1t)]
+ N^2 [l-exp(k2t)]
3
ZO
1
Tabatabai and
Al-Khafaji (1980)
N
m
4
FOZ
2
this study
N
m
= A [l-exp(kft)] + Kt

113
Objectives
Although the approach of Stanford and Smith (1972), as
described above, has much to recommend it, several questions
concerning its applicability to various situations have
arisen. These questions include both matters of methodology
and of data analysis.
One unfortunate aspect of the method is that a long (20
to 30 week) incubation period is required. This time can be
shortened considerably if the rate constant k is known. In
this context the assertion of Stanford and Smith (1972) and
others (Oyanedel and Rodriguez, 1977; Campbell et al., 1981)
*
that a "universal" k (k ) can be used to describe all soils
is particularly important.
Many Costa Rican soils are derived from volcanic ash
and contain varying amounts of allophane. The effects of
allophane are particularly evident in the Birrisito soil,
which is known to contain significant amounts of allophane
(Andriesse and Muller, 1973). Soil organic matter combines
with allophane and thereby may be "protected" from microbial
attack (Paul, 1984). This is consistent with the exception¬
ally high carbon content in that soil (Table 4-2). This may
result in distinctive kinetics of mineralization and there¬
fore require use of different k values.

114
Table 4-2. Selected soil properties.
Series*
Classif¬
ication
-# Crop*
%N
%OC
C/N
PH
B
TDa.
SC (>25 yrs.)
0.754
8.53
11.32
5.70
C
TDa.
CF (>15 yrs.)
0.437
4.48
9.88
4.33
I(P)
TDp.
M,M-B (7yrs.)
0.295
3.28
11.11
4.83
1(A)
TDp.
C-L-P (7yrs.)
0.284
2.95
10.40
5.16
+B, Birrisito series; C, Colorado series; I(P) Instituto
series with perennial crops; 1(A), Instituto with annual
crops.
#TDa is Typic dystrandept, and TDp is Typic Dystropept.
*SC, sugarcane; CF, coffee; M, maize; M-B, maize+beans.
C-L-P, cacao-Laurel-Plantain.
The method described above for estimation of N mineral¬
ization has been extended to mineralization of other nutri¬
ents (Tabatabai and Al-Khafaji, 1980; Maynard et al., 1983).
Such extension requires some alteration in the methods
originally proposed by Stanford and Smith (1972) because
nutrients present in the leaching solution they used may be
the object of study. If it is true, as has been claimed
(van Veen and Frissel, 1981), that the availability of C and
N as substrate limit mineralization rates, then the absence
of those nutrients in the leaching solution should not
effect N mineralization rates. In this case, the simultane¬
ous study of the mineralization of several elements would be
facilitated because a single soil sample (plus replicates)
could be used to measure the mineralization of all elements
of interest.
In addition, it was felt that the practice of
air-drying samples prior to incubation, as recommended by
Stanford and Smith (1972), should be investigated. The main
reason for this is that in both of the instances mentioned
above in which the zero-order kinetic model was used, field-

115
moist, rather than air-dried, soil was used. There were
other differences in methodology but is seems likely that
the kinetics of mineralization during incubation are influ¬
enced by the pretreatment used.
Although the first-order kinetic model has been used
widely, the agreement between that model and measured data
is seldom evaluated quantitatively. There have, however,
been several claims that some alternative models are more
appropriate.
With these considerations in mind, this study was
designed to answer the following four questions: (1) Does
the addition of nutrients following the leaching solution
affect mineralization rates? (2) Does air-drying prior to
incubation alter the kinetics of N mineralization? (3) Can
★
k be used to describe N mineralization in soil influenced
by volcanic ash?, and (4) Do the alternative models dis¬
cussed better describe N mineralization? Answering these
questions should lead to a better evaluation of the applica¬
bility of incubation procedures for estimating N mineraliza¬
tion in the field.
Materials and Methods
Soils
Four Costa Rican soils, representing 3 different soil
series, were used for this study (see Table 4-2). The
Instituto and Colorado soils were collected at CATIE located
near Turrialba and the Birrisito soil was collected near the

116
town of Juan Vinas about 20 km away. Classification,
cropping history and selected chemical properties of each
soil are given in Table 4-2. Several bucket auger fulls of
soil taken from a depth of 0 to 15 cm were composited. The
two samples of Instituto soil were taken from adjacent plots
with different cropping histories. Soil samples were frozen
shortly after collection for storage (approximately 5
months).
Incubation Procedure
Soil samples were thawed at room temperature, sieved,
thoroughly mixed, and divided into two roughly equal por¬
tions. One portion was spread over a laboratory bench and
allowed to dry for two weeks. The other portion was further
subdivided into two groups; one to be leached with 0.01 M
CaC^ (M treatment) only, the other (P treatment) to be
leached with a combination of the same solution and a -N
nutrient solution as described by Stanford and Smith (1972).
Three subsamples weighing approximately 30 g (oven-dry
basis) of each group and from each soil were then placed in
0.2 urn Nalgene filter units (Nalge Catalog Number 120-0020),
leached, and placed in an incubator maintained at 35 + Io C
and 98 to 99% relative humidity. After two weeks the air
dried soil samples were treated in the same manner.
The leaching procedure was as follows:
1. Place soils under a tension of 50 kPa using an appara¬
tus similar to that described by MacKay and Carefoot
(1981) ,

117
2. Add three 25 ml increments of 0.01M CaC^,
3. Place samples under 35 kPa tension,
4. Add 20 ml of 0.01M CaC^ to M treatment or 20 ml of -N
nutrient solution to the P treatment,
5. Maintain tension until more than 90 ml leachate had
accumulated (3 to 4 hours),
6. Place samples under tension of 35 kPa in incubator
over night,
7. Collect remaining leachate the next day,
8. Bring leachate samples to volume,
9. Close off filter units, and,
10. Place in incubator.
This procedure was performed approximately 2, 4, 7, 10,
14, 18, 22, 26, and 30 weeks after the start of the experi¬
ment. The weight of each sample was recorded biweekly and
evaporation loss was not permitted to exceed 0.5 g (1%). In
general, soil weights were constant within 1 g during the
course of incubation; however, in some cases the filter
membrane lost its integrity and subsequent samples were
lost. Air was circulated through each sample at least once
a week during the incubation period.
Chemical Analysis
Total soil N was determined using a semi-micro Kjeldahl
procedure (Bremner, 1965b). Total N in leachate was mea¬
sured using reduced iron with steam distillation (Nelson and
Sommers, 1975). Steam distillation with MgO and Devarda's
alloy (Bremner, 1965a) was used for inorganic N

118
determination. Total organic C was measured from CC>2
evolution after oxidation using a Leco (Model Number
572-100) carbon analyzer. Soil pH measurements were made in
a 1:2 soil:water paste. All soil analyses were performed in
triplicate. All references to soil weight are on the
oven-dry (105° C) weight basis.
Statistical Analysis
Analysis of variance of total cumulative N was per-
m
formed using Statistical Analysis System (SAS) general
linear models procedure (SAS, 1983). The experimental
design was a split-split-plot with soil as the main plot,
drying pretreatment as the subplot, and nutrient level the
sub-subplot.
Goodness-of-fit was analyzed using the nonlinear
regression procedure, NLIN (SAS, 1983), with the Marquad
technique.
Results
Cumulative N Mineralization
Figures 4-1 through 4-6 show the net cumulative N
mineralized over the 210 day incubation period for each soil
and treatment combination. The curves in these figures are
coded by three letters. Note that the first letter indi¬
cates the soil, or in the case of the Instituto series, the
cropping history. The letter B represents Birrisito soil
series; C denotes Colorado soil series; P stands for
Instituto soil series with perennial crops; and A represents

119
Time (days)
Figure 4-1. Cumulative net N mineralized with time for
three soils receiving the air-drying and plus nutrient
solution treatments. The FO and FOZ model best fits are
indicated by the dashed and solid lines, respectively.

Nm (mg kg"1)
120
Figure 4-2. Cumulative net N mineralized with time for
three soils receiving the air-drying and minus nutrient
treatments. The FO and FOZ models are indicated by the
dashed and solid lines, respectively.

Nm (mg kg"1)
121
Figure 4-3. Cumulative net N mineralized with time as
affected by nutrient solution treatment, with soil and
drying treatment constant. The FO and FOZ model best fits
are indicated by the dashed and solid lines, respectively.

Nm Cmg kg~1)
122
Figure 4-4. Cumulative net N mineralized with time as
affected by soil solution treatment. The FO and FOZ model
best fits are indicated by the dashed and solid lines,
respectively.

123
Figure 4-5. Cumulative net N mineralized with time as
affected by air-drying treatment. The FO and FOZ model best
fits are indicated by the dashed and solid lines,
respectively.

Nm (mg kg’1)
124
Figure 4-6. Cumulative net N mineralized with time as
affected by nutrient solution treatment. The FO and FOZ
model best fits are indicated by the dashed and solid lines,
respectively.

125
Instituto soil with annual crops. The second letter refers
to the pretreatment, either air-dried (A) or field moist
(F). The third letter refers to the nutrient solution
treatment with P for the plus nutrient solution (no N)
treatment and M for the minus nutrient solution treatment.
For example, the curve labeled BAP refers to Birrisito soil
that was air-dried prior to incubation and given nutrient
solution with leaching. The best nonlinear least sum of
squares fit of two of the models described above, FO and
FOZ, are represented by dashed and solid lines, respective¬
ly.
During the course of incubation, 8 of the 48 filter
units failed to maintain tension due to decomposition of the
cellulose filter membrane. This produced anomalous results
and forced their omission. All three of the filter units in
the CFM treatment failed after 150 days, hence the shortened
curve.
Only measured inorganic N (the sum of NO^ and NH4+)
are reported. Total N (the sum of inorganic and organic N)
was measured in the first five extractions. Although there
was significantly more total than inorganic N (a=0.95),
inorganic N comprised 96% of that total. The difference
between the two measures was not considered sufficient to
warrant further measurement and the practice was discontin¬
ued. With the exception of the first extraction, virtually
all of the inorganic N was NO^ •

126
Treatment Effects
This analysis was designed as a model-independent test
of the statistical significance of the effects of soils and
treatments on cumulative net N mineralization (N ) under the
m
described experimental conditions. At 210 days, for each
soil and treatment combination was analyzed as described in
the previous section. In the case of the soil-treatment
combination CFM, N and the associated standard deviation
m
were extrapolated from the last available measurement (after
150 days).
The effects of all three variables were significant
(a=0.95). In general, values were in the following
order: B=C>P>A (Figs. 4-1,2,5,6). Both air-drying and
addition of nutrient solution corresponded to significantly
higher N values,
m
Analysis of variance performed for over the incubation
perioid showed that both air-drying and nutrient addition
effects were significant (a=0.95) for each extraction. The
magnitude of the two effects, however, was quite different
over the incubation period. The differences between average
air-drying and field-moist values (A-F), and between average
nutrient addition and omission (P-M) values over time are
plotted in Fig. 4-7. The magnitude of the effect of
air-drying was maximal after 30 to 45 days and then remained
relatively constant, while the magnitude of the nutrient
addition effect increased steadily after about 45 days.

Treatment Effects (mg kg
127
50
40
30
20
A-F
✓
✓
✓
/
1 1 1 1 1 1 1 1 i i i i i i â– 
30 60 90 120 150 180 2 10
Time (days)
Figure 4-7. The effects of air-drying and nutrient addition
over time as indicated by the difference between average
air-dried and field-moist treatments (A-F), and by the
difference between the average plus-nutrient and
minus-nutrient solution (P-M) treatments.

128
These effects are evident in comparisons of the long,
nearly portions of the curves in Figs. 4-1 through 4-6 after
30 to 45 days of incubation. Those segments in Figs. 4-3
and 4-4, in which P and M vary, can be seen to diverge with
time. In contrast, a similar comparison in Figs. 4-5 and
4-6, where A and M are compared, shows nearly parallel
increases in after an initial separation.
Model Performance
The models described above will be discussed in the
following terms: visual (qualitative) agreement between best
fit curves and measured data, comparison of the total sum of
squares, the frequency with which calculated curves inter¬
sect the 90% confidence interval associated with measured
N , and the error associated with estimated parameter
values. Parameter values associated with each model are
summarized in Table 4-3.

Table 4-3. Model parameter values
Model
FO DFO FOZ
Soil
Trt.
PMN
k
N01
kl
N02
k2
A
X
hh
K
mg kg ^
day
mg kg ^
day 1
mg kg
1 day-1
mg kg ^
day
mg kg 1 day ^
BAP
264
0.022
127
0.067
232
0.0050
162
0.051
0.569
BFP
205
0.206
126
0.040
881
0.0005
128
0.039
0.425
BAM
224
0.025
158
0.040
618
0.0006
160
0.040
0.367
BFM
205
0.019
98
0.048
183
0.0046
126
0.038
0.415
CAP
226
0.027
115
0.087
197
0.0052
140
0.067
0.519
CFP
247
0.019
105
0.096
600
0.0015
113
0.085
0.736
CAM
204
0.033
135
0.076
2069
0.0002
135
0.076
0.456
CFM
156
0.033
93
0.087
1318
0.0004
94
0.086
0.496
PAP
174
0.022
89
0.074
244
0.0024
99
0.065
0.425
PFP
189
0.018
64
0.127
265
0.0035
81
0.083
0.569
PAM
139
0.034
106
0.055
2404
0.0009
106
0.055
0.205
PFM
124
0.032
76
0.099
3343
0.0010
78
0.095
0.293
AAP
113
0.014
37
0.128
264
0.0017
41
0.105
0.353
AFP
97
0.126
29
0.114
412
0.0009
31
0.099
0.318
AAM
65
0.037
49
0.065
287
0.0004
49
0.065
0.104
AFM
60
0.024
25
0.201
60
0.0052
31
0.129
0.168

130
The total sum of squares is a measure of the
goodness-of-fit between calculated and measured values. The
average sum of squares of all 16 incubation curves for each
of the 3 models is presented in Table 4-4.
Table 4-4. Indices of model qoodness-of-fit.
Mode1
FO DFO FOZ_
% intersection* 50.0 97.9 94.4
Ave. Sum of Sq. 161.1 25.8 26.4
Calculated as the percent of 90% con-
dence intervals about measured values
intersected by calculated curves.
The confidence interval (a=0.90) was calculated for
each individual extraction (there were 3 replicates). When
calculated values for N fall within the confidence interval
m
about measured values, the model calculation at the point is
considered to represent the measurement relative to the
precision of measurement. The proportion of calculated
values falling within the confidence intervals, expressed as
a percent, are also shown for each model in Table 4-4.
The precision with which parameter values were estimat¬
ed using the curve-fitting procedure described was described
by the relative error associated with those estimates (Table
4-5). The relative error was calculated from the average
standard deviation for all 16 curves, divided by the mean
for that estimate.

131
Table 4-5. Relative error of parameter
estimations.
Model
FO
DFO
FOZ
Para- RE*
Para- RE
Para-
RE
meter
meter
meter
N 0.03
No, 12.8
A
0.4
k° 0.10
k.1 20.2
kf
11
719
k°2 753
7
♦Relative error, RE, calculated as the
average standard deviation divided by
the mean estimate.
FO model
Comparison of the dashed lines in Figs. 4-1 through 4-6
with the data reveals a systematic difference between the
trend of the data and the fitted curves. In every case, the
fitted curve is lower than the measured data at the begin¬
ning and ending of the incubation period, and greater at
intermediate times. These trends are more clearly evident
in soils with high final N values.
m
Computed values of the two critical parameters in the
FO model are shown in Table 4-3. Values of PMN fall within
the range of those found by Stanford and Smith (1972) and
closely follow the trends of total discussed previously.
Values of k, however, which range from 0.0126 to 0.0373
(day â– *") clearly are not close to 0.0077, which was proposed
*
by Stanford and Smith (1972) as the k value. In all cases,
estimated k values were significantly greater (a=95%) than
0.0077 day ^. In addition, there was little internal
consistency among those estimates, the largest k value being
about 3 times greater than the smallest.

132
The goodness-of-fit, as indicated by the sum of squares
and percent intersection of estimated confidence intervals
(Table 4-4) indicates that, on both grounds, the fit is
relatively poor with the FO model. However, the error with
which the parameters are estimated is relatively low (Table
4-5) .
DFO model
Plots of the DFO model are not included because they
were virtually indistinguishable from those of FOZ model.
The improvement in fit is evident from the greatly reduced
sum of squares and greatly increased percent intersection of
confidence intervals (Table 4-4). The relative errors
associated with the parameters used to obtain these improved
fits, are however, quite high (Table 4-5). Estimates of
and k.2 are especially poor.
FOZ model
Visual examination of the solid lines in Figs. 4-1
through 4-6 indicates excellent agreement between calculated
and measured values throughout the incubation period. This
is reflected in the calculated goodness-of-fit parameters
shown in Table 4-4. In both these respects DFO and FOZ
models are very similar. The error associated with estimat¬
ed parameter values is, however, quite different in the two
models (Table 4-5). In this respect the FOZ model is
intermediate between the other two models.

133
Discussion
Treatment Effects
Significantly more N was mineralized in those samples
that were air-dried. Numerous studies have shown that
air-drying causes solubilization of highly decomposable
nitrogenous compounds (Stevenson, 1956; Powlson and
Jenkinson, 1976; Bartlett and James, 1980). This results in
a flush of N mineralization (a relatively high net mineral¬
ization rate) followed by slower rates similar to those of
soils that had not been air-dried (Williams, 1967; Nordmeyer
and Richter, 1985). The same pattern was observed in this
study.
The size of this flush is dependent on the severity of
the treatment (e.g., temperature and length of time) and the
soil used (Soulides and Allison, 1961; Marumoto et al.,
1982b). It is postulated that the solubilization of organic
compounds observed after air-drying is either the result of
physical disruption of organic matter structure or due to
the death of part of the microbial population, whose biomass
is then rapidly mineralized. Using isotope techniques,
Marumoto et al. (1982a) found that about 76% of the flush
was from the microbial biomass, thus demonstrating the
feasibility of the second explanation.
The anticipated fit of the F treatment to the ZO model
was not observed. There was, however, one major difference
between the procedure used by earlier investigators and that
used in the present study. In this study, all soils were

134
frozen prior to incubation. Effects of freezing similar to
those ascribed to air-drying have been documented with the
same explanations offered (Jenkinson and Powlson, 1976).
These effects are generally considered to be slight relative
to the effects of air-drying (Bartlett and James, 1980).
However, it is reasonable to expect that the soil microflora
in tropical regions would be more sensitive to freezing than
the soil microflora in temperate regions, where these
studies have been conducted. The effects of air-drying and
freezing can be added to a longer list of pretreatment
effects that can be expected to produce a flush of N miner¬
alization (Jenkinson and Powlson, 1976; Nordmeyer and
Richter, 1985 ) .
The effect of nutrient addition was also statistically
significant. This indicates that the microbial populations
that mineralize N suffered nutrient deficiencies under the
experimental conditions. Thus, C and N are not necessarily
limiting factors in mineralization. It should be noted,
however, that the comparison made here is between relatively
high nutrient inputs and extremely high leaching rates under
conditions of near optimal temperature and soil-water so
that the maximum effect is expected. The initial similarity
between P and M nutrient treatments may be due to nutrients
in the organic matter made soluble by the pretreatment.

135
Model Performance
FO model
Two major discrepancies between the measured incubation
curves and the FO model were noted in the previous section:
k values measured were significantly different from the
"universal" k; and there were systematic differences between
fitted and measured curves. These discrepancies are not
unique to this study although they have frequently escaped
notice and comment by earlier workers. Two reasons for this
are: the curve fitting methods used to obtain k and PMN
values; and the manner in which results are often presented.
The method of curve-fitting employed by Stanford and
Smith (1972) uses linear regression of log-transformed data
to estimate k values. Smith et al. (1980) and Talpaz et al.
(1981) have argued that a different approach, using nonlin¬
ear regression of untransformed data (NLIN), is more appro¬
priate on theoretical grounds. They also showed that the
NLIN method resulted in improved (in terms of sum of
squares) fit of curves to data. Talpaz et al. (1981) showed
that use of NLIN techniques tends to produce higher, more
variable, k values, which is a result also found by
Broadbent (1986). This difference is due to the relatively
greater weight given to larger values of N^ when the
log-transformed data are used.
Examples of both procedures can be found in the litera¬
ture. Although the k values obtained in this study are
considerably higher than those of Stanford and Smith (1972),

136
they fall well within the range found by workers using the
NLIN procedure (Smith et al, 1980; El-Harris et al., 1983).
Data in the literature are generally reported in two
forms; as cumulative net mineralization versus time or
1/2
versus t . Visual inspection of most of these curves
(e.g., Fiegin et al., 1974; Mary and Remy, 1979; El-Harris
et al., 1983; Hadas et al., 1986) shows a qualitative
similarity with those in Figs. 4-1 through 4-6. Those
, i/2
curves plotted against t generally show a linear rela¬
tionship (Stanford and Smith, 1972; Cassman and Munns,
1980). Figure 4-8 shows the best nonlinear least squares
1/2
fit to two curves that are linear with t . The fit is
good when the slope is small and much worse for greater
slopes. Note that the systematic differences observed in
both cases are similar to those shown in Figs. 4-1 through
4-6.
From these observation it may be concluded that the
discrepancies observed in this study are common to many
others. It appears that the course of mineralization on
these tropical soils of differing mineralogy is qualitative¬
ly similar to other soils studied. It also appears that the
applicability of the FO model deserves closer inspection.
The FO model predicts that, with time, the PMN will be
completely mineralized. If that pool is the only source of
N then accumulation of N should cease after that time.
m
This cessation was not observed in this study. In fact,
even when incubations have been carried for 50 weeks it is

Nm (mg Kg
137
Figure 4-8. Best nonlinear least squares fit of the FO
model (dashed line) to two curves that are linear with
respect to the square root of time.

138
not observed (Stark and Clapp, 1980). This suggests either
that the mineralization of PMN does not follow first order
kinetics or that more than one pool of N supplies the N that
is mineralized.
It is evident from the effects of air-drying observed
in this study and the effects of other pretreatments in
general, that part of the N mineralized in most incubation
studies can be attributed to pretreatment effects. It has
also been noted that, in the absence of pretreatment, there
is significant N mineralization. Therefore, at least two
pools of N must be considered to contribute to N mineraliza¬
tion.
Application of the FO model to field conditions intro¬
duces more problems. According to the FO model, the amount
of N residing in the PMN pool should decrease as soil
organic N is mineralized during the cropping season. In
fact, PMN has been measured during the course of a cropping
season in two recent studies (El-Harris et al., 1983;
Nordmeyer and Richter, 1985). One study found no discern¬
ible trend over time (Nordmeyer and Richter, 1985), and the
other found PMN to increase with time (El-Harris et al.,
1983). These findings suggest that the effects likely
increases in the microbial population during the cropping
season are at least partially countered by any diminution of
the "available" N.

139
DFO model
Additional pools of N have been represented in models
as mineralizing by first-order kinetics (e.g., Nuske and
Richter, 1981, Deans et al., 1986). The number of pools
considered is variable. From the above discussion it is
clear that one pool to account for pretreatment effects must
be considered. A second pool may be considered as a compos¬
ite of all other organic N, or as a specific pool that
mineralizes more rapidly than other organic matter.
Evidence that more than two pools contribute signifi¬
cant amounts of N has come from tracer studies (Paul and
Juma, 1981). We did not consider additional pools for two
reasons. First, the goodness-of-fit obtained using two
pools was excellent and there could be little improvement
made by adding terms. Thus, it was felt that the data were
not sufficient to justify additional terms. Second, addi¬
tion of pools would result in reduced precision with which
parameters were estimated. Even with two pools the error
associated with estimated parameters was very high (Table
4-5). These errors stem from the nature of the measured
mineralization curves. That is, an exponential relationship
for the second pool is fit to what must be assumed to be the
very base (virtually linear portion) of the second curve.
Using only this information it is possible to approximate
measured results with a wide range of Nq and k combinations.
The basic problem here is that simple incubation
studies, which have been designed to estimate N

140
availability, do not yield sufficient information to justify
the construction of complex models. Such models should be
capable of accurate description of the results from these
studies, but the parameters used must come from additional
experiments involving, for example, the use of 15N (see Paul
and Juma, 1981). If simple incubation studies are to be of
value in estimating N availability, the parameters derived
from them should be consistent with measured mineralization
rates and the model used be sufficiently simple that parame¬
ters can be precisely estimated.
In this study, the parameter values determined for the
DFO model resulted in estimates of N mineralization that
were consistent with measurements, the the precision with
which those parameters were estimated was relatively low
(Table 4-5).
FOZ Model
The FOZ model, as described previously, also consists
of two terms, a first order and a zero-order kinetic term.
The first-order term is interpreted as representing the pool
of N made mineralizable by pretreatment. The other term
describes the net mineralization of all other soil organic
N. When pretreatment effects are eliminated or are account¬
ed for by preincubation, the first term drops out and the
linear relationship that has been observed under such
conditions (Tabatabai and Al-Khafaji, 1980; Addiscott, 1983)
is predicted.

141
Analysis of variance performed on the parameters shows
significant differences that are consistent with the inter¬
pretations given above (Table 4-6). Values of A, for
example, are significantly greater for air-dried than
field-moist soils, which is expected due to the increased
severity of the former pretreatment. Values of K are
significantly greater for samples receiving added nutrients
than those not. This reflects the apparent stimulation of
microbial activity that results from nutrient addition. The
effect of soils on all parameters was significant, which is
a reflection of differing soil properties and management.
Table 4-6. Summary of treatment effects.
Variable Parameter
A k, K
Soils * *r *
Dryness * (A>F)
Nurient - 2 * (P> M)
* significance at 95% level
- no sigificane at 95% level
The first order term dominates the N mineralization
rates at small times (<30 day). It has been proposed
(Ayanaba et al., 1976) that the magnitude of A, or a quanti¬
ty analogous to it, is directly proportional to the size of
the microbial population which, in turn, should be related
to microbial activity and hence mineralization rates. Thus,
the magnitude of A should serve as an index of soil organic
N availability. This approach clearly demands standardiza¬
tion of pretreatment. Inspection of the data from this
study show that there is a fairly strong correlation between

142
A and K. The weakness of this approach is that, at best, it
provides an index of soil organic N availability.
After 30 to 45 days of incubation, a quasi-steady state
was achieved in which net N mineralization between leachings
was equal to N leached. This is described by the second,
zero-order term in the FOZ model. The success of this term
in describing the data implies that the rate of N mineral¬
ization is not limited by the amount of substrate during the
incubation period. The substrate involved may be composed
of smaller pools, but the implication is that these pools
are sufficiently large relative to the amount of N mineral¬
ized that they do not become limiting. The term "conglomer¬
ate kinetics" was used by Addiscott (1983) to describe this
situation. Differences in K values between soils may be
attributed to differences in the relative sizes of N pools
in the different soils.
The K value of a soil is considered to be a function of
temperature, soil nutrient content and soil-water potential.
Because it is related to large N pools it is expected to
remain fairly constant over time. Incubation studies of
soils over time have shown that, in general, N mineraliza¬
tion rates tend to stabilize at the same rate after long
incubation times irrespective of sampling date (Nordmeyer
and Richter, 1985), which supports that expectation.
K values may describe a "basal mineralization rate"
which, in the short run is augmented or reduced by addition
of exogenous organic N. In the long run, factors such as

143
the nature of crop planted and management practices are
expected to influence K. This may explain the measured
differences between P and A soils.
A number of other equations have been used to describe
N mineralization during incubation (Juma et al., 1984;
Broadbent, 1986). Some of those would undoubtedly produce
excellent agreement between measured and calculated curves
using relatively precisely estimated parameters. The
advantage of the FOZ model is that it differentiates between
pretreatment effects and N mineralization from other sourc¬
es.
In terms of application to the field conditions encoun¬
tered at the sites of this study (ie., humid tropical), it
seems reasonable to focus on a rates of mineralization as
opposed to a particular fraction of organic N (e.g., PMN).
In such a climate the mineralization of N is relatively
constant over the year, hence any given soil N fraction must
be continuously depleted and renewed, and the rate of this
renewal is what is of interest.
Conclusions
Addition of nutrients after leaching resulted in
significantly increased net N mineralization rates. Thus,
results from studies that use the same sample to measure
mineralization of more than one element (with accompanying
reduction in applied nutrients) cannot be directly compared
to studies that follow the original procedure proposed by

144
Stanford and Smith (1972). The increase in net N mineral¬
ization rate was constant over the duration of the incuba¬
tion period. It appears that microbial activity is stimu¬
lated by the more favorable environment induced by the
greater availability of nutrients.
Air-drying samples prior to incubation resulted in a
flush of mineralization for the first 30 to 45 days of the
incubation period. It is likely that other pretreatments,
freezing in particular, had qualitatively similar effects.
Previous work indicates that the flush results from the
solubilization of organic N compounds due to the death of
the microbial population. This flush greatly influences the
interpretation of measured cumulative N mineralized over
time in incubation studies.
*
The "universal" k value, k , for the FO model is not
appropriate for any of the soils or treatment combinations
studied. This does not appear to be the result of any
peculiar properties of the soils studied. Rather, it is a
result of the method of analysis used to determine k values.
In addition, it appears that the assumption that one N pool
contributes to mineralization is not valid when common
methods of pretreatment are used.
Both of the alternative models examined describe net N
mineralization better than the FO model. The DFO model was
shown to agree very well with measured net N mineralization,
but the precision with which parameters were estimated was
so poor that little confidence could be placed in them. The

145
FOZ model also described measured data very well, and the
precision of parameter estimation was considerably better
than for the DFO model. Other, more complex models could
undoubtedly achieve excellent agreement, but, unless the
required parameters were estimated independently, would
suffer the same limitations as the DFO model.
The first-order kinetic term in the FOZ model describes
the mineralization of N made mineralizable by pretreatment.
If the nature of pretreatment is carefully controlled then
the first term is probably indicative of the size of the
microbial populations. The zero-order term is a basal
mineralization rate. The successful application of the
zero-order term indicates that the source of mineral N in
these incubation studies was relatively large pool(s) of N.

CHAPTER V
OVERALL SUMMARY
Increased pressures for land use from a variety of
sources make it important to develop sustainable, productive
cropping systems in the humid tropics. One of the con¬
straints to this is the potentially large nutrient leaching
loss from agricultural fields in the region. It has been
suggested that cropping systems with high species density,
deep rooting habit, and continuous nutrient demand, should
be more conservative in terms of applied nutrient use than
annual, monocropped systems. In this study, we examined
water movement, nutrient leaching and release by mineraliza¬
tion in to contrasting cropping systems to determine differ¬
ences in nutrient use efficiency.
Water Movement and Nutrient Leaching
One of the main reasons that leaching has not been
studied extensively in tropical regions is that such studies
are difficult. There are no means currently available of
measuring leaching directly. For this reason, leaching
losses must be calculated from indirect measurements. This
requires that assumptions be made concerning the nature of
nutrient movement in the field.
The approach taken in this study was to calculate
nutrient movement on the basis of water fluxes and solute
concentrations. In order to apply this approach, the manner
146

147
in which water flows through the soil must be known. In
particular, there was concern that flow might take place in
localized channels (macropores) which would result in the
effective bypassing of the soil matrix.
Tritiated water was displaced through "undisturbed"
soil columns under a range of soil-water tensions to deter¬
mine the nature of soil-water flow in soil at the experimen¬
tal site. Although a high degree of dispersion was ob¬
served, significant bypassing was not observed under ten¬
sions other than at or very near 0 kPa (saturation).
Comparison of field-measured saturated hydraulic conductivi¬
ties with rainfall intensities indicated that such flow
conditions are very rare. This is consistent with measured
changes in the water table depth in response to rainfall
events and the observation that ponding did not occur at the
site. This led us to conclude that solute movement could be
described with modeling approaches that assume that all
soil-water participates in convective transport.
A field-scale, capacity-parameter based, model of water
and solute movement was then developed that was conceptually
based on the above findings. Solutes from a given input
were assumed to be dispersed about a solute front that
travels through the soil at the pore-water velocity, v . It
was further assumed that soil-water drained to a consistent
value, ©£C, after rainfall events. Problems associated with
the proximity of a water table were circumvented by using

148
the water table depth as an input and by not considering the
upward movement of water explicitly.
Simulated soil profile water contents closely approxi¬
mated those measured over the validation period of 96 days
in both plots. Simulation was then extended to a longer,
260 day period that commenced 150 days prior to the valida¬
tion time period. After 150 days of simulation, the agree¬
ment between measured and simulated profile soil-water
contents was reasonably good. These results were a further
confirmation that the basis of the model is sound.
Simulation results were used to calculate: (1) the net
deep percolation to be used to estimate net nutrient leach¬
ing loss, and (2), the movement of a nonadsorped solute
front through the soil profile.
The simulated net deep percolation was slightly greater
from the monocropped annual (MA) plot than from the mixed
perennial (MP) plot. The difference was due to the greater
actual evapotranspiration (AET) from the MP plot. The
+ 2+
measured soil solution concentrations of N, K , Ca , and
2+
Mg at 90 cm depth over the same time period were approxi¬
mately constant over time and much grater in the MA plot
than the MP plot. Estimation of net leaching loss (the
product of soil solution concentration and net deep percola¬
tion) were consequently much greater from the MA than the MP
plot.
The simulated movement of a nonadsorped solute front
was somewhat faster in the MA plot than the MP plot. The

149
greater AET and 0fc in the MP plot account for this. The
results indicate that succeeding crops in the MA plot do not
receive much beneficial effect from fertilizer NO^ (which is
weakly adsorped) applied to previous crops.
The concept of nutrient residence time was introduced
to characterize the sensitivity of the two cropping systems
to leaching losses. The solute residence is considered to
be the length of time a nonadsorped solute can be expected
to reside within a given soil depth. It is based on the
concepts used to calculate the movement of the solute front
in the model with a sight modification to allow the use of
average climatic data. Soil, crop, and climatic factors are
accounted for with three parameters. The soil parameter is
what was termed the leaching depth of the soil (0^CD),
which is the amount of water required to displace a
nonadsorped solute beyond the soil to the depth of interest
when the initial soil-water content is . In this study
only the movement of a nonadsorped solute was considered but
the concept could be extended to adsorped solutes by calcu¬
lating the leaching depth (LD) as
LD = 0fc D RF, (5-1)
where RF, the retardation factor, was described in Chapter
II.
The crop and climate parameters, AET and rainfall (TR),
respectively, were combined into a single parameter, the
cumulative effective rainfall (CER), which is simply AET -

150
TR. The solute residence time is then the length of time
required for the CER to exceed the leaching volume.
Solute residence times calculated for the MP plot were
about 1.4 times greater than those for the MA plot. Most of
that difference was due to the somewhat greater 0^ at the
MP plot. This result, which was somewhat surprising, can be
explained in terms of both the AET and the TR. AET differ¬
ences between the two systems were not as great as had been
expected for two reasons: (1) there was considerable loss
of water from the soil by evaporation and transpiration by
weeds during the fallow periods, and (2) the transpiration
from the MP plot was reduced during the dry season. These
effects are enhanced by the fact that the TR in this climate
is sufficiently greater than the AET that crop differences
were obscured.
Mineralization and Nutrient Cycling
Aside from differences in water movement, differences
in leaching losses from the two plots may be attributed to
differences in the rate of release of nutrients from litter
or crop residue and soil organic matter. This would effect
the rate at which nutrients enter the soil solution and
thereby effect the potential for leaching loss.
The laboratory incubation method of Stanford and Smith
(1972) was investigated as a possible basis for for deter¬
mining the rate of N mineralization from soil organic
matter. The kinetics of mineralization measured did not
follow those proposed by Stanford and Smith (1972). In

151
addition, the application of first-order kinetic N mineral¬
ization models to field conditions is questionable because
they do not account for the addition of N to the potentially
mineralizable N pool during the cropping season.
An alternative model, composed of two terms, one
zero-order and the other first-order kinetic, was proposed.
The first-order kinetic term was interpreted as describing
the effects of sample pretreatment on mineralization rates.
Results from the present study indicate that significantly
more N was mineralized from soils that were air-dryed prior
to incubation than those not. The zero-order term was
interpreted as describing the "basal mineralization" rate
that results from mineralization from large organic N
pool(s).
The alternative model quite successful at describing
cumulative net N mineralization during incubation. Either
of the two terms used might be used to estimate soil N
availability, but there are problems associated with both.
The first-order kinetic term, which is probably related to
the soil microbial biomass, could provide an index of the
capacity of the soil to mineralize organic N. However,
application requires knowledge of the relationship between
microbial biomass and N mineralization rate, which can be
expected to be dependent on numerous environmental condi¬
tions .
Application of the zero-order rate coefficient, K,
would seem to be appropriate since it describes

152
mineralization rates in the absence of experimental arti¬
facts. However, it too is certainly dependent on environ¬
mental conditions such as temperature, soil-water content,
and nutrient availability. These limitations make even
relative comparisons of parameter values questionable except
where environmental conditions are essentially equivalent.
Overall Assessment of Cropping Systems
It is clear that the net loss of nutrients from the
mixed perennial cropping system was much lower than from the
monocropped annual cropping system. This statement can be
based on the soil solution data alone. The reason for the
observed difference is not so clear, however. At the outset
of this work it was believed that differences in
transpirational demand would be sufficiently great that the
solute residence time would provide a reasonable explana¬
tion. However, for reasons discussed previously, the
differences in solute residence time were not as pronounced
as anticipated.
One explanation is simply that the mixed perennial
system received less nutrient input. It is necessary to
consider inputs from both fertilizer application and organic
matter decomposition. While the fertilizer additions are
known, this is not the case for organic additions. As an
alternative to using information from incubation studies,
the assumption can be made that the rate of nutrient entry
from organic sources is equal to the rate of litter or crop
residue addition. Eventually, at steady state, this must be

153
the case. The work of various authors (Jenny et al., 1949;
Sanchez et al., 1982) indicates that approximate equilibrium
in tropical regions is achieved relatively rapidly. Accord¬
ing to those studies, the equilibration time of 6 to 7 years
for the two cropping systems examined in this study is
likely sufficient for such conditions to prevail.
Following this approach, the annual addition of (kg
-1 + 2+ 2 +
ha ) of N, K , Ca , and Mg to the mixed perennial plot
of 95, 57, 108, and 43, respectively, measured by Alpizar et
al. (1983), can be added to annual fertilizer additions.
The resultant total addition is 235, 77, 108, and 43 kg ha 1
yr 1 of N, K+, Ca^+, and Mg^+, respectively. It should be
noted that the estimation of litter input is a considerable
underestimation because inputs from pruning were omitted by
Alpizar et al. (1983). Using residue inputs for maize
provided by Sanchez (1976), the estimated total annual input
+ 2+ 2
of N and K (estimates of Ca and Mg are questionable in
this context) are 300 and 100 kg ha ^, respectively. Viewed
from this perspective, it does not appear that differences
in nutrient input alone can explain the observed differences
in soil solution concentration.
In my opinion, there are two reasonable explanations
for the observed differences in cropping systems. The
first, and probably more important, is the well developed
rooting system in combination with nutrient demand, that
exists throughout the year in the mixed perennial system.
This means that any nutrient entering the soil solution must

154
travel through the entire rooting system before it is lost
by leaching. In contrast, much of the fertilizer nutrient
in the monocropped annual system is applied at planting,
when there are no roots at depth. In addition, active roots
are essentially eliminated from the profile at each harvest,
rendering the recovery of nutrients located in the lower
portions of the profile unlikely.
The other explanation is based on the effects of the
litter layer in the mixed perennial system. The presence of
carbonaceous material at the soil surface could result in
temporary immobilization of NO^ . This NO^ , plus that
released by mineralization of litter fall, is expected to
enter the soil solution slowly, allowing the crops greater
opportunity for absorption.
Future Work
As is often the case, the results from this study point
to the kind of work that should be undertaken in the future.
The basic approach to the study of cropping systems employed
in this study was to to monitor the various cropping system
properties (e.g., soil solution concentration and soil-water
content) as they changed "naturally" over time. While this
approach does provide considerable insight into the ranges
of values and relative importance of processes that occur in
the field, it does not provide detailed, quantitative
information about those processes.
For example, considerable information concerning the
nature of solute movement in the field could be obtained by

155
implementing smaller scale, intensively measured experiments
in which tracers and water are applied at critical intensi¬
ties. In addition, smaller scale, intensively measured
subplots could be monitored intensively to better assess the
movement of water through the soil and the response of the
water table to rainfall inputs. By the same logic, a much
clearer picture of the relative movement of applied N in
both systems could be obtained by more intensive measurement
of subplots.
On a slightly different level, the overall importance
of various processes would be better understood if more care
were taken to evaluate other components of the cropping
systems. In this study, considerable effort was devoted to
the evaluation of leaching losses, which were considered to
be the "missing link" in many nutrient balance studies, at
the expense of the rest of the chain. Probably the single
most important set a data that were missing was the actual
crop uptake of nutrients.
One conclusion from the N mineralization study was that
laboratory incubations provide a questionable basis for the
estimation of N mineralization under field conditions. This
result is not surprising in light of the overall failure of
any laboratory procedure to be widely accepted in spite of
considerable efforts in development. This is not to say
that further study into the mechanisms of N mineralization,
possibly using incubation techniques, is unwarranted. On
the contrary, such research is required if better

156
understanding and eventually better assessment of N mineral¬
ization is to be achieved. At present, however, it seems
that reasonably good evaluation of organic inputs can be
obtained simple mass balance considerations.
In general, it appears that there is considerable
potential for the use of cropping system manipulation as a
means of achieving high efficiency of applied nutrient
uptake. It should be possible to isolate the mechanisms
responsible. If this is done then we will have a rational
basis for the design of future cropping systems.

157
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169
BIOGRAPHICAL SKETCH
Mark S. Seyfried was born in Santa Rosa, California in
the year 1954. Most of his early years were spent in the
near by town of Modesto. The easy access to both the Sierra
Nevada mountains and the ocean from that town allowed him to
develop an appreciation of both. He graduated from high
school in 1972 and moved to Berkeley to attend the Universi¬
ty of California the following fall.
After investigating a variety of "careers" he became
disenchanted with academia briefly and quit. After working
a number of jobs a travelling considerably, he returned to
college and discovered the discipline of soil science. Two
years later, in 1977, he graduated with a degree that field.
In October of 1977 he started work with the Soil
Conservation Service as a soil mapper in New Mexico. This
provided a great opportunity to see the reality of soils in
"the field" as opposed to the textbooks or laboratory and
was a great experience. New Mexico was a fantastic place to
work such a job. Unfortunately, the job of mapping breeds
many questions but provides little opportunity to answer
them.
Because the university seemed to the place where such
an opportunity might exist, he started graduate studies in
the University of Florida in 1980. While studying soil
genesis he discovered the research approach that those in

170
soil physics were taking. After obtaining his M. S. in
1982, he immediately flew to Costa Rica to apply this
approach to many of the questions he had been asking since
the old mapping days. This work has constituted a Ph.D.
dissertation. Although many questions remain to be an¬
swered, the effort proved very satisfying.

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
a dissertation for the degree of Doctor of Philosophy.
P. S. C. Rao, Chairman
Professor of Soil Science
as
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Professor of Soil Science
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
o w,
cl' ^
c
D. A. Graétz
Associate Professor of Soil Science
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
N. B. Comerford
Associate Professor of Soil Science

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
J. M. Bennett
Associate Professor of Agronomy
This dissertation was submitted to the Graduate Faculty of
the College of Agriculture and to the Graduate School, and
was accepted as partial fufillment of the requirements for
the degree of Doctor of Philosophy.
August 1986
Dean, College of Agriculture
Dean, Graduate School

UNIVERSITY OF FLORIDA
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