Valuing water quality tradeoffs at different spatial scales: An integrated approach using bilevel optimization

This study evaluates the tradeoff between agricultural production and water quality at both the watershed scale and the farm scale, using an integrated economic-biophysical hybrid genetic algorithm. We apply a multi-input, multi-output profit maximization model to detailed farm-level production data from the Oregon Willamette Valley to predict each producer's response to a targeted fertilizer tax policy. Their resulting production decisions are included in a biophysical model of basin-level soil and water quality. Building on a general regulation problem for nonpoint pollution, we use a hybrid genetic algorithm to integrate the economic and biophysical models into one bilevel multiobjective optimization problem, the joint maximization of farm profits and minimization of Nitrate runoff resulting from fertilizer usage. This approach allows us to more fully endogenize fertilizer reduction cost, rather than assume an average cost relationship. The solution set of tax rates generates the Pareto optimal frontier at the watershed level. We then measure the tradeoffs between maximum profit and Nitrogen loading for individual farms, subject to the solution fertilizer tax policy. We find considerable variation in tradeoff values across the basin, which could be used to target incentives for reducing Nitrogen loading to agricultural producers under non-uniform control strategies.