摘要：The pressure on land as required input in competing uses fuelled research on trade-offs in land use due to agricultural land expansion to meet food demand which is explicitly and implicitly treated in global land use modelling. Global land use studies rely on assessing the trade-offs by assuming policy, environmental, and economic constraints on the availability of land but do not base on consistent land use budgets. Commonly, they lack the focus on a holistic view on land use which employs different categories including trade-offs without mixing land use with land cover categories. We pursue a spatially-explicit land use budgeting approach in global available land assessment to overcome overlaps in classification. Objectives pertain to (a) identifying and integrating plausible environmental datasets in consistent land use datasets and (b) identifying and analysing the available land base and plausible exogenous land conversion rates in the agricultural land use optimization model MAgPIE. Methodologically, consistent spatially-explicit land use datasets and incorporated climate, physical and normative constraints are used in a static geographical approach to overcome overlaps in classification. In a second step, the generated available-land-input datasets from pre-processing are implemented in MAgPIE as constraint equations. Assumed environmental trade-offs are complemented by economic trade-offs in terms of costs of land conversion and technological change. The analysis of model behaviour until 2055 builds on joint land expansion scenarios including available land stocks and historical cropland conversion rates. Results and conclusions refer to the available stock for conversion and land use patterns including the trade off between expansion versus intensification, the average technological change and relative total costs of production for three regions in two scenarios.
关键词：cropland expansion;available land;historical land conversion;spatially-explicit modelling;non-linear optimization;intact & frontier forest
