Using social network analysis to identify key stakeholders in agricultural biodiversity governance and related land-use decisions at regional and local level

In 2013 the European Commission launched its new green infrastructure strategy to make another attempt to stop and possibly reverse the loss of biodiversity until 2020, by connecting habitats in the wider landscape. This means that conservation would go beyond current practices to include landscapes that are dominated by conventional agriculture, where biodiversity conservation plays a minor role at best. The green infrastructure strategy aims at bottom-up rather than top-down implementation, and suggests including local and regional stakeholders. Therefore, it is important to know which stakeholders influence land-use decisions concerning green infrastructure at the local and regional level. The research presented in this paper served to select stakeholders in preparation for a participatory scenario development process to analyze consequences of different implementation options of the European green infrastructure strategy. We used a mix of qualitative and quantitative social network analysis (SNA) methods to combine actors’ attributes, especially concerning their perceived influence, with structural and relational measures. Further, our analysis provides information on institutional backgrounds and governance settings for green infrastructure and agricultural policy. The investigation started with key informant interviews at the regional level in administrative units responsible for relevant policies and procedures such as regional planners, representatives of federal ministries, and continued at the local level with farmers and other members of the community. The analysis revealed the importance of information flows and regulations but also of social pressure, considerably influencing biodiversity governance with respect to green infrastructure and biodiversity.

Assessing the resilience of a real-world social-ecological system: lessons from a multidisciplinary evaluation of a South African pastoral system

In the past decades, social-ecological systems (SESs) worldwide have undergone dramatic transformations with often detrimental consequences for livelihoods. Although resilience thinking offers promising conceptual frameworks to understand SES transformations, empirical resilience assessments of real-world SESs are still rare because SES complexity requires integrating knowledge, theories, and approaches from different disciplines. Taking up this challenge, we empirically assess the resilience of a South African pastoral SES to drought using various methods from natural and social sciences. In the ecological subsystem, we analyze rangelands’ ability to buffer drought effects on forage provision, using soil and vegetation indicators. In the social subsystem, we assess households’ and communities’ capacities to mitigate drought effects, applying agronomic and institutional indicators and benchmarking against practices and institutions in traditional pastoral SESs. Our results indicate that a decoupling of livelihoods from livestock-generated income was initiated by government interventions in the 1930s. In the post-apartheid phase, minimum-input strategies of herd management were adopted, leading to a recovery of rangeland vegetation due to unintentionally reduced stocking densities. Because current livelihood security is mainly based on external monetary resources (pensions, child grants, and disability grants), household resilience to drought is higher than in historical phases. Our study is one of the first to use a truly multidisciplinary resilience assessment. Conflicting results from partial assessments underline that measuring narrow indicator sets may impede a deeper understanding of SES transformations. The results also imply that the resilience of contemporary, open SESs cannot be explained by an inward-looking approach because essential connections and drivers at other scales have become relevant in the globalized world. Our study thus has helped to identify pitfalls in empirical resilience assessment and to improve the conceptualization of SES dynamics.