2.1. Study Location
The study was conducted in Borana zone rangelands, southern Ethiopia. The Borana zone is one of 13 administrative zones within Oromia National Regional State. It is located between 3°36 – 6°38’North latitude and 3°43’- 39°30’ East longitude and borders Kenya in the south, Somali Regional Government in the east, and the Ethiopian highland districts in the north. The Borana rangelands cover an area of about 50,000 km2 of which 75% consists of lowland, and frequently exposed to droughts. Based on CSA (2017) population census, the Borana administrative zone is currently inhabited by a projected population of about 1.2 million people.
Figure 1: Study location, Source: Tolossa (2018)
The Borana rangelands are characterized by an arid and semi-arid climate, with pockets of sub-humid zones. The average annual rainfall varies between 350 mm and 900 mm with a considerable variability of 21 to 68% among years (Lasage et al., 2010). Rainfall is characterized by bimodal, with 60% of the annual rainfall occurring between March and May (main rainy-season) followed by a minor peak between September and November (small rainy-season). The long-term variability in the quantity and the distribution of the rainfall results in recurrent droughts in the area. In the past, droughts lasting several years occurred approximately once in 20 years and isolated dry years (< 400 mm) once in five years (Coppock, 1994). Recently, the period between droughts has decreased to 5 years, and the latest drought was observed only 3 years after the previous one (Bekele, 2013).
The topography in the Borana rangelands is distinguished by plain rangelands, intersected with occasional mountain ranges, volcanic cones and depressions, and an altitude between 750 and 1700 m.a.s.l. They are dominated by tropical savannah vegetation, with varying proportions of open grasslands, and perennial herbaceous and woody vegetation (Alemayehu, 2002). Cossins and Upton (1988) classified the Borana rangelands into four ecological zones of different potential grazing capacity; high potential savannah in the northern part, bushlands with high shrub cover in the central area, medium potential grassland in the east and volcanic areas in the west.
Yabello district is one of the representatives of the core of Borana pastoralism system and the cultural sites of the Gada system (Indigenous governance system) which govern the overall rangelands resource use (Ayana and Oba, 2007). Recently, development interventions such as the establishment of ranches, intensification of cropping farm, curtailment of mobility, etc. are intensive in this district. Thus, for the purpose of this study, Yabello district was selected. Yabello district encompasses a total area of 5556.7 square kilometers. The study area is characterized mainly by lowlands and hills. It lies between 1350 and 1800 meter above sea level. Based on current demarcation, the district comprises 18 lowest administrative units called kebele of which 8 kebeles are mainly depended on pastoralism; and the remaining 10 kebeles are characterized by agro-pastoralist.
2.3. Actors’ identification process and data collection
Before discussing the process involved in identification of stakeholders’/actors’ considered in network analysis, it is worth highlighting the formal institutional setup related to rangeland resource governance in the country. In Ethiopia, generally, the political administration has five tiers: national or federal level, regional states, zones, districts and kebeles. Generally, institutional setup for natural resource management and governance follows the same jurisdictions. Rangeland use is mainly governed by the Federal Democratic Republic of Ethiopia Rural Land Administration and Land Use Proclamation No. 456/2005. The proclamation provides the general framework and power for national and regional governments to set hierarchical organizational structures down to district level to regulate sustainable land use system. However, there are no formal state organizations specifically dealing with rangeland resources management.
At national level, policy issues and development interventions related to rangeland management are coordinated by Ministry of Environment, Forest and Climate Change (recently named as commission), Ministry of Agriculture and Natural Resources, Ministry of Water, Minerals, and Energy, Ministry of Federal Affairs and Ministry of Livestock and Fishery Development (currently, merged to Ministry of Agriculture and Natural Resources). At regional level, rangeland resource governance and management is shared responsibilities of Bureau of Environment, Forest and Climate Change Authority, Bureau of Agriculture and Natural Resources, Bureau of Rural Land Administration and Use, Bureau of Water, Minerals and Energy and Bureau of Livestock and Fishery Development. The institutional structures at Zonal and district levels follow the same lines. Alongside with these state institutions, many organizations such as Research Institutes, Universities, Non-government organizations, State Enterprises and Community Based Organizations and/or customary authorities have interest in rangeland management practices.
The Ministry of Federal affairs is mandated to coordinate the overall pastoral development programs and projects in pastoral areas. However, existing reality shows that there have been poor coordination efforts. In connection with this, many argue that the intention of government is largely motivated by recognition of exerting pressure to control pastoral areas (Abdulahi, 2007). More importantly, as indicated by the key informants from stakeholders, the linkages between stakeholders institutions are quite weak at all levels of scales of management, especially between different lines of management.
To identify active stakeholders/actors involved in rangeland resources management, a combination of purposive and snow ball sampling techniques were employed. Initially, successive consultations were made with major organizations which have direct roles in rangeland governance including Borana Zone Pastoral Development Office, Pastoral Commission and Land Use and Environmental Protection offices to recommend other stakeholders actively involved in rangeland governance system. The snowball sampling method was followed until no new organizations were mentioned. The snowball sampling procedure was started from district level actors. From the local actors, 2 actors were interviewed. To avoid biasedness that may arise from the snowball sampling method in overlooking other stakeholders involved in rangeland management issues, available secondary data, specifically, policy documents were consulted to complement the initial process. Ultimately, a total of 53 actors were included in the social network analysis.
2.4. Network data analysis
To assess the effectiveness of the rangeland resource governance network, analyzing the nature of actors’ interactions in a network is a prerequisite (Bodin and Crona, 2009). The network structures (relational pattern) influence the behavior and actions of actors involved in the network and overall effectiveness of the governance system (Sandström and Rova, 2010). These network structural characteristics, which have strong functional implications for the resilience of the network, broadly include: number of social ties, degree of cohesion, subgroup inter-linkages network centralization and actor centrality (Bodin and Crona, 2009).
In shedding light on how each of the network structural characteristics affect the overall performance of natural resource governance, the number of social ties, captured by network density, greatly affect the outcomes of the network governance as the more social ties tend to increase the possibilities of collaboration, mutual trust development and joint action (Sandström and Rova, 2010). The existence of higher network density also facilitate for the co-production of knowledge that are useful in SES resilience building (Bodin and Crona, 2009).
The other important structural characteristic, the level of network cohesiveness, measures to what extent the network is separated into distinguishable subgroups. Looking at its effect on the outcome of the network governance, the existence of subgroups, low cohesion, can possibly pose challenges on collaborative process among subgroups (Hannemann and Riddle, 2011). Generally, less cohesive network exhibit high density in the overall network and produce positive governance outcomes as it facilitates sharing of important resources such as knowledge and information (Bodin and Crona, 2009). It is important to note that if connectivity exists between different subgroups (bridging ties) there would be a high possibility in using external resources which in turn improves the capacity of the network governance (Crona and Hubacek, 2010). In the Borana rangeland governance, for instance, the connections beyond the subgroups potentially promote and create collaborative partnership between various types of the actors including NGOs, government line organizations, higher education and research institutes, community based organizations, and customary authorities.
The relative position and influence of individual actors, covered in the concepts of network centralization and actor’s centrality, significantly affect the capacity of the natural resource governance regime in many ways (Bodin and Crona, 2009). The level of centrality deals with the distribution of linkage among actors (nodes) in the network and their structural importance (Bodin et al., 2006). Centrally, the network centrality helps to understand which actors and how they can use their structural position and able to influence the collaborative process and how they are advantageous over the others in accessing important resources (Crona and Hubacek, 2010). Though there are multiple ways to measure network centrality, for the purpose of this study we attempted to measure two metrics: degree and betweenness centrality.
In this way, following related literature (Hanneman and Riddle, 2005; Bodin and Crona, 2009; Sandström and Rova, 2010; Prell, 2012; Scott, 2015) methods of social network analysis were used to map, quantify, and analyze the relational patterns or connection between actors in rangeland governance. Structural properties of networks, the ones described above were measured to analyze the effectiveness of network governance. Brief descriptions of the quantitative network analysis on the selected metrics are highlighted in the subsequent sections.
The network density measures the proportion of all possible ties present in a network and used as proxy of social cohesiveness (Bodin and Crona, 2009). Density is calculated by dividing the number of existing connections in the structure by the maximum number of possible connections (Scott, 2015).
Where: D denotes density
l = maximum number of links
n= number of actors
The network structures in which all actors are completely connected have a density of 1 reflecting strong cohesiveness in the network structures and effective governance. However, the existence of subgroup in the network may mislead the interpretation of the whole network and the existence of various subsets of actors should be considered (Sandström and Rova, 2010). In our case, the data were clustered into 6 subgroups based on the main organizational goals and governance scales and the density of subgroup structures were examined.
To identify actors’ structural position, or coordinating actors that would otherwise have limited or no connections, parameters of the network centrality was analyzed. Network centrality measures how central or well-connected an actor in a network (Sandström and Rova, 2010). It also describes the patterns of power relation and how much an actor has access to the resources in the network (Dkamela et al., 2014; Angst et al., 2018). For the present study, from various versions of centrality parameters, degree centrality and betweenness centrality were considered.
Degree centrality measures the numbers of direct links to and from an actor (Sandström and Rova, 2010). Degree centrality parameter helps to visualize how tightly the network is organized around its most central point or how ‘star-like’ the network structure is (Bodin and Crona, 2009; Sandström and Rova, 2010; Scott, 2015). A high degree of centrality is interpreted as well-connected network structures (Sandström and Rova, 2010). In related front, to identify potential bridging actors within identified clusters, betweenness centrality scores were calculated for each actor in the network. Betweenness is the number of shortest paths from all nodes (actors) to all others that pass through one specific node (actor) (Dkamela et al., 2014).
Betweenness centrality helps to quantify how much each actor contributes to minimizing the distance between actors in the network (Bodin et al., 2006). It measures an actor bridging position with respect to other two actors in the network. It is the probability of an actor being on the shortest path between two points, reflecting how often a node lies on a shortest path between any two nodes in the network (Angst et al., 2018). To put it another way, betweenness centrality refers the number of times an actor located between two other actors who are disconnected. An actor with high betweenness centrality is considered to have a great influence over the course of interactions, hence affect how rangeland resources governance system delivers governance assets (Hanneman and Riddle, 2005).
To identify actors playing great role in connecting actors out of the clusters, following Vance-Borland and Holley (2011), we calculated brokerage scores. All network metrics used to describe the characteristics of the governance networks were calculated using UCINET version 6.591(Borgatti et al., 2002). In order to analysis the network structure, the data were transformed into binary network measures (1 for presence of collaboration and 0 for absence of collaborative relationship). To depict actors’ relationship visually, NetDraw software was employed.