Analysis of Social Networks with Focus on Ram Exchange in Four Communities in the Ethiopian Highland

Universität für Bodenkultur (University for Natural Resources and Life Sciences)

Department für nachhaltige Agrarsysteme (Department for Sustainable Agricultural Systems)

Institut für Nutztierwissenschaften (Division of Livestock Sciences)

Leiter (head): Univ. Prof. Dr. Christoph Winckler

Analysis of social networks with focus on ram exchange in four communities in the Ethiopian highland

(Soziale Netzwerkanalyse in vier Gemeinden im äthiopischen Hochland, unter besonderer Berücksichtigung von Zuchtbockaustausch)

Diplomarbeit ausgeführt zum Zweck der Erlangung des akademischen Grades einer Diplom Ingenieurin

vorgelegt von (submitted by) Marina Aigner

Betreuer (supervisors) PD Dr. Maria Wurzinger Univ. Prof. Johann Sölkner Dr. Aynalem Haile

Wien, January 2012

Contents

1. Index of tables...... 4

2. Index of images ...... 4

3. Index of graphs...... 5

4. Introduction ...... 7

4.1. Importance of livestock in Ethiopia...... 7

4.2. ICARDA-ILRI-BOKU-project ...... 9

5. 2 Literature review...... 10

6. 2.1 Social Network Analysis ...... 10 2.1.1 Idea and concept of Social Network analysis...... 10 6.1.1. Participatory wealth ranking ...... 16 6.1.2. Participatory resource mapping ...... 22

3.1 Description of the study areas...... 24 6.1.3. Menz...... 24 6.1.4. Horro...... 25

6.2. Data collection ...... 26 6.2.1. Interviews and Social Networks...... 26 6.2.2. Network properties ...... 27 6.2.3. Resource mapping...... 28

6.3. Data analysis...... 31

7. Results & Discussion ...... 32

7.1. General household information and family size ...... 32

7.2. Land holding...... 34 7.2.1. Private grazing land ...... 35 2

7.2.2. Communal grazing land ...... 35

7.3. Farming activity and land use pattern ...... 37

7.4. Livestock holding ...... 37

7.5. Different networks ...... 40 7.5.1. Iddir ...... 40 7.5.2. Debo ...... 42 7.5.3. Daddo/Wenfel...... 42 7.5.4. Dugdee...... 43

7.6. Wealth ranking...... 43

7.7. Resource mapping...... 46

7.8. Ram exchange...... 49 7.8.1. Ram management...... 50

7.9. Social networks...... 53 7.9.1. Ram lending...... 53 7.9.2. Ram borrowing ...... 58 7.9.3. Organizational structure of communal grazing areas...... 62 7.9.4. Relationships among community members...... 65 7.9.5. Network cooperation daddo...... 67 7.9.6. Network cooperation Iddir...... 70

8. Conclusions ...... 73

9. Summary ...... 74

10. Zusammenfassung...... 75

11. Glossary...... 77

12. References ...... 79

1. Index of tables

Table 1. Distribution of farmers across the study sites ...... 25

Table 2. Demographic and socio-economic characterization of the households ...... 32

Table 3. Land holding (ha) and land use system of the households...... 35

Table 4. Average flock size and composition of livestock in Menz and Horro area...... 38

Table 5 wealth status pattern of the four communities ...... 45

Table 6. Ram exchange behavior at Menz and Horro...... 48

Table 7. Number of breeding rams respectively wealth rank and age of farmers ...... 50

2. Index of images

Image 1. Map of Ethiopia, 1-Menz area, 2- Horro area ...... 23

Image 2. Resource mapping at Mehal Meda, Menz...... 29

Image 3. Resource mapping at Molale, Menz...... 29

Image 4. Resource mapping at Lakku, Horro...... 30

Image 5. Work cooperation Daddo for house construction...... 42

Image 6. Lakku farmer demonstrates the way how poor people sleep on andake ...... 44

3. Index of graphs

Graph 1. Resource map- Molale...... 46

Graph 2. Resource map, Mehal Meda...... 46

Graph 3. Resource mapping at Lakku ...... 47

Graph 4. Resource map Gitlo community...... 48

Graph 5. Ram lending at Gitlo, Horro area ...... 54

Graph 6. Ram lending at Lakku ...... 55

Graph 7. Ram lending at Molale, Menz area...... 56

Graph 8. Ram lending at Mehal Meda, Menz area...... 56

Graph 9. Ram borrowing at Gitlo...... 59

Graph 10. Ram borrowing at Lakku, Shambu...... 59

Graph 11. Ram borrowing at Mehal Meda...... 60

Graph 12. Ram borrowing at Molale...... 60

Graph 13. Grazing groups at Mehal Meda, Menz area ...... 62

Graph 14. Grazing groups at Molale, Menz area ...... 62

Graph 15. Grazing groups at Gitlo,Horro...... 63

Graph 16. Grazing groups at Lakku, Horro...... 63

Graph 17. Relationships among community members of Gitlo...... 64

Graph 18. Relationships among community members at Lakku...... 65

Graph 19. Relationships among community members of Mehal Meda...... 65

Graph 20. Relationship at Molale...... 66

Graph 21. Work group cooperation daddo at Mehal Meda, Menz area...... 67

Graph 22. Cooperation form daddo at Molale ...... 68

Graph 23. Cooperation form daddo at Lakku ...... 68

Graph 24. Cooperation form daddo at Gitlo ...... 69

Graph 25. Iddir cooperation at Gitlo, Horro area...... 70

Graph 26. Iddir at Mehal Meda, Menz area ...... 71

Graph 27. Iddir at Molale...... 72

Graph 28. Iddir, Lakku...... 73

4. Introduction

Ethiopia has the largest livestock population in Africa. Currently livestock ownership supports and sustains the livelihoods of an estimated 80 percent of the rural poor. There are approximately 51 million cattle, 39 million sheep and goats, 8.6 million equine, 1 million camels, and 55.4 million chickens in the country (FAOSTAT, 2009). In general, sheep are the predominant livestock in areas over 3000 metre above sea level (masl) and at altitude over 3500 masl farmers keep only sheep (Mengistu, 2008).

Given the country’s diverse topographic and climatic conditions, different breeds of animals evolved over time and adapted to the ecological conditions of their habitat. Ethiopia can be considered as a centre of diversity of animal genetic resources (Kebede and Yemane, 1992).

4.1. Importance of livestock in Ethiopia

Livestock are extremely important in Ethiopia for economic development and poverty reduction. From a holistic perspective livestock are more important in Ethiopia than in any other Sub-Saharan African country due to wide range of livestock species and their enormous populations, the dependence on livestock by a big proportion of the country’s 67 million, the livestock sectors contribution to national agricultural GDP and the total GDP. It is estimated that livestock contributes to the livelihoods of 60-70% of the Ethiopian population (Halderman, 2004).

Livestock carries out multiple functions in the Ethiopian economy by providing food, input for crop production and soil fertility management, raw material for industry, cash income as well as in promoting saving, fuel, social functions, and employment. Estimates show that the livestock sub-sector contributes 12-16% of the total and 30-35% of agricultural GDP, respectively (Halderman, 2004).

Small ruminants contribute significantly to the livelihood of resource-poor farmers in Ethiopia. However their productivity is in general low as a reflection of declining land 7

productivity and water scarcity that translates in feed scarcity, poor flock management and health, and inadequate use of animal genetic resources (ADA Proposal, 2006). In the Ethiopian highlands, small ruminants represent only 6.6% of the capital invested in farm livestock but provide 12.5% of the value of livestock products consumed on the farms and almost 50% of the cash income generated by livestock production (Kriesel and Lemma, 1989). Consequently, small ruminants in general and sheep in particular are important investments in the highlands of Ethiopia. The current level of on-farm productivity of the indigenous Ethiopian sheep genetic resources in the smallholder production systems is low (Tibbo, 2006).

Increased human population and urbanization have increased the demand for animal protein in the country and given place to opportunities for market-oriented productivity improvement. Therefore, missing genetic improvement programs represent a major constraint to achieve sustainable improvements.

Ethiopian indigenous sheep breeds can be as productive as exotic sheep, especially under conditions where environmental stress is high (Hassen et al., 2002; Tibbo, 2006). Studies in the indigenous Horro and Menz sheep breeds have revealed significant between- and within- breed variation for growth and survival that could be exploited to attain permanent improvements in productivity and income (Tibbo, 2006).

4.2. ICARDA-ILRI-BOKU-project

The project develops community-based breeding strategies for resource-poor sheep owners in the highlands of Ethiopia. Representative communities are actively involved in the project, from the definition of breeding goals and selection criteria to the identification of the most appropriate and acceptable strategy. Strategies are proposed to take full advantage of existing institutions and already available secondary information. An important element of the project was the understanding of institutional issues that can underpin successful institutional arrangements, from the production through to markets. To improve the ability of communities to manage the breeding programs, capacity building is embedded in the project.

Moreover, the project aims to develop decentralized and participatory breeding strategies for communities of sheep keepers. This strategy was flexible to adjust to the institutional arrangements and the communities’ capacity and ability to handle the breeding program. The project targets sustainable increases in agricultural productivity, by ensuring the better management of local genetic resources through farmer-participatory breeding, where breeding goals are defined by farmers.

Farmers’ competitiveness on markets increases through sustainable development in production and marketing systems, which contributes to better market supply in the country; hence to food security. Further aims are strengthening institutions and to foster policy development by improving rural institutions.

Four breeds at four different locations are involved in the project: Horro (Bako - Shambu areas), Menz (Debre Berhan or Mehal-Meda areas), Afar/Adal (Werer areas), Arsi (in the Mid-rift valleys around Adami-Tulu).

The project is led by ICARDA and BOKU in partnership with the International Livestock Research Institute (ILRI), and is implemented by institutions of the national programs, including Ethiopian Institute of Agricultural Research (EIAR), Oromiya Agricultural Research Institute (OARI) and the Amhara Regional Agricultural Research Institute (ARARI) at the 9

four locations of the project as well as the Ethiopian Ministry of Agriculture and Rural Development (MoARD).

The objective of this study is to analyze the social networks of four different communities for activities such as ram exchange both, lending and borrowing, grazing groups, relationships and local network co-operations (Iddir, Daddo). These networks are analyzed together with socio-economic information of farmers like age, sex, wealth, education status, religion and position in community. Based on these results and additional information on framework and organization of the local networks, importance and the idea of implementing social network analysis in sheep breeding will be discussed.

5. 2 Literature review

6. 2.1 Social Network Analysis

2.1.1 Idea and concept of Social Network analysis

A social network analysis (SNA) examines the structure of social relationships in a group to find out the informal connections between people. These relationships are often of communication, awareness, trust, and decision-making. SNA is used as an approach to look at these relationships (Ehrlich and Carboni, 2005).

Social network analysis has become famous for application in business problems. For example, in knowledge management and collaboration SNAs can help locate expertise, seed new communities of practice, develop cross-functional knowledge-sharing, and improve strategic decision-making across leadership teams. Furthermore, SNA can contribute to create innovative teams and facilitate to find out individuals, who are most likely exposed to new ideas (Ehrlich and Carboni, 2005).

Recently, SNA has also been applied in the field of rural development. For example, Hartwich et al (2007) analyzed in their study how knowledge management influences the behavior of farmers towards innovation. Through SNA the study approved that a multiple-agent knowledge management that involves public and private sector organizations and civil society advocates the adoption of innovations among farmers. Furthermore, one of the findings was the embeddedness of farmers in social networks determines the extent to which they adopt innovation. It was assumed that higher connected farmers would have higher levels of knowledge adoption (Hartwich et al., 2007).

Another study applied SNA to find out the role of the Bolivian government in guiding and managing the Bolivian Agricultural Technology System (SIBTA). The findings suggested that weak leadership and limited commitment have prevented the Bolivian government, particularly the Ministry of Agriculture, to operate more active in the SIBTA (Hartwich et al., 2007).

Another study identified patterns of social interaction among small farmers in the three agricultural subsectors in Bolivia- fish culture, peanut production, and quinoa production- and analyzed how social interaction influences farmers’ behavior towards the adoption of pro-poor innovations. The study tested a wide range of hypotheses on the impact that the embeddedness of farmers in social networks has on the intensity with which they adopt innovations. The study suggests that persuasion, social influence, and competition are significant influences in the decisions of farmers in poor rural regions in Bolivia to adopt innovations. Consequential, attention of policymakers and practitioners who are interested in the design and implementation of projects and programs fostering agricultural innovation and who may want to take into account the effects of social interaction and social capital, are meant to attract (Hartwich et al., 2008). Connectedness

An important property of a network is its connectedness. A graph is connected if there is a path between every pair of nodes in the graph. Thus, in a connected graph all pairs of nodes are reachable. In disconnected graphs the nodes can be partitioned into two or more subsets in

which there are no paths between the nodes in different subsets. The connected cohesions in a graph are called components. Particularly working co operations and grazing groups come up with several components within a graph (Wassermann and Faust, 1994).

Direction in graphs

A graph is directed if the relations are directed from one actor to another. A directed graph is represented in drawn form by attaching an arrow head to each line. The direction of the arrow indicates the direction of the relation (Scott, 1991).

Density

Network density describes the general level of linkage among the points in a graph. A ‘complete’ graph is one in which all the points are adjacent to one another, so that each point is connected directly to every other point. However, such completion is very rare, even in small networks. The concept of density is an attempt to summarize the overall distribution of lines in order to measure how far from this state of completion the graph is. The more points that are connected to one another, the more dense will the graph be (VisuaLyzer User Manual, 2007).

Centrality of actors

Actors’ centrality, in literature called point centrality, expresses the position of an actor in a network. For example, in a star-type network, the central located actor has the highest centrality, while in a chain-type network all actors are of the same centrality (Jansen, 2006).

There are different indices for point centrality measurement (Freeman, 1979).

1. Degree of actors- An actor is central if he has many relations.

2. Closeness of actors- Actors with small distances to other actors are central. This means, this actor does not depend on conveyance of information from other actors, and can deliver them to others without greater losses.

3. Betweenness of actors- Central is the actor who lies on the shortest connection paths of other dyads in the network. He can be used as middlemen and thus control many activities in the network

For this study the degree of actors (or node) has been considered. It tells us the total number of edges incident to the node and is important because it tells us the number of connections one farmer has to the others. In directed graphs, degree is computed in node in-degree, the number of edges received by the node, and the out- degree, the number of edges initiated by the node. Out degree indicates the role of an actor/node as a source of ties. It is expressed as the sum of the connections from the actor to others. Out- degree is usually a measure of how influential the actor may be. Usually the first set of actors has high out-degrees. In contrast, in-degree indicates the role of an actor/node as sinks or receivers of information. This is expressed as the sum of the connections to each node, in other words how many other actors send information or have ties to a specific actor of interest (VisuaLyzer user manual, 2007).

Another concept defining centrality within a network by Scott (1994) distinguishes between ‘local’ and ‘global’ centrality. A node is locally central if it has a large number of connections with the other points in its immediate environment, e.g. a large ‘neighborhood’ of direct contacts. On the other hand a point is globally central, when it has a position of strategic significance in the overall structure of the network. While local centrality is concerned with the relative prominence of a focal point in its neighborhood, global centrality concerns importance within the whole network. (Scott, 1991).

A measure of global centrality is based on the ‘closeness’ of the points. Global centrality is expressed in terms of the distances among the various points. Local centrality measures, whatever path distance is used, are expressed in terms of the distances among the various points. Hence, a point is globally central if it lies at short distances from many other points (Scott, 1991).

A simple way to measure point centrality is by the degrees of the various points in the graph. The degree is simply the number of other points to which a point is adjacent. A point is central if it has a high degree and hence, considered as well-connected within its local environment. In all directed graphs, each point will have two measures of its local centrality, one corresponding to its in degree and the other to its out degree. In-degree level is also considered as prestige level of an actor as it indicates the number of ties sending information to a specific actor of interest. The out-degree indicates the role of an actor/node as a source of ties and expresses the sum of connections from the actor to others. Out-degree is usually a measure of how influential the actor may be (Visualyzer user manual, 2007).

Centralization/Graph centrality The difference to point centrality is that graph centrality refers to particular properties of the graph structure as a whole. Therefore, it is not about relative prominence of points, but to the overall cohesion or integration of the graph. Graphs may be more or less centralized around particular points or sets of points (Scott, 1994).

To describe the centrality of networks, Freeman (1979) developed three concepts, based on the concepts of centrality of actors.

1. Degree based centralization - finds out if one actor of the network participates extraordinary in the direct relations of actors in the network. This can be the case in a star-type network, where centrality is highest. In a reciprocal network, centralization is lowest (0%).

2. Closeness based centralization - additionally to the difference of direct participation of actors in relations of the network, indirect participation is considered. It counts as measurement of possible independence and efficiency of actors in a network. Different to the previous concept of degree based centralization; normalized degrees are required.

3. Betweenness based centralization – degree of monopolization of information- and control of resources through outstanding central actors (Jansen, 2006).

Over all network degree centralization measures the extent to which a single actor, in this case farmer, has high centrality, and the others low centrality. The larger the centralization score, the more likely it is that a farmer is quite central with the remaining actors considerably less central, and probably at the periphery of the network. It expresses how unequal, variable or heterogeneous the actor centralities are in the network. Degree centralization is expressed as a percentage and a star-type network centralization achieves 100 %, while an all to all connected network centralization result in 0 %. In other words, degree centrality shows us the level of farmers’ integration into the network (VisuaLyzer user manual, 2007).

Standardized or normalized indices allow comparison of degree centrality scores across other networks of different sizes. Normalized degree scores are a ratio of the degree (out- or in- degree) to the number of actors in the network less one, expressed as a percentage. By the use of this figure networks of different sizes can be compared (VisuaLyzer user manual, 2007).

Cutpoints

Cutpoints are nodes, whose deletion would increase the number of components in a graph. The graph may contain many, just one, or even no cutpoints at all (Scott, 1991).

Bridge

A bridge behaves like a cutpoint, but is a line such that the graph containing the line has fewer components than the subgraph that is obtained when removing the line (Scott, 1991).

Opinion leader

The software ‘VisuaLyzer’ contains a function called opinion leaders. They have been recognized as critical elements in the diffusion process, largely because of their ability to reach and diffuse ideas, information or practices to their followers. The number of opinion leaders a network has, is defined by the in-degree of the nodes. Nodes with the highest in- degree values are nominated opinion leaders. The output of the opinion leaders procedure is

the list of leaders and a list of group members for each leader together with additional parameters showing selection reasons and sorting order (VisuaLyzer user manual, 2007).

Subgroups/cliques

Network substructures such as cliques and subgroups, are important in network analysis because they enable an analyst to gain a micro-view of the network, showing the various groupings that may be present in a network and the implications of the sub-group configuration for behavior of members (VisuaLyzer user manual, 2007).

Particularly in the graphs of grazing groups and community co operations, clustering provides insight into the number of communal grazing lands or number of ‘Iddir’ networks.

In all demonstrated graphs enabled nodes are illustrated yellow. Red nodes indicate cutpoints and green nodes opinion leaders.

6.1.1. Participatory wealth ranking

Participatory wealth ranking is one technique of the participatory rural appraisal (PRA) methodology, which is for interacting with farmers, understanding them and learning from them (Mukherjee, 1993). Wealth ranking is one tool of participatory research to measure the wealth status of different persons of one community. Participatory methods like wealth ranking were traditionally preferred mainly by sociologists and development practitioners. Now it is commonly used in combination with other more formal methods, particularly in poverty studies which focus on understanding of rural livelihoods in developing countries (Kebede, 2007). This is shown in a study about villages in Bangladesh where the accuracy of different poverty assessment methods has been compared. The method achieved a high accuracy level if its scores are calibrated at community level and therefore can be used as an adequate poverty targeting method for development policies and projects (Zeller et al., 2006).

Wealth ranking it is used for targeting poorer groups for specific activities and in monitoring the impact of aid distribution. It can also be applied to identify questions for focused research on specific constraints of different groups. Moreover, it demonstrates the understanding of

local criteria of wealth and well-being and of changes in wealth (Mikkelson, 2005). In a study of Braganca (1994), wealth ranking was applied to reveal the extent of poverty of farmers in an area of Mozambique. It helped to comprehend and identify how farmers perceive wealth, especially in relation to farm size and number of animals owned (RRA notes, 1994).

Wealth ranking was also accomplished to understand local perceptions of wealth and to produce a simple wealth classification of households in each brigade (Mearns et al., 1992). Wealth ranking is a very good method to explore the diverse range of circumstances and to begin to understand the different needs and priorities of poorer and better-off households’ (RRA Notes, 1994). Additionally, wealth ranking can be used in studies on privatization, as in a concrete example of a policy research and training project about policy alternatives for livestock development in Mongolia. The intention was to find out the influence of current privatization of the economy on herding households. The central research question was whether ‘wealth’ differentials between households took on other forms under the new system, such as gaining access to valued grazing areas for some households in key moments. Hence, wealth ranking provided some baseline information about wealth, status and power (Mikkelson, 2005).

Wealth ranking can also be applied for analysing factors which influence smallholder farmers’ market participation. Using this analytical tool, more wealth-ranking factors that influence market participation as well as strategies for improving this participation can be found out. Additionally, the way a cash crop development project affects a household’s wealth status could be analyzed (Green et al., 2006).

Wealth ranking has also been used for livelihood analysis by Swathi Lekshmi (2008) in India. Differences have been noticed in the size of land holding and number of livestock farmers possessed. It is recognized that wealthy farmers have differential access to land, labour, animal inputs and credit. In addition, wealthier farmers have different attitudes to risk and innovation than poorer ones (Cancian, 1978).

In smallholder communities, wealth is not only an economic aspect, but it correlates with social and political attributes. It has been long observed by social anthropologists that villagers take a passionate interest in the relative household standard of living and do their own subconscious rankings. As poverty measuring has become crucial for reducing poverty, the wealth status has gained importance in development work. Poverty cannot only be measured by income. ‘Multiple parameters on poverty and well-being originating from theoretical and empirical studies are now common, including for example entitlement and capabilities, assets, dignity and autonomy (Baulch, 1996), as well as a variety of context- specific parameters identified by poor people themselves in concrete participatory poverty studies ‘(Mikkelson, 2005).

In every human society exists some sort of inequality which is based on attributes like religion, race, ethnic group, caste and wealth or economic status which apply to family units as well as individuals. Additionally, age and gender are individual attributes which can also form the basis of social and economic inequality. ‘Wealth is defined in terms of access to or control over important economic resources (Grandin, 1988). Higher income can be considered as wealth, but rather as an indicator of wealth than themselves constituting wealth. ‘Wealth inequality is found in virtually every human community and is among the most important characteristics that differentiate people within a community. As the nature of economic resources varies from community to community, so do the specific defining characteristics of wealth Grandin (1988).

Concerning agricultural production, wealth has an effect on factors like the availability of labour, money for input purchase or for investments, which is often in form of livestock, and savings. Also the amount of cropping, type of crops cultivated and finally their use, whether consumed at home or sold, are all expected to differ with wealth status. Regarding livestock production, wealth affects numbers and species of animals hold, as well as the management and purpose of use. Farmers of different wealth have diverse necessities and problems and their ability to adopt technologies differ a lot. In addition, wealth status often defines the quality of the family’s physical life. For example, the type of housing, access to clean water, 18

access to medical care, diet, workload and level of education vary considerably. These material differences in life have effect on mental and spiritual well-being (Grandin, 1988).

Interviewed farmers, must be representatives of different wealth status such as rich, medium and poor ones. Planning for the ‘average’ farmer or household would be ineffective. There are some difficulties to find those members of a community which can be assumed as representatives for conducting a survey of wealth ranking, because it is hard to reach poor farmers or identifying them. This is due to the difficulties of obtaining information on the wealth of smallholders, because they are often indisposed to provide information on their land holdings, off-farm income and livestock sales. Poor people are usually less involved in community affairs (Grandin, 1988).

For choosing a ‘target area’, information on surface area, the human and livestock population should be gathered. Besides, differences between community because of distance to towns, markets, availability of roads, existence of farmers’ groups, current or past development programmes, population density, size of land holdings and the age of settlement, have to be considered. Areas of different ethnic groups, which prevail in different villages, can be affected in their social structure and agricultural production. The community and its boundaries should be defined by electing the unit, which depend on the number of households it contains. A local concept of wealth ensures the comparability of the data obtained from various informants. Moreover, it makes certain that households are ranked according to the criteria the researcher desires. It is essential to understand the major components of the local wealth concept, e.g. land holdings, wage employment, livestock holdings. Another important fact is to define the household (Grandin, 1988).

For the accomplishment of the ranking it is important to find several reliable local informants, who are long-standing members of the community and generally knowledgeable and honest. It is useful to identify informants from different socio-economic groups who represent a cross- section of the community (Grandin, 1988).

Usually in wealth ranking farmers agree easily, so that only 3-5 informants are necessary. Informants are only asked to group people, and not to provide any sensitive information like amount of land or number of cattle (Grandin, 1988).

A study by Barbara Grandin (1983) discusses the importance of wealth differences between producers in traditional pastoral production systems. It confirms that significant wealth differences exist and that these have a profound effect on production strategies. Additionally, it says that pastoral systems research has to pay attention to them at several stages from defining a target population or recommendation domain to developing and testing interventions (Grandin, 1983).

As with wealth ranking, the indication of variables, which go beyond income, is possible to measure poverty, the poverty line no longer serves as an effective policy tool to reflect the complexities and field realities of poverty. Therefore, it is an essential tool to bridge the gap between the official poverty line and the villagers’ description of poverty (Grandin, 1988).

Barbara Grandin (1988) developed wealth ranking by card sorting during work with pastoral communities in Kenya. There, two aspects of wealth ranking were important. One is the nature of wealth, status and power differences between households. The other is the way key issues emerged in relation to important production constraints. According to the second important aspect of wealth ranking is the way key issues emerged relating to production constraints (Grandin, 1988).

In a study of Hargreaves et al. (2005), a mixed-methods approach to participatory wealth ranking to identify the number of poor households in eight villages of rural South Africa and describe how poor they are. It presents a novel approach to wealth ranking that generates a rich appraisal of poverty (Hargreaves et al., 2005).

Applications of wealth ranking together with household surveys have been approached among farming households in Southern Zimbabwe by Ian Scoones (1994). The ranking was highly correlated with livestock ownership, farm asset holdings, crop harvests and crop sales. The method indicates relative wealth and it can be used complementary to surveys. Additionally, qualitative discussions during ranking exercises tell details of the historically, socially and economically constructed understandings of wealth and well-being of different actors (Scoones, 1994).

Factors like land ownership and livestock holding are routinely mentioned. First, there is given some economic value to resources. But also the cultural and symbolic value has to be considered. For example, additionally to the important sources of draft power, meat and milk, livestock in many communities is also a status symbol. Some religious and other beliefs may attach a value to resources that are not directly related to their economic value. Furthermore, the abundance of resources will most likely affect the given weight. Usually visibility of resources influences decisions of people doing wealth ranking. For example, livestock ownership is more visible than income or savings. Therefore, higher weight is expected for resources that are more visible. Also ‘cultural value’ of resources is expected to be positively affected by visibility. To be socially important, resources should be visible to other members of the community. This paper argues that the relative visibility of resources can help us understand the weights given to different types of resources in wealth ranking exercises. It is an analytical framework which was examined by using data collected from rural areas of four eastern Africa countries. It helps to a better understanding of the economic and ‘cultural’ values people attach to different resources. In addition, it can help as contribution to combine qualitative and quantitative methods of analysis and assist to understand how communities value different types of resources (Kebede, 2007).

6.1.2. Participatory resource mapping

Participatory resource mapping is a rich tool and is commonly used in Participatory Rural Appraisal (PRA). Maps by villagers provide a basis for knowing different aspects of a village,

for instance, the location of dwellings and buildings and land-use patterns as indicated by a social map showing the position of households in a village and their socio-economic characteristics. Also different kinds of natural resources and micro environments can be illustrated by a map. Farmers of villagers draw the resource profile of the village depicting different kinds of soils, rivers, ponds, trees, crops and micro environments. The problems can be described and discussed on the basis of a resource map along with the opportunities and the constraints (Mukherjee, 1993).

One kind of ‘resource mapping’ is the village resource map, which is a tool that helps us to learn about a community and its resource base. It is applied to get useful information about local perceptions of resources. A map of the area, containing all important resources, should be developed by the participants. The objective of this PRA tool is to learn about the villagers’ perception of the natural resources found in the community and how they are used. Mearns (1992) used participatory mapping with farmers in Mongolia to identify grazing and other key resources (Mearns, 1992).

Resource mapping has been used to identify resource clusters in a study in Zimbabwe. The maps provided a useful visual indication of resource clusters. It illustrated that information is communicated in a visual way, that illiterate people are able to understand and which enables them to contribute their knowledge. The mapping provided an opportunity for some local people to demonstrate their skills, as the woman who made the model windmill, which can build their confidence to participate (Hira et al., 2004).

Another interesting study where participatory methods are applied is using participatory epidemiology to assess the impact of livestock disease in Kenya. Since recent time, epidemiologists adapted methods of participatory rural appraisal to improve understanding of livestock diseases in resource-poor settings and in areas where conventional methods are difficult to use (Catley et al., 2003).

Participatory research mapping can also be a method for documenting the subsistence lands recognition of their land rights as used by indigenous populations in Panama. Community representatives were trained to complete land-use assessments using questionnaires and sketch maps. Together with specialists, this information has been transformed into standard cartographic and demographic results. As a result the first large-scale mapping of indigenous lands in this little-known region has been designed. The study demonstrates how indigenous people can work with researchers in data collection and interpretation to transform their cognitive knowledge into standard forms for producing excellent scientific and applied results while enhancing their ability to manage their own lands (Herlihy, 2003).

Additionally to the general socio-economic household characteristics, land- and livestock holding, grazing management, work groups and co operations, breeding ram management and institutional arrangements, the research methods, wealth ranking and resource mapping, provided information about wealth status, location of homesteads and private and communal resources. Based on that information, the study tried to find out the reasons for involvement in breeding ram exchange of farmers.

3. Materials and methods

3.1 Description of the study areas

The two study areas Horro and Menz districts in central and Western Ethiopia. Menz represents a mixed crop-livestock system in the high altitude areas and is home to the Menz breed. The Horro district is located in the medium to high altitude areas and the Horro breed is the target breed.

1 2

Image 1. Map of Ethiopia, 1-Menz area, 2- Horro area

6.1.3. Menz

The area is located at an altitude of 2800 meters above sea level and about 280 km north of Addis Ababa (Mengistu, 2008). The rainfall is bi-modal with a main rainy season from June to September, and an erratic unreliable short rainy season from February to March. According to the meteorological data from Debre Berhan Agricultural Research Centre from the years of 1985 to 2005, the annual precipitation at Mehal Meda town was about 900 mm and the

minimum and maximum average temperatures were 6.8°C and 17.6°C, respectively. The highland parts of Menz are the main habitat of Menz sheep with a potential for sheep production (MOA, 1998; Abebe, 1999).

About 98% of the population in the area depends on agriculture. The farming system in Menz area is largely characterized by mixed crop-livestock production system. Due to severe frost, unreliable rainfall and poor soil fertility, crop production is limited. Hence, the area is characterized as one of the drought prone areas of the Amhara National Regional State (Mengistu, 2008).

Two locations, Mehal Meda and Molale, have been involved in this study. Mehal Meda is located at 300 km from Addis Ababa but at altitudes ranging between 2700- 3300 m.a.s.l. The production system is largely characterised by mixed crop-livestock system, but is limited to sheep-barley system in very high altitude areas where extreme temperature limits other crops. Sheep are mainly raised on natural pasture with no supplementation. They graze them largely on communal grazing land but this has recently been diminishing. There is no formal controlled breeding. The main products from these animals are mutton and course wool for carpet and blanket industry (Mengistu, 2008). There is one research centre called Debre Berhan Agricultural Research Centre and two government ranches, namely Debre Berhan and Amed Guya, devoted to improvement of the breed. The crossbreeding programme of the later failed due to poor planning and disease introduction risk associated with exotic breed importations (Tibbo, 2006).

6.1.4. Horro

The second area is called Shambu in Horro district of Horro Guduru Wollega zone of Oromia regional state. The survey was conducted with the communities of the two sites Gittlo and Lakku. Shambu area is located in the western Ethiopian mid-highland region at an altitude from 1600 to 2500 masl. The average annual rainfall over the period of 1970–2003 was 1823 mm. About 80% of the annual rain falls between May and September (Tefera and Sterk, 2008). 25

In the small town Bako, 250 km from the capital, an Agricultural Research Centre, which has long-year research experience with the breed, is located. Shambu is closer to the epicentre of the Horro sheep breed origin. Briefly the Horro sheep is fat-tailed hair-type sheep with bigger growth potential compared with other indigenous breeds in Ethiopia (DAGRIS, 2009).

6.2. Data collection

6.2.1. Interviews and Social Networks

Data were generated by administrating a structured questionnaire (see ANNEX 1). In total, 231 farmers (Table 1) have been interviewed to collect information on general socio- economic household characteristics, land- and livestock holding, grazing management, work groups and cooperations, breeding ram management and institutional arrangements.

Table 1. Distribution of farmers across the study sites

Location Farmers (n)

Mehal Meda (Menz) 57

Molale (Menz) 58

Lakku (Horro) 59

Gittlo (Horro) 57

Total 231

To establish the matrices for the social network analysis, the following questions have been asked to each member of the communities.

• To whom did you borrow a ram in the last three years?

• From whom did you receive a ram in the last three years?

• Whose flock grazes together along with your flock?

• To which community members are you related; what kind of relationship (e.g. blood, in law)?

• With whom did you collaborate in the form of daddo in the last three years?

• Who from the community are your Iddir mates?

Based on this information, the social networks had been analyzed by different concepts of SNA. Moreover, group discussions were held with chairmen of Iddir and community members about definition, organization and by-laws of Iddir. By means of workshops wealth ranking and resource mapping have been conducted with informants of the community. Wealth ranking was conducted with several informant people. Criteria were set to identify community members in to rich, medium and poor for the ranking. The resource mapping has been conducted with 5 to 6 informants who know well the area and the homesteads of the community members. A map of the area, including all homesteads, the private and communal grazing areas, water points for livestock as well as for human, forest resources and infrastructure like roads, school and church, was plotted by informants. The material used for the resource mapping was a sheet of paper put on the ground and some pencils.

All information received from farmers has been translated by local extension personnel. Data from secondary sources on linkages of breeding ram exchange, grazing group formation, different forms of relationship among the community, relations of working co operation Iddir and Daddo was also collected and compiled.

6.2.2. Network properties

The number of nodes in a network informs about total nodes and enabled nodes. A low number of nodes means a low participation in ram exchange. Enabled nodes are those included into the network. Isolated nodes are in the graph but not connected at all to other nodes in the network. A dyad consists of a linked pair of actors and is the most basic level within a network. Dyads found in the graphs of ram exchange are caused by location and relationship.. (Wassermann and Faust, 1994)

Most social network applications focus on collections actors that are all of the same type, which is called one-mode network, since all actors come from one set like in this study all actors are farmers. There are also types of structural variables that are measured on two or even more sets of entities. For example, actors from two different sets, one set consisting of corporations and a second set consisting of non-profit organizations. In this case, flows of financial support from corporations to non-profit actors can be measured. A network dataset containing two sets of actors is referred to as a two-mode network (Wassermann and Faust, 1994).

6.2.3. Resource mapping

In Mehal Meda, the informants were aware of the requirements for resource mapping, because they were already experienced through other projects from the past. One informant started quickly to draw roads and rivers by discussing with other farmers. Then other informants started to sign in some homesteads and the names of the members who live there. Small villages and the two sites became visible very quickly. The school as well as the fence where the data sampling occurred, was an important orientation point for continuing the map. After indicating all homesteads, they started to plot private and communal grazing areas. To complete the map, the watering points for livestock and human, as well as forest resources has been drawn. In general, the map has been drawn quite quickly and flawless.

In Molale the informants are not experienced with such tools at all. Therefore, they had some problems at the beginning, how the map should look like and how to start with it. But when we showed them the map of Mehal Meda, it became clearer for them and they started drawing some homesteads. More and more small villages have been created and rivers, roads, school, mills, a fishpond and a nursery have been signed in. Ensuing, water points and communal grazing land have been pointed out. Finally, the map has been completed by indicating the distances between the villages. In the end the informants notified that the forest resources are mainly around the homesteads, and drew in some along the road. Compared to the first community, it took them longer in Molale to create the resource map due to lack of

experience. Nevertheless the result has been clearly represented and figured out well. Also the farmers themselves were very happy and proud with the finished map of their area.

In Horro, while explaining resource mapping, farmers were listening very carefully. First they started with Shambu as orientation point. Then the road from Lakku towards Shambu has been drawn; followed by a church and another building which was a NGO. After that they continued to sketch in one communal grazing land and roads which lead to small villages. Homesteads followed including names of household heads and the roads leading to them. The old man, who had been very dynamic in the wealth ranking, was not actively participating in resource mapping. Mainly two middle-aged informants were creating the map. One of them was commanding all locations and names to the other drawing informant. After finishing the homesteads, they continued with communal grazing areas. Additionally all rivers which serve as water points for animals has been plotted as well as the developing agent office, grain mills and tab water points.

At Gittlo, resource mapping has been conducted in the area including all homesteads very well. As orientation he plotted in Shambu town first. As next step he illustrated the extension office and the homesteads around there, followed by roads and rivers. By adding communal grazing areas, forest resources and water points he completed the map. The informant created a map clearly representing the area.

Image 2. Resource mapping at Mehal Meda, Menz

Image 3. Resource mapping at Molale, Menz 30

Image 4. Resource mapping at Lakku, Horro

6.3. Data analysis

For characterizing the four communities regarding socio-economic household information, land- and livestock holding, descriptive and the General Linear Model (GLM) procedures of SAS (SAS, 2003). Further evaluation of grazing management, co operations and breeding ram management, categorical model analysis were applied.

Social network analysis has been carried out with the software ‘VisuaLyzer’ on following activities: ram exchange behavior, grazing groups, relationships and co operations of communities. Additionally, the results of the SNA have been compared with the resource map created by each community.

7. Results & Discussion

7.1. General household information and family size

Two hundred and thirty-one (115 small holder farmers in Menz area and 116 farmers in Horro area) were interviewed for the household survey. Family size, sex, education background and age structure of the respondents are presented in Table 2.

Family size varies among Menz and Horro communities and is on average higher at Horro. The age of respondents ranged from 18 to 80 years with a mean of 37.7 years for Menz and ranged from 20 to 80 years with a mean age of 41.6 years for Horro district. The survey revealed that the majority of the households in both production systems were headed by males which accounted for 84.3% in Menz and 88.8% in Horro district. The remaining proportion of the households was headed by females. Similar to Mengistu (2008) description, female headed household would indicate either the husband has died or they are divorced.

As the survey showed, 38.3% of household heads in Menz were literate (grade 4-12), 40% were able to read and write either from religious school or from adult education and the remaining 21.7% of the smallholders farmers in Menz area were illiterate. This results differ from Mengistu (2008) in education level ‘reading and writing’ (30.8%) and ‘illiterate’ (33.3%). Almost half of the farmers at Horro (48.3%) were literates, 27.6% of them were able to read and write and 23.3% were illiterate. In contrast to this report, higher proportion of illiterate (59.7%) and lower level of primary and secondary attendants (21.7% and 5.4 %) were reported in southern Ethiopia (Takele, 2006).

The majority of farmers in Menz are between 30 and 50 years old, whereas at Horro the majority of farmers are above fifty. Mean (standard deviation) age of household head at Menz and Horro was 42.4 (13.50) and 45.5 (15.13) years, respectively.

Table 2. Demographic and socio-economic characterization of the households

Descriptor Menz Horro Overall

n=115 n=116 n=231

Mean±SD Mean±SD Mean±SD

Family Size 5.4±1.97 6.5±2.71 6±2.42

Percent Percent Percent

Sex of household head

Male 84.3 88.8 86.6

Female 15,7 11,2 13.4

Education Level

Illiterate 21.7 23.3 22.5

Reading and Writing 40 27.6 33.8

Literate 38.3 48.3 43.3

Age

<30 years 25.2 21.4 23.3

30- <50 years 45.2 39.4 41.1

>50 years 29.6 40.2 34.2

Illiterate=unable to read and write, Literate= having formal education of grade 5 or above

At Menz sheep production is the main income source as the harsh environment limits crop production. Therefore farmers are very interested in improving their sheep production through breeding ram selection and exchange.

Due to regional and cultural aspects, in Menz, all farmers are ‘orthodox Christians’ and 9 farmers at Mehal Meda community take in the position of priest. 25% of the community take administrative or leadership positions in the community such as chairman, secretary, auditor or cashier of the Kebele and Iddir. At Horro the majority of farmers are protestant (45.13%) followed by orthodox (42.48%), Waqefata (6.19%) and catholic (6.19%).

A considerable number of 48.28% is involved in an administrative or leadership positions in Horro communities.

7.2. Land holding

In both areas farming is dominated by mixed crop-livestock system. Individual sheep owners bring their small flocks to communal grazing land or tend them nearby marginal areas next to their neighbouring farms. Supplementation is rare except in case where farmers plan to fatten their castrated rams. There is no formal controlled breeding, which is wasted opportunity for improvement within breed.

Due to topographical reasons, crop production is the main income source at Horro area, therefore compared to Menz less value and importance is given to sheep production. Furthermore, farmers never received any service such as training or support to create awareness or improve their production.

The two types of grazing lands, private and communal are common at both areas. It was mentioned that both private and communal grazing lands are limited in terms of size. The number of households using a given plot of communal grazing area differs from village to village. For example, at Menz starting from 2 households up to 40 households graze their sheep together at communal grazing land. Among Horro communities, 2 up to 3 Kebele use one communal grazing land. The access right to a given grazing area is determined both by government bodies and respective community members.

7.2.1. Private grazing land

This is the most protected and well managed pasture land compared to the other two. It is commonly used for milking cows and oxen. Sheep and horses do not have access to such type of land indicating that sheep have been ignored even though they are considered to be the most important species for immediate income generation in the area. Some participants confessed that the lower attention given to sheep was due to lack of awareness. Every household has its own grazing land though the size may vary from household to household. It ranges from 0.06 ha up to 1.25 ha in Menz, and from 0.05 up to 2 ha at Horro.

Private grazing land is better managed than the communal due to single ownership and less grazing pressure because it gets rest during the rainy season. Private grazing land is from this plot of land that farmers harvest grasses and prepare hay for later use in the dry season. It is also protected from erosion by preparing dike and irken. It is used for grazing early in the morning and in the afternoon. The private grazing land is protected from being overgrazed during the wet season so as to allow growth of grass as it is mostly found in the upland areas because it will not recover if overgrazed during this time. Private grazing land ownership was more reflected in Menz (94.78%) as compared to Horro which was practiced in 48.28% of the respondents.

7.2.2. Communal grazing land

The number of households accessing a given communal grazing area varies from village to village ranging from 2 to 40 households in Menz and 2 to more than 100 households in Horro district. It is where every species of livestock are kept during the day. It is solely utilized during rainy season (from June to August). No kind of protection and development are undertaken. It was emphasized that animal fight, diseases transmission, uncontrolled mating, overgrazing due to competition, and lack of responsibilities are some of the major problems in wise utilization of the communal grazing lands. Currently, the sizes of most communal grazing areas are shrinking due to several reasons. One major problem is that the land is usually encroached by neighboring households possessing farm lands adjacent to it. By time

more and more grazing land gets cultivated for crop production. Especially newly emerging landless households put additional pressure on it. Some farmers privately plant trees upon it and later claim it as their own holdings.

The communal grazing lands are usually located near water banks that are marshy. Overstocking and trampling during the wet season (July to September) cause muddying of the land, which leads later on to a decline in its productivity. The major reason for its deterioration was reported as lack of legal protection and wise utilization by the users. Diseases like liver fluke cause liver and lung problems when sheep graze the wet communal grazing areas. Currently, the communal grazing areas generally serve as a mere holding area and the animals practically obtain no grass from it. The substantial number of farmers (35.24% in Menz and 32.60% in Horro) claimed that grazing land is not sufficient.

Average land holding of farmers in Menz area was 1.40 hectare, whereas it is 2.39 in Horro. Considering the figures in Table 3 a significant part of the total land was used for grazing.

Table 3. Land holding (ha) and land use system of the households

Land holding (ha) Menz Horro Overall

Total land 1.40 ± 0.61 2.39 ± 2.77 1.85 ± 1.47

Crop land 0.96 ± 0.40 2.34 ± 2.11 1.58 ± 1.25

Private grazing land 0.51 ± 0.28 0.91 ± 0.53 0.65 ± 0.37

Communal grazing 1.10 ± 0.81 6.14 ± 12.08 3.50 ± 6.42 land

Land rent (%) 0.00 26.70

In Menz area all farmers owned all the land they used, whereas at Horro 26.70% of the households cultivated or use others land on grain share basis which they call qit’e.

Communal grazing is practiced at both areas by a majority (93.51%) of respondents. More than half of the farmers named communal grazing problems. Menz farmers indicated overgrazing (29.57%), poor performance (23.48%) - due to degradation, and shortage of communal grazing land as the main problems for sheep production. 67.83% of Menz and 35.34% of Horro farmers indicated that grazing land is not sufficient. It was said that recurrent drought has become a common phenomenon and both grazing areas could not support their animals.

Because of intensive land cultivation at Horro, communal grazing lands comprised mostly of the swampy bottom lands. These areas are characterized by slow grass growth during the wet season, because of water logging, and harbor snail which is host for liver fluke. All farmers use communal grazing land in an average of 1.1±0.81ha per household.

In Menz area, farmers grow wheat, barley, bean, pea, lentil, chick pea, grass pea and rarely linseed and fenugreek. However, wheat, barley and bean are the main crops during the main rainy season in their order with area coverage of 37%, 34% and 21%, respectively. Barley is the major crop and covers about 74% of the area followed by wheat and lentil with coverage of 14% and 12%, respectively (Mengistu, 2008).

7.3. Farming activity and land use pattern

Compared to Menz, at Horro a majority of 33.33% ranked crop production as their first income source. 13.42% evaluated livestock and crop production as equally important. Only 3.46% ranked livestock production as first farming activity. The average land holding at Horro is 2.4 ha which is basically used for cropping as it is the main income source there. 7.36% are landless farmers and a considerable percentage of 26.72% are qit’e users. Basically elderly farmers with larger property provide land in form of rent for landless households.

7.4. Livestock holding

Menz and Horro farmers hold cattle, sheep, donkey, horse, mule, goat and poultry along with crop production, but in different proportions. Average flock size and livestock composition are presented in Table 3. Total livestock holding per household among crop-livestock system 37

in Horro area was higher (30.92 livestock/household) than smallholder farmers in Menz highlands (26.54 livestock/household).

Sheep was the predominant species in Menz and Horro district, 17.8 and 13.9 respectively. The dominancy of sheep composition followed by cattle recorded in Menz area was in agreement with previous study (Mengistu, 2008). Sheep flock size was 17.78 (range of 2 to 57) in Menz and 13.91 (range of 2 to 60) in Horro. The predominance of sheep in Menz might be because of the fact that highly degraded areas could not support crop production as well as maintain larger animals like cattle. There are more horses found at Horro (3.09), whereas at Menz almost 87% of the farmers posses donkeys, on average 1.18.

In sheep holding significant difference came up in wealth status between rich and poor or medium farmers (p>0.0001). In terms of gender there is no difference between male and female headed households in keeping sheep. Similarly education level and age does not determine the number of sheep.

Cattle holding at Horro is by far higher than at Menz (8.2 vs. 3.72). This is due to a favored environment and feeding conditions in Horro area. Regarding gender, male headed households posses higher number of cattle (7.08) than female headed (5.66). Educational background also influences cattle holding significantly, as literate farmers keep a higher number than illiterate or farmers with reading and writing knowledge do. The study verifies that age also effects as farmers above 50 years in average keep a higher number (8.32) than farmers below 50 years do.

Table 4. Average flock size and composition of livestock in Menz and Horro area

Site and species N Mean flock SD Range

size

Menz 1.93

Cattle 115 3.72 10.65 0-16

Sheep 115 17.78 0.53 2-57

Goat 115 0.14 0.53 0-3

Horse 115 0.25 0.67 0-2

Donkey 115 1.18 0.27 0-3

Mule 115 0.07 2.25 0-1

Chicken 115 3.40 0-12

Horro 10.19

Cattle 116 8.19 14.70 0-45

Sheep 116 13.91 3.94 2-60

Goat 115 0.96 3.60 0-15

Horse 115 3.09 0.66 0-13

donkey 115 0.09 0.32 0-4

Mule 115 0.05 7.30 0-1

chicken 116 4.63 0-22

N = number of observation, SD = standard deviation

7.5. Different networks

There are different kinds of co-operations found in the countryside of Ethiopia but as well in the cities. Farmers form co-operations based on location and time period of support. The most important cooperation, where usually all people participate is called Iddir.

7.5.1. Iddir

Iddir is an organization of people, based on the idea to help and support each other in difficult times. Everybody who has family is active in Iddir and usually men are considered as members. Participation is voluntarily and most Iddir activities are social ones. For example if somebody dies, Iddir members perform the funeral ceremony, prepare food for the time when family, relatives and Iddir members grieve together in the home of the deceased. At Gitlo, Iddir members stay for seven days together with the family to mourn with them. Additionally, each member has to contribute grain and money to victims’ family. Furthermore, Iddir members will maintain the house of the deceased member and support his family in ploughing during cropping season.

If someone gets sick, Iddir members can bring him to hospital or lend money for treatment without interest, except for Mehal Meda where interest rate is 3% per month. Moreover, Iddir supports poor members without oxen or if ones’ oxen died, in providing them one in ploughing his land. In case of house fire, Iddir members contribute grain for the victim. If theft occurs, Iddir forces people to contribute grain or domestic fuel.

Access grain is given on loan basis if somebody needs and will be repaid afterwards, in some cases even with interests. For example, somebody borrow a certain amount of grain in May and return it after harvesting period. Member fee for Iddir varies among regions and communities. At Lakku members contribute one ETB (Ethiopian Birr) (0.06€, 01.06.2010) per month. In case of house burnt or theft, Iddir contributes additionally grain so that this person receives 10 kg of grain which can be wheat, tef or bean, from each member. At Gittlo the contribution fee for Iddir is 0.5 ETB (0.03€, 01.06.2010) per month for each member. It is used to help sick members, to purchase material, coffee for funerals, tala, bench, furniture and tent. 40

At Mehal Meda contribution is about three ETB per month (0.18€, 01.06.2010) three birr per year, and 3 kg of wheat or beans per year, which must be submitted till January 29 of Ethiopian calendar. Additionally, there is penalty in form of money and grain if somebody fails to contribute to Iddir.

Iddir is well-regulated by means of by laws, which can be endorsed by the court like in case of Mehal Meda, and vary among Iddir groups. By-laws are individually different and in case of changes, it must be again endorsed by the court. If someone breaks the law, he is obligated to pay a fee. For example, those who do not attend funeral service are forced to pay a fine of 5 ETB (0.30€, 01.06.2010). In case of refusing this fine twice, he will be dismissed. Grain fine accounts for 1.5 kg, if somebody refuses to pay, the amount of fine increases to the double. Fine can also be in other form e.g. in case of death in a family which has not contributed the grain, then the amount of fine and the 3 kg grain are reduced from the 30 kg which they usually would receive and they have to sign it. If someone gets dismissed, he can appeal other Iddir group to judge again about his case for rejoining Iddir. He can also ask excuse for rejoining.

In case of refusing this fee, he will be judged based on legal regional laws as well as on the Iddir by-laws. There are individual rules for Iddir in case of Iddir law breaking. Fee penalty is common which varies depending on the community from 5 ETB (0.30€, 01.06.2010) at Lakku up to 15 ETB (0.90€, 01.06.2010) at Gittlo. In case of Gittlo the fee increases to 30 ETB (1.80€, 01.06.2010). If the accused person is not able to pay for 2 or 3 month, the penalty can be split into three stages. All members meet every month for assessing their members; ideas or suggestions can be proposed by members, so that all members decide if somebody gets suspended from Iddir or not. Depending on the culprits’ interest and if the person apologizes, he receives the opportunity to return to Iddir.

In case of law breakers at Lakku, another member seeks accuse at Kebele administration for the culprit, who is additionally to his penalty also requested to pay the daily loan for this person. At Mehal Meda someone needs clearance which confirms that he left Iddir

voluntarily, from his own Iddir for rejoining other Iddir. Iddir has a committee, which consists of five members, namely chairman, secretary, cashier, and two other members, who are elected when Iddir is formed and registered at Kebele level. People are even more afraid to break Iddir law than Kebele law, because they could lose what they are entitled to by Iddir laws, e.g. in case of house fire. Iddir is the sole and first help for most people in the study areas.

Participation differs among communities and network cooperation. In general, highest participation seeks Iddir-networks. Mehal Meda accomplishes 100% of participation, followed by Molale (96.55%), Lakku (96.55%) and Gittlo (64.91%). 91.56% is the average participation of Iddir at all four communities and half of them also practice ram exchange.

7.5.2. Debo

Debo is a common form of working cooperation at Menz and Horro area. Farmers support each other in activities like harvesting of hay or crop, weeding, house construction, threshing and ploughing for a longer period of time. As a kind of payment the hosting farmer prepares food for the laborer.

Participation in Debo differs a lot among the two study sites. While at Menz 97.4% of farmers participate, at Horro only 45.7% do so. The overall participation accounts for 72.69%, there from 30.40% lend or borrow rams with others.

7.5.3. Daddo/Wenfel

Daddo called at Horro and Wenfel at Menz area, are similar to Debo working co-operations in form of labor support among farmers. Typical wenfel activity among women at Menz is producing dung cake, harvesting, and different kind of field work and transportation of crops or other commodities. Period of time varies between 2 month e.g. harvesting time or longer, but is in general shorter than debo. This form of cooperation is widespread among the two

sites, with full participation at Gittlo (100%) and strong involvement at Mehal Meda and Molale, both 98.25%. Out of the average participation of all communities in daddo (95.11%), 52.44% exchange rams.

Image 5. Work cooperation Daddo for house construction

7.5.4. Dugdee

Dugdee is another form of working cooperation and common at Horro. Activities are similar to the previous mentioned ones with the difference that labor time has to be balanced. The rule is ‘if I work two days for you, you work one day for me’.

7.6. Wealth ranking

The community in Mehal Meda classified a farmer as rich if he owns 1 ha of land or above, one pair of oxen, 50 heads of sheep and above, 2-3 milking cows and 2 gotera - which is a container used for grain conservation- each with about 2000 kg of grain. A medium farmer has been ranged as owning about 0,5 ha of land, one ox and one cow, about 25 sheep and one gotera of 2000 kg of grain. Poor farmers have been categorized as holding 5 heads sheep, maybe having an ox and cow or not, and owning up to 0.5 ha, sometimes 1 ha.

In Molale the community classified a farmer as rich if he possess one pair of oxen, at least 2 cows or more, a mule plus a donkey, 30 sheep or more, land holding of 3 ha and above, 2000 kg grain and above and a corrugated iron house. For medium farmers the criteria were one ox, one cow, one donkey, about 10-15 sheep, 1.5 ha land holding, about 1000 kg grain and 2 houses of thatched roof, for residence and kitchen. Poor farmers has been categorized as no ox and no cow holding, keeping a few heads of sheep but maybe also keep for other’s as ribi about 200 kg of grain per year, 0.5 ha of land and a single house made of thatched roof.

The criteria for the wealth categories diverge in the two communities. In Mehal Meda the requirements for rich or medium farmers are more emphasized on the number of sheep and the amount of grain, whereas in Molale more value is set on land holding and the type of house. The criteria for poor farmers are very similar in both communities. Wealth status has been determined for each member participating in the project by discussing among informants. Agreement on wealth status determination was high.

In Horro area, wealth categories cannot be categorized with general criteria for a whole community. For example, Gittlo community consists of several Kebele; wealth status is determined by comparing each other. Informants reported that wealth status to a large extent is based on the annual income.

The classification factors for rich people were that they usually live in bigger, better constructed houses with furniture and posses also house in town. In general, the form of the house and type of roof differs between rich and poor. For example, rich farmers live in bigger, four-sided houses often with iron roof. Some may have an iron roof, but are not considered as rich. By contrast, poor farmers live in circled hats with grass roof.

Farmers commended that land holding can never be a criteria for wealth, because there are even people without land, but working hard in qit’e, so that one community member from Lakku is a very rich one without possessing land. Wealth status is more related with number of livestock holding, but there it is impossible to set criteria with exact numbers. At Lakku,

farmers possessing land, but without livestock, are considered as poor. Oxen play a significant role in wealth status, because of its importance for ploughing land for crop production.

Households with two or more oxen, up to 4 ha of land and with livestock number up to 15 are called rich. They can have their own grain to consume, may have an iron sheet house, but not much money as deposit.

Generally speaking, rich people lend but never borrow money and have money in bank. They possess 3 to 4 oxen for ploughing, and additionally keep horse, sheep and cattle. The amount of crop a family has, also play a role in wealth status. Another characteristic of rich people is a grain mill which works by diesel, meanwhile medium and poor farmers go to grain mill. Family size and having trees, especially apple trees, are as well indicators for wealth. Furthermore, the sort of bed people possess plays an important role in determining wealth status. While poor people are sleeping on andake or skin of animals, medium farmers sleep on mattress made of grass or straw of wheat or barley. Rich farmers sleep in beds with sponge mattress.

Image 6. Lakku farmer demonstrates the way how poor people sleep on andake

Table 5 wealth status pattern of the four communities

rich medium poor Mehal Meda 30 21 6 Molale 9 41 8 Lakku 21 30 8 Gitlo 15 29 11

7.7. Resource mapping

The method ‘resource mapping’ has been conducted for visualizing the community resources- especially grazing areas- and location of homesteads. Distance among community members has been taken into account as further factor for the importance of ram exchange. Moreover, location of communal grazing lands provided information on the influence of communal grazing group formation on ram exchange behavior of farmers.

While Menz communities live in small neighborhoods to each other, Horro communities form a center where usually the farmer training center is located.

Graph 1. Resource map- Molale

Gezahegn Chernet ChairmanW/Hanna Asnake Kebede Amete Tebikew Chernet Kebede Amete 700 m Gizachew Tesfaye Geramew Kebede Tebikew Chernet Tefera Aschenaki Sinke Tegegn Geramew Kebede Girma Tilahun Adefris Eshete Sinke Tegegn Shewatatek Yitna Yechale Zeleke Demissew Zewdu Agonafir Eshete school Mekona Negash Mulat Dejene W/Emanuel Sharew Getachew Asalif Kifelaw Tezera Andarge Debebe Tesfaye W/Tsadik Belay Breshet Aschalew Shewatatele Agachew Kasaay Agmassu Assefe Yitna Chekene AboteTeshone TilahunYeshimebet Getachew Debre Gerbet Demisse TekfeTirfe Trugeta Mekona Nekatibeb Feredegn Negash Beshahwered Negessa grain mill Kabtimer T/Haimonot Zemiyaw Feredegn Abate Kassay Endailalu Feredegn Alfo Assefa Eshete Ayalkbet Kefelegn Negash W/Georgis H/Mariam Demiss Mulat Shewareged Diresilgn Getachaw T/Wold Neges Andarge Yeshi Kifetew Fente Shinkut Aschenaki Bualew Tayitu Tegegn Deginet Tenaw church Shinelis Kifllew Zewdu Dires Fentahun Aschalew Shumete Mestawot Mullat Belayhun Zelalaw Alamaw Geremew Adefris Ayele Getaneh Alemy

Teketel Eshete Temalede Awgichena Mengisho Kasablot Shiferaw Erdaw Geta Balew Yerafu Desda Asefa Awgichew

Graph 2. Resource map, Mehal Meda 47

BiqilaFanta Amante Mi´o Namomisa Rorro Lamessa Inkosa Jani Barissa Ganatii Dirriba Fiqadu Barsissa Biritu Saqata Waqari Dhugassa Rafissa ShibruGelet Dagittu BayanaDessalegn Ragassa Namomissa Abboma Ofkola Marga Idessa Nagishoo Bayisa Alamaya Nagassa Adunya Bayu Nag´´o Gobana Tolera Dhaba Boka Dachassa Lamu Shubitu Guta Nagassa Gutama Ragassa Nagassa Tariku Ayana Merga Sori Lamessa Barassa Dessalegn Wajiraa Abbataa Admasuu DA Fiqadu Fayisa Waqira Mayibasu Tafari Fanta tab water church Inkoshe Fufa Alemitu Fiquadu NGO Shambu FTC grain mill Getachew Gobana Tiru Gobana Qitessa Nagassa tab water Nagishoo Bayisa Getachew Inkosa Tukulii Irena Lense Guluma Tashoma Qumbi Dirriba Bayisa Chala Asebe Inkoosa Bayisa Itticha Qumbii

Yadata Aman Kibltu Zamade

Graph 3. Resource mapping at Lakku

Shambu

Shibur Geleta Waqgari Dugassa Temesgen Terfa Biqila Birru Garoma Gudata Qixessa Raggassa Garama Belete Wirtu Mosissa Nagashi Galata Bizunesh Tesgera Jabana Ochoo Ganati Dirriba Namomissa Rorro Belay Qanasa Dessalegn Waqjira Lamessa Gafata Beyese Dhugassa Abbara Hinsarmu Adugna Dhaba Gafata Rorro Xayitu Gamada Edessa Shanqo Bultuma Rundassa Dachassa Dheressa Hordofa Tolera Haile Dheressa Katama Gamada Shushee Wirtu Akkuma Hundara Asfaw Gemechu Mulugeta Fanta Mekonnin Fayisa Zeleke Haile

c. grazing: Dessalegn Waqjira Bizunesh Tesgera Tuffa Hundessa Chaltu Gnabure LamessaTesso JamaneshGeremew Gete Dheressa Ababee Abdana Chaltu Gnabure Kibitu Dheressa Adugna Dhinsa Dadituu Dhaba Dheresa Hundara FTC Kuli Edessa Tesfaye Koche Ragassa Dhugassa Ilfinesh Dalala Kibre Amante Bizunesh Tesgera Gamada Tolera Mizane Waqgari Malkamuu Dheressa Habtamu Wirtur Gudata Fufa Desisa Waqwaya Birqi Bekele Banti Adula Dirribe Adam Sanbaba Galata Tilahun Aga Fekadu Hordofa Tariku Edessa school

Tariku Chali Tucho Dano Boka Ararso

Graph 4. Resource map Gitlo community

7.8. Ram exchange

Table 6. Ram exchange behavior at Menz and Horro

Communities (Active) ram exchange Among family and Ram exchange outside activity neighborhood (%) community (%)

(%)

Menz 47 2.63 85

Shambu 51 11.67 90

Ram exchange is practiced on communal grazing areas, mostly uncontrolled. Significant differences in ram exchange are found between the two areas, Menz and Horro, but also between communities. While 70 farmers (30.84%) practice ram exchange at Horro, at Menz only 50 (22.03%) do so. Among the four communities, highest ram exchange participation is found at Gitlo with 23.35%, followed by Mehal Meda with 20.26%. Farmers younger than 30 years participate less in ram exchange than farmers above 30 years. No significant differences could be found in education status, sex and wealth rank. A higher percentage at Shambu exchange rams with neighbours and relatives, 11.67%, compared to 2.63% at Menz.

In contrast, the ram exchange activity at Molale is weak. Only few farmers are aware of the importance to keep and select rams whether in a communal or individual way because they never faced any experience or influence from outside related to ram exchange.

In general, there is very little exchange with farmers from outside the community. 90% of Horro farmers told that they do not exchange rams with farmers outside the Kebele or neighborhood. Similar is the situation at Menz; 85% state the same.

7.8.1. Ram management

Out of all Menz sheep owners 31.4% do not posses breeding ram, 33.4% own one ram and 33.3% owned more than one breeding ram. These figures are different to the results of another study, where 20.6% had no breeding ram, 17.6% owned one ram and 61.8% owned more than one breeding ram (Getachew, 2008). Sheep breeders without a breeding ram indicated that they use neighboring rams or their ewe mated with breeding ram from other flock in communal grazing land. The same has been found in other study (Getachew, 2008).

At Horro 29.6% kept their own breeding ram. According to a previous study by Zewdu Edea the majority (75.8%) of breeding rams for farmers in Horro were originated from own flock and 24.2% were purchased from market. Moreover, it was reported that of the total ram owners about 94.4% share their rams with others. Most of the community practiced communal sharing of grazing lands (Zewdu, 2008).

Table 7. Number of breeding rams respectively wealth rank and age of farmers

Wealth rank Number of breeding ram

Rich 2.80±0.454

medium 1.71±0.454

poor 1.68±0.426

Age

<30 1.89±0.482

>30 - 50 2.27±0.36

>50 2.02±0.370

Average number of rams per household is higher at Menz (2.34±0.414) than at Horro (1.66±0.354). Among the four communities, Molale has the highest number of rams. Reasons are that on the one hand sheep production in general is much more important at Menz area than at Horro because of additional production systems e.g. crops. On the other hand, previous projects at Mehal Meda where breeding rams have been introduced into the community, is a reason for the higher number of ram.

Significant difference between rich and poor or medium farmer in the number of breeding rams has been found. Rich farmers keep on average 2.80±0.454 breeding rams, whereas poor and medium farmers keep 1.68±0.426 and 1.71±0.454 respectively (see table 7).

Age does not point out significant differences between young and elderly farmers, but young farmers with an average ram number of 1.89±0.482 keep less ram than farmers above 30 years (2.27±0.36 for farmers between 30 and 50, and 2.02±0.370 for farmers above 50 years; see table 7).

The majority of farmers at Menz (28.05%) hold the opinion that breeding rams in their community are sufficient. By contrast, 26.24% of Shambu farmers are not content with the

number of rams in their community. 40% of farmers there see the main reason for this lack in early selling of breeding rams, while at Menz only 11% do so. 11% see the problem in the poor performance of breeding rams and another 7.8% of Menz farmers think that there is a shortage in crossbred rams. 2.6% mentioned a missing access to other flocks.

Constraints of breeding and suggestion for improvementMain problems in ram management are that farmers mentioned grazing land scarcity, hence shortage of feed, as main problems in sheep breeding. Because of loss of breeding rams through predator or theft, farmers prefer to sell their rams early for cash income. Alternatively, rams get castrated and fattened for sale. Early selling of young rams also has been reported by Zewdu (2008). Even high quality rams are rarely used for breeding, as they are sold at early age (Zewdu, 2000). This causes shortage of breeding rams, thus difficulties in breeding management. The main reasons for diseases especially liver fluke1and parasites cause problems and farmers claimed the lack of health care service.

Another important issue was that farmers claimed that poor rams are sometimes poorly managed. Lack of good rams has been mentioned, as well as different opinions on the use of communal ram management. Thereby some farmers supported the use of communal rams and only one farmer stated his fear that flocks might not graze together all year round and insufficient responsibility or even theft in case of communal rams. Some farmers also mentioned inbreeding as a problem.

As suggestions for a ram exchange improvement, farmers claimed for better rams, either private or communal ones. They imply to arrange ram exchange within neighbors, or communal grazing land, or the project should organize the utilization of rams. An adequate

1 Liver fluke- widespread disease among pastoral production systems, flatworms located in liver of various animals (Fasciola hepatica- sheep liver fluke). It is common in wet marshy lands

ram management in case of borrowing has been pointed out. Especially Lakku advocated for communal ram from outside which means improved breed, and ram selection.

7.9. Social networks

Across different community activities, network characteristics vary. The following chapter presents the main findings of Social Network Analysis.

In this study, all graphs are directed, which means that all relations between the actors- in this case farmer- have a direction; except in the graph of Iddir at Lakku, relations are reciprocal, which means a two-way relationship (see Graph 23).

In this study both, connected and disconnected networks were found. For example, while at Gitlo all networks, except Iddir cooperation, are connected; at the other sites, the graph of several community activities is disconnected e.g. ram lending at Mehal Meda (see Graph 7). Especially the network of relationship shows a strong connection in all communities. Activities like grazing group, daddo and iddir co-operations demonstrate multiple components, however with high density within those (see Graph 19, daddo Molale).

Density value ranges from 0 to 1 and is highest in an all to all connected network with the value 1. Highest density values are achieved in the networks of relationships of communities (see Graph 16 and 17), while the graphs of ram lending and borrowing comes up with the lowest density (see Graph 6, 7, 10, 11).

7.9.1. Ram lending

Graphs are directed and only Gitlo community is connected. As shown in Graph 5, a network containing several subgroups is visible and even 81 cliques (minimum size of a clique is 5 farmers). In most cases subgroups are based on communal grazing land and location; in other words adjacency of farmers in form of neighborhood. Exchange participation is expressed by the total node number and is highest at Gitlo with 61 nodes and lowest at Molale with 11 nodes (see Graph 7 for Molale). Because of the low number of enabled nodes at Molale, results for the measurements density and degree centralization are not comparable.

Network density- an index expressing the proportion of links to nodes of a graph- is highest at Gitlo (0.311) and lowest at Mehal Meda (0.145). This low level is due to a disconnection of the graph, as it is split up into two groups, representing the community sites Boda and Sinamba (see Graph 8).

Degree centralization is similar among communities in ram lending. Gitlo comes up with lowest centralization score (71.3%), while Molale reaches the highest percentage with 104.4% (see Graph 7). Mehal Meda and Lakku have fairly similar centralization scores, 88.42% and 84.84%.

In total 14 opinion leaders are found at Gitlo, which transfer crucial information for the network.

At Gitlo ram lending is based on location and communal grazing groups. Additionally, the number of sheep owned by farmer, play a role for ram lending, because farmers with large number of sheep more likely own breeding ram and lend them to others. Thus, wealth status also has influence in ram borrowing.

Education level of farmers in different degree centrality positions vary. In several cases higher educated farmers are of high degree centrality and therefore central ones. By contrast, there are also farmers actively involved in ram exchange with grade level lower than 4, or even illiterate.

According to age, participation in exchange varies. As has been observed by the network analysis, farmers of high degree centrality are on average older than those of lower one. It is common that elder farmers more likely possess breeding ram. Similar is the case in position in community. While the majority of farmers with high degree centrality are active in community or Iddir administration, only 50% of the 10 farmers with lowest degree do so. A majority of 89.66% are male farmers at Gitlo, therefore gender differences does not play a significant role in ram lending.

Wealth status in general has an influence on ram exchange, because farmers possessing breeding ram usually keep a bigger flock size and are therefore considered as rich.

As already mentioned above, Mehal Meda is divided into two sides, Sinamba and Boda, which determine all network activities (see Graph 8). For example, ram borrowing as well as 54

lending are split into two subgroups. Six opinion leaders- three at each side- and one cutpoint characterize the network of Mehal Meda. The use of communal ram2 among neighborhood is very common so that ram lending is based on many small cliques. On the contrary, ram lending activity at Molale is scarce, as farmers never faced any experience from previous projects enhancing ram exchange as well as missing tradition on it (see Graph 7). Only 18% of the farmers at Molale are involved in ram lending that is carried out within two subgroups and one dyad. Those are mainly based on location. In the case of Molale, farmers who lend ram are of higher education level than the average and only rich and medium farmers are active.

Graph 5. Ram lending at Gitlo, Horro area

At Lakku ram lending activity is carried out within two groups. One neighborhood which lives far away from the center lend rams among themselves. The groups are based on neighborhood. The graph also contains one dyad- neighboring farmers located farer away from the center.

2 Breeding ram shared by a group of farmers, within this study mainly based on communal grazing groups 55

The network contains two cutpoints. Those nodes are important for the connectedness of the network. In case of their absence, the number of components would increase- consisting additionally 6 isolates, two dyads and 2 components (see Graph 6). Also 5 opinion leaders unify the graph by providing essential information to other nodes.

Graph 6. Ram lending at Lakku

Graph 7. Ram lending at Molale, Menz area

Graph 8. Ram lending at Mehal Meda, Menz area

7.9.2. Ram borrowing

Ram borrowing activity at Gitlo has a participation of 100%, as the total number of nodes amount to 61 nodes. Graph 12 shows Molale with a rather low involvement of 16 nodes out of 58 farmers. As shown in graph 10 and 12, the networks of the sites Lakku and Molale are not connected as there are two different sides in the community. Various network properties show that Gitlo community accomplishes highest activity in ram exchange. Network density (0.261) and degree centralization (26.12%) are highest. Gitlo has a dense network because of active ram exchange among the community. Lowest exchange activity is notable in density (0.092), degree centralization (9.17%) and number of enabled nodes and links at Molale.

Generally speaking, networks of ram lending and borrowing are very similar. In Horro area both sides Gitlo and Lakku demonstrate that location has considerable influence on farmer’s integration in ram borrowing. While the network at Gitlo is connected, at Lakku it is divided into two components because of communal grazing land location. As can be seen in Graph 6, the main component holds together through a bridge, which is due to location as these farmers live close to each other.

As demonstrated in graph 10, six cutpoints hold together the graph of Lakku. Cutpoints hold together a network to one compound. In case of Lakku, those cutpoints are based on the number of breeding ram and location of homesteads. Density is low (0.114) and a low number of enabled nodes with centralized position of nodes leads to a high value in degree centralization (92.25%).

In general core farmers possess higher number of sheep than those who live peripheral in the network. To some extent, education and position in community also influence ram exchange behavior. Farmers with high degree centrality are either rich or medium, they are educated and many of them are positioned in community cooperation or administration. Especially wealth determines this activity as mostly farmers with larger flocks have breeding ram and hence care

more about mating. While poorer farmers usually have smaller flocks without rams. Therefore, they are often not involved in borrowing rams to others.

Graph 11 demonstrates ram borrowing at Mehal Meda and confirms again that location cause ram exchange because two sides ‘Boda’ and ‘Sinamba’ represent two groups of ram exchange connected through a middleman based on relationship. Although number of total nodes (53) and therefore participation is high, network density is low (0.0965) and degree centralization high (93.89%). The network consists of one component and is connected through seven cutpoints. Those nodes play the role of middlemen between two groups which are often based on blood relationship.

Relationship is another determining factor in ram exchange at Mehal Meda. For example, in ram borrowing the two sides of the community are linked through a cutpoint based on relationship. As shown in Graph 11, Melesse Tilahun receives ram from his brother Tesfa Tilahun. At the side called ´Boda` one farmer borrows ram to all the farmers of the side. The main reason is that he is rich and keeps a large flock of sheep, therefore he also keeps breeding rams. Five opinion leaders- points with highest in-degree value in the network – characterize the network and all of them are located at ‘Boda’ side (see Graph 11: opinion leaders indicated as green nodes).

Mostly farmers without breeding ram, but with a high number of sheep do care about borrowing ram from others. However, there are some farmers with large flock size but their location in the network is peripheral.

Graph 9. Ram borrowing at Gitlo

Graph 10. Ram borrowing at Lakku, Shambu

Graph 11. Ram borrowing at Mehal Meda

Graph 12. Ram borrowing at Molale

Compared to Mehal Meda, the number of nodes at Molale is low. Only 28% of the community is participating in this activity. Only 16 out of 61 farmers are active in ram borrowing. Apart from two small components, which are connected through four cutpoints, the network consists 61

of three dyads. Ram borrowing activity is very low. Reasons are small number of rams in the community and lack of awareness and importance given to sheep breeding.

7.9.3. Organizational structure of communal grazing areas

Gathering on communal grazing areas is basically determined by location e.g. adjacency of homestead to the area. The formation of grazing groups is a strongly influencing organizational structure for breeding ram management, because mainly the communal grazing land is first meeting point where (uncontrolled) mating takes place.

In Menz communities, the number of cohesions complies with the number of communal grazing areas (see graph 13 and 14). For example at Mehal Meda there are seven communal grazing areas, thus seven subgroups are visible in the graph of the social network. Red indicated cutpoints- four at Mehal Meda and two at Molale (see in graph 13 and 14) - link groups of different communal grazing areas. Opinion leaders – nine at Mehal Meda and ten at Molale- are located in one of the main cohesions as they have the highest in-degree values.

Different to Menz, the graphs at Horro are connected to one compound. However, subgroups based on communal grazing areas are still discernible. There are no cutpoints, but a considerable number of opinion leaders at both sides.

At all four communities, participation is high, as a majority of farmers graze their sheep on communal land. Location influences the high number of links at Lakku, as the majority of farmers live central and graze their sheep together. Density 0.531, centralization 48.52%

Graph 13. Grazing groups at Mehal Meda, Menz area

Graph 14. Grazing groups at Molale, Menz area

Graph 15. Grazing groups at Gitlo,Horro

Graph 16. Grazing groups at Lakku, Horro

7.9.4. Relationships among community members

According to relationship, all networks are connected and dense. Highest number of links and degree centralization is found at Molale, 1020 total links and normalized degree centrality lies by 27.87%. However, relationship networks of the other communities come up with similar figures. All graphs come up with high density and a high number of opinion leaders, especially those having a big family.

Blood relationship of female is less than of male because women usually hail from neighboring districts but joined the community through marriage. Most frequent relationships at Gitlo are blood relationships with 349 links, followed by relatives with 264 linkages, 87 ties through marriage and 31 because of friendship e.g. muze, (best men during marriage), and abeliji, which means godfather. Often all members of a family live close to each other as it is common to share land with the children, when they reach the age of about 15 years. There was no observation of farmers having many relationships to other community members being more active in ram exchange.

Graph 17. Relationships among community members of Gitlo

Graph 18. Relationships among community members at Lakku

Graph 19. Relationships among community members of Mehal Meda

Graph 20. Relationship at Molale

7.9.5. Network cooperation daddo

Daddo is a common working cooperation in the study areas. Works such as harvesting or house constructions are accomplished in collaboration with farmers. Farmers merge into work groups, mostly based on neighborhood. While the graph at Mehal Meda is divided because of two different sides, at Molale the whole community is one big component. Almost all farmers participate in this activity but to different extent. Usually younger farmers up to 55 participate more active in daddo than elderly do because they cannot work hard anymore. Other daddo members often till the fields of elderly members, but those who can afford do not join daddo and hire laborer for tilling their fields. Profession can also matter e.g. at one household at Gitlo they are teacher and have some sheep but are not involved in any community activity because farming and sheep keeping are not their first income sources.

Graph 21 shows a graph split up into two sites, hence working cooperation daddo is carried out separately on each site; though among them many (in total 149) small cliques (minimum size three nodes) are found and strongly related to the location of homesteads. One node plays

the role of a bridge to connect the sites. The reason for the bridging function of this node might be the high number of relationships to others (number of degree 63).

Graph 21. Work group cooperation daddo at Mehal Meda, Menz area

Graph 22 (Molale) is well interconnected and dense (0.759) and contains a fairly high number of cliques with a minimum size of three farmers. Twenty-four opinion leaders characterize a strongly interlinked network out of one compound.

Graph 23 shows daddo network of Lakku community. Although the network is connected (one compound), the subgroups are visible based on neighborhoods. Twelve leaders with maximum group size of four nodes are located in the main component. Gitlo comes up with a similar graph; connected network out of one component, but subgroups recognizable (see graph 23). No cutpoints, but eleven opinion leaders characterize the network.

Centralization scores are in general low, especially Lakku and Molale (<25%). Mehal Meda seeks higher centralization because of practicing ram exchange separated in two sites. The graph of Gitlo has highest degree centralization regarding daddo cooperation (75.09%).

Graph 22. Cooperation form daddo at Molale

Graph 23. Cooperation form daddo at Lakku

Graph 24. Cooperation form daddo at Gitlo

7.9.6. Network cooperation Iddir

Iddir is the most important cooperation in countryside as well as in cities of Ethiopia. Almost everybody is member of Iddir, but especially elderly people participate and take position of chairman or administrator. The most influencing factor for participating in Iddir is location. Similar to daddo, the creation of Iddir is based on neighborhoods. Also blood relationships, relatives and friends are reasons for joining Iddir.

Women are less participating in Iddir than male farmers, because due to Ethiopian tradition men join Iddir. This affinity is caused by cultural reasons, as the empowerment is usually given to the household head. Household head is mostly father or son. In case of death of husband, women lead the household.

There are several differences in the organization of Iddir networks among the communities. While at Gitlo and Mehal Meda the networks are not connected, at Lakku only one Iddir group is showed where every member is connected to each other. At Gitlo, several Iddir associations are found which are based on the location of the neighborhoods. One large and

strong connected and three smaller Iddir groups. A bridge connects one of the smaller neighborhoods with the main compound.

At Gittlo there is one bigger and three smaller Iddir as indicated in Graph 5. In total four cutpoints interlink the graph, and especially one cutpoint link a group of farmers living farer away from the farmers training center, which is considered as community center. Therefore, network density is lower (0.34) - compared to the graphs of the other communities- and degree centralization of 68.16%. Four opinion leaders are located in the main component representing the biggest Iddir group.

While at Gittlo and Mehal Meda the networks are not connected, at Lakku only one Iddir group is illustrated where all members are connected to each other. Due to the location of the different neighborhoods at Gittlo several Iddir associations are found, a bigger and strong connected one and three smaller Iddir groups. One of them is disconnected to the other.

Graph 25. Iddir cooperation at Gitlo, Horro area

At Mehal Meda, the network is disconnected as two Kebele- ‘Boda’ and ‘Sinamba’- live far from each other, so that their Iddir associations are completely separated. Moreover, it is an undirected graph; every farmer elected all other farmers of the same site as his Iddir partner. Additionally the network is undirected because all farmers participate with everybody to the same extent. Although participation in Iddir reaches 100%, density and degree centralization only accounts for 0.49 and 52.46% respectively, due to a separated graph.

At Molale three Iddir associations are found, which are mainly based on location. Several bridges keep the subgroups connected to one component. Additionally two cutpoints- based on relationship- link the compounds and one single actor. These linking nodes are often based on family relationship and in community well integrated farmers. A considerable number of opinion leader play the role of transmitters and all, in total twenty-three, positioned in the biggest Iddir association. Network density is high with 0.738 with degree centralization accordingly low (27.12%).

Graph 26. Iddir at Mehal Meda, Menz area

Graph 27. Iddir at Molale

The network at Lakku shows a very strong connected network of one association of Iddir (with a number of enabled nodes of 10) with a degree centralization of 0% and density of 1, as each member is connected with all the others. These farmers live close to each other, thus their connectedness is based on location.

Graph 28. Iddir, Lakku

Properties of networks differ significantly among communities and activities. In general, participation (number of enabled nodes) is high. The networks of ram exchange (lending and borrowing) are well connected at the communities of Gitlo, Lakku and Mehal Meda; while at Molale low participation has been observed.

All graphs of grazing groups come up with 100% of enabled nodes, as all farmers join communal grazing. Similar cases are the graphs of relationship, Iddir and daddo cooperation- all farmers have some kind of relationships to other community members, and everybody is member of Iddir, as it is the most common and important social association in Ethiopia.

Network density varies a lot among the different networks. In Menz communities, density is accordingly low in ram lending and borrowing, and slightly higher at Gitlo. Values increase in communal grazing groups and become even higher in the graphs of Iddir; maximum level achieved at Lakku (density =1).

Degree centralization varies significantly among activities and communities. High centralization values are found in lending and borrowing of ram at Mehal Meda, because activities are basically carried out within two sites. Both subgraphs are centralized, because farmers possessing breeding ram play a central role in ram lending and borrowing. Lakku also 74

comes up with high degree centrality; ram exchange is mainly carried out on the behalf of some farmers, while at Gitlo ram exchange activity is better distributed.

In communal grazing groups centralization is low; the network contains several subgroups and farmers do not have central position. Among the graphs of grazing groups, Horro communities come up with significant higher network centralization, due to a big communal grazing land in the community.

Regarding daddo, centralization scores in general are low, especially Lakku and Molale (<25%). Mehal Meda seeks higher centralization because of practicing ram exchange separated in two sites. Gitlo has the most centralized graph of daddo. High degree centralization is found in iddir cooperations. The graph of Iddir cooperation at Lakku achieves a centralization of 100% because each member of the Iddir group is connected to every other Iddir member in the groups.

8. Conclusions

One important outcome of this study is that social networks are important especially in community-based activity projects. In the case of breeding, social network analysis is a useful method to figure out the networks having influence on ram exchange. Regarding this study, ram exchange practice varies between locations. Depending on farming activity, breeding activities are more or less important. While the farmers in the highlands of Horro area mainly depend on crop production, farmers at Menz were much more interested in sheep breeding, as it makes up their first source of income.

Furthermore, the analysis showed that ram exchange is basically carried out on communal grazing areas- to a large extent uncontrolled. Working cooperations Iddir and Daddo could be seen as a possible entry point for community-based activities. Social network analysis can be a useful scientific tool in the field of development issues, especially in studies where farmers become involved. Through Social Network Analysis the social structure and behavior of farmers come to light and thus can be considered.

9. Summary

This thesis is part of a joint project of ICARDA, ILRI, BOKU and National Agricultural Research Systems in Ethiopia on ‘Designing community-based breeding strategies for indigenous sheep breeds of smallholders in Ethiopia’. Ethiopia has about 24 million sheep which represent an important source of income and protein in the diets of the poor; however off-take is low. The project aims to develop decentralized and participatory breeding strategies for communities of sheep keepers, by ensuring the better management of local genetic resources through farmer-participatory breeding, where breeding goals are defined by farmers. Therefore, social networks of community members provided crucial information on ram exchange practices.

The project operates in four areas of Ethiopia, but for this thesis two areas (Menz and Horro) which are home for two breeds Menz- located in the northern highland- and the other Horro- located in the western mid-land region of Ethiopia, were considered.

The objective of this thesis was to analyse the social networks of different community activities such as ram exchange (lending and borrowing), communal grazing groups, different relationships among the community and local network cooperations (Iddir and Daddo) with additional information on farmers’ attributes like sex, age, wealth status, education status and religion. Information on these networks has been provided by farmers through questionnaire based interviews of all farmers of the communities participating in the project. Methods like ‘Resource mapping’ and ‘wealth ranking’ provided additional information on location of homesteads and community resources and wealth status of farmers.

Information on ram exchange behaviour, and thereby the importance of local networks has been figured out. Results showed that to a large extent location of farmers’ homestead influences the breeding practices and ram exchange. Especially communal grazing groups influence in ram exchange, because of widespread uncontrolled mating. Depending on farming activities, breeding activities are more or less important.

Working cooperations Iddir and Daddo are very common and are characterized by stabile relationships among farmers. Therefore, local networks can be seen as entry points for community-based activities and development projects.

10. Zusammenfassung

Die vorliegende Diplomarbeit ist Teil des ILRI-BOKU-Projektes: „Design von dörflichen Zuchtprogrammen für lokale Schafrassen in Äthiopien“. In Äthiopien gibt es ca. 24 Millionen Schafe, die eine wichtige Einnahmens- und Proteinquelle für viele Arme darstellen. Jedoch sind die Einnahmen gering. Das Ziel des Projektes ist es die dezentralisierten und partizipativen Zuchtstrategien für Schafthaltergemeinden zu entwickeln, und ein besseres Management von lokalen genetischen Resources durch eine partizipative Züchtung mit Bauern zu sichern, wobei die Zuchtziele von den Bauern gesetzt worden sind. Soziale Netzwerke von Gemeindemitgliedern bieten dazu eine äußerst wichtige Information zu Zuchtbockaustauschpraktiken.

Das Projekt hat in vier Regionen Äthiopiens stattgefunden, jedoch sind für diese Arbeit nur zwei Regionen mit indigenen Rassen berücksichtigt worden. Eine Rasse davon heisst Menz und ist im nördlichen Hochland vorzufinden. Die andere, Horro, ist im westlichen Mittelland gelegen.

Das Ziel dieser Arbeit ist die Analyse von sozialen Netzwerken verschiedener kommunaler Aktivitäten wie Zuchtbockaustausch, gemeinschaftliche Weidenutzung, Verwandtschaften und Beziehungen zwischen Gemeindemitgliedern und lokaler Netzwerk Kooperationen (Iddir und Daddo) mit zusätzlichen Informationen zu den Bauern wie Geschlecht, Alter, ökonomischer Status, Bildungsstatus und Religion. Informationen zu den Netzwerken wurde mit Hilfe von fragebogengestützten Interviews mit 231 Bauern aus vier Gemeinden gesammelt.

Durch die participative Methoden „resource mapping“ und „wealth ranking“ wurden zusätzliche Informationen über Wohnort und Gemeinderesourcen sowie der ökonomischer Status aller Bauern erstellt.

Informationen zu Zuchtbockaustausch und die Rolle lokaler Netzwerke sind dabei besonders betrachtet worden. Ergebnisse zeigen, dass der Wohnort von Bauern und die Entfernung zu anderen dabei eine wichtige Rolle in Zuchtpraktiken spielen. Besondere Verknüpfung von Zuchtbockaustausch und gemeinschaftliche Weidenutzung wurde ersichtlich, da zu einem großen Teil unkontrollierte Paarung aus diesen Flächen stattfindet. Ausserdem, spielt die Produktionsweise der Bauern für das Ausüben von Zuchtpraktiken eine wichtige Rolle. Lokale Netzwerkkooperationen Iddir und Daddo sind sehr verbreitet, und bieten starke Netzwerkstrukturen aufgrund stabiler Beziehungen unter den Bauern. Daurausfolgend, wären lokale Netzwerke gute Basisstrukturen für dörliche Entwicklungsprojekte, insbesondere dörfliche Zuchtprogramme.

11. Glossary

Iddir- most common and important cooperation in Ethiopia where farmers help and

support each other

Kebele- smallest peasant association in Ethiopia

Irken- A common method to avoid erosion in the highland areas of Ethiopia; small

terraces made of stones

Tef- Ethiopian traditional grain, used for the preparation of enshera (flat cake,

traditional Ethiopian dish)

Tala- Drapery which is used as cloth

Daddo/Wenfel- working co operations in form of labor support among farmers

Dugdee- form of working co operation and common at Horro

Gotera- container used for grain conservation

Ribi- A farmer keeps the ewes of another farmer, therefore he receives half of their

lambs

Andake- Andake is a matress made of local grass

Qit’e- Cultivating others land on grain share basis

Muze- a very close friend

Abeliji- godfather, relationship among farmers

Waaqefata- the worshiper- is a person who believes in God. The religion is called Waaqeffanna- means ‘Believing in God’, is a religious belief among Oromians (state of Ethiopia where Horro area is located). Informants defined their belief ‘we do not belief in anything, there is just god’.

The universe is source of everything: The earth, the sun, the solar system, all of the matter, the time and the energy and everything else.

Waaqeffanna means is the practice of worshiping God. Even thought the truthfulness of Waaqeffanna remained unchallenged, it’s universality is limited by the emergence of Middle Eastern and Hebraic mysticism where prophetic mystical traditions were forced nations around the world to abandon Waqeffanna via the use of dedicated intel lectual and metaphor ical teachings. Middle Eastern originated metaphorical stories were presented in a way they could touch people’s daily lives with long term goal of recreating or re-defining the meaning of God following their own world outlooks.

12. References

** Abebe, M. (1999): Husbandry practice and productivity of sheep in Lalo-mama Midir woreda of central Ethiopia. An M.Sc Thesis presented to the School of Graduate Studies of Alemaya University of Agriculture, Dire Dawa, Ethiopia. 99 p.

**Baulch, B. (1996): The New Poverty Agenda: A Disputed Consesus. IDS Bulletin, Vol 27 (1), Brighton.

**Kebede, B.,Yemane, B. (1992): Animal genetic resources and breed characterization work in Ethiopia. Research Planning Workshop on African Animal Genetic Resources: their Characterisation, Conservation and Utilisation, Addis Ababa (Ethiopia). ILCA.

**Cancian, F. (1978): The innovator's situation: Upper middle class conservation in agricultural communities. Stanford University Press, Stanford, California.

** Catley, A., Admassu, B. (2003): Using Participatory Epidemiology to Assess the Impact of Livestock Diseases. Consultative Group Meeting on Contagious Bovine Pleuropneumonia in Africa 12-14 November 2003, FAO Headquarters, Rome, Italy

**CSA (2004): Central Statistical Authority. National Integrated Household Survey. Agricultural Sample Survey 2003/2004 www.csa.gov.et/surveys/Agricultural.../docs%5Cquest_2003.pdf

**DAGRIS (2009): Domestic Animal Genetic Resources Information System. ILRI. Addis Abeba. Ethiopia.

** Ehrlich, K., Carboni, I. (2005): Inside Social Network Analysis. IBM Technical Report 05- 10. **FAO (2003): Livestock sector brief. Food and Agriculture Organization of the United Nations. pp 3.

**Freeman, L. C. (1979): Centrality in social networks. Conceptual clarification. Social networks 1, 215-239.

**Grandin, B. (1983): The importance of wealth effects on pastoral production: A rapid method for wealth ranking. ILCA, Kenya.

**Grandin, B. (1988): Wealth Ranking in Smallholder Communities: A field manual. London: IT Publications. **Green et al, (2006): Use of wealth ranking to analyse factors influencing smallholder farmers' market participation in northern Mozambique. Development Southern Africa, Volume 23, Issue 5 December 2006, pages 669 – 683.

**Halderman, M. (2004): The political economy of Pro-Poor Livestock Policy-Making in Ethiopia. Pro-Poor Livestock Policy Initiative, Working paper Nr. 19. FAO. Rome. Italy. http://www.fao.org/ag/againfo/programmes/en/pplpi/docarc/wp19.pdf

**Hartwich, F. and H. G. Jansen (2007). The Role of Government in Agricultural Innovation: Lessons from Bolivia. Research Brief. IFPRI, Washington D.C.

** Hartwich, F., M. Monge Pérez, L. Ampuero Ramos and J.L Soto, (2007): Knowledge management for agricultural innovation: Lessons from networking efforts in the Bolivian Agricultural Technology System. Knowledge Management for Development Journal 3(2): 21- 37 www.km4dev.org/journal

**Hargreaves, J.R., Morison, L.A., Gear, J.S.S., Mzamani, Makhubele, B., Porter, J.D.H., Busza, J., Watts, C., Kim, J.C., Pronyk, P.M. (2005): Hearing the Voices of the Poor: Assigning Poverty Lines on the Basis of Local Perceptions of Poverty. A Quantitative Analysis of Qualitative Data from Participatory Wealth Ranking in Rural South Africa. University of the Witwatersrand, Acornhoek, South Africa.

**Hartwich, F., Halgin, D., Monge, M., (2008): How Change Agents and Social Capital Influence the Adoption of Innovations among Small Farmers. Evidence from Social Networks in Rural Bolivia. IFPRI Discussion Paper.

** Hassen, Y., Sölkner, J., Gizaw, S., Baumung, R. (2002). Performance of crossbred and indigenous sheep under village conditions in the cool highlands of central-northern Ethiopia: growth, birth and body weights. Small Ruminant Research. 43, 195–202. 80

**Herlihy, P.H. (2003): Participatory Research Mapping of Indigenous Lands in Darién, Panama Human Organization, Vol. 62, No. 4.

** Hira, A., Parfitt, T. W. (2004): Development projects for a new millennium. British Library Cataloguing, p 110.

**Jansen, D. (2006): Einführung in die Netzwerkanalyse. Grundlagen, Methoden, Forschungsbeispiele. 3., überarbeitete Auflage. Verlag für Sozialwissenschaften/GWV Fachverlage GmbH Wiesbaden. **Kirsopp-Reed, K. (1994): A review of PRA methods for livestock research and development Source: RRA Notes (1994), Issue 20, pp.11–36, IIED London.

** Kebede, B. (2007): Community wealth-ranking and household surveys. School of Development Studies University of East Anglia and Centre for the Study of African Economies (CSAE). Universtiy of Oxford.

** Kebede, B., Yemane, B., Animal genetic resources and breed characterization work in Ethiopia. Proceedings of the Research Planning Workshop held at ILCA, Addis Ababa, Ethiopia 19-21 February 1992. ILRI. Addis Ababa, Ethiopia.

** Kriesel, D.A., Lemma, K. (1989): Farming Systems Development Survey Report. TCP/ETH/6659. MOA, Addis Ababa, Ethiopia.

**Mearns, R., Shombodon, D., Narangerel, G., Tuul U., Enkhamgalan, A., Myagmarzhav, B., Bayanjargal, A., Bekhsuren, B., (1992): Natural resource mapping and seasonal variations andstresses in Mongolia. RRA Notes (1994), Issue 20, pp.95–105, IIED London.

**Mengistu, T.G. (2008): Characterization of Menz and Afar indigenous sheep breeds of smallholders and pastoralists for designing community-based breeding strategies in Ethiopia. Department of Animal Sciences, Haramaya University.

**Mikkelson, B. (2005): Methods for Development Work and Research: A new guide for practitioners. Sage Publications India.

** MOA, (1998): Agro-ecological zones of Ethiopia. Ministry of Agriculture. Addis Ababa, Ethiopia.

** Mukherjee, N. (1993): Participatory rural appraisal. Methodology and Applications. Ashok Kumar Mittal. Concept Publishing Company. New Delhi. India.

** SAS (Statistical Analysis System), 2003. SAS for windows, Release 9.1. SAS Institute, Inc., Cary, NC, USA.

**RRA Notes (1994): Livestock rehabilitation programme in Mozambique. Issue 20, pp.157– 162, IIED London.

**Scoones, I., (1994): Investigating Difference: Applications of Wealth Ranking and Household Survey Approaches among Farming Households in Southern Zimbabwe IT Publications, London.

**Scott, J. (1991): Social Network Analysis- A Handbook. Sage Publications. London.

**Swathi Lekshmi, P.S., Venugopalan, R., Padmini, K. (2008): Livelihood Analysis using Wealth Ranking Tool of PRA. CMFRI, Mangalore Research Centre of CMFRI, Manglore, Karnataka.

**Takele, T. (2006): On-farm Characterization of Sheko breed of in Southern Ethiopia cattle. An M.Sc Thesis presented to the School of Graduate Studies of Alemaya University of Agriculture, Ethiopia.105p.

**Tefera B., Sterk G. (2008): Hydropower-induced Land use change in Fincha’a Watershed Western Ethiopia: Analysis and Impacts. Mountain Research and Development. Vol 28. 72- 80. http://www.bioone.org/doi/pdf/10.1659/mrd.0811

**Tibbo, M. (2006): Productivity and health of indigenous sheep breeds and crossbreds in the central Ethiopian highlands. PhD dissertation. Department of Animal Breeding and Genetics, Faculty for Veterinary Medicine and Animal Sciences, Swedish University of Agricultural Sciences (SLU),Uppsala, Sweden. Available at web: epsilon.slu.se/archive/00001142/01/Markos_Tibbo_corrected.pdf

**Wassermann, S., Faust K. (1994): Social Network Analysis. Methods and Applications. University of Cambridge.

**Zeller, M., Feulefack, J, Neef, A., (2006): How accurate is Participatory Wealth Ranking (PWR) in Targeting the Poor? A case study from Bangladesh. Annual Meeting, August 12-18, 2006, Queensland Australia. International Association of Agricultural Economists.

**Zewdu, E. (2008): Characterization of Bonga and Horro indigenous sheep breeds of smallholders for designing community based breeding strategies in Ethiopia. An M.Sc Thesis presented to the School of Graduate Studies of Haramaya University. Ethiopia.

Annex A: Questionnaire

General information and socio-economic aspects:

1. How many members have the family?

2. Who is the household head?

3. Education status of household head? Grade level?

a. Illiterate b. Writing and reading c. Grade

4. Age_____

5. Religion______

6. Role in the church______

7. Position in the community______

Land and livestock holding:

8. Land holding (in ha) ______a) Crops (including fallow land) ______b) Fallow land (private)______c) Grazing private ______d) Grazing communal______

Others (specify) ______

9. Livestock holding

a. Cattle b. Sheep c. Goat d. Horse e. Donkey f. Others

10. What is your major farming activity? 84

a. Crop production b. Livestock c. Both

Grazing management:

12. Where your animals are mostly grazing? Rank

a) on private grazing areas b) communal grazing areas c) both d) others______13. Can you tell me your experiences with grazing areas for your animals? Are there some problems with grazing of your animals? If yes, which ones?

14. What experiences do you have with communal grazing?

Work groups and co operations:

15. Are you a member of a work group or cooperation of the community?

a. Yes b. No

17. If yes, which one?

a. Iddir b. Dado c. Other

18. Do you think that you are an active member of the community/cooperation and involved in their activities?

a. Yes b. No c. Don´t know 85

d. Other

19. In which activity do you think are you an active member?

Evaluate yourself in the following community activities!

• What do you think about your integration into ram exchange with other community members? Please, tell us, how would you estimate yourself in the activity of ram exchange?

• How would you evaluate your integration/participation in grazing animals together with other households?

• How would you evaluate yourself in the participation of Iddir activities? Could you please tell us, how would you evaluate yourself as a member of Iddir?

• Could you please tell us, how would you evaluate yourself as a member of Daddo?

Breeding ram:

20. What do you think about the number of breeding rams in your flock or in the flock where your animals are grazing? Are there sufficient or is there a lack of breeding rams?

21. Could you please tell us what could be the reason for a lack of breeding rams?

22. Could you please tell us about your experiences with breeding ram exchange?

23. Did you make experiences with ram lending or borrowing or both?

24. With whom did you make experiences or with whom do you practice exchange?

25. Do you exchange rams with individuals from outside of the community?

a. Yes 86

b. No

26. If yes, could you please tell us your 28. If no, why you do not practice ram experiences about this exchange and with how exchange with individuals from outside of many you practice this exchange? the community?

27. And why do you prefer to exchange ram with farmers outside of the community?

a. Close friendship b. Family member c. They have better animals for breeding d. They are richer e. Other reasons______

29. Do you want to tell us something else about breeding rams and your experiences with it? Did you ever notice that there are some problems belonging breeding rams and their exchange?

30. What could be done in your opinion to improve the exchange of breeding rams in your community? What would you suggest?

Institutions for breeding ram ( Menz)

31. Do you know an institution or organization that offers breeding ram for the communities? 87

32. Do you know how it is called?

33. Please, could you tell us about your experiences with this institution? And what do you think about this institution?