Livestock Research for Rural Development 24 (9) 2012 Guide for preparation of papers LRRD Newsletter

Citation of this paper

Livestock innovation systems and networks: findings from smallholder dairy farmers in Ethiopia

Amlaku Asres, Johann Sölkner, Ranjitha Puskur* and Maria Wurzinger

BOKU-University of Natural Resources and Life Sciences, Department of Sustainable Agricultural Systems, Division of Livestock Sciences, Gregor-Mendel-Str. 33, 1180 Vienna, Austria
* ILRI-International Livestock Research Institute, P.O. Box 5689, Addis Ababa, Ethiopia


This paper uses household and key informant survey data from Ethiopia to: (1) understand the organizational structures that influence change in dairy production systems; (2) explore how local-level innovation system networks are functioning in the smallholder dairy production and (3) identify intervention points for strengthening innovation capacity. Results revealed that public sector actors are the major role players in the dairy production system despite their minor role in marketing linkages. We also found out that the private sector actors play peripheral roles in the network. Differences between innovator and non-innovator social networks were observed, with innovators exhibiting greater access to sources of production knowledge, inputs, credits and markets. Important institutions that could strengthen the stakeholders’ ability to identify, implement and adapt sustainable practices were not included in the processes. We recommend for policy guidance to reform the current agricultural extension system to address institutional and policy issues that constrain effective agricultural innovation system. 

Key words: Innovation capacity, Social network analysis, Dairy, Extension service, Ethiopia


Agriculture is considered central to African economies but its sustainable development faces enormous challenges. Low innovation capacity (Juma 2011), low productivity (UNDP 2012; World Bank 2008), demographic pressures, dwindling natural resources and climate change (Jayne et al, 2010) have all made agricultural development more complex. Recent studies (UNDP 2012; FAO 2012) argued that, the agriculture sector in Africa is performing below its potential and agricultural productivity remains low-much lower than in other regions and cereal yields stagnated for decades in sub-Saharan Africa and it remains the world’s most food insecure region.  

Similarly, in Ethiopia the agriculture sector is characterised as experiencing slow rate of technological change and the slow emergence of alternative institutional and organizational arrangements to enhance growth and development in the sector. For example, according to the Knowledge Assessment Methodology (KAM) report on Knowledge Index (KI) which measures the country’s ability to generate, disseminate and use knowledge, Ethiopia ranks 140th among the 145 countries assessed (KAM 2012). Despite of decades of research and development efforts, with the aim to provide farmers new technologies to improve their farming practices, agricultural productivity for both crop and livestock production is still very low (EARO 2006). Livestock production is a major agricultural activity in Ethiopia; including the value of ploughing services, it provided 45 % of the agricultural GDP in 2008-09 (IGAD 2010). However, the production is below the potential and characterized as low-input-low-output, for various reasons- technical, socio-economic, and institutional (MoARD 2010).  

This suggests that dissemination of new knowledge on agricultural technologies alone is not enough to deal with the aforementioned set of challenges and thereby lead to a more efficient use of resources, agricultural growth or development. Instead, it is equally important to understand the whole range of actors and ways of enhancing their capacity and thereby to follow novel agricultural innovation systems of relevance.  

Recent research has highlighted innovation systems perspective as a study framework (Hall et al 2008; World Bank 2006a; Clark 2002) to explore the capacity for innovation in several key areas of technology, organization and institutions.  However, in Ethiopia, this literature has yet to be juxtaposed in a detail manner with empirical evidence and little is known about how technological, organizational and institutional innovation collectively enhances smallholder productivity. To date, no study has been conducted using the innovation systems framework in the study area and also social network analysis as a method in the Ethiopian livestock sector. Furthermore, little use of the technique has been made so far for social network analysis in developing country agriculture in general and Ethiopia in particular (Spielman et al 2010).   

This paper examines how livestock /dairy smallholders innovate - how their social networks contribute to innovation processes; and how those decisions, networks, and processes are influenced by policy and market driven factors in Northwest Ethiopia as a case study area. The objectives of this research, therefore, are to: (1) understand the organizational structures that influence change in dairy production systems; (2) explore how local-level innovation system networks are functioning in the smallholder dairy production and (3) identify intervention points for strengthening innovation capacity. We argue that additional insights emerge from studying the innovation capacity of smallholders to better support farmers with strong networks and therefore greater opportunities to innovate and improve the productivity of dairy production systems. 

This paper is structured as follows: the second section describes the conceptual framework used for understanding the innovation systems perspective. The third section describes the methodology emphasizing the study area, the data collection process, the social network analysis (SNA) methods. Section 4 presents the results and discussion. Section 5 concludes. 

A conceptual framework 

We begin by rethinking dairy production as part of a bigger dynamic system of livestock innovation. The framework we are following for analysis is an innovation systems approach which allows us to explore the capacity to innovate. 

The concept of innovation systems by Hall et al (2008), Spielman et al (2007), World Bank (2006a) and Clark (2002) provides an alternative framework to that of the linear technology diffusion model. The latter has been criticized for its failure to understand the source, nature and dynamics of most innovations processes, particularly in the context of developing countries (Edquist 1997). 

As an element of the conceptual framework, we focus on innovation capacity (Hall 2009) rather than on the diffusion of innovation (Rogers 2003). Rogers defines ‘diffusion’ as the process by which an innovation is communicated through certain channels over time among the members of a social system. It is usually based on the idea that the innovation is ‘finished’ and can/should be adopted ‘as is’ and focuses on the rate of adoption. However in the current development context, the issue is rarely about ‘finished’ innovations that are adopted. More often, it is about co-developing innovations with stakeholders, and adapting them to suit specific demands. Given the current rapid pace of change (in markets, technologies, regulations, social and environmental circumstances) the focus also needs to be on strengthening flexible adaptive response capacities, so that stakeholders can adapt to upcoming changes.  

Innovation capacity focuses on policy and development interventions that mobilise knowledge and information to support a continuous process of innovation. Innovation is defined as the process of creating, accessing and using knowledge and information to create new products, processes, services, etc., that satisfy social and economic goals (Hall et al, 2008).  The question is partially about developing new knowledge, but it is also about the ability to mobilise available knowledge, and use this knowledge. This shifts the emphasis from technology and agricultural production techniques towards networks, linkages and institutional environments that enable the integration of different knowledge sources, and enable the adaptation of proposed technologies to meet the requirements of the local context.  

The innovation system concept is presented as a framework for examining the notion of innovation capacity. World Bank (2006a) provides the four point analytical framework to investigate agricultural innovation capacity. The checklist includes: (1) Actors, the roles they play and activities they are involved; (2) Attitudes (habits) and practices of the main actors; (3) Pattern of interaction; (4) Enabling environment (institutions and policies).  

Actors are individuals or organizations, in the public or the private domain, that have the ability to cause change. The livestock sub-sector is one of the major contributors to food security and growth within the agricultural sector. The following may be key actors in the system: smallholder farmers, firms that provide inputs and services (such as forage seed, equipment, credit, etc), agro-processing enterprises, organizations that influence policy and provide resources (Bureaus of agriculture, education, finance, industry and trade), market intermediaries (traders, brokers and their associations), research and development organizations (public, private), universities and other institutions of higher learning, organizations that provide information and services (extension and training services, animal health services), farmers associations (cooperatives, unions), religious social organizations, bilateral projects and NGOs that facilitate networking. 

Central to the process are the interactions of different people and their ideas (knowledge); the institutions (the attitudes, habits, practices, and ways of working) that shape how individuals and organizations interact; and learning as a means of evolving new arrangements specific to local contexts. Institutions are the formal and informal rules (laws and regulations, norms, values, and morals), that shape human behaviour, and the mechanisms (including certain organizations) for their enforcement (North 1990). The roles of institutions in innovation include “managing uncertainty, providing information, managing conflicts and promoting trust among groups(Oyelaran-Oyeyinka 2005). Recent innovation capacity studies in Europe (Loorbach 2007), Asia (Spielman and Kelemework 2007; Rist et al 2007), Latin America (Rist et al 2007) and Africa (Rist et al 2007; Spielman et al 2010; Tesfaye et al 2010; Davis et al 2006; Mazur 2006) have all concluded that the innovation systems approach is a useful encompassing framework to orient development strategy.  

Explicit in the innovation system concept is the notion that innovations are the product of networks of social and economic actors who interact with each other and, as a consequence of this interaction, create new ways to deal with social or economic processes (Hall et al 2001). Similar to Scott (2000) social networks are conceptualized in this study as relationships among actors. Actors build on connections and better connections create economic opportunity (Krebs and Holley 2002). Conceptually key data points in a network are the node (a single actor within a network), the ties (links between the nodes), and the dyad (pairs of actors). Networks potentially offer opportunities for taking advantage of economies of scale and scope as well as for developing capabilities necessary to respond to old challenges of underdevelopment and/or emerging new challenges. Networks aim to exploit comparative advantage and maximise spill-over effects.   

Social networks can be analysed using methods of SNA (Scott 2000). The SNA is a useful tool for investigating social structures. As it is a tool that can be applied in many fields, we study, in particular, its influence in the innovation system. It is useful in understanding and mapping innovation systems because of its analytical focus on relationships and interactions between people and groups, and its ability to capture knowledge flows and other attributes contained within such interactions (Spielman et al, 2009). In social network analysis (SNA) the nodes of concern are people, groups and organizations and the links may be social contacts, exchanges of information, political influence, money, joint membership in an organization, joint participation in specific events or many other aspects of human relationships (Davis et al 2006).  

Interest in SNA has only recently bloomed with the study of innovation systems approach in developing country agriculture (Spielman, 2010). There are, however, few related disciplines that use SNA to examine smallholder innovation systems and processes illustrate the tool’s value. Examples include social network effects on the adoption of agroforestry species in southern Ecuador (Gamboa et al, 2010); to analyse the agricultural biotechnology policy network (Philipp, 2010); to analyse the management of water resources network for agriculture (Rodriguez  et al 2006); the analysis of agricultural networks in Ethiopian smallholders (Spielman et al 2010); as an analytic tool within integrated pest management stakeholders’ practices (Raini et al 2006), building farmers’ capacities for networking (Clark 2006), and farmers social learning processes (Conley and Udry 2001). 

The innovation systems network study offers a locally relevant architecture that effectively links the different actors within the agricultural innovation system. This new architecture will also address the fundamental institutional and policy issues that currently constrain the emergence of effective agricultural innovation system.  


The SNA of smallholder dairy farmers involved both quantitative and qualitative data and a combination of different approaches such as: (1) desk review through document analysis and semi-structured interviews from key informants on innovations and key system actors; (2) household survey; (3) focus group discussion including Venn diagram and Institutional Ranking; and (4) key informant interviews. Prior to the actual data collection translation of the questionnaire into local language, and field visit to pre-test the questionnaire were conducted. Data was collected from July-November, 2010 in the North West of Ethiopia. 

Study area         

This study was conducted in North Gondar Zone of the Amhara Regional State, which is located in North-western Ethiopia. The study focused on innovations that were introduced by the Ethio-Austrian Integrated Livestock Development Project (ILDP) over 10 years (1998-2008) in three phases in 14 districts with the aim of improving livestock productivity,  income and food security. During this period ILDP was involved in implementing an integrated livestock development programme via packages of feed, health care delivery, genetic improvement, marketing and capacity building at the smallholder level. It has yielded encouraging livelihood enhancement results. Some of the success indicators showed that: (1) up to 0.25 hectare per household was used for improved forage crops; (2) mortality rate of cattle decreased by 3-4% and morbidity rates per animal reduced from 30-50% and (3)  milk production increased from 2 liters to 6 liters per day per cow on average. The success of the project can be attributed to, inter alia, the targeting, selection of technology, delivery mechanism (extension), input supply and credit schemes, and its attempt to link farmers to markets (ILDP, 2007). Thus focusing on practices, technologies and knowledge shared by ILDP for further study was important in order to capture lessons learned.  

The data collection was conducted in eight villages in the four phase I project districts (Table 1), where the project has been providing rural development support services since 1998. The selection was done mainly based on the longer duration of time that ILDP had been involved in these districts.  

Table 1. Selected sites for in-depth study



Livestock/ Technology Package Introduced

Agro-ecological Zone a

 Development Potential b



2. Degola

Dairy & Forage

M1, M2

Medium potential, low risk


3. Kerker

4. Shumara

Dairy & Forage

M1, M2

Medium potential, low risk




Dairy & Forage


Medium potential, low risk


7. Mikara

8. Zebena

Dairy & Forage


Medium potential, low risk

a M1 is hot-to-warm, moist lowlands (1500 – 2500 masl); M2 is tepid-to-cool, moist mid-highlands (2500-3000 masl). Source: MoA (2000).

b Source: World Bank (2004).

Household selection 

Households were selected through a stepwise process. Within each district, a two stage selection process was followed. First, two villages (case study sites) were purposively selected on the basis of their relative importance in having more project beneficiaries among the first year project districts. Then, a systematic random sampling was employed to select households from each village. The sample size, which mainly depended on the total number of beneficiaries in the sample districts, was determined by using the formula indicated in Jaeger (1984). Accordingly, a total of 224 households were selected from the four districts in eight enumeration sites for household survey. 

In each of the target districts and sample villages, representative individual beneficiaries were interviewed by using a semi-structured questionnaire. The  questionnaire focused on: (1) the socio-demography of the household (household members, age, sex, educational level, etc.); (2) household assets (land and livestock ownership); (3) access to rural services (extension, saving and credit service); (4) work groups and cooperative membership (how farmers have organized themselves to benefit); (5) participation in the ILDP project (the scope of support extended to farmers from all components of the project); (6) how government/ private service providers are organized to extend support or to administer the program; (7) what challenges they have faced and how these have been surmounted.  

Households for further study in the focus group discussions were selected from each village based on a rough index generated from the household survey data. The index was composed of equally weighted values for: (1) adoption of one or more of the improved technology packages introduced by the project calculated by computing the number of technology packages that the household was engaged in, divided by the total number of technology packages). Here, six packages are identified: dairy development, fattening of cattle and small ruminants, sheep and goat production, honey and wax production, poultry production, forage production; (2) adoption of one or more complimentary practices, calculated by computing the number of improved  practices applied by the household divided by the total number of  practices. Here, two practices are identified: genotype improvement (e.g. improvement of the local breed, implementation of cross breeding) and forage improvement [Forage improvement practice includes (e.g.  improving the feed quality of crop residues; natural pasture improvement; forage production through backyard development, under sowing, over sowing, strip planting, sequential cropping, and fodder bank (stored crop residue); improved forages production (e.g. Sesbania sesban, vetch, oats, tree lucern, napier grass, and fodder beet)]; (3) household practice of land allocation for forage production and or private grazing calculated as yes= (1) or no= (0); (4) ownership of modern production assets, calculated as the number of modern production assets owned by the household divided by the total number of production assets. Here three assets are identified (e.g. cream separator, milk churner, and aluminum milk container; and (5) contact with agricultural extension services: here two sources were identified (government or farmer development agents).  

The five households with the highest index scores and the five households with the lowest index scores were selected for separate focus group interviews and were denoted (for convenience only) as innovators or non innovators, respectively. As shown in Table 2, these groups statistically differed, with innovators exhibiting higher mean values. This approach allowed us to identify groups that, according to household survey data, were using livestock production in general and dairying in particular different from other members in the community, thus offering potentially valuable insights in to the role of smallholder innovation networks.  

Table 2. Descriptive statistics for focus group participants






Group mean difference test (p-value)

Female participants (%)




Mean family size (no.)

7.95 (2.3)

6.5 (1.9)


Mean age (years)

47.4 (9.6)

48.4 (11.7)


Mean education (years)

3.58 (2.8)

2.25 (2.4)


Mean land ownership size (ha)

1.85 (0.7)

1.48 (0.7)


Mean cross-breed bull (no.)

1.2 (1.0)

0.15 (0.4)


Mean cross-breed cow (no.)

2.5 (1.2)

1.15 (0.8)


Mean land size allocated for forage production (ha)

0.2 (0.2)

0.12 (0.2)


Mean land size allocated  as private grazing land (ha)

0.22 (0.3)

0.11 (0.2)


Notes: Numbers in parentheses show standard deviations.  Mean between innovators and non innovators significantly different at confidences interval of *95%; **99%.

Focus Group Discussion and Semi-structured Interviews 

Sixteen focus group discussions (FGD) were conducted (two at each village, one with innovators and one with non-innovators, in eight villages,) composed with five individuals each. In each of the target study sites, a checklist of questions was used as a flexible guide for discussions. The pre-tested checklist focused on: (1) identifying source of production knowledge and information; (2) inputs and materials; (3) credit and finance; and (4) market links and price information. 

The FGD was followed by a Venn diagramming exercise, followed in turn by an institutional -ranking exercise. Following the FGD interviews at each site, additional semi-structured interviews were conducted with key actors identified by the FGD participants. These include farmer development agents; government development agents; cooperative managers; village officials at local level; experts at agricultural offices and ILDP focal persons at district and zonal level; Bureaus of Agriculture and Amhara Research Institute at regional level.  Interviews were guided by questions similar to those posed to PRA participants. Data gathered from the PRA and semi-structured interviews were then used to conduct social network analysis of each site. 

Social network analysis: methods 

In this study the SNA is used to analyze the social networks in the dairy production system practiced among smallholder farmers. The analysis of local network data generally follows a sequence of steps that aim at identifying typologies of actors and interactions. The first step involves two different types of analysis. One refers to the entire network analysis and the other to the centrality analysis (Wellman 1992).  

The entire network analysis examines the structure of social networks (including groups or clusters), as well as the networks’ composition, functioning and links to external situations. With this analysis it is possible to examine questions such as: who interacts with whom, about what and how? How are ties and relationships maintained, or changed over time? The approach to the entire network analysis focused on the description of the structure of the local network through the examination of the size, density and cohesion of the network.  

The centrality analysis is the most important way of identifying the actors that play the most relevant roles within the network and refers to the extent to which a network revolves around a single node (Everett and Borgatti 1999). Centrality is an attribute of the actors in a network that refers to the structural position of an actor within the network. Measuring the centrality of different actors is a way of assessing the importance and influence of an actor with in the network. According to Freeman (1979), the most important measures of centrality are degree centrality, closeness centrality and betweenness centrality. Degree centrality allows the following questions to be answered: How active is each social actor with in the network? Who is the most active social actor within the network? Closeness centrality allows the following questions to be answered: who is the social actor with fastest access to all the actors within the network? Betweenness centrality allows the following question to be answered: Who is the best-connected social actor within the network? 

Definitions of the variables (elements) used in the SNA are presented in Table 3. Most of their definitions are adapted from Scott (2000) and regarding the mathematical formulas behind them, consult Scott (2000) for further details. 

Table 3. Definition of the variables (elements) used in the social network analysis




A single actor (any individual, organization, or other entity of interest) with in a network


 Interconnections between actors

Directed Tie

An ordered set of two nodes, i.e., with an initial/source and a terminal/destination node.


Actor of interest  within a network


Node directly connected to an ego

Ego network

Network that only shows direct ties to the ego and not between alters


Pair of nodes linked by a tie


Graphical representation of relationships that displays points to represent nodes and lines to represent ties; also referred to as a graph

Network size

Total number of nodes in a network

Network density

Nodes that are actually tied as a proportion of all possible ties in a network. When density is close to 1.0, the network is said to be dense, otherwise it is sparse.


Measure of the number of ties that a node has relative to the total number of ties existing in the network as a whole; centrality measures include degree, closeness, and betweenness. 


Total number of ties a node has to other nodes. A node is central, when it has the higher number of ties with other nodes.

In-degree centrality

Number of ties received by the node. The in-degree of an actor is an index of prestige /indicate its importance/.

Out-degree centrality

Number of ties initiated by the node. The out-degree is usually a measure of how influential the actor may be.


Measure of reciprocal of the geodesic distance (the shortest path connecting two nodes) of node to all other nodes in the network. A node is “close” if it lies at short distance from many other nodes (as in being physically proximate).


Number of times a node occurs along a geodesic path. It is a node that can play the part of a liaison or broker or gatekeeper with a potential for control over others.


Cohesive subgroup within a network in which the nodes are connected in some maximal sense


Nodes that are only loosely connected to the core and have minimal or no ties among themselves

Source: Authors;   Scott (2000), Hanneman and Riddle (2005), Wasserman and Faust (2005) and Spielman et al (2010).

Data Analysis 

Descriptive statistics were done using SAS 9.2 software package (SAS Inc. 2009). Qualitative data (80 primary documents) from FGD and KII interviews were transcribed and later coded with Atlas.ti 6.2 software (Atlas.ti 2010).  SocioMetrica VisuaLyzer 2.0 (VisuaLyzer 2007) was employed to look at the data from the PRA exercise and key informant interviews applying social network analysis. 

Results and Discussion

We perform a network analysis of innovation actors in the smallholder dairy production system. We examine the central players, the underlying collaborative relationships and its implication. The result is presented as follows. 

The actors in the dairy production innovation system 

We identified diverse actors in the system and their composition includes a range of public, private and civil society organizations. There were a total of 23 actors with a web of 179 ties (interconnections) taking part in the innovation systems network (Fig. 1).  The density, which is an indicator for the level of connectedness of a network for the innovators network, is 0.70, i.e. 70% of all possible direct linkages are present. Furthermore, the degree centrality (cohesion) of the whole network is low (0.32), showing that only 32% of the connections are reciprocated. In other words, this indicates that those actors responsible for the exchange of technology and information supply, inputs and materials, credit and finance sources and marketing are not well connected. Therefore, this data tells us that there is a potential to increase the interconnections among actors in this network, which could contribute to improving the productivity of the dairy sector. In a relatively dense network (density close to one), an individual’s network partners also communicate with each other, which implies that information may spread faster (Valente 1995). However, not all connections are important and needs to scrutinize connections based on their quality. 

Role of public service providers in linkage facilitation  

It was found that the public sectors play a central role in the dairy production innovation system. To determine which of the actors are more important (having a leading role), the analysis considered all the direct ties made by an actor (both originated and received) and the indirect ties (paths). The usual parameters of centrality were used to examine the centrality of the actors within the network in terms of: degree centrality (collaboration among actors), out-degree centrality (influence), in-degree centrality (prestige/prominence), closeness centrality (physical proximity) and betweenness centrality (liaison/most favoured position). Figure 1 provides the visual map of the central players and the underlying collaborative relationships and Table 4 presents the descriptive measures of the actors.  

Figure 1. Innovators’ Social Network and its Core Members (Note: The size of each node is determined by the node’s degree centrality. Please refer Table 4 for the abbreviations)

The analysis of Figure 1 and Table 4 reveals seven core institutions (service providers) that play a central role in smallholder innovation process. According to their order of degree centrality (collaboration), the institutions are: the District office of Agriculture (WoA), Integrated Livestock Development Project (ILDP), development agent (DA) at the village level, and the village administration (KA), cooperative promotion office (CPO), farmer development agent (FDA) [farmer development agents:  is farmers used by ILDP as agent farmers to effectively demonstrate livestock technologies and to persuade others. According to their peers, farmers stated that skill and knowledge transfer has been more convincing, long lasting and cost effective] and Zonal office of Agriculture (ZOA). These are all public rural service providers that are closely linked with smallholder households and they typically operate around an interwoven network of agencies.  

The results in Table 4 reveal that the WoA is the actor with the highest degree of centrality (0.79), i.e., it is the actor with more connections and hence can directly affect many of the actors. It is an actor with most influence (highest out-degree), most prominence (highest in-degree), highest closeness (is closest to the others) and highest betweenness (the actor with the most favoured position) because many other actors depend on it to make connections with other actors. This central position makes this actor more accessible to the smallholder dairy farmers and results from its role in supplying knowledge/information, inputs, credit and market services. Therefore, WoA is supposed to be an intensely involved actor in innovation facilitation and can be considered as the most important channel for the diffusion of information and innovations (decision, technical support, procedure, etc.). Moreover, because its betweenness is high, it also serves as a liaison between different actors in the network. 

Table 4. Measures of centrality of actors in the network







1 Smallholders






2District office of agriculture (WoA)






3 Integrated livestock development project (ILDP)






4 Development agent (DA)






5 Village Administration (KA)






6 Cooperative promotion office (CPO)






7 Farmer development agent (FDA)






8 Zonal office of agriculture (ZoA)






9 Farmers milk marketing cooperative (FMMC)






10 Farmer multi-purpose cooperative (FMPC)






11 Amhara credit & saving institute (ACSI)






12 Bureau of agriculture & rural development (BoARD)






13 Food security office (FSO)






14 Farmers milk marketing union (FMMU)






15 Sustainable resource management program (SRMP) – Bilateral Project






16 Agricultural research centre (ARC)






17 Farmers saving & credit cooperative (FSCC)






18 Traders






19 World Vision International (WV) - NGO






20 KFW - Bilateral Project






21 Orthodox (NGO)






22 Brokers






23 Religious social organization (RSO)






However, a high level of accessibility does not necessarily indicate good-quality service. The past 10 years performance assessment shows that the national Ministry of Agriculture and its branches at the region to district level couldn’t perform client-oriented extension service (MoARD 2010), and its system is rather unresponsive to user demand (World Bank 2010). The country’s extension service are said to be the  largest worldwide in-terms of manpower, for example, extension worker per farmer ratio is 1:1300 versus 1:7000 in India, however,   the service have generally failed to perform demand driven agricultural extension service to farmers (World Bank 2010). This implies that there are opportunities of improving the services of this actor. There are studies that indicate how agricultural extension needs to be re-oriented in Ethiopia (Habtemariam K. 2005; Berhanu et al 2006; Beyerlee et al 2007; World Bank 2006b and World Bank 2010). 

Within the smallholder dairy production innovation network, other stakeholders that play central role is ILDP, which is responsible for providing various kinds of services (knowledge, input, credit and marketing). The high value of betweenness of ILDP (next to WoA) represents its strong potential to control interface relationships. ILDP, responsible for the overall project management and as a technical arm, represents the most central position between the local actors and other entities situated outside the district. In this case though, the project was crowding out other actors. As a result, farmers in the project area commented that, smallholder dairy farm production and productivity has not continued sustainably after the project terminated. 

Similarly, ILDP used farmer development agents (FDAs), paravets, and community facilitators as a grassroots level extension agent with the assumption that the government development agents (DAs) were too overburdened to provide ILDP related extension services with the required intensity and quality. However, the capacity building effort of ILDP on these parallel structures was not used properly and stopped after the project terminated because of lack of coordination. In the context of definite time frames for development interventions, an important issue which was not well dealt with during the project period was how such innovative approaches can be institutionalized so that they can be sustained when development organizations leave. Moreover, ILDP was providing marketing extension services for livestock production. The unique nature of the extension service provided by ILDP was focused on activities, such as organizing farmers for collective marketing, providing market advice and market information, linking farmers with markets of both inputs and outputs etc. This experiences of the ILDP marketing extension can serve as a model for government extension service in the region as a whole, which does not consider marketing extension as an important component of the agricultural extension service. 

Overall, the above result is consistent with the study on smallholder innovation networks in the Ethiopian crop sector by Spielman et al (2010) which concludes that public extension and administration exert a strong influence over smallholder networks, potentially crowding out market based and civil society actors, and thus limiting beneficial innovation processes. 

Role of public sector actors in marketing 

While public sector actors are key providers of information, inputs and credit related, their role is by far less with respect to developing market linkages or transmitting marketing information (price) to small households. These actors have limited experience and capabilities with markets. The marketing actors which are dominantly private sector actors like the farmers’ milk marketing cooperatives, farmers’ multi-purpose cooperative, farmers’ milk marketing union, traders, and brokers have a peripheral position (Fig. 1).  

ILDP was opted for organizing farmers into specialized cooperatives to increase their bargaining power on input-output markets, to create financial capacity that producers as a group could have, and to increase their organizational capacity to pay for services (e.g. hiring professionals, to demand better services like extension, health, etc). However, this support was not kept on-going to make it sustainable by the current extension system. 

Apart from milk marketing, cooperatives shall provide important services such as breed improvement, animal feed, veterinary and AI services for their members. Therefore, organizing the farmers into cooperatives may later evolve into creating breeder associations, for instance, like the Boran cattle breeders society in Kenya (BCBS 2010). To institutionalize animal breeding, there is a need to develop interest group of breeders’ societies around the breed they are using to maintain their livelihoods. In addition, the dairy milk marketing cooperatives can potentially transform into business hubs, for instance, like the dairy business hub of Kenya (Mary et al 2010) in which farmers could access services such as education, credit facilities, artificial insemination, extension services and inputs such as feed, transport and veterinary services. For this transformation to occur, the structure of dairy cooperatives must separate policy making roles (such as those left for the decision of cooperative members) from professional management (such as the day to day technical work). The transformation could be accelerated by organizing the cooperatives into business entities that are publicly owned with farmers holding equity. 

Role of private sector actors  

Despite the lack of isolated actors (Figure 1 and Table 4), some of the actors are not so extensively involved in relationships with all the actors but have a peripheral position within the social network. These are mostly private sector actors such as market traders (input supply small businesses), brokers, farmers’ milk marketing cooperative/unions, are often peripheral to networks.  

On the other hand, the development of a vibrant private sector, which is capable of providing the essential input and support services, is critically important to unleash the growth potential of the dairy production system in the area.  In addition, private service providers are essential to accelerate rural economic growth, improve incomes and employment. There is a strong case for the extension service to allow and incentivize the entry and active participation of private sector actors. This implies that, in addition to the creation of enabling policies, laws and regulatory environment for private service delivery, public support for private service development is vital. This is because often market alone fails to allocate resources such as capital, skills and technological development to private sector and to ensure effective coordination with in a sector (Kurokawa et al 2008) 

The role of the civil society organizations  

The civil society organizations have a relatively strong relationship with the public sectors. Mostly they are peripheral but have ties with other actors. These civil society organizations are mainly the non-governmental organizations (e.g. World Vision), community-based organizations (e.g. farmers’ cooperatives) and bilateral projects. This is explained by the node degree (Fig. 2). In this case, it is possible to observe an attempt to become more integrated in the network. These actors are development partners to the WoA, and their work is often planned and implemented in consultation or collaboration with WoA. They have specific areas of expertise which they directly involve in actual activity implementation in specific areas or through training, technology delivery and financial support. Their comparative advantage lies in their ability to reach poor and marginalized people, and their operational flexibility and dynamism.  

Figure 2. Innovators Network Node Degree [Note: The size of each node is determined by the node’s degree centrality. Please refer Table 4 for the abbreviations]

Differences between innovators and non innovators 

Figure 1 and 3 provide a generalized overview of the typical networks for innovators and non innovators based on aggregated data from eight case study sites. Table 5 provides descriptive measures for both networks. 

Table 5. Descriptive measures of generalized networks



Non Innovators

Ego network size (no. of nodes)



Ego network density



Degree centrality



Closeness centrality




Figure 3. Generalised networks for Non-innovators (Note: Figures are calculated for complete network except for ego network size]

We found that the types of actors are different in number. The innovators network exhibits 22 actors, whereas the non-innovators network has 18. In the latter network one bilateral development project actor, 2 NGOs and one marketing actor are missing. 

Innovators are members of a larger network than non-innovators (explained by network size): implies that innovators have relatively greater access to formal and informal substitutes for knowledge/information, inputs, credit and markets than non-innovators who depend more on public service providers and quasi public institutions (Figures 1 and 3 and Table 5). 

Innovators’ network is less dense, denoting the presence of more actors than non innovators in the network; and innovators’ networks are more centralised and closer, denoting greater proximity (shorter walks) to other actors (Table 5). This implies that innovators are associated with better access to sources of production knowledge, inputs, credits and markets and thus a potentially greater number of livelihood options and opportunities than non-innovators have. This finding can be further substantiated from illustrations in Table 2.  

This finding has two implications to smallholder dairy innovation networks in the study area; that (1) Innovation networks vary within communities, (2) The diversity of connections helped to enhance innovation in the network. This has a widening consensus in other studies. Among the others, Scott states that actors who have more ties have greater opportunities because they have choices. This autonomy makes them less dependant on any specific other actors, and hence are more powerful (Scott 2000).  

Organizational and institutional differences among the study regions/ agro ecologies 

The study revealed that, whereas the type and number of NGOs and bilateral development projects slightly varies from district to district, the organizations involved in supporting and or dissemination activities the public service providers, quasi public and government supported farmers’ cooperative actors are similar across the 4 study areas.  

On the other hand the study districts are different in agro-ecology, distance from the major dairy production systems, and other supporting structures such as input-output market, credit, infrastructure, etc. which requires locally relevant organizational, managerial and institutional arrangements. Distance and transportation costs are clearly relevant when talking about physical goods such as milk, which is perishable and bulky the closer the service, the less the costs/charges of both service delivery and market transaction costs.  

Many development studies such as Altenburg et al (2008), Leeuwis (2004) and Pérez (1989) emphasise that development interventions to be successful, technological change should go hand in hand with institutional change. Ahmed et al (2004), for example, showed that the rate of adoption of fodder and pasture land management technologies in Ethiopia was extremely low for several reasons, which include factors relating to institutions, economic incentives, support service delivery and policy. 

In contrast to the above arguments, it is possible to suggest that there is a need to address the challenges of livestock production district by district. Considering the wide variation in capacity among districts, requires an innovation systems thinking. There is no ‘one size fits all’ solution to all problems. In this regard, the innovation systems framework offers insights on how to improve its capacity to innovate new locally relevant arrangements.  

Deficiency to incorporate other relevant service providers 

The study showed that the smallholder dairy production innovation network does not include Universities, breeding associations, private and NGO dairy advisory service providers and agro industries (agribusinesses). Private sector actors including traders, brokers, and input supply small businesses and companies are very weak or non-existent (Figure 1). 

A study by Altenburg et al (2008) showed that innovative capacity within a given sector depends on the quality and density of interactive relationships between producers, enterprise (market) and support services. The latter include public and private organizations which carry out research, train, advice, finance, coordinate and regulate. It is, therefore, an innovative network should encompass all direct and indirect actors from the point of production up to the point of consumption of the dairy products either as recipients of support or as lending support and service to ensure program success. The direct actors are rural traditional smallholder producers, improved market oriented dairy farmers and dairy cooperatives and Unions, milk collectors, small scale dairy processors, dairy input suppliers, commercial dairy farms, commercial dairy processors, retailers, consumers. Indirect actors and support/service providers are government offices at all levels, dairy and livestock development projects, Non Governmental Organization, Producers associations, professional associations, financial institutions are among the list. 



The authors greatly acknowledge the financial support of the OEAD-Gmbh (Austrian Agency for International Cooperation in Education and Research).


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Received 15 August 2012; Accepted 20 August 2012; Published 3 September 2012

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