GUIDELINES FOR CONSERVATION AND RESTORATION OF WATERSHED FORESTS USING COMMUNITY METHODS IN WANG NUEA DISTRICT, LAMPANG PROVINCE

Abstract

This research aims to 1) Study the conservation and restoration of headwater forests by community methods in Wang Nuea District, Lampang Province, 2) Develop the process of conservation and restoration of headwater forests by community methods, and 3) Propose the mechanism of conservation and restoration of headwater forests by community methods. This is a participatory action research with key informants in in-depth interviews, 25 monks/persons, and focus group discussions, 5 monks/persons, using a structured interview form. The data were analyzed in terms of content and presented in a descriptive manner. The results showed that 1) The study of conservation and restoration of headwater forests by community methods in the past consisted of the forest destiny ceremony, the forest ordination ceremony, the reforestation ceremony, the construction of water retention dams using local wisdom, and the construction of forest firebreaks. 2) The development of the process of conservation and restoration of headwater forests by community methods by integrating Buddhist principles with community methods in conservation and restoration of headwater forests, making people in the community aware of the importance of inherited local wisdom through rituals, and the interdependence between forests water, and people, which creates unity and harmony. Continue to drive networks to conserve and restore headwater forests sustainably. 3) The mechanism of conservation and restoration of watershed forests by community methods has been used for both direct and indirect benefits which is consistent with the way of life, beliefs, and cultures in each locality, adhering to the same practices. From the research results, the knowledge is summarized as follows: Knowledge K = (Knowledge), Participation P = (Participation), Wisdom innovation W = (Wisdom innovation) Conservation group C = (conservation), Network S = (Social Network).