Abstract
We assess empirically how agricultural lands should be used to produce the highest valued outputs, which include food, energy, and environmental goods and services. We explore efficiency tradeoffs associated with allocating land between food and bioenergy and use a set of market prices and nonmarket environmental values to value the outputs produced by those crops. We also examine the degree to which using marginal land for energy crops is an approximately optimal rule. Our empirical results for an agricultural watershed in Iowa show that planting energy crops on marginal land is not likely to yield the highest valued output.
Highlights
A vigorous debate has emerged over the past decade concerning use of the worldâs land resources as concern about feeding growing populations, in the developing world, has become prominent in public discourse (Ray et al 2013, Godfray et al 2010)
Despite recognition that the cause of the price spikes was multifaceted, the concern about competition between food and fuel production on agricultural lands has become something of a black eye for any form of ethanol production with some arguing that biofuel crops, which inherently compete for land with traditional food and fiber crops, should not be subsidized or grown at all
We evaluate the empirical tradeoffs between food, fuel, and water quality by applying a simulation-optimization framework to an important watershed in the U.S Corn Belt
Summary
Land for Bioenergy Crops: Tradeoffs between Food, Fuel, and Environmental Services. We assess empirically how agricultural lands should be used to produce the highest valued outputs, which include food, energy, and environmental goods and services. We explore efficiency tradeoffs associated with allocating land between food and bioenergy and use a set of market prices and nonmarket environmental values to value the outputs produced by those crops. Our model, combined with costs and various output prices for market goods (food and fuel), can be used to determine the market value associated with any particular assignment of one of these fourteen land uses to the 2,122 HRUs in the watershed. Solving for the highest valued land use in the watershed is nontrivial because of both a combinatorial challenge (with 2,122 HRUs and 14 options, there are 14^2,122 potential solutions to evaluate) and an interdependence issue: the effect of a type of land use on downstream water quality in one location depends on choices at other locations To address this optimization challenge, we take advantage of the tools of evolutionary algorithms. We take advantage of Strength Pareto Evolutionary Algorithm 2 (Zitzler, Laumanns, and Thiele 2002) as described in Rabotyagov et al (2010) to approximate solutions to a five-objective Pareto optimization problem: maximize food and fuel production, minimize
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
