134 Int. J. Agricultural Resources, Governance, and Ecology, Vol. 3, Nos. 1/2, 2004
Copyright © 2004 Inderscience Enterprises Ltd.
Mapping organisational linkages in the agricultural
innovation system of Azerbaijan
International Service for National Agricultural Research,
P.O. Box 93375, 2509 AJ, The Hague, The Netherlands
Abstract: This study describes the evolving context and organisational
linkages in the agricultural innovation system of Azerbaijan and suggests ways
to promote effective organisational ties for the development, distribution and
use of new or improved information and knowledge related to agriculture.
Graph-theoretic principles and concepts are employed to assess the existing
organisational linkages vital for agricultural innovations. This assessment
indicates: (i) the innovation system of Azerbaijan is in the early stages, and
significant accomplishments in especially policy-making, research and
development, and credit institutions are yet to come; (ii) ample scope exists for
intermediary organisations to be more active in facilitating the flow of
information and knowledge between the public and the private organisations in
the system and (iii) especially in public organisations, flexible management
styles should be practised for timely and effective interaction with private
Keywords: agricultural innovation system; Azerbaijan; systems analysis;
agricultural science, technology and innovation policy.
Reference to this paper should be made as follows: Temel, T. (2004) ‘Mapping
organisational linkages in the agricultural innovation system of Azerbaijan’,
Int. J. Agricultural Resources, Governance, and Ecology, Vol. 3, Nos. 1/2,
Biographical notes: Currently, Dr Temel works as a Research Fellow at
ISNAR in The Hague, The Netherlands. His career as an economist started in
1986 at the Research Department of the Central Bank of the Republic of
Turkey, continued at the Department of Applied Economics in the University
of Minnesota in the USA, then at the Treasurer’s Department of the
International Monetary Fund, the Department of Agricultural Economics and
Marketing at Rutgers University in the USA, and as the Deputy Director and
Associate Professor of Economics at the Center of World Food Studies – Free
University Amsterdan, The Netherlands. He published in peer-reviewed
journals, including Agricultural Systems, Growth and Change, Journal of
Economic Development among others.
1 Introduction 
The transition from a command to a market economy has, since the collapse of the
former Soviet Union (SU), received mounting attention from economists, as the newly
independent republics of the SU represented ideal experimental stations for practitioners
Mapping organisational linkages 135
of neo-classical economics. New institutions have been designed and put into effect;
however, the still-dominant hierarchical decision-making structure in the public sector
restrains the process of institutional change for effective interaction of public and private
organisations. Azerbaijan is one such republic. Although legal changes introduced after
1991 opened the way for the development of an enabling environment, major public and
private research and development organisations even now operate as stand-alone units.
Connectedness of these units is vital for innovations not only to take place but also to
spread over a large domain of organisations, as it will speed up the generation, exchange
and use of new or improved information and knowledge in a wider context.
Adopting the systems methodology [2,3] as its conceptual framework, this study
describes the evolving context and organisational linkages in the agricultural innovation
system (AIS) of Azerbaijan and suggests ways to promote effective organisational ties
for the development, distribution and use of new or improved information and knowledge
related to agriculture. The study defines the AIS as a set of agents that jointly and/or
individually contribute to the development, diffusion and use of new or improved
agriculture-related information and knowledge, and that directly and/or indirectly
influence technological change in agriculture. Graph-theoretic principles and concepts
are employed to assess the existing organisational linkages in the AIS. The conceptual
framework adopted should help policy makers identify constraints on the innovative
capacity of the economy and develop mechanisms to release them.
The systems methodology, first introduced by von Bertalanffy , applies systems
concepts and principles to aid a decision-maker in identifying, reconstructing, optimising
and controlling a system, while taking into account multiple objectives, constraints and
resources. The methodology has been widely applied to examine agricultural,
environmental, and cross-cutting issues. Examples include Goldsworthy and de Vries 
presenting a collection of studies adopting the systems approach as a tool to assess
opportunities in the developing country agriculture. Savory  and Gill  elaborate on
the potential of the systems approach in sustainability planning in agro-ecological issues.
Temel and Maru  apply the approach to design public–private partnerships in malaria
control. The list can be extended at will.
2 Systems analysis by graph-theoretic concepts
The following graph theoretic concepts are utilized to characterize a system:
• Matrix S maps all linkages of components organised around a specific system goal.
Consider an example system S with five components: Policy (P), Research (R),
Information (I), Farming (F), and External assistance (X). Each component consists
of one or more organisations that share similar objectives, and is placed in a diagonal
cell of S. Following the clock-wise convention, linkages between the components are
placed in the off-diagonal cells, PI (1st row, 3rd column) in S represents the linkage
between the organisations under Components P and I. Likewise, IP in the 3rd row,
1st column represents the linkage between the same organisations. The difference
between PI and IP lies in the direction of influence. In the case of PI, the component
P is the source of influence, while in IP the source is the component I.
The off-diagonal cells represent binary (or one-edge or one-to-one) linkages
between the two components. A linkage between P and I can also be established
136 T. Temel
through a pathway such as P → F → R → I. The maximum number of edges is equal
to (n – 1) = 4, where n denotes the number of components in S.
RP R RI RF RX
IP IR I IF IX
FP FR FI F FX
XP XR XI XF X
• Matrix S[c] codes linkages. 0 for the absence and 1 for the presence of a linkage.
S[c] indicates that P has a linkage with R, I and X; and R has a linkage with I and F
but not with P, etc. Note that, in this example, PR exists but not RP, which is
manifested by 1 in the 1st row, 2nd column and 0 in the 2nd row, 1st column.
The coding maps linkages that are claimed to exist; therefore, S[c] is not necessarily
symmetric. For example, the component P claims that the linkage PR exists, while
the component R claims the absence of the same linkage.
S[c] can also be mapped in a different format to detect visual patterns, where black
(white) cells indicate the existing (nonexistent) links.
Visual S[c] = I
• Matrix S[i] is the influence-adjusted version of Matrix S[c]. The adjustment is done
in this study by asking questions during the interview with representatives of the
organizations concerned. They are asked to comment on the degree of influence their
organisations exerted on others in the system. The answers to the questions were
scaled as none, weak, medium and strong. A value 0 was assigned for a nonexistent
influence, 1 for a weak, 2 for a medium and 3 for a strong influence [9–11]. This
procedure resulted in a vector of values representing the degree of influence the
interviewed organisation claims to exert on the rest of the organisations in the
system. Repeating the same procedure for each organisation in the system yielded as
many vectors as organisations. Next, the components were defined as subsets of the
organisations interviewed and the vector of values assigned to the organisations in
Mapping organisational linkages 137
each subset was reduced to a single vector by using the mode of the relevant values.
With this procedure we reduced the dimension of the system from the number of
organisations to the number of components. This procedure yielded a matrix like
S[i], values of which represent the degree of influence claimed only. The value 1 in
the 1st row, 3rd column of S[i], for example, indicates P’s claim of weak influence
on I, while I claims medium influence on P expressed by the value 2 in the 3rd row,
1st column. Note that a linkage might exist; however, if it does not have any
influence on the organisations involved, then that linkage will be assigned
a zero in S[i].
• The structure of S[i] is characterised by the Source (So) and Sink (Si) coordinates.
So and Si are, respectively, defined as the origin and the pool of influence. These
definitions, together with the clock-wise convention followed in the construction
of S, imply that rows (columns) in S[i] represent the source (sink). For example,
the 2nd row indicates R’s influence on P, I, F and X, indicating that R is the source
of influence. Furthermore, the 2nd column indicates others’ influence on R,
indicating that R is absorbing all the influence from others.
A value 3 assigned to the arrow from P to R in Figure 1 shows P’s strong influence
on R. Similarly, a value of 2 assigned to the arrow from I to P indicates the degree of
influence I exerts on P. Therefore, the sum of the values in the 1st row of S[i], which
amounts to 5, represents the total influence of P on others in the system. The effect
of others on P is calculated as the sum of the values in the 1st column, which is 2.
The (So, Si)-coordinates are then calculated as (5, 2) for P, (3, 6) for R, (8, 2) for I,
(3, 6) for F and (1, 4) for X. The scatter plot of these coordinates in Figure 1, shows
that I is dominant (implied by the observation that its influence on the system is
more than others’ influence on it) and R and F are both equally subordinate
components (implied by the observation that others influence it more than it
influences others). Furthermore, overlaying the binary influences in S[i] on Figure 1
by using directed arrows makes it easier to visualise the dynamics of the source–sink
• The density of S[i], denoted by d, is calculated as d = b/[n(n – 1)] with 1 ≥ d ≥ 0.
The parameters b and n denote the total number of existing binary influences and
the number of dimensions of S[i], respectively. For example, S[i] has a density
of 0.5, where b = 10 and n = 5. A structure is said to be fully identified if d = 1,
implying that all the components are linked to each other.
• A cluster is a subset of components concentrated around a (So, Si)-coordinate.
The So–Si structure is a useful tool for visually detecting clusters in the system.
The concept of cluster is, especially in large systems, helping to identify subsystems
and examine their characteristics.
138 T. Temel
Figure 1 The source–sink structure of S[i]
3 Data collection
A questionnaire  was designed and used to gather the information necessary for
analysing the AIS of Azerbaijan. This questionnaire has two distinct features. First,
organisational linkages are scaled as 0 for absent, 1 for weak, 2 for medium and 3 for
strong linkage, in order to show how effectively linkage mechanisms were used during
the interaction of the organisations concerned. Second, during the interviews, channels
through which the interviewed organisation influenced the others in the system were also
discussed. This information was later used to characterise the source–sink structure of the
AIS of Azerbaijan. The table below shows the number of components and persons
Components Number of persons interviewed
1. Policy 7
2. Research 12
3. Education 5
4. Credit 1
5. Extension and information 4
6. Inputs-processing-outputs marketing 8
7. Farm organisations 10
8. Private consultancy 11
9. External assistance 5
Mapping organisational linkages 139
The questionnaire is organised into five sections. The list of organisations visited was
prepared in collaboration with the director and deputy director of the Agrarian Science
Center of Azerbaijan. Section 1 concerns organisational profiles including information on
the respondent, classification of his/her organisation, and internal and external factors
that influence organisational performance. Section 2 concerns innovation activities, the
types and goals of these activities, sources of knowledge about innovations, funding
mechanisms and factors that constrain the activities. Section 3 relates to the type of
organisational linkages, strength of the linkages, linkage mechanisms used and
linkage-constraining factors. Section 4 relates to the most recent innovation developed or
diffused or used by the respondent’s organisation. Section 5 covers the agricultural
science and technology policy issues. In the analysis, national statistics available in the
literature were also used.
The main difficulty faced during the interviews was that some of the interviewees
were not able to clearly identify those organisational linkages that led to specific
innovations. When asked about organisational linkages related to innovations, they
simply reported all the existing linkages and constraints. Furthermore, due to ongoing
policy and organisational reforms, some of the respondents were not at liberty to respond
to some questions.
4 Agriculture innovation system
Various definitions of national innovation systems are quite similar at face value, but
there are some differences in meaning, emphasis and use of the concept. Freeman ,
for example, defines the concept as the network of institutions in the public and private
sectors whose activities and interactions initiate, import, modify and diffuse new
technologies. Nelson  defines it as a set of institutions whose interactions determine
the innovative performance of firms. Metcalfe  defines it as a set of institutions that
jointly and individually contribute to the development and diffusion of new technologies.
Smith  underlines the point that the innovative performance of an economy depends
not only on how the individual institutions perform in isolation, but on how they interact
with each other as elements of a collective system of knowledge creation and use.
The key difference between the above definitions is that some view the concept as a
simple aggregation of institutions, while others point at the synergies that originate from
their joint operation. The current study follows the spirit of the latter, viewing the
innovation system as not a simple aggregation of organisations but as a group of agents
who operate like members of an invisible orchestra. These members play pieces of one
big melody with an invisible harmony among them. This orchestra can be characterised
by coherence, harmony and synergy. Coherence brings different pieces under the same
melody; harmony creates a tune that keeps the members around the same spirit and
synergy ties the members more strongly around the common goal.
In this study we adopt a definition that views innovations as outcomes of
organisational learning through continuous interactions of organisations. Accordingly,
we define an AIS as a group of agents that jointly and/or individually contribute to the
development, diffusion and use of agriculture-related new technologies and that directly
and/or indirectly influence the process of technological change in agriculture.
140 T. Temel
4.2 Functions and linkages of organisations in the AIS of Azerbaijan
The organisational structure of the AIS of Azerbaijan is shown in Figure 2,
where organisations are classified by their functions: general policy-making (F1),
policy formulation, coordination, supervision and assessment (F2), financing R&D (F3),
R&D performance (F4), technology diffusion (F5), and technology application (F6) .
This figure also shows the links between the public and private organisations.
The organisations in the 1st layer perform F1 and F3 together; those in the 2nd layer,
F2 and F3, and those in the 1st and 2nd layers at the same time perform F1, F2 and
Figure 2 A structure for the AIS of Azerbaijan
Mapping organisational linkages 141
Below, we describe functions and roles of individual organisations that appear in
Policy component of the AIS. This includes five key public bodies operating under the
jurisdiction of the Cabinet of Ministers. The State Committee for Science and
Technology (SCST) supports the Cabinet in the formulation of science and technology
policies and coordinates all the science and technology-related activities. The Ministry of
Agriculture (MoA) as well as State Land Committee, State Amelioration Committee,
State Commission for Agricultural Reforms, State Commission for Assistance to
Agricultural Private Farm Sector and Agro-Industry Unit all support the Cabinet in the
formulation of the national agricultural policy. The Ministry of Education (MoEd), the
Ministry of Finance (MoF) and the Ministry of Economic Development (MoED),
respectively, support the Cabinet in the formulation of the national educational policy,
national budgetary policy and economic development policies.
Research component. This includes a total of 26 research institutes, 15 of which
operate under the Agrarian Science Center of Azerbaijan (ASCA) of the MoA and 11
under the Academy of Sciences and several committees. Of these 11 institutes, six belong
to the Academy of Sciences, three to the Committee for Water Economy, one to the State
Land Committee and one to the ‘Azerforest’ industrial amalgamation. The ASCA is
responsible for the coordination of national agricultural research. The other 11 research
institutes do not yet have clearly defined relations with the ASCA. Although the science
and technology committee is to coordinate research programmes of all research institutes
in the country, no initiative is taken to address the problem of a two-headed agricultural
After 1991, unfavourable conditions forced agricultural research institutes to use their
experimental stations for income generation purposes. In this process, they developed
relations with private seed supply and plant protection companies and large private
farms. Although uncommon, experts in the experimental stations have now and then
developed relations with international organisations via joint projects.
The Agency for Support to the Development of Private Agricultural Sector
(ASDPAS), responsible towards the State Commission for Assistance to Agricultural
Private Farm Sector, supervises internationally funded projects, including the Farm
Privatization Project, the Agricultural Development and Credit Project, and the
Development of Mountainous and Highlands Project. The Agricultural Research Board
monitors these projects funded through the competitive grant system.
Education component. This includes universities (25 public, 17 private) and colleges
(73 public, 4 private). The Agricultural Education Department of the MoEd assumes
responsibility for administrative coordination of 21 agricultural colleges and one
Agricultural Academy. The Scientific Research, Education, and Personnel Training
department of the MoA is responsible for administrative coordination of research,
education and training programmes. In addition to this, the Agricultural Academy
independently engages in both teaching and theoretical research. The agricultural
colleges, on the other hand, engage in both teaching and applied research. The MoA
supervises post-graduate education of students accepted to the 15 research institutes of
the ASCA. There are also several agriculture-related faculties in the Azerbaijan State
University and other technical universities.
Extension and information component. This includes the Information Dissemination
Unit (IDU) of the MoA as well as private enterprises engaged in extension activities.
The IDU supports the development of methodologies for the collection of farm
142 T. Temel
information, provides extension services and disseminates information on new farming
techniques. The Information and Consulting Services Center (ICSC), established in 2000
within the Agricultural Development and Credit Project of the Agency, coordinates
information and extension services through its regional branches. These branches provide
extension services to farmers in the preparation of business plans and in the accession to
credits or loans. Recent changes in the legal system have also paved the way for private
consultancy firms to engage in the capacity development and information dissemination
The actors involved in the diffusion of agricultural information are diverse. Experts in
research and educational institutions, professional meetings, international networks,
computer-based information services and mass communication means are among them.
Sources of funding for information and extension services are also diverse. The activities
of the IDU are financed from the government budget, those of private companies through
client contracts, subcontracts with international organisations and sometimes by own
resources. Project-based extension activities are funded by international development
International organisations, private consultancies, farmers and several research
institutes are the major actors in this component. Typically, they use linkage mechanisms
such as planning and review, technology diffusion, exchange of personnel and sharing of
information. Not surprisingly, the IDU, private seed-marketing firms and large farms are
also in close contact. Problem diagnosis, programme planning and review and joint
resource use are among the most commonly practised linkage mechanisms.
Private input supply, processing and marketing component. Around 20 private input
supply firms are gradually specialising in the supply of seeds and plant protection
materials. Large farms operate as producers, processors and middlemen. Few private
companies enjoy full control over the cotton production chain. International companies
become instrumental in introducing new seed varieties (grains, vegetables, cotton), and
chemicals, pesticides and herbicides, especially offering opportunities to new large
farms. Almost all these firms provide extension services to their clients.
The private firms have developed relations with research institutes under the MoA,
international input supply companies, the Seed Quality Control Unit (QCU) of the MoA
and farmers. They engage in joint problem diagnosis with experts from the research
institutes, in field demonstrations and training sessions with local customers and in the
preparation of TV programmes and information booklets for farmers.
Farm component. This includes large and small farms. Large ones play a
considerable role in the diffusion of new technologies, as they perform production,
processing and marketing activities simultaneously. They benefit from their
comparatively easy access to irrigation water and other production inputs and their close
connections with experimental stations of research institutes under the ASCA. Small
farms, on the other hand, literally lack everything, but most important of all, they lack
land large enough to think about commercial farming.
Large and small farms paint an opposite picture regarding their linkages with other
organizations in the system. A majority of the managers of the large private farms have
strong ties with the policy, research and education, input supply, processing, marketing
organisations, and with international organisations. Small farms are, however, a simple
expansion of garden plots, and thus, their relations are often with large farm operators.
The farm association is not really an important actor.
Mapping organisational linkages 143
Large farms have contacts with researchers at experimental stations, and are engaging
in joint problem diagnosis, technology development and exchange of personnel. They
have informal contacts with graduate students and professors and develop relations with
input supply firms and input supply enterprises. Through project implementation, these
farms rarely develop relations with international organisations offering new seed
varieties. They are involved in joint programme development, problem diagnosis,
technology demonstration, training and information sharing, and developed relations with
private input supply firms providing pesticides, agricultural chemicals and fertilisers.
Private consultancy service component. This includes as many as 35 firms run mostly
by academicians, researchers and post-graduate students. They usually collaborate with
international organisations. Most are spin-off entities growing around internationally
funded projects. They help farmers find appropriate sources of credits and prepare
business plans. Simply, they supply knowledge and their constantly increasing number
implies that the market for knowledge is growing.
Typically, a consultancy firm has relations with international organisations and the
ASDPAS. With skilled and energetic staff, these firms are capable of quickly adapting to
changing conditions. But, they are not proactive in business due to the lack of knowledge
and expertise in preparing project proposals and to inadequate international and national
linkages. They, however, play a pivotal role, facilitating international organisations to
Their immediate linkage with the ASDPAS is characterised by joint programme
development, review, and evaluation. With international organisations, they develop joint
programmes, participate in joint technology diffusion, and share information and
financial resources. With agricultural research institutes they develop joint programmes,
participate in technology diffusion activities, and rarely use facilities and staff jointly.
With farmers, they engage in joint problem diagnosis, priority setting, technology
demonstration and diffusion activities. Finally, they further develop linkages
with private input suppliers, processors and marketing agents. These firms obtain
innovation-related information from policy makers, research institutes, large processing
firms, universities, computer-based information sources and professional meetings.
Agricultural credit component. This includes public and private banks. Of a total of
70 banks in 1999, four state-owned banks dominate the banking system, basically
extending loans to public enterprises but no credit to the agricultural sector. At present, it
appears that no strategic plan has been put into effect to finance the agricultural sector.
Preparations are underway to merge the Agro-Industry and Security banks to create a
Universal Bank. But, it is not likely that it will extend credits to private farmers.
Recently, the government has established an oil fund to help mobilise resources to the
rural sector in general, and to the agricultural sector in particular. The government further
reduced the number of taxes to one (land tax) varying in the range of USD 5–25 for a
External assistance component.
This includes international organisations instrumental
in exposing national entities to improved knowledge, processes and practices through
joint project activities. These activities are usually carried out in collaboration with
private consultancy firms, as they have relatively better human and physical resources.
Public entities, however, have been slow in collaborating with international organisations
mainly due to the lack of qualified human resources.
144 T. Temel
Three types of linkages evolve between international organisations and the rest of the
system. First, formal relations are developed with policy units in order to legitimise goals
of the projects undertaken by these organisations. Second, direct interactions are
developed with beneficiaries of the projects. Close contacts are developed with farmers
through training programmes for promoting new farming techniques and agri-business
practices. Third, formal and informal relations are growing with private market
participants. Programme outputs are disseminated through newsletters, memos and TV
programmes. The weak legal system and local organisations’ inadequate material and
financial resources hamper the further development of these linkages.
5 Mapping organisational linkages in the AIS
Table 1 presents a structure for the AIS of Azerbaijan. The off-diagonal cells of this table
indicate specific linkage mechanisms that the relevant organisations utilise in
establishing ties with each other. The matrix S below represents the same table more
compactly, where f stands for formal linkages, i informal linkages, and m mixed
(both formal and informal) linkages. Furthermore, w stands for weak, m medium and
s strong linkages. Combining these notations, fw indicates a formal-weak linkage
between the two organisations; mm indicates a mixed-medium linkage; fs indicates a
formal-strong linkage, etc. Zeros that appear in some of the off-diagonal cells indicate
either a nonexistent or a negligible linkage, or a linkage that the investigator was not able
to identify. The notations with superscript 1 show those linkages established by using
specific linkage mechanisms in Table 1.
S is not fully identified. Of a total of 72 relations, only 45 are identified. Hence, it has
a density of 0.63 (= 45/72). S is fairly flexible, reflected by formal, informal and mixed
cross-component linkages. Of the 45 identified relations, 25 are formal, 11 informal and
9 mixed. The component C is fully detached from the rest of the system. Considering
those linkages established through linkage mechanisms only paints a different picture.
First, the density of the system declines to 0.35, which is very low compared to the
density 0.63 of S. Second, the private (D, I, F, X) and public (P, R, E) components tend
to move away from each other, tending to form two clusters.
The public (P, R, E) and the private (I, M, F, D) components are at early stages of
development, reflected by the dominantly informal relations between them. Equivalently
important is the willingness of (M, F, D) to develop contacts with P, which is implied by
(fm, im, im) in the first column and (0, 0, 0) in the first row. The relationship between the
private components is much stronger than that between the public components.
The relations among P, R, E and C are all formal and weak, while those among I, M, F
and D are overwhelmingly at medium strength. Finally, X has developed relations
with all the components in the system. Among these relations, the strongest ones are with
(I, F, D, P).
Mapping organisational linkages 145
Table 1 Linkage matrix S
146 T. Temel
S[r] is established by assigning 1 to a weak, 2 to a medium and 3 to a strong linkage in S.
For example, a value 2 in the 1st row, 9th column implies that the representative
organisation in the component P claims to have a medium level linkage with the
representative organisation in the component X. Similarly, a value 3 in the 9th row,
5th column indicates that the representative organisation in X claims to have a strong
linkage with the representative organisation in I. Figure 3 maps the implied structure in
which D is the most integrated and C is the least integrated component of the system.
This implies that policy interventions should target those organisations within the
component D. The component R is highly interactive  with the rest of the system,
and it is followed by (X, I, E, M). Finally, P is the most subordinate component, followed
Figure 3 The source-sink structure of S[r]
We assume that weak, medium and strong linkages generate weak, medium and strong
influences, respectively. Incorporating this assumption into S[r] would yield an
influence-adjusted matrix, S[i]. Figure 4 maps the source–sink structure of S[i].
According to this structure, P and F are the most subordinate, D and X are the most
dominant and I and E are the most interactive components.
Figure 4 The source-sink structure of S[i]
The visual format of S[r] below is an alternative representation of Matrix S[r], helping to
detect visually the areas where information is lacking. A cell coloured light (dark) grey
implies that linkage information between the relevant components is weak (medium).
For example, the three white cells in the 1st row imply that P did not declare any
significant activity relating to (M, F, D). But, the very same components declared
considerable activities relating to P implied by the corresponding dark grey cells in the
Mapping organisational linkages 147
fw fw fw mw fm
fw fw mw mm im im fw
fw fw iw im fw
fm fm fm fm fw
fm im mm mw
im im iw mw
im im mm fm iw mm fm
fm fw fs mw fm fm
P 0.330.330.330.33 0 0 0 1.32
0.33 R 0.33 0 0.33 1.32 1.32 1.32 0.33
0.33 0.33 E 0 0 0 0.33 1.32 0.33
0.33 0 0 C 0 0 0 0 0.33
1.32 1.32 0 0 I 0 1.32 1.32 0.33
1.32 1.32 0 0 0 M 1.32 0 0.33
1.32 1.32 0.33 0 0 0.33 F 0 0
1.32 1.32 1.32 0 1.32 0.33 1.32 D 1.32
1.32 0.33 0 0 2.97 0.33 1.32 1.32 X
Visual format of S[r]:
148 T. Temel
Spurring innovation culture should receive most attention at the moment because a
system without the spirit of innovation cannot be successful even if it has a strong
infrastructure and a resource base. To date, public organisations in the system lack
this spirit and have been preoccupied with the provision of basic services. Private
organisations, however, have been relatively more active in innovation activities,
gathering around international organisations. Tuning the existing knowledge
infrastructure into development goals and supporting it with an effective intellectual
property rights system, law enforcement mechanism, innovation-financing scheme and
human resources should usher in a new generation of innovation activities. An enabling
environment with these qualifications should pave the way for the growth of interface
agents that should glue related but at the moment disconnected organizations in the AIS
Well-defined science, technology and agriculture policies are the prerequisites for an
AIS to operate. Setting agricultural research priorities, revitalising the crucial parts of the
research system and tuning them to market developments, and preparing the curricula of
agricultural education institutions all require a clear direction at the policy level. In the
design of these triple policies, the following considerations should receive special
attention. First, labour and natural resource intensive pathways to growth are limited by
the availability of natural and human resources and are subject to decreasing returns,
while pathways driven by knowledge do not seem to face such constraints. Therefore,
information/communication technologies and human capital are far more important than
any other technology. Secondly, consideration should be placed on the observation that
the initial distribution of endowments determines the future knowledge accumulation
pathway. Therefore, the future implications of the privatisation of land and state-owned
agro-processing enterprises, should be carefully considered and remedial action should
be taken to avoid possible negative effects that are likely to emerge. Thirdly,
globalisation is expected to speed up regional technology transfer to the extent that
Azerbaijan is able to take advantage of opportunities in the international agricultural
Information and knowledge should be considered a vital input that drives national
innovation system. It brings good governance and financial soundness and improves the
quality of human resources, and as a result induces improved interactions between the
components of the system. The low degrees of partnership between public and private
actors and of the operation of exchange and communication mechanisms characterise the
AIS of Azerbaijan. The interactions are to be developed to link (P, R) to (M, F, D). In
this endeavour, priority must be given to the establishment of a strong extension and
information component because it is the key component that would facilitate the flow of
information from the private sector to the policy and research institutions or vice versa.
Research should not be an isolated phenomenon; it can be integrated into the system
at two stages. The first stage requires the development of intermediary organisations and
the private consultancy component to help (R, M, F) exchange information. In the next
Mapping organisational linkages 149
stage, R could, through joint research activities with X, expose itself to global processes.
With a successful completion of these stages, national research organisations would be
able to enhance their research capacity and develop effective research priorities.
Intermediary organisations, such as marketing associations, farmers’ organisations,
trade and commerce organisations, and platforms for constructive dialogues should play
a more active role in bringing together the components (I, M, F) and (P, R). Specifically,
links between these components could be strengthened through policy dialogues where
the intermediary organisations could pass information from (I, M, F) to (P, R) or vice
versa. Such exchange of information should help P and R reassess agricultural policy and
agricultural research priorities, respectively.
Stakeholders should be represented in research priority setting activities. Typically,
stakeholders exert pressure on the formation of the research agenda through two
channels. The first is to use funding as a threat, and the second is to influence general
policy making through interest group activities. The important point to make is that these
pressures are not always unproductive as they could provide information on the true
preferences of actors that are directly or indirectly influenced by research results.
Furthermore, NGOs, the private sector and other intermediaries might be important
voices for farmer concerns, and might be important partners in the governance of
agricultural research systems.
Effective financing mechanisms should be established and coupled with general
guidelines of the agricultural, science and technology policies. The allocation of
resources, accumulating in the newly established oil fund of Azerbaijan, should be based
on these guidelines. There is the need to speed up reforms concerning agricultural credit
institutions, as the presence of sound financial sources would promote investment in
agriculture, on the one hand, and induce synergy at least between the policy and the
credit components of the AIS of Azerbaijan on the other.
Adopting the systems methodology as its conceptual framework, this study describes the
evolving context and organisational linkages in the AIS of Azerbaijan and suggests ways
to promote effective organisational ties for the development, distribution and use of
agriculture related information and knowledge. Graph-theoretic principles and concepts
are employed to assess the existing organisational linkages vital for agricultural
innovations. This assessment indicates: (i) the innovation system of Azerbaijan is in the
early stages, and significant accomplishments in, especially, policy-making, research and
development, and credit institutions are yet to come, (ii) ample scope exists for
intermediary organisations to be more active in facilitating the flow of information and
knowledge between the public and the private organisations in the system and
(iii) especially in public organisations, flexible management styles should be promoted
for timely and effective interaction with private organisations.
The policy makers in Azerbaijan face many challenges. The first and foremost
challenge is to enhance the understanding of AIS among policy makers, which is a
necessary condition for the design and implementation of the coupled agricultural,
science and technology policies. During this endeavour, as argued by Cooper ,
theoretical frameworks that have been developed from empirical studies of innovative
firms in the industrialised countries could be of great importance since they could
150 T. Temel
provide useful guidelines for policy studies in the region from at least two points of view.
First, innovation theories contain insights into how and why technical capabilities are
developed in the advanced countries. In effect, they give some new dimensions of
meaning to the concept of “accumulation of local technological capabilities”, which has
come to play an important part in technology policies in developing countries. Second,
innovation studies have much to tell us about the structure of international markets, and
this kind of information is important in defining strategies for non-traditional agricultural
The second challenge is to develop methodological guidelines in order to evaluate
empirically national institutional set-ups with a view to obtaining comparable results at
the international levels. As argued by Capron and Cincera , the present literature does
not report any operational guidelines regarding the assessment of organisational linkages
underpinning national innovation systems. Such guidelines could also be used as a
benchmarking approach in the management of agricultural, science and technology
policies. An equivalently important issue, which has not received enough attention in the
literature, is, as argued by Nelson , the need for well-articulated and verified
analytical frameworks linking innovation systems to technological and economic
Reference and Notes
This study draws on ISNAR Country Report No 64. The author acknowledges the
contributions to this paper of Fuad Karimov and Willem Janssen.
OECD (1999) Managing National Innovation Systems, Paris, France, OECD.
European Commission (2000) ‘Innovation policy in a knowledge-based economy’, A Merit
Study Commissioned by the European Commission, Brussels, Belgium, Enterprise Directorate
von Bertalanffy, L. (1968) General Systems Theory: Foundations, Development, Applications.
Goldsworthy, P. and de Vries, F.P. (Eds.) (1994) Opportunities, Use and Transfer of Systems
Research Methods in Agriculture to Developing Countries, Kluwer Academic Publishers in
cooperation with ISNAR and ICASA, Boston.
Savory, A. (1991) ‘Holistic resource management: a conceptual framework for ecologically
sound economic modelling’, Ecological Economics, Vol. 3, pp.181–191.
Gill, R.A. (1996) Planning for Sustainable Agro-Ecosystems: A Systems Approach,
Temel, T. and Maru, A. (2003) ‘A systems approach to malaria control: institutional
perspective’, ISNAR Discussion Paper No. 03–05.
Note that if the scale consists of such categories as ‘very harmful influence’, ‘harmful
influence’, ‘neutral influence’, ‘useful influence’ and ‘very useful influence’, then an
appropriate set of values to be assigned to these categories would be –2, –1, 0 1, and 2,
respectively. For more reading on measurement techniques for surveys with scaled questions,
the reader is referred to Miller  and Tull and Hwakins .
Miller, G.A. (1956) ‘The magical number seven, plus or minus two: Some limits on our
capacity for processing information’, The Psychological Review, Vol. 63, No. 2, pp.81–97.
Tull, D.S. and Hawkins, D.I. (1984) Marketing Research: Measurement and Method, Mac
Millan, New York.
The Questionnaire (both in English and Russian) is available upon request. Also available
upon request is the list of persons interviewed.
Mapping organisational linkages 151
13 Freeman, C. (1987) Technology Policy and Economic Performance: Lessons from Japan,
14 Nelson, R.R. (Ed.) (1993) National Innovation Systems: A Comparative Analysis, Oxford
University Press, New York.
15 Metcalfe, S. (1995) ‘The economic foundations of technology policy: equilibrium and
evolutionary perspectives’, in Stoneman, P. (Ed.): Handbook of the Economics of Innovation
and Technical Change, Blackwell, Oxford, pp.409–512.
16 Smith, K. et al. (1996) ‘The Norwegian national innovation system: a pilot study of
knowledge creation’, STEP Report, Oslo.
17 Points on the 45-degree line in the source–sink diagrams represent the case in which the
source is equal to the sink in terms of influence.
18 Cooper, C. (1991) Are Innovation Studies on Industrialized Economies Relevant to
Technology Policy in Developing Countries? (UNU/INTECH Working Paper No. 3)’, The
United Nations University, Maastricht, The Netherlands.
19 Capron, H. and Cincera, M. (2000) Assessing the Institutional Set-up of National Innovation
System, OECD On Line.
Aghion, P. and Howitt, P. (1998) Endogenous Growth Theory, Massachussetts: MIT Press,
Azerbaycan Respublikasi Kend Teserrufati Nazirligi (ARKTN). (2000) Azerbaycan
Respublikasinin Kend Teserrufatina Dair Icmal, Baku, Azerbayca: Azerbaycan Respublikasi
Kend Teserrufatinda Ozel Bolmenin Inkisafina Yardim Agentligi.
Bahm, A. (1983) ‘Five systems concepts of society’, General Systems, Vol. 28, pp.43–57.
Bunge, M. (1979) ‘A systems concept of society: Beyond individualism and holism’, General
Systems, Vol. 24, pp.27–44.
Cormen, H.T., Leiserson, E.C. and Rivest, L.R. (1990) Introduction to Algorithms, The MIT Press,
David, P.A. (1975) Technical Choice, Innovation, and Economic Growth, Cambridge University
Debreu, G. and Herstein, I.N. (1953) ‘Nonnegative square matrices’, Econometrica, Vol. 21,
Hudson, J.A. (1992) Rock Engineering Systems: Theory and Practice, Ellis Horwood Limited,
Chichester, London, UK.
Klir, G. (Ed.) (1991) Facets of Systems, Science Plenum Press, New York.
Laszlo, E. (1986) Systems and Societies: The Basic Cybernetics of Social Evolution, in Geyer and
van der Zouwer, pp.145–171.
Mesarovic, M.D. and Reisman, A. (Eds.) (1972) Systems Approach and the City, North Holland,
Murota, K. (1987) Systems Analysis by Graphs and Matroids: Structural Solvability and
Controlability, Springer-Verlag, Berlin.
Nelson, R.R. and Winter, S.G. (1982) An Evolutionary Theory of Economic Change, Harvard
University Press, Cambridge.
North, D. (1995). Institutions and economic development. Paper presented at the OECD
(DSTI/Development Centre) Workshop on Institutional Framework and Economic
Romer, P.M. (1986) ‘Increasing returns and long-run growth’, Journal of Political Economy,
Vol. 94, No. 3, pp.1002–1037.
152 T. Temel
Romer, P.M. (1990) ‘Endogenous technological change’, Journal of Political Economy, Vol. 98,
Romer, P.M. (1994) ‘The origins of endogenous growth’, Journal of Economic Perspectives,
Vol. 8, No. 1, pp.3–22.
Shulin, G. (1996) Toward an Analytical Framework for National Innovation Systems (Discussion
Paper Series No. 9605). The United Nations University, INTECH Institute for New
Technologies, Maastricht, The Netherlands.
Shulin, G. (1999) Implications of National Innovation Systems for Developing Countries:
Managing Change and Complexity in Economic Development (Discussion Paper Series
No. 9903). The United Nations University, INTECH Institute for New Technologies,
Maastricht, The Netherlands.
State Statistical Committee of the Republic of Azerbaijan (SSCRA) (2000) Statistical Yearbook of
Azerbaijan, Seda Publications.
Mapping organisational linkages 153
ASCA Agrarian Science Center of Azerbaijan
ADCP Agricultural Development and Credit Project
ADMAP Agricultural Development Mountainous Areas Project
ARB Agricultural Research Board
AS Academy of Sciences
ASC Agricultural State Colleges
ASDPAS Agency for Support to the Development of Private Agricultural Sector
AzAA Azerbaijan Agricultural Academy
CGS Competitive Grants System
ERS Economic Reform Center
FPP Farm Privatization Project
HDSREPT Head Dept. Scientific Research, Education, and Personnel Training
IARC International Agricultural Research Center
ICARDA International Center for Agricultural Research in Dry Areas
ICSC Information and Consulting Services Center
IDAD International Development Agencies and Donors
IFAD International Fund for Agricultural Development
GTZ German Technical Support Society
MoA Ministry of Agriculture
MoEd Ministry of Education
MoED Ministry of Economic Development
MoEc Ministry of Ecology
MoF Ministry of Finance
MoT Ministry of Taxes
NGOF Non-governmental Organizations Forum
nAPC Non-agricultural Private Colleges
nASC Non-agricultural State Colleges
NARS National Agricultural Research System
NGO Non Governmental Organization
PACF Private Agro-Consulting Firms
PrU Private Universities
RC Regional centers
RES Regional Experimental Stations
RI Research Institutes
SCAR State Committee for Agrarian Reforms
SCADPF State Committee for Assistance to Development of Private Farms
SCSEC State Committee on Science, Education, and Culture
SSF State Seed Farms
SCAI State Committee for Amelioration & Irrigation
SLC State Land Committee
SU State Universities
SCST State Committee on Science and Technology
SCC State Customs Committee
TACIS Technical Assistance Committee for Independence States
USAID United States Agency for International Development
UNDP United Nation Development Programme
WB World Bank