Academic journal article Journal of Agricultural and Applied Economics

Agricultural Impacts of Biofuels Production

Academic journal article Journal of Agricultural and Applied Economics

Agricultural Impacts of Biofuels Production

Article excerpt

Analysis of the potential to supply 25% of projected 2025 U.S. transportation fuels indicates sufficient biomass resources are available to meet increased demand while simultaneously meeting food, feed, and export needs. Corn and soybeans continue to be important feedstocks for ethanol and biodiesel production, but cellulose feedstocks (agricultural crop residues, energy crops such as switchgrass, and forestry residues) will play a major role. Farm income increases, mostly because of higher crop prices. Increased crop prices increase the cost of producing biofuels.

Key Words: biodiesel, biofuels, biomass, cellulose feedstocks, crop residues, ethanol, forest residues, switchgrass

JEL Classifications: O11, Q11, Q41

Recently, a number of proposals have been advanced to use alternative fuels from biomass as a means to "break our addiction to oil." These initiatives propose to replace 20%-30% of U.S. fuel use with biomass-derived fuels within the next quarter century. This is a large task. The United States consumed about 140 billion gallons of gasoline in 2005. The feedstocks needed to produce biofuels will come largely from the agricultural and forestry sectors. Such a large increase raises numerous questions regarding feasibility, approach, impacts of such activities, and, most specifically, whether we can meet new fuel demands and still meet food, feed, and export demands (i.e., fuel versus food). Existing analyses do not fully address these questions, as they typically examine the impact of a single limited change (i.e., increasing ethanol production from corn grain or biodiesel production from soybeans).

The vision of a future biobased industry includes the simultaneous production of biofuels, bioelectricity, and bioproducts that uses not only corn grain and soybean oil, but also a host of cellulose feedstocks. We have developed a framework to rigorously evaluate feedstock-related issues associated with the development of a biobased industry utilizing a dynamic model of the U.S. agricultural sector (POLYSYS) that has been modified to include several cellulose feedstocks (endogenous feedstocks include corn stover, wheat straw, and switchgrass; exogenous feedstocks include forest and mill residues) and several bioenergy and bioproduct technologies (ethanol from corn starch and cellulose; biodiesel from soybeans; 1,3-propanediol, lactic acid, levulinic acid, succinic acid, and glycerol from starch, cellulose, and/or oil crops; and electricity from cellulose). This paper presents the results of an analysis that examines the replacement of 25% of the projected 2025 U.S. petroleum-derived transportation fuel use with ethanol derived from corn grain and cellulose feedstocks and biodiesel derived from soybeans.

The POLYSYS Model

POLYSYS includes national demand, regional supply, livestock, and aggregate income modules (De La Torre Ugarte et al.; Ray and Moriak) and is anchored to published baseline projections for all model variables (FAPRI; USDA 2006). Products included in POLYSYS are corn, grain sorghum, oats, barley, wheat, soybeans, cotton, rice, beef, pork, lamb and mutton, broilers, turkeys, eggs, and milk. Exogenous commodities include alfalfa and other hay and edible oils and meals. The model simulates the impacts of changes from the baseline on the national crop and livestock supply and demand variables, such as acres, yields, prices, commodity payments, and income. The crop supply module is composed of 305 independent regional linear programming models, each of which represents the land allocation decision in a specific geographic region with relatively homogeneous production characteristics (Agricultural Statistical Districts). Acres are allowed to enter crop production, shift production to a different crop, or move out of crop production on the basis of maximizing returns above costs. The crop demand module utilizes estimated demand elasticities and price flexibilities and is a function of own price, cross-price shifters, and nonprice shifter variables. …

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