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Title:   Author  item
                                            Archer, David
  Submitted to: Meeting Abstract
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 7/25/2010
                                    Publication  Date: 7/25/2010
                                    Citation: Archer, D.W. 2010. Modeling Bioenergy Feedstock Supply: Impacts of Temporal and Spatial Variability. Meeting Abstract. On-Line Reference T112.Interpretive Summary:
                                Technical Abstract: A theoretical model is constructed illustrating how transportation costs and spatial and temporal variability in feedstock production influence farm production practices and resulting impacts on bioenergy feedstock supply and the environment. The model is constructed for a bioenergy producer minimizing cost of feedstock supplied by profit maximizing producers at the field level. A biomass gasification facility that has been constructed at the University of Minnesota Morris (UMM) is used as an applied example. 

For the applied analysis, the EPIC simulation model is used to generate crop and biomass yields and related impacts on soil organic carbon, soil erosion, and nutrient leaching and runoff for crop rotation, tillage system, and biomass harvest alternatives. Simulation modeling is conducted for 20 years on 132 soil types within a 10-mile radius of the UMM facility. EPIC is calibrated using local field research data on crop production alternatives. Modeled feedstocks include corn cobs, corn stover, wheat straw, and alfalfa. EPIC simulation results are linked to a GIS, allowing spatial display of simulation output and facilitating analysis of feedstock supply costs including transportation to the UMM bioenergy facility. 

Bioenergy feedstock supply is estimated first using the 20-year means of grain and biomass yields for each of the biomass harvest treatments. This estimate includes impacts of spatial variability in feedstock production on profit maximizing crop production practices within the vicinity of the UMM facility.  The inclusion of transportation costs means that practices producing higher quantities of feedstocks will be favored closer to the UMM facility, but this effect declines with distance from the facility. This affects the mix of crop production practices used within the region, an effect that may not be captured when transportation costs and spatial variation in production are not included.

The effects of temporal variability are included using the annual simulation model results. Under temporal variability, the land used in producing feedstocks for the UMM facility may change from year to year. This variability affects profit maximizing production practices at the field level, with changes in tillage and rotation practices contingent on expected provision of feedstock to the UMM facility.


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