Indigenous Knowledge for Forest Management and Climate Change Adaptation in Forest Dependent Communities of Cross River, Nigeria

ABSTRACT

Climate change is a major global human development challenge. Modern technologies have been largely unsuccessful in tackling this challenge, thus indigenous knowledge for forestmanagement is being considered as an alternative solution. There is dearth of knowledge on the effects of cultural factors on climate change adaptation in forest-communities of Cross River, hence, this study examined the extent to which beliefs and practices of forestmanagement in forest-dependent communities of Cross River are engaged in addressing challenges of climate change.

 Ecological modernisation served as the theoretical framework while the research design was Participatory Rural Appraisal. A semi-structured questionnaire was used to collect information from 459 respondents purposively selected from three forest-dependent communities representing mangrove forest (Iko-Esai, 153), Ekuri forest (Agoi-Ibami, 191), and Mbe/Afi forest (Butatong, 115) blocks. Quantitative data collected was on sociodemographic characteristics, indigenous beliefs and practices, and their influence on climate change adaptation behaviour. Indigenous knowledge was assessed with an 8-item instrument which categorised indigenous forest-management practices into: zero-tilling, soil-mulching, bush-fallow, crop-rotation, green-manure, mixed-cropping, tree-felling, hunting taboos, and tree planting.  Key Informant Interviews were conducted with 12 officials of the Ministry of Environment, and 33 In-Depth Interviews with 18 community leaders, seven forest managers, five academics, and three policy makers on forest-related traditions and practices. Climate change and transforming social structures were assessed through reviews of archives, reports and maps. Transects were used to identify similarities and differences of paths. Seasonal calendars assessed sequences of events and their relationship with the people; while institutional analysis assessed communities‘ interests, layout, infrastructures, health and wealth patterns. Quantitative data were analysed using descriptive statistics and linear regression at p