Page Banner

United States Department of Agriculture

Agricultural Research Service


item Russelle, Michael
item Birr, Adam
item Tiffany, Douglas

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 5/8/2006
Publication Date: 11/14/2006
Citation: Russelle, M.P., Birr, A.S., Tiffany, D.G. 2006. Estimated net energy yields in a biomass fuelshed [abstract]. ASA-CSSA-SSSA Annual Meeting Abstracts. CD-ROM. Paper No. 167-2.

Interpretive Summary:

Technical Abstract: Cellulosic plant materials are crucial to building sustainable bioeconomies. Local sourcing of low-density cellulosic feedstocks will reduce energy use and expenses in transportation. We developed a GIS-based approach to mapping yield and net energy production of crops in potential fuelsheds. The model fuelshed was centered on Madelia, MN, site of several biomass-based initiatives. Maps of energy yield were based on expected crop yields from the Soil Survey Geographic (SSURGO) database. Fuelshed maps showed distinct differences in average yield related to physiographic location. Typical gross energy yields were about 50 GJ/ha for soybean [Glycine max (L.) Merr.], 100 GJ/ha for corn (Zea mays L.) grain, and 135 GJ/ha for alfalfa (Medicago sativa L.) hay. Direct energy inputs of fuel for tillage, transportation, and drying as well as indirect energy of inputs like seed, fertilizer, and chemicals were computed based on predicted yields and university guidelines. Corn grain contained about 7X the energy required to produce and deliver it, soybean about 9X, and alfalfa about 24X. Considered on the basis of MJ produced divided by the economic value of energy inputs, alfalfa was higher (about 2100 MJ/$) than corn grain and soybean (about 900 MJ/$). With removal of all grain and 50% of corn stover, the net energy gain from a corn-soybean rotation would be only one-half that of a rotation of 4 yr of alfalfa, 2 yr of corn, and 1 yr of soybean. Such map products with supporting crop yield and energy input data facilitate quantitative spatial analysis and allow facility planners to identify: 1) where yields will be most reliable on land closest to the processing facilities; 2) areas where additional environmental payments may be available to improve farm profitability; 3) areas more sensitive to biomass removal; and 4) relative comparisons among crop alternatives.

Last Modified: 08/17/2017
Footer Content Back to Top of Page