Wetlands are a critical part of the environment and perform important ecosystem functions, such as water quality and quantity balance and habitat for a number of species. Due to vast wetland conversion to agricultural lands, these functions are in decline. Specifically, Le Sueur watershed in South Minnesota contributes 30% of the Minnesota River’s annual sediment, although it drains only 6% of the basin area (the watershed is primarily agricultural fields). Previous research has shown that restoring parts of the watershed to its natural state can reduce sediment and improve water quality.
Our study is devoted to the analysis of how the integration of spatial ownership distribution and uncertainty in agricultural production affect optimal decision making for wetland restoration in South Minnesota. We propose a method based on real options analysis to assess the critical leasing payment level at which the farmer would prefer land retirement to agricultural production. Using these cost estimates and a mathematical modeling approach, we explore how clustering management units using ownership criteria can affect cost savings, ecosystem services and landscape characteristics in a wetland restoration project. We integrate all these factors using mathematical programming.
Results/Conclusions
We accounted for forward-looking farming strategies to estimate minimal payments that land owners would accept to retire land from agricultural use. These values were used in restoration cost calculations and provided the input for the mathematical programs. The output of the mathematical programs allowed an analysis of the total cost of the restoration projects, tradeoffs between the objectives and the effects of the ownership clusters on the landscape characteristics.
We used the output of the models to build Pareto optimal tradeoff curves to show the variety of management plans that can appeal to different groups of stakeholders. Our results demonstrate the importance of including information such as ownership clusters at the planning stages, because post-optimization adjustment of the solutions does not produce restoration plans with the lowest cost. The key contribution of our research is the integration of managerial, ecological and economic factors in our models for a well-informed analysis and projections of the potential consequences of restoration at the landscape level.