Description
Abstract Agroforestry has been practiced in the Mid-hills for generations as the main source of the supplement of timber, firewood, and fodder. However, its adoption is influenced by different site-specific factors necessitating site-specific studies. In this regard, this study was conducted in the Mid-hill section of Western Nepal to identify major agroforestry practices and analyze determinants of adoption decisions. This was accomplished through direct observation, focus group discussions (n = 3), key informant interviews (n = 9), and household surveys (n = 200), and collected data were analyzed using descriptive statistics and ordinal logistic regression. Altogether five different agroforestry practices were found viz. Silvopasture, Commercial crop under tree shade, Home Garden, Trees in and around farmland, and Fruit Tree Orchard. Among them, 50.5% were adopting only one practice, 39.5% were adopting two practices, 10% were adopting more than two practices as per their needs and resources. The households with a male as household head, larger household size, greater land-holding and livestock units, higher cash income, sup-port for integrating crops, and at a farther distance from community forest were significantly more likely to get higher ratings for the adoption of agroforestry practices. Whereas, variables like caste, occupation, education, age, access to the forest, and membership in community-based organizations weren’t statisti-cally significant. Findings from this study can provide guidelines to stakeholders working to develop effec-tive agricultural management strategies along with promoting the integration of trees into farming sys-tems that meet farmers’ needs and preferences. How-ever, more research on socially acceptable, ecologi-cally sound, and economically beneficial agroforestry practices as well as specific species combinations suitable to the study area is needed to maximize prod-ucts and benefits from limited available arable land.
Keywords Adoption · Factors · Ordinal logistic regression · Integration
