Using Stakeholder Preferences to Identify Optimal Land Use Configurations

Abstract

One way to solve multi-objective spatial land use allocation problems is to calculate a set of Pareto-optimal solutions and include stakeholder preferences after the optimization process. There are various land use allocation studies that identify the Pareto frontier (i.e., trade-off curve); to our knowledge, however, for the majority of them, the debate on which solutions are preferred by stakeholders or are preferred by stakeholders remains open. One reason could be that Pareto-optimal solutions, due to their multi-dimensionality, are difficult to communicate. To fill this gap, we give an example using the results of a multi-objective agricultural land use allocation problem that maximizes four biophysical objectives: agricultural production, water quality, water quantity, and biodiversity in the Lossa River Basin in Central Germany. We conducted expert interviews with 11 local stakeholders from different backgrounds, e.g., water experts, nature conservationists, farmers, etc. In addition to providing information about the case study area, we visualized the trade-offs between the different objectives using parallel coordinates plots that allowed the stakeholders to browse through the optimal solutions. Based on this information, the stakeholders set weights for each of the objectives by applying the Analytic Hierarchy Process (AHP). With these weights, we selected the preferred solutions from the Pareto-optimal set. The results show that, overall, stakeholders clearly ranked water quality first, followed by biodiversity, water quantity, and agricultural production. The corresponding land use maps show a huge difference in land management (e.g., less application of fertilizer, more linear elements, and conservation tillage) for the preferred solutions compared to the current status. The method presented in this study can help decision makers finding land use and land management strategies based on both biophysical modeling results and stakeholder expertise, and it shows how multi-objective optimization results can be communicated and used for an information-based decision-making process.

A Conceptualized Land Use System and Data to Support Integrated Landscape Assessments in Austria

“…However, neglecting important factors of the land use system at the landscape scale when assessing land use change effects might lead to biased recommendations. Thus, close cooperation with regional stakeholders is essential to represent key factors in the ILA, as also stressed, e.g., by Kaim et al (2020).…”

Riparian reforestation on the landscape scale: Navigating trade‐offs among agricultural production, ecosystem functioning and biodiversity

“…Previous studies selected and visualized individual solutions along the Pareto‐front, thus providing examples on how extreme land‐use configurations, as well as compromises, could look like (Gourevitch et al, 2016; Pennington et al, 2017; Schwarz et al, 2020; Strauch et al, 2019). Furthermore, additional stakeholder preferences could be used to weight and prioritize specific solutions (Kaim et al, 2020). We focused our analysis on the identification of priority areas of specific measures, based on an aggregation of the whole Pareto‐optimal solutions space (Karakostas, 2017; Verhagen et al, 2018).…”

“…Furthermore, additional stakeholder preferences could be used to weight and prioritize specific solutions (Kaim et al, 2020). We focused our analysis on the identification of priority areas of specific measures, based on an aggregation of the whole Pareto-optimal solutions space (Karakostas, 2017;Verhagen et al, 2018).…”

optimLanduse: A package for multiobjective land‐cover composition optimization under uncertainty

“…The robust approach provides one single best land‐cover composition under the specific set of preferences out of the entire frontier, using equally weighted decision criteria and uncertainty scenarios. For example, Kaim et al (2020) show the advantages of illustrating the effect of variable stakeholder preferences in the form of Pareto frontiers. The reference point nature of our model allows for identifying land‐cover compositions for which one indicator cannot be improved without compromising another indicator (see e.g.…”