A bayesian network analysis of reforestation decisions by rural mountain communities in Vietnam

Abstract

Reforestation is a primary factor in protecting upland forests providing economic sustenance for livelihood and keeping watersheds intact. In this study, we evaluated the importance of several direct and indirect drivers that can influence people’s decision for reforestation. Acquiring data from Cao Phong district of Vietnam, we utilized Bayesian Network (BN) to analyze how household characteristics, socio-economic status, biophysical environment, institutional support, and farm characteristics influenced reforestation decisions of local people. BN allowed us to identify 1) the main drivers that affect landholders ‘planted forest area, 2) how the key drivers affect among themselves, and 3) what causes constraints in tree planting. We surveyed 100 households for potential drivers, identified significant drivers by using bivariate analysis and stepwise linear regression, and created a BN to predict scenarios with different household’s perception regarding the planted forest area. The results revealed five direct drivers (attitude of household to tree planting, sources of investment capital for planting practice, land area, distance from household to market, experience of participating in forestry program) and seven indirect drivers (information about forestry program, incentives supported for tree planters, land tenure, accessibility to plantation forest, rotation length of planting trees, forest area, household income) that significantly influenced farmers’ reforestation decisions. Constraints in planting trees were due to the difficulties in protecting property from mortality and unreliable profit. Our results can assist design efficient forestry programs in Vietnam and in other comparable areas.

Keywords

• Bayesian network
• scenario analysis
• stepwise regression
• tree planting decision
• Cao Phong district