Combining participatory modeling and multi-criteria analysis for community-based forest management

• Published in: Forest ecology and management 2005-03, Vol.207 (1), p.145-156
• Main Authors: Mendoza, Guillermo A., Prabhu, Ravi
• Format: Article
• Language: English
• Subjects: analysis; Animal and plant ecology; Animal, plant and microbial ecology; Biological and medical sciences; collaboration; criteria; Forest management; Forest management. Stand types and stand dynamics. Silvicultural treatments. Tending of stands. Natural regeneration; Forestry; Fundamental and applied biological sciences. Psychology; Multi-criteria analysis; participation; Participative modeling; Participatory management; Synecology; Terrestrial ecosystems
• ISSN: 0378-1127; 1872-7042
• DOI: 10.1016/j.foreco.2004.10.024

Participatory approaches to forest management have gained wide acceptance and have also become the primary guiding principle in the management of natural resources worldwide. Despite their widespread popularity, participatory methods developed so far have often been criticized as lacking in rigor and in need of better structuring and analytical capabilities. This paper proposes and combines two approaches, namely multi-criteria analysis (MCA) and participatory modeling. MCA offers an analytical environment where multiple goals, objectives, and perspectives can be accommodated and analyzed collectively and holistically. Such framework is deemed appropriate under a community-based forest management (CBFM) setting that is typically characterized by plurality of opinions and interests. Participatory modeling, on the other hand, is a general framework that subscribes to the principles of participatory action research, where direct participation of local communities is deemed crucial to the success of any management strategy. To ensure that the modeling process is participatory, the modeling environment, model formulation, and model development must be transparent and within the grasp of local participants. This paper describes how these two approaches can be integrated in a decision support system. The integration of the two approaches takes advantage of the analytical capabilities of MCA and the open and collaborative nature of participatory modeling. Applications of these two approaches as stand alone models are briefly described. A more detailed case study describing the integration of the two approaches is also described.