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ARS Home » Pacific West Area » Corvallis, Oregon » Forage Seed and Cereal Research » Research » Publications at this Location » Publication #322457

Research Project: Multi-Objective Optimization of a Profitable and Environmentally Sustainable Agriculture to Produce Food and Fiber in a Changing Climate

Location: Forage Seed and Cereal Research

Title: Spatial targeting of agri-environmental policy using bilevel evolutionary optimization

Author
item Whittaker, Gerald
item Barnhart, Bradley
item Fare, Rolfe - Oregon State University
item Grosskopf, Shawna - Oregon State University
item Bostian, M - Lewis & Clark
item Mueller Warrant, George
item Griffith, Stephen

Submitted to: Omega - The International Journal of Management Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/20/2016
Publication Date: 3/31/2016
Citation: Whittaker, G.W., Barnhart, B.L., Fare, R., Grosskopf, S., Bostian, M., Mueller Warrant, G.W., Griffith, S.M. 2016. Spatial targeting of agri-environmental policy using bilevel evolutionary optimization. Omega - The International Journal of Management Science. doi: 10.1016/j.omega.2016.01.007.

Interpretive Summary: In this study we describe a method for the design of agri-environmental policy to target areas where emissions can be reduced for the least cost. The problem is characterized by a single leader, the agency, that establishes a policy with the goal of optimizing its own objectives, and multiple followers, the producers, who respond by complying with the policy in a way that maximizes their own objectives. The new method presented in this study is able to calculate the best possible trade-offs among several objectives, including producer profit, emissions reduction and program cost.

Technical Abstract: In this study we describe the optimal designation of agri-environmental policy as a bilevel optimization problem and propose an integrated solution method using a hybrid genetic algorithm. The problem is characterized by a single leader, the agency, that establishes a policy with the goal of optimizing its own objectives, and multiple followers, the producers, who respond by complying with the policy in a way that maximizes their own objectives. Almost all analysis and simulation of agri-environmental policy to date employ a single level of optimization and do not fully incorporate the impact of the optimizing response of producers on policy formulation. Our method seeks the optimal policy allocation to optimize conflicting objectives within a bilevel framework and integrates a biophysical model (Soil and Water Assessment Tool; SWAT) with an economic model (profit maximization; DEA) using a hybrid genetic algorithm. Application of the tax at different geographical resolutions demonstrated that bilevel optimization is effective for determining optimal spatial targeting of agri-environmental policy.