and policy makers is also an important factor for a successful policy uptake of this type of decision-support tools (e.g. McIntosh et al. 2011; Albert et al. 2014). Therefore identification of priority areas for restoration has the potential to inform policy makers and stakeholders by providing spatial explicit information on the location of restoration measures but should not be easily used as a technical replacement of stakeholder deliberation as tradeoffs may be judged in different ways by different stakeholders and other considerations, not accounted for in the optimization, may play an important role. One step to better include stakeholder deliberation in the approach is to assign relative weights to each outcome, instead of assuming an equal weight for all alternatives, as it is done in our identification of priority locations. Therefore, care should be taken in not interpreting the highest priority areas for restoration as a single best solution but as a way of presenting optimization results in a spatially explicit manner and to inform and not end a discussion among stakeholders. Limitations to our approach Any environmental model and computational optimization requires a set of simplifications and assumptions. In our case, a limitation is the necessity to implement LULM alternatives at the farm level. This assumes a uniform LULM across all cells in a farm. However, in reality many agri-environment measures can be implemented below the farm level. Therefore, the resolution at which the optimization model and the landscape planner allocate LULM is not aligned. A clear example of this is the establishment of linear elements, which in reality does not necessarily occur on all fields of a farm. We partly accounted for this by only allowing allocation of linear elements on field edges. An importation decision is the use of the optimization algorithm, in our case Several optimization algorithms exist that can be used for multi-objective optimization such as BORG, SPEA-2 or NSGA-III (Cholodowicz and Orlowski, 2017; Deb and Jain, 2013; Hadka and Reed, 2015). Here we choose to use the given its’ wide applicability in spatial allocation problems (Malczewski and Rinner, 2015). Our analysis focuses on the use of optimization algorithms in a real world case study and policy framework. A methodological comparison of individual optimization algorithms is outside the scope of this paper. Importantly, using NSGA-II algorithm
and policy makers is also an important factor for a successful policy uptake of this type of decision-support tools (e.g. McIntosh et al. 2011; Albert et al. 2014). Therefore identification of priority areas for restoration has the potential to inform policy makers and stakeholders by providing spatial explicit information on the location of restoration measures but should not be easily used as a technical replacement of stakeholder deliberation as tradeoffs may be judged in different ways by different stakeholders and other considerations, not accounted for in the optimization, may play an important role. One step to better include stakeholder deliberation in the approach is to assign relative weights to each outcome, instead of assuming an equal weight for all alternatives, as it is done in our identification of priority locations. Therefore, care should be taken in not interpreting the highest priority areas for restoration as a single best solution but as a way of presenting optimization results in a spatially explicit manner and to inform and not end a discussion among stakeholders. Limitations to our approach Any environmental model and computational optimization requires a set of simplifications and assumptions. In our case, a limitation is the necessity to implement LULM alternatives at the farm level. This assumes a uniform LULM across all cells in a farm. However, in reality many agri-environment measures can be implemented below the farm level. Therefore, the resolution at which the optimization model and the landscape planner allocate LULM is not aligned. A clear example of this is the establishment of linear elements, which in reality does not necessarily occur on all fields of a farm. We partly accounted for this by only allowing allocation of linear elements on field edges. An importation decision is the use of the optimization algorithm, in our case Several optimization algorithms exist that can be used for multi-objective optimization such as BORG, SPEA-2 or NSGA-III (Cholodowicz and Orlowski, 2017; Deb and Jain, 2013; Hadka and Reed, 2015). Here we choose to use the given its’ wide applicability in spatial allocation problems (Malczewski and Rinner, 2015). Our analysis focuses on the use of optimization algorithms in a real world case study and policy framework. A methodological comparison of individual optimization algorithms is outside the scope of this paper. Importantly, using NSGA-II algorithm
Verhagen_OptimizingKrommeRijn_EnvSciencePolicy_revisies_20180314.docx