K. K. Islam
Forest Policy Laboratory, Kyushu University, Hakozaki, Higashi, Fukuoka, Japan
Hyakumura Kimihiko
Institute of Tropical Agriculture, Kyushu University, Hakozaki, Higashi, Fukuoka, Japan
Masakazu Tani
Faculty of Design, Kyushu University, Shiobaru, Minami, Fukuoka, Japan
Max Krott
Chair of Forest and Nature Conservation Policy, Georg-August University Gottingen, Buesgenweg 3, Germany
Noriko Sato
Forest Policy Laboratory, Kyushu University, Hakozaki, Higashi, Fukuoka, Japan
Participatory Forestry, Livelihood Assets, Sal Forests
Resource Development and Management
Impact, Income generation
Study Area: The moist deciduous Sal forests cover an area of 120,000 ha and these forests owned by the Bangladesh Forest Department (FD, 2014; Islam & Sato, 2012a). Sal forests were distributed over the relatively drier central and north-western part of the country consists of mainly Tangail, Mymensingh, Gazipur and Dhaka districts. Majority of the Bangladesh Sal forests are located at the Tangail and Mymensing districts which is called Madhupur Sal forests and considered one of the most significance PF areas in Bangladesh (Islam & Sato, 2012a; Islam et al., 2013). The study was conducted at the whole Madhupur Sal forests area of Bangladesh.
Description of Participatory Forestry Program: In this program, each participant (local people who is a member of participatory forestry program called Participant) was allocated 1 ha of degraded forest land for PF plantation duration of 10 year rotation cycle. Each participant can continue up to three rotation cycles (30 years) if he/she maintain the program criteria properly. The fast growing firewood producing tree species (e.g. Acacia spp., Bokain, Gamar) were selected for plantation with a spacing of 2 m × 2 m (total 2500 tree/ha). After 4 years, 50% of the standing trees were thinned out (1st thinning) and this technique was repeated after 7 years (2nd thinning). The remaining 625 (approximately) trees were finally harvested at the end of the 10-year cycle. The FD and participants shared the benefit of the 2nd thinning and final tree harvest outputs at a ratio of 45%:45% and the remaining 10% benefit will store for future tree plantation called TFF (Tree Farming Fund). The participant can cultivate annual crops in association with trees at any time of the 10-year rotation cycle and the crops together with 1st thinning benefits were granted solely to the member. This type of participatory forest management approaches were gaining popularity in all over the Bangladesh.
Sampling, Data Collection and Analytical Techniques: Both qualitative and quantitative data were collected to visualize the impacts of PF on participants’ livelihoods assets and identify the actors’ power dynamics in this study. Quantitative data were collect through a semistructured questionnaire survey, and for qualitative data this study used interview of forest department staff, local people, journalists, non-government organizations staff, leaders and donor agencies key staff. This study also conducted focus group discussion, personal observation and literature review to collect data. The study randomly selected 60 participants for interview and 30 non-participants who were possessing similar socioeconomic conditions with participant before stated PF program. A total of 3327 PF participants were involved in Madhupur Sal forests area (Islam & Sato, 2012a). During field visit actors were asked about their views on other actors and this study tried to cover all PF actors listed in the result section. Interview questionnaire were pretested and improved before conducting the final interview and a research team consisting of 5 members were involving in data collection at Madhupur area during different months of 2012 to 2014. For the actor power analysis, the study covered every actors and also asked each actor their judgment on the power elements of coercion, incentives and trust for the other actors. To measure the different elements of power, the study used a simple scaling systems of 2 for powerful actors and 1 for non-powerful actors and finally the average round numerical figure were tabulated. On the other hand, various scaling and indexing me- thods was adopted to measure human, physical, social, natural and financial capitals so that it was possible to make them comparable and to allow meaningful interpretation. Most of the indicators are determined by using rating scale methods in terms of different weight: 0.33, 0.66 and 1.0 interpreted as poor, medium/average and good. The questions that have three answer choices measured as: I = Good% × 1 + Medium% × 0.66 + Poor% × 0.33. The two answer questions, Yes or No were interpreted as: I = Yes% × 1 + No% × 0. The economic benefit questions related to money was measured in different ways. Less than the mean value was classified as poor with the weight of 0.33; more than the mean but less than 1.5 × mean treated as medium/average with the weight of 0.66; and more than 1.5 × mean was classified as good with the weight of 1.0. Similar types of calculation procedure were followed for participants’ tree stocks and livestock indicators. After weight calculation of each indicator, we calculated the value of each type of livelihood asset and finally the overall livelihood asset value.
Open Journal of Forestry, 2014, 4, 1-9
Journal