ORIGINAL ARTICLE

Analysing integration and diversity in agro-ecosystemsby using indicators of network analysis

M. C. Rufino Æ H. Hengsdijk Æ A. Verhagen

Received: 7 June 2008 / Accepted: 12 December 2008 / Published online: 4 February 2009

� The Author(s) 2009. This article is published with open access at Springerlink.com

Abstract Diversity of farming activities may

increase income stability and reduce risks to resource-

poor households, while integration—using the outputs

of one activity as input in another activity—may reduce

dependency on external resources. In practice, diversity

and integration are poorly defined, and there is no

method to characterise them, hampering the explora-

tion of their benefits. We introduce a method based on

network analysis (NA) to assess the diversity and

integration in farm household systems by using the

Finn cycling index to characterise integration of

farming activities, and the average mutual information,

and the statistical uncertainty (HR) to characterise

diversity of flows. We used nitrogen (N) flows in an

application of NA to crop-livestock systems of the

highlands of Northern Ethiopia. N recycling was low

(FCI \ 3%) in these systems independently of the farm

type. Even with improved N management FCI was

lower than 10%. Since large amounts of N are

withdrawn from the system with the harvests, there

are relatively few opportunities for recycling. The

diversity in N flows increased from the poor to the

wealthier farm households, but differences were small.

The wealthier farm households did not recycle more N

than the relatively less diverse and poorer farm

household. The definition of the system and compart-

ments boundaries must be made explicit in any

application of the method as these strongly affect the

results. NA appears useful to assess the effects of farm

management practices on system performance and to

support discussions on diversity and integration of

agro-ecosystems.

Keywords Farm management � Farming systems

analysis � Africa � Nitrogen flows � Systems design

Introduction

Farm household systems are agro-ecosystems in

which rural households are a central component.

Diverse and integrated farm household systems are

often associated with sustainable agro-ecosystems

(Dalsgaard and Oficial 1997), because diversity and

integration enable the realisation of complementari-

ties between different activities and may improve

resource use efficiencies. Diversity in farming activ-

ities may increase income stability and reduce

income risks of resource-poor households (Ellis

2000; Niehof 2004). Integrated farm household

systems use the outputs of one activity as inputs in

M. C. Rufino (&) � H. Hengsdijk � A. Verhagen

Plant Research International, Wageningen University

and Research Centre, P.O. Box 16, 6700 AA Wageningen,

The Netherlands

e-mail: mariana.rufino@wur.nl

M. C. Rufino

Plant Production Systems Group, Wageningen University,

P.O. Box 430, 6700 AK Wageningen, The Netherlands

123

Nutr Cycl Agroecosyst (2009) 84:229–247

DOI 10.1007/s10705-008-9239-2

another activity, which may reduce adverse effects to

the environment and decrease the dependency on

external resources through recycling (Edwards et al.

1993; Vereijken 2002). Cycling of energy and

nutrients are considered two of the most important

features that confers stability to ecosystem function-

ing (Allesina and Ulanowicz 2004).

In practice, diversity and integration are still poorly

defined and, although there have been several studies

that focus on integrated agro-ecosystems (Prein 2002;

Pant et al. 2005), there is no practical method to

characterise, quantify, and assess integration of

diverse agro-ecosystems. We define integration in

agro-ecosystems as the degree to which the com-

partments (or activities in such systems) are

interconnected by flows of material. In agro-ecosys-

tems that are diverse, the number of options for flows

of material is larger than in relatively simple, often

specialised non-diverse agro-ecosystems. We intro-

duce and apply network analysis (NA) to quantify the

degree of integration and diversity of farm household

systems using a set of indicators. NA is basically an

input–output analysis originally developed in eco-

nomics (Leontief 1951) that was introduced into

ecology by Hannon (1973) to quantify relationships

within ecosystems (Fath and Patten 1999). Leontief

developed input–output analysis to estimate the

amount of materials needed to produce a certain

quantity of goods. It is applied in systems analysis,

which conceptualizes systems as networks of inter-

acting compartments exchanging resources. In farm

household systems, it may be used to analyse input–

output relationships among different compartments or

household activities. The flow analysis of Finn (1980),

belongs to the early developments of NA where it was

used to study throughflow of nutrients or energy, and

cycling in ecosystems. The Shannon index, derived

from communication theory (Shannon 1948), was

introduced in ecology by MacArthur (1955) to

evaluate flow patterns in ecosystems. Later, Rutledge

et al. (1976) introduced another measure of commu-

nication theory, i.e. the average mutual information

(AMI) to study the organisation of nutrients and

energy flows in ecosystems. AMI has been proposed

by Ulanowicz (1980, 1997, 2001) to measure systems

organisation, and how the structure of the flows in an

ecosystem is refined to increase autocatalysis (Odum

1969). Since the earlier developments of NA, there

have been several applications to study ecosystem

properties (e.g. Baird and Ulanowicz 1993; Christian

et al. 1996; Heymans et al. 2002), but seldom to study

agro-ecosystems (e.g. Fores and Christian 1993;

Dalsgaard and Oficial 1997; Groot et al. 2003).

The objective of this study was to assess the

potentials and limitations of NA to evaluate integra-

tion of diverse agro-ecosystems, specifically indi-

cators of flow analysis (throughflow, throughput and

cycling) and indicators from communication theory

(i.e. measures of organisation and diversity) are

addressed. We introduce the method, the system

conceptualisation and the indicators using theoretical

examples to illustrate their meaning. Then we present

a case study from the highlands of Northern Ethiopia

where the method was applied, and the consequences

of different management options for the degree of

integration and diversity were explored. We end the

article with conclusions on the appropriateness of the

indicators to characterise diversity and integration of

agro-ecosystems.

Materials and methods

Network analysis of nutrient flows

The NA uses matrices built with the resource flows of

the systems under study, and a number of indicators.

The resource flows characterise the organisation of

the system. In this study, we use flows of nitrogen (N)

to perform the NA because this resource is often the

most limiting production factor in low-input agricul-

ture, and it can—to a large extent—be managed by

farm households. The selection of the system bound-

ary depends on the purpose of the study. In the

application presented later the system definitions

were defined by the resource base of the farm

household, which consists of a number of compart-

ments that interact. We used one year as the temporal

unit of analysis, because this is a common time

horizon for agricultural production.

Conceptualising the system

After having defined the boundaries of the system/

network, the next steps in NA are to define the n

compartments, and to quantify their interactions (N

flows). For farm households, compartments are

defined as farming activities that contribute directly

230 Nutr Cycl Agroecosyst (2009) 84:229–247

123

(e.g. provide food) or indirectly (e.g. through cash

income) to the consumption of the farm household

and have an impact on the N resources (Langeveld

et al. 2008). Farming activities can be characterised

in terms of N inputs and N outputs of which the latter

can be used in other farming activities or can be

exported from the system.

Indicators from network analysis to assess

integration

In this section the indicators used to assess size,

activity and cycling in ecosystems (Finn 1980) are

explained using a theoretical example of a simple

network, i.e. a system with two compartments (H1

and H2), for which storage (x1, x2) and flows are

quantified (y01, z10, f12, f21, y02, z20) (Fig. 1). This

system is characterised by the following elements: Hi

is the compartment i, _xi is the change in the storage of

compartment Hi, yoi is the outflow from compartment

Hi to the external environment, zio is the inflow from

the external environment to compartment Hi, and fij is

an internal flow from compartment Hj to compart-

ment Hi. The flows are expressed in kg N year-1, and

storage and the size of the compartments in kg N.

Nitrogen flows move from one compartment (j = 0,

…, n) to another (i = 1, …, n, n ? 1, n ? 2), where

n ? 1 accounts for usable exports (e.g. grain, milk)

and n ? 2 accounts for losses (e.g. animal excreta in

pastures, human excreta). Here compartment j = 0 is

used to keep track of the imports. We use the

convention of usable (n ? 1) and unusable export or

losses (n ? 2) from Hirata and Ulanowicz (1984).

Storage in a compartment is an estimation of the

amount of N contained in the total human and animal

mass (expressed as kg N per compartment) while for

cropping activities or field compartments storage is

an estimation of the amount of N contained in the top

soil layer (e.g. 0–30 cm), also expressed in kg N per

compartment.

Based on this conceptualisation of the network,

Finn (1980) developed a number of indicators that

characterise N flows in the system:

Imports (IN) is the amount of N that is imported

from the external environment into the system (Eq. 1).

IN ¼Xn

i¼1

zio ð1Þ

Total inflow (TIN) into the system is the sum of N

flows from external inputs (z) into all n compartments

plus the amount of N contributed to the system total

flows by the storage of all compartments _xið Þ, i.e.

negative changes in the storage (Eq. 2).

TIN ¼Xn

i¼1

zio �Xn

i¼1

_xið Þ� ð2Þ

These definitions take the input perspective (Finn

1980), and are used to assess whether a network

accumulates or loses material.

Throughflow (Ti) is the total flow from other

compartments to compartment i (fij) plus the inflow

from the exterior (z) and the N flows contributed by

the storage of compartment Hi (the negative changes

in storage _xi) (Eq. 3). This definition takes the input

perspective.

Ti ¼Xn

j¼1

fij þ zio � _xið Þ� ð3Þ

Total system throughflow (TST) is the sum of all

the Ti in the system (Eq. 4). It represents the N pool

within the system that contributes to the production

or activity. The ratio IN/TST is an indicator of

dependency of the system on external inputs.

TST ¼Xn

i¼1

Ti ð4Þ

Path length (PL) is the average number of

compartments that a unit of inflow passes through

(Eq. 5). It is a measure of the cycling intensity within

the system. Part of the nutrients entering the system

may flow through one or more compartments and

leave the system, while another part may be recycled

repeatedly before leaving the system.

H1x1

H2x2

y02

z20

y01

z10

f21

f12

Fig. 1 System representing a network with two compartments

H1 and H2, and their respective storages x1 and x2, the internal

flows f12 and f 21, and exchanges from (z10 and z20) and to the

external environment, (y01 and y02). The rectangular box

defines the system boundaries. Source: Finn (1980)

Nutr Cycl Agroecosyst (2009) 84:229–247 231

123

PL ¼ TST

TINð5Þ

Throughput (T..) is the sum of all flows in the

system (Eq. 6).

T:: ¼Xnþ2

i¼1

Xn

j¼0

Tij ð6Þ

Each flow fij can be expressed as a fraction q��ij of

the total flow (Tj) leaving the compartment Hj, then

throughflow can be expressed as:

Ti ¼Xn

j¼1

q��ij Tj þ zio � _xið Þ� ð7Þ

Expressed in matrix form:

T ¼ Q��T þ z� _xið Þ� ð8Þ

where Q** is a matrix with the q��ij elements, T is a

column vector of throughflows, z is a column vector

of inflows and (xi)– is a vector of negative state

derivatives. Solving for T gives:

T ¼ ½I � Q����1 z� ð _xiÞ�� �

ð9Þ

where I is the identity matrix, the matrix [I-Q**]-1

is called N**, whose i, j element indicate the flow in

Hi due to an unit of flow starting in Hj. Cycling

efficiency (REi) (Eq. 10) is the fraction of through-

flow (Ti) that returns to the compartment Hi, and it

can be found by examining the diagonal of matrix

N**. The element n**ii represents the flows gener-

ated by a unit of flow that started in Hi.

REi ¼n��ii � 1

n��iið10Þ

The Finn’s cycling index (FCI) is the proportion of

TST that is recycled (Eq. 12) within the system. FCI

is calculated by dividing the relative cycling effi-

ciency of all compartments (TSTc) (Eq. 10) by the

total TST (Eq. 11). It yields values between 0 and 1,

indicating either no recycling or complete recycling.

TSTc ¼Xn

i¼1

REiTi ð11Þ

FCI ¼ TSTc

TSTð12Þ

See Finn (1980) for more details on the calculation

of the flow analysis indicators. We use the indicators

FCI, PL and the relationship between IN/TST to

assess integration in agro-ecosystems, because

according to our definition (see ‘‘Introduction’’) a

more integrated system shows more internal recy-

cling and less dependency from the external

environment. Additionally, the ratio of TST/T.. can

be used to characterise the role of the storage in the

compartments to the system total flow.

The analysis focused on N flows associated with

management decisions and controlled by farmers,

such as the imports of N through fertilisers or food and

the exports to the market in harvested products. We

did not estimate the size of flows such as N leaching,

volatilisation, runoff, wet deposition, N2-fixation or

redistribution of nutrients at the farm and landscape

level. Information related to the size of these flows is

difficult to obtain at the farm scale. The assumption

was made that these flows do not largely differ for the

studied farm households. Clearly, omission of these

flows may affect the contribution from and to the soil

N storages, and losses to the environment.

Illustration of integration indicators

Here, we present examples of different systems with

four compartments to illustrate the calculations of the

indicators and to facilitate their interpretation

(Fig. 2A, B). Systems A and B receive both inputs

from the external environment (IN). For system A the

total inflow (TIN) is five, and for network B it is four.

Comparing IN and TIN allows to assess whether a

system accumulates or loses material because TIN

combines the external input (IN) with the changes in

compartment storage needed to support the total

network flow. In these systems TIN and IN are the

same because the compartment storages do not

contribute to the network flows. Both systems do not

accumulate or lose material; they are in a steady-state

as storage xi = 0 and total inflows (TIN) and imports

(IN) are equal. The ratio IN/TST shows that system A

depends more on imports to support the system

activity (TST) than system B. The total system

throughflow (TST) is the sum of all material flowing

through the system compartments, while the T.. sums

all inputs and outputs flowing from and to all system

compartments. System B differs from system A in that

imports are smaller and recycling is larger. As a result,

the ratio TST/T.. is larger for B than for system A,

which means that the storage compensates for the

difference between inputs and outputs. As an example

232 Nutr Cycl Agroecosyst (2009) 84:229–247

123

the detailed calculations of FCI (Eqs. 1–12) for

system A (Fig. 2) are presented in Table 1.

Systems C and D are not in steady-state and the

change in storage is negative in all compartments.

External inputs (IN) are zero and the total inflows

(TIN), which are supported by the change in storage,

are in both cases four. These systems export material

to the external environment. System D recycles more

material than system C, which increases the ratio

TST/T.. because part of the activity in the network is

supported by cycling and not only by the change in

storage. An increase in PL is associated with an

increase of cycling (Fig. 2C, D).

Indicators to assess diversity

Diversity in farm household systems may be assessed

straightforwardly from the number of farming activ-

ities, equivalent to assessing species richness in

ecosystems. This is, however, rather limited because

different compartments/activities use different types

and amounts of resources (e.g. land, fertilisers) to

produce plant or animal products that contribute

differently to the household consumption.

The Shannon index (Shannon and Weaver 1949) is

the most common index used to assess (bio) diversity

(Clergue et al. 2005) (Eq. 13).

S ¼ �X

i

pi log2 pið Þ ð13Þ

where pi is the fraction of flow Ti

T ::. The Shannon index

(Eq. 13) sums over all ith linkages in the system, and

it quantifies the diversity of the network connections.

When a flow Ti is a large proportion of T.. then log

(pi) is close to zero, and the contribution of that flow

to the system diversity is small. This happens in

systems with few compartments, where the flow of

one compartment dominates T... Such systems have a

low diversity in their flows network.

The Shannon index was further elaborated to study

how the pattern of flows is refined or organised in a

network (Rutledge et al. 1976; Ulanowicz 1980). The

(0) (0)

(0) (0)

1

1

2

1

11

2

2

1 2

3 4

11

1(0) (0)

(0) (0)

1

1

1

11

1

3

1 2

3 4

12

1

1

1

(-1) (-1)

(-1) (-1)

12

1

1

1 2

3 4

11

1(-1) (-1)

(-1) (-1)

1

1

3

1 2

3 4

12

1

1

1

(A) (B)

(D)(C)

TIN = 4 IN = 0TST = 7 T.. = 8PL = 2.0TSTc =FCI = 0.125

TIN = 4 IN = 0 TST = 11 T.. = 11PL = 2.8TSTc =2.1FCI = 0.194

TIN = 5 IN=5TST = 9 T.. = 14PL = 1.8TSTc =0.83FCI = 0.093

TIN = 4 IN = 4TST = 11 T.. = 15PL = 2.8TSTc =FCI = 0.194

Fig. 2 Examples of four

systems with four

compartments to illustrate

the calculations of the

indicators and to explain

their interpretation. Flows

are represented by the

arrows and storage is

indicated between brackets.

N flows in systems A and B

are in steady-state with no

change in storage ( _xi ¼ 0)

and total inflows (TIN) and

imports (IN) are equal.

Systems C and D are not in

steady state with a negative

change in storage ( _xi\0),

imports (IN) = 0 and differ

from total inflows (TIN)

which are supported by the

change in storage (see

‘‘Illustration of integration

indicators’’ for further

explanation)

Nutr Cycl Agroecosyst (2009) 84:229–247 233

123

diversity in network connections is not necessarily

used to its full extent. Mageau et al. (1998) defined

the AMI as: ‘‘… a measure of the information we

have regarding the exchange of material within the

system. If material from any compartment had the

equal chance of flowing into any other compartment,

then we have no information regarding the flow in the

network. If all material from one compartment was

transferred to only one of the potential recipients, we

would have complete information regarding the flow

structure’’. AMI quantifies the organisation of the

flows in the network (Eq. 14). In the log part of

Eq. 14, the conditional probability that a flow enter-

ing i comes from j is quantified. That probability is

the fraction of the flow Tij to all flows that enter Ti,

divided by the product of the fraction of Ti to total

flows T.. and Tj to total flow T... Each of these

conditional probabilities are weighted by the joint

probability of that flow (Tij/T..), and these weighted

‘constraints’ are summed over all combinations of i

and j in the network. For example, in a system where

the total flow is divided equally among all compart-

ments, and all compartments are connected, AMI will

be zero or very close to zero. If few flows, which are

a large proportion of T.., connect few compartments,

the value of AMI will approach its upper boundary.

AMI ¼ kXnþ2

i¼1

Xn

j¼0

Tij

T::log2

TijT::Ti:T:j

ð14Þ

Statistical uncertainty (HR) is the upper boundary

for AMI, it is the Shannon-diversity (Eq. 13) of flows

given a certain value of T.. (Eq. 15). When the

contribution of the flow out of a compartment (T.j) to

total system T.. is small and different across com-

partments, diversity increases. HR increases when T..

is partitioned among a greater number of flows.

HR ¼ �Xn

j¼0

T:jT::

log2

T:jT::

ð15Þ

AMI/HR is the proportion of diversity that is

reduced by the actual pattern of flows. This may be

used to evaluate the organisation of N flows to total

diversity of the network connections. The units of

AMI and HR are bits and the scalar k = 1 for AMI.

For more detail on AMI and its derivation we refer to

Ulanowicz (2001) and Latham and Scully (2002).

Illustration of diversity indicators

In Fig. 3 two groups of three systems are presented to

show the meaning of AMI and HR. T.. is kept constant

to show differences in organisation of the flows

reflected in changes in AMI, and in diversity shown

in changes in HR. In the first group (Fig. 3A–C), the

diversity of flows changes slightly because the contri-

bution of each of the flows (T.j) to T.. changes little

from system A to system C. However, AMI increases

considerably from A to C, reflecting a selection of flow

paths from almost all connections possible in system A

Table 1 Flow matrix for the network shown in Fig. 2A

H1 H2 H3 H4 Inflows (z) Ti

H1 0 0 1 0 1 2

H2 1 0 0 0 1 2

H3 1 0 0 0 2 3

H4 0 1 0 0 1 2

Outflows (y) 0 1 1 2

Ti 2 2 3 2

TIN 5 (Eq. 1)

TST 9 (Eq. 4)

PL 1.8 (Eq. 5)

T.. 14 (Eq. 6)

Q** matrix (Eq. 8)

H1 0 0 0.33 0

H2 0.5 0 0 0

H3 0.5 0 0 0

H4 0 0.5 0 0

N** matrix

H1 1.2 0 0.4 0

H2 0.6 1 0.2 0

H3 0.6 0 1.2 0

H4 0.3 0.5 0.1 1

REi 0.17 0 0.17 0 (Eq. 10)

TSTc 0.83 (Eq. 11)

FCI 0.093 (Eq. 12)

H1–H4 are the compartments of the network with their internal

flows expressed in kg N year-1. Compartmental throughflow

(Ti) is calculated according to Eq. 3. total inflows (TIN), total

throughflow (TST), total throughput (T..) and path length (PL)

are calculated according to Eqs. 2, 4, 6 and 5, respectively. The

elements of the Q** matrix are calculated as the fraction of

the inter-compartmental flow to total compartmental flow. The

N** matrix is the inverse matrix [I-Q**]-1, whose elements

represent throughflow values for a unit of flow that enter the

column compartment

234 Nutr Cycl Agroecosyst (2009) 84:229–247

123

to very few in system C. This happens when e.g. the

most efficient path is selected for nutrient flows.

In the second group of systems (Fig. 3D–F), the

diversity of flows in the system changes due to

differential contribution of each compartment to total

flows. System D is less diverse than system A (each

compartment contributes similar amounts to total

flows), but the network of flows is more constrained,

many flows of system A are eliminated in system D,

and therefore the value of AMI increases. In system

E, the contribution of the compartments to total flows

is not uniform, diversity decreases and AMI is

relatively high because of the limited number of

connections between the compartments. In system F,

diversity decreases further, and because the total

flows are dominated by one compartment, i.e. the

ratio of AMI/HR is high.

In practice, AMI and HR can be used to assess

nutrient allocation between activities and resulting

efficiencies. It is expected that in specialised systems,

HR will be relatively low and AMI will be close to HR.

These adapted systems use the most efficient paths. In

less specialised and more diverse systems AMI will be

smaller. These systems are more adaptable, and may

keep several (more inefficient) network connections

active as a risk management strategy.

Application to mixed farm household

systems in Ethiopia

In this section, we aim at gaining understanding on

how the diversity in flow patterns relates to integra-

tion by using the proposed indicators.

1 2

3 4

1 2

3 4

(A) (C)(B)

1 2

3 4

T.. = 24AMI = 0.8HR = 2.2AMI/HR = 0.36

T.. = 24AMI = 2.0HR = 2.0AMI/HR = 1.0

T.. = 24AMI = 0.0HR = 2.3 AMI/HR = 0.14

1 2

3 4

1 2

3 4

1 2

3 4

T.. = 24AMI = 1.0HR = 1.7AMI/HR = 0.60

T.. = 24AMI = 0.8HR = 0.9AMI/HR = 0.8

T.. = 24AMI = 0.9HR = 2.2AMI/HR = 0.41

22

8

8

11

1

1

1

10

1

1

110

(F)(E)(D)

2222

22 2

2

2 22

2

66

6

6

2

2

2

22 2

22

22

2

2

Fig. 3 Examples of six systems with four compartments to

show how simplification of flow patterns decreases diversity

and increases the information content in networks. Flows are

represented by the arrows, and the size of the flows is indicated

next to the arrow, except for system A, where all flows equal

one. From system A to C, flows become less random and

therefore organisation increases. From system D to F diversity

decreases, and because the flow network is simple, AMI

approaches HR. (See Sect. ‘‘Illustration of diversity indicators’’)

Nutr Cycl Agroecosyst (2009) 84:229–247 235

123

The study area

The method was applied to farm household systems

of the village Teghane (13�450N, 39�410E), Atsbi

Wonberta district in Northern Ethiopia. Average farm

size is about 0.5 ha and most households grow

cereals for subsistence and legumes (faba beans,

common beans). Steep slopes, stony soils, frost-risk

during part of the year and seasonal rainfall constrain

agricultural production. Average annual rainfall is

540 mm, of which most is concentrated in a period of

only 75 days (from June to September). Livestock

(dairy and beef cattle, donkeys, and sheep) graze on

communal pastures and are fed crop residues and

other grasses cut and carried to the farm.

Data collection and processing

During the 2002 growing season, a farm household

survey was conducted in Teghane as part of the

research programme ‘Policies for Sustainable Land

Management in the Ethiopian Highlands’ sponsored

by the Dutch ministry of foreign affairs (DGIS).

During a rapid diagnostic appraisal, farmers in

Teghane (n = 50) identified three household wealth

classes based on land, livestock and labour (Mulder

2003; Abegaz 2005). The poor households had no or

few livestock and little land, the medium wealth

households had at least one ox, one donkey and few

sheep, and usually a labour surplus, the wealthier

households had several oxen, some cattle, donkeys

and sheep and they were most of the time food self-

sufficient. We used three farm households, each

representing a typical wealth class (Table 2).

Detailed information on household composition

and consumption, farm and fields characteristics,

input use to different activities, flows between

activities, crop yields, animal production, sales and

input and output prices were collected using the

participatory NUTrient MONitoring (NUTMON)

approach (De Jager et al. 1998; Van den Bosch

et al. 1998). The combination of farm household

surveys, field observations and measurements, and

simple models provided the basis for the NA

application. Intake and excretion of the livestock

was estimated using a model that uses as inputs

animal type, size, grazing time and feed availability

(Vlaming et al. 2001). To quantify N flows we used

conversion coefficients obtained from analysis of

plant and soil samples taken during the survey and for

those flows that were more difficult to quantify we

used conversion coefficients from the literature

(Table 4 in Appendix). A more detailed description

of the farming systems and data used can be found in

Rufino et al. (2008) and Langeveld et al. (2008).

Exploring the effect of management options

NA indicators for the three farm household types

were calculated for the situation at the moment of the

survey (baseline scenario), followed by an explora-

tion of the consequences of farm management

changes for the indicator values (improved manage-

ment scenario). IN, TIN, TST, T.. and TSTc were

expressed on a per capita basis to allow comparison

of N use of the different farm household types. The

management changes included increased yields of

barley from its current value of 2 to 3 t ha-1, and

faba-beans from 1 to 2 t ha-1, these yield levels were

recorded in similar agro-ecosystems in the highlands

of Ethiopia (Agegnehu et al. 2006). It was assumed

that the associated increase in the availability of crop

Table 2 Main characteristics of three types of farm house-

hold systems from Teghane, Ethiopia representing three

different wealth classes, i.e. poor, medium wealth and wealthy

Poor Medium Wealthier

Farm household characteristics

Arable land (ha) 0.30 0.70 2.40

Own land (ha) 0.30 0.70 1.60

Household members 5 9 10

Animals (TLUs)a

Cattle 1 4.7 6.3

Sheep 0.2 0.9 3.0

Donkeys – 0.9 0.7

Mules – 0.6 –

Poultry 0.01 0.02 0.04

Crops (ha)

Barley 0.49 0.55

Barley irrigated 0.23 0.10 0.38

Barley rented-in 0.57

Wheat 0.15

Wheat rented-in 0.68

Faba beans 0.08 0.10

Prickly pears 0.07

a TLUs are tropical livestock units, one tropical livestock unit

equals an animal of 250 kg body mass

236 Nutr Cycl Agroecosyst (2009) 84:229–247

123

residues was subtracted from the feed imported from

common pastures. More manure N was retained on-

farm because of improved management within fea-

sible ranges as reported by Rufino et al. (2006). We

assumed that in the improved management scenario,

70% of the manure available for recycling on-farm

was conserved contributing to higher application

rates to crops, and resulting in higher crop yields.

Sensitivity analysis

The objective of the partial sensitivity analysis was to

evaluate the effect of changes in the underlying data

used to estimate N flows, and the conceptualisation of

the system on the NA indicators. We used only the

wealthier farm household for the sensitivity analysis.

First, all parameters associated to plant and animal

products and fertilisers were changed to their max-

imum and minimum values (Table 4 in Appendix).

Second, parameters related to management were

changed to their maximum and minimum. Third,

we compared three network configurations of the

same farm household system to evaluate the impact

of (dis)aggregation of compartments on NA indica-

tors, i.e. (1) the baseline configuration with 12

compartments (Fig. 4), and (2) a configuration with

four compartments where all animal compartments

were aggregated into one livestock compartment and

all cropping activities into one crop compartment,

and (3) a configuration with 14 compartments where

two crop compartments were each split into two

compartments, i.e. fields were divided into two plots.

BarleyFababeans Barley

irrigated

Mule

Milk

Manure

Manure

Manure

Feed

Manure

Grain Straw

FeedManure

Straw

Straw

Grain Grain

Straw

Sheep

Manure

Manure

Seed

Faeces

Waste

Straw Straw

Manure

Manure

Milk

Manure

Manure

Urine Food

Eggs

Household

Feed

Seed Fertilisers

Feed

Grain

Meat Meat

Manure

Meat

ChickenCattle

Manure storage

Donkey

Barley Wheat

Donkey

Cattle

Manure storageFeed

Manure

Manure

Barley(rented)

Wheat(rented)

Manure

Seeds

Straw

Chicken

Sheep

Straw

Eggs

Manure

Seeds

Grain Grain

Straw

Grain Eggs

GrainSeeds

Straw

Grain

Grain

Seeds

Urine Grain

Feed Manure

Manure

Household

Straw

Grain

Manure

Straw Manure

Faeces

GrainGrain

Fababeans

Manure

Meat + milk

Feed

Manure

Barley irrigated

Seeds

Manure

Fertiliser

(Poor) (Medium)

(Wealthier)

Barleyirrigated

Household

Sheep

Prickly pears

Cattle

Chicken

Milk

Manure

Milk

Manure

Manure storage

Feed Manure Meat

Grain

Straw

Feed Manure

Manure

Leaves Fruits

Excreta

Meat Manure

Seed

Fertiliser

Grain

Meat

ExcretaFood

Grain

WasteManure

Straw

Fig. 4 Flow diagrams of three different farm household types,

i.e. poor with seven compartments/activities, medium with ten

compartments/activities and wealthier with 12 compartments/

activities. Dashed lines are relatively small flows, solid linesare large N flows

Nutr Cycl Agroecosyst (2009) 84:229–247 237

123

Results

The farm households as a network of N flows

The poor, medium and wealthier farm household were

each conceptualised as networks of N flows in Fig. 4.

The poor farm household had 0.3 ha of land, cattle,

sheep and few chickens. Livestock fed mainly with

biomass from communal land and with on-farm

produced crop residues. No feed was purchased to

support animal production. Manure from the corral

was used only as household fuel. Most milk was sold

and only a small portion was used for household

consumption. Two crops were grown, i.e. (irrigated)

barley (Hordeum vulgare L.) and prickly pear (Opun-

tia spp.). Part of the barley harvest was exchanged for

labour and traction by means of share-cropping.

Mineral fertilisers were applied exclusively to the

irrigated barley crop. A large amount of food was

imported because on-farm production could not meet

the household requirements (see Table 5 in Appen-

dix). A significant amount of cash came from off-farm

employment of the family head. There were no other

important sources of income.

The medium wealth farm household had 0.7 ha

with rainfed and irrigated barley, faba beans (Vicia

faba L.), cattle, a mule, a donkey, sheep, and

chickens. Animals were fed on communal land, crop

residues produced on-farm, and purchased feed.

Manure was collected from the corral, composted in

heaps and used as fertiliser. Milk was partly sold and

partly consumed by the household members while

eggs were sold. Mineral fertilisers were exclusively

applied to the irrigated barley crop. Some food was

purchased, but most household consumption was met

by on-farm production (see Table 6 in Appendix).

Cash was generated mainly through the sale of honey,

eggs, sheep hides, and leasing out the mule.

The wealthier farm household had 2.4 ha with

common wheat (Triticum spp.), buckwheat (Fagopy-

rum esculentum), barley and faba beans, cattle, sheep,

donkeys and chickens. The animals were fed on

communal land, with crop residues produced on-farm,

and with purchased supplements. Manure from the

corral was partly used as fertilisers and partly as fuel.

Neither manure nor fertilisers were applied to the

rented land. Milk was used for home consumption.

Half of the grain production of the rented land was

used to pay this rent. Mineral fertilisers were applied

only to the irrigated plots. Household food require-

ments were met by on-farm production and the food

surplus was marketed (see Table 7 in Appendix). The

N flow within each of the three farm households was

dominated by the N supply to the household and the

livestock. The largest N inflow was the result of the

livestock grazing in the common pastures. The

collected livestock excreta was recycled and used as

fertiliser and fuel for cooking. A part of the crop

residues was used to feed livestock but their contri-

bution to the total N flow in the system was relatively

small.

Indicators to assess integration and diversity

Baseline scenario under current management

All farm households depended largely on imported N

(IN) to support the system throughflow (TST)

(Table 3). IN represented between 66 and 70% of

TST for the three farm types. IN comprised N

fertilisers, feed N and food N. Fertiliser N use was

limited in all three farms. The poor farm household

used more fertiliser N on a per hectare basis, and

imported more feed N per tropical livestock unit

(TLU) than the other types. The medium and

wealthier farm households applied manure N (109

and 30 kg ha-1, respectively) while the poor farm

household used manure mainly as fuel. Imported feed

N represented the largest proportion (78–92%) of IN,

with a daily average of 100–150 g N per TLU.

Purchased food N as grain accounted for about

3 kg N per capita per year in the poor and medium

wealth farm households.

The amount of N recycled (TSTc) was small for all

three systems (between 1 and 2.5 kg N per capita) as

compared with the total system throughflow (TST),

and therefore FCIs and path lengths (PL) were also

relatively small. Statistical HR showed that diversity

in the network connections (N flows) increased from

the poor to the wealthier farm households, but

differences were small. The relatively more diverse

and wealthier farm households (HR = 2.4) did not

recycle more N (FCI = 2.2–2.6%) than the relatively

less diverse (HR = 2.2) and poor farm household

(FCI = 2.9%). Since the three farm households

manage their N resources similarly, the degree of

integration in the poor, medium and wealthier farm

households was also similar.

238 Nutr Cycl Agroecosyst (2009) 84:229–247

123

Scenario under improved management

In the alternative management scenario the integra-

tion of farming activities increased (FCI ranged from

4.2 to 7.7%, see Table 3) because the amount of N

recycled (TSTc) more than doubled. The dependency

on external N inputs decreased (IN/TST) from 66–70

to 53–58%, while PL increased only slightly. N flows

of the improved management scenario are shown in

Tables 5, 6, and 7. The diversity in the N flow pattern

also increased somewhat (HR = 2.4–2.6 vs. 2.2–2.4

in the baseline) because the size of internal flows

increased. AMI was slightly reduced because the N

flows were more homogeneously distributed.

Sensitivity analyses

The change of parameters associated to plant and

animal products and fertilisers to the maximum and

minimum values found in the literature caused a

relative change of 26–29% in IN, TIN, TST, 10–15%

in TSTc and FCI and practically no change in the

other indicators (Fig. 5A). Changes in the conversion

coefficients alter the size of the N flows, and therefore

all the indicators related to system size, activity and

cycling. The change in TSTc and FCI is different than

for the other indicators because there are few cycling

flows in the network, i.e. the change in TSTc is

relatively smaller than the change in TST. PL does

not change as it depends much on the number of

activities which was not altered.

The change in management parameters had a

relatively greater effect on the integration indicators

(TSTc, FCI and PL) (Fig 5B) than the change in

conversion coefficients of plant and animal products

and fertilisers. PL changed because of the changes in

TIN and TST. Management parameters determine the

amount of N retained in the system resulting in a

much larger effect on TSTc, FCI and PL (Fig. 5B).

The conceptualisation of the system has a large effect

on the recycling indicators (TSTc, FCI and PL) and

on the structure/organisation related indicators (AMI,

HR) (Fig. 5C), and relatively no or little effect on the

system size related indicators (IN, TIN and TST). By

removing compartments, the amount of N cycled

increased because we aggregated the flows of several

compartments into one. The total flow in the system

did not change due to the aggregation, and therefore

the largest effects were observed in TSTc and FCI.Ta

ble

3N

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T(k

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ita-

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Nutr Cycl Agroecosyst (2009) 84:229–247 239

123

The aggregation also had an effect on the diversity of

N flows because the indicator sums the contribution

of each compartmental flow to obtain the system

diversity.

Discussion

In order to study N flows within agro-ecosystems the

relevant sub-systems or compartments need to be

identified (Hirata and Ulanowicz 1986). NA is sensi-

tive to the system definitions, a common issue in using

systems analytical tools to study ecological systems

(Fath et al. 2007), and even more so in agro-

ecosystems where biophysical and socio-economic

aspects interact (Stomph et al. 1994). The aim of the

study determines the system boundaries and its

compartments. Only explicit definition of the system

characteristics allows comparison across different

studies. Furthermore, sensitivity analysis of aggregat-

ing compartments is helpful to assess the

consequences on indicators as illustrated in ‘‘Sensi-

tivity analyses’’ and Fig. 5C. This analysis showed

that some indicators are more (TSTc, FCI), and others

are less (IN, TIN and TST) affected by changes in the

conceptualization of the system. Similarly, effects can

be assessed of neglecting resource flows because of

the difficulty to measure or quantify them accurately.

In any analysis technique, the accuracy of the results is

as good as the data available (Fath et al. 2007). The

uncertainty in parameter values (conversion coeffi-

cients) affecting the indicator values (cf. Fig. 5a) can

be addressed by improving the accuracy of parameters

and flows size estimations. Estimation of flows (e.g.

feed intake from grasslands, crop residues removal

from fields) and system processes represent a major

challenge, one in which we can build experience, and

that should not prevent us from using NA to charac-

terise the integration of agro-ecosystems.

The indicators of organisation are useful to

compare diversity and organisation of the flows

across farm household systems. AMI and HR provide

information on the configuration and diversity of the

network of flows resulting from the management by

the farm household. These measures can be used to

compare systems within a region but also across

environments. AMI will approach its upper boundary

when a few flows dominate the total flow in a system

for a given system size (T..) as shown in Fig. 3. The

three farm households types evaluated in this study

did not differ much in diversity and organization of

the flows (cf. Table 3). Probably differences in these

indicators would be evident in systems with different

production structure, or when comparing systems

across regions of different agroecology. Using NA

the impact of technologies aimed at intensifying crop

or livestock production on the whole farm household

can be evaluated ex post in terms of integration and

dependency of external inputs. This allows to assess

properties that are otherwise not evident from direct

observation or measurements from individual com-

partments of the system, and offers opportunities to

test configurations of flow patterns resulting in more

efficient use of resources which may be confronted

with economic indicators.

-1.0

-0.5

0.0

0.5

1.0n=4 n=14

-1.0

-0.5

0.0

0.5

1.0min max

-1.0

-0.5

0.0

0.5

1.0min max

Fra

ctio

nal c

hang

e in

the

indi

ces

valu

es

IN TIN TST TSTc FCI PL AMI HR

(A)

(B)

(C)

Fig. 5 Fractional changes in the indicators IN, TIN, TST,

TSTc, FCI, PL, AMI, and HR for three situations: A changes in

conversion coefficients for N concentrations, dry matter and

energy values of plant and animal products; B changes in

management related parameters; C changes in the indicator

values as a result of aggregating (n = 4) or disaggregating

compartments (n = 14) as compared to the observed situation

(n = 12). The fractional changes refer to the observed values

in Table 3 and were obtained by using the maximum and the

minimum of the ranges presented in Table 4

240 Nutr Cycl Agroecosyst (2009) 84:229–247

123

The NA indicators showed that the farm house-

holds were different in size (TST), but equally small

in recycling and dependency on large N imports from

common pastures to support livestock production.

According to the analyses of this study, N cycling in

farm household systems is much smaller compared to

natural ecosystems (Finn 1980) since the principal

aim of agro-ecosystem is to produce food and other

products that are exported from the system. The three

farm types hardly differed in organisation of flows

and diversity although the poor household appeared

to be somewhat less diversified than the wealthier

household. On-farm production of fodder crops could

substitute or supplement the feeds from common

pastures, and add to the opportunities to increase

recycling. However, household objectives and limi-

tations imposed by other farm resources (e.g. labour

constraints) determine whether this strategy could

improve integration.

In the case study, collected excreta contributed to the

manure heap, but most urine from livestock was lost

reducing the amount of recycled N (TSTc). In addition

to mineral fertilisers, nitrogen input of crop activities

comprised household waste and (a part of) human

excreta. Both N sources contribute to the recycled N

(TSTc), and the cycling index of the systems (FCI). The

number of animals largely determined the amount of

imported N, because most of the feed requirements

were met with biomass from communal grazing land.

Wealthy and medium households imported relatively

large amounts of N for feeding livestock, but at least

half of the N excreta returned to the common pastures

during grazing. The amount of recycled N could

increase considerably if the animals were fed with

fodder produced on-farm, but this may compete for

land, labour and other resources.

Integrated systems which use nutrients efficiently

and reduce the dependency on external inputs should be

aimed at, especially in situations where farmers have no

or limited access to external inputs. In marginal

environments such as Northern Ethiopia, where the

availability of external inputs is uncertain (Abegaz et al.

2007), recycling of nutrients for crop and livestock

production may increase the adaptability and reliability

of farm household systems (Lopez-Ridaura et al. 2002).

NA can be used ex ante to compare farm

household systems across environments. In this study

the farm household system was the unit of analysis

but NA may be applied at other aggregation levels

(e.g. village or watershed), requiring a different

conceptualisation of the system. In the quantification

of N flows within the farm household systems we did

not include losses of N through leaching, and gaseous

losses. Provided data is available these flows can be

included in the NA, although estimation of their

importance is highly problematic (Faerge and Magid

2004). Linking integration indicators with farm

economic indicators may enable the identification of

synergies and trade-offs and the design of more

resource use efficient and robust farming systems.

Evaluating the relative importance of different flows

into and within the systems and comparing systems

across regions will be the focus of further research.

Conclusions

NA provides a tool to analyse the degree to which

household activities are integrated. Diversity of farm

household activities does not necessarily lead to inte-

gration of these activities through increased exchange of

nutrient resources. Conceptualising and measuring

processes and flows remain a major challenge in agro-

ecosystems studies, but this should not prevent us from

applying NA that assist us in quantifying integration and

diversity of agro-ecosystems. N cycling indicators of

farm household systems are much lower than those

calculated for natural ecosystems due to export of food

and other products from these systems. The relative

large amounts of N that are withdrawn limit opportu-

nities for recycling within farm household systems. But

still opportunities to increase N cycling in farm

household systems can be indentified using NA.

Increasing the input use to increase harvests, also

increase the amounts of nutrients prone to losses. To our

view, farming system (re)design or a system shift will be

needed to aim at sustainability.

Acknowledgments This study was possible due to the

financial support of the Dutch Ministry of Agriculture,

Nature and Food Quality and the Food and Agriculture

Organization of the United Nations. We thank the WUR-

IFPRI research program RESPONSE for sharing data, Girmay

Tesfay, Assefa Abegaz and Herma Mulder for helping with

interpretation of data, Pablo Tittonell, Santiago Lopez-Ridaura,

Mark van Wijk, Pytrik Reidsma, and Ken Giller for their

suggestions on a previous version of the manuscript, Luke

Latham for sharing the algorithms to calculate the network

analysis indicators, and Marcel Lubbers for recoding the

algorithms.

Nutr Cycl Agroecosyst (2009) 84:229–247 241

123

Open Access This article is distributed under the terms of the

Creative Commons Attribution Noncommercial License which

permits any noncommercial use, distribution, and reproduction

in any medium, provided the original author(s) and source are

credited.

Appendix

See Tables 4, 5, 6, and 7.

Table 4 Conversion coefficients and management parameters to estimate N flows in the networks of the three types of farm

household systems in Teghane, Northern Ethiopia

Coefficient values

ReferenceActual Minimum Maximum

Plant products

Barley grain DM (g kg FW-1) 880 840 920 NUTMON database, Vlaming et al. (2001)

Barley grain N (% DM) 1.55 1.40 1.71

Barley grain energy (MJ kg DM-1) 14.80 13.32 16.28

Barley crop residues DM (g kg FW-1) 920 870 970

Barley crop residues N (% DM) 0.70 0.63 0.77

Wheat grain DM (g kg-1FW) 870 830 910

Wheat grain N (% DM) 2.23 2.01 2.45

Wheat grain energy (MJ kg DM-1) 14.00 12.60 15.40

Wheat crop residues DM (g kg FW-1) 920 870 970

Wheat crop residues N (% DM) 0.40 0.36 0.44

Faba beans grain DM (g kg FW-1) 860 820 900

Faba beans grain N (% DM) 4.00 3.60 4.40

Faba beans pods energy (MJ kg FW-1) 3.70 3.33 4.07

Faba beans crop residues DM (g kg FW-1) 860 820 900

Faba beans crop residues N (% DM) 1.40 1.26 1.54

Grass DM (g kg FW-1) 170 140 200

Grass N (% DM) 2.40 2.04 2.76

Organic and inorganic fertilisers

Ruminant manure DM (g kg FW-1) 350 200 500 De Ridder and Van Keulen (1990)

Ruminant manure N (% DM) 2.00 1.00 3.00

Poultry manure DM (g kg FW-1) 350 300 500

Poultry manure N (% DM) 3.10 2.64 3.57

Ash N (% DM) 2.00 1.70 2.30 NUTMON database, Vlaming et al. (2001)

Urea N (% FW) 46.0 45.5 46.5

DAP N (% FW) 18.0 17.8 18.2

Animal products

Sheep meat N (% FW) 2.65 2.52 2.78 USDA Nutrient Data Laboratory (2004)

Sheep meat energy (MJ kg FW-1) 11.80 10.62 12.98

Chicken meat N (% FW) 2.90 2.76 3.05

Chicken meat energy (MJ kg FW-1) 9.00 8.10 9.90

Cattle meat N (% FW) 3.40 3.23 3.57

Cattle meat energy (MJ kg FW-1) 9.00 8.10 9.90

Cattle milk N (% FW) 0.50 0.48 0.53

Cattle milk energy (MJ kg FW-1) 2.90 2.61 3.19

Donkey meat N (% FW) 3.00 2.85 3.15

Mule meat N (% FW) 3.00 2.85 3.15

242 Nutr Cycl Agroecosyst (2009) 84:229–247

123

Table 4 continued

Coefficient values Reference

Actual Minimum Maximum

Eggs DM (g kg FW-1) 250 240 260

Eggs N (% DM) 1.85 1.76 1.94

Eggs energy (MJ kg FW-1) 6.80 6.46 7.14

Fraction N retention animal tissue 0.20 0.10 0.30 NUTMON database, Vlaming et al. (2001)

Fraction N retention human tissue 0.20 0.10 0.30

Humans daily energy needs (MJ d-1) 9.10 8.19 10.01 Bender (1997)

Management related parameters

Fraction excreta N retention (ruminants) 0.50 0.05 0.95 Rufino et al. (2006)

Fraction excreta N retention (poultry) 0.50 0.20 0.80

Fraction excreta N retention (humans) 0.50 0.05 0.95

Fraction time spent on-farm by animals 0.50 0.20 0.80

Fraction household wastage 0.20 0.05 0.50 NUTMON database, Vlaming et al. (2001)

DM dry matter, FW fresh weight, N nitrogen

Table 5 Nitrogen flow matrix for the poor farm household type from Teghane, Ethiopia consisting of seven compartments

i (to)j (from) ? 0 1 2 3 4 5 6 7

Compartment Inflows Household Prickly pears Barley irrigated Cattle Sheep Chicken Heap

(a) Baseline scenario with current yields and manure management

1 Household 15.7 0 1.5 6.1 0.3 0 0.1 0

2 Prickly pears 0 0 0 0 0 0 0 0

3 Barley irrigated 7.7 6.6 0 0 0 0 0 0

4 Cattle 43.4 0 1.0 1.1 0 0 0 0

5 Sheep 41.3 0 0 0 0 0 0 0

6 Chicken 0.4 0 0 0 0 0 0 0

7 Heap 4.2 6.0 0 0 9.1 8.3 0.2 0

Usable export 0 0 0 5.5 1.2 0 0 0

Unusable export 0 6.6 0 0 27.3 24.8 0.1 18.8

Storage 7.5 672 2,025 12 1.8 0.3 10

(b) Scenario with increased barley yields and internal recycling through improved manure management and cattle feeding

1 Household 7.8 0 3.0 12.6 0.3 0 0.1 0

2 Prickly pears 0 0 0 0 0 0 0 0

3 Barley irrigated 7.7 6.6 0 0 0 0 0 10.0

4 Cattle 34.9 0 2.0 8.5 0 0 0 0

5 Sheep 41.3 0 0 0 0 0 0 0

6 Chicken 0.4 0 0 0 0 0 0 0

7 Heap 4.2 5.9 0 0 11.6 8.3 0.2 0

Usable export 0 0 0 1.2 0 0 0

Unusable export 6.6 0 0 21.5 24.8 0.1 19.8

Storage 7.5 672 2,025 12 1.8 0.3 10

N flows move from one compartment (column j) to another (row i = 1, …, n, n ? 1, n ? 2) and are expressed in kg of N per compartment

per year. Inflows (column j = 0) and outflows (row n ? 1) are the total amounts of N imported to and exported from a compartment.

Storage is expressed in kg N per compartment (See ‘‘Indicators from network analysis to assess integration’’ for more detail)

Flows in italics changed in relation to the baseline

Nutr Cycl Agroecosyst (2009) 84:229–247 243

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123

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0

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‘‘In

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sto

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sch

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tio

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bas

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