Non-Timber forest products and their role in rural livelihoods: A case study from the Annapurna Conservation Area, Nepal - Sherpa, Chungla (Ethnobotany Research and Applications, 2025)
Background: This study investigated the role of Non-Timber Forest Products (NTFPs) in supporting the livelihoods of communities in Parche area of Madi Rural Municipality, Kaski District, Nepal. Despite rich biodiversity and cultural diversity, communities face economic challenges from low agricultural productivity, reliance on remittances, and unstable tourism income. Sustainable NTFP use offers income potential, but limited awareness, value addition, and market access hinder its impact. Methods: Data from 100 respondents were collected via surveys, interviews, and focus group discussion. Socioeconomic data were analyzed using descriptive statistics and Ordinary Least Square (OLS) regression to assess relationships between variables like age, education, landholding, livestock, family size, forest distance, and NTFP income. A t-test evaluated the significance of each coefficient. Results: The study identified eight key NTFPs, with Allo (Girardinia diversifolia) and Nigalo (Drepanostachyum falcatum) significantly contributing to middle class household income. NTFPs also serve important medicinal, food, and household purposes. Analysis revealed that education (p < 0.001), landholding size (p < 0.001), family size (p < 0.001), and forest proximity (p = 0.025) significantly influence NTFP income, highlighting these factors as key determinants. Conclusion: This study underscores the critical role of NTFPs in supporting rural livelihoods in the Parche area, with Girardinia diversifolia and Drepanostachyum falcatum identified as key income sources. Socioeconomic factors such as, education, family size, landholding, and forest proximity significantly influence NTFP income. Promoting sustainable use, value addition, and market access can enhance their economic and cultural benefits. Keywords: Allo, Domestication, Income generation, Ordinary Least Square (OLS) Model