Using mechanistic land-use models to explore the potential of agroforestry for enhancing ecological and economic functions

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Agroforestry has been advocated as a pathway to satisfy growing food and energy demands, while enhancing the provision of ecosystem services. Past research in this field has often focused only on the plot level, while scientists and policy makers are calling for research on multifunctional landscapes. The research proposed here therefore seeks to develop a mechanistic land-use modelling approach for multifunctionality to identify the conditions under which agroforestry could contribute to reducing land-use conflicts. It aims at investigating which level of diversification will be most appropriate at the plot, farm and landscape level to bring together public interests and farmer s objectives. For this purpose, novel single and multi objective modelling approaches to optimize land use allocation under uncertainty will be further developed. For a first prototype model, the approach will be developed for a typical tropical landscape at the forest frontier in eastern Panama. A landscape level approach shall first answer the question, how much of agroforestry will be desirable in a landscape portfolio to achieve multiple ecosystem services, while reducing trade-offs between them (multi objective model). A farm level model shall investigate how agroforestry should be designed to become an efficient part of a farm portfolio that satisfies the farmer s (most likely economic) needs (single objective model). A range of already common and potentially new agroforestry systems and layouts could be simulated under different conditions using the process based WaNuLCAS. Among these, specific layouts will be pre selected according to the conditions identified during the optimization approach. Finally, the land use composition and provision of ecosystem services of the aggregated farm models shall be contrasted to the landscape model, in order to investigate whether including efficient agroforestry systems may reduce potential differences. The proposed research is innovative in that it compares agroforestry to other forms of land-use diversification and incorporates uncertainties into the decision making process. Novel robust optimization techniques allow for a reduction of model complexity and thus inclusion of a large number of land-use options. The proposed research may therefore make an important contribution to designing resilient multifunctional landscapes and directing future agroforestry research.