This report identifies the driving forces for reforestation in three villages of Northern Vietnam. Using an institutional analysis focused on the rules governing upland access and use, the authors assess the relative impact of state policies (reforestation programs and forestland allocation) on land use change. Findings show that the latter are indirectly responsible for reforestation, but not because of the incentives they provided. Instead, they disrupted the local rules governing annual crop cultivation and grazing activities leading to the end of annual cropping.

Agricultural soils in the tropics have undergone significant declines in their native carbon stock through the long-term use of extractive farming practices. However, these soils have significant capacity to sequester CO2 through the implementation of improved land management practices. This paper reviews the published and grey literature related to the influence of improved land management practices on soil carbon stock in the tropics.

Most forests in Sri Lanka are secondary, are mostly confined to the dry and intermediate zones of the country, and arise out of swidden agriculture. The majority of secondary forests which regenerate after swidden farming in the dry parts of Sri Lanka are grown from vegetative parts, that are from remaining roots and stumps. Secondary forests provide numerous products of importance to local people. They also help to bridge seasonal gaps in livelihoods.

Secondary forests comprise a large and growing proportion of the forest cover in the tropics and are very important at the local, national and regional levels for a wide range of products and environmental services. However, knowledge and expertise regarding secondary forests is still limited, and they are inadequately addressed in forest policy, planning and research.

Many modelers and spatial analysts engaged in the Mekong, Ganges, Andes, Volta, Limpopo and Nile basins are grappling with similar issues:

How do we get hold of and share quality information?

How do we integrate bio-physical and socio-economic data?

What are the best methods to fill data gaps and move across scales?

How do we select the most appropriate models from the plethora available and transfer these tools and technologies to partners with limited means?

How do we link different models and build feed-back loops...?