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Biomass supply chain management in North Carolina (part 1): predictive model for cropland conversion to biomass feedstocks

1 BASF Corp., 26 Davis Dr., Research Triangle Park, NC, 27709 USA
2 NC State University, Biological and Agricultural Engineering, Box 7625, Raleigh, NC 27695 USA
3 Bayer Crop Science LP, 2 T.W. Alexander Dr., Research Triangle Park, NC 27709 USA

Special Issues: Renewable energy systems and agro-residue management

Increased interest in biomass cultivation requires detailed analysis of spatial production potential of possible biorefinery locations, with emphasis on feedstock production cost minimization. Integrated assessment of publicly available spatial data on current crop production, soil type, and yield potential, coupled with techno-economic production cost estimates, can support a functional method for rapid analysis of potential biorefinery sites. A novel predictive model was developed to determine cropland conversion using a probabilistic profit based equation for multiple biomass crops: giant reed, miscanthus, switchgrass, and sorghum (with either canola or barley as a winter crop). The three primary regions of North Carolina (Mountains, Piedmont, and Coastal Plain) were used as a case study and with a single parameter uncertainty analysis was completed. According to the model, the county chosen to represent the Coastal Plain (Duplin County) had the largest potential acreage that would be converted (15,071 ha, 7.1% total land, 9.3% of cropland) primarily to sorghum with canola as a winter crop. Large portions were also predicted to convert to giant reed and switchgrass, depending on the price and yield parameters used. The Piedmont (Granville County, 7697 ha, 5.5% total land, 6.9% cropland) and Mountain (Henderson County, 2117 ha, 2.2% total land, 2.3% cropland) regions were predicted to convert primarily to switchgrass acreage for biomass production, with much less available biomass overall compared to the Coastal Plain. This model provided meaningful insight into regional cropping systems and feedstock availability, allowing for improved business planning in designated regions. Determination of cropland conversion is imperative to develop realistic biomass logistical operations, which in conjunction can assist with rapid determination of profitable biomass availability. After this rapid analysis method is conducted in-depth on-ground biorefinery feasibility analysis can occur, ensuring resource are used only in locations with a high potential for available low cost biomass feedstocks.
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Keywords Biomass; techno-economic; feedstock modeling; bioenergy; spatial analysis; yield determination; giant reed; switchgrass; sorghum; miscanthus

Citation: Kevin R Caffrey, Mari S Chinn, Matthew W Veal. Biomass supply chain management in North Carolina (part 1): predictive model for cropland conversion to biomass feedstocks. AIMS Energy, 2016, 4(2): 256-279. doi: 10.3934/energy.2016.2.256

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