Combining biophysical optimization with economic preference analysis for agricultural land-use allocation

Agricultural production provides food, feed, and renewable energy, generates economic profits, and contributes to social welfare in many ways. However, intensive farming is one of the biggest threats to biodiversity. Although current market forces and regulations such as the European Union's Common Agricultural Policy, seem to foster agricultural intensification, a socially and ecologically optimal land-use strategy should seek to reconcile agricultural production with biodiversity conservation. Research on spatial land-use allocation lacks studies that consider both aspects simultaneously. Therefore, we developed a method that finds land-use strategies with a maximum contribution to social welfare, taking into account the landscape's biophysical potential. We applied a multiobjective optimization algorithm that identified landscape configurations that maximize agricultural production and biodiversity based on their contribution to social welfare. Social welfare was approximated by the profit contribution of agricultural production and society's willingness to pay for biodiversity. The algorithm simultaneously evaluated the biophysical outcomes of different land uses using the Soil and Water Assessment Tool (SWAT) and a biodiversity model. The method was applied to an agricultural landscape in central Germany. The results show that, in this area, overall social welfare can be increased compared to the status quo if both social benefits from biodiversity and economic profits from agricultural production are considered in land-use allocation. Further, the resulting optimal solutions can create win-win situations between the two, usually conflicting, objectives. The integration of preference information into the biophysical optimization allows reducing the usually large set of Pareto-optimal solutions and thus facilitates further stakeholder-based analyses. Our explorative study provides an example of how socioeconomic data and biophysical models can be combined to support decision making and the development of land-use policies.