Maputo City has a very high population density and sources of domestic energy such as electricity, gas or even kerosene are not yet available for the majority of the population. Demand for wood products is high both in the rural and urban areas for consumption and generation of income. Coupled with agricultural expansion, this raises concerns over the sustainability of use of natural forest resources. Nevertheless, the government has limited financial and human resources to establish plantations which can satisfy the increases in wood demand, especially in the urban areas. The 1991 Reforestation policy attempted to overcome this by adopting as strategy the involvement of the users, especially the rural community, in the replacement of exploited forest resources. However, reported failure in implementation of this strategy has suggested that there is need to elaborate a decision support tool, which would encapsulate the multidisciplinary nature of the problem, at both farm and regional levels. The underlying hypothesis of this research is, therefore, that despite data scarcity and/or unreliability, it is possible to develop a planning framework applying a relatively sophisticated planning tool such as Mathematical Programming. The aim of the thesis is to perform an ex-ante analysis of the impact of the strategy, stressing a bottom-up and integrated planning procedure and including decision makers at the two levels. Both single and multiobjective mathematical programming methods are applied, preceded by the use of Geographical Information Systems in the analysis of the spatial distribution of resources. The results show that by integrating agriculture, forestry and animal husbandry activities and constraints in the farm planning framework, it is possible to assess the individual potential responses to reforestation alternatives. Furthermore, conflicts among national goals are assessed using aggregation techniques. This provides policy makers with information on the opportunity cost that may be associated with changes in government priorities.