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DecisionSupport 8Key strengths• Can generate an understanding of socioinstitutionalstructures, actors and linkages,and ways to improve information andknowledge transfer• Can provide information on decision framingand key actors.• Can provide quantitative information andcorrelations to understand network variables(quantitative SNA).• Qualitative SNA is quick and relatively easy todo and encourages participation acrossdiverse viewpoints and actors.Potential weaknesses• Subjective bias and can be difficult togeneralise.• Time-consuming, intensive process(quantitative SNA).• Does not have a temporal or spatialdimension.• Networks have artificial boundaries (oftennecessarily).• Design of process is critical to get as manydiffering viewpoints as possible.Case StudiesThe MEDIATION study has reviewed existing literature examples and has applied network analysesto a number of adaptation case studies (see Varela-Ortega et al., 2013, Bharwani et al. 2012, Zhuand van Ierland, 2013 for more details). The case studies are summarised in the box below.Case Study 1 – Quantitative Social Network Analysis in FinlandThe first MEDIATION case study, focusing on Finland, used egocentric SNA to investigate farmers’involvement in environmental conservation through their relationships with other actor-types, i.e. withconservation stakeholders. The research was investigating the effectiveness (for the local ecologyand economy) of agri-environmental schemes (AES), and their role as potential adaptation options inthe context of future climate change. These schemes are an existing policy instrument for enhancingbiodiversity in Finnish agricultural landscapes, promoting active management and maintaining theconservation values of semi-natural grasslands and other traditionally managed biotopes.A survey was sent to farmers in two locations (SW Finland and Pirkanmaa), most of whom had anagro-environmental agreement (AEA) in place. The survey collected information about farmingpractices, farmer attitudes towards conservation, and other farmer attributes. It also included asection (related to SNA) which asked whether they had a connection or not to each of 14 differentactor categories. The response rate was 20%, with just under 400 answers, with over 250 of thesefully completing the survey section on their networks.The network analysis method was adapted to understand the role of different organisations in thecommunications about adoption of AEAs and the channels of potential influence and dissemination.Information about networks was included as an attribute, and was used to visualise and to makestatistical comparisons across several other variables.While a complete network and structure properties could not be constructed from the responses,because connections were directed to categories of actors/ actor-types rather than individually(uniquely) identified actors, the survey provided important information on individual networks.8
Social Network AnalysisThe overall set of responses were analysedcalculating the proportion of farmers having aconnection with each actor type. Several actorgroups had an overall connectivity of > 80%(administration of rural affairs, Forestry Union,Other farmers and forest owners and localhunters).Figure 2 shows results presented using Rstatistical software (R Core Team, 2012) and itspackages for network analysis and visualisation(Csardi and Nepusz, 2006).Figure 2 Caption-size of node labels and size of node isscaled to proportion farmers of connections.Subgroups in the farmer responses were analysed and compared. Subgroup categories were farmlocation (alternatives were SW Finland and Pirkanmaa), farm type (alternatives were animalhusbandry and crop) and type of AEA (alternatives were normal and special). Differences amongeach pair of alternatives were assessed visually, by comparing network diagrams, and statistically, byusing Chi-squared tests of differences in the proportions. The tests showed that, significantly,1) farmers in Pirkanmaa were more connected to local forest management than farmers in SW Finland2) farmers with special AEA were more connected to environmental administration and lessconnected to local industry than farmers with normal AEA3) farmers involved in animal husbandry were more connected with the Union for agriculture andforest producers than farmers involved in crop agriculture.The analysis showed differences in the way that various farmer subgroups connect with otherconservation stakeholders. While further network analysis and sampling would help in understandingthe networks and the wider survey results (i.e. regression analysis), the study has provided interestingfindings that identify areas for further research.Case Study 2 – Qualitative Social Network Analysis in SpainThe second case study focused on Guadiana river basin, presenting an illustrative example ofadaptation decision-making in the agricultural and water sectors (see Varela-Ortega et al.,submitted). This basin is expected to be one of the most seriously affected by climate change inSpain, with a potential decrease in water resources of 11% by 2030 and associated impacts onirrigated agriculture. A social network mapping exercise was undertaken to analyse the social andinstitutional framework of climate change adaptation. This was applied to a group of basinstakeholders: the water administration, representatives of the main irrigation communities, activeenvironmental groups and the different climate change offices involved in the basin (National andRegional). The analysis focused on ‘how are climate change adaptation related decisions taken inthe Guadiana basin, in the agricultural and water sectors?’A stakeholder workshop was used, grouping attendees into water administration, farmers andenvironmental / CC organizations. Each group built a socio-institutional network map, which weresubsequently discussed to help learning of all perspectives. Each box represents a differentstakeholder group (name, main objective, and influence [where a higher number indicates higherinfluence]). The linkages represent flows of information, funds and implementation capacity betweenstakeholder groups.9
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Decision SupportMethods for Climate
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