Structure, function, and stability of intercropping systems in Tanzania


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Structure, function, and stability of intercropping systems in Tanzania
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xi, 399 leaves : ill. ; 28 cm.
Benedict, Faye Frances, 1951-
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Subjects / Keywords:
Agriculture -- Tanzania   ( lcsh )
Intercropping -- Tanzania   ( lcsh )
Mischkultur   ( swd )
Tansania   ( swd )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )


Thesis (Ph. D.)--University of Florida, 1982.
Includes bibliographical references (leaves 388-398).
Statement of Responsibility:
by Faye Frances Benedict.
General Note:
General Note:

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University of Florida
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notis - ABU5872
oclc - 09260659
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Copyright 1982


Faye Frances Benedict


This project was made possible by the support of many people and

agencies. The Center for African Studies of the University of Florida

provided National Defense Foreign Language Fellowship support both

before and after the field research phase, which was funded by Fulbright-

Hays and American Association of University Women doctoral research

grants. I am also grateful to the Tanzania National Scientific Research

Council, the Tanzania Ministry of Agriculture, and the University of Dar

es Salaam for permission to conduct the research in Tanzania, and for

the substantial logistic support they provided. Jack Ewel, Ariel Lugo,

Hugh Popenoe, Haig Der-Houssikian, and Hossiah Kayumbo stimulated me to

initiate the project, and provided invaluable guidance and support to

carry it through to completion. R. Hunt Davis, Jr., also provided useful

comments on the final draft.

I am particularly grateful to Asha, Abbas, Ngatimwa, and the other

Tanzanian workers who not only provided superb field assistance, but also

shared their ideas, humor, and culture with me. Special thanks go to my

parents, who have supported my educational efforts for many years, and to

my husband, Harald, for the many kinds of help and support he has given.


ACKNOWLEDGEMENTS. . . ... ...... .iii

ABSTRACT. . . ... . x



The Ecological Issues . . 2

Diversity, Stability and Competition ........ 2
Definitional Problems. . 4
Stress and its Relation to Productivity and
Stability .................. .. 5

The Agronomic Issues ................. 7

Productivity in Crop Mixtures .......... 8
Resource Partitioning and Competition Reduction. 9
Pests and Diseases . .. 11
Stability and Risk . ... 12
Methodological Problems. . ... 13
A New Index ................... 16

Hypotheses. . . .. 19

Experimental Approach to the Hypotheses .. 21

The Tanzanian Agricultural Setting.. .. 21

Overview ............... 21
Survey of Local Crop Systems ............ 24
Methods . . .. .24
Results ................ .. 25

TWO METHODS . ...... ...... 32

The Study Site. . . .. .... 32

Location and History. ... . 32
Climate. . .. 33
Soils. . . .. 34

Experimental Design . . .

Crop Systems, Stress Treatments, and
Planting Density . . .
Plot Layout. . . .

Plot Management . . .

Agronomic Management . .
Application of Stress Treatments . .

Measures of System and Species Response .

Growth and Productivity. . .
Total and edible biomass. . .
Biomass at physiological maturity (flowering)
Leaf mass at defoliation. . .
Miscellaneous productivity measures .
LAI and canopy cover . .
Cowpea third-leaf area. . .
Specific leaf mass. . .
Root biomass. . .
Stem length . . .
Survivorship. . .
Resource Use . . .
LAI . . .
Root biomass. . .
Soil moisture . .
Labor . . .
Resistance to Natural Stressors. . .
Weed growth . . .
Lodging . . .
Pests and diseases. . .

Methods of Analysis . . .

Three Types of Analysis. . .
Absolute comparison of system performance
Comparison of intercrops and
corresponding monocultures. . .
Comparison of species performance in
intercrops and monocultures . .
Analyses of Variance . .
Analysis of Stability. . ....


. 73


Introduction. . . .

Results: Productivity Differences Among Systems .. ...
Measures of Biomass Accretion. . .
Edible and total aboveground biomass
at harvest. . . .


Total aboveground biomass at flowering. ... 86
LAI and canopy cover. . ... 86
LAI at flowering. . .. 95
Leaf mass at defoliation. . ... 98
Root biomass. . ... 101
System fullstandedness. . ... 104
Measures of Biomass Distribution .. .107
Percent monocot LAI . ... 107
Allocation ratios . .. 111
Root/shoot ratios .. . .. 111
Correlations Among Productivity Measures .116
Year-to-Year Productivity Differences. .. .119
Overall System Performance . ... 119
Overall Response to the Stress Treatments. ... .123

Results: Productivity in Intercrops and Corresponding
Mlonocultures. . . 123

Edible and Total Aboveground Biomass at Harvest. 127
LAI and Canopy Cover .. .... .135
Leaf Mass at Defoliation . ... 135
Root Biomass, Year 1 ............... 135
Aboveground Biomass, LAI, and Root Biomass
at Flowering, Year 2 . ... 140
Fullstandedness. . . ... 140
Yield Equivalent Ratios (YERs) . .. 140

Results: Productivity of Species from System to System. 141

Introduction . .... ... 141
Measures of Biomass Accretion. . ... 144
Edible and total aboveground biomass
at harvest. . . 144
Aboveground biomass at flowering. .. .161
LAI . . 161
LAI at flowering. . ... 165
Leaf mass at defoliation. . ... 165
Root biomass. ............... 168
Survivorship and mortality. . ... 168
Stem length . ... 179
Measures of Biomass Distribution ... .186
Allocation ratios . ... 186
Root/shoot ratios . ... 187
Specific leaf mass. . ... 193
Miscellaneous yield parameters. ... 193
Correlations Among Direct Productivity Measures. .. 204
Correlations Among Indirect and Direct
Productivity Measures. . ... 215
Summary of Year-to-Year Differences in
Productivity . .... .222
Summary of System-to-System Productivity
Differences. . .... .222
Summary of Response to Stress Treatments 233

Discussion. . . .

Comparison with Regional Yields. . .
Food Yield as a Measure of Productivity. .
Absolute Comparisons Among Systems .. ....
Comparisons of Intercrops with
Corresponding Monocultures . .
Response to the Stress Treatments .. ...
Effects of the Stress Treatments on Intercrop
Advantage . . .
Stress and Biomass Distribution. . .
Species YER as a Measure of Competitiveness
and Aggressiveness . .
Height and aggressiveness . .


Introduction . . .

Results . . .

System Stability . . .
Year-to-year fluctuations . .
Plot-to-plot variability within years .
Responsiveness to the stress treatments .
Stability at different intensities of stress.
Stability with respect to competition ..
Summary . . .
Species Stability. . .
Year-to-year fluctuations . .
Plot-to-plot variability within years .
Responsiveness to the stress treatments .
Summary of species stability in
intercrops and monocultures . .
Stability with respect to competition ..

Discussion: Sources of System Stability .

An Artifact of Coefficient of Variation
Calculations? . .
Masking of Species' Fluctuations .
Buffering of Species' Fluctuations .
Increased Stability of Component Species .
Stress Intensity and Stability .
Interaction Among Stressors. .


Results . . .

Weed Biomass . .
Maize and Sorghum Lodging. .
Striga . . .















. 277
. 282

. 286


. 291

Rats . . ... .. .. 294
Maize Pests and Diseases . ... 294
Sorghum Pests and Diseases . ... 294
Cowpea Pests and Diseases. . ... 298
Pumpkin Pests and Diseases . ... 308
Summary of Pests and Diseases in Intercrops
and Monocultures . ... .312
Effects of Pests on Productivity ... 312
Weeds . . ... .. 317
Maize and sorghum pests . ... 321
Cowpea pests. . ... 325
Pumpkin pests . ... 327
Summary of Diversity-Pest Level Productivity
Relationships. . . ... 328
Pest Stability . . ... 331

Discussion. . . ... .332

Previous Studies . ... 332
Pest Levels. . .... .334
Pest Stability . ... 336
Plot Size, and the Range of Diversity Tested 337

SIX RESOURCE USE. . . ... .338

Introduction. . . ... 338

Light, Water, and Nutrient Use . 338

Results. . . ... .338
Amount and vertical distribution of LAI 338
Amount and vertical distribution of roots 343
Soil moisture . ... 343
Discussion . . ... ... .345
Efficiency of total resource use. .. .345
LAI and light use . ... 348
Root biomass amount and distribution:
use of soil resources ............ 351
Complementary moisture and
nutrient response . ... 352
Efficiency of nutrient use. . ... 353
Efficiency of water use . ... 354
Compensation. . ... 355
Temporal partitioning . ... 359

Human Labor Use . . ... .. .360

Results. . . ... .360
Labor use efficiency. . ... 367
Discussion . . ... ... .371



Species Composition of Intercrops . .. 374

Environmental Stability and Intercropping .. 378


NINE CONCLUSIONS .................... . 381


BIOGRAPHICAL SKETCH ........................ 399


Abstract of Dissertation Presented to the Graduate Council
of the University of Florida in Partial Fulfillment of the Requirements
for the Degree of Doctor of Philosophy



Faye Frances Benedict

December, 1982

Chairperson: John J. Ewel
Major Department: Department of Botany

Effects of crop diversity on system productivity and stability

formed the subject of a two-year study of nine Tanzanian cropping systems

and successional vegetation. Planting density was controlled so that

intercrop-monoculture differences were due solely to diversity, not

density, effects.

Various measures of seasonal biomass accretion were used as

indicators of net productivity. The ordering of systems by productivity,

from high to low, was successional vegetation, maize-sorghum, maize-

pumpkin, sorghum, maize-sorghum-cowpea-pumpkin, sparse maize, recommended

density maize, maize-cowpea, cowpea, and pumpkin. Intercrop performance

was compared with that of a mixture of corresponding monocultures by

Yield Equivalent Ratio (YER), a new index defined as the ratio of yield

in intercrop to expected yield based on planting densities and monoculture

yields. By this measure, intercrops were more productive than correspond-

ing monocultures, but the advantage was lowest under a 50 percent

defoliation treatment. Species YERs were greater than one for maize,

sorghum, and cowpea, and less than one for pumpkin.

The productivity advantage of diverse systems was due primarily to

a greater amount and a more even distribution of leaves and roots, temporal

partitioning of growth, complementary resource response curves, and

compensatory growth. Several of the 23 pests and diseases inventoried

were correlated with crop yield, but few were significantly more or less

abundant in intercrops than monocultures. Efficiency of labor use in

intercrops was equal to or greater than that in monocultures.

The ordering of systems by yield stability (constancy), from high

to low, was sorghum, maize, succession, four-crop intercrop, cowpea, and

pumpkin. Intercrop stability was higher than that of component crops

grown as monocultures. Yield of sorghum, cowpea, and pumpkin, but not

maize, was less stable in intercrops than monocultures; decreased species

stability in diverse systems may enhance system stability through compen-

satory growth. Stability was greatest under low levels of stress.



Parallel lines of research in the agronomic and ecological sciences

have focused on the effects of vegetation diversity on ecosystem pro-

ductivity and stability. The agronomic approach has consisted mostly

of experimental studies of various crop combinations,with relatively

little attention (until recently) to the causes of differences in

yield, and considerable confusion over the indices used to compare

the performance of diverse and simple systems. Comparisons of systems

of unlike density have further confused the issue. The ecological

approach has emphasized theoretical mechanisms underlying the

diversity-stability relationship, mathematical models of competition

and resource partitioning, computer simulation of diverse and simple

systems, and broad comparative ecosystem studies, with relatively few

controlled experimental studies. In this study I integrate these two

approaches by using agronomic systems to conduct a controlled

experiment testing the effects of diversity on various aspects of

ecosystem structure and function. The experimental design allows

conclusions to be made regarding the effects of spatial diversity,

not confounded by varying species composition and planting density.

The Ecological Issues

Diversity, Stability, and Competition

The question of whether increasing species diversity increases

stability, and the relationship of diversity and stability to productivity,

have been recurrent issues in the ecological literature for many years.

Several volumes have been devoted to these topics, drawing on evidence

ranging from global latitudinal comparisons to laboratory microcosm

experiments, mathematical models, and computer simulations (Woodwell and

Smith 1969, May 1973, Pielou 1975, van Dobben and Lowe-McConnell 1975),

van Voris 1976). The issue is still far from being resolved, but

Goodman (1975) and Murdoch (1975) concluded that although species

diversity per se is not a good predictor of stability (especially in

agricultural and model systems), certain correlates of diversity may be

causally related to stability.

Current literature tends to substitute more specific, mechanistic

hypotheses for the generalized diversity-stability question. A large

increase in entomological studies, for example, has begun to elucidate

the roles of resource concentration, pest dilution, feedbacks between

trophic levels, time lags, search behavior, microclimate, and natural

enemy dynamics on pest levels, pest stability, and various methods of

pest control in diverse and simple systems (reviewed by de Loach 1970,

Southwood and Way 1970, Hassell and May 1973, Comins and Blatt 1974,

Huffaker 1974, van Emden and Williams 1974, Barclay and van den Driessche

1975, Murdoch 1975, Murdoch and Oaten 1975, Shattock 1976, Trenbath

1976, Way 1976, van Emden 1977, Levins and Wilson 1980). No clear

conclusions can be drawn on theoretical grounds regarding the effects

of diversity on pests; there are many mechanisms that may stabilize and

reduce pest levels in simple systems as well as complex systems. Per-

haps the best way to summarize the theoretical effect of plant diversity

on pest levels and pest stability is that it is unpredictable due to the

complexity of interactions involved.

There has also been a resurgence of interest in competition

reduction through resource partitioning as a mechanism that may increase

productivity and stability in diverse systems (reviewed by Pianka 1981).

May (1981) and Nunney (1980) modeled growth rates of two interacting

populations using Lotka-Volterra competition equations. Pianka (1981)

noted, however, that while these models are useful, they greatly

oversimplify the competitive mechanisms involved among species. It is

also likely that the competition coefficients in these equations vary

with growing conditions and planting diversity (Vandermeer 1981).

The theory of niche separation was presented by Pianka (1981),

and biological mechanisms of resource partitioning were reviewed by

Schoener (1974). Increased light use efficiency in canopies composed

of sun- and shade-adapted species was discussed by Donald (1961), Black

(1975), and Boardman (1977). Bray (1974) noted that root competition

for nitrogen and water can occur at low root densities due to those

substances' high mobility in soil, and Litav and Wolovitch (1971),

Parrish and Bazzaz (1976) and Berendse (1979) discussed the temporal

and spatial separation of root zones. Partitioning of nutrient element

demand was suggested by the results of Garten (1978), who showed that

concentrations of elements in plant tissue may vary from species to

species at a site.

The question of whether natural successional regrowth con-

stitutes the most effective possible use of resources at a site has

not been completely resolved. It may not be, at least in the

short term; pastures and sugar cane are two examples of human-

managed systems composed of a few highly productive species that

may produce more biomass on a site than succession (Odum 1971, Mott

and Popenoe 1977). Whether, and by what biological mechanisms,

the productivity and stability of successional systems can be improved

upon is still an open question.

Definitional Problems

The definitions of diversity and stability have been a source of

confusion. Hurlbert (1971) reviewed the various diversity indices in

use and concludes that they are not necessarily correlated, cannot be

shown to be useful measures of a meaningful community property, and

should be replaced by other measures of ecosystem characteristics more

appropriate to the specific issue at hand. Stability is an even more

confused concept; its meaning includes properties of constancy (both

absolute and in relation to the mean), predictability, resistance, and

resilience (Holling 1973, Goodman 1975, Murdoch 1975 and Orians 1975).

The most commonly used measures of species constancy are coefficient

of variation (e.g., Rao and Willey 1980 and other studies cited in

Trenbath 1974), and standard error of population levels or changes

(Watt 1965). Other measures of stability, less commonly used, relate

to degree and rate of response to perturbations (Orians 1975).

Constancy on the community level could be measured as coefficient

of variation or standard error of system attributes such as total

productivity or leaf area, rather than as population counts; Murdoch

et al. (1972) evaluated insect community stability with a similarity

index of two years' sweep net samples. Margalef (1969) proposed a

community stability index that is the sum of the quantity (biomass'biomass

half-life) for component species. Other measures of community stability

with respect to perturbations have been suggested by Orians (1975).

Stress and its Relation to Productivity and

The general definition of stress by Odum (1967) as diversion of

potential energy in response to a stressor is robust, and will be used

in this study in preference to that of Grime (1979), who limited the

term to chronic low-productivity conditions and excluded other possible

energy drains such as competition or disturbance. Lugo (1978) pointed

to the type of stressor, its duration (chronic vs. acute), point of

impingement, and degree of system adaptation as factors that will

determine the rate and degree of system response to stressors. Rapport

(1981) differentiated between "alarm" reactions in non-preadapted systems

and "coping" reactions in resistant, preadapted systems (an analogy with

human response to disease). Stress can be measured as decreased

productivity, increased energy drains, or acceleration of repair within

the system (Odum 1967). Stressors, like limiting factors, are thought

to interact multiplicatively, rather than additively, with respect to

productivity (Odum 1971, Lugo 1978).


If we accept the definitions of stress as reduced productivity,

and stability as low fluctuation in productivity, a link between the

two concepts becomes apparent. Stability can be viewed as a system's

ability to adjust to varying intensities of stressors to maintain

productivity at a constant level. Degree of preadaptation will largely

determine the stability of the system with respect to a stressor (as was

pointed out, in the context of pest populations, by Huffaker 1974,

Goodman 1975, and Murdoch 1975). If stressors do interact multi-

plicatively, then reduced intensity of some stressors (such as competition

or herbivore drains) should reduce the magnitude of productivity response

to other stressors. If diverse systems are more productive than simple

ones due to resource partitioning, reduced drains, or other factors, it

would not, then, be surprising to find that they are also more stable

than simple systems.

Environmental parameters such as level, constancy, and predictability

of limiting factors, describe the setting in which stabilizing biological

adjustment occurs. Such parameters may be critical in determining eco-

system productivity and stability across a range of environments (Zaret

1982). Environmental effects may also explain the noncorrelation of

productivity and stability in (high-productivity, low-stability) marsh

and pond ecosystems, and (low-productivity, high-stability) paramo

vegetation and marine benthos systems. It is within a given environment

that the biological mechanisms leading to system stability can best be

explored, since differences in system behavior due to differences in

environmental parameters are controlled.

The Agronomic Issues

Green Revolution technology assumed abundant supplies of fossil-

fuel energy at affordable prices. Dramatic increases in the cost of

energy inputs to agriculture (mechanization, improved seed, fertilization,

pesticide, irrigation) have raised the issue of how these inputs can be

used most effectively. Subsistence agricultural systems that evolved in

low-fossil-fuel-energy environments might contain features that optimize

the use of resources and/or reduce the risk of crop failure. One such

feature is the high diversity of crops frequently planted side-by-side

in traditional tropical agricultural systems.

The term intercropping has been widely used to designate simultaneous

planting of more than one species in the same field at the same time,

while "multiple cropping" is a more general term that includes relay

and series planting of the same or different species (Dalrymple 1971,

Beets 1975, Norman 1979). Kass (1978) used "polyculture" in reference

to any situation in which two or more crops are simultaneously grown

together; "multiple cropping,"to any situation in which more than one

crop is grown on a given area in one year; and "intercropping," to

simultaneous alternate-row plantings. The planting patterns used in

this study sometimes mixed species within rows, and therefore do not

conform to Kass's use of the term intercrop, but they do fit the

generally accepted use of the word by other researchers.

Intercropping, and other forms of multiple cropping, have been

credited with increased productivity, reduced risk of crop failure,

and reduced incidence of pests and diseases (Janzen 1973, Dasman et al.

1974, Trenbath 1975a, Gibson and Jones 1976, Papendick et al. 1976,

Pimentel 1976, Sanchez 1976). These systems are looked to with great

hope as potentially stable, productive, low-energy-input cropping

systems for the developing tropics.

Productivity in Crop Mixtures

The experimental evidence to back up claims of increased productivity

in crop mixtures is abundant, but the results are seriously confused by

the varying methods used to determine intercrop planting densities and

compare productivities. Kass (1978) gave an excellent review of the

massive literature on this topic. Regarding yield and resource use,

he concluded that by many measures polycultures are clearly superior to

monocultures in terms of yield. Trenbath (1974) reviewed 344 data

papers in which 60 percent of the two-crop mixtures studied yielded

more than the mean yield of corresponding monocultures. Significantly

more of the 572 mixtures reviewed by van den Bergh (1968) yielded more

than monocultures as measured by the Land Equivalent Ratio (see below)

than yielded less than monocultures.

Trenbath (1977) reviewed possible mechanisms responsible for

productivity increases in mixtures. Competition for light, water, and

nutrients may be reduced through temporal partitioning of growth and

resource demand, spatial partitioning of root and leaf systems,

differences in limiting factor response curves (different resource

"requirements"), shading and leaf angle effects on light utilization,

and shading effects on water use. These mechanisms typically result

in higher-than-monoculture yields of one species (the "aggressor"),

the lower-than-monoculture yields of the second species (the "sub-

ordinate"), and increased productivity of the system as a whole.


Increased productivity in mixtures may also be due to allelopathic or

allelophilic interactions, changes in the rhizosphere flora, mechanical

factors (e.g., wind protection and support for understory crops), and

reduced pest and disease impact as a result of reduced resource concen-

tration and microclimate effects (Trenbath 1974). Mixtures also have the

capacity for compensatory growth, or the exploitation by one species of

resources liberated through the demise of an unlike neighbor.

Resource Partitioning and Competition Reduction

The most convincing body of evidence of resource partitioning in

crop mixtures regards spatial partitioning of rooting zones (Kurtz et al. 1952,

Litav and Harper 1967, O'Brien et al. 1967, Whittington and O'Brian 1968,

Ellern et al. 1970, Raper and Barber 1970, Baldwin and Tinker 1972,

Kauraw and Minko 1972, Portas 1973, Nelliat et al. 1974, Allmaras et al.

1975a, 1975b, Trenbath 1975b, Nair 1979). Other aspects of competition

reduction have been less well studied. Complementarity of nutritional

and water demands was suggested by lover (1948, 1959), Kolb (1962), and

Davies and Snaydon (1973). Reduced competition for nitrogen may be

especially important in mixtures containing legumes (de Wit et al. 1966).

Light distribution in canopies, reduced leaf angles in the lower canopy,

and displacement of shade-adapted species with large leaves and lower

optimum leaf temperatures lower in the canopy can all contribute to

improved light use efficiency in mixed crop systems (Monteith 1965,

Parkhurst and Loucks 1972, Trenbath 1974, Terjung and Louie 1973).

Temporal partitioning of leaf and root development and grain-filling

was thought to be responsible for high intercropping yields in two

studies (van den Bergh and de Wit 1960, Kassam and.Stockinger

1973). Labor inputs have been quantified in a number of studies

(reviewed by Kass 1978), but the results are not clear, and there is

no obvious reason to expect labor inputs to be reduced in intercropping

systems. In the studies reviewed by Kass, labor use was usually higher

in intercrops than monocultures, but the reverse was sometimes also

found to be true. The reasons for differences in labor use have not

been investigated.

A number of researchers have proposed models and/or field measures

of competition among crops in mixtures. Vandermeer (1982) obtained

whole-system yield graphs (at all density combinations) from intercrop

competition equations based on the Lotka-Volterra equations. He also

showed (1981) that the same nonlinearity of yield-density functions

that allows species coexistence in nature (Lotka-Volterra equations) also

occurs in intercrops that yield more than corresponding monocultures

(Land Equivalent Ratio > 1). That the mathematical expression for

nonlinearity is of the same form in both cases should perhaps not, however,

be taken as proof that the competitive function and Land Equivalent

Ratio are "mathematically identical" criteria, as Vandermeer states.

Other models of competition are not derived from the Lotka-

Volterra equations, and seem to lack a theoretical common ground; the

relations between de Wit's (1960) crowding coefficient, McGilchrist's

(1965) measure of aggressivity in mixtures, Donald's (1963) competitive

index, and Willey and Rao's (1980) competitive ratio are not clear.

Part of the confusion stems from the varying use of per-plant and per-

area measures, and the lack of differentiation between competition within

a species and competition among species, which creates difficulties in

situations where overall planting density is not held constant. The

de Wit and Lotka-Volterra equations contain competition coefficients

that vary under different growing conditions and overall planting

densities; the other three measures do not contain such coefficients,

and are much more readily applied to field observations. Only the

de Wit and Lotka-Volterra equations can be easily expanded to include

more than two species, however.

As our awareness of the complexity of biological interactions in

intercrops increases, the use of simplified models and indices of

competition to explain yield differences become less and less tenable

on theoretical grounds. It is nevertheless desirable that researchers

agree on a few measures of species and system performance that are

robust under varying planting densities, crop composition, and number

of crops interplanted.

Pests and Diseases

Insect ecology has contributed much to our understanding of

mechanisms operating to determine pest levels and stabilities in agri-

cultural systems (see the numerous reviews listed above). Due to the

many complex interactions involved, differences in pest mobilities, plot

size effects, and many other factors, generalizations cannot be made

regarding the effects of intercropping on pests and diseases. The

field data available support this conclusion: .Kass (1978) and Trenbath

(1976) gave numerous examples of both increased and decreased pests and

diseases in intercrop systems. Pest stability is much less well

researched; virtually no data are available on that topic, and both

stabilizing and destabilizing mechanisms may operate in intercrop


Stability and Risk

Risk reduction is an especially critical issue in the developing

tropics, where obtaining at least a minimum harvest each year may be

more important than obtaining maximum average yield over a period of

several years. The term risk is not synonymous with ecological

instabilityt, however. Risk contains both a productivity and a stability

component, since a system having generally high productivity is less

likely to fall below a given disaster level than a less-productive

system. Productivity rather than stability may be the most important

factor determining degree of risk (Rao and Willey 1980). Stability

rather than risk was the parameter evaluated in the present study, but

inferences regarding risk reduction are drawn in the discussion.

Stability in intercrop systems is much less well-researched than

productivity, resource partitioning, and pest and disease levels. Two

mechanisms underlying the hypothetically greater stability of intercrops

are compensatory growth and greater stability of pest populations.

Donald (1963) found that in 51 of 70 mixtures examined, per-plant

yield of one component increased and the other decreased (compared to

monocultures), but it was not possible to distinguish between uneven

resource partitioning and compensatory growth as possible causes of

the imbalance. Few clear examples exist in the literature, but increased

growth of understory plants in response to natural and artificial

damage to the overstory was reported by Fisher (1977) and Liboon et al.


Trenbath (1974) reviewed several data papers in which seed yield

stability of a mixture of two genotypes was greater than that of the

more stable component. This situation is the exception rather than the

rule; he stated that the stability of mixtures usually lies between

that of the component species or genotypes. Kass (1978)) cited several

studies in which year-to-year variability was less in mixtures than

in monocultures. The usual measure of yield variability in such studies

has been the coefficient of variation (C.V.) over time or space.

Rao and Willey (1980) found that stability of a sorghum-pigeonpea

intercrop in 51 experiments over a period of six years was higher than

either monoculture (C.V.=39 percent in intercrop, 49 and 44 percent in

monocultures), and very slightly higher than that of a weighted mean of

the monocultures (C.V.=39 vs. 42 percent). These differences are not

striking, however, and are confounded by the fact that the intercrop

consisted of full stands of both component crops. Rao and Willey

also determined degree of regression of intercrop and monoculture

yields with a site favorability index, and found that the intercrop

system response was intermediate between that of the less responsive

sorghum and the more responsive pigeonpea. Risk of crop failure was

much lower in intercrop than monoculture, due primarily to differences

in productivity.

Methodological Problems

The major difficulty with interpretation of the wealth of experi-

mental intercropping data concerns uncontrolled planting densities.

Overall level of biological activity in intercrops may be higher due

to planting more seed of species that grow into larger plants for

seeds of small plants. This increased "functional density" (density

of biological function) may bias yield results in favor of the intercrop.

Fisher (1977) found, for example, no yield advantage in maize-bean

mixtures beyond that due to increased planting densities. Increased

functional density of plants also leads to early cessation of growth

and altered microclimate (Pimentel et al. 1962), reduced root/shoot

ratios (Milthorpe and Moorby 1975) and increased risk of moisture stress

(Dowker 1963, Milthorpe and Moorby 1975). The question arises as to

how effects of inter- and intraspecific competition can be compared

without altering overall planting density. The problem becomes critical

for species of very different plant size, and is a serious factor that

confounds the results of most intercropping experiments.

One of three methods is normally used to determine densities

of the two interplanted species (Haizel 1974). In the additive method,

normal full stands of two crops are superimposed to give a double stand.

In the substitutive method, a given number of individuals of species A

are replaced by the same number of individuals of species B, giving either

a less or more full stand than normal, depending on which species uses

more resources per plant. In the replacement series method (Osiru and

Willey 1972, Willey and Osiru 1972), a given fraction of the optimum

stand of species A is replaced by the same fraction of the optimum

stand of species B. In addition to the above three methods, in many

intercropping studies the component species are planted in unusual

combinations of densities. Any of these experimental designs might

be suitable if one's goal is to find the crop or crop combination having

maximum yield. Only the "replacement series" method controls for over-

all functional density of plants, however, and can show the effects of

spatial diversity (not density) on system structure and function.

A second problem with the literature on intercropping concerns

methods used to compare intercrop and monoculture yields. The simplest

method is to compare the total yield from an intercrop with that from

monocultures of the component crops. Trenbath (1974), in a review of

over 300 studies, found that situations in which the intercrop yielded

either more or less than both components transgressiveve yielding")

were relatively rare. When two crops have been planted in equal pro-

portions in the mixture (by either the substitutive or replacement

method), the intercrop yield is most often compared with the mean yield

of the two monocultures (also known as the mid-monoculture yield or the

sum of the half-hectare yields). When used with the substitutive

design, the meaning of this comparison is not clear, since the density

of the species that was "substituted in" is usually not equal to half

its density in monoculture.

The most common method for comparing intercrop and monoculture

yields is the Land Equivalent Ratio (LER), also called the Relative

Yield Total (RYT) and defined as the sum of the component species'

per-hectare yield in intercrop, divided by per-hectare yield in

optimum-density monoculture. Each species' ratio represents the area

of monoculture required to give the same yield as one hectare of

intercrop; the sum of the ratios represents the area required to equal

the yield of all crops from one hectare of intercrop. Land Equivalent

Ratio has the advantage that it is easy to conceptualize and calculate.

One disadvantage is that it may have little meaning when applied to

systems having different planting densities, especially if the mono-

cultures are more sparsely planted than the intercrops, as is very often

the case, giving a falsely high LER. Land Equivalent Ratio also should


not be used when intercrop/monoculture yield differences are large,

because yield increases in the intercrop are given greater weight than

decreases (an artifact of the ratio calculations). For example, if

species A yields twice as much in intercrop as in monoculture (LERA = 2)

but species B yields four times as much in monoculture as in inter-

crop (LERB = .25), the overall performance of the intercrop is judged

highly favorable (LER = 2.25). Perhaps the most serious criticism of

LER is that it gives equal weight to both high- and low-yielding

components of an intercrop. This weighting has been justified by the

argument that both crops may be necessary to the farmer, but can result

in high LER's in situations where a minor crop failed in the monoculture.

Table 1 gives a hypothetical example of this situation, where LER = 2.3

even though the mid-monoculture yield was greater than that of the

intercrop! This distortion occurs because the species' performances

are not weighted by yield.

A New Index

A more meaningful index of intercrop performance than the area

of land required to produce the exact same yield of all crops as the

intercrop would be the yield difference of intercrops and monocultures

grown on the same land area. Yield could be expressed in terms of any

parameter of interest (biomass, kcals, protein, money, etc.). I propose

to call this new index the Yield Equivalent Ratio (YER) because it is

the ratio of yields from equivalent land areas, in contrast to the Land

Equivalent Ratio, which is the ratio of land areas for equivalent yields.

YER is defined as the ratio of intercrop yield on an area to yield of

Table 1. Distortion of Land Equivalent Ratio (LER) by
failure of a minor crop in monoculture. This hypothetical
example assumes equal planting densities (by the substitute
or replacement methods) in the intercrop.




A/B .3 2

LER = .3 + 2 = 2.3


monocultures of the component crops grown on the same area, divided among

the monocultures in the same proportion as their intercrop planting

proportions. Planting proportions are expressed as percent of optimum

monoculture stands, and should add to one. With modifications the ratio

could also be applied where planting proportions added to more than one

(overpacked intercrops); the resulting ratio would then reflect packing

effects as well as diversity effects.

In mathematical form,

Y +Y +Y + Y
YER = _Y1,I 2,I 3,I 1 Yi,I
Y1,M P1) + Y2,M P2) + (Y3,M P3) + (Yi,M Pi

where Yi,I = per-area yield of species i in intercrop

Yi,M = per-area yield of species i in optimum-density monoculture

pi = proportion of optimum density monoculture planted of
species i planted in the intercrop

Yield Equivalent Ratios greater than one indicate that greater

yield per area is obtained from intercropping than from the same area

divided among monocultures of the same functional density, and reflect

reduced competition or other effects associated with increased spatial

diversity. When applied to a half-full-stand + half-full-stand mixture

of two crops, YERreduces to the ratio of intercrop yield to the mid-

monoculture yield, a comparison that has been widely used to evaluate

the performance of two-crop intercrops (Trenbath 1974). Unlike LER,

YER can be evaluated statistically through comparisons of the numerator

and denominator.

Performance of each species in intercrop and monoculture can also

be compared with YERs calculated on the basis of intercrop and monoculture

yields of that species only:

YER = 1,I
species 1 Y,

Y1M 1


The goal of this study was to compare the productivity and

stability of diverse and simple systems of equivalent density, and

to elucidate some of the mechanisms responsible for differences

found. The following hypotheses and corollaries formed a framework

for the study.

Hypothesis 1. Productivity of diverse cropping systems is

greater than a weighted mean of monocultures of their

component crops.

Corollary A. Intercrop productivity is intermediate

between that of the most productive and least

productive components.

Corollary B. The greater the intensity of stress (the less

productive the growing conditions), the greater the

productivity advantage of intercrops compared with a

weighted mean of corresponding monocultures.

Hypothesis 2. Yield stability (constancy) of diverse systems is

greater than the stability of a weighted mean of the yields

of corresponding monocultures.

Corollary A. Intercrop stability is intermediate between

that of its most stable and least stable components.

Corollary B. Stability of all systems is greatest under

the most productive conditions (lowest intensity of


Hypothesis 3. Some species are more productive in diverse systems

than in monoculture; others are more productive in monoculture.

Hypothesis 4. Some species are more stable in diverse systems than

in monoculture; others are more stable in monoculture.

Hypothesis 5. Pest and disease levels are lower and pest

stability higher in diverse systems than in monoculture.

Hypothesis 6. Efficiency of resource use (light, water, nutrients,

human labor) is greater in diverse systems than in monocultures.

Corollary A. Root and leaf systems of different species

occupy different vertical zones, and vertical profiles

of leaves and roots are more evenly filled in diverse

systems than in monocultures.

Corollary B. Compensation occurs among components of diverse

systems; low productivity of one species causes increased

productivity of others.

Corollary C. Intercrop systems composed of species with

unlike resource-response curves and temporal growth

patterns have the greatest productivity advantage

compared with corresponding monocultures.

Hypothesis 7. Natural successional vegetation of the same age

as the crop systems is the most stable and productive of

all systems and has the most evenly-filled root and leaf


Experimental Approach to the Hypotheses

Intercrop systems selected for study were based on those commonly

used in the area. A four-crop system was included as a more diverse

system than the usual two-crop intercrop systems. Planting densities were

determined by the replacement method in order to maintain equal functional

densities. Monocultures of each crop were grown, and successional

vegetation and bare ground plots were included as controls. Four "stress

treatments" were applied: fertilization, pesticide spraying, defoliation,

and watering. Effects of these four types of stressors were compared

with a control treatment that was an imitation of local, low-energy-

input farming practices.

Response parameters included measures of biomass accretion and

distribution, pest and disease incidence, and resource use. Hypotheses

were tested concerning system performance, species performance in various

systems, and comparisons of intercrop performance with that of correspond-

ing monocultures.

The Tanzanian Agricultural Setting


The economy of Tanzania is largely dependent upon the productivity

of its small-scale farmers, who comprise an estimated 90 percent of the

population. Agriculture produces 40 percent of the country's GNP and

80 percent of its import earnings (Witucki 1978). Fifty percent of

food production is consumed on-farm. Tanzania is a net importer of

food, and a pattern of frequent food shortages has been documented from

1850 to recent years (Brooke 1967a, 1967b). The most frequent cause of

crop failure over this period was drought, but other causes included

excessive rainfall, locusts, birds, and armyworms. The level and

stability of agricultural production on small farms are therefore

questions of prime importance both to the national economy and the

welfare of the Tanzanian people.

Agricultural change has been rapid in Tanzania in the 21 years

since independence. In 1967 President Nyerere introduced a socialist

development policy called ujamaa. Development efforts were concentrated

in newly formed ujamaa villages, consolidated from previously scattered

settlements to facilitate introduction of improved social services and

agricultural technology (McKay 1968). This unusual development approach

and the philosophy of self-reliance are most eloquently expressed in

Nyerere's own writings (1973, 1974).

Under the new development approach, agricultural productivity

rose at a rate exceeding the population growth rate from 1965 to 1975

(Ruthenberg 1973, Witucki 1978). A campaign to increase farmer's

agricultural efforts on both private and communal fields stimulated

small farmer activity. International aid projects provided assistance

for improved seed, crop agronomy, and appropriate agricultural

technology. The most notable project was the National Maize Project,

begun in 1975. Its goal was to increase maize production in favorable

areas through improved seed and fertilizer inputs and improved crop

management (Fortmann 1976). Villages and individuals were particularly

encouraged to increase the area of maize and cotton planted and to

plant maize at higher densities. In many cases partially subsidized

inputs including improved seeds, fertilizer, pesticide, and tractors

were supplied.

Several years of bad weather in the mid-1970's, involvement in

the Uganda war in 1978-1979, increasing oil prices, and internal

management problems all contributed to a reversal of the progress made

from 1965 to 1975. Tanzania's foreign exchange situation is now

extremely serious, and the country is highly dependent on foreign

grants and loans to finance the present five-year development plan

(Witucki 1978).

In the present energy and economic environment, the best strategy

for agricultural development may be a two-pronged one. Higher productivity

through use of fossil-fuel inputs is desirable where economically

feasible; other, low-risk systems that make the most efficient use of

available resources are also desirable to minimize small farmer

dependence on unpredictable fossil-fuel-based resources. Development

of traditional, diverse cropping systems for increased productivity

may be one way to achieve this second goal. Identification of specific

mechanisms that may operate in those systems to reduce labor demand,

reduce risk of pest and disease attack, increase efficiency of water,

light, and nutrient use, and reduce the risk of crop failure is the

first step in the process of adapting traditional systems for increased

productivity without increasing risks for small farmers.

Traditional agricultural systems in Tanzania are extremely diverse

and highly adapted to the local climate and soils (Ruthenberg 1964, 1968).

Fortmann (1976) surveyed 96 farmers in Morogoro District (the district

where this study was conducted) and found that 64 percent of the area

planted to maize was intercropped wili one or more other crops. Seventy

percent of total maize area planted had no improved inputs; another

22 percent received only improved seed input, and a very low percent

was fertilized. The villages she surveyed were generally located on

favorable mountain slopes and river floodplains. The most common

species intercropped with maize in those areas were beans, rice, sun-

flower, and bananas.

Survey of Local Crop Systems

Local farmers' crop systems and cultural practices were used to the

greatest extent possible in this study, for three reasons. First,

results of this study will be of more practical value in suggesting

possible changes and improvements in cropping systems if they are taken

from authentic systems widely used by farmers. Second, little experi-

mental ecological research has been done with indigenous systems (and

cultural practices), especially intercropping system composed of three

or more species. Third, if intercropping is advantageous in terms of

stability and yield, one might hypothesize that it is most advantageous

for the unpredictable and low-resource environments in which small-

scale farmers operate, and for cropping systems that have persisted

over time, presumably because farmers found them to be successful.


To determine the most common cropping systems in the study area,

I conducted 30 interviews with farmers in 15 villages in the Kilosa

district of Morogoro Region in October and November, 1978. The District

Agricultural Development Officer of Kilosa District kindly accompanied

me, provided transportation, and introduced me to village leaders.

Selection of farmers was not random, but rather an attempt was made to

select experienced, longtime residents who were willing and able to

provide detailed descriptions of their farming practices. Interviewees

were actually selected by the Village Chairman (or his representative)

or the village agricultural officer, and so may represent the modernized

sector of the farming population. The sample was approximately 30

percent female and 70 percent male.

Interviewees were asked to describe qualitatively each of the

fields they had cultivated the previous year, including species

composition, plant arrangement, cultivation practices, and use of high-

energy inputs (seeds, tractors, fertilizer, pesticide). They were also

asked how long they had used each system and what they felt its advantages

and disadvantages were, particularly with regard to mixing species in

the same field. Finally, they were asked to describe systems they had

used in the past, and to tell what they felt their main agricultural

problems were. Survey results pertaining to crop combinations used

and farmers' perceptions of their advantages and disadvantages are

reported here.


The survey revealed that an agricultural transition of surprising

depth and breadth is taking place in the surveyed villages, that is

probably typical of the situation in villages throughout Tanzania.

Farmers' ideas about their traditional cropping systems are changing

under the influence of agricultural officers and programs aimed at

introducing Green Revolution technology and increasing the area of

maize and cotton planted. Farmers are experimenting with the new

systems, but almost always keep some fields planted in traditional


Great diversity of cropping systems was present, both among

each farmer's fields and among different farmers' fields. All 30

farmers used more than one cropping system; a maximum of 9 and mean

of 3.9 systems per farmer were reported. Almost all farmers (28 out

of 30) planted at least one kind of monoculture, of which maize was

the most common (22 farmers out of 28). More farmers used monocots as

monocultures than used dicots as monocultures (26 and 18, respectively.

In addition to the major-season monoculture field crops, many farmers

(10) relay-planted bean monocultures after monocultures of Gramineae,

and many (9 of 20 queried) planted a mixed vegetable garden, invariably

composed of several species planted in separate rows, or more commonly

separate sections, of the garden. Most farmers interviewed (19 of 30)

cultivated some fields as monocultures and some as intercrop systems.

Of the remaining 11 farmers, 9 planted exclusively monocultures and

two planted exclusively intercrops.

Most farmers reported that the monoculture systems were relatively

new in the last 2-20 years, although sorghum, cassava, greengram, and

sparsely spaced maize were mentioned as monoculture systems used in the

past that are less widespread now. Many farmers (11 of 30) also

described intercrop combinations they had planted previously but no

longer used at the time of the interview. Two farmers mentioned that

they had changed from scattered to row planting, and two more noted

that they used to plant two or more species in the same hole but now

planted them in separate holes in the same field.

A total of 33 intercrop species combinations were given by the

interviewed farmers; these are broadly categorized and listed in

Table 2. The great majority of the systems were combinations of

monocots and dicots; only six of the 33 intercrop systems were all-

monocot or all-dicot. Three all-grass systems were mentioned, one

being in quite widespread use (maize-sorghum, 8 of 33 farmers). Three

systems containing no monocots were used, all of them composed of one

legume and one "other dicot" (= not Leguminosae or Cucurbitaceae).

Interestingly, two of the "other dicots" were tall species (sunflower

and cassava) which may function much like graminaceous species in these

all-dicot intercrop systems.

Grass-legume systems accounted for 13 and grass-legume-cucurbit

systems for 8 more, of the 33 intercrop combinations. Many of these

contained more than one grass and/or legume. In addition to the grass-

legume and grass-legume-cucurbit systems, six other monocot-dicot

systems were given: two grass-cucurbit, two grass-other dicot, and

two grass-legume-other dicot. The most common spatial arrangement for

monocot-dicot systems was relatively widely spaced rows of grass species

(in alternate rows if more than one grass species was present) with

legumes or other dicots scattered, or in rows, between the rows of

graminaceous species. Cucurbits were described as being planted "here

and there" or at several-meter intervals between the rows of monocot


Interviewees' responses were mixed regarding the advantages and

disadvantages of intercropping; 24 of the 30 farmers interviewed did,

however, have one or more comments to make on the subject. The responses


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were categorized, and for the sake of simplicity the specific intercrop

systems involved were ignored. In almost all cases the farmer was

comparing maize-legume or maize-legume-cucurbit systems with maize

monoculture or monocultures of the species comprising the intercrop


Of nine farmers who mentioned intercrop/monoculture yield differences,

seven said intercrops had higher yield than monocultures and two said

monocultures yield more. Two farmers preferred intercrops because of

the diversity of crops one harvests and the early harvest of legumes

from intercrop systems. Two interviewees said that the chance of total

crop failure was reduced in intercrops, but ten noted that at least one

crop is likely to fail in an intercrop. Of those ten, six were referring

to the high risk of maize failure in two-grass systems, and one gave

shading as the reason. One farmer felt that maize is stronger in

intercrops in general. These comments are not necessarily incompatible:

risk of losing one crop and risk of losing all crops are not the same

thing, and it is quite possible that maize is at high risk in all-monocot

systems but not in other intercrop combinations.

One farmer said intercrop was better than monoculture during

drought, and one noticed reduced erosion on intercropped fields. One

stated that intercrops had more pests than monocultures; two said there

was no difference. Two said monocultures had more weeds than (maize-

pumpkin) intercrops; five said there was no difference.

The numbers of farmers who noted higher or lower labor requirements

for various tasks in intercrops compared to monocultures were as follows:

planting--three (intercrop more), five (less), one (no difference);

weeding--six (intercrop more), three (less), one (no difference);

harvest--one (intercrop more), one (less); total labor--two (intercrop

more), one (less). These results suggest that intercrops may be easier

to plant but harder to weed than monocultures. One interviewee felt

that monocultures are preferable for village communal fields (ujamaa

fields) because they are easier to manage when many people are involved.

Finally, three farmers mentioned crop mixtures that do not work well:

maize-fiwi bean, maize-rice, and any intercrop with cassava. Two more

admonished against planting two species in the same hole; one of these

specified that two grasses or two legumes should not share the same


Planting density is an issue of some interest in Tanzania because

farmers are being encouraged to plant at higher densities than they

traditionally have. Of six farmers who mentioned planting density, five

preferred sparse to dense plantings for the following reasons: survives

better in drought (four respondents); wilts less (one); lodges less in

drought (one); stronger roots (one); easier to plant (1). One farmer

commented that sparse plantings yield the same as dense; one that insect

levels are the same in both; and one that weed growth is the same in

both. One farmer preferred dense plantings because of higher yields in

wet years.


The Study Site

Location and History

The study was conducted during the 1978 and 1979 growing seasons

(called Year 1 and Year 2) near the town of Morogoro, Tanzania. At

380E and 7S, Morogoro lies approximately 200 km west of Dar es

Salaam and the Indian Ocean, and sits at the base of the Uluguru

mountain range.

Field plots occupied 2 ha on the campus of the Faculty of

Agriculture, Forestry, and Veterinary Science, a branch of the University

of Dar es Salaam situated a few kilometers from Morogoro town. The

plots were located approximately 1 km north of the main campus build-

ings, occupying nearly flat land 550 m in elevation, 60 m above the

present level of the Ngerengere River to the west, and between small

streams flowing from the mountains toward the river. The plots were

bordered on three sides by a strip of young successional vegetation and

by a dirt road on the fourth. Beyond the successional strip was a

variety of field crop and pasture experiments.

The land on which the study site was located was part of the

Tanke Sisal Estate until the founding of the Faculty of Agriculture in

1970, after which it was used for agricultural experiments. Small

patches of Brachystegia- and Combretum-dominant miombo woodland occur


locally at uncultivated sites and suggest that this fire-adapted

vegetation once covered the area. The study site was in fallow for

two years preceding this study and was covered with a dense 2-3 m tall

brush dominated by a diverse mixture of grasses, including Chloris gayana,

Panicum maximum, Digitaria scalarum, Rottboellia exaltata, and Cynodon

dactylon. The successional regrowth also contained occasional sedges

(Cyperus rotunda and C. tuberosus), and an understory of herbaceous

and occasionally woody dicots including Celosia trigyna and C. laxa,

Bidens pilosa, Sonchus examiculatus, Crotelaria spp., Ipomoea mombasana

and 1. tenurostris, Galinosoga parviflora, Tridax procumbens, Amaranthus

spp., and many others. Several small vines were present, but they did

not dominate the grassy overstory.


Amount and distribution of rainfall are the most critical climatic

factors affecting crop production in the Morogoro area. As would be

expected for a site of low latitude and moderate elevation, temperature

and solar radiation do not limit the production of the major food

crops. Slightly lower temperatures occur in June and July (mean

21-22C) than during the rest of the year (mean 23-260C). Lowest

insolation is during the high-cloud-cover rainy period from March

through May (Jackson 19 75b).

Rainfall patterns in Tanzania result from the interaction of two

seasonal monsoons that dictate the movement of the intertropical

convergence zone, and complex local convergence patterns (Jackson 1975i).

Most of the midelevation Central Plateau of Tanzania is in the Tropical

Very Dry Forest life zone of Holdrige (1967), receiving 400-900 mm annual

rainfall in a single season from approximately November to May. Morogoro,

because of its proximity to the Uluguru Mountains, receives about 1000

mm annually, distributed rather unpredictably from November to May,

and is therefore at the edge of the Tropical Dry Forest life zone.

In most years a one- to three-week dry period sufficient to kill young

crops occurs in January or February. This short drought divides the

season into the "short rains" (November to January), generally considered

too unpredictable to be relied upon for the main crop, and the "long

rains," beginning in late February or March, during which most agricul-

tural activity takes place. The unpredictability of both amount and

distribution of rainfall makes annual cropping a risky occupation on

the Central Plateau, although less so in Morogoro due to the local

higher total precipitation.

The rains were considered by local people to be slightly heavier

than average in both study years. Tanzania Meteorological Station No.

96-3776 (less than 1 km from the study site) recorded 1117 mm for 1978

and 735/882 mm for the main growing season from January through July

in 1978 and 1979, respectively. Measures of precipitation taken daily

on the site between Harch 27 and July 28, 1978,were only 3 percent

higher than those from the meteorological station.


Soils on the study site are thought to be the product of uplift,

colluvial and alluvial deposition, and erosion by water. The Morogoro

area is geologically young compared with the sountern part of Tanzania

(Temple 1975, Pratt and Gwynne 1978). The yellow-red soil (color

7.5YR 4/6) was tentatively identified as an alfisol. Alfisols and oxisols

are both present in the Morogoro area (National Academy of Science

1972, Dregne 1976) but subsurface clay accumulation (see below), pre-

dominance of kaolinite clays, and moderate levels of weatherable

minerals at the site (Nicholaides and King 1980) suggest that the soil

is an alfisol.

To determine the overall nutrient status of the soil, 33 composite

samples of surface soil (10, 0-15 cm deep cores from each of 33, 15 x

21 m experimental plots) were collected. They were analyzed by the

Department of Soil Science in Morogoro for pH (1:1 in water), total

nitrogen (micro-Kjeldahl, Black 1965), available phosphorus (Bray and

Kurtz 1945, method no. 1), exchangeable bases, and organic carbon.

While the soils are not excessively acidic, they are low in total

nitrogen, available phosphorus, and organic carbon, and have low cation

exchange capacity and high carbon/nitrogen ratio. Ranges and means are

given in Table 3. The broad ranges of element concentrations and pH

indicate high spatial heterogeneity, but no clear spatial trends across

the study site were evident for any of the parameters.

In addition, the distinguishable soil horizons were sampled to a

depth of 100-130 cm in five soil pits located throughout the study

site (Figure 1). Determinations of pH, organic carbon, total nitrogen,

and particle size distribution were made by the Department of Soil

Science in Morogoro. Particle size distribution was by the Bouyoucous

hydrometer method; other methods were as above. Values of pH, organic

carbon, and total nitrogen in the top 20 cm agreed with those from the

Table 3. Surface soil pH, total N, available P, organic C,
and base concentrations. Range and mean are given for 33
composite samples of 10 0-15 cm deep cores each, except for
total N, where three anomalously low readings were discarded.




pH 4.8 6.0

TOTAL N (percent)


ORGANIC C (percent)

Na (me/100 g soil)

K (me/100 g soil)

Ca (me/100 g soil)

Mg (me/100 g soil)

.05 .10

.2 2.0

.8 1.2

.09 .27

.90 2.79

9.08 29.33

.47 12.25






5.0 6.0 7.0 0





III 0-


IV 0-


V 0-



.5 1.0 1.50 .05.10 .150 25 50 75 100

5060 700 5 10 150 05 10 150 25 50 75 100

Figure 1. pH, percent C, percent total N, and particle size
distribution in five soil profiles (I-V) taken at the study

composite samples. Organic carbon and total nitrogen were very low

below 20-40 cm in all profiles: less than 0.5 percent organic carbon

(minimum 0.1 percent) and less than 0.05 percent total nitrogen

(minimum 0.01 percent). Profiles I and II, located in the slightly

elevated southeast corner of the site, had increasing clay particles

and decreasing pH below 80 cm, suggesting an argyllic horizon, but

profiles III, IV and V did not show consistent trends in pH and particle

size distribution with depth. The silt fraction was consistently low

(less than 12 percent), and in all profiles-the higher pH values were

associated with a greater sand fraction. Soil texture was categorized

as clay to sandy clay.

Some parts of the site had a tendency to waterlog, but most of

the site experienced only temporary standing water after long and/or

hard rainy periods.

Experimental Design

Crop System, Stress Treatments, and Planting Density

Based on the farmer survey, four intercrop combinations were

chosen for study: maize-sorghum, maize-cowpea, maize-pumpkin, and a

four-crop system, maize-sorghum-cowpea-pumpkin. These four represent

a range of types of crop combinations (grass-grass, grass-legume,

grass-cucurbit, and diverse grass-legume-cucurbit). For comparison,

each crop was also grown as a monoculture. Sorghum, cowpea, and

pumpkin densities were those recommended by the Ministry of Agriculture

for local conditions, while maize monoculture was grown both at the

recommended density and half the recommended density (hereon called

"sparse maize"). Two more systems were included as controls for

measurements of yield, Leaf Area Index (LAI), weed growth, soil moisture,

and recovery after defoliation: natural succession (on ground prepared

the same as the rest), and a vegetation-free or "bare ground" system. In

the second year of the study the maize-sorghum, maize-cowpea, maize-

pumpkin, sparse maize, and bare ground systems were omitted to allow

greater replication and inclusion of more "stress treatments."

The planting density of each crop in the intercrop system was

adjusted so that all crop systems except sparse maize had the same

functional density of plants; that is, differences in system structure

and function were due solely to differences in diverstiy and not to

differences in crowding or plant density. Equal functional density

was achieved by planting the species in a mixture in proportions adding

to one, for example one-half maize and one-half sorghum. The pro-

portion allocated to each species, multiplied by its monoculture or

full-stand density, gives that crop's planting density in the intercrop.

In the example given, a hectare of maize-sorghum intercrop would contain

the same number of maize plants as one-half hectare of maize monoculture

(being planted at half the monoculture density), and the same number of

sorghum plants as one-half hectare of sorghum monoculture. This method

of determining planting density so that all systems have the same

functional density is based on the "replacement series" methods of

Willey and Osiru (1972). To compare performance of either species or

whole systems, yields (or other measures) are weighted according to

planting density.

The proportions for mixtures used in this study were based on the

farmer interviews, and were as follows: maize-sorghum (1/2 + 1/2),

maize-cowpea (1/2 + 1/2), maize-pumpkin (2/3 + 1/3), four-crop (1/4 +

1/4 + 1/4 + 1/4). The actual planting densities these proportions

represent, and the full-stand densities on which they are based, are

given in Table 4. Since individuals of different species do not replace

each other one-to-one in this method, total plant densities of the

systems differ.

Plant arrangements in the intercrop systems are shown in Figure 2,

and the spacing for each species in both intercrop and monoculture

systems is given in Table 4. Intercrop arrangements were patterned

after those described by local farmers, with the exception of maize-

sorghum, which consisted of two rows of sorghum 40 cm apart alternating

with one row of maize. This arrangement prevented crowding of sorghum

within rows and gave a more even spatial distribution than would

alternating single rows. Local farmers interplant maize and sorghum

in a variety of geometric and nongeometric patterns.

Four stress treatments were applied to the systems. In both

Year 1 and 2 half the leaves were systematically removed from the site

from all species in the defoliation treatment. In Year 2 resource

levels were increased by fertilization and watering treatments; losses

to herbivores were reduced in a pesticide-sprayed treatment. The

"control" treatment was managed with typical local methods (periodic

hand weeding and no fossil fuel inputs).

Table 4. Planting proportions, densities, and
mental systems.

spacings in the experi-










90 x 25

90 x 50

60 x 15

75 x 20

120 x 120a

180 x 25

90 x 20





























1/2 (.48)

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every fifth hole in every other row planted to pumpkin, not cowpea


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Figure 2. Intercrop planting patterns. Inter-row distance
of maize is 90 cm in the maize-cowpea and maize-pumpkin
intercrops, and 180 cm in the maize-sorghum and four-crop
intercrops; other distances are to scale.

Plot Layout

In both years a rectangular array of plots was constructed, then

systems and/or treatments were randomly assigned to the array. In Year

1, two separate experiments were conducted (Figure 3). In the main

experiment, the 11 experimental systems were compared in the "control"

treatment only (farmer's conditions). Three replicates per system, a

total of 33, 15 x 21 m plots, were separated by 2 m walkways. A 2 m

buffer strip within each plot surrounded the sampling area, which was

divided into 6, 5 x 5 m subplots with 1 m walkways between for easier

management. In the second experiment, response to defoliation was

investigated in an adjacent set of 60, 8 x 10 m plots (2 treatments x

10 systems x 3 replicates). These plots were also separated by 2 m

walkways and contained 2 m buffer strips surrounding 4 x 6 m sampling


Results from Year 1 suggested several changes in design for Year

2. Due to extremely high plot-to-plot variability, more replicates of

smaller plot size were planted. In addition, the three two-crop systems,

sparse maize, and the bare ground system were eliminated to allow

greater experimentation with.stress treatments. The stress treatments

and main plots were combined into a single experiment in Year 2, shown

in Figure 3. All plots were 8 x 10 m, separated by 1 m walkways and

containing 1 m buffer strips around 6 x 8 m sampling areas. The

watering and pesticide treatments were isolated in blocks separated by

3 m walkways from the other three treatments to avoid contamination.

The watering treatment was located in the southeast corner of the site,

near the water source, and pesticide plots were located along the

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south edge. (Daytime wind direction was unpredictable.) Plots of the

six experimental systems were randomly assigned within each of these

two treatments, and plots for the other three treatments were randomly

assigned tc the remaining area. To save labor and increase replication

of the control plots, watered plots were replicated only four times

(24 plots total), control treatment plots six times. The other three

treatments were replicated five times per system. Two plots were

eliminated due to standing water on the entire plot, and three due

to planting or treatment errors. Plots covered incompletely or briefly

with standing water were not eliminated. No system-by-treatment

combination contained fewer than four replicates.

Plot Management

Agronomic Management

The chronology of various agronomic and experimental activities

in Years 1 and 2 is shown in Figure 4. Weekly rainfall is given as a

corresponding histogram. Each cultural operation (planting, thinning,

weeding, harvesting) took a maximum of six days to complete (usually

fewer than four). To minimize age differences, operations were conducted

systematically throughout the field, going up one row of plots and

down the next, following the original planting order. The succession

system was slightly older than the crop systems due to natural seeding,

and a correction for this was applied in the results where possible.

In Year 1 the land was plowed and harrowed just before planting.

Judging from the survey of farmers, this was a common practice, and

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was almost a necessity in view of the time requirements and expense of

hand-clearing fallow land. In Year 2 the land was too wet to plow

during the last two weeks of February, and finally a crew of 40

workers hand-cultivated the site in time for planting in mid-March.

In both years the ground was cleared of weedy residue by hand and

quickly surface-hoed a second time to make a smooth surface for


The middle day of planting (March 18 in Year 1, March 12 in Year

2) was designated day 0; planting took five days in Year 1, 3 in Year

2. Between-row and within-row planting distances were measured with

marked ropes. Using the traditional dibble-stick method, maize

(variety Ilonga Composite) was planted 3 seeds/hole; sorghum (Serena),

5 seeds/hole; cowpea (a nonclimbing SVS3 variety), 3 seeds/hole; and

pumpkin (local variety), 4 seeds/hole. Good germination occurred in

both years, but a few seedlings were transplanted when 5-10 cm high

to fill gaps and achieve the desired planting density. The stands

were thinned at two to three weeks to one plant per hole for all

species except pumpkin (two per hole). All'systems except natural

succession were weeded three times, as necessary, in both years. The

third weeding was used to monitor weed growth and was not necessary

from an agronomic standpoint. Cowpea pods did not mature synchronously

and were harvested several times over the period shown in Figure 4.

Pumpkins were also continuously harvested, but all other crops were

harvested only once. All aboveground biomass was harvested and

separated into edible and nonedible components.

Application of Stress Treatments

On the fertilizer-treated plots, P205 from triple super phosphate

was hoed into shallow furrows along the rows or (for pumpkin only) holes

at planting, at the rate of 40 kg/ha. Nitrogen was applied as a

top-dressing along the rows at day 28 at the rate of 40 kg/ha as

200 kg/ha ammonium sulfate, the most commonly used and available nitrogen

fertilizer. In successional plots, fertilizer was applied along

imaginary rows 90 cm apart. Fertilization rates were those recommended

by the Soil Science Department in Morogoro for local conditions.

Plots designated for the watering treatment were in fact not

watered much, due to abundant rainfall during most of the growing season.

Seedlings were watered once after emergence (day 21) and from day 63

until harvest. Water was applied directly to the plant bases using

a hose. Successional plants were watered by standing outside the

sampling area as much as possible, inserting the hose into the vegetation

layer, and letting it run for several minutes before moving it to the

next point 1-2 m away.

Plots designated for defoliation were cut on day 20-25 (Year 1)

and day 49-50 (Year 2) and the cuttings removed from the plots. The

delay in Year 2 was due to the later first weeding, creating a labor

shortage during the hectic third through seventh weeks. Whenever

possible, every other leaf was cut where it joined the stem. This

system worked well for maize, sorghum, and pumpkin, but was difficult

in cowpeas due to the high degree of branching and in succession because

of the density of stems. Instead, in cowpeas, all leaves on one side

of the plant (partitioned by an imaginary line along the row) were taken.

In the successional plots, all biomass above 15 cm (Year 1) and 20 cm

(Year 2) was removed. This represented approximately 71 percent of

successional leaf area in Year 1 (estimated from the first LAI measure-

ment) and 57 percent of successional LAI in Year 2 (estimated from the

second LAI measurement and linearly interpolated by height). While the

cowpea and succession defoliation methods do not imitate natural leaf-

removing processes as well as the alternate-leaf method, they could be

used reliably by workers with minimal additional damage to the vegetation

by stem breakage.

The "pesticide" treatment was intended to reduce but not to

completely eliminate pest populations in a regime a farmer might

conceivably use. Spraying was stopped more than two weeks before the

harvest to avoid contamination. Sixty percent diazinon was selected

as a broad spectrum organo-phosphate pesticide effective against most

groups of insect pests. The pesticide-treated plots were sprayed five

times with diazinon at approximately ten-day intervals, beginning

day 17 (Figure 4). Application rate was 2.2 liters emulsifiable

concentrate/ha (recommended rate is 0.4-3.5 liters/ha). The second

diazinon spraying was mixed with the rainfast fungicide Blitox (50

percent copper oxychloride) at an application rate of 1 kg/ha. The

mixture caused moderate leaf damage to maize and sorghum, and was

clearly phytotoxic to pumpkin. Finally, as systematic protection

against sap feeders and stem borers during crop maturation, dimecron

(phosphamidon) was sprayed at approximately 300 g active ingredient/ha

on day 58.

Measures of System and Species Response

The response variables may be conceptualized as falling into four

groups: growth and productivity, resource use, resistance to natural

stressors, and response to artificial stress augmentation or amelioration.

These groups are not entirely exclusive; one variable may contribute

information in two or more of these categories.

Sampling regimes for the variables measured are described below.

Sampling took four or fewer days except where noted (the days given are

mid-days of the sampling periods). Systematic, rather than random,

sampling was frequently used to save time. Systematic samples were taken

such that individuals were selected from all locations in all plots;

sample size varied somewhat to meet this condition. The first individual

to be sampled in each row or plot was chosen randomly, and the interval

between individuals did not correspond to any regularity in the planting

patterns. For example, in the four-crop system cowpeas were not sampled

at an interval of four, since this is the interval at which pumpkins

were interplanted.

Growth and Productivity

The term productivity is used here in the broad sense of biomass

accumulation per unit time (one growing season), and not in the usual

sense of net primary productivity (NPP). Net primary productivity is

rarely measured, and would theoretically include biomass that dies,

drops off the plant, decomposes, or is consumed by herbivores. Turnover

of plant parts was not measured in this study because of the short

growing period involved and high rate of biomass accumulation of the

systems being studied, and because the primary purpose of the study was

to make comparisons among systems rather than to determine NPP. In

mature ecosystems, changes in standing biomass may be unsatisfactory

indicators of net productivity because of high rates of death, decom-

position, and consumption. For agricultural and early successional

systems, however, where biomass accumulates rapidly, changes in stand-

ing crop of biomass, leaves, roots, etc., should be reasonably reliable

measures of productivity. Accumulation of edible biomass is an important

system attribute in its own right. Throughout the presentation and

discussion of the results of this study, the unit of time over which

growth or biomass accretion was measured is either given or assumed to

be the entire growing season (final harvest data).

Total and edible biomass

Total and edible biomass production, perhaps the best indicators

of species and system performance, were determined at harvest. All

crops were harvested at their appropriate time rather than synchronously.

Cowpea pods were harvested three times, pumpkins were harvested as they

ripened, and maize and sorghum were harvested nearly synchronously

(Figure 4). Natural succession plots were harvested after the cropping


Although some systems (e.g., cowpea) matured slightly before

others, all systems essentially filled the main growing season. There-

fore, all parameters related to yield are implicitly expressed as

production per growing season, rather than per day or per month. Chang-

ing the time unit might affect tests of differences among systems but

should not influence comparisons of a given species' performance

among systems or differences between intercrops and corresponding


All aboveground biomass, including dead material lying on the

ground and/or attached to the plants, was collected, divided into various

components, subsampled if necessary, dried at 800C, and weighed. Edible

and total aboveground yield per area, and edible and total yield per

plant were calculated. Edible yield from the succession plots was

assumed to be zero, and per-plant calculations were not performed for

the succession system. Total biomass at harvest of defoliated plots

did not include the biomass of defoliated leaves unless specified.

Allocation ratio was calculated as.(edible biomass)/(total aboveground biomass).

Biomass at physiological maturity (flowering)

In Year 2, subplots of all systems in the control and fertilized

treatments were harvested approximately at flowering (day 52, cowpea;

day 71, succession; day 73, sorghum; day 77, maize; day 79, pumpkin).

Plants in a 12 m2 agronomicc systems) or 6 m2 (successional system)

area were harvested by hoe, including roots to a depth of approximately

15 cm. In the successional system, monocots and dicots were separated;

the roots cut off at soil level; and the roots and shoots washed, air

dried, subsampled, ovendried at 800C, and weighed. Maize plants were

divided into leaves, stems, tassels, ears, and roots; sorghum into

leaves, stems, panicles, and roots; cowpeas into leaves, stems, pods,

and roots; and pumpkin into leaves, stems, flowers, fruits, roots, and

shoots. Each category was washed, subsampled, dried, and weighed.

Moisture content of the subsamples of each category was averaged for

each system-by-treatment combination. Root/shoot ratios were calculated

for the four agronomic species, successional monocots, and successional


Leaf mass at defoliation

The mass of defoliated leaves represents one-half the standing crop

of leaves at the time of defoliation, and was used to compare leaf

production up to the time of defoliation, and to calculate a measure of

total biomass production that included cut leaves. Defoliated leaves

of each species were collected, subsampled if necessary, dried at 800C,

and weighed.

Miscellaneous productivity measures

In addition to the basic productivity measures, other measures of

yield or biomass distribution were derived from the harvest data, which

included counts of cowpea seeds and pods, maize cobs, sorghum panicles,

and pumpkin fruits. For cowpeas, these measures were percent edible

seeds (by mass, Years and 2), percent edible seeds (by number, Year 1

and 2), number of pods per plant (Year 1), number of seeds per pod

(Year 1), and number of edible seeds per pod (Year 1). For sorghum,

the additional yield parameters were number of heads per plant (all

plots, Year 2) and percent of heads with greater than 75 percent of

the head surface area filled with grain (all plots, Year 2). Tillering

was also monitored in sorghum. Number of maize cobs per plant and

maize grain yield per cob were determined for all plots in Year 1;

number of pumpkin fruits per plant and edible yield per pumpkin fruit

were determined for all plots in Year 2.

LAI and canopy cover

Leaf Area Index (LAI) and canopy cover are measures of leaf production

as well as a species' or system's potential to capture light (discussed

in the section on resource use, below). Leaf Area Index was measured

by the "plumb-bob" method using a fishing rod and reel apparatus (Benedict

1976) to lower a weighted string vertically through the canopy. The

number of leaves hitting the string, averaged over a number of trials

at different points, equals LAI. This method underestimates actual leaf

surface area because leaves oriented vertically are less likely to be

touched by the string than horizontal leaves of the same area. This

error may be rather significant when comparing LAI of different systems

on an absolute basis, but should not be important when comparing a

given species' LAI among systems or LAI of intercrops and corresponding


Leaf Area Index was measured in control-treatment plots on days

28, 46, and 74 in Year 1 and in control, fertilized, and pesticide-

sprayed plots on days 40 and 65 in Year 2. Samples were not taken in

wet or windy weather; each sample took two people a maximum of seven

days to complete. Sixty measurements were made on each plot: six at

each of 10 random points located at least 2 m from each other and 1 m

from the edge of the sampling area. The average LAI from the 60

points was treated as a single replicate in the analysis. Height by

'15 cm intervals and by the species of each leaf the line touched was

recorded in all systems except succession, where only plant class

monocott or dicot) was noted. Since successional vegetation had a

"head start" on the crop systems, the LAI of the successional system

was set back seven days on each sampling date by linear interpolation.

The "system LAI" of an intercrop system is the sum of the LAIs of

its component species. Canopy cover was calculated as the percent of

trials (of the 60 per plot) in which one or more leaves was hit. Canopy

cover isa system-level variable; ground cover by species was not


Cowpea third-leaf area

A measure related to cowpea LAI, the area of the third leaf up

from the cotyledon, was obtained as part of herbivory measurements on

day 22 (Year 1) and day 30 (Year 2). The sampling and area measure-

ment techniques are given in the section on cowpea leaf herbivory,


Specific leaf mass

Specific leaf mass (g/m2 leaf) was determined on day 71, Year 2.

Leaves of three systematically selected plants of each crop species

were harvested from the border strips of each plot, the leaf area

determined, and the samples dried at 800C and weighed to determine mass

per unit leaf area. In the successional system, specific leaf mass of

monocots and dicots was determined for a composite sample of three

systematically-selected, approximately 10-cm-diameter samples of

vegetation from the border strip of each plot. Leaf Area Index of the

crop species was calculated as leaf biomass divided by specific leaf

mass. Biomass of leaves and stems was not separated in the successional

plots,so LAI could not be determined.

Root biomass

In Year 1, root biomass was determined by taking soil cores to a

depth of 40 cm with an 8 cm diameter root auger on day 96. The cores

were taken in 10 cm depth intervals and the soil carefully rinsed off

each segment of each core through approximately 12 strand/cm screens.
The roots were then dried at 80C and weighed to 10 g. Living and

dead roots, and roots of different species, could not be differentiated.

Root biomass data were corrected for roots remaining from the previous

fallow by subtracting (by depth interval) root biomass determined on

the bare ground plots.

Root cores were taken along a line perpendicular to planting rows,

at a systematic interval that did not correspond to the inter-row

distance. It was felt that this gave a more representative sample than

would coring at random points, in view of the very limited sample size.

Ten cores were taken from two plots in the maize-sorghum and maize-cowpea

systems, five cores from one plot in each of the other nine systems.

Roots were also sampled by a complete harvest method at flowering

in Year 2 (see above). Year 2 values were not corrected for residual

roots because the previous vegetation (the Year 1 experiment) was less

than one year old and residual roots were few at the start of the Year

2 growing season.

Root biomass was used as an indicator of root activity in this

study. Root surface area may be a better measure because large roots

are not active in proportion to their biomass; all systems in this

study were of equal and low age, however, and large roots were absent.

Stem length

Height or length of individuals of each crop species was measured

on days 28, 46, and 89 (Year 1) and days 36 and 59 (Year 2). Control

plots (Year 1) and plots of all treatments (Year 2) were sampled.

Systematic samples were taken in each system, ranging from every 15th

plant in every other row (sorghum monoculture) to every plant (pumpkin

in four-crop system). Sample size was 39-51 in Year 1 and 13-32 in

Year 2 (except pumpkin,n = 1-30). Maize and sorghum plants were

measured from the soil surface to the straightened tip of the longest

leaf; cowpea was measured along the primary stem to the main meristem;

pumpkin was measured along its longest branch to the farthest meristem.


Survivorship was calculated from stand counts taken on days 24,

53, and 74 (Year 1) and days 28, 56, and 96 (Year 2). In Year 1, the

subplots of each main plot were surveyed and the results summed; in

Year 2 the 6 x 8 m sampling areas of all plots were inventoried.

Natural successional vegetation was not inventoried due to the difficulty

of distinguishing individuals. Mortality/30 days was calculated from

survivorship for the intervals between samples and for the growing

season as a whole. Stand counts were also converted to percent of

full stand (recommended or target planting density). At the first

sampling date, percent full stand ("fullstandedness") gives a measure

of how well the desired planting density was achieved (success of

crop establishment). For later sampling dates, fullstandedness of

species in a system can be summed to give a measure of plant survival

on a system level, a property I called "system fullstandedness." If

no mortality occurred, system fullstandedness would be one in all

systems except sparse maize, where it should be 0.5.

Resource Use

Ability of the experimental systems to exploit available resources

was of interest as a possible explanation for differences in productivity.

While it was difficult in most cases to obtain direct measures of resource

utilization, several collateral measures that should be correlated with

use of certain resources were obtained.


Amount and vertical distribution of LAI were used as indicators of

light interception in the experimental systems. Sampling methods are

given above.

Root biomass

Amount and distribution of root biomass are indicators of utili-

zation of the soil volume and soil resources (primarily, water and

nutrients). Profiles of root biomass in 10-cm intervals from 0-40 cm

depth were constructed for all system in Year 1, as described above.

Soil moisture

Soil moisture was monitored by gravimetric methods in Year 2. A

tube-type soil auger was used to collect soil samples every two to three

days from the 15-30 cm depth zone. The samples were plastic-bagged in

the field, weighed, dried at 800C, reweighed, and the moisture content

calculated as (wet weight-dry weight)/(dry weight). Each day's samples

were taken from random points in a random selection of 25 plots; these

were later grouped into ten-day intervals to allow sufficient

replication by system and treatment to perform analysis of variance

for each interval.


A resource that is not usually monitored but is very important

to small farmers is labor. It was evident from the interviews that

human labor often limits the area planted. In Year 1, planting labor

was measured for the 60 small control and defoliation plots and weeding

labor was measured in the 33 main control plots, for the first and

second weedings. In Year 2, planting labor and first and second weeding

labor were measured in all plots. Crews of five to seven workers were

timed to the nearest five seconds and this was converted to person-

hours/ha. Since planting time included time to align and hold ropes and

to plant at the proper marked intervals, these determinations of planting

labor almost certainly overestimate the labor a small farmer would use.

The same is true of weeding labor, since it included thoroughly cleaning

the ground of weeds and collecting them for weighing. The measures

are nevertheless useful for comparative purposes. Efficiency of labor

use was also calculated, as the ratio of edible and total biomass output

to labor input.

Resistance to Natural Stressors

Stressors provided by nature were used to test hypotheses about

the resistance of the experimental systems to energy drains. All of

the growth and productivity measures in control plots reflect the

species' and systems' reaction to the sum of all naturally occurring

stressors, and are therefore included in this category. In addition,

the impact of a number of specific natural stressors was monitored,

including weed growth; lodging in windstorms; and population levels

of various pests and diseases, usually measured in terms of frequency

of affected host plants.

Weed growth

Competition by weeds is especially important to small farmers,

who often do not weed their fields as often or as thoroughly as was

done in this study. Weeds were hoed, collected, trimmed of roots,

subsampled, dried at 800C, and weighed. Samples were taken from control

plots in the first, second, and third weedings in Year 1 (days 17,

39 and 78) and from control, fertilized, and watered plots in the

second and third weedings in Year 2 (days 44 and 107). The first weed

sample in Year 2 was discarded due to a sampling error.


In both years a windstorm occurred as the crops were approaching

maturity, conveniently providing an opportunity to measure susceptibility

to lodging. The number of maize and sorghum plants inclined at greater

than 450 from vertical was recorded for all control plots on day 76 in

Year 1, and for control and defoliated plots on day 74, Year 2. No

measure could be devised that would meaningfully compare the degree

of lodging in maize and sorghum with that in the other experimental


Pests and diseases

The surveys conducted were intended to be representative, rather

than exhaustive, of the important pests and diseases, and were limited

by time, the need to isolate and identify pests, and constraints on

destructive sampling. In some cases, non-pest-specific plant symptoms

(e.g., leaf discoloration) were used as variables due to the difficulty

of separating and identifying the multiple causes of those symptoms.

Monitoring methods are described below by crop. Most pest and disease

levels are expressed as percent of plants, leaves, pods, or other plant

part affected.

Rat holes. Shortly after planting, rats excavated some of the

planting holes, eating the seeds. The number of rat holes per plot was

counted in the 60 small control and defoliated plots on approximately

day 4, Year 1, for possible correlation with crop system.

Maize streak virus. Maize streak is a viral disease carried by

the maize leafhopper Cicadulina mbila (Homoptera: Cicadellidae), and

is easily identified by broken white lines parallel to the leaf veins.

Systematic samples of maize plants were scored by degree of infection:

0 (no symptoms), 1 (five or fewer leaves with symptoms), or 2 (more

than five leaves with symptoms). From 91-367 plants were scored in

each control plot on day 31 of Year 1. From 27-91 plants were sampled

on all plots on day 34, and 17-59 plants per plot on day 80, in Year

2. The data were later reduced to percent of plants having any symptoms.

Maize stalk borer. The maize stalk borer Busseola fusca

(Lepidoptera: Noctuidae) completes one generation in the stem of the

growing maize plant and a second generation in the cobs and stems of

maturing plants and trash left on the field after harvest. Populations

of stalk borers. were counted in systematic samples of 100 seedlings per

plot on day 13 in Year 2 by counting the number with "windowing,"

characteristic strips of holes in leaves that result from borers

entering the stem.

Sorghum shoot fly and tillering. Maggots of the sorghum shoot fly

Atherigona indica (Diptera: Muscidae) bore into young sorghum shoots,

causing death of the main shoot ("dead heart") and a compensatory

sprouting of new side shoots (tillering). Systematic samples of 380-676

plants were monitored for dead heart in control plots on day 32, Year 1,

and complete samples of 104-533 plants were taken during the day 22

stand count for all plots in Year 2. Percent of plants tillering was

also measured on day 47, Year 1 (50 systematically selected individuals

per control plot) and on day 57 in Year 2 (complete samples of 105-512

plants in each plot, measured in plots of all treatments).

Sorghum stalk borer. Sorghum stalk borers, primarily Busseola

fusca but possibly some individuals of Chilo and Sesamia species also,

were counted at harvest on day 130 in Year 2. Systematic samples of

25 main stems per plot were scored by number of borer exit holes. These

data were later reduced to percent of stems having any exit holes.

Striga. Striga hermontheca and Striga asiatica (Scrophulariaceae)

are plant parasites that attach to and feed on roots of maize and

sorghum. The Striga individuals were counted in all maize and sorghum

plots on day 77, Year 1, and the data expressed as individuals per

unit area.

Pumpkin melonfly. The pumpkin melonfly Dacus cucurbitae (Diptera:

Trypetidae) oviposits under the skin of developing pumpkin fruits, where

the larvae cause the fruit to partially or totally discolor and in

extreme cases to rot and fall off the vine. Melonfly damage was

assessed for all fruits in control plots on approximately day 70,

Year 1, and in all plots on day 80, Year 2. Data from plots having

fewer than five fruits were deleted. Fruits were scored by degree of

melonfly attack: 0 (no visible damage), 1 (one or more entry holes

visible), or 2 (fruit discolored or fallen off the vine). These data

were later reduced to percent of fruits having any visible damage.

Pumpkin leaf discoloration. Pumpkin leaves are susceptible to a

number of leaf diseases, of which powdery mildew (Erysiphe cichoracearum),

downy mildew (Pseudoperonospora cubensis) and an unidentified mosaic

virus were present on the experimental pumpkin plants. (Other diseases

may also have been present.) Rather than attempt to differentiate the

symptoms of these diseases, percent of leaves with any kind of leaf

discoloration was measured. Leaf miner trails, discoloration around

holes, etc., were not included, so the discoloration variable should

reflect the status of the plant with respect to leaf diseases in

general. A systematic sample of 44-56 pumpkin plants was surveyed in

each control plot on day 76, Year 1. Each plant was scored according

to the number of leaves that were partially or totally discolored:

0 (none discolored), 1 (five or fewer leaves discolored), or 2 (more

than five leaves discolored). These data were later reduced to percent

of plants with more than five leaves discolored.

Cowpea leaf herbivory. Young cowpea plants were heavily infested

with the beetle Ootheca bennigseni, causing considerable loss of leaf

area through herbivory. Leaf area loss was determined for systematic

samples of 43-71 leaves per plot (day 22, Year 1) and 10-17 leaves per

plot (day 30, Year 2). Only the third leaf up from the cotyledon was

sampled for each plant since the object was to make comparisons among

systems rather than to measure total leaf consumption. No correlation

was made for leaf expansion because the data were to be used to compare

herbivory on fully expanded leaves of like ages. Total leaf area (area

if herbivory had been zero) was determined by tracing the leaves on

opaque paper, approximating the original margins where missing, cutting

out the traces, and measuring their area on a leaf area meter. Area of

tracings were adjusted downward by 8.7 percent to correct for error

during tracing and cutt!iAg,- a determined by a sample of 55 uneaten

leaves and their paper counterparts. The "residual area," or leaf

area remaining after herbivory, was determined by measuring the leaves

themselves in the area meter. To improve accuracy since the meter had

a faulty 0.I cm2 display, the leaf samples from each plot were measured

as a group. Control plots were sampled in Year 1, all plots in Year 2.

Cowpea aphids. Percent of cowpea plants on which Aphis fabae

(Homoptera: Aphididae) were found was measured in control plots on day

88, Year 1 and in all plots on day 80, Year 2. The samples contained

23-121 (Year 1) and 28-122 (Year 2) systematically selected plants.

Cowpea leaf diseases. Three cowpea leaf diseases could be

distinguished well enough to be reliably scored. Top necrosis, a

common viral disease in the Morogoro area, causes characteristic

yellow mottling and leaf shrivelling very similar to that caused by

cowpea mosaic virus. Occurrence of top necrosis was recorded for

systematic samples of 139-349 plants for control plots on day 39,

Year 1, and 28-71 plants for all plots on day 53, Year 2. Three

degrees of severity of top necrosis were recorded: 0 (no symptoms),

1 (fewer than five leaves with symptoms), and 2 (five or more leaves

with symptoms). These data were later reduced to percent of plants

having any symptoms of top necrosis.

The pseudorust Synchitrium dolichi produced obvious orange

patches on leaves and stems, and powdery mildew (Erysiphe polygoni)

created easily distinguished white patches on leaves. Presence or

absence of these two diseases was recorded in the same sample in which

aphids were counted (above).

Cowpea seed and pod discoloration. Many cowpea seeds and pods

were disformed and discolored as a result of numerous pests and diseases.

Discoloration seemed to be primarily caused by unidentified fungal

diseases but may also have been caused to some extent by heteropteran

feeding. Pod discoloration was scored as 0 (no discoloration), 1 (less

than 25 percent of the pod surface discolored), or 2 (more than 25

percent of the pod surface discolored) for random samples of 59-120

pods per plot from the first harvest in control plots, Year 1, and

5-20 pods per plot from the three harvests from all plots, Year 2.

Most samples contained the maximum number of pods in this range;

occasional smaller samples were from low-yielding plots, where all

pods were sampled. The data were later reduced to percent of pods

having any fungal discoloration. In Year 2, a three-harvest average

was calculated by weighting percent discolored pods by pod mass for

the three harvests.

A distinctive oval, off-white growth on the pods thought to be

a scab disease was also monitored in Year 2 in the same pod samples


that were scored for pod discoloration. Percent of scab-infected pods

was determined for all three harvests and the weighted average calcu-

lated as above.

Cowpea seeds were inspected for several types of damage, including

seed discoloration, in samples from the first harvest of control plots

(Year 1) and all three harvests from all plots (Year 2). Seeds were

scored as discolored or not discolored; browning around the edges of

holes was not included. In Year 1 a small cup was used to remove a

random sample of seeds from each subplot's well-mixed bag of seed

yield. In Year 2 all seeds were sampled. Sample size ranged from

648-865 (sum of six subplots) in Year 1 and from 0-1196 in Year 2.

In Year 2, a three-harvest average was calculated by weighting the

percent of discolored seeds by the number of seeds in each harvest.

Cowpea Maruca damage. Maruca testiculatis (Lepidoptera) larvae

feed on cowpea flowers and seeds, causing considerable yield reduction.

The population of Maruca larvae in closed cowpea flowers was sampled on

day 81, Year 2. Presence or absence of larvae was scored in 1-40

flowers from the border strips surrounding the sampling areas of cowpea

plots. Samples containing fewer than five flowers were later discarded.

In addition, percent of pods having one or more Maruca exit holes was

recorded and the three-harvest average calculated for the pod samples

described above. Percent of seeds partially eaten by Maruca larvae was

also recorded and the three-harvest average calculated for the seed

samples described above.

Other cowpea seed damage. Seed shrivelling and seed bruchid

beetle holes were recorded and the three-harvest average calculated for

the seed samples. In addition, the percent of seeds without any visible

damage was recorded as a composite measure of susceptibility to pests

and diseases. Shrivelling or wrinkling of seeds was due primarily to

piercing-sucking feeding by Acanthomia horrida but may also be attributable

in part to thrips. Acanthoscelides obtectus (Coleoptera: Bruchidae)

produced small holes or characteristic transparent "windows" in the

seed coat that could be easily distinguished and counted.

Methods of Analysis

Some of the above response measures are related to whole-system

function, while others pertain to a given species' performance. For

convenience, successional monocots and successional dicots as well as

the four crop species are referred to as "species." Some parameters,

such as LAI and yield, were measured both on the species and system


Three Types of Analysis

Three general types of analysis were performed: (1) absolute

comparisons of system performance (system-level measures only), (2)

comparisons of intercrop performance with that of a weighted mean of

corresponding monocultures (system-level measures), and (3) comparison

of each species' performance in intercrop and monoculture systems.

Absolute comparison of system performance

The response measures were compared among systems and treatments

by analysis of variance (ANOVA). The measures were not adjusted for

planting density. This type of analysis tests for absolute differences

among systems and treatments, without regard for crop composition or

planting density.

Comparison of intercrops and corresponding monocultures

Whole-system performance of the intercrops was compared with a

weighted mean of corresponding monocultures by the Contrast procedure

of the Statistical Analysis System (SAS) statistical computer program

package (Helwig 1978). In this procedure, the given variable is compared

with several other variables that are weighted as specified. The

weighting factors were the fractions of the intercrop system allocated

to each species. Thus, system LAI of the maize-sorghum intercrop

could be compared with maize and sorghum LAI's, weighted equally. In

the maize-cowpea system the weighting factors were also 1/2 + 1/2; in

maize-pumpkin, 2/3 + 1/3; and in the four-crop system, 1/4 + 1/4 +

1/4 + 1/4. Intercrop performance was also compared with that of

corresponding monocultures using Yield Equivalent Ratio (YER).

Comparison of species performance in intercrops and monocultures

Measures of species performance (e.g., sorghum LAI) were corrected

for planting density, creating a new variable ("adjusted sorghum LAI")

on which the ANOVA was performed. In this example, adjusted sorghum

LAI equals sorghum LAI x 2 for the maize-sorghum system, and sorghum

LAI x 4 for the four-crop system. Adjusted variables equal the unadjusted

value for the monoculture systems (and successional monocots and dicots).

Analyses of variance performed on density-adjusted species variables test

for differences in a species' performance in different systems or

treatments, and are roughly equivalent to testing for differences in

per-plant performance. Variables expressed as frequencies were not

adjusted for planting density. Species performance was also evaluated

by the species' Yield Equivalent Ratio (YER), the ratio of species'

yield in intercrop to its expected yield based on planting density and

monoculture yield.

Analyses of Variance

Data from the three experimental data sets (Year 1 main control

plots; Year 1 small control and defoliated plots; Year 2 plots of all

treatments) were analyzed separately due to inherent differences in-

cluding plot size, weather, and sampling dates. One-way ANOVA by

system was performed on the data from the main control plots, Year 1,

and two-way ANOVA by system and treatment for the other two data sets.

Equality of variance among groups being compared is one of the

assumptions of ANOVA. Levene's test, in Biomedical Data Programs

program P7D (Dixon and Brown 1979) was used to test this assumption for

each variable. Species variables were tested for equality of variance

both before and after adjustment for planting density. Normality was

not tested for because ANOVA is known to be relatively insensitive to

deviations from this assumption (Sokal and Rohlf 1969, Steele and

Torrie 1980).

Almost all variables met the equal variance assumption as tested

by the BMDP program. This may have been due partly to small sample

size and partly to the fact that many variables were taken as means,

and should therefore be normally distributed with reduced variances.

Four variables had unequal variance (p< .05): system edible yield

(Year 2), system total yield (Year 2), pumpkin edible yield (Year 2),

and adjusted pumpkin edible yield (Year 2). Log transformation would

be expected to reduce variance inequality due to differences in growth

rate of difference species, but log transformation did not equalize the

variance of the above variables (except adjusted pumpkin edible yield,

and that because the program eliminated variances of zero, from the

four-crop system, from the calculation). On closer inspection it was

evident that the unequal variance of all four variables was due to

great fluctuation in pumpkin edible and total yield in the pumpkin

monoculture plots. I ignored this relatively minor departure from the

ANOVA assumptions, and did not transform any of the response variables.

The spatial isolation of the watered and pesticide-sprayed plots

in Year 2 raises the question of whether the ANOVA assumption of

randomness was seriously violated. This was unlikely since all plots

were located within approximately 250 m of each other, and soil analysis

had revealed no clear trends in soil pH or nutrients across the site.

As a further check the test of equality of variance was performed on all

variables without the water and pesticide plots, and little or no

improvement in variance equality resulted. Plot-to-plot variability by

system and treatment was no greater when water and pesticide plots were

included than when they were omitted.

The ANOVA was conducted with the General Linear Model (GLM)

procedure of the SAS package, due to unequal replication, followed by

the Duncan procedure (Duncan's multiple range test) to detect specific

differences among systems and treatments. In two-way ANOVAs where

system-by-treatment interacttonwas significant, Duncan's tests of system

differences were performed for each system. When system-by-treatment

interaction was nonsignificant, tests for differences among systems were

performed on data from plots of all treatments, and tests for differences

among treatments were performed on data from plots of all systems. In

the figures, the composition of the data sets or "samples" on which the

Duncan's tests were performed is described where any ambiguity exists.

A significance level of p < .05 was used throughout the analyses unless

otherwise specified.

In many cases, striking differences in mean levels of response variables

were not found to be statistically significant by the ANOVA and Duncan's

tests. This was especially true of differences among systems in Year 1,

where the number of systems was high and the number of replicates low.

Duncan's tests performed by system or treatment tended to give lower signif-

icance than those performed for all systems or treatments combined, due

to reduced sample size. In interpretation of results, therefore, some weight

has been given to consistency of trends (among several variables, across

treatments, and in the two study years) as well as the results of the statis-

tical tests.

Analysis of Stability

Several approaches were taken to assess the stability of the experi-

mental systems and the species comprising them. Levels of naturally

occurring stressors were measured (e.g., pest frequencies) and system

comparisons made by ANOVA. Pest levels were also correlated with pro-

ductivity; strong negative correlations suggest that either high produc-

tivity reduces pest levels or pests reduce productivity. The effects of the

pesticide treatment on both pest levels and productivity indirectly indicate

the importance of pests as productivity drains in the agricultural

systems studied.

The second measure of stability was the productivity change of

the systems and their component species in the fertilization, pesticide,

defoliation, and watering treatments. Both absolute magnitude of

change and percent change from controls were calculated; absolute change

is here called "response," and percent change is called "responsiveness."

The sign of the change is ignored. Differing system responses to change

in stress level indicate differences in resource use. Responsiveness

is a measure of instability; those systems or species least responsive

to changes in levels of stressors should also be the most constant.

Responsiveness of species to increases in diversity (reduced competition

stress) was also used as a measure of species stability.

Finally, the coefficient of variation (standard deviation/mean)

of systems or species over the range of stress treatments was determined

as a measure of overall constancy. Year 1 and Year 2 data were not

combined for this measure due to differing plot size. Temporal

(year-to-year) variability was also assessed, by the coefficient of

variation of the Year 1 and Year 2 control plot means of a number of

productivity variables (analyzed by-system and by-species).



This chapter is divided into three results sections, that cover the

three approaches taken to analysis of the productivity data, and a

discussion of these results as a whole. The three approaches to the data

analysis are (1) comparison of whole-system productivity of the ten

experimental system monoculturess, intercrops, and successional

vegetation), (2) comparison of the productivity of each intercrop

system with a weighted mean of its component crops grown as monocultures,

and (3) comparison of each species' productivity in various crop systems.

The first approach compares systems on an absolute basis, without regard

for species composition; the second evaluates system response to changes

in diversity (spatial mixing of species); the third evaluates individual

species' responses to changes in diversity.

The terms "growth" and "productivity" are used in the general

sense of biomass accretion per unit time and biomass distribution, rather

than as net primary productivity (NPP), which includes drains to

herbivores, death, and decomposition (see Methods chapter). The

variables used to evaluate productivity of systems and species were

divided into those that relate directly to survival and biomass accretion

(called "direct" measures) and those that contribute information on the

distribution of biomass ("indirect" measures). The direct variables

include edible and total aboveground biomass (called "edible biomass"

and "total biomass"); aboveground biomass at flowering (called "biomass

at flowering"); root biomass (by two different methods in the two study

years); LAI (by the plumb-bob method in both years and also by harvest

at flowering in Year 2); a measure of leaf biomass (determined at the

time the systems were experimentally defoliated); canopy cover; stem

length agronomicc species only); mortality (for each agronomic species);

and fullstandedness (percent of full stand count, for each agronomic

system as a whole). The indirect measures include allocation ratio (the

ratio of edible to total aboveground biomass); root/shoot ratio (at

flowering); specific leaf mass (mass per unit leaf area); and a number

of "miscellaneous yield measures" that were measured for the four

agronomic species, such as maize cob production.

For clarity, most measures of productivity are first examined in

control plots to establish baseline trends. Data from the four stress

treatments (fertilization, pesticide spraying, defoliation, and watering)

are then added to evaluate effects of higher or lower levels of stress

compared with the controls, which represent local farmers' conditions.

In Year 1, two separate experiments were performed, and the data from

them are not mixed. Discussion of Year 1 control plots refers to the

large main control plots, while discussion of effects of defoliation

refers to the small defoliated plots (and their own controls).

Whenever interaction between cropping system and treatment effects

was low (Pinteraction > .05) tests for differences among systems and among

treatments were performed on complete samples of all treatments or systems

combined to increase the power of the test. When interaction was high

(Pinteraction < .05), Duncan's tests for differences among systems and

among treatments were performed separately for each treatment and system.

For the sake of expediency in the field, not all measures were

taken in all systems and treatments. Such missing blocks of data are

omitted from tables and figures or may appear as blanks in tables.

Results: Productivity Differences Among Systems

Measures of Biomass Accretion

Edible and total aboveground biomass at harvest

Control treatment. The mean total biomass of the systems studied

ranged from 10-350 g/m2, and the mean edible biomass ranged from 0-122

g/m2 (Figures 5 and 6). Edible and total yields were generally the same

or slightly lower in Year 2 than Year 1. Edible and total biomass of

crop systems containing either maize or sorghum was consistently higher

than that of either cowpea or pumpkin monocultures. The difference was

significant in all cases except one; pumpkin edible yield was not

significantly lower than that of the maize and sorghum monocultures in

Year 2, although it was less than half the naize and sorghum edible

yields. Total biomass of the successional system was approximately equal

to that of the maize- and sorghum-containing crop systems and was also

significantly higher than cowpes and pumpkin monocultures. Edible yield

of the succession systan was assumed to be zero.

Among the crop systems containing monoc.ts, there were no significant

differences in total biomass, but there were significant differences in

edible biomass in Year 1. Edible yield was significantly lower in the

recommended-density maize monoculture than in the sorghum, maize-sorghum,

sparse maize, and maize-cowpea systems. The four-crop system had


500 -

400 -

300 -





p p--


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Figure 5. Total aboveground biomass in the control treatment,
Years 1 and 2. Systems not sharing a common line are
significantly different by Duncan's tests.

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500 -


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Figure 6. Edible biomass in the control treatment, Years 1 and 2.
Systems not sharing a common line are significantly different
by Duncan's tests.

the second-lowest edible yield, significantly lower than that of the

sorghum monoculture.

Stress Treatments. Defoliation had no consistent or significant

effect on whole-system edible and total yield compared with controls

(Figures 7-10), except significantly higher total biomass in control

than defoliated maize-pumpkin plots in Year 1. When the defoliated

leaf biomass was added to the final biomass, this difference was still

significant. After adding the defoliated biomass, total production

was significantly higher in defoliated than control plots in the maize-

cowpea (Year 1) and succession (Years 1 and 2) systems. Thus, all

systems except maize-pumpkin recovered from defoliation in terms of

edible and total biomass. Significant stimulation of production (when

defoliated leaf mass is included in the calculation) was demonstrated in

the maize-cowpea and succession systems.

Fertilization significantly increased total aboveground biomass

production compared with controls in all systems except cowpea monoculture,

and significantly increased edible biomass in all systems except sorghum

and cowpea. Edible biomass was, however, more than 50 percent greater

in fertilized than control sorghum plots. Watering had no consistent or

significant effect on edible and total yield. Pesticide spraying had

no effect on total biomass in the maize and sorghum monoculture, but

did cause a slight (nonsignificant) increase in the succession system,

a large (nonsignificant) increase in cowpea monoculture, and a decrease

in the four-crop and pumpkin systems. Spraying decreased edible yield

(nonsignificantly) in the maize, pumpkin, and four-crop systems, and

substantially (but nonsignificantly) increased it in the sorghum and

cowpea monocultures.


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Figure 9. Total aboveground biomass in five treatments,
Year 2. C = control treatment, F = fertilized, P =
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leaves is not included in total biomass. Treatments not
sharing a common line are significantly different by
Duncan's tests.










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Figure 10. Edible biomass in five treatments, Year 2.
C = control treatment, F = fertilized, P = pesticide,
D = defoliated, W = watered. Treatments not sharing a
common line are significantly different by Duncan's

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The ordering of whole-system edible and total biomass varied some-

what among the five stress treatments (Figures 11 and 12). Maize mono-

culture responded most to fertilization while sorghum monoculture

responded little; as a result, maize had the greatest edible and total

biomass production in the fertilization treatment. Maize total biomass

was significantly higher than all other systems in the fertilization

treatment. Pumpkin total biomass increased with fertilization to a

level significantly higher than that of cowpea monoculture; pumpkin

edible biomass increased to a level not significantly different from

that of the four-crop and sorghum systems. Pesticide spraying raised

the total biomass of the succession system to first place (significantly

higher than that of the four-crop and maize systems), and raised sorghum

and cowpea edible biomass relative to the other crop systems. The ordering

of systems by total biomass was the same in the watering treatment as in

controls, but there were minor changes in the ordering of systems by

edible biomass due to slight increases in maize and cowpea edible bio-

mass and slight decreases in the four-crop and pumpkin systems. The

defoliated maize-cowpea system was nearly as productive as the highest-

yielding system, sorghum monoculture. The four-crop system was adversely

affected by defoliation, and as a result its total biomass in the de-

foliated treatment was significantly lower than that of defoliated

succession and not significantly different from that of the two lowest-

yielding systems. Edible biomass of the four-crop system was also

reduced by defoliation to a level not significantly higher than that

of the cowpea and pumpkin monocultures.


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Total aboveground biomass at flowering

Aboveground biomass at flowering (Figure 13) corresponded closely

with the final harvest biomass values (r = .91 for all systems and

treatments sampled). Biomass at flowering was 50-80 percent of final

biomass in the control and fertilized maize, sorghum, four-crop and

succession systems. In pumpkin monoculture, biomass did not increase

between flowering and the final harvest in control plots, but it did

increase dramatically in the fertilization treatment, where biomass at

flowering was only 36 percent of final biomass. In cowpea monoculture,

biomass increased from flowering to final harvest in control plots,

where biomass at flowering was 42 percent of final biomass, but declined

by almost 50 percent in the fertilized plots.

In both the control and fertilization treatments, highest biomass

at flowering was found in the sorghum monoculture, successional vegetation,

and maize monoculture, followed by the four-crop system (Figure 13).

Lowest biomass was found in the pumpkin and cowpea monocultures. Bio-

mass at flowering was significantly greater in fertilized than control

plots in the sorghum, maize, four-crop, and succession systems.

LAI and canopy cover

Temporal patterns of leaf area development varied from system to

system (Figures 14 and 15). Early development of LAI was most rapid in

successional vegetation, maize monoculture, maize-sorghum intercrop,

and pumpkin monoculture. In the second half of the growing season (after

day 46 and day 40 in Years 1 and 2, respectively) successional LAI increased

only slightly or declined, whereas LAI of systems containing maize continued










































Figure 13. Total aboveground biomass at flowering, Year 2. Entries are
x s, in g/m2. Systems not sharing a common vertical line are signif-
icantly different by Duncan's tests. Asterisks mark systems in which
biomass was significantly greater in fertilized than control plots.




.5 -

X 0- -----------

I.o0 \

< O 5

PUMPKIN f----.
.5 /

0 -- **.-,
0 10 20 30 40 50 60 70 80

Figure 14. Temporal development of LAI in three treatments
in the monoculture systems, Years 1 and 2. Dashed lines
are Year 1; solid lines are Year 2.

X o









0 -

0 10 20

30 40 50 60 70

Figure 15. Temporal development of LAI in three treatments
in the succession and intercrop systems, Years 1 and 2. Dashed
lines are Year 1; solid lines are Year 2. Successional system
data were set back seven days by linear interpolation to
correct for growth during planting of the crop systems.


1 I 1 1 I