Incorporating Agroforestry Into Water Quality Trading: Evaluating Economic-Environmental Tradeoffs
Nonpoint source nitrogen runoff from agriculture is a significant contributor to eutrophication in the Chesapeake Bay. The state of Virginia has developed several market and incentive-based water quality credit trading programs to meet federal water quality objectives. In theory, these programs offer a mechanism to achieve environmental goals at least cost. However, in practice these programs face ongoing challenges arising from limited participation by farmers who supply water quality credits and, as a result, often fail to achieve cost efficiency. We build a flexible, accessible, and modular bioeconomic modeling system as a proof-of-concept to evaluate economic-environmental tradeoffs farmers face in an effort to support program participation and achieve environmental goals. We couple a biophysical nitrogen mass-balance model with an agricultural production model and apply the tool to study diverse agroforestry practices. We evaluate the relative efficiency of these practices by empirically estimating a production possibility frontier. We then use our bioeconomic modeling results to define the minimum willingness to accept of farmers, in terms of water quality credit prices, to adopt agroforestry practices that deliver water quality improvements. We extend our model results to estimate water quality credit premiums to compensate risk-averse farmers for undertaking production practices subject to relatively volatile prices in niche fruit markets. We demonstrate that the model generally simulates real-world credit prices, and highlight potential improvements in design for Virginia's trading program. In particular, quality credit trading programs could be more effective and efficient if credits awards reflect heterogeneity in the environmental benefits associated with nuanced land-use alternatives. Our modeling tool offers a framework to support incentive programs that are both economically sound and biophysically grounded.