More than Just Trees: Assessing Reforestation Success in Tropical Developing Countries

background

This study presents a review of reforestation assessment that highlights the need to consider longer-term growth and maturation success, environmental success, and socio-economic success alongside the more common focus on short-term establishment success in tropical developing countries. The authors argue that reforestation assessment should not be based on success indicators alone, but should incorporate the drivers of success, encompassing an array of biophysical, socio-economic, institutional, and project characteristics, especially to understand why reforestation programs succeed or fail in order to design more successful future projects. This study presents a conceptual model for reforestation success assessment that links key groups of success indicators and drivers, and aims to provide a basis for a more comprehensive evaluation of reforestation success and the basis for the development of predictive systems-based assessment models.

Conclusions & Takeaways

Indicators of establishment success include survival rate and area planted. Indicators of forest growth success include tree growth, stand density, area remaining intact or area maintained long term, and actual production of timber, fuelwood, resin, and fruit. Indicators of vegetation structure include canopy cover, canopy height, ground cover, litter cover, and shrub cover. Indicators for measuring ecosystem functions include stable soil surfaces, soil erosions, soil fertility, landslide frequency, quantity of surface water and ground water, water quality, soil organic matter, biomass productivity, and carbon stock or carbon sequestration. Indicators of the socio-economic success of reforestation include increased local income, local employment opportunities, other livelihood opportunities, availability of food and fiber supplies, the stability of market prices, and local employment and capacity building. Finally, biophysical and technical drivers and reported to include site-species matching, tree species selection, site preparation, quality of seeds and seedlings, time of planting, technical capability of implementers, post-establishment silviculture, and site quality. The authors express that the predictive systems-based assessment models are essential to better orient reforestation projects planning and policy design and a strong tool to assist rural communities in tropical developing countries to ameliorate quality of life and poverty issues.