Exploring Potential U.S. Switchgrass Production for Lignocellulosic Ethanol

Gunderson, Carla A¹  Davis, Ethan¹  Jager, Yetta¹  West, Tristram O.¹  Perlack, Robert D¹  Brandt, Craig C¹  Wullschleger, Stan D¹  Baskaran, Latha Malar¹  Webb, Erin¹  Downing, Mark¹ 

[1] ORNL

关键词: BIOFUELS;    BIOMASS;    CLIMATES;    CLIMATIC CHANGE;    CROPS;    ETHANOL;    FERTILIZATION;    FORECASTING;    FOSSIL FUELS;    GEOGRAPHIC INFORMATION SYSTEMS;    MANAGEMENT;    MOTORS;    PRECIPITATION;    PRODUCTION;    SCHEDULES;    STATISTICAL MODELS;    WEATHER switchgrass;    yield;    biofuels;    climate;    Panicum virgatum;    empirical model;   

DOI  :  10.2172/936551
RP-ID  :  ORNL/TM-2008/103
PID  :  OSTI ID: 936551
Others  :  Other: BM0101020
Others  :  CEBM001
Others  :  ORNL/TM-2007/183
Others  :  TRN: US200818%%1223

美国|英语

来源: SciTech Connect

【 摘 要 】

In response to concerns about oil dependency and the contributions of fossil fuel use to climatic change, the U.S. Department of Energy has begun a research initiative to make 20% of motor fuels biofuel based in 10 years, and make 30% of fuels bio-based by 2030. Fundamental to this objective is developing an understanding of feedstock dynamics of crops suitable for cellulosic ethanol production. This report focuses on switchgrass, reviewing the existing literature from field trials across the United States, and compiling it for the first time into a single database. Data available from the literature included cultivar and crop management information, and location of the field trial. For each location we determined latitude and longitude, and used this information to add temperature and precipitation records from the nearest weather station. Within this broad database we were able to identify the major sources of variation in biomass yield, and to characterize yield as a function of some of the more influential factors, e.g., stand age, ecotype, precipitation and temperature in the year of harvest, site latitude, and fertilization regime. We then used a modeling approach, based chiefly on climatic factors and ecotype, to predict potential yields for a given temperature and weather pattern (based on 95th percentile response curves), assuming the choice of optimal cultivars and harvest schedules. For upland ecotype varieties, potential yields were as high as 18 to 20 Mg/ha, given ideal growing conditions, whereas yields in lowland ecotype varieties could reach 23 to 27 Mg/ha. The predictive equations were used to produce maps of potential yield across the continental United States, based on precipitation and temperature in the long term climate record, using the Parameter-elevation Regressions on Independent Slopes Model (PRISM) in a Geographic Information System (GIS). Potential yields calculated via this characterization were subsequently compared to the Oak Ridge Energy Crop County Level data base (ORECCL), which was created at Oak Ridge National Laboratory (Graham et al. 1996) to predict biofuel crop yields at the county level within a limited geographic area. Mapped output using the model was relatively consistent with known switchgrass distribution. It correctly showed higher yields for lowland switchgrass when compared with upland varieties at most locations. Projections for the most northern parts of the range suggest comparable yields for the two ecotypes, but inadequate data for lowland ecotypes grown at high latitudes make it difficult to fully assess this projection. The final model is a predictor of optimal yields for a given climate scenario, but does not attempt to identify or account for other limiting or interacting factors. The statistical model is nevertheless an improvement over historical efforts, in that it is based on quantifiable climatic differences, and it can be used to extrapolate beyond the historic range of switchgrass. Additional refinement of the current statistical model, or the use of different empirical or process-based models, might improve the prediction of switchgrass yields with respect to climate and interactions with cultivar and management practices, assisting growers in choosing high-yielding cultivars within the context of local environmental growing conditions.