Ecological Interactions Tropical AFS

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Agroforestry Systems 61: 221236, 2004.

2004 Kluwer Academic Publishers. Printed in the Netherlands.221

Ecological interactions, management lessons and design tools in tropicalagroforestry systems

L. Garca-Barrios1,

and C.K. Ong2

1El Colegio de la Frontera Sur, Mexico. Carretera Panamericana y Perisur (s/n); San Cristobal de las Ca-

sas, Chiapas, Cp.29290 Mexico; 2World Agroforestry Centre, P.O. Box 30677, Nairobi, Kenya; Author for

correspondence: e-mail: [email protected]

Key words: Growth resources, Indices, Predictive understanding, Roots, Simulation models, Treecrop interactions

Abstract

During the 1980s, land- and labor-intensive simultaneous agroforestry systems (SAFS) were promoted in the trop-

ics, based on the optimism on tree-crop niche differentiation and its potential for designing tree-crop mixtures using

high tree-densities. In the 1990s it became clearer that although trees would yield crucial products and facilitate

simultaneous growing of crops, they would also exert strong competitive effects on crops. In the meanwhile,

a number of instruments for measuring the use of growth resources, exploratory and predictive models, and

production assessment tools were developed to aid in understanding the opportunities and biophysical limits of

SAFS. Following a review of the basic concepts of interspecific competition and facilitation between plants in

general, this chapter synthesizes positive and negative effects of trees on crops, and discusses how these effects

interact under different environmental resource conditions and how this imposes tradeoffs, biophysical limitations

and management requirements in SAFS. The scope and limits of some of the research methods and tools, such as

analytical and simulation models, that are available for assessing and predicting to a certain extent the productive

outcome of SAFS are also discussed. The review brings out clearly the need for looking beyond yield performance

in order to secure long-term management of farms and landscapes, by considering the environmental impacts and

functions of SAFS.

Introduction

Traditional low-input agricultural systems involving

trees have been designed and managed for centuries

by poor peasants around the world, and are still con-

spicuous in the tropics. During the past century, land-

use intensification, agroecosystem simplification and

other social changes have undermined the functional-

ity of many of these low-inputsystems, and confronted

peasant agriculture with enormous sustainability chal-

lenges (Nair 1998; Garca-Barrios and Garca-Barrios

1992; Garca-Barrios 2003). In the past two decades,

great expectations have been set on the promotion of

traditional and novel agroforestry practices as a means

for slowing down or reversing such trends, once it

became clear that high-input strategies promoted by

development agencies had failed to be adopted and/or

to deliver benefits to smallholders (Sanchez 1995).

Where there is still scope for fallow agriculture in the

tropics, sequential agroforestry systems such as en-

riched fallows have been proposed; where land-use

intensification and fragmentation is more severe, the

bet has been on simultaneous agroforestry systems

(SAFS) such as alleycropping, alley farming, parkland

systems and trees on field boundaries.

During the 1980s, alleycropping was promoted

throughout the tropics as a sustainable option for low-

input agriculture. By the 1990s it was recognized

that the density, management intensity and envir-

onmental scope of new tree-based SAFS had been

pushed too far. It became clear that introducing trees

in croplands was in some cases like walking on a

razor-edge because trees provide peasants with both

crucial products and strongly facilitate crops, but can


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deeper roots than crops. Trees are perennial and there-

fore: a) they eventually explore a relatively large space

and can substantially modify their biophysical envir-

onment to their benefit, b) they are better adapted to

resist cyclic environmental harshness and to use and

recycle resources when it is more efficient to do so,

and c) they commonly have a canopy and a root systemin place when crops starts growing (Ong et al. 1996).

Mature trees in SAFS can have the following

positive effects on crop fields:

1. They can add considerable amounts of organic mat-

ter to the soil and slow down its decomposition rate,

improving soil fertility and physical structure. Trees

roots can stabilize loose soil surfaces, which together

with tree litter cover, reduces erosion (Young 1997).

They recover leached nutrients from deep soil or bed-

rock layers inaccessible to crops (Rao et al. 1998).

Trees can make more phosphorus available via mycor-

rizha and fix important amounts of nitrogen, which can

be transferred to the crops via shoot and root prunings

(Giller 2001).

2. Although trees can increase the potential soil water-

holding capacity, they have variable and conflicting

effects on the actual water volume available in the

treecropsoil system: Rainfall intercepted by the can-

opy that evaporates without reaching the soil can be as

much as 50% when tree density is high (Ong et al.

1996). Yet, reduced soil evaporation by tree shade can

help offset such losses as long as rainfall is lower than

700 mm per annum (Ong and Swallow 2004). Trees

can eventually increase infiltration but this strongly

depends on slope and soil characteristics (Ong andSwallow 2004). In general, tree presence produces a

net increase in total water used by the system (Ong

and Swallow 2004).

3. Tree shade can reduce leaf temperature and evap-

orative demand experienced by crops, increasing the

latters water productivity (i.e., g of dry mass per g of

water transpired). The net shade effect is more positive

(or less negative) when the annual crop is a C3 plant

which is normally light saturated in the open, so partial

shade may have little effect on assimilation or even

be beneficial (Ong 1996). This improvement in con-

version efficiency is relatively modest when compared

to the effect described in the previous point (Ong and

Swallow 2004).

4. Tree hedgerows provide protection against wind and

runoff (Rao et al. 1998.).

5. Trees can reduce weed populations and change

weed floristic composition towards less aggressive,

slow growing species (Leibman and Gallandt 1997).

6. Little is known about the complex and sometimes

conflicting effects of trees on pest and disease con-

trol, but Schroth et al. (2000) and Rao et al. (2000)

providecomprehensive reviews on the topic. Increased

pest and disease incidence has often been observed

directly at the treecrop interface. Trees increase air

humidity, which favors microorganisms, provide shel-ter for herbivores (insects, birds, and small mammals),

which damage the crops, and reduce pest and disease

tolerance of competition-stressed crops. Trees them-

selves can be more susceptible to pest and disease

attack when sown at densities and spatial arrange-

ments uncommon to their natural environments. Yet,

tree hedges have the potential to slow down windborne

pests and diseases, to act as repellants, and to attract

natural enemies; recent evidence is provided by Girma

et al. (2000).

On the other hand, with increase in density of trees,

their size, and/or ability to capture resources in SAFS,

they can exert strong competition for light, water and

nutrients, and reduce annual crop yields beyond the in-

terests of farmers if improperly selected and managed

(Garca-Barrios 2003). Nevertheless, weak competi-

tion is possible under certain circumstances such as

the following:

1. Tree roots can potentially reach below the crop root

zone, and thus they can use water accumulated deeper

in the ground when the crop is growing; after the crop

is harvested, they can use whatever residual available

water is found in the crop root zone; and they can

use any additional rain which falls outside the crop

growing season (Ong et al. 1996). It is important tostress that trees used in SAFS do not always have deep

pivotal roots, and that mixed and superficial tree root

architectures are common (van Noordwijk et al. 1996).

Moreover, if water recharge below the root zone is

infrequent and/or nutrients are superficial, most trees

will tend to develop or redirect their roots to the upper

soil layers (Rao et al. 2004) and only a few species

will develop roots that can reach relatively deep water

tables. Consequently, there seems to be less scope for

vertical root complementarity than originally thought

(Sanchez 1995; Ong and Swallow 2004).

2. Some deciduous tree species used in SAFS in semi-

arid regions such as Faidherbia albida exhibit reverse

phenology: they produce their leaves and demand

water only during the dry season, while their litter

provides nutrients and their trunk and bare branches

cast a light shade over crops during the rainy season

(Rao et al. 1998).


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itive and negative effects explicitly, and without con-

sidering resource distribution and use. An extensive

literature has been developed on this matter during

the past 40 years (Vandermeer 1989). A number of

competition- and productivity indices have been de-

veloped and their scope, limitations, and pitfalls dis-

cussed (Connolly et al. 2001; Williams and McCarthy2001). Most productivity indices have been developed

for two species intercrops. They assume that the

farmer is interested in finding whether combinations of

both crops in the same field can outperform the corres-

ponding sole crops. We will give a very brief account

of how crop and tree yields relate separately to their

net intra and interspecific interactions by developing a

graphical analysis of a hypothetical and generic SAFS.

We will then discuss the simultaneous assessment of

crop and tree yields.

Consider the crop and tree sole stands in Fig-

ure 1(a) and (b). Both are sown at their optimum sole

crop densities (N.), i.e., the number of individuals

per unit area which produces the maximum possible

yield (Y.) in that surface. These parameters result

from the reasonably hyperbolic relation between sole

crop yields and sole crop plant densities (Willey and

Heath 1969) depicted in Figure 1d and are a direct

consequence of net intra-specific interactions. In our

example, the sole crop stand parameters are Nc = 6

and Yc = 6, while the sole tree stand parameters are

Nt = 2 and Yt = 18. Intra-row plant distances are the

same for both crops. In the treecrop mixture depicted

in Figure 1c, every sixth crop row has been substituted

with a tree row. As a consequence, the crop density inmixture (nc) is 5 and the tree density in mixture (nt) is

1. In other words, nc = 5/6*Nc and nt = 1/2*Nt. For

simplicity, we will assume that both tree competition

and facilitation on the crop can be present, and that

no tree pruning is practiced so that facilitation results

from other mechanisms. Crop plants nearer to the tree

are generally smaller as they experience more compet-

ition (see Figure 1c), but here we consider the average

plant performance within the unit area.

Different crop yield outcomes in mixture (yc) are

possible; they are shown in Figure 2a. An interesting

starting point is case 3, where yc = 5/6*Yc, i.e., mix-

ture crop yield is reduced in the same proportion as

crop density. The average crop individual has the same

weight in sole crop and intercrop, which means that

the average net effect of a tree on crop plants matches

the net intra-specific effect of the average crop indi-

vidual in the sole stand. Considering the asymmetry

in tree and crop sizes, this seems highly unlikely, un-

less it results from very weak tree competition due

to high resource complementarity, or from facilitation

almost compensating competition. In case 4, either

tree competition is zero (perfect complementarity) or

facilitation exactly compensates competition, because

in this case, yc equals the yield expected for five

plants per unit area in the sole crop (here we make theassumption that given a near-optimum crop density,

particular plant arrangement has little effect). In case

5, the crop stand experiences enough net facilitation to

match the maximum sole crop yield, and in case 6 to

surpass it. In case 1 and 2 the average crop plant ex-

periences significant net competition and crop yields

are strongly reduced.

A similar analysis is possible from Figure 2(b),

for the trees performance in mixture; the roles are

simply inverted. Cases 1 and 2 are highly unlikely,

unless strong crop allelopathy effects on young trees

are present. The most probable outcome is somewhere

between case 3 and 4. Cases 5 and 6 are not realistic,

given asymmetry between tree and crop.

Of course, the crop and tree yield outcomes in our

example will be coupled and depend on one another

such that not all outcome combinations make sense.

Some possible combinations are depicted graphically

in Figure 3. In more general terms, each specific

spatio-temporal mixture design for a given pair of

plant species in a given environment produces a pair of

coupled yields. The set of coupled yield outcomes of

all possible designs (which include all sole crop yields

as well) is called a yield set (Vandermeer 1989); the

subset of points which constitute the exterior envelopeof this set is called the production possibility fron-

tier (PPF; Ranganathan and De Wit 1996); (Figure 4

presents a possible yield set and PPF for our hypothet-

ical SAFS). A PPF can be analyzed to compare sole

and mixed crop outcomes and to find the optimum

mixed crop designs according to different biological

and economical performance criteria. An example of

such criteria is the land equivalent ratio (LER), defined

as (yc/Yc) + (yt/Yt). A LER> 1 means that there

mixture is advantageous because more land would

be required to obtain yc and yt by sowing each spe-

cies separately as sole crops. (For further details on

LER and other mixture performance indices, see Van-

dermeer 1989). A few scores of yield pairs can be

obtained through field experiments, while more thor-

ough yield set and PPF constructions can be aided by

experimentally fitted models. Simple analytical mod-

els have been developed for this purpose, which only

consider global population densities and render final


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Figure 1. Schematic representation of the yield vs. density relation of a tree-crop mixture, and its corresponding sole crops stands. Bar areas

represent the aboveground biomass of the individual plants. (a) sole crop stand; (b) sole tree stand; (c) tree-crop mixture; (d) sole stand yield

vs. density relations.

Figure 2. Sole stand yield vs. density relations and some possible yield outcomes (16) after mixture with the other species. (a) Crop yields;

(b) Tree yields. See text for further explanation of mixture outcomes.


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biomass or yield (e.g., Ranganathan and De Wit 1996).

Spatially explicit, individual-based plant growth mod-

els have also been developed. They require empirical

growth and competition parameters and render yields

at any growth stage for any spatio-temporal design

(e.g. Garca-Barrios et al. 2001). Both models need

to be re-parameterized in different environments.

Quantifying the effects of positive and negative

interactions separately

The idea to separate and quantify positive and negative

tree effects on crop yield in SAFS was formalized by

Ong (1995) in the simple equation:

I = F + C (1)

where I is the overall interaction, i.e., the percentage

net increase in production of one component attribut-

able to the presence of the other component, F is the

fertility effect, i.e., the percentage production increase

attributable to favorable effects of the other component

on soil fertility and microclimate, and C is the compet-

ition effect, i.e., the percentage production decrease

attributable to competition with the other component

for light, water and nutrients. The I value is based on

total area, including that occupied by trees; therefore,

a positive I value means net increase in total crop yield,

irrespective of crop population in mixture relative to

that of sole crop (Rao et al. 1998). In alleycropping

systems, the measurement of F and C has been accom-

plished with four treatments: Co = sole crop; Cm =

sole crop + mulch from pruned trees; Ho = crop +

tree with mulch removed; Hm = crop + tree with itsmulch. The equation then becomes

I = (Cm Co) (Hm Cm) (2)

The competition term can also be calculated as (Ho

Co) or, more conveniently, as the average of (Hm

Cm) and (Ho Co). This equation motivated the ana-

lysis of numerous previous alleycropping experiments

and the establishment of new ones (Ong 1996). Un-

fortunately, few proved to have these four treatments

and/or the appropriate experimental designs and field

display. Nevertheless, successful positive and negative

separation in a few experiments made evident, among

others, the following important facts (Sanchez 1995;

Ong 1996; Rao et al. 1998; Ong et al. 2004): 1) Strong

tree competition is more conspicuous than originally

thought; 2) high tree growth rates and tree biomass, in-

tuitively associated with the potential for a significant

fertility effect are also strongly related with tree com-

petitiveness, so tradeoffs between both interactions are

high; 3) positive and negative component effects are

very site specific and change with the environment .

After a modification by Ong (1996), the equation

evolved to (Rao et al. 1998):

I = F + C +M + P + L + A (3)

where F refers to effects on chemical, physical andbiological soil fertility, C to competition for light, wa-

ter and nutrients, M to effects on microclimate, P to

effects on pests, diseases and weeds, L to soil con-

servation and A to allelopathy effects. The benefit of

Equation (3) is that it encapsulates a comprehensive

overview of the possible effects involved. However, as

emphasized by the authors, many of these effects are

interdependent and cannot be experimentally estim-

ated independently of one another. Limitations have

been identified for this approach (Ong et al. 2004):

1) due to interdependence, the individual terms most

likely will give a sum that exceeds I, such that the rel-

ative importance of each term cannot be established. 2)

It cannot predict delayed effects and long-term trends.

3) It is not meant to predict the consequences of mov-

ing from one environment to another, as it does not

explicitly consider growth resource capture and use,

and their interaction (e.g. water x P interaction in P-

fixing soils). Cannell et al. (1996) attempted to clarify

the resource base of Ongs equation but did not make

plant interaction with resources sufficiently explicit.

Mechanistic research may be necessary to understand

SAFS functioning and performance over time and/or

in different environments.

Methods that explicitly consider growth resources

Positive and negative interactions between plant spe-

cies largely depend on how the latter affect each

others ability to capture and use growth resources.

The principles of light, water and nutrient capture

and use efficiency first applied to sole crop growth

analysis and modeling have been extended to inter-

cropping and agroforestry research in the past decade.

The field has seen enormous advances in the gather-

ing of relevant experimental data, theory development,

modeling capability, and construction of very sensitive

instruments for measuring direct above- and below-ground resource flow and capture (Black and Ong

2000). They have also made evident the challenges for

studying these processes in multispecies systems, and

the limitations of some basic idealized and simplified

assumptions about the relation between plant growth

and resource capture and use efficiency.


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Figure 3. Coupled tree and crop yields in mixture. Points 16 represent the six crop yield cases depicted in figure 2(a), and reasonable associated

tree yields. Three additional outcomes are included: in A, tree excludes crop; In B crop excludes young tree through strong allelopathy. In C

allelopathy effect on tree is milder. The slope of the dashed line indicates the intensity of the slightly negative crop effect on tree biomass. Stars

are maximum sole crop yields, and the line that crosses them represents all outcomes with a land equivalent ratio (LER) = 1. Above this line,

LER > 1.

A central tenet of mechanistic plant-plant interac-

tion analysis has been that, according to the Law of

the Minimum (Blackman 1905), as long as a resource

is the most limited, growth depends linearly on the

capture of this resource. If eventually another resourcebecomes the most limiting, the formers concentration

ceases to have an effect and the latter dictates growth.

As a consequence, biomass production (W) should be

easily modeled as the product of the capture of the

most limiting resource, and the efficiency with which

the captured resource is converted into biomass (Mon-

teith et al. 1994; Ong et al. 1996). The conversion

efficiency of the limiting resource is considered to be

conservative for a given species for a given environ-

ment, and the growth response of the plant is attributed

to the increased capture of the resource. Research

data (e.g., Demetriades-Shah et al. 1992; Black andOng 2000) and theoretical developments (Kho 2000;

Ong et al. 2004) have shown that: 1) Usually sev-

eral resources are limiting, in which case the relation

between biomass production and the capture of one

single resource should be viewed as a correlation, not

a causal relation that can be readily modeled. 2) The

variation in conversion efficiencies between environ-

ments is larger than that between species; therefore

such efficiencies are more determined by the environ-

ment than by species. 3) Plants alter the availability

of resources simultaneously and thus conversion ef-ficiencies by changing resource limitation. Moreover,

the most limiting resource for a species can differ

in sole system and mixture. 4) Efficiencies should

be studied and modeled in relation to the availabilit-

ies of the other resources, and treated as variables in

process-based models. 5) Dynamic simulation models

for different resources should be linked.

In recent years, different resource-based model-

ing approaches have been developed and/or used to

explore or predict how tree-environment-crop inter-

actions (and the productive performance of SAFS)

change when environmental resource availability ismodified. Some relevant examples are cited below:

The mulchshade model

Tree canopies in alleycropping provide N-rich mulch

but reduce radiation available for crops. The net tree

effect should then be a function of at least three

factors: 1) the trees mulch:shade ratio (MSR), 2) the


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Figure 4. A yield set and Production Possibilities Frontier for the hypothetical tree-crop mixture presented in figures 1 (c). Thin solid lines

result from fixing a global density and modifying the crop-tree proportion; thick dashed line from fixing a tree-crop proportion and modifying

global density. The thick solid line is the production possibility frontier, and the area under it is the yield set. The thin dashed line represents all

outcomes for which LER = 1.0, and the black dot on the PPF is the tree crop mixture which renders the highest attainable LER value.

importance in the particular environment of nitrogen

supplied by mulch, expressed as a Nsoil: Nmulch ra-

tio (NNR), and 3) tree row distance as a surrogateof tree leaf area index. Van Noordwijk (1996) de-

veloped a static-analytical alleycropping model, which

links these species attributes and management factors,

and produces explicit algebraic solutions. The simple

MSR offers a basis for comparing and selecting tree

species. The equation variables and parameters require

a lengthy explanation; here we are interested only in

discussing the type of results it delivers. The model

predicts that at low soil fertility, where the soil fer-

tility improvement due to mulch can be pronounced,

there is more chance that an agroforestry system im-

proves crop yields than at higher fertility where the

negative effects of shading will dominate. Moreover,it defines combinations of MSR and NNR for which

some alleycropping systems should be expected to

work (Figure 5a). Although not validated with em-

pirical data, it has been parameterized for typical

alleycropping tree species in order to estimate their

optimum hedgerow density at different Nsoil values

(Figure 5b). The mulch/shade model provides use-

ful insights, but does not incorporate the interactions

between resource dynamics, and crop and tree growth.Incorporating these elements extends the model bey-

ond what can be solved analytically and into the realm

of dynamic simulation models.

Integrated water, nutrient and light simulation models

in agroforestry systems

In the late 1990s complex agroforestry simulation

models and user platforms were constructed for ex-

ample, WaNuLCAS (van Noordwijk and Lusiana

1999) and HYPAR (Mobbs et al. 2001), and they are

still undergoing development, parameterization, and

validation. These two models are process-based and

have a useful level of spatial structure. Both require

weather databases and a considerable number of soil-

and plant parameters as inputs. WaNuLCAS is quite

elaborate in its soil sub-models and HYPAR in its can-

opy sub-models. They are too complex to allow a use-

ful description here, but excellent specifications and

user manuals are available. Once proper inputs and


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Figure 5. Some of the results obtained by van Noordwijk, (1996) with the mulch and shade model. (a) shows combinations of MSR and

NNR for which some hedgerow systems should be expected to work, given two different values of N mulch. P, C and L stand for estimatedMSR values for Peltophorum dasyrrachis, Calliandra calothyrsus and Leucaena leucocephala, respectively. (b) shows predicted optimum

tree density in an alley cropping system for five tree species (Peltophorum dasyrrachis, Erytrina poeppigiana, Gliricidia sepium, Leucaena

leucocephala and Calliandra calothyrsus) as a function of Nsoil. Tree density is expressed in linear meters of hedgerow per hectare. Species

parameters were estimated empirically and are reported in van Noordwijk, (1996), table 3.2.

parameters are in place, these models can be powerful

tools for systematically exploring the possible con-

sequences of diverse management practices and of

one or more resource gradients, before engaging in

costly and time consuming experiments and product-

ive projects. Van Noordwijk and Lusiana (1999) used

WaNuLCAS to model grain- and wood production,

water use, water-use efficiency and N limitation for

a wide range of annual average rainfall conditions

(240 mm to 2400 mm) in parkland systems with dif-

ferently shaped trees; an example of their results is

presented in Figure 6a and 6b. As yet, no experi-

mental data set exists on the same agroforestry system

at the same soil but widely differing rainfall condi-

tions, so they used data and parameters from different

sources and from theoretical assumptions. Model res-

ults generally agreed with conclusions derived from

experimental evidence (Breman and Kessler 1997).

Similar conclusions were reached with the HYPAR

model about the effect of climatic gradients on tree-crop interactions and productive outcomes (Cannell

et al. 1998).

Complex process-based models are potentially

powerful tools but have their own limitations and

pitfalls; they are expected to confront problems of

parameter estimation, validation and input gathering

similar to or more severely than those found in sole

crop physiological models (Aggarwal 1995; Hakan-

son 1995). These models can be used with caution

in order to gain insight about management practices

and about the consequences of theoretical assump-

tions; their outputs should be constantly confronted

with empirical results and farmers experience.

A general treeenvironmentcrop interaction

equation for predictive understanding of SAFS

Kho (2000) has developed a simple but powerful ana-

lytical model in which the overall tree effect on crop

production is explained as a balance of (positive and

negative) relative net tree effects on resource availabil-

ity to the crop. We will describe here some of its basic

features, consequences and applications. When trees

are introduced in a crop field, they simultaneously

change the availability of several resources in the en-

vironment of the crop, some for the better and somefor the worse. Notwithstanding the Law of the Min-

imum stated earlier, it has become clear that not one

but many resources can limit growth simultaneously

and that the degree to which a resource affects produc-

tion at a given level is dependant on the availability of

the other growth resources (Kho 2000). Consequently,

the relation between a given resource and production


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Figure 6. Some of the results obtained by van Noordwijk and Lusiana (1999) with the WaNuLCAS model for grain and wood production, for

a range of annual rainfall conditions in an hypothetical agroforestry system where trees provide N-rich mulch. For comparison, the sole crop

and sole tree stand are grown also. (a) shows a gradual shift from water to N as the major factor limiting crop production as rainfall increases.

At low rainfall levels, tree competition for water dominates over positive effects of N supplied by tree mulch. (b) Because of crop competition,

tree yield is lower in the SAFS stand than in sole tree stand.

is not linear but asymptotical when other resources are

held constant. As a particular resource becomes lessavailable in relation to others, its influence on produc-

tion becomes greater and in this last sense it becomes

more limiting. The limitation of a resource Ai (i.e.,

Li) can be formally defined as the ratio between the

slope of the production response curve (at a given re-

source level) and the average use efficiency of that

level of resource by the crop; it is therefore a rel-

ative non-dimensional term. Li can also be defined

(more intuitively) as the relative change in production

in response to a relative change in resource availabil-

ity (Kho 2000), which corresponds to the definition of

elasticity in economic theory. Kho 2000 has fruitfully

applied Eulers law from economics to demonstrate

that the sum of limitations (or elasticities) of all growth

resources ( Li) should reasonably be equal to 1.0 if

the so called constant return to scale assumption holds.

De Wit (1992) shows agricultural data supporting this

latter assumption of proportional relation of output to

input.

As a consequence of the previous arguments, when

trees interact with a crop, the expected relative cropyield change (dW/W) should be equal to a weighted

sum of the relative changes induced by the tree on

each resource Ai; the weights should be the Lis which

result from the particular balance of resources in the

specific environment. This leads to the equation:

dW/W=

n

i=1

(dAi/Ai) Lii (4)

In short, each resource contributes to the relative

change in production proportionally to its degree of

limitation and proportionally to its relative change in

availability. From Equation (4), Kho (2000) derivesEquation (5), which predicts the relative change in

crop production (I, as in Ong 1995), as a function

of tree effects on resource availability and of resource

balance in the particular environment considered:

I=

n

i=1

Li Ti (5)


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Figure 7. Trees influence crop production through altering (the balance of) resource availabilities to the crop. Each rectangle represents a

different resource: water (W), nitrogen (N), phosphorus (P) and radiation (R). The height of each shaded area relative to the height of the

rectangle represents the relative change in availability of the resource (Ti). The width of each shaded area relative to the total width represents

the limitation of the resource in the tree-crop interface (Li). The sum of positive and negative shaded surfaces relative to the total surface of therectangle represents the overall tree effect I expressed as fraction of sole crop production. The figures show possible tree effect balances of an

alley cropping technology in a humid climate. A) on nitrogen deficient soils, B) on acid (phosphorus deficient) soils, C) on nitrogen deficient

soils with nitrogen fertilizer (applied to the alley crop and the sole crop), and D) on acid soils with phosphorus fertilizer (applied to the alley

crop and the sole crop). The relative net tree effects on availability of each resource (Ti) are equal in AD; only the environments (i.e. resource

limitations Li) change, and explain the different overall effects (I). Source: Ong, et al., 2004.

where

Ti =Ai

Ai=

Ai;multi Ai;mono

Ai;mono(6)

Ti is the relative net change in availability of resource

i because of the tree, Ai;multi is the availability of re-

source i to the crop in the SAFS and A i;mono in thesole crop. Robust estimations of L and T values for

a particular SAFS and environment can be obtained

experimentally and/or derived from the literature fol-

lowing relatively simple methods which are described

and applied by Kho (2000) and Kho et al. (2001).

The relation between resource balance and limit-

ation combined with Equation (5) leads to two rules

that can be viewed as counterparts of classic crop

production principles (Kho 2000): 1) The greater the

availability of a resource in the environment, the smal-

ler is its share in the overall tree-crop interaction. 2)

The greater the availability of other limiting resources

in the environment, the greater is the share of a re-

source in the overall tree-crop interaction. These rulesare helpful for predicting the performance of a SAFS

technology when it is extended to another environment

and for developing a SAFS technology. For example,

Kho (2000) showed that for the alleycropping tech-

nology the net effect of the alleys on the availability

(to the crop) of the resources light, water, and phos-


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233

phorus is most likely negative, and that for nitrogen

it is most likely positive. Consequently, in a (sub-)

humid climate on nitrogen- deficient soils, the overall

alleycropping effect is most likely positive, because

of the high limitation for the positive nitrogen effect

and the low limitations for the negative net effects on

other resources (Figure 7A). In the same climate, buton acid soils, phosphorus is relatively less available,

which will increase the share of the negative phos-

phorus effect (rule 1) and will decrease the share of

the positive nitrogen effect (rule 2), resulting in a neg-

ative overall effect (Figure 7b). Through management

practices, the share of positive net effects can be in-

creased and the share of negative net effects decreased.

Compare, for example, Figure 7b and 7d for the effect

of phosphorus fertilizer applied to both the alleycrop-

ping system and the sole crop system. External inputs

of organic or inorganic nitrogen are most likely inap-

propriate, because these will reduce the share of the

positive nitrogen effect (rule 1) and increase the share

of the negative effects (rule 2; cf. Figure 7a and 7c).

Beyond yield performance

During the past two decades, agroforestry research in

the tropics has focused, naturally, on the prospects

for higher productivity in low-input agriculture, which

are based on plot-level research just as in agronomic

research. In recent years, there is a growing realiz-

ation that recommendations based on productivity at

the plot level alone are insufficient for the long-termmanagement of farms and landscapes. Sustainability

of land use practices, a major incentive for prefer-

ring agroforestry to high input agriculture, depends

(among other things) on ensuring that the flow of en-

ergy through the agroecosystems is in close balance to

those of the natural ecosystems (Lefroy and Stirzaker

1999). Furthermore, extrapolation of results from plot

to landscape level is often flawed because they fail to

account for the lateral movement of water and soil,

which are greatly influenced by filters in the landscape

(van Noordwijk and Ong 1999).

Young (1998) argues that much of the future in-

crease in food and wood production in the humid

tropics will have to be achieved from existing land and

water resources. Therefore, future research agenda

should aim to improve the efficiency with which land

and water are currently used. One promising option

for improvements of this kind is by using agroforestry,

with the ultimate aim of achieving sustainability of

production and resource use. The principal agro-

forestry systems suited to humid tropical environments

are multistrata systems, perennial crop combinations,

managed tree fallows, contour hedgerows and reclam-

ation agroforestry (ICRAF 1996; Young 1997; Tomich

et al. 1998). These mixtures of trees and crops have

the potential to improve land management via theirability to reduce soil erosion and improve soil condi-

tions for plant growth. There is some evidence for the

utility of agroforestry systems for soil conservation,

soil organic matter maintenance, nutrient retrieval,

and nutrient recycling (Buresh and Tian 1998; Young

1997). In farming for annual crops, contour hedgerows

may provide a viable alternative to conventional con-

servation measures (Kiepe and Rao 1994). However,

despite the demonstration that such systems can dra-

matically reduce soil losses and improve soil physical

properties, the beneficial effects on crop yield are of-

ten unpredictable and insufficient to attract widespread

adoption (Alegre and Rao 1996).

Agroforestry is now receiving increasing attention

by researchers, landowners and policy makers in Aus-

tralia as a potential solution to the salinity problems

caused by the rising water-table (Lefroy and Stirzaker

1999). Replacement of the native vegetation, mainly

trees and shrubs, have resulted in a steady increase

in the water table for much of the semiarid wheat

(Triticum sp.) belt of Eastern and Western Australia,

because the vegetation has been replaced by winter-

growing crops such as wheat (Triticum aestivum),

barley (Hordeum vulgare), canola (Brassica napus),

and lupin (Lupinus spp.), which cannot fully utilizethe annual rainfall. The salinity problem is more com-

plex than that experienced in the tropics because of

the nature of the duplex soils there, which slow lateral

movement of water across the landscape and there are

few profitable tree species to replace the existing an-

nual crops. Widely spaced tree rows (10 mm to 300 m

and also called alleycropping) of fast-growing nat-

ive (e.g., Eucalyptus spp.) and exotic origin (e.g., tree

lucerne Chamaecytisus palmensis) were originally

seen as the practical alternative for solving this huge

landscape problem. As with the tropical alleycrop-

ping experience, there is a strong tradeoff between

environmental function and crop performance in the

Australian environment. Therefore, there is now a ser-

ious emphasis on identifying trees that can provide

direct value to farmers (e.g., oil malle, Eucalyptus

polybractea), as suggested by Ong and Leakey (1999)

for sub-Saharan Africa, in addition to the hydrological

function.


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designing and evaluation of complex systems needs

to be tested. Research methods are needed to derive

useful and flexible rules from adaptive management.

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