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

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

Eli SagorUniversity of Minnesota Extension, St. Paul

Mark RickenbachUniversity of Wisconsin, Madison

Amanda KueperUniversity of Minnesota Extension, St. Paul

Outline

Background: Social networks

Case: Kickapoo Woods Cooperative

Cases: Five qualitative case studies

Policy recommendations

Policy objectives

Keep private forest lands forested

Maximize the flow of benefits from private forests.

Social influence on behavior

Social networks enable

Social influence on behavior

Social networks constrain

Social network analysis

Theory and analytical tools

Social network analysis

Theory and analytical tools

Relationships matter

Low-density network

Social network analysis

Theory and analytical tools

Relationships matter

High-density network

Social network analysis

Theory and analytical tools

Relationships matter

Highly centralized network

Social network analysis

Theory and analytical tools

Relationships matter

Social network analysis

Theory and analytical tools

Network attributes Structural analysis

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 2003Granovetter 1973

Policy relevance: Community capacity for adaptive management and SN attributes

Social memory

Heterogeneity

Resilience

Learning

Crona, Bodin, and Ernstson 2006

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

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

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

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

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

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

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?

Case examples: the real world

Wisconsin landowner cooperative

Wisconsin landowner cooperative

Rickenbach 2009, Fig. 4

Wisconsin landowner cooperative

Rickenbach 2009, Fig. 3

Reaching unengaged landowners

Most KWC members had not previously participated in other available landowner assistance programs.

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.

5 peer learning network models

Kueper, Sagor, and Becker: preliminary data

Oregon: Master Volunteer prog.

SW Wisconsin: landowner co-operative

Virginia: Landcare

Queensland, Australia: Landcare

Queensland, Australia: Landcare

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.

DavisPolicy recommendations

Invest in social-ecological systems research

Need for investment in social-ecological systems research.

Role and impacts of personal networks on landowner behavior.

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?

Understand interventions and outcomesBuild new networks

Moderate information flow

Support volunteers

Support independent local organizations

Ning site screenshot

Steering committee

Brett Butler, US Forest Service FIA / NWOSMark Buccowich, US Forest Service, NAShorna Broussard Allred, Cornell UniversityKarl Dalla Rosa, US Forest Service, Co-op ForestryDylan Jenkins, TNC PennsylvaniaDavid Kittredge and Paul Catanzaro, UMass AmherstAmanda Kueper, University of MinnesotaJim Johnson, Oregon State UniversityMaureen McDonough, Michigan State UniversityJames Malone, AL Treasure Forest Assoc.Don Mansius and Kevin Doran, Maine Forest ServiceEric Norland, CSREES

http://WoodlandOwnerNetworks.ning.comor http://bit.ly/10G9ky

Eli Sagor, esagor@umn.edu