Application of a Multi-Objective Optimization Method to Provide Least Cost Alternatives for NPS Pollution Control

Abstract

Nonpoint source (NPS) pollutants such as phosphorus, nitrogen, sediment, and pesticides are the foremost sources of water contamination in many of the water bodies in the Midwestern agricultural watersheds. This problem is expected to increase in the future with the increasing demand to provide corn as grain or stover for biofuel production. Best management practices (BMPs) have been proven to effectively reduce the NPS pollutant loads from agricultural areas. However, in a watershed with multiple farms and multiple BMPs feasible for implementation, it becomes a daunting task to choose a right combination of BMPs that provide maximum pollution reduction for least implementation costs. Multi-objective algorithms capable of searching from a large number of solutions are required to meet the given watershed management objectives. Genetic algorithms have been the most popular optimization algorithms for the BMP selection and placement. However, previous BMP optimization models did not study pesticide which is very commonly used in corn areas. Also, with corn stover being projected as a viable alternative for biofuel production there might be unintended consequences of the reduced residue in the corn fields on water quality. Therefore, there is a need to study the impact of different levels of residue management in combination with other BMPs at a watershed scale. In this research the following BMPs were selected for placement in the watershed: (a) residue management, (b) filter strips, (c) parallel terraces, (d) contour farming, and (e) tillage. We present a novel method of combing different NPS pollutants into a single objective function, which, along with the net costs, were used as the two objective functions during optimization. In this study we used BMP tool, a database that contains the pollution reduction and cost information of different BMPs under consideration which provides pollutant loads during optimization. The BMP optimization was performed using a NSGA-II based search method. The model was tested for the selection and placement of BMPs in Wildcat Creek Watershed, a corn dominated watershed located in northcentral Indiana, to reduce nitrogen, phosphorus, sediment, and pesticide losses from the watershed. The Pareto optimal fronts (plotted as spider plots) generated between the optimized objective functions can be used to make management decisions to achieve desired water quality goals with minimum BMP implementation and maintenance cost for the watershed. Also these solutions were geographically mapped to show the locations where various BMPs should be implemented. The solutions with larger pollution reduction consisted of buffer filter strips that lead to larger pollution reduction with greater costs compared to other alternatives.

A mixed discrete-continuous variable multiobjective genetic algorithm for targeted implementation of nonpoint source pollution control practices

“…The optimization procedures were implemented using a population size of 108, a crossover probability of 0.5, and a mutation rate of 0.005. Optimal values of crossover probability and mutation rates were obtained from a study conducted by Maringanti et al [2011].…”

“…However, many real world problems consist of continuous decision variables. While, most of the conservation practice optimization studies considered binary decision variables (1s and 0s, respectively, indicating that the corresponding conservation practice ''be'' or ''not be'' implemented) [Veith et al, 2004;Arabi et al, 2006], recent studies have dealt with discrete (integer) decision variables [Maringanti et al, 2011]. Incorporating continuous decision variables may lead to the selection of more realistic and better solutions in terms of the final ''optimal'' set of type and placement conservation practices, also known as ''Pareto-optimal front'' solutions.…”

“…Government agencies promote implementation of NPS pollution control practices by cost-sharing with land owners and farmers that is deemed as a field-by-filed approach [WilliaVeith et al, 2003]. Recent studies have shown that holistic watershed-scale strategies for targeted implementation of conservation practices could enhance environmental benefits gained from conservation expenditure [Arabi et al, 2006;Rao et al, 2007;Maringanti et al, 2011].…”

A Review and Discussion on Modeling and Assessing Agricultural Best Management Practices under Global Climate Change

“…Many optimization algorithms have been developed at different scales for cost effective placement of BMPs to reduce pollutant loadings in streams (e.g. Rodríguez et al, 2011;Maringanti et al, 2011). These complex optimization searches have shown significant advantages compared to conventional targeting and random placement techniques.…”

Assessment of beyond-the-field nutrient management practices for agricultural crop systems with subsurface drainage

“…While intensive monitoring can show water quality improvements in a given location with a given set of practices, watershed modeling enables assessments of combinations of practices at a range of temporal and spatial scales and across geographies, management regimes, and climates. In addition, models can be used with an optimization approach to choose conservation practices for water quality benefit at least cost (Gitau et al 2004;Arabi et al 2006;Maringanti et al 2011;Kaini et al 2012;Artita et al 2013;Kalcic et al 2015b). Others have tested targeting approaches or other predefined watershed management strategies (Jha et al 2010;Scavia et al 2017).…”