Integrated biophysical and socio-economic analysis of regional land use

Abstract

A methodology is presented with which regional land use options can be explored in order to provide policy support. This methodology is called SOLUS (Sustainable Options for Land USe) and integrates a linear programming model called REALM (Regional Economic and Agricultural Land-use Model) with Technical Coefficient Generators for crops (called LUCTOR) and for livestock activities (called PASTOR) used to quantify the technical coefficients of land use systems, and with a geographic information system (GIS). SOLUS is implemented for a case study in the northern Atlantic Zone of Costa Rica, with the objectives of i) analyzing regional land use practices while taking into account economic and environmental objectives and restrictions, and ii) evaluating economic and agrarian policies that influence the agricultural sector. Salient characteristics of the SOLUS methodology are the geographic explicit delineation of land and labor resources, the incorporation of endogenous prices of outputs and labor, and the variation of output prices according to quality of roads and distance to markets. The latter two aspects are related to the size of the region, while endogenous prices and wages are necessary because the supply originating in the region is capable of influencing prices and wages. Seven policy scenarios are studied that address policy and sustainability issues relevant to the region: technological change, zero soil nutrient depletion, limiting biocide use, taxing biocides, forest conservation, lowering interest rates and increasing real wages. It is shown that the SOLUS methodology is a suitable tool for the analysis of policy options in order to support policy decisions, as well as to analyze future land use options in view of their effects on income and the environment.KeywordsNatural ForestReal WageConsumer SurplusLinear Programming ModelFarm TypeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.