Multi‐objective optimization for diffuse pollution control at zero cost

Abstract

AbstractAgricultural best management practices (BMPs) are gaining ground as a means of mitigating diffuse nutrient pollution of surface waters in agricultural catchments; however, their cost‐effectiveness depends on the location‐specific characteristics of the land on which they are applied. To identify acceptable catchment management solutions with respect to environmental and economic objectives, a decision support tool (DST) is used in this study. The DST integrates the river basin soil and water assessment tool (SWAT) model that serves as the nonpoint source pollution estimator into an optimization framework consisting of a multi‐objective genetic algorithm that searches for optimal selection and location of BMPs in the landscape. A three‐objective optimization problem has been previously solved for the Arachtos catchment in western Greece including the implementation costs of several types of BMPs such as nutrient application, crop, soil and livestock management and total annual diffuse losses of total phosphorus (TP) and nitrate‐nitrogen (NO3‐N) from land to surface waters. In the present study, a solution of negligible total cost for the whole catchment was selected from optimal two‐dimensional trade‐off curves of cost‐TP and cost‐NO3‐N, aiming to complement previously analysed management options and further enhance decision‐making in this catchment. The zero cost solution led to 30 and 20% reductions in TP and NO3‐N river concentrations, respectively, corresponding to contour cultivation without tillage in corn, fertilizer management in alfalfa as well as livestock and manure management along with the establishment of filter strips at the edge of some corn and pastureland fields. The proposed methodology enabled the identification of a low cost, and possibly more favourable, compared to previous findings, combination of BMPs that ensures good quality of river water. It helps to provide the basis for sustainable land‐use planning and management in large agricultural landscapes, thus aiding decision‐making and cost‐effective implementation of Environmental Directives.