A network approach to private forest owner assistance: Theory, models, and policy recommendations

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Presented at the 2009 Society of American Foresters convention in Orlando, FL.

Presented at the 2009 Society of American Foresters convention in Orlando, FL.

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  • For the past four decades at least, there’s been increasing recognition of the importance of people-centered approaches to the problem of optimizing the flow of benefits from private forests. The policy goals are to keep private forest lands forested and to maximize the flow of benefits from those lands.
  • Social networks enable behaviors that require specialized knowledge, skills, or equipment. Without access to the right people, these things don’t get done, or get done in less than optimal ways.
  • Social networks constrain behaviors to a large degree. This argument is advanced in Granovetter’s 1985 paper “Economic action and social structures: The problem of embeddedness.” Landowners consistently report freedom from outside constraints on land management behavior as a top priority. But particularly for new landowners, social norms and other influences may constrain options. In addition, as noted in the previous slide, access to trusted help may be an important constraint.
  • Wisconsin – Case 2
  • This section based on Rickenbach 2009, Forest Policy & Economics, in press. KWC: 180 members at time of study SW Wisconsin Co-op provides a variety of services to members, mainly forest planning & management and information / education Mail survey to evaluate co-operative performance and map social networks within the membership a. While ties among members are quite limited (and thus limiting strong links among members), ties among network members and the larger community suggest that the cooperative has connections that Extension, public agency foresters, and others lack. b. Members see other members as highly trustworthy yet rarely list other members in personal networks. Member attendance at events is high, as are opps to discuss issues with other attendees. Measurement problem? c. Most KWC members had not obtained forestry assistance from other sources.
  • Many ties between KWC staff and members. Few internal ties. Members report a very high level of trust with other members, and several well-attended events every year promote member-to-member interaction and exchange, but surprisingly few ties with other members are reported.
  • Members have many ties outside KWC, with both professionals and non-member landowners. KWC connects members to “trusted” sources of professional assistance outside the co-op. Information moving from KWC staff to members moves well beyond members to other landowners.
  • Master volunteer, landowner cooperative, US Landcare, two Australian Landcare orgs. Preliminary data: Analysis complete on one case of five.
  • Oregon – Case 1
  • Wisconsin – Case 2
  • Virginia – Case 3
  • Case 4 – Dalrymple Landcare – Charters Towers area, QLD Australia
  • Preliminary findings: i. Atmosphere / community aspect: opportunity to engage with similar people. Homophily. Comfortable, safe space for dialogue. Professionals with connections to the group considered trusted. ii. Importance of both professional and experienced landowner perspectives and ways of knowing in a safe discussion space can be highly conducive to learning. Without participating, would not have had access to the experience-based knowledge from other landowners. (KWC study suggests might not have prior access to professionals either—most members not served by cost-share or tax subsidies.) iii. Social learning through observation and discussion: Visits to other landowners’ properties as well as informal discussions of what worked, what didn’t, and why. iv. Participation positively impacted landowner knowledge and confidence through both knowledge gain and resource network: If don’t know the answer, know where to find it.
  • a. Early research suggests that approaches emphasizing peer learning opportunities may present an opportunity to reach landowners unengaged by other policy tools. Networks can build trust through relationships, which positively impact resilience of social-ecological systems. b. Need for better understanding of the influence of personal networks on LO behavior, efficient roles for professionals, and how networks complement and add value to other policy tools to promote sustainable private forest management. (FIA investment in counting trees vs. people). c. Future research: Measuring networks, DBK’s idea of decision archi Consider joining WON network online!

Transcript

  • 1. A network approach to private forest owner assistance: Theory, models, and policy recommendations Eli Sagor University of Minnesota Extension, St. Paul Mark Rickenbach University of Wisconsin, Madison Amanda Kueper University of Minnesota Extension, St. Paul
  • 2. Outline
    • Background: Social networks
    • Case: Kickapoo Woods Cooperative
    • Cases: Five qualitative case studies
    • Policy recommendations
  • 3. Policy objectives
    • Keep private forest lands forested
    • Maximize the flow of benefits from private forests.
  • 4. Social influence on behavior
      • Social networks enable
  • 5. Social influence on behavior
      • Social networks constrain
  • 6. Social network analysis
      • Theory and analytical tools
  • 7. Social network analysis
      • Theory and analytical tools
      • Relationships matter
      • Low-density network
  • 8. Social network analysis
      • Theory and analytical tools
      • Relationships matter
      • High-density network
  • 9. Social network analysis
      • Theory and analytical tools
      • Relationships matter
      • Highly centralized network
  • 10. Social network analysis
      • Theory and analytical tools
      • Relationships matter
  • 11. Social network analysis
      • Theory and analytical tools
      • Network attributes Structural analysis
  • 12. Tie strength
    • Weak ties: Distant, infrequent contacts. Most efficient for easily codified knowledge.
    • Strong ties: Close, frequent, trusted contacts. Most efficient for tacit knowledge.
    Reagans & McEvily 2003 Granovetter 1973
  • 13. Policy relevance: Community capacity for adaptive management and SN attributes
    • Social memory
    • Heterogeneity
    • Resilience
    • Learning
    Crona, Bodin, and Ernstson 2006
  • 14. Policy relevance: Community capacity for adaptive management and SN attributes
    • Social memory
    • Heterogeneity
    • Resilience
    • Learning
    Crona, Bodin, and Ernstson 2006 Network density Reachability Centrality Betweenness / modularity
  • 15. Policy relevance: Community capacity for adaptive management and SN attributes
    • Social memory
    • Heterogeneity
    • Resilience
    • Learning
    Crona, Bodin, and Ernstson 2006 Network density Reachability Centrality Betweenness / modularity
  • 16. Policy relevance: Community capacity for adaptive management and SN attributes
    • Social memory
    • Heterogeneity
    • Resilience
    • Learning
    Crona, Bodin, and Ernstson 2006 Network density Reachability Centrality Betweenness / modularity
  • 17. Policy relevance: Community capacity for adaptive management and SN attributes
    • Social memory
    • Heterogeneity
    • Resilience
    • Learning
    Crona, Bodin, and Ernstson 2006 Network density Reachability Centrality Betweenness / modularity
  • 18. Policy relevance: Community capacity for adaptive management and SN attributes
    • Social memory
    • Heterogeneity
    • Resilience
    • Learning
    Crona, Bodin, and Ernstson 2006 Network density Reachability Centrality Betweenness / modularity
  • 19. Policy relevance: Community capacity for adaptive management and SN attributes
    • Social memory
    • Heterogeneity
    • Resilience
    • Learning
    Crona, Bodin, and Ernstson 2006 Network density Reachability Centrality Betweenness / modularity
  • 20. Research questions
    • What kind of information flows through woodland owner networks, and how?
    • What are the outcomes of different models of peer-to-peer outreach?
    • How does participation affect network size and access to trusted information?
    • How do personal networks affect woodland owner behavior?
  • 21. Case examples: the real world
  • 22. Wisconsin landowner cooperative
  • 23. Wisconsin landowner cooperative Rickenbach 2009, Fig. 4
  • 24. Wisconsin landowner cooperative Rickenbach 2009, Fig. 3
  • 25. Reaching unengaged landowners
    • Most KWC members had not previously participated in other available landowner assistance programs.
  • 26. Lessons learned
    • Professional assistance: Either KWC staff or recommended others: References vetted, hence trusted.
    • Members report frequent contact with other members (field days, etc) and high trust, yet few name members in personal networks.
  • 27. 5 peer learning network models Kueper, Sagor, and Becker: preliminary data
  • 28. Oregon: Master Volunteer prog.
  • 29. SW Wisconsin: landowner co-operative
  • 30. Virginia: Landcare
  • 31. Queensland, Australia: Landcare
  • 32. Queensland, Australia: Landcare
  • 33. Preliminary findings
    • Atmosphere matters. Safe space, trust highly conducive to active learning.
    • Variety of perspectives highly valued.
    • Learning through observation of similar properties and landowners: Homophily.
    • Participation positively impacts knowledge, confidence, and connections.
  • 34. Davis Policy recommendations
  • 35. Invest in social-ecological systems research
    • Need for investment in social-ecological systems research.
    • Role and impacts of personal networks on landowner behavior.
  • 36. Investigate the role of peer learning in current LO assistance programs
    • Flat budgets and limited capacity: Must increase outreach impact and efficiency.
    • Nothing substitutes for one-on-one professional – landowner contact. But how to supplement and add value to it?
  • 37. Understand interventions and outcomes
    • Build new networks
    • Moderate information flow
    • Support volunteers
    • Support independent local organizations
  • 38. Ning site screenshot
  • 39.
    • Steering committee
    • Brett Butler , US Forest Service FIA / NWOS
    • Mark Buccowich , US Forest Service, NA
    • Shorna Broussard Allred , Cornell University
    • Karl Dalla Rosa , US Forest Service, Co-op Forestry
    • Dylan Jenkins , TNC Pennsylvania
    • David Kittredge and Paul Catanzaro , UMass Amherst
    • Amanda Kueper , University of Minnesota
    • Jim Johnson , Oregon State University
    • Maureen McDonough , Michigan State University
    • James Malone , AL Treasure Forest Assoc.
    • Don Mansius and Kevin Doran , Maine Forest Service
    • Eric Norland , CSREES
  • 40. http://WoodlandOwnerNetworks.ning.com or http://bit.ly/10G9ky Eli Sagor, [email_address]