The quality of Dutch nature reserves is threatened by high nitrogen input, a problem which
to a large extent is caused by agricultural activities. The Dutch government intends to solve
this by designating some areas where the emission level is allowed to increase and other areas
where the emission level will have to decrease. Theoretically, this problem can be seen as a
reallocation of emission sources.
In earlier research, the optimal spatial distribution of agricultural ammonia emissions to
minimize atmospheric nitrogen deposition in nature reserves was determined. Linear programming
(LP) has been applied because of the approximately linear atmospheric transport relations
between emission and deposition locations. A more thorough analysis necessitates the
addition of other nitrogen contributions important for the quality of nature, such as by
groundwater and surface water transport. These processes can no longer be considered linear,
so the application of non-linear optimization methods is necessary. Several non-linear programming
methods can solve large-scale problems, but are not capable of dealing with nonsmoothness
and qualitative relations, especially when the number of variables and/or relations
is large. In this study, the potential of genetic algorithms (GA) is evaluated, by comparing the
GA results for the linear atmospheric emission–deposition process with results of LP. Kappa
statistics and regression analysis were used to test the similarity of the spatial emission and deposition distributions on the GA and LP output maps. GA was shown to perform well, producing
similar results to LP. Calculations in this article also showed that almost identical minimal
deposition patterns may be achieved with somewhat different emission patterns. This is a
potentially interesting feature for policy-makers, who may evaluate alternative emission distributions
on a small scale, each with their specific socio-economic impacts, while still achieving
optimal results for nature quality
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