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WATER AND NUTRIENT MOVEMENT RELATED TO SOIL PRODUCTIVITY
IN AN AGGREGATED GRAVELLY OXISOL FROM CAMEROON
By
PAUL R. ANAMOSA
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
1989
ACKNOWLEDGEMENTS
During the course of this project I have been fortunate to receive
a great deal of assistance. The IFAS International Programs Office
provided most of my assistantship and travel fare to and from Cameroon.
In Cameroon, Eric van Ranst, Soil Science Department Chair, provided me
with access to vehicles and field technicians. Philip Mokoko and
Maurice Ndazame gave invaluable help to my efforts; aiding in the
management of the field project, translating French and the local
Dschang dialect to English, and advising me on matters of protocol as
well as cultural values.
I owe a great debt of gratitude to Dr. W. G. Blue, chairman of my
graduate committee, for his support of my field and laboratory
activities as well as his editorial review of this dissertation. I am
fortunate to have studied under his guidance and am appreciative of his
personal generosity and understanding.
I am grateful to Dr. P. Nkedi-Kizza, who was willing to join my
graduate committee mid-term and who provided new perspectives to my
objectives. He gave constructive guidance and criticism to my
laboratory experiments.
I would also like to thank the other members of my graduate
committee, cochairman Dr. J. B. Sartain, Dr. B. L. McNeal, Dr. P. E.
Hildebrand, and Dr. G. Kidder for the interest and feedback they
provided. Dr. Hugh Popenoe graciously substituted for Dr. Blue while he
was in Cameroon.
Lastly, I would like to thank those on the home front. My wife,
Frances, was joyfully willing to pull up stakes and move to Cameroon,
put up with late-night runs to the lab to check pumps, and gave constant
encouragement throughout the course of this ordeal. Our feline
housemates Ferguson and Abigail helped with typing the manuscript.
TABLE OF CONTENTS
ACKNOWLEDGMENTS.................................................ii
ABSTRACT.......................................................... iv
CHAPTERS
1. INTRODUCTION...........................................1
2. REVIEW OF THE LITERATURE................................ 3
Introduction...................................3
Formation Processes............................... 4
Agricultural Productivity............................9
Research Topics................................... 12
3. SOIL CHARACTERIZATION................................ 15
Introduction..................................15
Materials and Methods ........................... 15
Results and Discussion.......................... 17
4. CHARACTERISTICS OF SOIL WATER MOVEMENT IN UNDISTURBED
SOIL COLUMNS........................................... 24
Introduction. ................................... 24
Materials and Methods ........................... 31
Results and Discussion .......................... 34
Conclusions.....................................59
5. CROP RESPONSE TO PLANTING DENSITIES AND FERTILIZER
APPLICATION SCHEDULING............................... 61
Introduction....................................61
Materials and Methods ........................... 63
Results and Discussion .......................... 66
Conclusions........................................ 94
6. NUTRIENT MOVEMENT IN UNDISTURBED SOIL COLUMNS...........98
Introduction....................................... 98
Materials and Methods .......................... 101
Results and Discussion........................... 106
Conclusions .................................... 137
7. OVERALL CONCLUSIONS....................................140
Introduction....... .............................140
Soil Characterization ......................... 141
Crop Response ................................. 142
Nutrient Leaching............................... 143
APPENDIX A SOIL PROFILE DESCRIPTION..............................145
APPENDIX B CROP COMPONENT YIELDS.................................147
REFERENCES .....................................................149
BIOGRAPHICAL SKETCH................................................159
v
Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements of the Degree of Doctor of Philosophy
WATER AND NUTRIENT MOVEMENT RELATED TO SOIL PRODUCTIVITY
IN AN AGGREGATED GRAVELLY OXISOL FROM CAMEROON
By
Paul R. Anamosa
August 1989
Chairman: W. G. Blue
Cochairman: J. B. Sartain
Major Department: Soil Science
Gravel decreases the water- and nutrient-holding capacities of
soil. Soils with gravel horizons (stone lines) are being increasingly
utilized for crop production in equatorial Africa. This study was
conducted to differentiate between the relative effects of water and
nutrient stress for crops grown on stone-line soils, and to determine if
preferential water flow and mobile/immobile water concepts should be
considered in describing nutrient and water behavior. The effects of
plant densities of maize (Zea mays L.) and bean (Phaseolus vulgaris L.)
and of split applications of plant nutrients were investigated for a
clayey-skeletal, oxidic, isohyperthermit, Typic Gibbsiorthox near
Dschang, Cameroon. The movement of soil nutrients was studied in soil
columns subjected to simulated rainy seasons. The nature of the porous
network of the soil was studied using miscible-displacement techniques
with tritiated water.
Increased splitting of mobile-nutrient applications (K, NO3, and
NH4) resulted in increased grain yields, but had no effect on stover
yields. Early-season moisture stress apparently decreased plant
emergence. However, high-density plantings yielded more grain and
stover than did similarly fertilized, low-density plantings. Thus, once
plants were established, grain yields were not adversely affected by
moisture stress. A 30-d delay in planting resulted in a 40% increase in
seasonal rainfall and 50 and 70% grain-yield reductions for bean and
corn, respectively.
Leaching of Ca, K, and Mg from 70-cm long soil columns was not
affected by rainfall regimes or fertilizer-application schedules,
although the distribution of Ca, K, and Mg in the columns indicated more
downward movement under higher rainfall. Leaching of K was negligible
under all treatments used in this study. Split applications of
fertilizer composed primarily of K, NO3, and NH4 resulted in greater
concentrations of Ca and Mg with depth.
Moisture-release curves showed that the soil drained nearly 30%
of total water content at 50-mbar tension, but still held 30% at 15-bar
tension. Miscible-displacement experiments indicated that, under
saturated conditions, the soil had a high dispersivity and held about
50% of it water in immobile-water regions.
Delays in planting to avoid early-season water stress result in
greater leaching losses and reduced grain yields. Splitting the
applications of mobile nutrients should increase their plant
availability later in the growing season. Gravel porosity and immobile-
water regions in the soil harbored highly mobile plant nutrients and
reduced leaching.
vii
Chapter 1
INTRODUCTION
In light of the present food-production crisis facing most
countries of sub-Saharan Africa, numerous policy priorities have been
proposed by academics and politicians to encourage the rapid development
of technology to improve Africa's food production capacity (Swindale,
1980; USAID, 1985; Mellor et al., 1987; lyegha, 1988). High on many
priority lists is the need for scientific and technological research
directed towards the development of efficient fertilizer utilization
practices specifically adapted for the low-fertility soils common to
tropical regions.
Shallow gravel horizons, frequently referred to as stone lines,
are common in soils throughout equatorial Africa. Stone-line soils are
generally considered to be agriculturally marginal; however, in a
continent where population growth is out pacing increases in
agricultural productivity, the development and utilization of marginal
lands for farming are increasing.
Stones in the root zone of a soil reduce root penetration and
water- and nutrient-holding capacities. These characteristics in turn
reduce root exploitation of the soil mass and increase both the
susceptibility of crops to water stress and the potential loss of
nutrients by leaching.
Several recent studies have indicated vesicular voids (pores) in
the gravel from stone-line soils (Muller and Bocquier, 1986; Amouric,
1986). The effects of porous gravel on soil-water behavior are not
easily inferred and depend on the porosity and pore-size distribution of
the gravel. In addition to possible storage of plant-available water,
the gravel porosity may also act as a sink/source for the storage of
leachable nutrients, and thereby, harbor nutrients from convective-water
flow.
The purpose of this dissertation was to assess several behavioral
characteristics regarding water and nutrient movements through a stone-
line soil from the western highlands of Cameroon. Specifically, the
objectives were:
1. To differentiate between the relative effects of possible
water and nutrient stresses on field crops grown on a stone-
line soil; and
2. To determine if preferential water flow and immobile-water
regions should be considered in describing nutrient-leaching
behavior in these soils.
CHAPTER 2
REVIEW OF THE LITERATURE
Introduction
Soils with gravel horizons are common on the hilly landscapes of
equatorial Africa. Commonly referred to as stone lines, these gravel
horizons were first discussed in the soils' literature in the mid 1930s,
and have experienced intermittent periods of scientific examination in
every decade since. Initial interests in morphology and formation
processes have given way to evaluation of aspects of agricultural
productivity.
Owing to the limitations of slope and tillage, these soils are
generally considered to be agriculturally marginal (Hidlebaugh, 1984).
However, increasing population pressures in many of the regions where
they occur have necessitated their increased usage. The few published
studies evaluating agricultural behavior have focused on effects of the
gravel on root penetrability and water redistribution. Inferences
regarding appropriate-management practices for stone-line soils under
agricultural production have not been addressed.
The purpose of this review is to examine the scientific literature
de ling with various aspects of stone-line formation processes and
agricultural productivity, and to develop a consensus of the needs for
future research, specifically in the area of crop-management practices.
Formation Processes
The term "stone line" was originally proposed by Sharpe (1938) to
designate "a line of angular to subangular fragments which parallels a
sloping surface to a depth of several feet." Ruhe (1959, p. 223),
summarizing the definitions of several studies (Sharpe, 1938; De
Heinzelin, 1955; Parizek and Woodruff, 1957), defined a stone line as "a
concentration of coarser rock fragments in soils; in cross section it
may be a line, one stone thick or more than one stone in thickness, that
generally overlies material weathered in place from bedrock and that
usually is overlain by variable thicknesses of finer-textured sediment."
De Heinzelin (1955) objected to the term when used to designate the
gravel horizons common to equatorial African soils. He proposed instead
the term "nappe de gravat" (sheet of gravel), because it more
appropriately described the three-dimensional nature of the structure.
However, at present the term stone line is widely used in both the
English and French pedological literature.
The formation processes that create stone lines instill specific
morphological characteristics to the soil profile. It is these
morphological characteristics that have been used to develop hypotheses
concerning the formation processes. Pedologists working throughout
equatorial Africa on a variety of landscapes have developed two
different schools of thought concerning stone-line formation. These
were categorized as either autochthonous (same) or allochthonous
(different) with respect to the parent material of the stones and of the
underlying material (Collinet, 1969). The distinction between the two
categories involves whether the stones are residual from the underlying
parent material or were transported from elsewhere and then covered with
sediment. This distinction was the core of debate among pedologists
originally hypothesizing the formation processes. It is still a point
of contention, considering that allochthonous processes rarely exhibit
the transport of stones over distances greater than several hundred
meters (Riquier, 1969; Sdgalen, 1969; Fairbridge and Finkl, 1984).
The stone lines produced by the two widely accepted autochthonous
processes are relatively uncommon and show vast differences in
morphology. The reworking of soil materials by termites, resulting in
concentration of the finer above the coarser sediments, has been studied
throughout equatorial Africa (De Heinzelin, 1955; Nye, 1955; Sys, 1955;
Gennart et al., 1961). Variations exist among termite species and
geographical locations, but such stone lines generally consist of a
diffuse gravel horizon rarely exceeding 25% by weight of small, 2 to 7
mm, fragments of residual quartz and occasional ironstone nodules. The
gravel horizons range in thickness from 10 to 250 cm, and rarely exceed
depths of 300 cm.
Surface stone lines, frequently called "desert pavement," are
found in extremely arid climates that receive occasional torrential
rains. It is generally believed that these surface stones result from
the fracture of exposed bedrock due to large daily temperature
fluctuations. Sheet erosion during heavy rains then removes any
overlying soil, which either collects in crevices between the stones or
is washed away (Springer, 1958; Finkl, 1979).
By far the most common type of stone line in equatorial regions of
Australia, South America, and Africa is presently attributed to an
allochthonous process. However, several autochthonous-process theories
have been proposed and subsequently refuted. Sharpe (1938, cited in
Ruhe, 1956) and Ireland et al. (1939, cited in Ruhe, 1956) proposed a
theory involving surface creep, in which soil flowing slowly downslope
shears off resistant rock projecting up into the subsoil and carries the
rock along the bottom of the creeping mass. Ruhe (1956) and Parizek and
Woodruff (1957) rejected this theory. They concluded that the sheets of
gravel were originally surface deposits later covered by an over-lying
mantle. Ruhe (1959) later described in detail this theory, which
assumes the stones to be highly resistant residual parent material that
became concentrated on a developing erosional surface by the removal of
finer material with runoff water. Finer-textured sediment derived from
an upper-valley slope then is deposited on the sheet of gravel. This
process is autochthonous in nature, and can not explain soils with
multiple stone lines (Ollier, 1959).
An allochthonous process was first proposed by de Craene (1954)
and later applied to both quartz lines and gravel horizons by Collinet
(1969) and Riquier (1969). In its most basic form, the process begins
with the deposition of rock material from exposed escarpments (rock
outcroppings) onto sloping eroded surfaces. This material then is
covered by a fine colluvial mineral deposit. Therefore, both rock and
fine fraction are genetically different from the soil below the stone
line. The process can be repeated as long as a rock escarpment exists
above the erosional surface.
A similar process can lead to the development of thin, quartz-
stone lines. Quartz veins of geologic origin are frequently sandwiched
between layers of sedimentary rock. If near the surface, such rock may
be transformed into soil or saprolite, leaving the resistant quartz vein
intact. Where the quartz vein intercepts the earth's surface it
provides a source of quartz pebbles that then may be spread over the
soil depending on the slope of the land. If the surface is sloping, the
pebbles will be scattered downslope. If the surface is flat the pebbles
will form mounds or ridges that may run for a considerable length across
the landscape.
The allochthonous process requires winnowing (the movement,
deposition, and concentration of coarse material by wind and running
water), which in turn usually requires climatic instability so that
slopes may go through both erosional and stabilizing periods (Fairbridge
and Finkl, 1984). Such periods are attributed to torrential rains
during arid to semi-arid climatic phases within a normally humid era.
This pattern would allow for erosion of vegetatively bare surfaces
during intermittent heavy rains in an arid phase and subsequent slope
stabilization by vegetation upon return of the humid climatic phase.
Several independent lines of evidence suggest that the
pleniglacial age of the late Wisconsinan cycle was responsible for the
climatic conditions favorable for stone-line formation in the tropics.
Bruckner (1955) working in Africa, Bigarella and de Andrade (1965)
working in Brazil, and Finkl (1979) working in Australia have all
identified regional occurrence of common but discontinuous stone lines
dating from the late Wisconsinan period. The arid phases during the
Wisconsinan period were brought about by a combination of lower solar
radiation, disruption of major air-flow patterns, and extension of the
cold polar oceanic currents into low latitudes (Fairbridge, 1964;
Cailleux and Tricart, 1973).
Stone lines formed from the allochthonous processes of escarpment
retreat, rock-fragment deposition, and fine-fraction sedimentation
frequently have common physical characteristics (Fairbridge and Finkl,
1984). Such stone lines occur as slope deposits on the paleoslopes of
interfluves and residual pediplains (Ojanuga and Wirth, 1977).
Distances of transport usually range from several meters to several
hundred meters. The stones are angular to rounded, but are usually
similar within any singular continuous horizon (Ojanuga and Lee, 1973).
The stones are quite resistant to weathering either because they are
inherently durable such as quartz or because they consist of resistant
lateritic pseudomorphs (similar shape but different mineralogy) of the
original rock fragments (Ojanuga and Lee, 1973; Muller and Bocquier,
1986). Such pseudomorphs result from the natural weathering and
dissolution of rock parent materials, along with the precipitation of Fe
and Al minerals leached from overlying soil horizons rich in Fe and Al
oxides/hydroxides. Lateritic material in the stone line may also come
from pistolitic duricrust that forms with desiccation and hardening of
exposed surface soil resting on top of the fragmenting escarpment
(Frankel and Bayliss, 1966; Amouric et al., 1986). Further erosion and
retreat of the escarpment face causes fragments of the surface duricrust
to drop along with escarpment-rock fragments to the erosional plain
below.
Agricultural Productivity
Little is known about the behavior of tropical stone-line soils or
the influence they exert on agricultural systems which they support.
The paucity of internationally available literature on soils of the
tropics, in general, is well known. The habit of national governments
to establish research stations on a region's best soils frequently
limits the generation of knowledge concerning hillside and
agriculturally marginal soils (Zandstra et al.,1981).
Lal, formerly of the International Institute of Tropical
Agriculture in Ibadin, Nigeria, has conducted several studies
investigating plant-root development and water availability on natural
and synthetic gravelly soils. Babaloa and Lal (1977a) evaluated the
effects of varying gravel concentrations on shoot growth and rooting
depth using soil/gravel mixtures in greenhouse pot studies. The weight
of corn shoots harvested after 21 d decreased by up to 50% as gravel
concentration increased from 10 to 75%. Rooting depth decreased only
slightly as gravel increased from 0 to 10%, but then decreased to 40% of
the non-gravel rooting depth as gravel increased to 25%. The rooting
depth decreased to 5% of the non-gravel rooting depth as the gravel
increased to 75%. Total root length was affected similarly. Shoot
weights increased by 20% as the depth to a 60%-gravel horizon increased
from 5 to 10 cm. Shoot tissue had a nonsignificant increase in
concentrations of N, P, and K as depth to the gravel horizon increased.
The researchers concluded that the gravel retarded rooting depth and
thereby decreased root exploitation of soil nutrients, resulting in
reduced nutrient uptake and consequent reduced overall growth.
Babalola and Lal (1977b) evaluated the effects of various gravel
sizes and mixtures and the effects of modifying a natural gravelly soil
on corn-seedling growth. Increased gravel size (4 to 8, 8 to 15, and 15
to 40 mm) decreased shoot weight, root weight, root depth, and overall
root length of 7-d-old seedlings harvested from soils of varying gravel
concentrations. Field studies were performed on a naturally occurring
gravel horizon following removal of the overlying 15 cm of surface soil.
Treatments included gravel horizon undisturbed; gravel horizon removed
and repacked at a lower bulk density; gravel horizon removed, sieved to
remove gravel, and repacked as only the fine fraction; and gravel
horizon removed and area repacked with the original surface soil. All
treatments with gravel had seedling emergence delayed 1 to 2 d. The
treatments did not produce differences in shoot height, shoot dry
weight, or root dry weight. In comparison to the undisturbed treatment,
reduction in bulk density, accomplished by removal and repacking of the
soil, increased root length and rooting depth by 50%. Removal of the
gravel and substitution of surface soil for the subsoil increased root
length and rooting depth by 80% over the undisturbed control. There
were no differences in root length, root depth, or shoot weight between
the subsoil without gravel and the replacement of subsoil by surface
soil. Roots in gravelly horizons exhibited an increased mean diameter,
stunted tips, and marked crookedness. Although the gravel had no impact
on dry-weight yields in this short 7-d trial, the stunted growth and
limited access of the roots to soil would probably have detrimental
repercussions on full-season, plant-yield components.
The influence of gravel on the moisture characteristics of the
whole soil results from the quantity of gravel, its arrangement in the
soil fabric, and its own hydrologic properties. Several researchers
have attributed the scarcity of information regarding water relations in
gravelly soils to difficulty in adapting standard laboratory and field
techniques to gravelly soil, which display a high degree of micro-
variability within repetitive samples (Reinhart, 1961; Hanson and
Blevins, 1979).
Experiments using drastically-disturbed and mixed-gravel soils
will be discussed and distinguished from those of naturally occurring
soils with gravel horizons. Miller and Bunger (1963) and later Unger
(1971a and 1971b) constructed soils with "pea gravel" horizons to study
water infiltration and redistribution. In all treatments of the three
studies, screens or special repacking techniques were used to prevent
soil from filling the interstitial spaces of the gravel horizons. These
studies showed that the gravel slowed downward percolation, and for all
practical purposes, prevented upward redistribution of soil water. The
behavior of these soils should probably not be extrapolated to soils of
the tropics with naturally occurring gravel horizons, in which a fine-
mineral fraction occupies the inter-gravel space and provides a
continuum of fine pores that can participate in the redistribution of
soil water.
Babalola and Lal (1977a) reported soil moisture-release curves for
the sieved, gravel-mixed soil used in their previously reported studies.
They showed an incremental decrease in soil-water content at tensions of
0 to 60 cm of water for each incremental increase in gravel
concentration from 0 to 75%. They concluded that, as gravel
concentration and, therefore, total solids increased, porosity and
consequently water-holding capacity decreased.
Ghuman and Lal (1984) studied differences in field-water
percolation and redistribution rates on a tropical Alfisol under
conventional plowing and no-till management. The soil had a naturally
occurring gravel horizon from the 10 to 80-cm depth that contained about
45% gravel by weight. Soil having an initial water content of 0.035
cm3/cm3 exhibited infiltration rates of 43 and 120 cm/h for the
conventional and no-till systems, respectively, upon application of 5 cm
of floodwater to the surface. The infiltrating water under both tillage
treatments reached the 30-cm depth before flood conditions ceased, at
which time the plots were covered to prevent surface evaporation.
Within 1 h the water had passed the 80-cm depth. The redistributing
soil water had stabilized after 5 h and the soil water content with
depth remained constant until cessation of observations at 48 h. Higher
initial soil-water contents resulted in slower infiltration rates. Even
under very dry conditions, the gravel horizon did not prohibit the
downward movement of infiltrating water.
Research Topics
The unique physical properties and generally unknown behavior of
tropical, stone-line soils lead to many questions regarding their
agricultural management. However, extrapolation of properties and
behavior of soils that simply contain stones can lead to the development
of management practices based on incorrect assumptions. Soil scientists
have historically witnessed the difficulty of transferring management
techniques developed for temperate soils to soils of the tropics
(Swindale, 1980). Research into the properties and consequent behavior
of a soil is generally considered the most sound approach for the
development of management practices (Dudal, 1980).
Vine and Lal (1981) concluded that gravel reduces volumetric-
moisture content, reduces nutrient-retaining capacity, and retards
plant-root development. The extent of such effects is related to the
properties of the gravel. The porosity of the gravel will influence the
degree of any reduction in soil-moisture content, and rainfall patterns
will influence the degree to which soil moisture becomes detrimental for
plant growth. Although Flint and Childs (1984) have demonstrated that
gravel can hold up to 40% of available water in temperate forest soils,
and Muller and Bocquier (1986) and Amouric et al. (1986) have
photographed voids in the gravel from tropical, stone-line soils from
both Cameroon and Senegal, the porosity and water-holding capacity of
tropical, stone-line soils have not been established. Babalola and Lal
(1977a and 1977b) and Ghuman and Lal (1984) made no mention of water
retention by the gravel in their studies of water relations in tropical,
stone-line soils.
Reductions in nutrient-retaining capacity result from volumetric
reductions in the soil's fine fraction with the increase in volume of
gravel. The fine fraction typically contains greater surface area and
organic matter, and consequent nutrient-retaining charge. However,
porous gravel may harbor weakly held mobile nutrients. Studies in soil
physics have firmly established the presence of immobile-water regions
in the fine porosity of soil aggregates (Kirda et al., 1973; van
Genuchten and Wierenga, 1977; Rao et al., 1980a).
Considerable evidence has shown that gravel contents above 10 to
20% by weight have deleterious effects on root development and soil
penetration (Babalola and Lal, 1977a and 1977b; Vine and Lal, 1981).
Regardless of gravel hydrologic properties, the gravel limits rooting
depth and, therefore, limits the volume of soil from which the plant can
extract immobile nutrients.
In light of the previously mentioned considerations, any research
effort with respect to the development of management practices may be
most productive if the research is designed to determine the combined
behavior of the processes and their combined effects on soil
productivity, instead of investigating separately the numerous
interdependent processes.
CHAPTER 3
SOIL CHARACTERIZATION
Introduction
In the late nineteenth century, Russian earth scientists
introduced the concept of soils as independent natural bodies, each with
unique morphology resulting from a unique combination of climate, living
matter, earthy materials, relief, and age (Buol et al., 1980). Since
that time, characterization of soil morphological, physical, and
chemical properties has played a fundamental role in the development of
soil taxonomic and resulting classification systems (Soil Survey Staff,
1975). Data characterizing soil properties, and subsequent taxonomic
classification of the soil are useful tools in the development of a
unified concept of soil behavior in its natural environment (Sanchez et
al., 1982b). The purpose of this chapter is to describe the physical
and chemical properties, and to taxonomically classify, the soil used in
the experimentation discussed throughout this dissertation.
Materials and Methods
The field site was located 1 km north of the Leppo primary
school and 60 m west of the road passing from Dschang to Djuttitsa
through the Leppo quarter of the village of Bafou, in the Western
Province of the Federal Republic of Cameroon. Following the field
experiments (Chapter 4), a 2-m deep pit was excavated in the middle of
the site and the soil profile was described (Appendix A). Samples
from each horizon were taken for physical and chemical analysis. Soil
texture was measured by the pipette method (Gee and Bauder, 1986).
Bulk density, porosity, and moisture-retention characteristics were
determined from undisturbed soil samples in 5-cm long by 5-cm
internal-diameter cores (Klute, 1986). Mineralogy of the fine
fraction (<2 mm) was determined by x-ray diffraction following removal
of organic matter with hydrogen peroxide and removal of noncrystalline
material with ammonium oxalate in the dark (Kunze and Dixon, 1986).
Exchangeable Ca+2, Mg+2, K%, and Na* were extracted from the fine
fraction with 1 M NH4OAc at pH 7 and determined by atomic absorption
spectrophotometry. Exchangeable H' and Al+3 were extracted with 1 M
KC1 and determined by the titration procedure of Yuan (1959). Organic
matter was determined by the modified Mebius procedure (Nelson and
Sommers, 1982). Phosphorous adsorption isotherms were determined for
both fine-fraction (<2 mm) and gravel (>2 mm) samples of the Ap
horizon, using the method of Fox and Kamprath (1970).
Gravel was sieved from the fine fraction, washed, and then
separated according to its four predominant colors. Mineralogy of the
gravel separates was determined by x-ray diffraction of powder mounts
following pulverization with a ball mill. Porosity of the gravel was
determined by the Brunauer-Emmett-Teller (BET) method on a
Quantachrome AUTOSORB-6 surface-area unit. Gravel-particle density
was determined using a gas pycnometer (Danielson and Sutherland,
1986). The soil was classified in the USDA Soil Taxonomy (Soil Survey
Staff, 1975) system based on its morphological description and its
physical and chemical properties.
Results and Discussion
Geographical Location
The soil-profile description is presented in the Appendix. The
soil rests on a 12 to 16% convex slope. The surrounding landscape is
steeply dissected and supports numerous small fields cropped to mixed
cultures of corn (Zea mavs L.), beans (Phaseolus vulgaris L.), cocoyams
(Colocasia esculenta), coffee (Coffea arabica L.), and peanuts (Arachis
hypooaea L.). The soil is derived from a basal parent material of
basalt, along with surface deposits of volcanic ash. The profile is
well-drained and has a lithologic discontinuity between the Btc and 2BCt
horizons, where a gravel horizon meets a buried clayey horizon.
Soil climate at the weather station of the Institute of Agronomic
Research in Dschang, 8 km south of the field site, is udic
isohyperthermic. The soil's control section (50 to 100 cm) is dry no
more than 90 d/yr, and maintains a mean annual temperature greater than
22 OC with less than a 5 OC fluctuation from the warmest to coolest
temperature at a depth of 50 cm.
Physical and Chemical Properties
Selected physical properties are shown in Table 3-1. The gravel
content of the top 72 cm ranges from 33 to 72% by weight. The fine
fraction is dominated by clay and composed of kaolinite, quartz,
goethite, and gibbsite. Selected chemical properties are shown in Table
3-2. Contents of organic carbon and exchangeable bases are calculated
on the basis of the fine fraction only. Trace quantities of acidity
were extractable, but never exceeded 0.02 cmol(+)/kg of soil for any
horizon.
Selected physical properties of the soil.
Bulk Fine fraction Clay minerals
Horizon Depth density Gravel Sand Silt Clay K Q GB GE
cm g/cm3 kg/kg -- kg/kg of <2 mm -- ---- % -----
Ap 0 11 0.88 0.335 0.211 0.355 0.435 55 15 15 15
Ac 11 22 1.00 0.588 0.111 0.403 0.486 60 10 15 15
Btc 22 72 1.46 0.720 0.112 0.337 0.551 60 5 20 15
2BCt 72 138 1.26 0 0.118 0.145 0.737 60 5 15 20
2CB 138 194+ 1.28 0 0.064 0.193 0.743 55 5 20 20
t K = Kaolinite Q = Quartz GE = Goethite GB = Gibbsite
Table 3-2. Selected chemical properties of the fine fraction (<2 mm)
of the soil.
Organic Extractable basest Extract. acidity Sum of pH
Horizon carbon Ca Mg K Na NH4OAct KCIt bases H20 KC1
g/kg ------- cmol (+)/kg fine fraction ---------
Ap 68.5 7.2 3.1 0.27 0.04 30.1 trace 10.6 5.33 4.76
Ac 57.0 4.5 2.6 0.13 0.03 22.9 trace 7.3 5.12 4.52
Btc 20.6 1.4 1.7 0.03 0.02 10.6 trace 3.1 4.98 4.85
2BCt 11.0 2.1 2.2 0.03 0.02 10.0 trace 4.3 5.52 5.43
2CB 7.5 3.6 2.4 0.05 0.05 8.4 trace 6.0 5.61 5.46
t extracted with 1 M NH OAc (pH 7.
f extracted with 1 M KC1.
less than 0.02 cmol (+)/kg.
Family designation: clayey-skeletal, oxidic, isohyperthermic,
Typic Gibbsiorthox
I
Table 3-1
19
The gravel is a composite of four visually distinguishable classes
composed of goethite, gibbsite, kaolinite, and an unidentified mineral
containing Mn (Table 3-3). The porosity of the gravel ranges from 0.13
to 0.32 mL/mL, with a natural-composite sample porosity of 0.2 mL/mL.
The moisture-release curve for the top 72 cm of soil is shown in
Fig. 3-1. The soil-water content exhibited no initial plateau at low
tension, thereby suggesting the presence of some very large pores that
are full when the soil is saturated, but which drain under relatively
low tensions. The soil lost nearly 0.2 mL of water per cm3 of soil
between saturation and 350-mbar tension (hypothetical field capacity).
It maintained 0.14 mL of water per cm3 of soil between 350-mbar and 15-
bar tension (hypothetical plant-available water).
Phosphorus adsorption isotherms are presented in Fig. 3-2. The Ap
horizon exhibits a strong affinity for P, and required nearly 500 ug P/g
soil (750 kg P/ha to a depth of 15 cm) to support a solution
concentration of 0.2 ug/mL. The gravel displayed a low affinity for P.
Taxonomic Classification
The Ap and Ac horizons constitute an umbric epipedon. The
epipedon has weak, medium, subangular-blocky structure that breaks to
moderate crumb. The color has a moist Munsell value and chroma darker
than 3.5. The organic-carbon content is greater than 2.5%, and the
depth of the epipedon is greater than 18 cm. Base saturation as
measured by 1 M NH4OAc at pH 7 is less than 50%.
The Btc, 2BCt and 2Cb horizons constitute an oxic horizon. This
horizon is at least 30-cm thick. The cation-exchange capacity using
NH4OAc (pH 7) is less than 16 cmol(+)/kg clay. There are no more than
Table 3-3. Physical and mineralogical characteristics of the gravel.
Color Predominant Pore Average Particle Bulk Porosity
minerals volume pore radius density density
mL/g nm g/mL g/mL mL/mL
Yellow Geothite (90)t 0.145 3.62 3.31 2.24 0.32
(12)$
Pink Kaolinite (60) 0.184 14.40 2.55 1.74 0.320
(8) Gibbsite (20)
Red Gibbsite (80) 0.087 8.69 2.63 2.14 0.186
(75) Kaolinite (10)
Black Manganese 0.043 4.91 3.55 3.08 0.133
(5) oxides
Composite -- -- 2.71 2.18 0.195
t Approximate percentage of mineral content.
t Percentage content of natural-composite total
(by mass).
0.6
S05 0 0.526 8.20x10-2 log(T)
R2. 0.998**
C.E
S0.4
CD-
. ...... ....... .. ........ ,
z
L 0.3
I-
O
0.2
I-.
o 0.1
0 4
0 100 200 300 400 15000
SOIL WATER TENSION, T (mbar)
Fig. 3-1. Soil moisture-release curve.
1000
Ap Horizon
S = 1420 C939
800 =0.8
R2 =0.89*
600
400
200
0.01
Gravel
S = 106 C1.30
R2 0. 91*
0.1 1.0 10.0
SOLUTION P, C (ug/mi)
Fig. 3-2. Phosphorus adsorption isotherms for the gravel and the Ap
horizon.
trace quantities of weatherable primary aluminosilicates. The texture
is finer than sandy loam and the horizon has more than 15% clay, with no
or very few clay skins.
The soil is an Oxisol because 1) the oxic horizon in the top 2 m,
2) there is no plaggen epipedon, and 3) there is neither an argillic nor
natric horizon above the oxic horizon. The soil is in the Orthox
suborder, because it has no continuous phases of plinthite within 30 cm
of the surface, is not saturated with water at any time during the year,
has neither a torric nor an ustic moisture regime, and has less than 16
kg of organic carbon per square meter to a depth of 1 m. The soil is in
the Gibbsiorthox great group, by virtue of the presence of a horizon
within 1.25 m of the surface that contains 20% or more by volume of
gravel-sized aggregates that contain 30% or more of gibbsite. This
Gibbsiorthox is in turn Typic, because the gibbsitic gravel is within 50
cm of the surface and there are no mottles in the upper 1 m of the soil.
The particle-size class of the soil is clayey-skeletal, because
gravel makes up 35% or more by volume and the fine earth contains 35% or
more clay by weight. The mineral class is oxidic, because the soil
contains less than 90% quartz and less than 40% each of hydrated
aluminum (reported as gibbsite or bohemite) and iron oxides extractable
by citrate-dithionite, and the sum of the percentages of these two
mineral groups divided by the percent clay is greater than 0.2.
Therefore, the family designation for the soil is clayey-skeletal,
oxidic, isohyperthermic, Typic Gibbsiorthox.
CHAPTER 4
CHARACTERISTICS OF SOIL WATER MOVEMENT IN UNDISTURBED SOIL COLUMNS
Introduction
A model is the representation of a form or process in an
alternative media. In modern science, chemical and physical processes
are modeled by representing behavioral processes with mathematical
relationships based on empirical and theoretical concepts. Models of
natural systems are frequently quite complex, because numerous
interrelated processes must be considered.
The ultimate goal in the conceptual development of a model is the
integration of mathematical relations that represent the true
mechanisms of the natural process. However, mechanistic approaches are
limited by insufficient understanding of processes and/or their
interactions. The limitations take the form of unverifiable
assumptions and exclusion of known but seemingly insignificant factors.
In lieu of mechanistic descriptions, processes may be lumped such that
the mathematical expression reflects the relation of several different
and detailed processes. Such a deterministic approach is advantageous
when the effects of a process can be modeled but the actual mechanisms
are unknown, or when a true mechanistic model requires extensive
characterization of the modeled media.
The value of a model lies in its ability to simulate the natural
process from measured or estimated parameters that characterize the
natural setting. Although concurrence of model-simulated and
independently derived parameters does not prove the correctness of the
model's underlying theoretical basis per se, overall confidence in the
model's theoretical basis is increased as concurrence continues to
exist under a variety of characterized conditions. Increased
confidence allows greater use of the model for purely predictive and
managerial purposes.
Numerous models have been proposed for describing solute
transport in aggregated porous media. Modeling solute transport in
aggregated or structured soils presents some unique problems due to the
complex three-dimensional nature of the inter-connected network of
irregularly sized and shaped soil pores. Attempts to model
displacement processes quantitatively have been based generally on the
convective-dispersive equation (Lapidus and Amundson, 1952),
ac/at = D a2C/az vo ac/az [4-1]
where C is the concentration (mg/mL), D is the dispersion coefficient
(cm2/day), vo is the pore-water velocity (cm/day), z is the distance
(cm), and t is time (days).
Adsorption of the solute to the porous
media may be considered using an adsorption coefficient derived from a
linear adsorption isotherm, defined by
S = KC [4-2]
where S is the sorbed solute concentration (mg/g), C is the equilibrium
solution solute concentration (mg/mL), and Kd is the adsorption
coefficient (mL/g) giving
R ac/at = D a2/az2 vo ac/az [4-3]
where R, the chemical retardation factor, is defined by
R = 1 + pKd/. [4-4]
where p is the soil bulk density (g/cm3) and 0 is the volumetric water
content (mL/mL). Eq. 4-3 can be rearranged to include the
dimensionless parameters:
T = vo t/L [4-5]
x = z/L [4-6]
P = vo L/D and [4-7]
C = C/C, [4-8]
where vo, t, z, and D have been previously defined and L is the column
length (cm), P is the Peclet number, T is the pore volumes of solution,
x is dimensionless distance, and C the ratio of effluent concentration
(Cb) to influent concentration (Co) to give the convective-dispersive
(CD) model,
R(aC/aT) = (1/P)(a2c/ax2) ac/ax [4-9]
The CD water-flow model has been used satisfactorily to simulate
nonadsorbed solute transport under laboratory and field conditions
(Nielsen and Biggar, 1961; Warrick et al., 1971). However, the model
has been relatively poor at simulating solute transport through well-
aggregated and structured soils (Green et al., 1972; Rao et al., 1974;
van Genuchten and Wierenga, 1976 and 1977).
Solutions of Eq. [4-1] predict nearly sigmoidal or symmetrical
concentration distributions (Coats and Smith, 1964; Gershon and Nir,
1969; van Genuchten and Wierenga, 1976). However, numerous
experimental studies have shown distinctly asymmetrical effluent curves
(Nielsen and Biggar, 1961; Biggar and Nielsen, 1962; Green et al.,
1972; van Genuchten and Wierenga, 1977). It was noted that this
asymmetry or tailing of effluent curves was more pronounced in
aggregated versus nonaggregated media and as solution velocities
increased. Coats and Smith (1964) hypothesized the existence of
regions of immobile water in small and dead-end pores. They modified
Eq. [4-1] to incorporate solute transfer by diffusion from mobile-
flowing water regions to stagnant immobile water regions, to give
0m (aCJaT) + Om (ac,/at)
= OmD, (a2Cm/az2) vmO, (aC,/dz) [4-10]
and
aim (ac,.Dt) = a(C, Cim) [4-11]
where 9m and 9im are the fractions of the soil filled with mobile and
stagnant water, respectively (cm3/cm3); Cm and Cim are the solute
concentrations (g/mL) in the mobile and immobile regions; vm is the
average pore-water velocity in the mobile region; Dm is the mobile-
water dispersion coefficient; and a is a mass-transfer coefficient
(day-').
van Genuchten and Wierenga (1976) have extended this model to
account for solute adsorption to the porous media through the inclusion
of an adsorption coefficient in the retardation factor. To account for
the possibility of unequal distribution of adsorption sites between the
mobile- and immobile-water regions, f is defined as the fraction of
sites in the mobile region. Including these concepts in the model of
Coats and Smith (1964), van Genuchten and Wierenga (1976) derived
(0m + fpKd) aCm/at + [lim + (1 f)pKd] aci/at
= OmDm (a2caz2) vmOm (acwaz) [4-12]
and
[Oi, + (1 f)pKd] ac/at = a(C. C). [4-13]
The model may be described in terms of the dimensionless parameters
Peclet number, P; the mobile water partition coefficient, fl; and the
dimensionless, mass-transfer coefficient, w, defined as:
P = vmL/D, [4-14]
R = (0m + pfKd)/(e + pKd) and [4-15]
w = aL/q [4-16]
Additionally, the concentrations of the solutes in the two regions (Cm
and Cim) may be normalized with the original-solute pulse
concentration, CO by defining
c1 = CJ/C [4-17]
and
c2 = Cm/Co [4-18]
With these definitions of P, 0, w, c,, and c2, Eqs. [4-12] and [4-13]
become:
fR (aC,/aT + (1 P)R aC2/aT = (1/P)(a2c1/ax2) ac,/ax [4-19]
and
(1 f)R aC2/aT = w(C, C2) [4-20]
The mobile-immobile model (MIM) (Eqs. [4-19] and [4-20]) contains
four dimensionless parameters; P, R, f and w. Agreement between model
simulation and experimental data is generally accepted as verification
of the conceptual basis of the model. However, experimental methods
are generally unavailable to measure f and w independently. When
experimental techniques are inadequate to measure parameters
independently, they are frequently estimated on the basis of a best-fit
of the model to experimental data (van Genuchten et al., 1977; Rao et
al., 1979; Nkedi-Kizza et al., 1983 ). van Genuchten (1981) has
developed a non-linear least-squares, curve-fitting computer program
that estimates MIM and CD parameters from miscible displacement
effluent data. Although such a technique is useful for parameter
estimation, it does not ensure process identification (Davidson et al.,
1980; Rao et al., 1980a).
Independently estimated model parameters for soil and synthetic
porous media have demonstrated slight deviations from those parameters
estimated from curve-fitting procedures based on the MIM. Rao et al.
(1980b) performed miscible-displacement experiments on fabricated media
consisting of mixtures of porous ceramic spheres, glass beads, and fine
sand. Parameters calculated by the MIM curve-fitting program were
compared to those experimentally measured or independently estimated
for the various mixtures (Rao et al.,1980a). Over a broad range of
pore-water velocities, close agreement was found between values
estimated by MIM curve-fitting to those independently determined.
Owing to the ease of utilization, unavailability of accurate
conclusive methods to determine some model parameters experimentally,
and otherwise general agreement between experimentally determined and
model-estimated parameters, the MIM has become a popular tool for
estimating soil-water behavioral characteristics. Seyfried and Rao
(1987) used the model in a study to examine the relative contributions
of soil-water characteristics to leaching in an aggregated tropical
Typic Dystropept derived from volcanic ash. Field studies monitoring K
movement were not successfully simulated by a simple convective-
dispersive water model (Seyfreid, 1986). Miscible-displacement
experiments on saturated soil columns and subsequent analysis of
effluent data by the MIM model estimated the mobile-water content at
about 55% of the total soil water. Low Peclet numbers and consequent
high dispersion coefficients indicated a high degree of preferential
water flow that bypassed large portions of the soil water.
Schulin et al. (1987) used the CD and MIM models to determine
behavior of water in undisturbed columns of soil containing about 55%
by volume gravel. Back-calculation of presented data indicated that
the gravel was not porous and contained no water. The MIM model
calculated the mobile-water content to be about 85% of the total water
present in unsaturated columns maintained at volumetric-water contents
of between 0.135 and 0.175 mL/mL for soils with total porosities
ranging from 0.25 to 0.30 mL/mL. Due to the low immobile-water
fraction, the CD model, which considers all water as mobile, was able
to estimate parameters capable of simulating the experimental BTC
nearly as well as the MIM model.
Several independently conducted studies have suggested that the
gravel resulting from mineral dissolution and precipitation in tropical
stone-line soils is porous (Amouric et al., 1986; Muller and Bocquier,
1986; Chapter 3.). Although the only apparent study on the
mobile/immobile-water content of gravelly soil indicates that the
immobile fraction is relatively small (Schulin et al., 1987), the
presence of porosity in gravel from tropical stone-line soils would
suggest that these soils may have a considerable immobile-water
fraction. The purpose of this study was to use the MIM and CD models
to evaluate tritiated water breakthrough curves from an aggregated
gravelly Oxisol, to determine if preferential water flow and immobile-
water regions should be considered when describing nutrient-leaching
behavior for this soil.
Materials and Methods
Column Preparations
Undisturbed soil columns were taken from unfertilized plots at
the previously described experimental site at the end of the growing
season. A soil-core sampler was constructed of a steel water pipe (102
mm i.d./ 114 mm o.d.) fitted with a sharp, hardened-steel, cutting edge
and a removable, threaded steel cap. An 80-cm length of PVC pipe was
inserted into the steel corer so that one end rested on a 1-mm wide
shelf at the base. The whole piece was held in place by tightening the
cap. The sampler was hammered into the ground until the top of the cap
was nearly level with the soil surface. The sampler was then lifted up
and out of the soil with a hydraulic jack. The PVC pipe full of soil
was removed from the sampler, sealed, and boxed for transport to the
laboratory in Gainesville. Excess PVC pipe was cut from the top of
each column so that the new end was 5 mm above the soil surface.
Approximately 5 mm of soil was removed from the bottom of the columns
and both ends were fitted with porous, fritted-glass plates (maximum
pore radius of 15um) and plexiglass end plates.
Miscible Displacement
The columns were held vertically and saturated from the bottom with
approximately 5 pore volumes of a degassed solution of 0.01 M CaCl.
32
The columns were then turned horizontally and the end plate in contact
with the surface Ap horizon was connected to an influent solution by a
three-way valve which allowed switching between tritiated and
nontritiated solutions of 0.01 M CaCl2 (Fig. 4-1). Effluent was
collected in a fraction collector from the other end of the column.
The 3H20 activity in the effluent fraction was monitored using liquid-
scintillation techniques. The resulting breakthrough curves were
fitted to the Convective-Dispersive (CD) and Mobile-Immobile (MIM)
transport models using the program CFITIM3 (van Genuchten, 1981), which
is based on a nonlinear, least sum of squares criteria for goodness of
fit. Boundary conditions assumed for the model analyses were constant
influent-solute concentration and a semi-infinite column.
Adsorption Isotherms
Adsorption isotherms for 3H20 were determined using a batch
technique similar to that described by Dao and Lavy (1978). Sieved (<2
mm) soil samples from each of the three soil horizons present in the
column, and a composite gravel sample (2 to 4 mm), were assayed. Moist
triplicate 4-g samples of each material were placed in a pre-weighed
10-mL plastic, screw-top centrifuge vial that had a 1-mm hole drilled
in the bottom. The vials were sealed and reweighed. Solution having
varying activities of 3H20 were injected into the basal hole until the
materials appeared near saturation. The vials were reweighed and then
placed on top of a glass marble resting on the bottom of a 30-mL
plastic, screw-top centrifuge tube. These larger tubes were then
sealed and set on their sides for 48 h to allow for equilibration of
the tritium throughout the sample. The 30-mL tubes were centrifuged at
Soil Column
3 Way Switches
A~AA-~AAAAAAA A~AAAAA~A .
S- U U
iip J
/
Pump
Fraction Collector
0.01 M CaCl2
Tritiated 0.01 M CaCl
2
Fig. 4-1. Schematic illustration of apparatus used in the
miscible-displacement experiments.
.
30 times the force of gravity, forcing the soil solution out of the
soil, and through the basal hole to be collected around the marble in
the bottom of the larger tube. The extruded solution was retrieved and
the 3H20 activity was measured. The soil sample was dried at 105 C
for 48 h and weighed. The adsorbed 3H20 was determined by subtraction
of the 3H20 activity in the extruded solution from the initial 3H20 in
the injected solution after accounting for the original water content
of the samples. Adsorption isotherms were constructed by plotting
adsorbed versus solution 3H20 activity. Linear-adsorption coefficients
were calculated using linear regression forced through the origin. An
overall soil-column retardation factor, R, was calculated using
weighted mean adsorption coefficients of the gravel and fine-fraction
samples from each horizon.
Results and Discussion
Description of Model Parameters
Information input into the non-linear, least-squares curve-
fitting program that optimizes dimensionless parameters for the CD and
MIM models consists of the observed tritium breakthrough curve (BTC),
which is composed of data pairs consisting of the pore volumes of
solution and the radioactivity of that solution relative to the
activity of the tritiated-pulse solution. The curve-fitting program is
capable of estimating the retardation factor, (R), Peclet number (P),
fraction of solutes in the mobile water region (f), dimensionless mass-
transfer coefficient (w), and tritiated-pulse volume (T). Confidence
in the predictive capacity of the model is improved as the number of
parameters which the model is required to predict decreases. The
curve-fitting procedure that estimates the model parameters from the
BTC, bases the parameter-selection process on the goodness of fit of a
model-predicted BTC with the observed effluent data. The model
calculates a 95% confidence interval for each estimated parameter;
however, the confidence interval measures the goodness of fit of the
estimated parameters to the effluent curve and does not involve any
consideration of random experimental error. Therefore, the final
estimation of soil-property parameters requires judicious
interpretation of the model-estimated parameters.
Several of the dimensionless parameters are measurable by
laboratory techniques. The retardation coefficient may be calculated
from an adsorption coefficient, KD, the water content, and the bulk
density. The tritiated-pulse volume may be measured during the
miscible-displacement process. Experimental methods to measure the
other three parameters, P, 8, and w, are generally unavailable.
The dimensionless parameters P and w are specific to the
particular conditions of the experiment from which they are derived.
The Peclet number relates [Eq. 4-14] the column's length and pore-water
velocity to the dispersion coefficient. Dispersion results from
physical mixing of soil water travelling at different velocities or
following different paths. The dispersion coefficient is an indicator
of soil-pore sizes and the pore-size distribution. Since the velocity
of water in a confined capillary is dependent on the capillary radius,
large capillaries can transport water more rapidly than smaller pores
under similar pressure gradients. The preferentially rapid transport
of water in large pores is called channelling, and results in solutes
travelling further and more rapidly than simple piston-displacement
concepts would allow.
The parameter f represents the fraction of solutes present in the
mobile region under equilibrium conditions. The mobile-water fraction,
0, may be calculated with Eq. 4-21:
0 = O/J = OR f(R-1) [4-21]
where 0 is the mobile-water fraction, Om is the mobile-water content, 8
is the total-water content, R is the chemical-retardation factor, and f
is the fraction of total adsorption sites in the mobile-water region.
The parameter f is typically approximated. Nkedi-Kizza et al.(1982)
argued that, since the surface area associated with a unit volume of
water in the small pores of the immobile region is probably much
greater than the surface area associated with a unit volume of water in
the mobile region, f may be approximated to be zero. However, NKedi-
Kizza et al. (1983) have also proposed equal distribution of the sites
between the two regions such that f = p and, therefore, 0 = #.
Seyfried and Rao (1987) proposed an intermediate approximation of f =
0/2. In all approximations, the severity of any error in the eventual
estimation of the mobile-water content is influenced by the value of R.
If there is almost no chemical adsorption or repulsion (R approaches
1.0), then the location of the sites becomes less important because the
value OR f(R-1) approaches both f and the mobile-water fraction, 0.
The dimensionless parameter, w, relates the mass-transfer
coefficient [Eq. 4-16] to column length and solution flux (volume per
time area). The mass-transfer coefficient is a lumped term including
both a tortuosity factor and a diffusion coefficient. Diffusion is the
transport of solutes from an area of high concentration to an area of
low concentration independent of any movement of the media. The
tortuosity of the media limits the exposure between a concentration
gradient. Both dispersion and diffusion will occur during water
transport through soil. The contribution of the dispersion process to
solute mixing is generally of greater magnitude than the diffusion
process, such that the diffusion process is frequently insignificant.
However, in soils that contain immobile-water regions, diffusion is the
only process that transports solutes into, through, and out of the
immobile regions. In such soils the magnitude of the diffusion process
becomes significant.
Parameter Estimation
Adsorption isotherms
The tritium-adsorption isotherms for the three column horizons
and composite gravel samples are presented in Fig. 4-2. A weighted
mean of the slope of the line obtained by plotting the adsorbed versus
solution concentrations of tritium was calculated considering the
depth and gravel content of each horizon. This column adsorption
coefficient, Kd, was applied to Eq. [4-4] with other column parameters
to calculate a retardation factor of 1.05 for both columns. This value
indicates that the tritium is slightly adsorbed to the soil and is
consistent with other values measured for soils of similar mineralogy
(Nkedi-Kizza, 1983; Seyfried and Rao, 1987).
Ap -11 cm
S 0.047C
2 **
r -.79**
Ac 11-22 cm
'Cp
6-
4
2
0 L
Gravel
6
S =-0.013
4 2= 0.92*
2
0
0 10(-
0 10(
C
"kr
f^
200
Btc 22-72 cm
&
S =0.031C
r2= 0. 93**
200
SOLUTION CONCENTRATION, C (Bq/mL)
Fig. 4-2. Tritium adsorption isotherms for column horizons and
composite gravel.
S =0.052C
r 0. 94**
j 1
i
Column physical properties
Selected physical properties of the soil columns are presented in
Table 4-1. The saturated-water content, bulk density, and particle-
size distribution of the two columns exhibited only slight differences.
The Darcian flux and number of pore volumes applied to each miscible-
displacement experiment for both soil columns are shown in Table 4-2.
The solution flux varied from slowest to fastest by a factor of over
40. The tritium concentration in the column effluent was monitored
during both pulse injection and clearing.
CD model analysis
The parameters estimated by the CD model for the four
displacement experiments of Column I are shown in Table 4-3. The
tritium-pulse volume was held constant during each curve-fitting
process, but the retardation factor, R, and the Peclet number were
allowed to vary. The lowest retardation factor, R = 0.74, which was
estimated for the most rapid flux, Expt. I-i, implies chemical
repulsion of the tritium from some regions of the soil. Since the
retardation factor was measured (R = 1.05), the model-estimated lower
value of 0.74 is an indication of immobile-water regions that were not
in physical equilibrium with the mobile effluent, due to the short
residence time of the pulse in the soil. The CD model, which considers
all soil water to be mobile, was unable to describe the observed BTC of
Expt. I-1 when the value of R was fixed at 1.05 (Fig. 4-3). As the
experimental flux was decreased, the CD-estimated retardation factor
approached the measured value, and the CD model was able to simulate
Table 4-1. Dimensions and selected physical properties of
soil columns.
Soil or
column property Units Column I Column II
Length
Surface area
Volume
Weight, oven-dry
Bulk density
Porosity
cm
cm2
L
kg
kg/L
L/L
Particle-size fractions
by mass
<2 mm
2-12 mm
12-75 mm
by volume
<2 mm
2-12 mm
12-75 mm
Particle density
<2 mm
2-12 mm
12-75 mm
g/g
g/g
g/g
L/L
L/L
L/L
kg/L
kg/L
kg/L
71.6
72.38
5.18
6.55
1.26
0.525
0.37
0.42
0.21
0.18
0.20
0.10
2.61
2.71
2.65
68.9
72.38
4.99
6.42
1.29
0.527
0.38
0.44
0.18
0.19
0.21
0.09
2.67
2.81
2.66
t Intra-gravel porosity excluded.
Table 4-2. Set-up for tritium miscible-displacement
experiments on Columns I and II.
Column-Experiment Flux, q Pulse, T
no. t
(cm/d) (pore volume)
I-1 111 1.43
1-2 16.8 2.58
I-3 2.71 2.84
I-4 36.7 2.65
II-1 2.69 2.88
II-2 36.7 2.59
t Order of execution
CD water model optimized dimensionless parameters.
Experiment Flux Peclet number Retardation factor
no. q P R
(cm/d)
I-i 111 1.4 0.74
(0.2)t (0.05)
1-4 36.7 1.0 1.02
(0.1) (0.06)
1-2 16.8 1.9 1.01
(0.2) (0.05)
1-3 2.71 4.0 1.12
(0.3) (0.02)
t Numbers in parenthesis (+) are 95% confidence intervals.
Table 4-3.
Expt. I-1
q 111 cm/d
0 Measured data points
CD
P R
1.4 0.74
- 0.82 1.05 (fixed)
0 2 4 6
PORE VOLUMES
Fig. 4-3. Measured and CD-simulated BTCs for Expt. I-1 with R
optimized or fixed at 1.05.
0.8
0.6
0.4
0.2
the observed BTCs (Fig. 4-4). At the slower flow rates, the pulse
resided in the column long enough to allow diffusion to bring the
mobile and immobile regions closer to physical equilibrium, thereby
masking the presence of an immobile-water region. Thus, at slow flux,
the conceptual assumption of the CD model, which considers all water to
be mobile, is falsely satisfied.
MIM model analysis
The dimensionless parameters estimated by the MIM model for the
four displacement experiments through Column I are presented in Table
4-4. The retardation factor for all of the trials was held constant at
the measured value of 1.05 during each curve-fitting process. The
Peclet number, B, and w were allowed to vary.
Although # is generally considered a constant for any given soil
sample, it showed a slight increase as the flux decreased. This
behavior is attributed to the inability of the model to distinguish
easily between the mobile and immobile regions when the flux is slow
enough to allow considerable diffusion between the two regions. This
implies that the best estimate of f is when the flux is infinitely
fast. Since the trial with the fastest flux exhibited the highest
degree of physical nonequilibrium (CD-model analysis), its MIM-model
analysis should yield the best estimate of f. Therefore, the BTC were
refitted to the MIM model holding # constant at 0.53 (Table 4-5).
In this study, f = 0.53 will be used as the value to approximate 0,
since R is very close to 1.0. The measured and MIM-estimated BTCs for
the four experiments from Column I are smooth, asymmetrical and show
Expt. 1-3
q = 2.71 cm/d
O Measured data points
CD P R
-- 4.0 1.12
4.6 1.05 (fixed)
b
i'
- I
p
-
PORE VOLUMES
Measured and CD-simulated BTCs for Expt. I-3 with R
or fixed at 1.05.
0.8
0.6
0.4
0.2
Fig. 4-4.
optimized
(CY
Table 4-4. MIM water model optimized
Experiment Flux, q P
no.
cm/d
I-1 111
I-4 36.7
I-2 16.8
I-3 2.71
t Numbers in parenthesis (+)
2.9
(0.6)t
2.7
(0.7)
5.8
(1.5)
6.2
(1.4)
are 95%
dimensionless parameters.
R W
fixed
1.05 0.53 0.20
(0.04) (0.05)
1.05 0.58 0.30
(0.07) (0.07)
1.05 0.61 0.38
(0.05) (0.08)
1.05 0.62 2.5
(0.07) (1.2)
confidence intervals.
Expt. I-1
q = 111 cm/d
SMeasured data points
P R
MIM 2.9 1.05 0.53 0.20
0 2 4 6
PORE VOLUMES
Fig. 4-5. Measured and MIM-simulated BTCs for Expt. I-1 with R
fixed at 1.05.
1.2
1
0.8
0.6
0.4
0.2
1.2
0.8
0.6
0.4
0.2
0
Expt. 1-2
q = 16.8 cm/d
0 Measured data points
SP R
-- MIM 9.3 1.05 0.53 0.51
2 4 6
PORE VOLUMES
Fig. 4-6. Measured and MIM-simulated BTCs for Expt. I-2 with R
fixed at 1.05 and f fixed at 0.53.
1.2
1
0.8
0.6
0.4
0.2
0
Expt. 1-3
q 2.71 cm/d
0 Measured data points
P R # (J
MIM 8.1 1.05 0.53 2.80
0 2 4 6
PORE VOLUMES
Fig. 4-7. Measured and MIM-simulated BTCs for Expt. I-3 with R
fixed at 1.05 and f fixed at 0.53.
Expt. 1-4
q 36.7 cm/d
0 Measured data points
P R (J
- MIM 2.7 1.05 0.58 0.30
0 2 4 6
PORE VOLUMES
Fig. 4-8. Measured and MIM-simulated BTCs for Expt. I-4 with R
fixed at 1.05 and f fixed at 0.53.
1.2
1
0.8
0.6
0.4
0.2
Table 4-5. MIM water model optimized dimensionless parameters
with R and B fixed.
Experiment Flux, q P R B
no. fixed fixed
(cm/d)
I-1 111 2.94 1.05 0.53 0.198
(0.55)t (0.052)
I-4 36.7 3.18 1.05 0.53 0.342
(0.24) (0.032)
1-2 16.8 9.28 1.05 0.53 0.510
(1.32) (0.036)
1-3 2.71 8.13 1.05 0.53 2.80
(3.52) (1.63)
t Numbers in parenthesis () are 95% confidence intervals.
very close agreement at both the fastest and slowest flow rates (Figs.
4-5, 4-6, 4-7, and 4-8).
The immobile-water fraction of 0.47 cannot be totally attributed
to the intra-gravel porosity. Since the volumetric-gravel content
(including intra-gravel porosity) of the column was 0.375 mL/mL, and 20
% of that was interior porosity, the intra-gravel porosity was only
0.075 mL/mL, or 14% of the total column porosity of 0.526 mL/mL. Even
if the intra-gravel porosity contains only immobile water, the
remaining immobile-water fraction (0.40) of the volumetric-water
content was associated with the aggregated fine-earth fraction of the
soil. Although the fine-earth fraction of the Ap and Ac horizons has a
weak-crumb structure, the Btc horizon has medium-sized, moderately
strong aggregates (Appendix A). Nkedi-Kizza et. al. (1983) have shown
that packed columns of sieved (2 to 4.7 mm), strongly aggregated, peds
from an Oxisol harbored over 50% of the total volumetric-water content
in immobile regions.
Schulin et al. (1987), using similar techniques, found an
equally small R = 1.12 but a f = 0.87 under unsaturated conditions.
Back-calculation of presented data indicated that the gravel was not
porous. The total porosity (0.255 mL/mL) was associated entirely with
the 20% by volume fine fraction. Extrapolation of their data to
saturated conditions, assuming the water volume between saturation and
the experimental water contents to be mobile, would yield a mobile
water fraction 0 = 0.92. Therefore, the difference in the volume of
the mobile-water regions of these two gravelly soils is more likely due
to differences in aggregate structure of the fine fraction than to
differences in gravel porosity.
The dimensionless parameters P and w are a function of
experimental conditions, and are used with their functional
relationships to determine soil-water properties. The Peclet number
relates the pore-water velocity and column length to the dispersion
coefficient (Eq. [4-14]). One theoretical exponential relationship of
the dispersion coefficient to the pore-water velocity is:
D = \ vmn [4-22]
where Dm is the hydrodynamic dispersion coefficient, X the
dispersivity, v, the mobile pore-water velocity, and n an empirical
constant. For most laboratory-displacement experiments involving
disturbed repackedd) soils, A is about 1.0 cm (van Genuchten and
Wierenga, 1986). For displacement experiments involving undisturbed
field soils, especially when aggregated, X can be one or two orders of
magnitude higher. The degree of dispersivity in a soil is increased as
the pore-size distribution in the mobile-water regions becomes broader.
The dispersivities of different soils are more easily compared if the
empirical constant, n, is assumed to be 1.0 and the equation is linear.
Schulin et al. (1987) and Russo (1983) determined dispersivities of
2.24 and 2.91 cm from soils containing 55 and 43% gravel by volume,
respectively, using a linear relationship. The Peclet numbers
estimated from the BTCs of Column I tended to increase with decreasing
flux, suggesting a nonlinear relation between dispersion coefficient
and pore-water velocity within the velocity range used in this study
(Fig. 4-9). The nonlinear plot provides a dispersivity of
14000
12000 -
10000
8000
6000 -
4000 -
2000 -
Dm = 3.29 Vm3
R = 0.96*
100 200 300 400
MOBILE PORE WATER VELOCITY, Vm (cm/d)
Fig. 4-9. Relationship between MIM-estimated dispersion
coefficient, Dm, and mobile pore-water velocity, Vm.
E_
h-
z
LL
Q
LL
UJ
0
0
z
O0
cl)
rr
03
3.3 cm2"n dn'1 with n = 1.3, which is of a magnitude similar to values
from the two previously mentioned studies on stony soils. Several
factors may be contributing to the high dispersivity of this soil.
Dispersion increases as the range of small to large pore sizes in a
soil increases, thereby providing a wide range of water velocities
within a single soil sample. Edwards et al. (1984) have shown that the
presence of non-porous gravel increases the total macropore volume at
the expense of the micropore volume. The large reduction in the water
content of the soil under slight tensions (0 to 50 mbar) indicates that
the soil possesses a considerable volume of large pores (Fig. 3-1).
Similarly, the retention of nearly 33% of the total soil water at 15
bars of tension indicates that the soil also contains a large volume of
very small pores.
The values of the dimensionless parameter w, estimated for each
experiment on Column 1, is presented in Table 4-5. It relates the
mass-transfer coefficient, a, to the column length and solution flux
(Eq. [4-16]). The mass-transfer coefficient is a lumped diffusion
parameter that relates the solute diffusion transfer to the molecular
diffusion coefficient, the mobile/immobile-water fraction, the
tortuosity, the radius of soil aggregates, and the solution flux. The
relationship between the mass-transfer coefficient and the solution
flux is shown in Fig. 4-10. The mass-transfer coefficient is not a
constant and has been shown to increase with solution flux using both
theoretical postulations and experimental methods (Rao, 1980a and
1980b; van Genuchten, 1985). The mathematical description of the
diffusion process employed by the MIM model has an underlying
0.4
S= 2.0x103 (q) + 8.7x10-2
r 2= 0. 99**
x
7
0.2
0.1 -L
'"
//
A
7
FLUX, q (cm/d)
Fig. 4-10. Relationship between MIM-estimated mass-transfer
coefficient, a, and flux, q.
56
assumption of first-order exchange kinetics. However, conceptually the
assumption is only valid for dead-end pores with a neck of negligible
volume (Coats and Smith, 1964). van Genuchten and Dalton (1986) have
shown that first-order kinetics represent a very close approximation in
the case of radial diffusive exchange between the soil matrix and
hollow, cylindrical macropores but, for other pore geometries, first-
order exchange is only a crude approximation. Because a is a lumped
parameter, it depends not only on the pore-space geometry, solute
diffusivity and the magnitude of the immobile region, but also on the
changing solute concentration within the two regions. The increase in
the mass-transfer coefficient with the increase in solution flux is due
to the rapidity at which the concentration gradient reaches its extreme
(more diffusive force) as a solute front approaches and leaves a given
point in a soil column.
Estimation of Column II Parameters
An additional enhancement to the validity of the estimated soil-
column parameters lies in their transferability to a different sample
of the same soil. Model-simulated BTC parameters derived from
experiments using Column I were applied to the experimental data from
Column II (Figs. 4-11 and 4-12). The values of R and # were set at
1.05 and 0.053, respectively, and the values for P and c were derived
from the line:- and curvilinear relationships presented in Figs. 4-9
and 4-10. The derivations included consideration of slight differences
in column lengths and bulk densities (Table 4-1). The simulated BTCs
based on the parameters derived from Column I exhibit later break
Expt. 11-1
q 2.69 cm/d
0 Measured data points
P R # J
- MIM 10 1.05 0.53 2.4
( all fixed)
PORE VOLUMES
Measured and MIM-simulated BTCs for Expt. II-1.
1.2
1
0.8 -
0.6
0.4
0.2
0
O
0
Z
O
0
0
0
w
O
I--
w
cr
Fig. 4-11.
1.2
1
0.8
0.6
0.4
0.2
0
Expt. U-2
q 36.7 cm/d
0 Measured data points
P R #
- MIM 4.3 1.05 0.53 0.30
(all fixed)
0 2 4 6 8
PORE VOLUMES
Fig. 4-12. Measured and MIM-simulated BTCs for Expt. 11-2.
through and a higher peak concentration than the experimentally
observed BTCs. The differences between the two estimated and observed
BTCs could be due to natural variability between the two soil samples.
Although the two columns contained nearly identical volumes of gravel,
the particle density of the gravel in Column II was higher. Since the
more dense gravel had less porosity (Table 3-3), less of the total
porosity of Column II was associated with the gravel and a greater
portion is associated with the remaining fine-earth fraction. However,
considering that all model parameters were independently estimated, the
MIM-model-generated estimates closely described the observed
asymmetrical BTCs.
Conclusions
The data from this study show that this soil, which contains a
strongly aggregated fine fraction and porous gravel, produced
asymmetrical BTCs for tritiated water. The degree of asymmetry
increased with increasing flow rates. The classical CD model was found
to be inadequate in describing water movement in this soil, due to the
inability of the model to account for diffusive mass transfer of water
into stagnant or immobile-water regions. The MIM model adequately
described water movement at all flow rates, and estimated that about
50% of the total-water content was in immobile regions. The high
immobile-water content and the relatively large dispersivity indicated
that, under natural field conditions consisting of short but intense
tropical rain storms, water transport in the larger soil pores could
60
carry small amounts of unadsorbed solutes beyond the root zones,
whereas considerable quantities of the solute could remain relatively
unaffected, harbored in immobile-water regions. Although this could
cause pollution of ground-water from nutrients and pesticides leaching
through macropores, the presence of immobile-water regions in this soil
will act as a source/sink for solutes that will be slowly released to
crops.
CHAPTER 5
CROP RESPONSE TO PLANTING DENSITIES
AND FERTILIZER APPLICATION SCHEDULING
Introduction
An integrated knowledge of the behavior of plant nutrients in soil
has been a major interest of agricultural scientists. Even after many
decades of concentrated research, fine-tuning of management practices
for agricultural soils still generally requires experimentation based on
trial and error. The basis of our remaining lack of knowledge lies in
the complex interrelations of the plant nutrients among themselves, and
with plants, soil, water, and the atmosphere. The seemingly simple
concept of a nutrient being "plant-available" involves complicated
physical processes regulating chemical speciation among three phases,
two of which are readily mobile, and the third of which changes
continuously and irregularly with depth (Addiscott et al., 1986). In
addition to comprehension of these processes, there is also a problem
with instrumentation and quantification of measurements for specific
events and objects (Harris and Hansen, 1975). It is no wonder that the
most reliable method for assessing the availability of a soil-borne
plant nutrient is still a field trial followed by analysis of the
resultant plant material (Melsted and Peck, 1977; Sumner, 1987). What
such a technique loses in its overall contribution to the knowledge of
individual processes, is offset by immediate knowledge regarding on-site
agricultural behavior.
High concentrations of stones in the rooting zone of an
agricultural soil impact both water-holding and water-movement behavior
(Epstein and Grant, 1966; Ghuman and Lal, 1984). The relative impact of
gravel on any one soil property is dependent on the amount and
properties of that gravel. Generally, for gravel with little or no
porosity, increased gravel concentrations in the soil increase bulk
density, decrease total volumetric water-holding capacity, increase
macroporosity, decrease microporosity, decrease saturated hydraulic
conductivity and change the proportion of water held at various tensions
when compared to values for the same soil minus the gravel (Edwards et
al., 1984). Logical inferences concerning the agricultural productivity
of gravelly (non-porous gravel) soils that may be deduced from these
characteristics include:
1. Decreases in water-holding capacity increase droughtiness
and, therefore, make crops more susceptible to water stress.
2. Decreases in total porosity increase the amount of water
transported by the remaining soil pores.
3. Increased water transport increases the potential loss of
nutrients by leaching.
The effect of porous gravel on soil-water behavior is not easily
inferred and depends on the porosity and pore-size distribution of the
gravel (Reinhart, 1961; Hanson and Blevins, 1979). Porous gravel has
been shown to hold considerable portions of plant-available water (Flint
and Childs, 1984). Soil with gravel of porosity similar to that of the
63
fine fraction may still limit a high quantity of the transported water
to the pores of the soil's fine fraction, due to the noncontinuous
and/or small size of the pores in the gravel. The potential loss of
soil nutrients by leaching will be influenced by the differential
effects of pore size and pore location on water transport and
channeling. Therefore, water in porous gravel may act as a sink for
leachable nutrients and thus may harbor nutrients from convective-water
flow.
Soils with shallow, subsurface gravel horizons are a common
occurrence in the western highlands of Cameroon. Although these soils
are typically not preferred by local farmers, increasing population
pressures have resulted in their increased utilization for food-crop
production. Scientific studies indicate that, depending on the
quantities and properties of the gravel, different practices are
required for effective agricultural management of these soils. The
purpose of this experiment was to develop a basic understanding of the
dynamics of water and nutrient availabilities for a soil with a shallow
gravel horizon, throughout a crop-growing season and using both locally
prevalent and modified management practices. The objective of this
experiment was to differentiate between the relative effects of possible
water and nutrient stress on corn and beans, by subjecting the crops to
combinations of plant densities and seasonal nutrient availabilities.
Materials and Methods
The experimental site was located 8 km north of the University
Center of Dschang campus in the Leppo quarter of the village of Bafou,
~
Western Province of Cameroon, Africa. The field, rented from a local
farmer, was on a 12 to 16% slope and contained a dense gravel horizon
from a depth of 22 to 72 cm. The depth, thickness, and location of the
gravel horizon were first determined by augering and later confirmed by
soil-pit sampling. The field had been planted to corn, peanut, and
cocoayam the previous year following a 3 to 5-yr fallow, and had
received no commercial fertilizers for at least the previous 5 yr.
Weeds and former crop residues were cut by hand, aligned in the furrows,
and buried under newly established ridges based on a 1-m row spacing.
A randomized, complete-block design with 4- by 12-m plots and four
treatment replications was composed of a 2 by 5 factorial consisting of
two planting densities (intra-row mix of corn Zea mays L. CIMMYT Z-290;
and red bean Phaseolus vulgaris L.) and five fertilizer schemes (Table
5-1). The two planting densities were (1) 30,000 corn plants/ha mixed
with 40,000 bean plants and (2) 45,000 corn plants/ha mixed with 60,000
bean plants. The fertilizer treatments consisted of a non-fertilized
control and four split-application treatments; all consisting of 400
kg/ha of a locally available 20-10-10 (N-P,20-K20) mixed fertilizer plus
248 kg/ha of triple superphosphate (TSP) (50 kg P/ha). The four
fertilized treatments had the 20-10-10 material applied (1) all
preplant; (2) one half preplant and one half after 8 wk; (3) one third
preplant and one third after 4 and 8 wk; and (4) one fourth preplant and
one fourth after 4, 8, and 12 wk. All of the fertilized plots had the
TSP applied preplant. The preplant fertilizers were first mixed
together, spread in a 33-cm band down the center of the ridge, and
spaded to a 10-cm depth. All subsequent applications were applied to
Table 5-1. Description of experimental design.
Design : Randomized Complete Block
4 blocks
2 x 5 factorial
Factor 1. Plant density (within-row mix)
Level 1. 30,000 pl/ha corn Zea mays L.
40,000 pl/ha bean Phaseolus vulqaris L.
Level 2. 45,000 pl/ha corn
60,000 pl/ha bean
Factor 2. Fertilizer application timing
Fertilizer Time after planting, wk
application
level 0 4 8 12
0 -
1 M+P
2 1/2 M + P 1/2 M
3 1/3 M + P 1/3 M 1/3 M
4 1/4 M + P 1/4 M 1/4 M 1/4 M
M = 400 kg/ha
equivalent
P = 50 kg P/ha
20-10-10 (N-P20 -K 0)
to 80-17-34 kg N-P-K /ha
as triple superphosphate
the soil surface in a 33-cm band down the center of the ridge.
The field site was planted 22 and 23 Mar. 1986, following
initiation of the rainy season in early March and upon the advice of
local farmers. The plots were planted initially to 1.5 times the
desired densities. Plots were thinned to proper densities after 3 wk.
Two of the four blocks were stripped and replanted after 4 wk, due to
low plant densities in several plots of each block. Weeds were
controlled by hand cultivation every 4 wk. Beans and corn were
harvested 75 and 140 d, respectively, after planting. Corn grain and
stover were analyzed for N, P, K, and Ca contents.
Soil samples were taken from all of the high-density plots at
depths of 0 to 5, 5 to 10, 10 to 15, 15 to 25, 25 to 35, 35 to 45, 45 to
55, 55 to 65, and 65 to 75 cm, just after corn harvest. Soil samples
were analyzed for gravel content and for Mehlich I-extractable P, K, and
Ca.
Results and Discussion
Plant densities in several plots from two of the four blocks
were below required levels (Table 5-2). Analysis of variance of percent
plant emergence for the first planting of the four blocks is presented
in Table 5-3. The percent emergence was apparently not affected by
either planting density or fertilizer treatment, but was affected by
block location (Table 5-4).
Several factors may have contributed to the lower plant stands.
There was an uncommon lull in seasonal rains during the first 4 wk after
planting. This caused afternoon wilting throughout the field. The
Table 5-2. Plant emergence percentages for plots falling below required
levels (< 66%).
Planting Fertilizer Crop
Block density schedule Corn Bean
-- Emergence, % --
3 low 0 61 58
3 low 4 48 46
3 high 2 51 45
3 high 4 53 55
4 low 1 70a 57
4 low 2 69a 56
4 high 0 49 72a
4 high 3 58 60
Adequate > 66% emergence
Table 5-3. Analysis
percentage.
of variance for early-season plant-emergence
Crop
Source D.F. Corn Bean
---F value ----
Block 3 6.27** 8.39**
Density 1 1.68 < 1
Fertilizer 4 < 1 < 1
Density*Fertilizer 4 < 1 < 1
Error 27
Total 39
C.V. 12.4 12.3
**99% level of probability
Table 5-4. Comparison of percent emergence of corn and bean by
replicate.
Factor / Level Crop
Corn Bean
--- Emergence, % ---
Block
1 80.8a* 81.2a
2 78.7ab 76.3ab
3 65.1c 62.3c
4 70.9bc 69.7bc
Means in the same column followed by the same letter are not
significantly different at the 95% level of probability, as
determined by Duncan's Multiple Range Test.
69
experimental site was on a west-facing slope. Blocks 1, 2 and a portion
of block 4 were on a 12% slope. All of block 3 and much of block 4 were
on a 16% slope. All of the plots with inadequate densities were on the
16 % slope. The slope of the land could have affected plant
establishment in two ways. The steeper areas would have received less
direct morning, but more direct afternoon sunlight. In addition, since
the field was laid out according to the sloping surface area and not the
level surface area, the plots on the most sloping land had the least
amount of soil beneath them. Therefore, the most sloping land probably
had the highest evaporative demand but the least quantity of soil from
which to draw water. In relation to later discussions, it should be
kept in mind that the lower plant densities did not constitute a drastic
failure (the lowest density was still 75% of that required), though they
were lower than the design of the experiment allowed.
Due to inadequate plant densities in some of the plots, all of the
plants in the two affected blocks were removed and the area was
replanted on 23 and 24 April, following additional rain 4 wk after the
first planting. The replanting changed the experimental design of the
study (Gomez and Gomez, 1984). An F test of the error mean squares from
the analysis of variance (Table 5-5) for the two planting dates was
performed for grain, stover, and total dry-matter yields (Appendix B).
In all cases, the error mean squares were not different. Consequently,
the data from the two sites were pooled and planting date was added to
the experimental design as an additional factor with two levels.
Because the planting-date levels were not randomized within the blocks,
but were instead imposed over complete blocks, a whole-block error term
Table 5-5. Analysis
two planting dates.
of variance of grain and stover yield for the
Planting Error Error mean
period D.F. mean square square ratio F,, F99
Grain yield
First 9 40442 2.21 ns 3.18 5.35
Second 9 18301
Stover yield
First 9 411685 1.66 ns 3.18 5.35
Second 9 247991
ns not significantly different
replicationss nested within dates) became the appropriate error term to
evaluate the effects of planting date on yield components. However, the
whole-block error term did not have sufficient degrees of freedom (<6)
to constitute a valid F test (Gomez and Gomez, 1984; Montgomery, 1984).
Therefore, the whole-block error term was pooled with either the three-
way interaction term, or the subplot pooled-error term, on the condition
that the newly added error term was not different from the whole-block
error term at the 75% level of probability.
Differences in environmental conditions during the two time
periods when the crops were in the field are impossible to assess. One
of the more obvious differences was in the quantity and distribution of
rainfall (Fig. 5-1). Seedlings in the first-planting period experienced
considerable wilting due to the slow-starting rainy season. Seedlings
of the second-planting had frequent early rainfall and showed no
wilting. Both plantings experienced frequent mid- and late-season
rains; however, the second planting received more total water because
the rainy season peaked in August and September, after the first
planting had been harvested.
The differential effects of climatic factors on grain and stover
yields may be attributed to the seasonal partitioning of plant
photosynthetic and mineral resources into different yield components.
Corn plants continue to increase in total dry-matter accumulation
throughout the season, until near harvest. However, once past silking,
most of the increase is due to grain filling. The dry-matter content of
other plant components remains relatively constant during this period
(Fig. 5-2)(Hanway, 1962). Tropical maize, in general, including the
1000
/
800 Second Planting
600
JI
400
200 Frst Planting
0 14 28 42 56 70 84 98 112 126 140
DAYS AFTER PLANTING
Fig. 5-1. Cumulative rainfall for the first and second planting
seasons.
Dry matter(g/m2)
1600-
1400-
1200 -
1000 -
800 -
600 -
400 -
200 -
0 -r----
Days after sowing
Fig. 5-2. Total crop and grain dry matter accumulation for Tuxeno-1
and Pioneer 3369A Zea mavs ,grown at Tlaltizapan, winter cycle
1974, at 80,000 plants/ha (from Fisher and Palmer, 1983).
line used in this study, CIMMYT Z-290, is late maturing, tall, leafy,
and less efficient in translocating to the grain photosynthates which
were previously deposited in the stems and leaves (Evans, 1975).
Although grain yields are intimately related to early-season plant
health, differences in grain and stover yields may be attributed to
differential early- and late-season environmental influences (Fisher and
Palmer, 1983).
The effects of these combined factors on corn grain, stover
(above-ground portion of the plant minus the grain), and total dry-
matter yields are presented in Table 5-6). The effect of the two
planting dates was large and significant on corn-grain yield, but
insignificant on stover yield. The second planting yielded only 35% the
amount of grain of the first-planting treatment, even though the stover
yields for the two dates were nearly identical (Table 5-7). The lack of
interaction between the effects of planting date and fertilizer
scheduling on stover yields indicated that the fertilizer schedule
affected the non-grain, plant dry-matter accumulation similarly over the
two crop-growth periods. The interaction between planting date and
fertilizer schedule on corn-grain yields reflects the environmental
effects that planting date had on this indicator of late-season
conditions. The differences in total rainfall for the two plant-growth
periods increased as the season continued (Fig. 5-1). This difference
may be used to explain differential effects on the yield components.
During the early part of each growing season the difference in
rainfall and in subsequent probable nutrient leaching were less
pronounced. If one estimates the evapotranspiration and effective
Table 5-6. Analysis of variance for corn grain, stover, and total dry
matter yields.
Plant component
Source D.F. Error term Grain Stover Dry matter
------- F value------
Date 1 Rep(Date) / Date*Den*Fert 131**a
Rep(Date) / Pooled error < Ia 16.3**a
Rep(Date) 2 Pooled error 3.14 1.62 2.51
Density 1 Pooled error 9.50** 30.5** 32.2**
Fertilizer 4 Pooled error 141** 36.9** 67.2**
Density*Fert 4 Pooled error 2.13 2.11 2.44
Date*Density 1 Pooled error 4.37 < 1 < 1
Date*Fert 4 Pooled error 22.8** < 1 1.34
Date*Den*Fert 4 Pooled error 1.29 < 1 < 1
Pooled Error 18
Total 39
C.V. 12.2 16.4 13.3
Date = Planting date
Rep = Replication
Den = Density = Planting density
Fert = Fertilizer = Fertilizer application schedule
a Mean square error pooled to increase degrees of freedom in order to
enhance validity of the F test.
** 99% level of probability
Table 5-7. Comparison of selected main-effect yield-component means.
Plant component
Factor / Level Grain Stover Dry matter
------------------- kg / ha ------------
Planting date
First 2060A* 3630A 5690A
Second 7558 3370A 41208
Density
Low 1320B 3000B 4320B
High 1490A 4000A 5490A
Fertilizer
4 3710a** 5650a
3 3860a 5560a
2 4060a 5890a
1 4500a 5900a
0 1360b 1530b
Planting date by fertilizer interaction
Fertilizer First Second
4 2760a 1130a
3 2470ab 918b
2 2680a 984ab
1 2070b 714c
0 322c 26d
Planting date by density interaction (90% level of probability)
Density First Second
Low 1920b 728a
High 2200a 782a
M n in tha came rnliimn inrfr th samP shhbheadino and followed by
the same
level of
errors.
uppercase letter, are not significantly different at the 95%
probability according to an F test analysis of mean square
Means in the same column, under the same subheading and followed by
the same lowercase letter, are not significantly different at the 95%
level of probability according Duncan's Multiple Range Test.
**
77
rainfall (actual rainfall minus evapotranspiration) during the two crop-
growth periods, it can be demonstrated that little or no leaching of
soil nutrients occurred in the first 8 wk of either season (described in
greater detail in Chapter 5). Availability of nutrients would have been
affected by the fertilizer-application schedule, but their possible
early-season leaching would not have been affected by the planting date,
because of the early-season dry period.
Stover yields, an early-season indicator, showed decreasing (but
not significantly different) yields as the application of fertilizer was
distributed over time, but no differences due to planting date.
However, the late-season indicator, grain yield, was affected by the
fertilizer schedule and planting-date interaction. Grain yields from
the first planting showed no differences among the split-fertilizer
schedules. The all-preplant, fertilizer-application treatments yielded
less grain than the split-application treatments, but still considerably
more than the non-fertilized control. The grain yields of the late-
planted corn showed greater separation of means and greater differences
in magnitude among the split-application schedules. The 4 by 1/4 split
schedule outyielded the one preplant application, the 3 by 1/3 split
schedule, and the 2 by 1/2 split schedule. The 3 by 1/3 and the 2 by
1/2 split schedules in turn yielded more grain than the all-preplant
schedule, all of which outyielded the non-fertilized control.
These differences indicate that the greater effective rainfall
during the later growing period caused more leaching and thereby reduced
plant availability of nutrients between application schedules. Due to
the low magnitude of these yields in relation to yields from the first
planting date, and the lower grain yields in relation to stover yields,
the differences in grain yields for the second planting associated with
fertilizer-application schedule did not translate into differences in
overall dry-matter yields. Dry-matter yields among the four fertilized
treatments showed no differences, although they all out-yielded the non-
fertilized control by nearly four-fold.
Effects of the three factors on nutrient uptake by the corn at
harvest are presented in Tables 5-8 and 5-9. Total uptake of N, P, K,
and Ca was greater for the first planting date than for the second date.
Second-growth-period uptake for each of the nutrients was a relatively
constant 75% of the values for the first growth period, which is
consistent with the differences in total dry-matter yields. There were
no differences among the fertilized treatments for uptake of any of the
four nutrients, although all of the fertilized treatments had higher
uptake than did the unfertilized controls. This information supports
the yield data, in that there were no differences in uptake among the
fertilized treatments, whereas uptake among all fertilized treatments
was much greater than for the unfertilized controls. The non-
significant planting-date by fertilizer-application interaction term
indicates that the later planting date decreased total nutrient uptake,
but that nutrient uptake between fertilizer treatments within the same
planting date was similar.
The plant-density factor was included in the experimental design
as a means to detect the effects of water stress on yields. The
utilization of high plant densities to induce stress, or early harvest
to reduce plant densities and reduce stress, are common tools used to
Table 5-8. Analysis of variance for uptake of N, P, K, and Ca by corn
dry matter.
Plant nutrient
Source D.F. Error term N P K Ca
-------- F value --------
Date 1 Rep(Date)a
Rep(Date)/Pooled errorb 6.24* 8.42** 3.50** 4.75*
Rep(Date) 2
Density 1 Pooled error 18.7** 10.8** 7.73* 22.5**
Fertilizer 4 Pooled error 41.8** 92.8** 16.2** 19.7**
Density*Fert 4 Pooled error 1.34 2.05 < 1 1.46
Date*Density 1 Pooled error < 1 < 1 < 1 < 1
Date*Fert 4 Pooled error < 1 2.01 < 1 < 1
Date*Den*Fert 4 Pooled error < 1 1 < 1 2.46
Pooled Error 18
Total 39
C.V 16.2 12.3 27.9 23.7
Date = Planting date
Rep = Replication
Den = Density = Planting density
Fert = Fertilizer application schedule
*95% and **99% level of probability
a Insufficient mean square error degrees of freedom for a valid F test.
b Mean square error pooled to increase degrees of freedom in order to
enhance validity of the F test.
Table 5-9. Comparison of means for uptake of N, P, K, and Ca from corn
dry matter.
Corn dry-matter nutrients
N P K Ca
Factor / Level
-------------- kg/ha ------------
Date
First
Second
Density
Low
High
Fertilizer
4
3
2
1
0
45.0a*
37.lb
36.5a
45.6b
50.9a
45.8a
48.5a
45.9a
14.lb
7.04a
5.07b
5.55a
6.57b
7.88a
6.82a
7.56a
6.39a
1.62b
50.2a
36.8b
38.la
48.9b
51.5a
45.8a
51.4a
45.9a
13.lb
8.92a
6.79b
6.45a
9.25b
8.67a
8.54a
9.18a
10.la
2.75b
owed by
level of
* Means in the same column under the same factor heading foll
the same letter are not significantly different at the 95%
probability, as determined by Duncan's Multiple Range Test.
develop a qualitative understanding of field-crop behavior where more
determinate methods (irrigation) are unavailable (Frey, 1981). Overall,
water requirements increase with planting densities. In this field
study, the higher plant densities yielded more grain, stover, N, P, K,
and Ca than the lower plant densities (Tables 5-6 and 5-8). The higher
densities and consequent greater demand on soil moisture did not induce
sufficient stress to affect yield components. However, superior yield
production by all of the fertilized treatments (irrespective of the
density) over the unfertilized control is sufficient evidence to support
the hypotheses that ambient soil fertility and not water availability
limited production of non- or minimally-fertilized plants for this soil.
The larger grain and stover yields of the fertilized treatments versus
those for the non-fertilized controls resulted from much larger plants,
which would have required larger quantities of soil water.
The historical rainfall-distribution pattern for this area
suggests that moisture stress would most likely occur early in the
growing season. Early-season water stress has been shown to be less
detrimental to eventual grain yields than stress during silking or grain
filling (Denmead and Shaw, 1960; Claassen and Shaw, 1970; Grant et al.,
1989). The need to replant part of this experiment was most likely due
to the infrequent occurrence of rainfall during a 4-wk drought following
a seemingly normal to slightly-wetter-than-normal start of the rainy
season. The early-season stress experienced by the plants in the first
crop-growth period was insufficient to decrease grain yield for the
higher densities relative to the lower densities.
The insignificant planting-date by density interaction indicated
that yields responded similarly to both densities within the two crop-
growth periods (Table 5-6). It is interesting to note that, if the
level of probability for the F test were reduced to 90%, the planting-
date by density interaction would become significant for corn-grain
yields. However, the significant difference in grain yields among
densities is only for the first planting date, where the high-density
yield was greater than the low-density yield (Table 5-7). Water stress
would have affected the higher-density plots to a greater degree than
the low-density plots. This is not to say that water stress did not
occur, but only that it did not detrimentally affect grain or stover
yields. Grain yields from the two planting densities for the second
planting date were not different. This would indicate that nutrient
availability and not water stress limited yields for the second planting
date.
Bean Yields
Bean yields between the two planting dates were also
differentially affected by extraneous conditions. Angular leaf spot
(Xanthomonas malvacearum E.F. Sm.) became very prevalent during the last
week before harvest of the plants in the first crop-growth period.
Although this probably had little effect on yields for the first crop-
growth period, it impacted the plants of the second crop-growth period
for 5 wk, and caused considerable premature leaf drop. Additionally,
drying facilities were inoperative and thus incapable of drying the
beans of the first harvest. They underwent some spoilage before
alternative drying facilities could be arranged.
~
The analysis of variance table for bean yields is presented in
Table 5-10. The effect of fertilizer on bean-grain yields was somewhat
peculiar. The beans were harvested after 75 d and, therefore, were
unaffected by the last (84-d) application of fertilizer for the 4 by 1/4
split. The second application in the 2 by 1/2 split and the third
application in the 3 by 1/3 split were applied at 56 d, which should be
about half way through the normal pod-filling period (Fig. 5-3).
Comparison of treatment means indicated that bean-grain yields increased
with the more numerous applications, even when one of the applications
occurred after the beans had been harvested (Table 5-11). Shading may
be the best explanation for bean-plant behavior in this mixed-crop
arrangement. The trend in bean-grain yields as affected by fertilizer
schedule is just the opposite of that for corn-stover yield. Maturation
of the bean plant, including pod filling, occurred simultaneously with
maturation of the corn stover tasselingg at 78 d). The increased
splitting of fertilizer applications that limited stover yields also
reduced the potential of the corn plant to shade the shorter beans.
Further evidence is the lack of a plant-density effect on the bean-grain
yields. Corn-grain yields in the high-density plots were higher than
for the low-density plots. The additional corn plants would have
provided more shade and consequently may have reduced the high-density
bean yields to levels comparable to those of the low-density bean plots.
Post-harvest soil samples were taken from the high-density plots
to monitor gravel content and discern end-of-season differences in
nutrient availability between fertilizer treatments for any given depth
of soil. The concentrations of gravel in the plots showed no
Table 5-10. Analysis of variance for bean grain yields.
Source D.F. Error term F value
Date 1 Rep(Date) / Pooled errors 51.1**
Rep(Date) 2
Density 1 Pooled error < 1
Fertilizer 4 Pooled error 31.3**
Density*Fertilizer 4 Pooled error < 1
Date*Density 1 Pooled error < 1
Date*Fertilizer 4 Pooled error < I
Date*Den*Fert 4 Pooled error < 1
Pooled Error 18
Total 39
C.V. 14.4
Date = Planting date
Rep = Replication
Den = Density = Planting density
Fert = Fertilizer = Fertilizer application schedule
*95% and **99% level of probability
a Mean square error pooled to increase degrees of freedom in order to
enhance validity of the F test.
Dry weight (g/m2),no.of nodes, pods(> 2.5cm)
600-
LAI-
500
Total biomass/
400 -
300 -
200 Pod nL
100
Seed (g/
0 n A- A
Leaf area index
ode no./m2
2
L ,
Days from emergence
Fig. 5-3. Key Phaseolus vulqaris component growth-accumulation
parameters for cultivar Porrillo Sint6tico planted at 25
plants/m2 at Palmira-CIAT (from Laing et al., 1983).
Table 5-11. Comparison of selected bean yield-component means.
Factor / Level Bean yield
kg/ha
Planting Date
Early 336A*
Late 223B
Fertilizer Schedule
4 355a**
3 328ab
2 300ab
1 265b
0 151c
* Means in the same column, under the same sub-heading and followed
by the same uppercase letter, are not significantly different at the
95% level of probability as determined by an F test of mean square
errors.
** Means in the same column, under the same sub-heading and followed
by the same lowercase letter, are not significantly different at the
95% level of probability as determined by Duncan's Multiple Range
Test.
Table 5-12. Analysis of variance for soil gravel percentage.
Source D.F. Error term F value
Date 1 Rep(Date) a
Rep(Date)+Pooled error < 1
Rep(Date) 2
Fert 4 Rep(Date)*Fert < 1
Date*Fert 4 Rep(Date)*Fert < 1
Rep(Date)*Fert 8
Depth(Fert) 40 Pooled error 9.99**
Date*Depth(Fert) 40 Pooled error 1.37
Pooled Error 80
Total 179
C.V. 36.7
Date = Planting date
Rep = Replications
Fert = Fertilizer scheduling
Depth = Depth of sampling
a Insufficient mean error square degrees of freedom for a valid F test.
b Mean error squares pooled to increase degrees of freedom in order to
enhance validity of the F test.
**99% probability of significantly different treatment means.
Table 5-13. Mean comparison of percent gravel associated with depths
for the fertilizer application schedule.
Depth Fertilzer application schedule
0 1 2 3 4
-- cm -- ------------------- % gravel -----------------------
0 5 36.6a 39.8a 42.Oa 29.7a 29.5a
5 10 30.8a 45.0a 34.6a 39.2a 39.1a
10 15 42.la 33.5a 41.6a 36.0a 40.la
15 25 59.4a 56.9a 52.4a 56.3a 60.5a
25 35 74.3a 68.8a 72.5a 71.8a 74.9a
35 45 74.3b 84.5a 78.9ab 80.3ab 84.5a
45 55 71.5a 79.4a 76.7a 77.5a 70.8a
55 65 66.8a 75.1a 70.4a 71.4a 65.3a
65 75 50.4a 59.la 60.1a 53.2a 54.0a
* Means in the same row followed by the same letter are not
significantly different at the 95% level of probability, as
determined by Duncan's Multiple Range Test.
Table 5-14.
Mean gravel content with depth.
Depth
cm
I- 5
i- 10
) 15
25
S- 35
- 45
55
i 65
S- 75
Gravel content
35.3c*
37.7c
38.6c
57.1b
72.5a
77.6a
75.2a
69.8a
55.4b
* Means followed by the same letter are not significantly different at
the 95% level of probability, as determined by Duncan's Multiple
Range Test.
_. _
---- % -----
significant trends associated with experimental treatments (Table 5-12).
The gravel content showed differences between depths, but not between
fertilizer schedules (Table 5-13 and 5-14).
The split fertilizer applications were for the most part
applications of N and K, because most of the P and Ca applied were in
the triple superphosphate which had been applied preplant in all
application schedules (Table 5-15). Analysis of variance for the
effects of the experimental factors on Mehlich I-extractable P, K, and
Ca indicated that planting date and fertilizer schedule had an
insignificant effect on overall nutrient concentrations averaged over
all depths (Table 5-16). The effects of sampling depth on nutrient
concentrations were significant. Since depth was nested within
fertilizer treatment and our experimental interest was in the location
of nutrients as affected by fertilizer schedule, mean separations were
made to distinguish differences among fertilizer-application schedules
within each depth, instead of differences between depths among
fertilizer schedules. Clear patterns are difficult to discern. The
concentration of K from 5 to 25 cm in all of the fertilized plots was
less than for the unfertilized control (Table 5-17). This would suggest
that fertilizer application enhanced K uptake to an even greater extent
than the amount applied. Limiting of this effect to the top 25 cm is
most likely related to the large increase in gravel concentration at the
top of the Btc horizon at about 22 cm, and to the subsequent effect of
the gravel on root growth (Table 5-18).
The concentrations of Ca at the various depths showed no
discernable pattern for the fertilized treatments or the control (Table
Table 5-15. Relative nutrient concentrations associated with each
fertilizer-application schedule.
Fertilizer-application schedule
Element 0 1 2 3 4
----------- percent of total applied t --------------
N 0 0 100 0 50 50 33 33 25 25
P 0 0 100 0 87 13 83 8.5 80 6.5
K 0 0 100 0 50 50 33 33 25 25
Ca 0 0 100 0 87 13 83 8.5 80 6.5
t Preplant applications Each subsequent application
Table 5-16. Analysis of variance for concentrations of Mehlich I-
extractable soil P, K, and Ca after harvest.
Soil nutrients
Source D.F. Error term P K Ca
----- F value -----
Date 1 Rep(Date) a a a
Rep(Date)+Pooled error 3.84b
Rep(Date) 2
Fert 4 Rep(Date)*Fert 2.17 1.07 < 1
Date*Fert 4 Rep(Date)*Fert < 1 < 1 < 1
Rep(Date)*Fert 8
Depth(Fert) 40 Pooled error 20.3** 5.55** 46.2**
Date*Depth(Fert) 40 Pooled error 1.88** < 1 < 1
Pooled error 80
Total 179
C.V. 25.5 76.1 17.6
Date = Planting date
Rep = Replications
Fert = Fertilizer scheduling
Depth = Depth of sampling
a Insufficient mean error square degrees of freedom for a valid F test.
b Mean error squares pooled to increase degrees of freedom in order to
enhance validity of the F test.
*95 and **99% probability of significantly different treatment means.
Table 5-17. Effects of fertilizer-application schedule on Mehlich I-
extractable soil K and Ca concentrations.
Depth Fertilizer application scheme
0 1 2 3 4
-- cm -- Mehlich I-extractable soil nutrients (ug/g)
K
0 5 165a* 151a 175a 127a 136a
5 10 155a 83b 88b 118ab 65b
10 15 195a 67b 70b 88b 76b
15 25 116a 46b 50b 87b 71b
25 35 38ab 35b 44a 47a 33b
35 45 23a 29a 27a 50a 23a
45 55 17a 21a 19a 26a 17a
55 65 14ab 12b 14ab 19a 13b
65 75 11a 9a 11a 14a 22a
Ca
0 5 1800a 1830a 2180a 1920a 1660a
5 10 1870b 2130ab 2330a 1944ab 1914ab
10 15 2041a 1855a 1931a 1995a 1711a
15 25 1686a 1391a 1440a 1370a 1580a
25 35 1120a 1000a 1250a 1480a 1040a
35 45 730a 729a 712a 1032a 677a
45 55 505a 509a 548a 583a 470a
55 65 361a 384a 433a 419a 390a
65 75 295a 315a 356a 310a 322a
* Means in the same row followed by the same letter are not
significantly different at the 95% level of probability, as
determined by Duncan's Multiple Range Test.
|
|
PAGE 1
WATER AND NUTRIENT MOVEMENT RELATED TO SOIL PRODUCTIVITY IN AN AGGREGATED GRAVELLY OXISOL FROM CAMEROON By PAUL R. ANAMOSA A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCT0R OF PHILOSOPHY UNIVERSITY OF FLORIDA 1989
PAGE 2
ACKNOWLEDGEMENTS During the course of this project I have been fortunate to receive a great deal of assistance. The IFAS International Programs Office provided most of my assistantship and travel fare to and from Cameroon. In Cameroon, Eric van Ranst, Soil Science Department Chair, provided me with access to vehicles and field technicians. Philip Mokoko and Maurice Ndazame gave invaluable help to my efforts; aiding in the management of the field project, translating French and the local Dschang dialect to English, and advising me on matters of protocol as well as cultural values. I owe a great debt of gratitude to Dr. W. G. Blue, chairman of my graduate committee, for his support of my field and laboratory activities as well as his editorial review of this dissertation. I am fortunate to have studied under his guidance and am appreciative of his personal generosity and understanding. I am grateful to Dr. P. Nkedi-Kizza, who was willing to join my graduate committee mid-term and who provided new perspectives to my objectives. He gave constructive guidance and criticism to my laboratory experiments. I would also like to thank the other members of my graduate committee, cochairman Dr. J. 8. Sartain, Dr. 8. L. McNeal, Dr. P. E. Hildebrand, and Dr. G. Kidder for the interest and feedback they provided. Dr. Hugh Popenoe graciously substituted for Dr. Blue while he i i
PAGE 3
,--was in Cameroon. Lastly, I would like to thank those on the home front. My wife, Frances, was joyfully willing to pull up stakes and move to Cameroon, put up with late-night runs to the lab to check pumps, and gave constant encouragement throughout the course of this ordeal. Our feline housemates Ferguson and Abigail helped with typing the manuscript. i i i
PAGE 4
TABLE OF CONTENTS ACKNOWLEDGMENTS ..................................................... ii ABSTRACT ........................................................... i V CHAPTERS 1. INTRODUCTION ....................................... 1 2. REVIEW OF THE LITERATURE ................................. 3 Introduction ...................................... 3 Formation Processes ................................ 4 Agricultural Productivity .......................... 9 Research Topics ................................. 12 3. SOIL CHARACTERIZATION ................................... 15 Introduction ..................................... 15 Materials and Methods ............................. 15 Results and Discussion ............................ 17 4. CHARACTERISTICS OF SOIL WATER MOVEMENT IN UNDISTURBED SOIL COLUMNS ............................................ 24 Introduction ...................................... 24 Materials and Methods ............................ 31 Results and Discussion ........................... 34 Conclusions ...................................... 59 5. CROP RESPONSE TO PLANTING DENSITIES AND FERTILIZER APPLICATION SCHEDULING .................................. 61 Introduction ...................................... 61 Materials and Methods ............................. 63 Results and Discussion ............................ 66 Conclusions ...................................... 94 6. NUTRIENT MOVEMENT IN UNDISTURBED SOIL COLUMNS ........... 98 Introduction ...................................... 98 Materials and Methods ............................ 101 Results and Discussion ........................... 106 Conclusions ............................. ....... 137 7. OVERALL CONCLUSIONS .................................... 140 Introduction ..................................... 140 Soil Characterization ............................ 141 Crop Response .................................... 142 Nutrient Leaching ................................ 143 iv
PAGE 5
APPENDIX A SOIL PROFILE DESCRIPTION ............................ .. 145 APPENDIX B CROP COMPONENT YIELDS .................................. 147 REFERENCES ........................................................ 149 BIOGRAPHICAL SKETCH ............. .................................. 159 V
PAGE 6
Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements of the Degree of Doctor of Philosophy WATER AND NUTRIENT MOVEMENT RELATED TO SOIL PRODUCTIVITY IN AN AGGREGATED GRAVELLY OXISOL FROM CAMEROON By Paul R Anamosa August 1989 Chairman: W G. Blue Cochairman : J B. Sartain Major Department: Soil Science Gravel decreases the waterand nutrient-holding capacities of soil. Soils with gravel horizons (stone lines) are being increasingly utilized for crop production in equatorial Africa. This study was conducted to differentiate between the relative effects of water and nutrient stress for crops grown on stone-line soils and to determine if preferential water flow and mobile/immobile water concepts should be considered in describing nutrient and water behavior The effects of p l ant densities of maize (Zea mays L.) and bean (Phaseolus vulgaris L ) and of split applications of plant nutrients were investigated for a clayey-skeletal oxidic, isohyperthermi~ Typic Gibbsiorthox near Dschang, Cameroon. The movement of soil nutrients was studied in so i l columns subjected to simul~ted rainy seasons. The nature of the porous network of the soil was studied using miscible-displacement techn i ques with tritiated water. Increased splitting of mobile-nutrient applications (K N0 3 and vi
PAGE 7
NH 4 ) resulted in increased grain yields, but had no effect on stover yields. Early-season moisture stress apparently decreased plant emergence. However, high-density plantings yielded more grain and stover than did similarly fertilized, low-density plantings. Thus, once plants were established, grain yields were not adversely affected by moisture stress. A 30-d delay in planting resulted in a 40% increase in seasonal rainfall and 50 and 70% grain-yield reductions for bean and corn, respectively. Leaching of Ca, K, and Mg from 70-cm long soil columns was not affected by rainfall regimes or fertilizer-application schedules, although the distribution of Ca, K, and Mg in the columns indicated more downward movement under higher rainfall. Leaching of K was negligible under all treatments used in this study. Split applications of fertilizer composed primarily of K, N0 3 and NH 4 resulted in greater concentrations of Ca and Mg with depth. Moisture-release curves showed that the soil drained nearly 30% of total water content at 50-mbar tension, but still held 30% at 15-bar tension. Miscible-displacement experiments indicated that, under saturated conditions, the soil had a high dispersivity and held about 50% of it water in immobile-water regions. Delays in planting to avoid early-season water stress result in greater leaching losses and reduced grain yields. Splitting the applications of mobile nutrients should increase their plant availability later in the growing season. ~ravel porosity and immobile water regions in the soil harbored highly mobile plant nutrients and reduced leaching. vii
PAGE 8
Chapter 1 INTRODUCTION In light of the present food-production crisis facing most countries of sub-Saharan Africa, numerous policy priorities have been proposed by academics and politicians to encourage the rapid development of technology to improve Africa's food production capacity (Swindale, 1980; USAID, 1985; Mellor et al., 1987; Iyegha, 1988). High on many priority lists is the need for scientific and technological research directed towards the development of efficient fertilizer utilization practices specifically adapted for the low-fertility soils common to tropical regions. Shallow gravel horizons, frequently referred to as stone lines, are common in soils throughout equatorial Africa. Stone-line soils are generally considered to be agriculturally marginal; however, in a continent where population growth is out pacing increases in agricultural productivity, the development and utilization of marginal lands for farming are increasing. Stones in the root zone of a soil reduce root penetration and waterand nutrient-holding capacities. These characteristics in turn reduce root exploitation of the soil mass and increase both the susceptibility of crops to water stress and the potential loss of nutrients by leaching. 1
PAGE 9
2 Several recent studies have indicated vesicular voids (pores) in the gravel from stone-line soils (Muller and Bocquier, 1986; Amouric, 1986). The effects of porous gravel on soil-water behavior are not easily inferred and depend on the porosity and pore-size distribution of the gravel In addition to possible storage of plant-available water, the gravel porosity may also act as a sink/source for the storage of leachable nutrients, and thereby, harbor nutrients from convective-water fl ow. The purpose of this dissertation was to assess several behavioral characteristics regarding water and nutrient movements through a stone line soil from the western highlands of Cameroon. Specifically, the objectives were: 1 To differentiate between the relative effects of possible water and nutrient stresses on field crops grown on a stone line soil; and 2. To determine if preferential water flow and immobile-water regions should be considered in describing nutrient-leaching behavior in these soils.
PAGE 10
CHAPTER 2 REVIEW OF THE LITERATURE Introduction Soils with gravel horizons are common on the hilly landscapes of equatorial Africa. Commonly referred to as stone lines, these gravel horizons were first discussed in the soils' literature in the mid 193Os, and have experienced intermittent periods of scientific examination in every decade since. Initial interests in morphology and formation processes have given way to evaluation of aspects of agricultural productivity. Owing to the limitations of slope and tillage, these soils are generally considered to be agriculturally marginal (Hidlebaugh, 1984). However, increasing population pressures in many of the regions where they occur have necessitated their increased usage. The few published studies evaluating agricultural behavior have focused on effects of the gravel on root penetrability and water redistribution. Inferences regarding appropriate-management practices for stone-line soils under agricultural production have not been addressed. The purpose of this review is to examine the scientific literature de~ling with various aspects of stone-line formation processes and agricultural productivity, and to develop a consensus of the needs for future research, specifically in the area of crop-management practices. 3
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4 Formation Processes The term "stone line" was originally proposed by Sharpe (1938) to designate "a line of angular to subangular fragments which parallels a sloping surface to a depth of several feet." Ruhe (1959, p. 223), summarizing the definitions of several studies (Sharpe, 1938; De Heinzelin, 1955; Parizek and Woodruff, 1957), defined a stone line as "a concentration of coarser rock fragments in soils; in cross section it may be a line, one stone thick or more than one stone in thickness, that generally overlies material weathered in place from bedrock and that usually is overlain by variable thicknesses of finer-textured sediment." De Heinzelin (1955) objected to the term when used to designate the gravel horizons common to equatorial African soils. He proposed instead the term "nappe de gravat" (sheet of gravel), because it more appropriately described the three-dimensional nature of the structure. However, at present the term stone line is widely used in both the English and French pedological literature. The formation processes that create stone lines instill specific morphological characteristics to the soil profile. It is these morphological characteristics that have been used to develop hypotheses concerning the formation processes. Pedologists working throughout equatorial Africa on a variety of landscapes have developed two different schools of thought concerning stone-line formation. These were categorized as either autochthonous (same) or allochthonous (different) with respect to the parent material of the stones and of the underlying material (Collinet, 1969). The distinction between the two
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5 categories involves whether the stones are residual from the underlying parent material or were transported from elsewhere and then covered with sediment. This distinction was the core of debate among pedologists originally hypothesizing the formation processes. It is still a point of contention, considering that allochthonous processes rarely exhibit the transport of stones over distances greater than several hundred meters (Riquier, 1969; Segalen, 1969; Fairbridge and Finkl, 1984). The stone lines produced by the two widely accepted autochthonous processes are relatively uncommon and show vast differences in morphology. The reworking of soil materials by termites, resulting in concentration of the finer above the coarser sediments, has been studied throughout equatorial Africa (De Heinzelin, 1955; Nye, 1955; Sys, 1955; Gennart et al 1961). Variations exist among termite species and geographical locations, but such stone lines generally consist of a diffuse gravel horizon rarely exceeding 25% by weight of small, 2 to 7 mm, fragments of residual quartz and occasional ironstone nodules. The gravel horizons range in thickness from 10 to 250 cm, and rarely exceed depths of 300 cm. Surface stone lines, frequently called ''desert pavement," are found in extremely arid climates that receive occasional torrential rains. It is generally believed that these surface stones result from the fracture of exposed bedrock due to large daily temperature fluctuations. Sheet erosion during heavy rains then removes any overlying $Oil, which either collects in crevices between the stones or is washed away (Springer, 1958; Finkl, 1979).
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6 By far the most common type of stone line in equatorial regions of Australia, South America, and Africa is presently attributed to an allochthonous process. However, several autochthonous-process theories have been proposed and subsequently refuted. Sharpe (1938, cited in Ruhe 1956) and Ireland et al. (1939, cited in Ruhe, 1956) proposed a theory involving surface creep, in which soil flowing slowly downslope shears off resistant rock projecting up into the subsoil and carries the rock along the bottom of the creeping mass. Ruhe (1956) and Parizek and Woodruff (1957) rejected this theory. They concluded that the sheets of gravel were originally surface deposits later covered by an over-lying mantle. Ruhe (1959) later described in detail this theory, which assumes the stones to be highly resistant residual parent material that became concentrated on a developing erosional surface by the removal of finer material with runoff water. Finer-textured sediment derived from an upper-valley slope then is deposited on the sheet of gravel. This process is autochthonous in nature, and can not explain soils with multiple stone lines (Ollier, 1959). An allochthonous process was first proposed by de Craene (1954) and later applied to both quartz lines and gravel horizons by Collinet (1969) and Riquier (1969). In its most basic form, the process begins with the deposition of rock material from exposed escarpments (rock outcroppings) onto sloping eroded surfaces. This material then is covered by a fine colluvial mineral deposit. Therefore, both rock and fine fraction are genetically different from the soil below the stone line. The process can be repeated as long as a rock escarpment exists above the erosional surface.
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7 A similar process can lead to the development of thin, quartz stone lines. Quartz veins of geologic origin are frequently sandwiched between layers of sedimentary rock. If near the surface, such rock may be transformed into soil or saprolite, leaving the resistant quartz vein intact. Where the quartz vein intercepts the earth's surface it provides a source of quartz pebbles that then may be spread over the soil depending on the slope of the land. If the surface is sloping the pebbles will be scattered downslope If the surface is flat the pebbles will form mounds or ridges that may run for a considerable length across the landscape. The allochthonous process requires winnowing (the movement, deposition, and concentration of coarse material by wind and running water), which in turn usually requires climatic instability so that slopes may go through both erosional and stabilizing periods (Fairbridge and Finkl, 1984). Such periods are attributed to torrential rains during arid to semi-arid climatic phases within a normally humid era. This pattern would allow for erosion of vegetatively bare surfaces during intermittent heavy rains in an arid phase and subsequent slope stabilization by vegetation upon return of the humid climatic phase. Several independent lines of evidence suggest that the pleniglacial age of the late Wisconsinan cycle was responsible for the climatic conditions favorable for stone-line formation in the tropics. Bruckner (1955) working in Africa, Bigarella and de Andrade (1965) working in Brazil, and Finkl (1979) working in Australia have all identified regional occurrence of common but discontinuous stone lines dating from the late Wisconsinan period. The arid phases during the
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Wisconsinan period were brought about by a combination of lower solar radiation, disruption of major air-flow patterns, and extension of the cold polar oceanic currents into low latitudes (Fairbridge, 1964; Cailleux and Tricart, 1973). Stone lines formed from the allochthonous processes of escarpment retreat, rock-fragment deposition, and fine-fraction sedimentation frequently have common physical characteristics (Fairbridge and Finkl, 1984). Such stone lines occur as slope deposits on the paleoslopes of interfluves and residual pediplains (Ojanuga and Wirth, 1977). 8 Distances of transport usually range from several meters to several hundred meters. The stones are angular to rounded, but are usually similar within any singular continuous horizon (Ojanuga and Lee, 1973). The stones are quite resistant to weathering either because they are inherently durable such as quartz or because they consist of resistant lateritic pseudomorphs (similar shape but different mineralogy) of the original rock fragments (Ojanuga and Lee, 1973; Muller and Bocquier, 1986). Such pseudomorphs result from the natural weathering and dissolution of rock parent materials, along with the precipitation of Fe and Al minerals leached from overlying soil horizons rich in Fe and Al oxides/hydroxides. Lateritic material in the stone line may also come from pistolitic duricrust that forms with desiccation and hardening of exposed surface soil resting on top of the fragmenting escarpment (Frankel and Bayliss, 1966; Amouric et al., 1986). Further erosion and retreat of the escarpment face causes fragments of the sutface duricrust to drop along with escarpment-rock fragments to the erosional plain below.
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9 Agricultural Productivity Little is known about the behavior of tropical stone-line soils or the influence they exert on agricultural systems which they support. The paucity of internationally available literature on soils of the tropics, in general, is well known. The habit of national governments to establish research stations on a region's best soils frequently limits the generation of knowledge concerning hillside and agriculturally marginal soils (Zandstra et al .,1981). Lal, formerly of the International Institute of Tropical Agriculture in Ibadin, Nigeria, has conducted several studies investigating plant-root development and water availability on natural and synthetic gravelly soils. Babaloa and Lal (1977a) evaluated the effects of varying gravel concentrations on shoot growth and rooting depth using soil/gravel mixtures in greenhouse pot studies. The weight of corn shoots harvested after 21 d decreased by up to 50% as gravel concentration increased from 10 to 75%. Rooting depth decreased only slightly as gravel increased from Oto 10%, but then decreased to 40% of the non-gravel rooting depth as gravel increased to 25% The rooting depth decreased to 5% of the non-gravel rooting depth as the gravel increased to 75%. Total root length was affected similarly. Shoot weights increased by 20% as the depth to a 60%-gravel horizon increased from 5 to 10 cm. Shoot tissue had a nonsignificant increase in concentrations of N, P, and K as depth to the gravel horizon increased. The researchers concluded that the gravel retarded rooting depth and thereby decreased root exploitation of soil nutrients, resulting in reduced nutrient uptake and consequent reduced overall growth.
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10 Babalola and Lal (1977b) evaluated the effects of various gravel sizes and mixtures and the effects of modifying a natural gravelly soil on corn-seedling growth. Increased gravel size (4 to 8, 8 to 15, and 15 to 40 mm) decreased shoot weight, root weight, root depth, and overall root length of 7-d-old seedlings harvested from soils of varying gravel concentrations. Field studies were performed on a naturally occurring gravel horizon following removal of the overlying 15 cm of surface soil. Treatments included gravel horizon undisturbed; gravel horizon removed and repacked at a lower bulk density; gravel horizon removed, sieved to remove gravel, and repacked as only the fine fraction; and gravel horizon removed and area repacked with the original surface soil. All treatments with gravel had seedling emergence delayed 1 to 2 d. The treatments did not produce differences in shoot height, shoot dry weight, or root dry weight. In comparison to the undisturbed treatment, reduction in bulk density, accomplished by removal and repacking of the soil, increased root length and rooting depth by 50%. Removal of the gravel and substitution of surface soil for the subsoil increased root length and rooting depth by 80% over the undisturbed control. There were no differences in root length, root depth, or shoot weight between the subsoil without gravel and the replacement of subsoil by surface soil. Roots in gravelly horizons exhibited an increased mean diameter, stunted tips, and marked crookedness. Although the gravel had no impact on dry-weight yields in this short 7-d trial, the stunted growth and limited access of the roots to soil would probably have detrimental repercussions on full-season, plant-yield components.
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11 The influence of gravel on the moisture characteristics of the whole soil results from the quantity of gravel, its arrangement in the soil fabric, and its own hydrologic properties. Several researchers have attributed the scarcity of information regarding water relations in gravelly soils to difficulty in adapting standard laboratory and field techniques to gravelly soil, which display a high degree of micro variability within repetitive samples (Reinhart, 1961; Hanson and Blevins, 1979). Experiments using drastically-disturbed and mixed-gravel soils will be discussed and distinguished from those of naturally occurring soils with gravel horizons. Miller and Bunger (1963) and later Unger (1971a and 1971b) constructed soils with "pea gravel" horizons to study water infiltration and redistribution. In all treatments of the three studies, screens or special repacking techniques were used to prevent soil from filling the interstitial spaces of the gravel horizons. These studies showed that the gravel slowed downward percolation, and for all practical purposes, prevented upward redistribution of soil water. The behavior of these soils should probably not be extrapolated to soils of the tropics with naturally occurring gravel horizons, in which a fine mineral fraction occupies the inter-gravel space and provides a continuum of fine pores that can participate in the redistribution of soil water. Babalola and Lal (1977a) reported soil moisture-release curves for the sieved, gravel-mixed soil used in their previously reported studies. They showed an incremental decrease in soil-water content at tensions of 0 to 60 cm of water for each incremental increase in gravel
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12 concentration from Oto 75%. They concluded that, as gravel concentration and, therefore, total solids increased, porosity and consequently water-holding capacity decreased. Ghuman and Lal (1984) studied differences in field-water percolation and redistribution rates on a tropical Alfisol under conventional plowing and no-till management. The soil had a naturally occurring gravel horizon from the 10 to 80-cm depth that contained about 45% gravel by weight. Soil having an initial water content of 0.035 cm 3 /cm 3 exhibited infiltration rates of 43 and 120 cm/h for the conventional and no-till systems, respectively, upon application of 5 cm of floodwater to the surface. The infiltrating water under both tillage treatments reached the 30-cm depth before flood conditions ceased, at which time the plots were covered to prevent surface evaporation. Within 1 h the water had passed the 80-cm depth. The redistributing soil water had stabilized after 5 hand the soil water content with depth remained constant until cessation of observations at 48 h. Higher initial soil-water contents resulted in slower infiltration rates. Even under very dry conditions, the gravel horizon did not prohibit the downward movement of infiltrating water. Research Topics The unique physical properties and generally unknown behavior of tropical, stone-line soils lead to many questions regarding their agricultural management. However, extrapolation of properties and behavior of soils that simply contain stones can lead to the development of management practices based on incorrect assumptions. Soil scientists
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13 have historically witnessed the difficulty of transferring management techniques developed for temperate soils to soils of the tropics (Swindale, 1980). Research into the properties and consequent behavior of a soil is generally considered the most sound approach for the development of management practices (Dudal, 1980) Vine and Lal (1981) concluded that gravel reduces volumetric moisture content, reduces nutrient-retaining capacity, and retards plant-root development. The extent of such effects is related to the properties of the gravel. The porosity of the gravel will influence the degree of any reduction in soil-moisture content, and rainfall patterns will influence the degree to which soil moisture becomes detrimental for plant growth. Although Flint and Childs (1984) have demonstrated that gravel can hold up to 40% of available water in temperate forest soils, and Muller and Bocquier (1986) and Amouric et al (1986) have photographed voids in the gravel from tropical, stonel ine soils from both Cameroon and Senegal, the porosity and water-holding capacity of tropical, stone-line soils have not been established. Babalola and Lal (1977a and 1977b) and Ghurnan and Lal (1984) made no mention of water retention by the gravel in their studies of water relations in tropical, stone-line soils. Reductions in nutrient-retaining capacity result from volumetric reductions in the soil's fine fraction with the increase in volume of gravel. The fine fraction typically contains greater surface area and organic matter and consequent nutrient-retaining charge. However, porous gravel may harbor weakly held mobile nutrients. Studies in soil physics have firmly established the presence of immobile-water regions
PAGE 21
14 in the fine porosity of soil aggregates (Kirda et al. 1973; van Genuchten and Wierenga 1977; Rao et al., 1980a). Considerable evidence has shown that gravel contents above 10 to 20% by weight have deleterious effects on root development and soil penetration (Babalola and Lal, 1977a and 1977b; Vine and Lal, 1981). Regardless of gravel hydrologic properties, the gravel limits rooting depth and, therefore, limits the volume of soil from which the plant can extract immobile nutrients. In light of the previously mentioned considerations, any research effort with respect to the development of management practices may be most productive if the research is designed to determine the combined behavior of the processes and their combined effects on soil productivity, instead of investigating separately the numerous interdependent processes.
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CHAPTER 3 SOIL CHARACTERIZATION Introduction In the late nineteenth century, Russian earth scientists introduced the concept of soils as independent natural bodies, each with unique morphology resulting from a unique combination of climate, living matter, earthy materials, relief, and age (Buol et al., 1980). Since that time, characterization of soil morphological, physical, and chemical properties has played a fundamental role in the development of soil taxonomic and resulting classification systems (Soil Survey Staff, 1975). Data characterizing soil properties, and subsequent taxonomic classification of the soil are useful tools in the development of a unified concept of soil behavior in its natural environment (Sanchez et al., 1982b). The purpose of this chapter is to describe the physical and chemical properties, and to taxonomically classify, the soil used in the experimentation discussed throughout this dissertation. Materials and Methods The field site was located 1 km north of the Leppo primary school and 60 m west of the road passing from Dschang to Djuttitsa through the Leppo quarter of the village of Bafou, in the Western Province of the Federal Republic of Cameroon. Following the field experiments (Chapter 4), a 2-m deep pit was excavated in the middle of 15
PAGE 23
16 the site and the soil profile was described (Appendix A). Samples from each horizon were taken for physical and chemical analysis. Soil texture was measured by the pipette method (Gee and Bauder, 1986). Bulk density, porosity, and moisture-retention characteristics were determined from undisturbed soil samples in 5-cm long by 5-cm internal-diameter cores (Klute, 1986). Mineralogy of the fine fraction (<2 mm) was determined by x-ray diffraction following removal of organic matter with hydrogen peroxide and removal of noncrystalline material with ammonium oxalate in the dark (Kunze and Dixon, 1986). Exchangeable Ca+ 2 Mg+ 2 K+, and Na+ were extracted from the fine fraction with 1 M NH 4 0Ac at pH 7 and determined by atomic absorption spectrophotometry. Exchangeable H+ and Al+ 3 were extracted with 1 M KCl and determined by the titration procedure of Yuan (1959). Organic matter was determined by the modified Mebius procedure (Nelson and Sommers, 1982). Phosphorous adsorption isotherms were determined for both fine-fraction (<2 mm) and gravel (>2 mm) samples of the Ap horizon, using the method of Fox and Kamprath (1970). Gravel was sieved from the fine fraction, washed, and then separated according to its four predominant colors. Mineralogy of the gravel separates was determined by x-ray diffraction of powder mounts following pulverization with a ball mill. Porosity of the gravel was determined by the Brunauer-Emmett-Teller (BET) method on a Quantachrome AUTOSORB-6 surface-area unit. Gravel-particle density was determined using a gas pycnometer (Danielson and Sutherland, 1986). The soil was classified in the USDA Soil Taxonomy (Soil Survey Staff, 1975) system based on its morphological description and its physical and chemical properties.
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17 Results and Discussion Geographical Location The soil-profile description is presented in the Appendix. The soil rests on a 12 to 16% convex slope. The surrounding landscape is steeply dissected and supports numerous small fields cropped to mixed cultures of corn (Zea mays L.), beans (Phaseolus vulgaris L ), cocoyams (Colocasia esculenta). coffee (Coffea arabica L.), and peanuts (Arachis hypogaea L.). The soil is derived from a basal parent material of basalt, along with surface deposits of volcanic ash. The profile is well-drained and has a lithologic discontinuity between the Btc and 2BCt horizons, where a gravel horizon meets a buried clayey horizon. Soil climate at the weather station of the Institute of Agronomic Research in Dschang, 8 km south of the field site, is udic isohyperthermic. The soil's control section (SO to 100 cm) is dry no more than 90 d/yr, and maintains a mean annual temperature greater than 22 C with less than a 5 C fluctuation from the warmest to coolest temperature at a depth of 50 cm. Physical and Chemical Properties Selected physical properties are shown in Table 3-1. The gravel content of the top 72 cm ranges from 33 to 72% by weight. The fine fraction is dominated by clay and composed of kaolinite, quartz, goethite, and gibbsite. Selected chemical properties are shown in Table 3-2. Contents of organic carbon and exchangeable bases are calculated on the basis of the fine fraction only. Trace quantities of acidity were extractable, but never exceeded 0.02 cmol (+)/kg of soil for any horizon.
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18 Table 3-1. Selected physical properties of the soil. Bulk Fine fraction Clai'. minerals Horizon Depth density Gravel Sand Silt Clay K Q GB GE cm g/cm 3 kg/kg -kg/kg of <2 mm -% Ap 0 11 0.88 0.335 0.211 0.355 0.435 55 15 15 15 Ac 11 22 1.00 0.588 0.111 0.403 0.486 60 10 15 15 Btc 22 72 1.46 0.720 0.112 0.337 0.551 60 5 20 15 2BCt 72 138 1. 26 0 0.118 0 .145 0.737 60 5 15 20 2CB 138 194+ I. 28 0 0.064 0 .193 0.743 55 5 20 20 t K = Kaolinite Q = Quartz GE = Goethite GB= Gibbsite Table 3-2. Selected chemical properties of the fine fraction of the soil. Organic Extractable bases Extract. aciditi'. Sum of Horizon carbon Ca Mg K Na NH 4 0Act KClt bases g/kg ------cmol (+)/kg fine fraction -------Ap 68.5 7.2 3.1 0.27 0.04 30.1 trace 10.6 Ac 57.0 4.5 2.6 0 .13 0.03 22.9 trace 7 3 Btc 20.6 1.4 1. 7 0 03 0.02 10.6 trace 3.1 2BCt 11.0 2 .1 2.2 0.03 0.02 10 0 trace 4.3 2CB 7.5 3.6 2.4 0.05 0.05 8.4 trace 6.0 t extracted with 1 M NH,OAc (pH 7). t extracted with 1 M KC less than 0.02 cmol (+)/kg. Family designation: clayey-skeletal, oxidic, isohyperthermic Typic Gibbsiorthox (<2 mm) ~H H 2 0 KClt 5.33 4.76 5.12 4.52 4.98 4.85 5.52 5.43 5.61 5.46
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19 The gravel is a composite of four visually distinguishable classes composed of goethite, gibbsite, kaolinite, and an unidentified mineral containing Mn (Table 3-3). The porosity of the gravel ranges from 0.13 to 0.32 ml/ml, with a natural-composite sample porosity of 0.2 ml/ml. The moisture-release curve for the top 72 cm of soil is shown in Fig. 3-1. The soil-water content exhibited no initial plateau at low tension, thereby suggesting the presence of some very large pores that are full when the soil is saturated, but which drain under relatively low tensions. The soil lost nearly 0.2 ml of water per cm 3 of soil between saturation and 350-mbar tension (hypothetical field capacity) It maintained 0.14 ml of water per cm 3 of soil between 350-mbar and 15bar tension (hypothetical plant-available water). Phosphorus adsorption isotherms are presented in Fig. 3-2. The Ap horizon exhibits a strong affinity for P, and required nearly 500 ug P/g soil (750 kg P/ha to a depth of 15 cm) to support a solution concentration of 0.2 ug/ml. The gravel displayed a low affinity for P. Taxonomic Classification The Ap and Ac horizons constitute an umbric epipedon. The epipedon has weak, medium, subangular-blocky structure that breaks to moderate crumb. The color has a moist Munsell value and chroma darker than 3.5. The organic-carbon content is greater than 2.5%, and the depth of the epipedon is greater than 18 cm. Base saturation as measured by 1 M NH 4 0Ac at pH 7 is less than 50%. The Btc, 2BCt and 2Cb horizons constitute an oxic horizon. This horizon is at least 30-cm thick. The cation-exchange capacity using NH 4 0Ac (pH 7) is less than 16 cmol(+)/kg clay. There are no more than
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20 Table 3-3. Physical and mineralogical characteristics of the gravel. Color Predominant Pore Average Particle Bulk Porosity minerals volume pore radius density density ml/g nm g/ml g/ml ml/ml Yellow Geothite (90)t 0 145 3.62 3.31 2.24 0.32 ( 12); Pink Kaolinite (60) 0.184 14.40 2.55 1. 74 0.320 (8) Gibbsite (20) Red Gibbsite (80) 0.087 8.69 2.63 2 .14 0 .186 (75) Kaolinite (10) Black Manganese 0.043 4.91 3.55 3.08 0 .133 (5) oxides Composite 2.71 2 18 0 195 t Approximate percentage of mineral content. ; Percentage content of natural-composite total (by mass).
PAGE 28
"'E (.) ...... 1: (.) CD. z w 1z 0 () a: w <( _J 0 (/) 0.6 0.4 0 3 0.2 0. 1 0 0 0 = 0. 526 8. 20x 102 1og(T) Ft0 998** 100 200 300 400 SOIL WATER TENSION, T (mbar) Fig. 3-1. Soil moisture-release curve. 21 15000
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22 1000 Ap Horizon :::=s .. 1420 c 0 939 o 800 fJ'J Ft= 0.89 0) ........ 0) :::i en 600 a: Cl LU ca 400 cc Gravel 0 en S = 106 C 1 30 Cl ,-----, <( 200 R 2 0. 91* 0 w.... l ____ _,__--1::3 ___ ......___ ____ L_ ___ __; 0.01 0. 1 1.0 10.0 SOLUTION P, C (ug/ml) Fig 3-2 Phosphorus adsorption isotherms for the gravel and the Ap horizon.
PAGE 30
23 trace quantities of weatherable primary aluminosilicates. The texture is finer than sandy loam and the horizon has more than 15% clay, with no or very few clay skins. The soil is an Oxisol because 1) the oxic horizon in the top 2 m, 2) there is no plaggen epipedon, and 3) there is neither an argillic nor natric horizon above the oxic horizon. The soil is in the 0rthox suborder, because it has no continuous phases of plinthite within 30 cm of the surface, is not saturated with water at any time during the year, has neither a torric nor an ustic moisture regime, and has less than 16 kg of organic carbon per square meter to a depth of 1 m. The soil is in the Gibbsiorthox great group, by virtue of the presence of a horizon within 1 25 m of the surface that contains 20% or more by volume of gravel-sized aggregates that contain 30% or more of gibbsite. This Gibbsiorthox is in turn Typic, because the gibbsitic gravel is within 50 cm of the surface and there are no mottles in the upper 1 m of the soil. The particle-size class of the soil is clayey-skeletal, because gravel makes up 35% or more by volume and the fine earth contains 35% or more clay by weight. The mineral class is oxidic, because the soil contains less than 90% quartz and less than 40% each of hydrated aluminum (reported as gibbsite or bohemite) and iron oxides extractable by citrate-dithionite, and the sum of the percentages of these two mineral groups divided by the percent clay is greater than 0.2. Therefore, the family designation for the soil is clayey-skeletal, oxidic, isohyperthermic, Typic Gibbsiorthox
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CHAPTER 4 CHARACTERISTICS OF SOIL WATER MOVEMENT IN UNDISTURBED SOIL COLUMNS Introduction A model is the representation of a form or process in an alternative media. In modern science, chemical and physical processes are modeled by representing behavioral processes with mathematical relationships based on empirical and theoretical concepts. Models of natural systems are frequently quite complex, because numerous interrelated processes must be considered. The ultimate goal in the conceptual development of a model is the integration of mathematical relations that represent the true mechanisms of the natural process. However, mechanistic approaches are limited by insufficient understanding of processes and/or their interactions. The limitations take the form of unverifiable assumptions and exclusion of known but seemingly insignificant factors. In lieu of mechanistic descriptions, processes may be lumped such that the mathematical expression reflects the relation of several different and detailed processes. Such a deterministic approach is advantageous when the effects of a process can be modeled but the actual mechanisms are unknown, or when a true mechanistic model requires extensive characterization of the modeled media. The value of a model lies in its ability to simulate the natural process from measured or estimated parameters that characterize the 24
PAGE 32
25 natural setting. Although concurrence of model-simulated and independently derived parameters does not prove the correctness of the model's underlying theoretical basis per se, overall confidence in the model's theoretical basis is increased as concurrence continues to exist under a variety of characterized conditions. Increased confidence allows greater use of the model for purely predictive and managerial purposes. Numerous models have been proposed for describing solute transport in aggregated porous media. Modeling solute transport in aggregated or structured soils presents some unique problems due to the complex three-dimensional nature of the inter-connected network of irregularly sized and shaped soil pores. Attempts to model displacement processes quantitatively have been based generally on the convective-dispersive equation (Lapidus and Amundson, 1952), 8C/8t = D 8 2 C/8z V 0 8C/8z [4-1] where C is the concentration (mg/ml), Dis the dispersion coefficient (cm 2 /day), v 0 is the pore-water velocity (cm/day), z is the distance (cm), and t is time (days). Adsorption of the solute to the porous media may be considered using an adsorption coefficient derived from a linear adsorption isotherm, defined by S = K C d [4-2] where Sis the sorbed solute concentration (mg/g), C is the equilibrium solution solute concentration (mg/ml), and Kd is the adsorption coefficient (ml/g) giving R 8C/8t = D 8 2 C/8z 2 v 0 8C/az [4-3]
PAGE 33
where R, the chemical retardation factor, is defined by R = 1 + pK/fJ. 26 [4-4] where pis the soil bulk density (g/cm 3 ) and fJ is the volumetric water content (ml/ml). Eq. 4-3 can be rearranged to include the dimensionless parameters: T = V 0 t/l X = z/L P = v 0 L/D and C = CJC 0 [4-5] [4-6] [4-7] [4-8] where v 0 t, z, and D have been previously defined and l is the column length (cm), Pis the Peclet number, Tis the pore volumes of solution, xis dimensionless distance, and C the ratio of effluent concentration (Cb) to influent concentration (C 0 ) to give the convective-dispersive (CD) model, R(ac;aT) = (l/P)(a 2 c/ax 2 ) ac;ax [4-9] The CD water-flow model has been used satisfactorily to simulate nonadsorbed solute transport under laboratory and field conditions (Nielsen and Biggar, 1961; Warrick et al., 1971). However, the model has been relatively poor at simulating solute transport through well aggregated and structured soils (Green et al., 1972; Rao et al., 1974; van Genuchten and Wierenga, 1976 and 1977). Solutions of Eq. [4-1] predict nearly sigmoidal or symmetrical concentration distributions (Coats and Smith, 1964; Gershon and Nir 1969; van Genuchten and Wierenga, 1976). However, numerous experimental studies have shown distinctly asymmetrical effluent curves (Nielsen and Biggar, 1961; Biggar and Nielsen, 1962; Green et al.
PAGE 34
1972; van Genuchten and Wierenga, 1977). It was noted that this asymmetry or tailing of effluent curves was more pronounced in aggregated versus nonaggregated media and as solution velocities increased. Coats and Smith (1964} hypothesized the existence of regions of immobile water in small and dead-end pores. They modified Eq. [4-1] to incorporate solute transfer by diffusion from mobile flowing water regions to stagnant immobile water regions, to give 8m (aCJaT} + 8;m (aC;Jat} 27 = 8m0m (a 2 Cm/az 2 ) vm8m (aCJdz) [4-10] and (4-11] where am and aim are the fractions of the soil filled with mobile and stagnant water, respectively (cm 3 /cm 3 }; Cm and Cim are the solute concentrations (g/ml) in the mobile and immobile regions; vm is the average pore-water velocity in the mobile region; Om is the mobile water dispersion coefficient; and a is a mass-transfer coefficient ( day 1 ). van Genuchten and Wierenga (1976} have extended this model to account for solute adsorption to the porous media through the inclusion of an adsorption coefficient in the retardation factor. To account for the possibility of unequal distribution of adsorption sites between the mobileand immobile-water regions, f is defined as the fraction of sites in the mobile region. Including these concepts in the model of Coats and Smith (1964}, van Genuchten and Wierenga (1976) derived (8m + fpKd) acm;at + [Oim + (1 f)pKd] ac;Jat (4 12]
PAGE 35
28 and [4-13] The model may be described in terms of the dimensionless parameters Peclet number, P; the mobile water partition coefficient, p; and the dimensionless, mass-transfer coefficient, w, defined as: P = vml/Dm, [4-14] P = (Om+ pjKd)/(8 + pKd) and [4-15] w = al/q [4-16] Additionally, the concentrations of the solutes in the two regions (Cm and C;m) may be normalized with the original-solute pulse concentration, C 0 by defining c, = CjCO [4-17] and [4-18] With these definitions of P, P, w, c 1 and c 2 Eqs. [4-12] and [4-13] become: [4-19] and [4-20] The mobile-immobile model (MIM) (Eqs. (4-19] and [4-20]) contains four dimensionless parameters; P, R, P and w. Agreement between model simulation and experimental data is generally accepted as verification of the conceptual basis of the model. However, experimental methods are generally unavailable to measure p and w independently. When experimental techniques are inadequate to measure parameters independently, they are frequently estimated on the basis of a best-fit
PAGE 36
29 of the model to experimental data (van Genuchten et al., 1977; Rao et al., 1979; Nkedi-Kizza et al., 1983 ). van Genuchten (1981) has developed a non-linear least-squares, curve-fitting computer program that estimates MIM and CD parameters from miscible displacement effluent data. Although such a technique is useful for parameter estimation, it does not ensure process identification (Davidson et al 1980; Rao et al., 1980a). Independently estimated model parameters for soil and synthetic porous media have demonstrated slight deviations from those parameters estimated from curve-fitting procedures based on the MIM. Rao et al. (1980b) performed miscible-displacement experiments on fabricated media consisting of mixtures of porous ceramic spheres, glass beads, and fine sand. Parameters calculated by the MIM curve-fitting program were compared to those experimentally measured or independently estimated for the various mixtures (Rao et al .,1980a). Over a broad range of pore-water velocities, close agreement was found between values estimated by MIM curve-fitting to those independently determined. Owing to the ease of utilization, unavailability of accurate conclusive methods to determine some model parameters experimentally and otherwise general agreement between experimentally determined and model-estimated parameters, the MIM has become a popular tool for estimating soil-water behavioral characteristics. Seyfried and Rao (1987) used the model in a study to examine the relative contributions of soil-water characteristics to leaching in an aggregated tropical Typic Dystropept derived from volcanic ash. Field studies monitoring K I movement were not successfully simulated by a simple convective
PAGE 37
30 dispersive water model (Seyfreid, 1986). Miscible-displacement experiments on saturated soil columns and subsequent analysis of effluent data by the MIM model estimated the mobile-water content at about 55% of the total soil water. Low Peclet numbers and consequent high dispersion coefficients indicated a high degree of preferential water flow that bypassed large portions of the soil water. Schulin et al. (1987) used the CD and MIM models to determine behavior of water in undisturbed columns of soil containing about 55% by volume gravel. Back-calculation of presented data indicated that the gravel was not porous and contained no water. The MIM model calculated the mobile-water content to be about 85% of the total water present in unsaturated columns maintained at volumetric-water contents of between 0.135 and 0.175 ml/ml for soils with total porosities ranging from 0.25 to 0.30 ml/ml. Due to the low immobile-water fraction, the CD model, which considers all water as mobile, was able to estimate parameters capable of simulating the experimental BTC nearly as well as the MIM model. Several independently conducted studies have suggested that the gravel resulting from mineral dissolution and precipitation in tropical stone-line soils is porous (Amouric et al., 1986; Muller and Bocquier, 1986; Chapter 3 ). Although the only apparent study on the mobile/immobile-water content of gravelly soil indicates that the immobile fraction is relatively small (Schul in et al., 1987), the presence of porosity in gravel from tropical stone-line soils would suggest that these soils may have a considerable immobile-water fraction The purpose of this study was to use the MIM and CD models
PAGE 38
31 to evaluate tritiated water breakthrough curves from an aggregated gravelly 0xisol, to determine if preferential water flow and immobile water regions should be considered when describing nutrient-leaching behavior for this soil. Materials and Methods Column Preparations Undisturbed soil columns were taken from unfertilized plots at the previously described experimental site at the end of the growing season. A soil-core sampler was constructed of a steel water pipe (102 mm i.d./ 114 mm o.d.) fitted with a sharp, hardened-steel, cutting edge and a removable, threaded steel cap. An 80-cm length of PVC pipe was inserted into the steel corer so that one end rested on a 1-mm wide shelf at the base. The whole piece was held in place by tightening the cap. The sampler was hammered into the ground until the top of the cap was nearly level with the soil surface. The sampler was then lifted up and out of the soil with a hydraulic jack. The PVC pipe full of soil was removed from the sampler, sealed, and boxed for transport to the laboratory in Gainesville. Excess PVC pipe was cut from the top of each column so that the new end was 5 mm above the soil surface. Approximately 5 mm of soil was removed from the bottom of the columns and both ends were fitted with porous, fritted-glass plates (maximum pore radius of !Sum) and plexiglass end plates. Miscible Displacement The columns were held vertically and saturated from the bottom with approximately 5 pore volumes of a degassed solution of 0.01 M CaCl 2
PAGE 39
32 The columns were then turned horizontally and the end plate in contact with the surface Ap hofizon was connected to an influent solution by a three-way valve which allowed switching between tritiated and nontritiated solutions of 0.01 M CaC1 2 (Fig. 4-1). Effluent was collected in a fraction collector from the other end of the column. The 3 H 2 0 activity in the effluent fraction was monitored using liquid scintillation techniques. The resulting breakthrough curves were fitted to the Convective-Dispersive (CD) and Mobile-Immobile (MIM) transport models using the program CFITIM3 (van Genuchten, 1981), which is based on a nonlinear, least sum of squares criteria for goodness of fit. Boundary conditions assumed for the model analyses were constant influent-solute concentration and a semi-infinite column. Adsorption Isotherms Adsorption isotherms for 3 H 2 0 were determined using a batch technique similar to that described by Dao and Lavy (1978). Sieved (<2 mm) soil samples from each of the three soil horizons present in the column, and a composite gravel sample (2 to 4 mm}, were assayed. Moist triplicate 4-g samples of each material were placed in a pre-weighed 10-ml plastic, screw-top centrifuge vial that had a 1-mm hole drilled in the bottom. The vials were sealed and reweighed. Solution having varying activities of 3 H 2 0 were injected into the basal hole until the materials appeared near saturation. The vials were reweighed and then placed on top of a glass marble resting on the bottom of a 30-ml plastic, screw-top centrifuge tube. These larger tubes were then sealed and set on their sides for 48 h to allow for equilibration of the tritium throughout the sample. The 30-ml tubes were centrifuged at
PAGE 40
Soil Column I I n : 1 n u u u Fraction Collector 3 Way Switches I ' 3/ : I I I I I Pump i I 0.01 M CaCl2 33 I I / \ Tritiated O 01 M CaCl 2 Fig. 4-1. Schematic illustration of apparatus used in the miscible-displacement experiments.
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34 30 times the force of gravity, forcing the soil solution out of the soil, and through the basal hole to be collected around the marble in the bottom of the larger tube. The extruded solution was retrieved and the 3 H 2 0 activity was measured. The soil sample was dried at 105 C for 48 hand weighed. The adsorbed 3 H 2 0 was determined by subtraction of the 3 H 2 0 activity in the extruded solution from the initial 3 H 2 0 in the injected solution after accounting for the original water content of the samples. Adsorption isotherms were constructed by plotting adsorbed versus solution 3 H 2 0 activity. Linear-adsorption coefficients were calculated using linear regression forced through the origin. An overall soil-column retardation factor, R, was calculated using weighted mean adsorption coefficients of the gravel and fine-fraction samples from each horizon. Results and Discussion Description of Model Parameters Information input into the non-linear, least-squares curve fitting program that optimizes dimensionless parameters for the CD and MIM models consists of the observed tritium breakthrough curve (BTC), which is composed of data pairs consisting of the pore volumes of solution and the radioactivity of that solution relative to the activity of the tritiated-pulse solution The curve-fitting program is capable of estimating the retardation factor, (R), Peclet number (P), fraction of solutes in the mobile water region (~), dimensionless mass transfer coefficient (w), and tritiated-pulse volume (T). Confidence
PAGE 42
35 in the predictive capacity of the model is improved as the number of parameters which the model is required to predict decreases. The curve-fitting procedure that estimates the model parameters from the BTC, bases the parameter-selection process on the goodness of fit of a model-predicted BTC with the observed effluent data. The model calculates a 95% confidence interval for each estimated parameter; however, the confidence interval measures the goodness of fit of the estimated parameters to the effluent curve and does not involve any consideration of random experimental error. Therefore, the final estimation of soil-property parameters requires judicious interpretation of the model-estimated parameters. Several of the dimensionless parameters are measurable by laboratory techniques. The retardation coefficient may be calculated from an adsorption coefficient, ~' the water content, and the bulk density. The tritiated-pulse volume may be measured during the miscible-displacement process. Experimental methods to measure the other three parameters, P, ~' and w, are generally unavailable. The dimensionless parameters P and ware specific to the particular conditions of the experiment from which they are derived. The Peclet number relates [Eq. 4-14] the column's length and pore-water velocity to the dispersion coefficient. Dispersion results from physical mixing of soil water travelling at different velocities or following different paths The dispersion coefficient is an indicator of soil-pore sizes and the pore-size distribution. Since the velocity of water in a confined capillary is dependent on the capillary radius, large capillaries can transport water more rapidly than smaller pores
PAGE 43
under similar pressure gradients. The preferentially rapid transport of water in large pores is called channelling, and results in solutes travelling further and more rapidly than simple piston-displacement concepts would allow. 36 The parameter~ represents the fraction of solutes present in the mobile region under equilibrium conditions. The mobile-water fraction, ,maybe calculated with Eq. 4-21: = 8j8 = ~R f(R-1) [4-21] where is the mobile-water fraction, 8m is the mobile-water content, 8 is the total-water content, R is the chemical-retardation factor, and f is the fraction of total adsorption sites in the mobile-water region. The parameter f is typically approximated. Nkedi-Kizza et al .(1982) argued that, since the surface area associated with a unit volume of water in the small pores of the immobile region is probably much greater than the surface area associated with a unit volume of water in the mobile region, f may be approximated to be zero. However, NKedi Kizza et al. (1983) have also proposed equal distribution of the sites between the two regions such that f =~and, therefore, = p. Seyfried and Rao (1987) proposed an intermediate approximation off= /2. In all approximations, the severity of any error in the eventual estimation of the mobile-water content is influenced by the value of R. If there is almost no chemical adsorption or repulsion (R approaches 1.0), then the location of the sites becomes less important because the value ~R f(R-1) approaches both~ and the mobile-water fraction,. The dimensionless parameter, w, relates the mass-transfer coefficient [Eq. 4-16] to column length and solution flux (volume per
PAGE 44
i 37 time area). The mass-transfer coefficient is a lumped term including both a tortuosity factor and a diffusion coefficient. Diffusion is the transport of solutes from an area of high concentration to an area of low concentration independent of any movement of the media. The tortuosity of the media limits the exposure between a concentration gradient. Both dispersion and diffusion will occur during water transport through soil. The contribution of the dispersion process to solute mixing is generally of greater magnitude than the diffusion process, such that the diffusion process is frequently insignificant. However, in soils that contain immobile-water regions, diffusion is the only process that transports solutes into, through, and out of the immobile regions. In such soils the magnitude of the diffusion process becomes significant. Parameter Estimation Adsorption isotherms The tritium-adsorption isotherms for the three column horizons and composite gravel samples are presented in Fig. 4-2. A weighted mean of the slope of the line obtained by plotting the adsorbed versus solution concentrations of tritium was calculated considering the depth and gravel content of each horizon. This column adsorption coefficient, Kd, was applied to Eq. [4-4] with other column parameters to calculate a retardation factor of 1.05 for both columns. This value indicates that the tritium is slightly adsorbed to the soil arrl is consistent with other values measured for soils of similar mineralogy (Nkedi-Kizza, 1983; Seyfried and Rao, 1987).
PAGE 45
O> ....... S" (f) z 0 ..... <( a: ..... z w () z 0 () 0 0 (f) 0 <( a ~----------, 6 4 2 4 Ap 0-11 cm 0 0 0 Gravel S 0 047C r 2 0 79** S = 0 013C r 2 z 0 92** 0 Btc 22-72 cm 0 / co S=0.031C r 2 =0.93** 100 200 SOLUTION CONCENTRATION, C (Bq/mL) Fig. 4 2. Tritium adsorption isotherms for column horizons and composite gravel 38
PAGE 46
39 Column physical properties Selected physical properties of the soil columns are presented in Table 4-1. The saturated-water content, bulk density, and particle size distribution of the two columns exhibited only slight differences. The Darcian flux and number of pore volumes applied to each miscible displacement experiment for both soil columns are shown in Table 4-2. The solution flux varied from slowest to fastest by a factor of over 40. The tritium concentration in the column effluent was monitored during both pulse injection and clearing. CD model analysis The parameters estimated by the CD model for the four displacement experiments of Column I are shown in Table 4-3. The tritium-pulse volume was held constant during each curve-fitting process, but the retardation factor, R, and the Peclet number were allowed to vary. The lowest retardation factor, R = 0.74, which was estimated for the most rapid flux, Expt. 1-1, implies chemical repulsion of the tritium from some regions of the soil. Since the retardation factor was measured (R = 1.05), the model-estimated lower value of 0.74 is an indication of immobile-water regions that were not in physical equilibrium with the mobile effluent, due to the short residence time of the pulse in the soil. The CD model, which considers all soil water to be mobile, was unable to describe the observed BTC of Expt. 1-1 when the value of R was fixed at 1.05 (Fig. 4-3). As the experimental flux was decreased, the CO-estimated retardation factor approached the measured value, and the CD model was able to simulate
PAGE 47
Table 4-1. Dimensions and selected physical properties of soil columns. Soil or column property Units Column I Column II Length cm 71. 6 68.9 Surface area cm 2 72.38 72.38 Volume L 5.18 4.99 Weight, oven-dry kg 6.55 6.42 Bulk density kg/L 1. 26 1. 29 Porosity L/L 0.525 0.527 Particle-size fractions by mass <2 mm g/g 0.37 0.38 2-12 mm g/g 0.42 0.44 12-75 mm g/g 0.21 0 .18 by volumet <2 mm L/L 0.18 0 .19 2-12 mm L/L 0.20 0.21 12-75 mm L/L 0.10 0.09 Particle density <2 mm kg/l 2.61 2.67 2-12 mm kg/L 2. 71 2.81 12-75 mm kg/L 2.65 2.66 t Intra-gravel porosity excluded. Table 4-2. Set-up for tritium miscible-displacement experiments on Columns I and II. Column-Experiment no. t I -1 I-2 I-3 I-4 I I -1 II-2 t Order of execution Flux, (cm/d) 111 16.8 2. 71 36.7 2.69 36.7 q Pulse, T (pore volume) 1. 43 2.58 2.84 2 65 2.88 2.59 40
PAGE 48
41 Table 4-3 CD water model optimized dimensionless parameters Experiment Flux Peclet number Retardation factor no. q p R (cm/d) I-1 111 1.4 0.74 (0.2)t (0.05) I-4 36.7 1.0 1.02 ( 0. 1) {0.06) I -2 16.8 1. 9 1.01 (0.2) (0 05) I-3 2 71 4.0 1.12 (0.3) (0.02) t Numbers in parenthesis are 95% confidence intervals.
PAGE 49
1 2 Expt. 1-1 1 q 111 cm/d 0 Measured data points 0 0 CD p R () ...... 0 () 0.8 tP 1. 4 0 74 CJ z I 0 I 0.82 1 05 (fixed) I0. < 0 6 a: Iz w () z 0 4 0 0 w > i= 0 2 < ...J w a: 0 0 2 4 6 PORE VOLUMES Fig 4-3. Measured and CD-simulated BTCs for Expt. I-1 with R optimized or fixed at 1.05. 42 8
PAGE 50
43 the observed BTCs (Fig. 4-4). At the slower flow rates, the pulse resided in the column long enough to allow diffusion to bring the mobile and immobile regions closer to physical equilibrium, thereby masking the presence of an immobile-water region. Thus, at slow flux, the conceptual assumption of the CD model, which considers all water to be mobile, is falsely satisfied. MIM model analysis The dimensionless parameters estimated by the MIM model for the four displacement experiments through Column I are presented in Table 4-4. The retardation factor for all of the trials was held constant at the measured value of 1.05 during each curve-fitting process. The Peclet number, fi, and w were allowed to vary Although fi is generally considered a constant for any given soil sample, it showed a slight increase as the flux decreased. This behavior is attributed to the inability of the model to distinguish easily between the mobile and immobile regions when the flux is slow enough to allow considerable diffusion between the two regions. This implies that the best estimate of~ is when the flux is infinitely fast. Since the trial with the fastest flux exhibited the highest degree of physical nonequilibrium (CD-model analysis), its MIM-model analysis should yield the best estimate of fi. Therefore, the BTC were refitted to the MIM model holding fi constant at 0.53 (Table 4-5). In this study fi = 0 53 will be used as the value to approximate~, since R is very close to 1.0. The measured and MIM-estimated BTCs for the four experiments from Column I are smooth, asymmetrical and show
PAGE 51
1. 2 Expt 1-3 1 q = 2 71 cm/d 0 Measured data points 0 CD p R <.) ....... 0 8 <.) 4 0 1. 12 z 4.6 1 05 (fixed) 0 I < 0 6 I I ex: 1P Iz /4' , w 1) (.) I z 0 4 t 0 (.) w > 0 2 < .J ' w a: 0 0 2 4 6 PORE VOLUMES Fig. 4-4 Measured and CD-simulated BTCs for Expt. I-3 with R optimized or fixed at 1.05. 44 8
PAGE 52
45 Table 4-4. MIM water model optimized dimensionless parameters. Experiment Flux, q p R B w no. fixed cm/d I-1 111 2.9 1.05 0.53 0.20 (0.6)t (0.04) (0.05) I-4 36.7 2.7 1.05 0.58 0.30 (0. 7) (0. 07) (0. 07) 1-2 16.8 5.8 1.05 0.61 0.38 ( 1. 5) (0.05) (0.08) I-3 2. 71 6.2 1.05 0.62 2.5 ( 1. 4) ( 0. 07) ( 1. 2) t Numbers in parenthesis are 95% confidence intervals.
PAGE 53
0 1. 2 () Expt. 1-1 ....... () 1 q = 111 cm/d z 0 'J Measured data points I<( 0.8 p R {J a: I-MIM 2 9 1 05 0 53 0 20 z w 0 6 () z 0 () w 0.4 > j::: <( -' 0.2 w a: 0 0 2 4 6 PORE VOLUMES Fig. 4-5. Measured and MIM-simulated BTCs for Expt. 1-1 with R fixed at 1.05. 46 8
PAGE 54
1 2 Expt 1-2 0 () 1 q = 16 8 cm/d ....... () 0 Measured data points z p R p (.J 0 0.8 .._ -MIM 9 3 1 05 0 53 0 51 <( a: .._ 0 6 z w () z 0 () 0 4 w > .= 0 2 <( _J w a: 0 0 2 4 6 8 PORE VOLUMES Fig 4-6 Measured and MIM simulated BTCs for E x pt. 1 2 with R fixed at 1 05 and fi fixed at 0.53 47
PAGE 55
I ~ ---------0 () () z 0 1. 2 1 .= 0 <( .8 a: 1z LU 0 z 0 0.6 () LU 0.4 > .= <( ....J LU a: 0.2 Expt. 1-3 q a 2. 71 cm/d 0 Measured data points p R p (.J MIM 8. 1 1 05 o. 53 2 80 0 { ~----L.--~-----1....--.1-----'--~ ~:....j.........------' 0 2 4 6 8 PORE VOLUMES Fig. 4-7. Measured and MIM-simulated BTCs for Expt. 1-3 with R fixed at 1 05 and~ fixed at 0.53. 48
PAGE 56
1. 2 0 0 ....... 0 1 z 0 I0.8 <( a: Im o.s 0 z 0 0 0.4 UJ > .:= <( 0.2 _J w a: 0 0 2 Expt. 1-4 Q 36. 7 cm/d 0 Measured data points p R (J MIM 2 7 1.05 0.58 0 30 4 6 PORE VOLUMES 8 Fig. 4-8. Measured and MIM-simulated BTCs for Expt. I-4 with R fixed at 1.05 and P fixed at 0.53. 49
PAGE 57
50 Table 4-5. MIM water model optimized dimensionless parameters with Rand B fixed. Experiment Flux, q p R B w no. fixed fixed (cm/d) 1-1 111 2.94 1.05 0.53 0.198 (0.55)t (0.052) I-4 36.7 3 .18 1.05 0.53 0.342 (0 24) (0.032) 1-2 16.8 9.28 1.05 0.53 0.510 (1.32) (0.036) I-3 2.71 8.13 1.05 0.53 2.80 (3.52) ( 1. 63) t Numbers in parenthesis are 95% confidence intervals.
PAGE 58
51 very close agreement at both the fastest and slowest flow rates (Figs. 4-5 4-6, 4-7, and 4-8). The immobile-water fraction of 0 47 cannot be totally attributed to the intra-gravel porosity Since the volumetric-gravel content (including intra-gravel porosity) of the column was 0.375 ml/ml, and 20 % of that was interior porosity, the intra-gravel porosity was only 0 075 ml/ml, or 14% of the total column porosity of 0.526 ml/ml. Even if the intra-gravel porosity contains only immobile water, the remaining immobile water fraction (0 40) of the volumetric-water content was associated with the aggregated fine-earth fraction of the soil. Although the fine-earth fraction of the Ap and Ac horizons has a weak-crumb structure, the Btc horizon has medium-sized, moderately strong aggregates (Appendix A). Nkedi Kizza et al. (1983) have shown that packed columns of sieved (2 to 4.7 mm), strongly aggregated peds from an Oxisol harbored over 50% of the total volumetric-water content in immobile regions. Schul in et al. (1987), using similar techniques, found an equally small R = 1 12 but a fi = 0.87 under unsaturated conditions. Back-calculation of presented data indicated that the gravel was not porous. The total porosity (0.255 ml/ml) was associated entirely with the 20% by volume fine fraction. Extrapolation of their data to saturated conditions, assuming the water volume between saturation and the experimental water contents to be mobile, would yie1~ a mobile water fraction= 0.92. Therefore, the difference in the volume of the mobile-water regions of these two gravelly soils is more likely due
PAGE 59
52 to differences in aggregate structure of the fine fraction than to differences in gravel porosity. The dimensionless parameters P and ware a function of experimental conditions, and are used with their functional relationships to determine soil-water properties. The Peclet number relates the pore-water velocity and column length to the dispersion coefficient (Eq. [4-14]). One theoretical exponential relat i onship of the dispersion coefficient to the pore-water velocity is : Om = ). vmn [4-22] where Om is the hydrodynamic dispersion coefficient, ). the dispersivity vm the mobile pore-water velocity, and nan empirical constant. For most laboratory-displacement experiments involving disturbed (repacked) soils,). is about 1.0 cm (van Genuchten and Wierenga, 1986). For displacement experiments involving undisturbed field soils, especially when aggregated, ). can be one or two orders of magnitude higher. The degree of dispersivity in a soil is increased as the pore-size distribution in the mobile-water regions becomes broader The dispersivities of different soils are more easily compared if the empirical constant, n, is assumed to be 1.0 and the equation is linear. Schul in et al (1987) and Russo (1983) determined dispersivities of 2.24 and 2.91 cm from soils containing 55 and 43% gravel by volume, respectively, using a linear relationship The Peclet numbers estimated from the BTCs of Column I tended to increase with decreasing flux, suggesting a nonlinear relation between dispersion coefficient and pore-water velocity within the velocity range used in this study (Fig. 4-9). The nonlinear plot provides a dispersivity of
PAGE 60
53 14000 l "O N' Dm = 3 29 Vm 1 33 E 12000 I ft= 0. 96* Q E 0 10000 / V t-=" I / / z w 0 8000 / iI / / LL / w 6000 ,,/ 0 ;(..) I z 0 4000 Cl) 0 a: w a.. 2000 Cl) 0 0 0 0 100 200 300 400 500 MOBILE PORE WATER VELOCITY, Vm (cm/d) Fig. 4-9. Relationship between MIM estirnated dispersion coefficient, Orn, and mobile pore-water velocity, vm.
PAGE 61
54 3.3 cm 2 n d" 1 with n = 1.3, which is of a magnitude similar to values from the two previously mentioned studies on stony soils. Several factors may be contributing to the high dispersivity of this soil. Dispersion increases as the range of small to large pore sizes in a soil increases, thereby providing~ wide range of water velocities within a single soil sample. Edwards et al. (1984) have shown that the presence of non-porous gravel increases the total macropore volume at the expense of the micropore volume. The large reduction in the water content of the soil under slight tensions (0 to 50 mbar) indicates that the soil possesses a considerable volume of large pores (Fig. 3-1). Similarly, the retention of nearly 33% of the total soil water at 15 bars of tension indicates that the soil also contains a large volume of very small pores. The values of the dimensionless parameter w, estimated for each experiment on Column 1, is presented in Table 4-5. It relates the mass-transfer coefficient, a, to the column length and solution flux (Eq. [4-16]). The mass-transfer coefficient is a lumped diffusion parameter that relates the solute diffusion transfer to the molecular diffusion coefficient, the mobile/immobile-water fraction, the tortuosity, the radius of soil aggregates, and the solution flux. The relationship between the mass-transfer coefficient and the solution flux is shown in Fig. 4-10. The mass-transfer coefficient is not a constant and has been shown to increase with solution flux using both theoretical postulations and experimental methods (Rao, 1980a and 1980b; van Genuchten, 1985). The mathematical description of the diffusion process employed by the MIM model has an underlying
PAGE 62
0 4 I a = 2. Ox 1 cr 3 (q) + 8. 7x 10 2 'O ...... ,r2= 0. 99** / <::S 0 3 I I z // w 0 / / / u:::: LL w 0 0.2 '0 a: w LL / Cl) V z <( a: 0 1 .,_ Cl) 0 Cl) <( 0 0 so 100 FLUX q ( cm/ d) Fig. 4-10. Relationship between MIM estimated mass-transfer coefficient a, and flux, q. 55 150
PAGE 63
56 assumption of first-order exchange kinetics. However, conceptually the assumption is only valid for dead-end pores with a neck of negligible volume (Coats and Smith, 1964). van Genuchten and Dalton (1986) have shown that first-order kinetics represent a very close approximation in the case of radial diffusive exchange between the soil matrix and hollow, cylindrical macropores but, for other pore geometries, first order exchange is only a crude approximation. Because~ is a lumped parameter, it depends not only on the pore-space geometry, solute diffusivity and the magnitude of the immobile region, but also on the changing solute concentration within the two regions. The increase in the mass-transfer coefficient with the increase in solution flux is due to the rapidity at which the concentration gradient reaches its extreme (more diffusive force) as a solute front approaches and leaves a given point in a soil column. Estimation of Column II Parameters An additional enhancement to the validity of the estimated soil column parameters lies in their transferability to a different sample of the same soil. Model-simulated BTC parameters derived from experiments using Column I were applied to the experimental data from Column II (Figs. 4-11 and 4-12). The values of Rand P were set at 1.05 and 0.053, respectively, and the values for P and w were derived from the line:; and curvilinear relationships presented in Figs. 4-9 and 4-10. The derivations included consideration of slight differences in column lengths and bulk densities (Table 4-1). The simulated BTCs based on the parameters derived from Column I exhibit later break
PAGE 64
57 1 2 Expt. 11-1 0 () q .. 2 69cm/d ........ 0 0 Measured data points z p R (.J 0 t I0 8 <( MIM 10 1 05 0 53 2 4 a: ( all fixed) Iz 0 6 w 0 z 0 0 0.4 w > I0.2 _J w a: 0 0 2 4 6 8 PORE VOLUMES Fig 4-11. Measured and MIM-simu l ated BTCs for Expt. II-1.
PAGE 65
58 1. 2 Expt 11-2 q. 36.7 cm/d 0 1 (.) 0 Measured data points ....... (.) p R (.J z 0 8 i r 0 I MIM 4 3 1 05 0 53 0.30 IL ( all fixed ) I <( r er: 0.6 Iz w i(.) I z 0.4 0 (.) w > 0 2 I<( _J w er: 0 0 2 4 6 8 PORE VOLUMES Fig. 4-12. Measured and MIM-simulated BTCs for Expt. II-2.
PAGE 66
59 through and a higher peak concentration than the experimentally observed BTCs. The differences between the two estimated and observed BTCs could be due to natural variability between the two soil samples. Although the two columns contained nearly identical volumes of gravel, the particle density of the gravel in Column II was higher. Since the more dense gravel had less porosity (Table 3-3), less of the total porosity of Column II was associated with the gravel and a greater portion is associated with the remaining fine-earth fraction. However, considering that all model parameters were independently estimated, the MIM-model-generated estimates closely described the observed asymmetrical BTCs. Conclusions The data from this study show that this soil, which contains a strongly aggregated fine fraction and porous gravel, produced asymmetrical BTCs for tritiated water. The degree of asymmetry increased with increasing flow rates. The classical CD model was found to be inadequate in describing water movement in this soil due to the inability of the model to account for diffusive mass transfer of water into stagnant or immobile-water regions. The MIM model adequately described water movement at all flow rates, and estimated that about 50% of the total-water content was in immobile regions. The high immobile-water content and the relatively large dispersivity indicated that, under natural field conditions consisting of short but intense tropical rain storms, water transport in the larger soil pores could
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60 carry small amounts of unadsorbed solutes beyond the root zones, whereas considerable quantities of the solute could remain relatively unaffected, harbored in immobile-water regions. Although this could cause pollution of ground-water from nutrients and pesticides leaching through macropores, the presence of immobile-water regions in this soil will act as a source/sink for solutes that will be slowly released to crops.
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r CHAPTER 5 CROP RESPONSE TO PLANTING DENSITIES AND FERTILIZER APPLICATION SCHEDULING Introduction An integrated knowledge of the behavior of plant nutrients in soil has been a major interest of agricultural scientists. Even after many decades of concentrated research, fine-tuning of management practices for agricultural soils still generally requires experimentation based on trial and error. The basis of our remaining lack of knowledge lies in the complex interrelations of the plant nutrients among themselves, and with plants, soil, water, and the atmosphere. The seemingly simple concept of a nutrient being "plant-available" involves complicated physical processes regulating chemical speciation among three phases, two of which are readily mobile, and the third of which changes continuously and irregularly with depth (Addiscott et al., 1986) In addition to comprehension of these processes, there is also a problem with instrumentation and quantification of measurements for specific events and objects (Harris and Hansen, 1975). It is no wonder that the most reliable method for assessing the availability of a soil-borne plant nutrient is still a field trial followed by analysis of the resultant plant material (Melsted and Peck, 1977; Sumner, 1987). What such a technique loses in its overall contribution to the knowledge of individual processes, is offset by immediate knowledge regarding on-site agricultural behavior. 61
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62 High concentrations of stones in the rooting zone of an agricultural soil impact both water-holding and water-movement behavior (Epstein and Grant, 1966; Ghuman and Lal, 1984). The relative impact of gravel on any one soil property is dependent on the amount and properties of that gravel. Generally, for gravel with little or no porosity, increased gravel concentrations in the soil increase bulk density, decrease total volumetric water-holding capacity, increase macroporosity, decrease microporosity, decrease saturated hydraulic conductivity and change the proportion of water held at various tensions when compared to values for the same soil minus the gravel (Edwards et al., 1984). Logical inferences concerning the agricultural productivity of gravelly (non-porous gravel) soils that may be deduced from these characteristics include: 1. Decreases in water-holding capacity increase draughtiness and, therefore, make crops more susceptible to water stress. 2. Decreases in total porosity increase the amount of water transported by the remaining soil pores. 3. Increased water transport increases the potential loss of nutrients by leaching. The effect of porous gravel on soil-water behavior is not easily inferred and depends on the porosity and pore-size distribution of the gravel (Reinhart, 1961; Hanson and Blevins, 1979). Porous gravel has been shown to hold considerable portions of plant-available water (Flint and Childs, 1984). Soil with gravel of porosity similar to that of the
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63 fine fraction may still limit a high quantity of the transported water to the pores of the soil's fine fraction, due to the noncontinuous and/or small size of the pores in the gravel. The potential loss of soil nutrients by leaching will be influenced by the differential effects of pore size and pore location on water transport and channeling. Therefore, water in porous gravel may act as a sink for leachable nutrients and thus may harbor nutrients from convective-water flow. Soils with shallow, subsurface gravel horizons are a common occurrence in the western highlands of Cameroon. Although these soils are typically not preferred by local farmers, increasing population pressures have resulted in their increased utilization for food-crop production. Scientific studies indicate that, depending on the quantities and properties of the gravel, different practices are required for effective agricultural management of these soils. The purpose of this experiment was to develop a basic understanding of the dynamics of water and nutrient availabilities for a soil with a shallow gravel horizon, throughout a crop-growing season and using both locally prevalent and modified management practices. The objective of this experiment was to differentiate between the relative effects of possible water and nutrient stress on corn and beans, by subjecting the crops to combinations of plant densities and seasonal nutrient availabilities. Materials and Methods The experimental site was located 8 km north of the University Center of Dschang campus in the Leppo quarter of the village of Bafou,
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64 Western Province of Cameroon, Africa. The field, rented from a local farmer, was on a 12 to 16% slope and contained a dense gravel horizon from a depth of 22 to 72 cm. The depth, thickness, and location of the gravel horizon were first determined by augering and later confirmed by soil-pit sampling. The field had been planted to corn, peanut, and cocoayam the previous year following a 3 to 5-yr fallow, and had received no commercial fertilizers for at least the previous 5 yr. Weeds and former crop residues were cut by hand, aligned in the furrows, and buried under newly established ridges based on a 1-m row spacing. A randomized, complete-block design with 4by 12-m plots and four treatment replications was composed of a 2 by 5 factorial consisting of two planting densities (intra-row mix of corn Zea mays L. CIMMYT Z-290; and red bean Phaseolus vulqaris L.) and five fertilizer schemes (Table 5-1). The two planting densities were (1) 30,000 corn plants/ha mixed with 40,000 bean plants and (2) 45,000 corn plants/ha mixed with 60,000 bean plants. The fertilizer treatments consisted of a non-fertilized control and four split-application treatments; all consisting of 400 kg/ha of a locally available 20-10-10 (N-P 2 0 5 -K 2 0) mixed fertilizer plus 248 kg/ha of triple superphosphate (TSP) (50 kg P/ha). The four fertilized treatments had the 20-10-10 material applied (1) all preplant; (2) one half preplant and one half after 8 wk; (3) one third preplant and one third after 4 and 8 wk; and (4) one fourth preplant and one fourth after 4, 8, and 12 wk. All of the fertilized plots had the TSP applied preplant. The preplant fertilizers were first mixed together, spread in a 33-cm band down the center of the ridge, and spaded to a 10-cm depth. All subsequent applications were applied to
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Table 5-1. Description of experimental design. Design : Randomized Complete Block 4 blocks 2 x 5 factorial Factor 1 Plant density (within-row mix) Level 1. 30,000 pl/ha corn Zea mays L. 40,000 pl/ha bean Phaseolus vulqaris L. Level 2. 45,000 pl/ha corn 60 000 pl/ha bean Factor 2. Fertilizer application timing Fertilizer Time after planting, application 1 eve l 0 4 8 0 1 M + p 2 1/2 M + P 1/2 M 3 1/3 M + P 1/3 M 1/3 M 4 1/4 M + P 1/4 M 1 / 4 M M = 400 kg/ha 20-10 10 (N-P O -K 0) equivalent to 80-17-34 kg N-~-K /ha p = 50 kg P / ha as triple superphosphate wk 12 1 / 4 M 65
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66 the soil surface in a 33-cm band down the center of the ridge. The field site was planted 22 and 23 Mar. 1986, following initiation of the rainy season in early March and upon the advice of local farmers. The plots were planted initially to 1.5 times the desired densities. Plots were thinned to proper densities after 3 wk. Two of the four blocks were stripped and replanted after 4 wk, due to low plant densities in several plots of each block. Weeds were controlled by hand cultivation every 4 wk. Beans and corn were harvested 75 and 140 d, respectively, after planting. Corn grain and stover were analyzed for N, P, K, and Ca contents. Soil samples were taken from all of the high-density plots at depths of Oto 5, 5 to 10, 10 to 15, 15 to 25, 25 to 35, 35 to 45, 45 to 55, 55 to 65, and 65 to 75 cm, just after corn harvest. Soil samples were analyzed for gravel content and for Mehlich I-extractable P, K, and Ca. Results and Discussion Plant densities in several plots from two of the four blocks were below required levels (Table 5-2). Analysis of variance of percent plant emergence for the first planting of the four blocks is presented in Table 5-3. The percent emergence was apparently not affected by either planting density or fertilizer treatment, but was affected by block location (Table 5-4). Several factors may have contributed to the lower plant stands. There was an uncommon lull in seasonal rains during the first 4 wk after planting. This caused afternoon wilting throughout the field. The
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67 Table 5-2. Plant emergence percentages for plots falling below required levels (< 66%). Block 3 3 3 3 4 4 4 4 Adequate Planting density low low high high low low high high Fertilizer schedule 0 4 2 4 1 2 0 3 > 66% emergence Crop Corn Bean -Emergence, % -61 58 48 46 51 45 53 55 70a 57 69a 56 49 72a 58 60
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Table 5-3. Analysis of variance for early-season plant-emergence percentage. Source Block Density Fertilizer Density*Fertilizer Error Total c.v. D.F. 3 1 4 4 27 39 **99% level of probability Crop Corn F value 6.27** 1.68 < 1 < 1 12.4 Bean 8.39** < 1 < 1 < 1 12.3 Table 5-4. Comparison of percent emergence of corn and bean by replicate. Factor / Level Crop Block 1 2 3 4 Corn Bean --Emergence,% --80.8a* 78.7ab 65. lc 70 9bc 81.2a 76.3ab 62.3c 69.7bc Means in the same column followed by the same letter are not significantly different at the 95% level of probability, as determined by Duncan s Multiple Range Test. 68
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69 experimental site was on a west-facing slope Blocks 1, 2 and a portion of block 4 were on a 12% slope. All of block 3 and much of block 4 were on a 16% slope. All of the plots with inadequate densities were on the 16 % slope. The slope of the land could have affected plant establishment in two ways. The steeper areas would have received less direct morning, but more direct afternoon sunlight. In addition, since the field was laid out according to the sloping surface area and not the level surface area, the plots on the most sloping land had the least amount of soil beneath them. Therefore, the most sloping land probably had the highest evaporative demand but the least quantity of soil from which to draw water. In relation to later discussions, it should be kept in mind that the lower plant densities did not constitute a drastic failure (the lowest density was still 75% of that required) though they were lower than the design of the experiment allowed. Due to inadequate plant densities in some of the plots, all of the plants in the two affected blocks were removed and the area was replanted on 23 and 24 April, following additional rain 4 wk after the first planting. The replanting changed the experimental design of the study (Gomez and Gomez, 1984). An F test of the error mean squares from the analysis of variance (Table 5-5) for the two planting dates was performed for grain, stover, and total dry-matter yields (Appendix B). In all cases, the error mean squares were not different. Consequently, the data from the two sites were pooled and planting date was added to the experimental design as an additional factor with two levels. Because the planting-date levels were not randomized within the blocks, but were instead imposed over complete blocks, a whole-block error term
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70 Table 5-5. Analysis of variance of grain and stover yield for the two planting dates. Planting Error Error mean period D.F mean square square ratio F 95 F99 Grain Yield First 9 40442 2.21 ns 3 .18 5.35 Second 9 18301 Stover yield First 9 411685 1. 66 ns 3 .18 5.35 Second 9 247991 ns not signficantly different
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71 (replications nested within dates) became the appropriate error term to evaluate the effects of planting date on yield components. However, the whole-block error term did not have sufficient degrees of freedom (<6) to constitute a valid F test (Gomez and Gomez, 1984; Montgomery 1984). Therefore, the whole-block error term was pooled with either the three way interaction term, or the subplot pooled-error term, on the condition that the newly added error term was not different from the whole-block error term at the 75% level of probability Differences in environmental conditions during the two time periods when the crops were in the field are impossible to assess. One of the more obvious differences was in the quantity and distribution of rainfall (Fig. 5-1) Seedlings in the first-planting period experienced considerable wilting due to the slow-starting rainy season. Seedlings of the second-planting had frequent early rainfall and showed no wilting. Both plantings experienced frequent midand late-season rains ; however, the second planting received more total water because the rainy season peaked in August and September, after the first planting had been harvested. The differential effects of climatic factors on grain and stover yields may be attributed to the seasonal partitioning of plant photosynthetic and mineral resources into different yield components. Corn plants continue to increase in total dry-matter accumulation throughout the season, ~ ~ til near harvest. However, once past silking most of the increase is due to grain filling The dry-matter content of other plant components remains relatively constant during this period (Fig 5-2)(Hanway 1962). Tropical maize, in general including the
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E E _J _J < u. z < cc L1J > < _J ::> ::> (.) 1000 800 600 400 I 200 o .., -~ [ ,../ Second Planting I r _ _,. / j I/ .., .r .I I ,.. .I -' / J ,..i i First Planting 0 14 28 42 56 70 84 98 112 126 140 DAYS AFTER PLANTING Fig 5-1 Cumulative rainfall for the first and second planting seasons. 72
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Dry matter ( g/m 2 l 1600 1400 1200 1000 800 600 400 200 0 0 28 Tuxpeiio-1 0 Pio-ieer 3369A 56 84 Days after sowing 112 140 Dry Motter Grain Dry Matter 73 Fig. 5-2. Total crop and grain dry matter accumulation for Tuxeno-1 and Pioneer 3369A Zea mays ,grown at Tlaltizapan, winter cycle 1974, at 80,000 plants/ha (from Fisher and Palmer, 1983).
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74 line used in this study, CIMMYT Z-290, is late maturing, tall, leafy, and less efficient in translocating to the grain photosynthates which were previously deposited in the stems and leaves (Evans, 1975). Although grain yields are intimately related to early-season plant health, differences in grain and stover yields may be attributed to differential earlyand late-season environmental influences (Fisher and Palmer, 1983). The effects of these combined factors on corn grain, stover (above-ground portion of the plant minus the grain), and total dry matter yields are presented in Table 5-6). The effect of the two planting dates was large and significant on corn-grain yield, but insignificant on stover yield. The second planting yielded only 35% the amount of grain of the first-planting treatment, even though the stover yields for the two dates were nearly identical (Table 5-7). The lack of interaction between the effects of planting date and fertilizer scheduling on stover yields indicated that the fertilizer schedule affected the non-grain, plant dry-matter accumulation similarly over the two crop-growth periods. The interaction between planting date and fertilizer schedule on corn-grain yields reflects the environmental effects that planting date had on this indicator of late-season conditions The differences in total rainfall for the two plant-growth periods increased as the season continued (Fig. 5-1) This difference may be used to explain differential effects on the yield components. During the early part of each growing season the difference in rainfall and in subsequent probable nutrient leaching were less pronounced. If one estimates the evapotranspiration and effective
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Table 5-6 Analysis of variance for corn grain, stover, and total dry matter yields. Plant component 75 Source D.F. Error term Grain Stover Dry matter F value------Date 1 Rep(Date) / Date*Den*Fert 13l**a Rep(Date) / Pooled error < la 16. 3**a Rep(Date) Density Fertilizer Density*Fert Date*Density Date*Fert Date*Den*Fert Pooled Error Total c.v. 2 1 4 4 1 4 4 18 39 Date= Planting date Rep= Replication Pooled error Pooled error Pooled error Pooled error Pooled error Pooled error Pooled error Den= Density= Planting density 3 .14 9.50** 141** 2 .13 4.37 22.8** 1. 29 12.2 Fert =Fertilizer= Fertilizer application schedule 1. 62 30.5** 36.9** 2 .11 < 1 < 1 < 1 16 4 2.51 32.2** 67.2** 2.44 < 1 1. 34 < 1 13.3 a Mean square error pooled to increase degrees of freedom in order to enhance validity of the F test. ** 99% level of probability
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76 Table 5-7. Comparison of selected main-effect yield-component means. Plant component Factor/ Level Grain Stover Dry matter ------------------kg/ ha -----------Planting date First 2060A* 3630A 5690A Second 7558 3370A 41208 Density Low 1320B 30008 4320B High 1490A 4000A 5490A Fertilizer 4 3710a** 5650a 3 3860a 5560a 2 4060a 5890a 1 4500a 5900a 0 1360b 1530b Planting date by fertilizer interaction Fertilizer 4 3 2 1 0 Planting date by density Density Low High First Second 2760a 1130a 2470ab 918b 2680a 984ab 2070b 714c 322c 26d interaction (90% level of probability) First Second 1920b 728a 2200a 782a Means in the same column, under the same subheading and followed by the same uppercase letter, are not significantly different at the 95% level of probability according to an F test analysis of mean square errors. ** Means in the same column, under the same subheading and followed by the same lowercase letter, are not significantly different at the 95% level of probability according Duncan's Multiple Range Test.
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77 rainfall (actual rainfall minus evapotranspiration) during the two crop growth periods, it can be demonstrated that little or no leaching of soil nutrients occurred in the first 8 wk of either season (described in greater detail in Chapter 5). Availability of nutrients would have been affected by the fertilizer-application schedule, but their possible early-season leaching would not have been affected by the planting date, because of the early-season dry period. Stover yields, an early-season indicator, showed decreasing (but not significantly different) yields as the application of fertilizer was distributed over time, but no differences due to planting date. However, the late-season indicator, grain yield, was affected by the fertilizer schedule and planting-date interaction. Grain yields from the first planting showed no differences among the split-fertilizer schedules. The all-preplant, fertilizer-application treatments yielded less grain than the split-application treatments, but still considerably more than the non-fertilized control The grain yields of the late planted corn showed greater separation of means and greater differences in magnitude among the split-application schedules. The 4 by 1/4 split schedule outyielded the one preplant application, the 3 by 1/3 split schedule, and the 2 by 1/2 split schedule. The 3 by 1/3 and the 2 by 1/2 split schedules in turn yielded more grain than the all-preplant schedule, all of which outyielded the non-fertilized control. These differences indicate that the greater effective rainfall during the later growing period caused more leaching and thereby reduced plant availability of nutrients between application schedules. Due to the low magnitude of these yields in relation to yields from the first
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78 planting date, and the lower grain yields in relation to stover yields, the differences in grain yields for the second planting associated with fertilizer-application schedule did not translate into differences in overall dry-matter yields. Dry-matter yields among the four fertilized treatments showed no differences, although they all out-yielded the non fertilized control by nearly four-fold. Effects of the three factors on nutrient uptake by the corn at harvest are presented in Tables 5-8 and 5-9. Total uptake of N, P, K, and Ca was greater for the first planting date than for the second date. Second-growth-period uptake for each of the nutrients was a relatively constant 75% of the values for the first growth period, which is consistent with the differences in total dry-matter yields. There were no differences among the fertilized treatments for uptake of any of the four nutrients, although all of the fertilized treatments had higher uptake than did the unfertilized controls. This information supports the yield data, in that there were no differences in uptake among the fertilized treatments, whereas uptake among all fertilized treatments was much greater than for the unfertilized controls. The non significant planting-date by fertilizer-application interaction term indicates that the later planting date decreased total nutrient uptake, but that nutrient uptake between fertilizer treatments within the same planting date was similar. The plant-density factor was included in the experimental design as a means to detect the effects of water stress on yields. The utilization of high plant densities to induce stress, or early harvest to reduce plant densities and reduce stress, are common tools used to
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Table 5-8 Analysis of variance for uptake of N, P, K, and Ca by corn dry matter. Plant nutrient Source D.F. Error term N P K Ca Date 79 F value 1 Rep(Date)a Rep(Date)/Pooled errorb 6.24* 8.42** 3.50** 4.75* Rep(Date) 2 Density 1 Pooled Fertilizer 4 Pooled Density*Fert 4 Pooled Date*Density 1 Pooled Date*Fert 4 Pooled Date*Den*Fert 4 Pooled Pooled Error 18 Total 39 c.v Date= Planting date Rep= Replication error error error error error error Den= Density= Planting density Fert = Fertilizer application schedule *95% and **99% level of prbability 18.7** 10.8** 7.73* 22.5** 41. 8** 92.8** 16.2** 19.7** 1.34 2.05 < 1 1.46 < 1 < 1 < 1 < 1 < I 2 01 < 1 < 1 < 1 < 1 < 1 2.46 16.2 12.3 27 9 23.7 a Insufficient mean square error degrees of freedom for a valid F test. b Mean square error pooled to increase degrees of freedom in order to enhance validity of the F test.
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80 Table 5-9. Comparison of means for uptake of N, P, K, and Ca from corn dry matter Corn dry-matter nutrients Factor/ Level N p K Ca -------------kg/ha-----------Date First 45.0a* 7.04a 50.2a 8.92a Second 37. lb 5.07b 36.8b 6 79b Density Low 36.Sa 5.55a 38. la 6.45a High 45.6b 6.57b 48 9b 9 25b Fertilizer 4 50.9a 7.88a 51. Sa 8.67a 3 45.8a 6.82a 45.8a 8.54a 2 48.Sa 7.56a 51.4a 9 .18a 1 45.9a 6.39a 45.9a 10. la 0 14. lb 1.62b 13. lb 2.75b Means in the same column under the same factor heading followed by the same letter are not significantly different at the 95% level of probability as determined by Duncan's Multiple Range Test
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81 develop a qualitative understanding of field-crop behavior where more determinate methods (irrigation) are unavailable (Frey, 1981). Overall, water requirements increase with planting densities. In this field study, the higher plant densities yielded more grain, stover, N, P, K, and Ca than the lower plant densities (Tables 5-6 and 5-8). The higher densities and consequent greater demand on soil moisture did not induce sufficient stress to affect yield components. However, superior yield production by all of the fertilized treatments (irrespective of the density) over the unfertilized control is sufficient evidence to support the hypotheses that ambient soil fertility and not water availability limited production of nonor minimally-fertilized plants for this soil. The larger grain and stover yields of the fertilized treatments versus those for the non-fertilized controls resulted from much larger plants, which would have required larger quantities of soil water. The historical rainfall-distribution pattern for this area suggests that moisture stress would most likely occur early in the growing season. Early-season water stress has been shown to be less detrimental to eventual grain yields than stress during silking or grain filling (Denmead and Shaw, 1960; Claassen and Shaw, 1970; Grant et al., 1989). The need to replant part of this experiment was most likely due to the infrequent occurrence of rainfall during a 4-wk drought following a seemingly normal to slightly-wetter-than-normal start of the rainy season. The early-season stress experienced by the plants in the first crop-growth period was insufficient to decrease grain yield for the higher densities relative to the lower densities. The insignificant planting-date by density interaction indicated
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82 that yields responded similarly to both densities within the two cropgrowth periods (Table 5-6). It is interesting to note that, if the level of probability for the F test were reduced to 90%, the planting date by density interaction would become significant for corn-grain yields. However, the significant difference in grain yields among densities is only for the first planting date, where the high-density yield was greater than the low-density yield (Table 5-7). Water stress would have affected the higher-density plots to a greater degree than the low-density plots. This is not to say that water stress did not occur, but only that it did not detrimentally affect grain or stover yields. Grain yields from the two planting densities for the second planting date were not different. This would indicate that nutrient availability and not water stress limited yields for the second planting date. Bean Yields Bean yields between the two planting dates were also differentially affected by extraneous conditions. Angular leaf spot (Xanthomonas malvacearum E.F. Sm.) became very prevalent during the last week before harvest of the plants in the first crop-growth period. Although this probably had little effect on yields for the first crop growth period, it impacted the plants of the second crop-growth period for 5 wk, and caused considerable premature leaf drop. Additionally, drying facilities were inoperative and thus incapable of drying the beans of the first harvest. They underwent some spoilage before alternative drying facilities could be arranged.
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83 The analysis of variance table for bean yields is presented in Table 5-10. The effect of fertilizer on bean-grain yields was somewhat peculiar. The beans were harvested after 75 d and, therefore, were unaffected by the last (84-d) application of fertilizer for the 4 by 1/4 split. The second application in the 2 by 1/2 split and the third application in the 3 by 1/3 split were applied at 56 d, which should be about half way through the normal pod-filling period (Fig. 5-3). Comparison of treatment means indicated that bean-grain yields increased with the more numerous applications, even when one of the applications occurred after the beans had been harvested (Table 5-11). Shading may be the best explanation for bean-plant behavior in this mixed-crop arrangement. The trend in bean-grain yields as affected by fertilizer schedule is just the opposite of that for corn-stover yield. Maturation of the bean plant, including pod filling, occurred simultaneously with maturation of the corn stover (tasseling at 78 d). The increased splitting of fertilizer applications that limited stover yields also reduced the potential of the corn plant to shade the shorter beans. Further evidence is the lack of a plant-density effect on the bean-grain yields. Corn-grain yields in the high-density plots were higher than for the low-density plots. The additional corn plants would have provided more shade and consequently may have reduced the high-density bean yields to levels comparable to those of the low-density bean plots. Post-harvest soil samples were taken from the high-density plots to monitor gravel content and discern end-of-season differences in nutrient availability between fertilizer treatments for any given depth of soil. The concentrations of gravel in the plots showed no
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Table 5-10. Analysis Source D.F. Date Rep(Date) Density Fertilizer Density*Fertilizer Date*Density Date*Fertilizer Date*Den*Fert Pooled Error Total c.v 14.4 Date= Planting date Rep= Replication 1 2 1 4 4 1 4 4 18 39 of variance for bean Error term Rep(Date) / Pooled Pooled error Pooled error Pooled error Pooled error Pooled error Pooled error Den= Density= Planting density grain yields error 3 Fert =Fertilizer= Fertilizer application schedule *95% and **99% level of prbability F value 51.1** < 1 31 3** < 1 < 1 < 1 < 1 a Mean square error pooled to increase degrees of freedom in order to enhance validity of the F test. 84
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Ory weight (g/m 2 ),no of nodes, pods ( > 2 5cm) Leaf area index 600 3 500 400 2 300 200 100 80 Days from emergence Fig. 5-3 Key Phaseolus vulqaris component growth-accumulation parameters for cultivar Parrillo Sintetico planted at 25 plants/m 2 at Palmira-CIAT (from Laing et al 1983) 85
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Table 5-11 Comparison of selected bean yield-component means. Factor/ Level Bean yield Planting Date Early Late Fertilizer Schedule 4 3 2 1 0 kg/ha 336A* 223B 355a** 328ab 300ab 265b 151c Means in the same column, under the same sub-heading and followed 86 by the same uppercase letter, are not significantly different at the 95% level of probability as determined by an F test of mean square errors. ** Means in the same column, under the same sub-heading and followed by the same lowercase letter, are not significantly different at the 95% level of probability as determined by Duncan's Multiple Range Test.
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Table 5-12. Analysis of variance for soil gravel percentage. Source Date Rep(Date) Fert Date*Fert Rep(Date)*Fert Depth(Fert) Date*Depth(Fert) Pooled Error Total c.v. 36.7 D.F. Error term I Rep(Date) Rep(Date)+Pooled errorb 2 4 Rep(Date)*Fert 4 Rep(Date)*Fert 8 40 Pooled error 40 Pooled error 80 179 Date= Planting date Rep= Replications Fert = Fertilizer scheduling Depth= Depth of sampling F value a < I < I < 1 9.99** 1.37 87 a Insufficient mean error square degrees of freedom for a valid F test. b Mean error squares pooled to increase degrees of freedom in order to enhance validity of the F test. **99% probability of signficantly different treatment means.
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88 Table 5-13. Mean comparison of percent gravel associated with depths for the fertilizer application schedule. Depth cm -0 5 5 10 10 15 15 25 25 35 35 45 45 55 55 65 65 75 Fertilzer application schedule 0 1 2 3 ------------------% gravel 36.6a 39.8a 30.8a 45.0a 42. la 33.5a 59.4a 56.9a 74.3a 68.8a 74.3b 84.Sa 71. Sa 79.4a 66.8a 75.la 50.4a 59. la 42 0a 34.6a 41.6a 52.4a 72.Sa 78.9ab 76.7a 70.4a 60. la 29.7a 39.2a 36.0a 56.3a 71.8a 80.3ab 77 .Sa 71.4a 53.2a Means in the same row followed by the same letter are not significantly different at the 95% level of probability, as determined by Duncan's Multiple Range Test. 4 29 Sa 39. la 40. la 60.Sa 74.9a 84.5a 70.8a 65.3a 54.0a
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Table 5-14. Mean gravel content with depth. Depth cm 0 5 5 10 10 15 15 25 25 35 35 45 45 55 55 65 65 75 Gravel content ---% ----35.3c* 37.7c 38.6c 57. lb 72.5a 77 .6a 75.2a 69.8a 55.4b 89 Means followed by the same letter are not significantly different at the 95% level of probability, as determined by Duncan's Multiple Range Test
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90 significant trends associated with experimental treatments (Table 5-12). The gravel content showed differences between depths, but not between fertilizer schedules (Table 5-13 and 5-14). The split fertilizer applications were for the most part applications of N and K, because most of the P and Ca applied were in the triple superphosphate which had been applied preplant in all application schedules (Table 5-15). Analysis of variance for the effects of the experimental factors on Mehlich I-extractable P, K, and Ca indicated that planting date and fertilizer schedule had an insignificant effect on overall nutrient concentrations averaged over all depths (Table 5-16). The effects of sampling depth on nutrient concentrations were significant. Since depth was nested within fertilizer treatment and our experimental interest was in the location of nutrients as affected by fertilizer schedule, mean separations were made to distinguish differences among fertilizer-application schedules within each depth, instead of differences between depths among fertilizer schedules. Clear patterns are difficult to discern. The concentration of K from 5 to 25 cm in all of the fertilized plots was less than for the unfertilized control {Table 5-17). This would suggest that fertilizer application enhanced K uptake to an even greater extent than the amount applied. Limiting of this effect to the top 25 cm is most likely related to the large increase in gravel concentration at the top of the Btc horizon at about 22 cm, and to the subsequent effect of the gravel on root growth {Table 5-18). The concentrations of Ca at the various depths showed no discernable pattern for the fertilized treatments or the control (Table
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Table 5-15. Relative nutrient concentrations associated with each fertilizer-application schedule. Element N p K Ca Fertilizer-aQQlication schedule 0 1 2 3 4 ----------percent of tot a 1 applied t -------------0 0 100 0 50 50 33 33 25 25 0 0 100 0 87 13 83 8.5 80 6.5 0 0 100 0 50 so 33 33 25 25 0 0 100 0 87 13 83 8.5 80 6.5 t Preplant applications Each subsequent application 91
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Table 5-16. Analysis of variance for concentrations of Mehlich extractable soil P, K, and Ca after harvest. Source D.F. Date 1 Rep(Date) 2 Fert 4 Date*Fert 4 Rep(Date)*Fert 8 Depth(Fert) 40 Date*Depth(Fert) 40 Pooled error 80 Total 179 c.v. Date= Planting date Rep= Replications Error term Rep(Date) Rep(Date)+Pooled Rep(Date)*Fert Rep(Date)*Fert Pooled error Pooled error Fert = Fertilizer scheduling Depth= Depth of sampling Soil nutrients P K Ca F value a a a error 3.84b 2 17 1.07 < 1 < 1 < 1 < 1 20.3** 5.55** 46.2** 1.88** < 1 < 1 25.5 76 .1 17.6 92 a Insufficient mean error square degrees of freedom for a valid F test. b Mean error squares pooled to increase degrees of freedom in order to enhance validity of the F test. *95 and **99% probability of signficantly different treatment means.
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93 Table 5-17. Effects of fertilizer-application schedule on Mehlich Iextractable soil Kand Ca concentrations. Depth Fertilizer agglication scheme 0 l 2 3 4 -cm -Mehlich I-extractable soil nutrients (ug/g) K 0 5 165a* 151a 175a 127a 136a 5 10 155a 83b 88b 118ab 65b 10 15 195a 67b 70b 88b 76b 15 25 116a 46b 50b 87b 71b 25 35 38ab 35b 44a 47a 33b 35 45 23a 29a 27a 50a 23a 45 55 17a 21a 19a 26a 17a 55 65 14ab 12b 14ab 19a 13b 65 75 11 a 9a 11 a 14a 22a Ca 0 5 1800a 1830a 2180a 1920a 1660a 5 10 1870b 2130ab 2330a 1944ab 1914ab 10 15 2041a 1855a 1931a 1995a 171 la 15 25 1686a 1391a 1440a 1370a 1580a 25 35 1120a 1000a 1250a 1480a 1040a 35 45 730a 729a 712a 1032a 677a 45 55 505a 509a 548a 583a 470a 55 65 361a 384a 433a 419a 390a 65 75 295a 315a 356a 310a 322a Means in the same row followed by the same letter are not significantly different at the 95% level of probability, as determined by Duncan's Multiple Range Test.
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94 5-17). This was probably a reflection of the small amount of fertilizer Ca added, as compared to the amount naturally present. Fertilizer applications resulted in a marked increase in P concentrations for the top 10 to 15 cm (Table 5-18). Gibbsite and goethite, the predominant soil minerals, are known to adsorb P strongly. This fixation prevents the P from leaching, and makes much of it unavailable for plant uptake. The lack of discernable differences in soil-nutrient concentrations among the fertilized treatments may have been related to several other experimental observations. The total uptake of N, P, K, and Ca by corn was not different among the four fertilization schedules no matter how the fertilizer application was split. The same amount of fertilizer was applied to each plot, and the same quantities of these nutrients were harvested (within experimental error). Partitioning of the plant's photosynthetic resources into grain and stover was affected by fertilizer schedule, but the overall dry matter harvested was similar. It is also important to note that, by the time the 4 by 1/4 treatments had received their last fertilizer application, they had only received 39 and 50% (first and second planting, respectively) of the total season's rainfall. The last 8 wk of the season accounted for at least half of the total rainfall. Conclusions Differences among the effects of split fertilizer applications and planting date on corn-grain and stover yields indicated that early season nutrient losses due to leaching were minimal. Although stover
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Table 5-18. Effects of planting date and fertilizer application schedule on concentrations of Mehlich I-extractable soil P. Depth -cm -0 5 5 10 10 15 15 25 25 35 35 45 45 55 55 65 65 75 0 5 5 10 10 15 15 25 25 35 35 45 45 55 55 65 65 75 Fertilzer application 0 1 2 3 4 --------Mehlich I-extractable soil P (ug/g) --------First planting 10.5d* 10.6b 10. 7b 10. la 8. la 7.Sa 6. la 5 0a 3.2a 11. 2b 10.2b 10.6a 10.0a 8. la 5.4a 4.6a 4.4a 5.0a 12.6cd 17.5a 15.0ab 10.8a 9.4a 7.0a 5.0a 3.3a 3.0a 11. 6 20 .1 a IO.la 8.3b 5.7a 4.9a 4.3a 3.Sb 3.7a 22.7a 18.7a 12.0ab 9 .1 a IO.Sa 7 9a 7. la 5.4a 3.9a Second planting 19. la 22. la 9.8a 9.2ab 8.6a 6.2a 4.6a 4.0ab 4.6a 19.2ab 17.6a 17.Sa 10.2a 8.Sa 6.7a 6.2a 5.2a 3.8a 18.6a 15.3ab 11. la 8.7ab 9 .1 a 6.6a 4.6a 4.3ab 4.4a 16. 1 be 17.3a 11.8b 10.3a 7.la 5.la 4.0a 3.3a 3.0a 20. la 14.8ab 9. la 8.3b 6. la 6.Sa 4.2a 3.9ab 5.2a 95 Means in the same row under the same planting date and followed by the same letter are not significantly different at the 95% level of probability, as determined by Duncan's Multiple Range Test.
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96 yields decreased as the fertilizer was split over more applications extending later into the growing season, an increase in grain yield with later applications resulted in similar total dry-matter yields among all treatments receiving fertilizer An inadvertent delay in planting had no significant effect on stover yields, but drastically reduced grain yields. Additionally, grain yields for the two planting dates were affected differently by the fertilizer schedules. For the first planting date, the 4 by 1/4 fertilizer split application yielded 33% more grain than the all-preplant fertilizer schedule, whereas the 4 by 1/4 schedule yielded 60% more grain than the all-preplant schedule for the second planting. Although the second-planting grain yields were much smaller than those of the first planting, increased splitting of the fertilizer applications became more efficient as the growing season extended further into an intensifying rainy season. Amounts of rain were low during the early part of the rainy season when this particular experiment was conducted. Although early-season moisture stress was sufficient to cause wilting and probably reduce plant stands, higher plant densities did not use enough additional soil moisture to reduce grain or stover yields. Considering the significant but relatively small differences in grain yield between the two densities, and the large differences in grain yield between the two planting dates, replanting due to poor plant stands may only be advisable under the most extreme circumstances. The low plant densities that resulted from the unseasonably dry conditions of this experiment were probably still sufficient to give larger yields than the treatments of the second planting
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97 The end-of-season soil-nutrient contents did not show discernible differences among the four fertilized treatments. However, all of the fertilized plots had less Kand more P than the non-fertilized controls. Soil strength and local laboratory facilities limited sampling and analytical methods that could be used to monitor nutrient movement during the growing season. Yield and nutrient-uptake results suggest that nutrient leaching became prevalent late in the season. Further studies, designed specifically to monitor water and nutrient movement, could give more conclusive evidence with respect to location of surface applied nutrients throughout the season.
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CHAPTER 6 NUTRIENT MOVEMENT IN UNDISTURBED SOIL COLUMNS Introduction The efficiency of field-applied fertilizers is dependent on their availability to the plant roots. In the high-rainfall tropics, efficient management of applied fertilizers involves practices that maintain the fertilizer nutrients in the active crop root zone (Sanchez et al., 1982a). The availability of a plant nutrient in a root-zone is affected in turn by the interactions of the specific nutrient with the soil and by the transport mechanisms involved in bringing the nutrient to the root surface. Quantification of nutrient availability is complicated by the technology employed to assess the root/soil environment. The quantification of nutrient availability in soils of the tropics is further complicated by the limited local access to scientific infrastructure (trained scientists and equipped laboratories) (Lipton, 1987; Vallaeys et al., 1987). The soil column is a frequently used tool in soil-science research because it allows the soil to be moved intact from the field to laboratories which have controlled environments and sophisticated equipment. It is generally assumed that the chemical and physical characteristics, as well as the combined dynamic behavior of a soil, simulate the behavior of the soil in its natural field setting. 98
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99 Rainfall, evapotranspiration, and time all influence the behavior of water and solutes in field soils. Soil columns have the additional environmental influences created by the soil interface at the surface of the cylinder casing and at the bottom of the column with the atmosphere. Modifications to the soil column's laboratory environment are frequently made to create the effects of a natural field environment. The influence of the column casing on solute transport under both saturated and unsaturated-water conditions has been shown to be minimal when the casing is made of a non-adsorbing material (Cronan, 1978). The exposure of the water at the bottom of the soil column to atmospheric pressure disrupts the soil-water tension present under field conditions, so that the soil will saturate upon the addition of sufficient water to the column. A zone of saturated water will remain at the bottom of the column until sufficient gravitational pressure is obtained to begin forcing water from the column. The development of a water-saturated zone at this interface may be prevented by sealing the interface with a porous plate and subjecting the plate to a partial vacuum (Gaudet et al., 1977; Topp and Zebchuk, 1979; Gish and Jury, 1982). The natural rainfall and evapotranspiration conditions of a field site are difficult to impose under laboratory conditions. The simulations need not necessarily mimic a field environment, such as can be attempted with environmental chambers, but need only simulate the effects. Doorenbos and Pruitt (1975) developed a model that permits calculation of the effective rainfall and evapotranspiration from meteorological and plant water-use data. The effective rainfall is considered to be that water responsible for moving solutes in the
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100 profile. This approach has been used to derive water parameters for solute-leaching models (Seyfried and Rao, 1989) and soil-moisture budget models (Dyer and Mack, 1984). The porous ceramic cup has become a popular tool for sampling soil solutions in situ (Wood, 1973; Harris and Hansen, 1975). Guidelines developed from research evaluating the properties of the cups and the effects such properties have on soil-solution samples have brought surprising conformity by users regarding accepted limitations to the utilization of the cups (Grover and Lamborn, 1970; Hansen and Harris, 1975; Alberts et al., 1977; van der Ploeg and Beese, 1977; Barbarick et al., 1979; Talsma et al., 1979; Nagpal, 1982; Neary and Tomassini, 1985). The purpose of this study was to determine the seasonal movement of applied nutrients in the field experiment (Chapter 4) through utilization of soil columns subjected to an environment simulating the effects of the natural setting. Implicit in the incorporation of each simulated factor is the error associated with each deviation from the true effect of the natural factor. Although the propagation of errors from multiple simulated factors may actually cancel each other and result in a perfect simulation of the natural site, the opposite effect is equally possible. Therefore, it is deemed appropriate that the interpretation of the experimental results be more qualitative than quantitative in nature.
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101 Materials and Methods Column Preparation Undisturbed soil columns were taken from one of the unfertilized control plots used in the field trials. Polyvinyl chloride (PVC) pipes 80 cm in length and of 9.6 cm internal diameter were put in a soil corer constructed of a steel pipe fitted at one end with a hardened steel cutting edge and having a heavy, threaded steel cap at the other. The unit was hammered 75 to 80 cm into the soil and removed with a hydraulic jack (Foale and Upchurch, 1982). The columns were sealed, crated, and transported to the laboratory for analysis. The bottoms of the columns were cut so that each column contained a 70-cm depth of soil. The columns were then fitted with 1-bar bubble pressure porous alundum plates 95 mm in diameter and 10 mm thick. The plates had been previously washed with 50 pore volumes of 0.1 M HCl and rinsed with 100 pore volumes of deionized water (Neary and Tomassini, 1985). Similarly washed 1-bar bubble-pressure ceramic tensiometer cups and 1-bar bubble-pressure alundum solution extraction cups (28 6 mm long by 6.35 mm outer diameter) were inserted at depths of 5, 15, 25, 35, 45, 55, and 65 cm from the top of the soil columns. The tensiometer cups were connected by means of water-saturated tubing to mercury reservoirs for monitoring soil-water potential. The solution-extraction cups were connected by means of tubing to a collection vial in a pressure chamber (Harris and Hansen, 1975). The bottom end plates were connected to a constant suction of 50 cm H 2 0 (Fig. 6-1).
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Rainhead Soil column n / Extraction cups To mercury manometers \ / ~ 102 Effluent collector I Extraction vials Vacuum chamber Fig 6-1. Schematic illustration of apparatus used in the experiments monitoring fertilizer leaching in undisturbed soil columns.
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103 Experimental Design Daily rainfall was measured at the Cameroon field site and evapotranspiration data were recorded from a grass-covered lysimeter at the Institute of Agronomic Research meteorological station at Dschang, 8 km south of the field site. Weekly crop evapotranspiration and effective precipitation were calculated using equations derived by Doorenbos and Pruitt (1975). Deionized water equal to the weekly effective rainfall (total rainfall minus crop evapotranspiration) was added to the columns on a daily schedule for 20 d (corresponding to a 20-wk growing season) through a rainhead consisting of seven hypodermic needles which applied the water to the soil surface at a rate of 4 cm/h. Following a redistribution period of 8 h, soil-solution samples of 5 to 15 ml were extracted via the extraction cups by applying a suction of 80 cm H 2 0 to the collection chamber. The total volume of solution extracted via the cups was calculated and an equal quantity was added to the increment applied the next day. The extracted solutions and the effluent from the bottom of the column were acidified with H 2 S0 4 and stored at 4 C until analyzed for N0 3 NH 4 Ca, Mg, and K. The columns were subjected to five treatments designed to model several of the field fertilizer applications discussed in Chapter 5. The column treatments consisted of: (a) No fertilizer and subjected to the rainfall of the first planting season (non fertilized); (b) 400 kg/ha of a 20-10-10 (N-P 2 0 5 -K 2 0) plus 50 kg P/ha as 1 I triple superphosphate (TSP), all of which was applied
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104 preplant (before water application) and subjected to the rainfall of the first field-planting season. (c) 400 kg/ha of 20-10-10 plus 50 kg P/ha as TSP applied before water application, but subjected to the rainfall of the second planting season. (d) 400 kg/ha of 20-10-10 divided such that 100 kg/ha was applied with 50 kg P/ha (TSP) before the addition of water to the column, and the remainder of split into 100 kg/ha portions applied after 4, 8 and 12 wk of simulated field "rainfall"; and (e) 1600 kg/ha of 20-10-10 and 450 kg P/ha as TSP applied before water application and subjected to the rainfall of the first planting season. The actual fertilizer rates applied to the columns were based on the surface area of the column, and were three-fold those applied in the field. This was to account for the field application of fertilizer in a 33-cm band within a 1-m row. The top 10 cm from each column were removed, mixed with the appropriate preplant fertilizer, and replaced in the column before addition of the first applications of water. Split fertilizer applications were spread evenly over the surface of the soil. Following the last simulated rainfall and solution-extraction event, the columns were cut into 5-cm segments and each segment was analyzed for gravel content, organic carbon, Mehlich I-extractable P, Ca, Mg and K, and 1 M KCl-extractable Ca and Mg.
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105 Extraction Cup Testing Ceramic extraction cups are made from an assortment of ingredients and the exact composition changes with availability of raw materials (Silkworth and Grigal, 1981; Nagpal, 1982). This has resulted in seemingly contradictory conclusions regarding the adsorption and screening of solutes by such cups. Researchers have recommended that, due to the changing assortment of basic ingredients, cups should be tested for adsorption and screening with the specific solutes to be used in the subsequent experimental procedures (Grover and Lamborn, 1970; Hansen and Harris, 1975; Neary and Tomassini, 1985). An experiment was performed on two sets of six ceramic extraction cups The first set of six cups was washed by passing through each cup 250 ml of 0.1 M HCl followed by 250 ml of deionized water (Neary and Tomassini, 1985). The cups were then placed in 30 ml of a solution containing 2.0, 2.1, and 1.6 ug/ml of Ca, K, and N0 3 respectively. Following passage of 15 ml of the solution, the cups were removed and the passed and remaining solutions were saved for analysis. This process was then repeated two more times. The other set of six cups was washed similarly but then submerged for 24 h in 30 ml of a spiked solution containing Ca, K, and N0 3 at 0.05 ug/ml (a concentration at the detection limit of the Jarrel Ash ICAP used for the chemical analysis). The six cups were then blotted dry and treated similarly to the first six cups by placing them in 30 ml of solution containing 2.0, 2.1, and 1.6 ug/ml Ca, K, and N0 3 respectively, and then passing 15 ml of the solution through the cups.
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106 The adsorption of P to the ceramic cups was investigated using the method of Nair et al. (1984). Duplicate extraction cups were placed, one per 50-ml screw-top centrifuge tube, and allowed to equilibrate for 24 h in 25 ml of 0.01 M CaC1 2 solution with one of several concentrations of KH 2 P0 4 Solution P concentrations were determined by the ascorbic acid/ammonium molybdate method (Olsen and Sommers, 1982). Results and Discussion Extraction Cups Test The concentrations of solutes in the passed and remaining solutions from each extraction-cup treatment are presented in Table 6-1. Analysis of variance and orthogonal contrasts were calculated to determine the effects of the treatments on the various solutions {Table 6-2). Concentrations of both Ca and Kin the passed solutions were higher than in the remaining solutions, after the first passage of the test solution. This indicated that some contaminating Ca and K remained in the cups after the initial washing process. However, the concentrations of Ca and Kin the passed and remaining solutions for the next two increments were not different. The concentrations of N0 3 in the passed and remaining solutions were not different for any of the three increments. The dilute spike solution was used to satisfy minor complexing sites and, thereby, reduce the possible adsorption of solutes during later experimental use. After pre-soaking the cups in the spike solution, the concentration of Ca in the passed solution was significantly less than that in the remaining solution. This result was
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107 Table 6-1. Mean concentrations of solutes in treatment solutions remaining and after being passed through extraction cups. Solutes Treatment Ca K N0 3 --------ug/ml ---------0 Control 2.0 2 .1 1.6 First pass 1 Passed 2.3 2.8 1. 7 2 Remaining 2 .1 2.2 1. 7 Second pass 3 Passed 2.0 2 .1 1. 7 4 Remaining 2.0 2 .1 1.6 Third pass 5 Passed 2.0 2.1 1. 7 6 Remaining 2.0 2.2 1.6 First pass After spike solution 7 Passed 1.8 1. 9 1. 6 8 Remaining 2.0 2.0 1.6 Second pass After spike solution 9 Passed 2.0 2.0 1. 7 10 Remaining 2.0 2.0 1. 7 Table 6-2. Orthogonal contrasts of solute concentrations remaining and after being passed through extraction cups. Solutes Contrast Ca K N0 3 ------significance ------1st pass vs 1st remaining ** ** n. s. 2nd pass vs 2nd remaining n. s. n. s. n. s. 3rd pass vs 3rd remaining n. s. n. s. n. s. 1st pass vs 1st remaining(after spike) ** n. s. n. s. 2nd pass vs 2nd remaining(after spike) n .,;. n. s. n. s. Control vs all passed n. s. n. s. n. s. Control vs all remaining n. s. n. s. n. s. ** Significantly different at 99% level of probability. n.s Not significantly different at 95% level of probability.
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108 different from that for the non-spiked set of cups, which had higher concentrations of Ca and Kin the passed solutions. The spike-soaked set of cups showed no contamination and only minor adsorption of Ca during the first passage. There were no differences between the passed and remaining solution concentrations following the second increment through the spike-soaked cups. Taken as a whole, the passed and the remaining solutions showed no differences in solute concentration when compared to the original control solution. Under no circumstances was the remaining solution solute concentration higher than for the control solution, thereby ruling out the possibility of screening (restricted passage due to steric or electrostatic effects). Even when differences existed between passed and remaining solute concentrations, they were small and near the detection limits of the analyzing instrument. Therefore, it may be concluded that the cups had negligible adsorption, screening, or contaminating effects when passing solutions of Ca, K, or N0 3 The P-adsorption isotherm is presented in Fig. 6-2. The cups exhibited a strong affinity for P. Such behavior precludes using the cups for extraction and consequent determination of soil-solution P concentrations. Similar behavior has been previously reported (Bottcher et al 1984) Nutrient Movement The soil solutions were stored for 1~ to 30 wk before chemical analysis due to problems with instruments. Although the samples were acidified (pH< 2) and kept refrigerated, the time period exceeded generally-accepted standards for quantitative analysis of N0 3 and NH 4
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Adsorbed = 52(Solution P) + 22 r 2 400 r 0. 0 C.) .E cu L.. 300 (l) C.) C) ........ 0 C) ::, 200 a. Cl w 100 0 (f) Cl <( 0 0 0 2 4 6 8 SOLUTION P (ug/mL) Fig. 6-2. Phosphorus adsorption isotherm for ceramic extraction cups. 109
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110 due to the susceptibility of these compounds to biological transformations (Environmental Protection Agency, 1983). Soil-solution concentrations of both NO 3 and NH 4 were erratic between day-to-day samples, depth-to-depth samples, and experimental replicate samples. Therefore, the data for soil solution concentrations of NO 3 and NH 4 were not used to monitor their movement in the columns. Distribution of the effective rainfall during the two planting seasons is presented in Fig. 6-3. The first planting season had no effective rain until the fifth week. This absence of effective rainfall prevented soil-solution extractions from the column during the simulated initial 5 wk of the season. Except for the second week, the second planting season had effective rainfall every week of the season. An analysis of variance was calculated with the concentration of gravel, the cation-exchange capacity, and the percentage of organic carbon at different depths in the columns (Table 6-3). The cation exchange capacity and percent of organic carbon were determined only on the fine fraction. These parameters showed no differences across treatments, but did show differences with depth. The value of the parameters within each 5-cm increment of soil are presented in Table 64. In reviewing the behavior of the soil-applied nutrients, the reader should again note the application distribution of the nutrients under the various treatments (Table 5-15). Although the all-preplant treatments received all applied nutrients before the beginning of the water-application period, the split-application treatment received most (80%) of the Ca and P, but only 25% of the K, preplant. The subsequent
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E E _J _J < LL z < a: UJ > 1() UJ LL LL UJ 80 7 First Planting Season 60 50 40 30 20 10 0 -10 -20 0 28 56 84 112 140 80 ,------------------------, i >70 ._ 60 50 -40 30 -20 10 0 ,10 -20 0 Second Planting Season I 28 56 84 DAYS AFTER PLANTING 112 140 I I 111 Fig 6-3. Distribution of effective rainfall for first and second planting seasons.
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112 Table 6-3. Analysis of variance table for column soil-gravel content, effective cation-exchange capacity, and organic-carbon content. Source D.F. Error term Treatment 4 Error Gravel Effective cation Organic exchange capacity carbon F value 1.04 1. 64 1.66 Depth(Treatment 65 Error 8.16** 13.5** 44.7** Error 70 Total 139 c.v 24.0 17.4 10.7 ** 99% probability of significance Table 6-4. Column soil-gravel content, effective cation exchange capacity, and organic carbon content with depth. Depth Gravel Effective cation Organic carbon exchange capacityt cm % cmol (+) I kg g/kg 0 5 42.6 9.84 58.8 5 10 49.5 10.00 56.3 10 15 46.8 8.31 55 5 15 20 61. 7 7 16 49.8 20 25 77 .8 5.80 42.5 25 30 84.6 5.08 33.6 30 35 82.2 4 45 29.7 35 40 82.9 4.21 25.1 40 45 80.2 3.91 23.l 45 50 74.9 3.86 19.7 50 55 72.5 3.72 19.1 55 60 68.0 3.56 16.1 60 65 62.1 3.41 16.2 65 70 37.9 3 19 14 5 t Sum of Ca, Mg, and titratable acidity extracted with 1 !1 KCl.
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113 three split-applications each added 6.5% of the total Ca and P, but 25% of the total K (as well as N0 3 and NH 4 ). It is also important to note that the placement of the ceramic extraction cups and the suction used to withdraw the soil solution influence the size and location of the extraction zone (van der Ploeg and Beese, 1977). Due to the clayey nature of the soil's fine fraction several procedural considerations are required to prevent migration of clay into the cups and subsequent plugging of the cups' porous network. The holes drilled into the columns were first lined with a thin layer of fine sand before insertion of the extraction cup. This requires the soil solution to first cross a thin sand bridge before reaching the extraction cup. The suction used to extract the soil solution was chosen so that sufficient soil water was available for extraction and so that migration and lodging of clay in the cups was minimal (Hansen and Harris, 1975; Talsma et al., 1979). The size of the extraction cups (28.6 mm long by 6.35 mm o.d.) preclude their installation into exclusively small or large pores. Since the previous chapter indicated the presence of both immobile-water regions and preferential flow in macropores at near saturated conditions, the soil solution was probably extracted predominantly from the larger pores.
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114 The concentration of soil-solution Ca, K, and Mg with depth during the water-application periods for the five column treatments are shown in Figs. 6-4, 6-5, and 6-6, respectively. The intercepts (I) and coefficients (xi) of the response surface equations including quadratic and crossproduct effects: Solute (ug/ml) = I+ x 1 (Depth) + x 2 (Day) + x 3 (Depth*Day) + X4(Depth 2 ) + X5(Day 2 ) (6-1] for each nutrient and treatment are presented in Table 6-5. The concentrations of Ca, K, and Mg in the soil solution for all treatment exhibited an initial peak in the soil surface (0 to 10 cm) and a near absence of the nutrient in the subsoil (SO to 70 cm). As the season progressed, the surface concentrations decreased greatly and the subsoil concentrations increased slightly, as the solutes moved convectively down with flowing water and moved diffusively into immobile soil water regions. The magnitudes of the initial peaks were dependent on the fertilizer treatment. The highest fertilizer application rate demonstrated the highest soil-solution concentration, while the non fertilized control typically had the lowest initial solution concentration. For Ca and K, this may be explained by their presence in the fertilizer; however, for Mg the increased soil solution concentrations with increased fertilizer application, was due to the competitive selectivity the fertilizer ions have for the soil surface and the ability of the newly added ions to displace Mg from the surface phase to the solution phase.
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\ !~ (\) -lb"< s~~ "' .s%-~ o ... .., \ '-i 0 ... (j Q) (\) -lb"< s~ .>, .... ..,-~ ~/..,_"""' ... ..... '1.-!<" ..:r o..,. ......_ P...(l) <::)~Co NO FERTILIZER Q) q> r{P~..,_~ r '<"' ,,_ ... ..... 'l, ,;; / '-..:r ......_ P... Cl> ......._Co Q .... -lb"< \ i i~ (~ J~ (\) ,, (\)~ -lb"'< Q) <.:Jo/..,_"""' ... .._ '1.-!<"..:r ...... -.,eo Q .... 1600 tg / ho PREPLANT Q) ...... 'l, ,1,"..:r ""' --6'.$-.-,,.~ ,.::1 00 kg / ho X 4 APPLI C ATION S 4 00 kg / ho PREPLANT 400 kg / ho PREPLAMT HIGH RAINFALL Fig. 6-4. Concentrations of soil solution Ca with depth during simulated rainy seasons. I-' I-' u,
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' 0, ::i 'Ill I.: sa,_ ..S ~ A:::I~ %-o .., 0, ::i 'Ill~ I.: s4-.:,~ :-s"'{so.._ q, ... ... 'l, ,t,i' ........ Cg <::> ..... NO FERTILIZER q, qJ>--' ... .._ 'l, .... ..... ........ Cg <:> ..... "\ 1 \ 'I.: Ill s4-.:,~ ,,.....s-C{s,-0 ... ,~ 0, ::i 'I.:~ Ill ,'l........ .:tr ......_ci,{l> ......... <::> ..... 1600 lg / ha PREPLANT q, qJ>, 'l......... ..... 0, ::i 'I.: ..... 1 00 kg / ha X 4 APPLI C ATION S 400 kg / ha PREPLANT 400 kg / ho PREPLANT HI G H RAINFALL Fig. 6-5. Concentrations of soil solution K by depth during simulated rainy seasons. I-' I-' (j\
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117 e\ ...J i~i ...J < lJ.. z ,~ < a:: 1, I cl, f} { 1,_ ('.) V) 9 '1-1 I C: I 0 0 .... V) ~\ I z <'IS < QJ ...J V) a.. \\ w .... 0:: z a.. C: ~'lJ. < ...J 0 <'IS ti) { a.. .c s.. w 'i ~ o a:: "' "0 a.. 8t!Jt l!J 9A QJ 0 ( "'J',U/6n J q.; 0 .+-J I .c ~ 'v 0 <'IS ql ,r ,Cl ::, E 0 0 V) --0 e '1-, C, C: Bet e i\ s.. ( 7"'//5n J i5J.I ::, ~" \\ "0 cP .... ..s::::. '& 'lJ. z .+-J < C. I} ...J QJ ~ { 1,_ a.. w "0 I 0 0:: a.. ..c I 0 .c C, '0, :E: e '1-1 C: ~ / 0 0 i\ 0 .... r/1,f ,r .+-J ::, 8t!Jt e ,\\ (7"'/ 6 n J 5J.I 0 V) '& 'lJ. a:: cP ,tll w .... ~ / <1,. N 0 I 0 V) ...J e \ <+I .... 0 a:: i ~ \ w lf) V) lJ.. z 0 C: 0 0 z \\ .... ... < .+-J '& 'lJ. u <'IS ...J s.. e I} { a.. .+-J 8t!Jt i, ~o a.. C: ( 7W/6n J 5J.I < QJ ~" eA u cP ,:C: I X 0 u 0 .c "' I.O I 0 I.O 0 l!Jet e ( -,.,., _,, 5 n J 6J.I O"I ~" L.... cP
PAGE 125
118 Table 6-5. Variable coefficients for the response-surface equations esitmating concentrations of Ca, Kand Mg with depth and over time for column fertilizer-application treatmentst. Element Intercept Day Depth Day2 Depth*Day Depth 2 Ca K Mg Ca K Mg Ca K Mg Ca K Mg Ca K Mg 264 35 121 254 45.8 138 355 107 161 190 45 77 405 193 149 No f ert il i zer -3.09 nst -0.352 -0.743 ns 0.0359 -0.0742 8.0x10 4 4.31x10 3 3.0lxl0 3 -1.18 ns ns 0.0210 -0.0250 100 kg/ha x 4 applications ns 2.21 -0.0102 0.0653 -0.0694 -0.150 -1.66 ns 5 l 7x10 4 9 22x10 4 ns -1.71 -5.70x10 3 0.0425 -0.0312 400 kg/ha preplant -2.82 ns -0.687 -2.76 -1.28 ns ns ns ns 0.0804 0 0150 0 0416 -0 .113 0 0102 -0.0547 400 kg/ha preplant high rainfall -2.69 2 19 -0.414 -0.935 -0.952 1.24 ns ns ns 0.0315 -0 0667 6.14x10 3 ns 0.0156 -0.0296 1600 kg/ha preplant ns ns -0.155 -3.22 ns ns ns ns ns 0 0913 0.0255 0 0455 -0.142 ns -0.066 ns = nonsignificant at 90% level of probability Depth= depth in soil column (0-70 cm) Day= days after planting t The fertilizer indicated was 20-10-10 (N-P 2 0 5 -K 2 0). In addition, triple superphosphate was applied to give a ratio of 1 kg P/8 kg 20-10-10 r2 0 51** 0.48** 0.40** 0.53** 0.83** 0.45** 0 44** 0 60** 0.43** 0.40** 0.33** 0.44** 0.35** 0.40** 0.33**
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119 The amount of water applied affected the distribution of the nutrient in the soil solution during the season. The 400 kg/ha, high rainfall treatment had a lower initial concentration of the solution nutrients in the surface soil than the similarly fertilized, low rainfall treatment. Additionally, the loss of the nutrients from the surface to the subsoil occurred earlier in the season. The end of season, soil-solution nutrient concentrations throughout the column for the high-rainfall treatment were lower than all other fertilized treatments and generally the same as the non-fertilized control. The increased washing of the larger soil pores decreased the solute concentration in the extracted solutions. Splitting the fertilizer application reduced the initial early season surface-soil solution peak, but maintained greater soil-solution concentrations later in the season. Only the highest fertilizer treatment was able to maintain end of season soil-solution nutrient concentrations at levels similar to the split-application treatments. For K this may be explained by the repeated application of small amounts of K to the surface throughout the season. However for Ca and Mg, their lower initial solution concentrations and higher late-season solution concentrations indicated the diminished competitive effects of a smaller initial K application and the continued intermittent competition for the soil surface as more K was applied. Within the generalization made above, Ca, Mg, and K exhibited slight differences in behavior. The soil-solution concentrations of K in the non-fertilized control were barely above the detection level of the analytical instrumentation. The initial early-season peak showed no
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120 dissipation with depth and disappeared completely by day 70 (after only 3.8 cm of water). Soil-solution K concentrations between the depths of 30 to 70 cm were near 0.0 ug/ml for all treatments except the highest fertilizer application and that was only at the end of the water application season. This suggested that as soil solution K moved down in the column it also moved to regions unavailable to the extraction cups. The distribution of concentrations of Mg with depth during the water-application seasons for all treatments was similar to that of Ca. However, Mg was not applied in any of the fertilizer treatments. The magnitude of the soil-solution concentrations of Mg with depth increased with increasing quantities of the fertilizer. The cumulative distributions of Ca, K, and Mg passing the 70-cm depth are presented in Figs. 6-7, 6-8, and 6-9, respectively. The quantities of applied and leached Ca, K, and Mg are presented in Table 6-6. The effects of the fertilizer treatments on the leaching of Ca and Mg were similar. All treatments showed a large increase in leaching after 84 d, when the rainy season intensified and the amount of water per application increased greatly. The columns receiving no fertilizer exhibited the smallest loss of Ca and Mg to leaching below the 70-cm depth. This again suggested the competitive effects (and increased susceptibility to leaching) of the fertilizer additions on applied and non-applied nutrients. The three treatments with applications of 400 kg/ha of 20-10-10 plus 50 kg P/ha lost similar quantities of Ca and Mg throughout the season. Both the treatments with split-fertilizer applications and high rainfall showed leaching behavior similar to the
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121 250 -e-No fertilizer ro .c ........ G 100 kg/ha X 4 applications 0) 200 I .: .. I 8 I I 400 kg/ha preplant I ro () I 150 L -~ 400 kg/ha preplant high rainfall I I I UJ r 6 > I :' \ ~ .,_ -1600 kg/ha preplant / <( 100 I 6 .i I _J I ,' _I => ~ /J r-; f::::.D. m _. => I ,7 A () 50 6 I e / 0 0 28 56 84 112 140 DAYS AFTER PLANTING Fig. 6-7. Cumulative concentrations of Ca leached from 70-cm soil columns. The fertilizer indicated was 20-10-10 (N-P 2 0 5 -K 2 0). In addition, triple superphosphate was applied to give a ratio of 1 kg P/8 kg 20-10-10.
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122 I I ,., No fertilizer 15 ---c::::r.. 1 ct5 .c L __ r::,_ __ 100 kg/ha X 4 applications c.....J ........ l 0) I I I /.\ ... I I 400 kg/ha preplant I <---> I 10 I I i I I --A400 kg/ha preplant high rainfall I w i I I > II -1600 kg/ha prepiant I <( I _J r I I :::, 5 r ,II ::E I --:::, I 0 I I 0 0 28 56 84 112 140 DAYS AFTER PLANTING Fig. 6-8. Cumulative concentrations of K leached from 70-cm soil columns. The fertilizer indicated was 20-10-10 (N-P 2 0 5 -K 2 0). In addition, triple superphosphate was applied to give a ratio of 1 kg P/8 kg 20-10-10.
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123 175 '' No f ert~izer ..t'tj 150 --, ......... 100 kg/ha X 4 applications I 0) I JI 125 400 kg/ha preplant 1111 0) I I --AI 100 400 kg/ha preplant high rainfall ~ UJ r I > -,-1600 kg/ha preplant '\ l I75 I w I <( I ~ I , _J I \ 50 ~ 66, i pW ::::> I D. :& () I ( 25 J:i~ ~r, 0 _. o I 0 28 56 84 112 140 DAYS AFTER PLANTING Fig 6-9. Cumulative concentrations of Mg leached from 70-cm soil columns. The fertilizer indicated was 20-10-10 (N-P 2 0 5 -K 2 0). In addition triple superphosphate was applied to Jive a ratio of 1 kg P/? kg 20-10-10.
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124 Table 6-6. Quantities of applied, Mehlich I-extractable, and leached P, Ca, K, and Mg. Treatmentt Column Actual total quantity Mehlich I applied extracted+ Nutrient in leachate passing 70-cm depth Leached minus leached from non-fertilized treatment --------------kg/ha -------------No fertilizer 100 kg/ha x 4 application 400 kg/ha preplant 400 kg/ha high rainfall 1600 kg/ha preplant No f ert il i zer 100 kg/ha x 4 application 400 kg/ha preplant 400 kg/ha high rainfall 1600 kg/ha preplant No fertilizer 100 kg/ha x 4 application 400 kg/ha preplant 400 kg/ha high rainfall 1600 kg/ha preplant No fert i l i zer 100 kg/ha x 4 application 400 kg/ha preplant 400 kg/ha high rainfall 1600 kg/ha preplant 3190a* 3270a 2610a 2730a 2912a 141c 240b 227b 182bc 368a 971a 930a 836ab 777b 806ab 5.6a 10. lb 14.0b 10.7b 87.2c o 137 137 137 548 0 97 97 97 388 0 0 0 0 0 0 201 201 201 804 Ca K 86a 126b 140b 123b 210c 1.3a 1. 9a 1. 9a 1. 9a 14.6b M 49a 104b 108b 106b 151c p o 40 54 37 124 o 0.6 0.6 0.6 13.3 0 55 59 57 102 t The fertilizer indicated was 20-10-10 (N-P 2 0 5 -K 2 0). In addition, triple super phosphate was applied to give a ratio of 1 kg P/8 kg 20-10-10. + Determined only on the fine fraction (<2 mm). Means in the same column under the same subheading followed by the same letter are not significantly different at the 95% level of probability, as determined by Duncan's Multiple Range Test.
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125 low rainfall non-split fertilizer treatment. The columns with the highest fertilizer application rate had the largest amount of leaching of Ca and Mg. Therefore, the loss of solutes from the more mobile convective water path does not necessarily result in increased leaching from the root zone. The higher rainfall treatment caused more rapid early-season decreases in soil solution concentrations, but the increased washing of the larger pores had little effect on the overall loss of nutrients from the column. The effects of treatments on the leaching of K were different from those of Ca and Mg. There was almost no leaching of K from the non fertilized columns or any of the three moderately fertilized treatments. The split application of fertilizer and the high-rainfall treatment had leaching losses similar to the low-rainfall, all preplant, and the non fertilized treatments. The treatments with the highest fertilization rate exhibited the highest loss of K, but even this loss was small compared to the quantities applied (less than 4%). The concentrations of Mehlich I-extractable P, K, Ca, and Mg present in the columns at the end of the water application are presented in Tables 6-7, 6-8, 6-9, and 6-10, respectively and shown graphically in Figs. 6-10, 6-11, 6-12, and 6-13, respectively. The effects of the fertilizer treatments on the concentration of P with depth were limited to the surface Oto 15 cm. The high adsorptive capacity of this soil for P prevents the downward movement in the convective water flow. The three treatments with 400 kg/ha 20-10-10 plus 50 kg P/ha showed no differences in P concentration in the top 10 cm; however, they were much less than the 1600 kg/ha treatment, and
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126 Table 6-7. Column Mehlich I-extractable soil P by treatment and depth. Fertilizer application treatmentst Depth No Fert. 100 kg/ha 400 kg/ha x 4 appl. preplant 400 kg/ha preplant high rain 1600 kg/ha preplant cm ---------Mehlich I-extractable soil P (ug/g) ----------0 5 3.7d* 22b 19b llc 92a 5 10 3.5c llb 15b llb 130a 10 15 3.3b 3 0b 3 9b 3 4b 59a 15 20 2.9b 3.0b 3.6ab 3.4ab 3.8a 20 25 2.6a 2.8a 2.9a 3.2a 3. la 25 30 2.2a 2.4a 1. 9a 2.3a 2.0a 30 35 l.3ab 1.8a 0.7b l 5ab l.5ab 35 40 0.4b 1.4a 0.4b I.lab l. lab 40 45 0.3ab 0.9ab 0 2b 0.2b l.Oa 45 50 0. la 0 4a O.Oa 0.3a 0.4a 50 55 O.Ob 0.3ab O.Ob 0.7ab 0.8a 55 60 O.Oa 0.3a O.Oa 0. la 0 3a 60 65 0 2a 0.4a O. la O. la O Oa 65 70 O.Oa 0.4a 0. la O.Oa O.Oa t The fertilizer indcated was 20 10-10 (N-P 2 0 5 -K 2 0) In addition, triple superphosphate was applied to give a ratio of 1 kg P/8 kg 20-10-10. Means in the same row followed by the same letter are not significantly different at a 95% level of probability as determined by Duncan's Multiple Range Test
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E (.) w 0 LL er: => en 0 er: LL I a. w 0 0 30 50 60 70 MEHLICH I EXTRACTABLE P (ug/g) 25 50 75 100 125 No fertilizer -0-100 kg/ha X 4 applications l~ 400 kg/ha preplant -A400 kg/ha preplant high rainfall - 1600 kg/ha preplant Fig. 6-10. Concentrations of Mehlich I-extractable Pin soil columns at the end of simulated rainy seasons. The fertilizer indicated was 20-10-10 (N-P 2 0 5 -K 2 0) In addition, triple super phosphate was applied to give a ratio of 1 kg P/8 kg 20-10-10. 127
PAGE 135
128 Table 6-8. Column Mehlich I-extractable soil K by treatment and depth. Depth No Fert. Fertilizer application 100 kg/ha 400 kg/ha x 4 appl. preplant treatmentst 400 kg/ha preplant high rain 1600 kg/ha preplant cm -------Mehlich I-extractable soil K (ug/g) --------------0 5 5 10 10 15 15 20 20 25 25 30 30 35 35 40 40 45 45 50 50 55 55 60 60 65 65 70 lOlb* 114c 83c 45c 27c 20c 16d 12b 10b 9.Sb 7.3b 6.5b 6.7b 4.7b 191a 157bc 150bc 112bc 62bc 37bc 23cd 17b 14b llb 8.8b 8.6b 7.3b 7.8b 142ab 179b 167b 149ab 106b 69b 42bc 28b 17b 13b lOab 8.0b 6.2b 5.0b 98b 145bc 137bc 122bc 91b 69b 46b 29b 19b 13b 14ab 7.4b 6.0b 6.5b 182a 300a 258a 211a 174a 122a 102a 78a 58a 36a 22a 20a 15a 16a t The fertilizer indcated was 20-10-10 (N-P 2 0 5 -K 2 0). In addition, triple superphosphate was applied to give a ratio of 1 kg P/8 kg 20-10-10. Means in the same row followed by the same letter are not significantly different at a 95% level of probability as determined by Duncan's Multiple Range Test.
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E (.) UJ () < LL. a: ::::> (/) 0 a: LL. :::c a. UJ 0 0 MEHLICH I EXTRACTABLE K (ug/g) 100 200 300 4) 6 Ill} _ ~--~ 6 kJ6 0 --G u. No f erti6zer 100 kg/ha X 4 applications 400 kg/ha preplant 400 -..&.400 kg/ha preplant high rainfall j -a1600 kg/ha preplant : I Fig. 6-11. Concentrations of Mehlich I-extractable Kin soil columns at the end of simulated rainy season. The fertilizer indicated was 20-10-10 (N-P 2 0~-K 2 0). In addition, triple super phosphate was applied to give a ratio of 1 kg P / 8 kg 20-10-10. 129
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130 Table 6-9. Column Mehlich I extractable soil Ca by treatment and depth. Fertilizer agglication treatments No 100 kg/ha 400 kg/ha 400 kg/ha 1600 kg/ha Depth Fert. x 4 appl. preplant preplant preplant high rain cm ---------Mehlich I-extractable soil Ca (ug/g) ---------0 5 1760b* 1740b 1800ab 1830ab 2110a 5 10 1740b 1790b 1680b 1820b 2280a 10 15 1620a 1700a 1130b 1480ab 1310b 15 20 1190ab 1510a 963b 1263ab 985b 20 25 1010a 1170a 860a 910a 863a 25 30 837a 931a 782a 785a 741a 30 35 697a 747a 688a 695a 625a 35 40 640a 656a 632a 582a 583a 40 45 554a 569a 549a 498a 537a 45 50 505a 448a 550a 439a 517a 50 55 443a 405a 444a 410a 441a 55 60 416a 364a 400a 351a 392a 60 65 379a 348a 376a 323a 347a 65 70 323a 341a 351a 348a 335a t The fertilizer indcated was 20-10-10 (N-P O -K 0) 1 d 2 s \ In addition, triple superphosphate was app ,e to give a ra 10 of I kg P/8 kg 20-10-10. Means in the same row followed by the same letter are not significantly different at a 95% level of probability as determined by Duncan's Multiple Range Test.
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E C.) w (.) Cl) 0 cc u. J: ta. w 0 0 0 f I 10 1 i I 20 1 30 40 1 s J 60 MEHLICH I EXTACTABLE Ca (ug/g) 500 I I I I 1000 I I I 0 --G -t:s --~-II1500 2000 2500 I I I I I I ' -- No fertilizer 100 kg/ha X 4 applications i 400 kg/ha preplant I I I 400 kg/ha preplant high rainfall I 1600 kg/ha preplant 70 '---~-------------Fig. 6-12. Concentrations of Mehlich I-extractable Ca in soil columns at the end of simulated rainy season The fertilizer indicated was 20-10-10 (N-P 2 0 5 -K 2 0) In addition, triple super phosphate was applied to give a ratio of 1 kg P/8 kg 20-1012. 131
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Table 6-10. Column Mehlich I-extractable soil Mg by treatment and depth. Fertilizer application treatmentst 132 No 100 kg/ha 400 kg/ha 400 kg/ha 1600 kg/ha Depth Fert. x 4 appl. preplant preplant preplant high rain cm ---------Mehlich I-extractable soil Mg (ug/g) ----------0 5 395a* 250b 325ab 342ab 267b 5 10 387a 307b 296b 330b 285b 10 15 369a 34lab 223b 288ab 234b 15 20 307ab 352a 220b 279ab 202b 20 25 287ab 318a 216b 237ab 212b 25 30 260a 282a 224a 226a 216a 30 35 244a 263a 225a 232a 205a 35 40 247a 271a 230a 221a 213a 40 45 251b 287b 241b 220b 391a 45 50 264b 267b 300b 242b 412a 50 55 270b 271b 279b 253b 388a 55 60 287ab 274ab 279ab 241b 343a 60 65 282a 278a 280a 228a 307a 65 70 257a 278a 277a 245a 292a t The fertilizer indcated was 20-10-10 (N-P O -K 0). In addition triple superphosphate was 1 d 2 s 2. app ,e to give a ratio of 1 kg P/8 kg 20-10-10. Means in the same row followed by the same letter are not significantly different at a 95% level of probability as determined by Duncan's Multiple Range Test.
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E Q UJ () < u.. a: :::> Cl) ::E 0 cc u.. J: a.. UJ 0 133 MEHLICH I EXTRACTABLE Mg (ug/g) 0 100 200 300 400 500 0 rl ----r ---,-----,-----.-1 ---,, ---.----.---,I ----., ---,, --, --,1 -----,, --r------r-----r 1 --r ----r--r------r1 ,---, ---,-----,-----,----r[11 \ i 10 1 Jili l -'La",,. Ji. -._ -. i 0 20 I i J,.. o \ : _. / ff ,,....._ 0 No fertilizer 100 kg/ha X 4 applications 30 400 kg/ha preplant I i ---o-6 --~-400 kg/ha prepiant high rainfam : 40 50 60 -------,, / \ I 1600 kg/ha preplant Fig 6-13. Concentrations of Mehlich I-extractable Mg in soil columns at the end of simulated rainy seasons. The fertilizer indicated was 20-10-10 (N-P 2 0~-K 2 0). In addition, triple super phosphate was applied to give a ratio of 1 kg P/8 kg 20-10-10. I I I
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134 higher than the non-fertilized control. This indicated that the applied P stayed where it was placed. The three moderately fertilized treatments showed no apparent leaching of P from the top 10 cm to greater depths. Phosphorus did exhibit some leaching to the 10 to 15-cm region in the highest fertilizer application rate treatment, where high localized P concentrations were able to maintain high soil solution concentrations and overcome adsorption, allowing some movement to greater depths. Below 15 cm, small and seemingly erratic differences in P concentrations existed between treatments that can not be readily attributed to treatment effects. The distribution of Mehlich I-extractable concentrations of Ca in the soil at the end of the water-application period is different from the distribution of the soil-solution concentrations of Ca for the same period. By the end of the water-application period, soil-solution concentrations of Ca were negligible in the surface but much higher in the 50 to 70-cm depths, whereas the concentrations of Mehlich extractable Ca were highest in the surface soil. The concentrations of Kin the soil determined by the two methods gave similar distribution patterns. By the end of the season, K concentrations were still highest in the surface soil, although the addition of the higher rates of fertilizer caused more leaching to greater depths. Of the three 400 kg/ha fertilizer rates, the split application resulted in the highest surface concentration and the least leaching with depth. This was due to the intermittent application of K to the surface throughout the season, thereby decreasing its contact with the leaching rainwater.
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135 All of the fertilizer treatments caused leaching of Mg from the surface soil to greater depths. The 400 kg/ha high-rainfall ferttilizer treatment caused the least displacement and leaching, whereas the 400 kg/ha split and 1600 kg/ha application rates displaced large quantities of Mg to the 10 to 35-cm and the 45 to 65-cm depths, respectively. The movement of Mg is attributable to the competitive ability of the applied solutes to dislodge adsorbed Mg to the soil-solution phase and increase its susceptibility to leaching with the downward convective-water transport. Crop Availability The movement of the nutrients in the soil columns may be used to support concepts regarding the effects of field-fertilizer treatments on yield responses. The rapid declines in the soil-solution concentrations of Ca, K, and Mg with the intensification of the rainy season (Fig. 63) after about 70 d were consistent with previously discussed (Chapters 4 and 5) indications that midto late-season leaching restricted grain yields, especially in the second planting season. Greater water transport increases the leachability of soluble nutrients. The presence of immobile-water regions and the preferential flow of water in larger pores decreases the speed of solute movement out of the root zone, but the increased water applications of the second season increased the susceptibility of the solutes to leaching The split-fertilizer applications to the columns were responsible for maintaining midto late-season (78 to 140 d; tasseling to harvest) concentrations of Ca, K, and Mg higher than the similar preplant application rate and at least as
PAGE 143
136 high as the 1600 kg/ha preplant rate. Comparing the responses to the two seasons, field-trial grain yields exhibited greater relative differences between the all-preplant and 4 x 1/4 split in the second planting season when the split application could increase late-season soil nutrient levels .. Comparisons of column and field end-of-season Mehlich extractable nutrient concentrations may also be used to explain the field-trial behavior. The Mehlich I-extractable concentrations of Kin the field surface soils were lowest among the four treatments receiving the fertilizer applications (Table 5-17), suggesting that the plants in the non-fertilized plots were unable to use available K, and thereby limited by nutrients other than K. Concentrations of column Mehlich extractable K from the 400 kg/ha treatments were higher than the non fertilized treatments to depths of 40 cm after which the concentrations were similar. The ability of the soil column to store the K from the highest fertilizer in the top 55 cm and experience only negligible leaching of K from the columns is a strong indication that K is not readily leached from the field soils. The leaching losses of Ca and Mg relative to their total content in the soil columns were not much above that of K. This indicated that if a pattern of selectivity of these cations exists, it minimally affects their overall susceptibility to leaching. There was a potential long-term loss of Mg from field soils undergoing N-P-K fertilization if rates were excessive The movement of a large quantity of the Mg from the surface to greater depths under high fertilization rates indicated the degree to which Mg could be forced from the root zone by cationic competition.
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137 Conclusions The leaching of Ca, K, and Mg were differentially affected by the fertilizer and water treatments. Although soil-solution concentrations of K decreased to near zero for all depths at the end of the water application period for all treatments, the loss of K from the column due to leaching was negligible. Except for the highest fertilization rate, Mehlich I-extractable K concentrations were greater than the non fertilized control in only the top 35 cm of the soil columns. Among the three 400 kg 20-10-10/ha plus 50 kg P/ha fertilizer treatments, the split application resulted in higher soil K concentrations in the top 20 cm. The predominance of Kin the split applications also affected the leaching of Ca and Mg The additions of K later in the season caused displacement and leaching of Ca to greater depths (20 to 30 cm) in the columns. A similar pattern was exhibited by the distribution of Mg. Among the 400 kg 20-10-10/ha plus 50 kg P/ha treatments, the split fertilizer applications had the lowest Mg concentrations in the top 10 cm of soil, but the highest concentrations of Mg in the 20 to 30-cm region. The competitive strength of K bonding to exchange sites in acid tropical soils relative to the divalent Ca and Mq also has been shown by Pleysier and Juo (1981) and Juo (1979). They found a "leachability" (preferred selectivity) sequence of K < Al z Caz Mg< Na on a Typic Paleudult from southern Nigeria. However, the distribution with depth
PAGE 145
138 of Mehlich I-extractable Ca, K, and Mg under the 1600 kg/ha treatment (548 kg Ca/ha, 368 kg K/ha, and 0.0 kg Mg/ha actually applied) suggests the slightly lower selectivity the soil has for K as compared to Ca Both Ca and K had concentration maximums at 5 to 10 cm. The concentration of K decreased gradually with depth, but remained above all other treatments until the 50-cm depth. The concentration of Ca exhibited a rapid decrease with depth such that at 15 cm Mehlich-1 concentrations of Ca were no different for the non-fertilized control treatment. This indicates that K moved more readily than Ca to the greater depths. The 1600 kg/ha fertilizer-application rate affected both the distribution of nutrients in the columns and the magnitude of nutrient leaching. In addition to the high concentrations of applied nutrients in the surface soils of the columns, the high fertilizer rate also induced redistribution of Mg from the surface to lower depth (45 65 cm). The high fertilizer application increased the leaching of Ca, K, and Mg. The high-rainfall treatment had no effect on the eventual location of Ca, K, and Mg. The high-rainfall treatment did cause the soil solution concentrations of Ca, K, and Mg to decrease earlier in the season than the similarly fertilized, low-rainfall treatment. However, the rainfall affected neither the distribution of Mehlich I-extractable nor the total leached amounts of Ca, K, or Mg. Therefore, the movement of greater quantities of water in the soil takes place in regions that have low solute concentrations. The increased repetitive washing of the macropores with greater rainfall has little effect on the overall
PAGE 146
139 distribution of the solutes in the soil columns, thereby indicating the importance of the immobile-water region as a region for storage of solutes in the soil mass
PAGE 147
CHAPTER 7 OVERALL CONCLUSIONS Introduction The industrial development of Africa requires concurrent advances in the productive capacity of the farmer to meet an ever-increasing demand for agricultural products. Success in the transformation from subsistence to market-driven surplus farming will depend on development and utilization of farming practices that are based on the efficient use of available resources. The preponderance of soils with low nutrient holding capacities under high-rainfall climates presents a formidable challenge to the scientific community to characterize the behavior of these soils and to aid in the development of resource-efficient management practices. Strict adherence to soil-fertility evaluation criteria developed in temperate-climate schools-of-thought may overlook the potential of "agriculturally poor or marginal land". The soil studied in this dissertation is a case in point. The presence of stones in high fixing soils actually reduces the quantity of P-adsorption sites per soil volume and land area. The size of farming systems in Africa is frequently limited by labor shortages during s~Jrt periods of peak demand (e.g., during immediate planting at the onset of rains). Development of fertilization schemes for these types of soils based on preplant application of non-leachable nutrients but repeated application 140
PAGE 148
141 of leachable nutrients throughout the season may be a method to take advantage of an otherwise marginal situation. It is important that we be imaginative in the evaluation of soil properties and the development of associated soil-management practices. The behaviors of crops, nutrients, and water in a stone-line soil have been examined in this dissertation to enhance the scientific knowledge that may contribute to development of appropriate-management technology Soil Characterization The top 70 cm (approximate root zone) of this Typic Gibbsiorthox has a relatively high amount of organic carbon, low cation-exchange capacity, and about 35% gravel (by volume). The gravel content in the Ap, Ac and Btc horizons is also sufficient (> 25%) to reduce rooting depth and cause root deformities (thickening and crookedness), thereby reducing the ability of the plant to exploit greater depths of soil (Babalola and Lal, 1977a and 1977b; Vine and Lal, 1981). The fine fraction is composed of residual primary and secondary minerals which have limited capacity for both holding nutrients from leaching and from providing soil nutrients from mineral weathering. The fine fraction has the capacity to adsorb large quantities of Pin forms unavailable for plant uptake (Fig. 3-2). The soil column/fertilizer-leaching study can also be evaluated as a short term P adsorption/incubation experiment. Only 10% of a 804 kg P/ha application was Mehlich I-extractable after 30 d of soil contact. Only 5~ of a 201 kg P/ha application was Mehlich I
PAGE 149
142 extractable over the same period (Table 5-6). A 10% reduction in adsorption of P could double the amount available for plant uptake The shape of the soil moisture-release curve and the behavior of tritiated water indicated that the soil had a broad pore-size distribution. At saturation the soil exhibited a high degree of preferential water flow (by-passing), and held nearly 50% of its water in stagnant or immobile regions. The porosity of the gravel accounted for about 20% of the total porosity. Even if all of the pore volume of the gravel held immobile water, 80% of the immobile water was still associated with the fine fraction. The combination of by-passing and of large immobile-water regions is well documented for highly structured, aggregated soils. These properties allow for the rapid downward transport of surface-applied water along with only minimal mixing with water in immobile regions. Crop Response The response of corn and beans to fertilizer scheduling and plant density treatments indicated that late-season leaching of nutrients was more detrimental to grain yields than early-season moisture stress. Delaying the planting date 30 d, and subjecting the crops to about 40% more rainwater, resulted in 50 and 70% reductions in beanand corn grain yields, respectively. Within the later planting, there was a greater grain-yield response to increased splitting and later application of mobile nutrients.
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143 Reductions in yields due to early-season moisture stress appeared minimal for crops planted during the local, traditional planting season at the initiation of the rains in March. The unusually slow start of the rainy season during the field trial apparently decreased seedling emergence. The high-density, first-planting treatments had 150% the number of plants of the low-density treatments, but yielded only 13% more grain. The densities in the "low emergence" plots were about 75% of the lowdensity treatments. Therefore, even a 20% reduction in yield among the low-emergence plots compared to the regular low-density plots would still have doubled yields for the plots of the second planting season. Delays in second growth season subjected the crop to more late-season rain, greater losses of nutrients due to increased leaching, and associated reductions in grain yields. Nutrient Leaching Soil solution and Mehlich I-extractable concentrations of Ca, K, and Mg were differentially affected by fertilizer and simulated rainy season water treatments to the soil columns. The soil-solution concentrations of Ca, K, and Mg declined earlier in the season under a high-rainfall simulation than for a similarly fertilized low-rainfall regime. Higher rainfall caused more washing of the larger pores (preferential flow). Compared to columns receiving similar quantities of an all-preplant fertilizer treatment, split-fertilizer applications composed primarily of K, N0 3 and NH 4 resulted in lower overall concentrations of soil-solution Ca, K, and Mg early in the season. The
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144 split applications caused end-of-season Mehlich I-extractable K to be highest in the surface soil; however, the distribution of Ca and Mg indicated that there was leaching from the top 10 cm to the 15 to 30-cm region. High fertilizer rates caused a large displacement of Mg to regions lower in the columns. Leaching of K past the 70-cm depth was negligible, and the same as for the non-fertilized columns, for all but the highest fertilization rate. The columns under the highest fertilization rate lost only 4% of the K fertilizer to leaching. Leaching of Ca and Mg from the columns increased with increasing application rates of fertilizer, but was unaffected by application splitting or by higher simulated rainfall amounts. The loss of data concerning the movement of N0 3 in this soil is unfortunate, because N is generally considered to be the nutrient most susceptible to leaching. Since field grain yields showed the greatest response to split-fertilizer applications, and since the columns studied indicated that the leaching of K is minimal even under high fertilization rates, there is considerable circumstantial evidence to indicate that the leaching of N0 3 is of agronomic importance.
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APPENDIX A SOIL PROFILE DESCRIPTION Profile number: 1 Soil name: none Higher category classification: clayey-skeletal, oxidic, isohyperthermic, Typic Gibbsiorthox; USDA, Soil Taxonomy, 1975. Date of examination: 15 November 1986 Authors: Eric van Ranst, Johann DeBave, and Paul R. Anamosa Location: 1 km north of the Leppo primary school and 60 m west of the road passing through the Leppo quarter of the village of Bafou, in the Northwest Province of the Federal Republic of Cameroon. Approximately 5 31' N., 10 5' E. Elevation: 1580 m Land-form: 1. physiographic position: convex slope (14%) 2. surrounding land forms: steeply dissected 3. microtopography : contour furrowed Slope on which profile is sited: 14% (class 4) Vegetation or land-use: Mixed cultures of corn, Zea mays L., and beans, Phaseolus vulgaris L Climate: Annual rainfall 1900 mm; dry season November to March (60 mm). Parent material: Basalt and volcanic ash. Drainage: Class 4 well drained. Moisture conditions in profile: profile moist throughout. Depth to groundwater table: Unknown; probably greater than 4 m; no appare11t influence on the prof i 1 e. Presence of stones and rock outcroppings: surface stones present; rock outcroppings are a common characteristic of geomorphology. Evidence of erosion: agricultural activity limits visible signs of active erosion. 145
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146 Human influence: very slight, confined to the plow layer. Profile Description: Ap 0 11 cm Ac 11 22 cm Dark reddish brown (5 YR 3/2) moist and dark reddish grey (5 YR 4/2) dry, slightly gravelly clay; weak medium to coarse subangular blocky breaking to moderate crumb; slightly sticky, slightly plastic, friable; no cutans, few iron impregnated basaltic saprolite, averaging 1 3 cm in diameter; abundant fine and few medium roots; abrupt smooth boundary. Very dark grey (5 YR 3/1) moist and dark reddish brown (5 YR 3/2) dry, very gravelly clay; very frequent gravel, 45% by volume, coarse to very coarse iron-impregnated basaltic saprolite, slightly rounded platy and blocky; too much gravel to describe structure; slightly plastic, slightly sticky, friable; cutans; common fine roots; clear smooth boundary. Btc 22 72 cm Red (2.5 YR 4/6) moist and red (2.5 YR 4 6) dry, gravelly heavy clay; fine to coarse gravel of iron-impregnated basaltic saprolite, blocky and rounded with yellow coatings; moderate medium subangular blocky; very few thin patchy clay coatings; sticky, plastic, friable; very few fine roots; clear smooth boundary. 28Ct 72 138 cm Red (2.5 YR 4/6) moist and red (10 R4/6) dry, clay; moderate medium subangular blocky; sticky, plastic, friable; few thin patchy cutans; few, medium, distinct, sharp boundary purple mottles with white centers; pedotubules; very few, fine, roots; gradual smooth boundary. 2CB 138 194+ cm Red (10 R 4/6) moist clay ; moderate, medium, angular to subangular blocky; sticky, plastic, friable; very few thin patchy clay coatings; many medium and coarse distinct sharp purple mottles with white centers; very few, fine, roots.
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APPENDIX B CROP COMPONENT YIELDS Table B-1. Corn grain yields. Fertilizer application schedule 0 1 2 3 4 0 1 2 3 4 Corn grain Low density High density Rep 1 Rep 2 Rep 1 Rep 2 ----------------------kg/ha-----------------------First planting season 375 306 345 264 2175 1970 2222 1923 2375 2536 2937 2871 2136 2368 2995 2378 2760 2207 3249 2824 Second planting season 34 39 24 10 686 748 793 629 741 907 1310 978 757 1034 1019 863 1164 1177 976 1222 147
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148 Table B-2. Corn stover yields. Fertilizer Corn stover application Low densitl High densitl schedule Rep 1 Rep 2 Rep 1 Rep 2 ----------------------kg/ha -----------------------First planting season 0 1356 1238 1822 1711 1 4022 4119 6409 4253 2 3103 3576 5667 4082 3 2928 3644 4771 4250 4 3758 3444 4763 3637 Second planting season 0 1340 835 1825 738 1 3264 4443 5035 4479 2 2855 3007 4877 5304 3 3342 3168 4192 4644 4 3450 3029 3355 4204 Table B-3. Bean grain yields. Fertilizer Corn stover application Low densitl High densitl schedule Rep 1 Rep 2 Rep 1 Rep 2 ----------------------kg/ha -----------------------First planting season 0 166 208 242 124 1 300 341 361 295 2 352 328 418 375 3 443 373 404 315 4 397 466 373 444 Second planting season 0 102 131 127 108 1 169 198 252 204 2 228 202 223 272 3 281 229 251 321 4 310 261 298 291
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150 Claassen, M.M and R.H. Shaw. 1970. Water deficit effects on corn. II. Grain components. Agron J 62:652-655. Coats, K.H., and B.D. Smith. 1964. Dead-end pore volume and dispersion in porous media. Soc. Petr. Eng. J. 4:73-84. Collinet, J. 1969. Contribution i l'etude des "stone-lines" dans la region du Moyen-Ogooue (Gabon). Cah. ORSTOM, ser. Pedal. VII:3-42. Cronan, C.S. 1978. A soil column tension lysimeter that minimizes experimental edge effects. Soil Sci. 125:306-309. Danielson, R.E., and P.L. Sutherland. 1986. Porosity. ln A Klute (ed.) Methods of soil analysis. Part 1. 2nd. ed Agronomy 9:443-462. Dao, T.H., and T.L. Lavy 1978. Extraction of soil solution using a simple centrifugation method for pesticide adsorption-desorption studies. Soil Sci. Soc. Am. J. 42:375-377. Davidson, J.M., P.S.C. Rao, R.E. Green, and H.M Selim. 1980. Evaluation of conceptual models for solute behavior in soil water systems. ln A. Banin and U. Kafkafi (ed.) Agrochemicals in soils. Proc. of Inter Congress of ISSS held at Jerusalem, Israel. June 1976. Permagon Press, London. de Craene, A. 1954. Les sols de pedimentation ou les sols i "stone line" du nord-est du Congo belge. Int. Congr. Soil Sci Leopoldville, 4:451-460. de Heinzelin, J. 1955. Observations sue la genese des nappes de gravats dans les sols tropicaux. Publ. I.N E.A.C., Ser. Science 64:1-37. Denmead, Q.T and R H. Shaw. 1960 The effects of soil moisture stress at different stages of growth on the development and yield of corn. Agron. J. 52:272-274. Doorenbos, J., and W.O. Pruitt. 1975. Guidelines for predicting crop water requirements. FAQ Irrig. and Drain Paper #24, FAQ UN, Rome. Dudal, R. 1980. Soil-related constraints to agricultural development in the tropics. p. 23-37. ln Priorities for alleviating soil-related constraints to food production in the tropics. International Rice Research Institute Los Banos Philippines. Dyer J .A., and A.R. Mack 1984. The versatile soil moisture budget: Ver 3. Tech Bull. 1984-lE. Research Branch A~riculture Canada.
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157 Unger, P.W. 1971b. Soil profile gravel layers: II. Effect on growth and water use by a hybrid forage sorghum. Soil Sci. Soc. Am. J. 35:980-983. USAID, 1985. Plan for supporting agricultural research and faculties of agriculture in Africa. United States Agency for International Development. U.S. Govt. Printing Office, Washington, D.C. Vallaeys, G., P. Silvestre, M.J. Blackie, and C.L. Delgado. 1987 Development and extension of agricultural production technology. p. 148-160. ln J.W. Mellor, C.L. Delgado, and M.J. Blackie (ed.), Accelerating food production in sub-Saharan Africa. John Hopkins Univ. Press, Baltimore. van der Ploeg, R.R and F. Beese. 1977. Model calculations for the extraction of soil water by ceramic cups and plates. Soil Sci. Soc. Am. J. 41:466-470. van Genuchten, M.Th. 1981. Non-equilibrium solute transport parameters from miscible displacement experiments. Res. Rep. 119, U.S. Salinity Lab. and Dept. of Soil and Environ. Sci., Univ. of Calif., Riverside. van Genuchten, M.Th. 1985. A general approach for modeling solute transport in structured soil. Memoires IAH 17:513-526. van Genuchten, M.Th., and F.N. Dalton. 1986. Models for simulating salt movement in aggregated field soils. Geoderma 38:165-183. van Genuchten, M.Th., and P.J. Wierenga. 1976. Mass transfer studies in sorbing porous media: I. Analytical solutions. Soil Sci. Soc. Am. J. 40:473-480. van Genuchten, M.Th., and P.J. Wierenga. 1977. Mass transfer studies in sorbing porous media: II. Experimental evaluation with tritium ( 3 H 2 0). Soil Sci. Soc. Am. J. 41:272-278. van Genuchten, M.Th., and P.J. Wierenga. 1986. Solute dispersion coefficients and retardation factors. In: A. Klute (Editor), Methods of soil analysis. Part 1. 2nd ed. Agronomy 9:1025-1054. Vine, R.N., and R. Lal. 1981. The influence of sands and gravels on root growth of maize seedlings. Soil Sci. 131:124-129. Warrick, A.W., J.W. Biggar, and D.R. Nielsen. 1971. Simultaneous solute and water transfer from an unsaturated soil. Water Resour. Res. 7:1216-1225. Wood, W.W. 1973. A technique using porous cups for water sampling at any depth in the unsaturated zone. Water Resour. Res. 9:486-442. Yuan, T.L. 1959. Determination of exchangeable hydrogen in soils by titration method. Soil Sci 88:164-167.
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158 Zandstra, H.G E.C. Price, J.A. Litsinger, and R.A. Morris. 1981. A methodology for on-farm cropping systems research International Rice Research Institute, Los Banos, Philippines.
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BIOGRAPHICAL SKETCH Paul R. Anamosa was born in Washington D.C. on 22 Nov. 1954. He spent the early years of his life in rural Maryland, scavenging the woods near his home for the simple pleasures nature bestows to little boys; tadpoles, crayfish, and the glee of song-birds resounding from the protective canopy of a forest. At age ten he and his family moved to the stark desert of New Mexico, where discovery of the joys of nature follows the development of an appreciation for browns and grays, and the patience to look for more subtle evidence of life and change. Paul graduated from Sandia High School in Albuquerque in 1973, and moved to Las Cruces, to attend New Mexico State University. His interests traversed the life sciences and in May of 1978, he graduated with Bachelor of Science degrees in Biology, Agricultural Biology, and Agricultural Pest Management and a Bachelor of Arts degree in Chemistry. He began graduate studies in entomology at Colorado State University, only to find that pest management was a dirty job, that somebody had to do it, but not him. In 1979, sponsored by the U.S. Peace Corps, Paul became an extension agent in the mountains of Jamaica within the Ministry of Agriculture. There he saw the joys and miseries of people intimately exposed to the power, fruits, and deprivations of nature There he learned to speak patois, roast cashews, eat mangoes, and listen to the old, the young, and the Rastafarians talk of life, honesty, and 159
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160 heartbreak. He was impressed by the wasted human potential and the strategies of individuals to find challenges, comfort, and happiness in an exacting environment. In 1982, Paul enrolled in graduate studies in soil chemistry at the University of Wisconsin to deepen his understanding of soil processes. Paul enrolled in a Ph.D. program at the University of Florida in 1984 to hone his skills in the utilization of scientific knowledge to aid in the development of sound agricultural policy and technology for agricultural developing regions of the world He interrupted his studies to take a teaching position with the University Center of Dschang, Cameroon where he was able to conduct his dissertation field studies. In 1985, Paul married Frances Adele Egan a friend and confidante since his time in the Peace Corps. They have since been adopted by two cats, Ferguson a native of Cameroon, and Abigail, a kitten hellbent on contributing to universal entropy.
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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 cert i fy 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. 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. Brian L. McNea l 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 Peter Nkedi-Kizza Assistant Professor of Soil Science
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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. Pe E. Hildebrand Professor of Food and Resource Economics I certify that I have read this study and that in my op1n1on 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. Gerald Kidder Professor of Soil Science This dissertation was submitted to the Graduate Faculty of the College of Agriculture and to the Graduate School and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy. August 1989 Dean, of Agric ture Dean, Graduate School
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