Abstract:
How forest income in Community Based Forest Management (CBFM), enters into
peoples’ livelihood diversification strategies, income distributional profile, degree of
dependency and variations due to contextual factors is unknown. This study was carried in
Sunya, Lengatei and Dongo wards in Kiteto district from September to December 2008 to
assess the impact of community based forest management on rural livelihoods. Livelihood
framework was the main approach used. Data collection involved Participatory Rural
Appraisal, household questionnaire survey, participant observation and focused group
discussion. The study population was grouped into; poor, medium and less poor wealth
groups. Descriptive and inferential statistics were used for quantitative data using
Statistical Package for Social Science (SPSS version 12) and Excel. The inferential
statistic analysis was used to determine the influence of household socio-economic
characteristics on household income and how environmental incomes influenced by other
sources of household income. Forest resource use dominated by fodder and firewood,
improved after CBFM. Household income contributions were as follows; Agriculture
96.3%, environmental income 2.8% and non-farm, off-farms and remittances contributed
0.9%. Livestock owning was potentially a variable with respect to household asset. Total
household income increased with increase in number of cattle and the relationship was
significant (P<0.001). Lower income households registered lower earnings in agriculture
than environmental income. Environmental income reduced income inequality, the Gini
coefficient without environmental income in respective study villages of Sunya,
Asamatwa, Lesoit and Olkitikiti was increased to 0.01, 0.01, 0.03 and 0.00 units
respectively. The overall Gini coefficient increased to 0.02 units. Community’s perception
on CBFM towards livelihood was generally positive. The study recommends; use of forest
resources in CBFM to identify actual needs of the local community in regard to existing
income category and social groups’ needs, improvement of existing livelihood options and
identification of other alternative livelihood options and income generating activities.
