oakridgenationallaboratory

Relationships between people and their environment are largely defined by land use. Space and soil are needed for native plants and wildlife, as well as for crops used for food, feed, fiber, wood products and biofuel (liquid fuel derived from plant material). People also use land for homes, schools, jobs, transportation, mining and recreation. Social and economic forces influence the allocation of land to various uses. The recent increase in biofuel production offers the opportunity to design ways to select locations and management plans that are best suited to meet human needs while also protecting natural biodiversity (the variation of life within an ecosystem, biome or the entire Earth). Forethought and careful planning can help society balance these diverse demands for land. At the same time, current energy infrastructure must become less reliant on the earth’s finite supply of fossil fuels because they contribute to greenhouse gas emissions, cause environmental pollution, and jeopardize energy security. The sustainable development of renewable fuel alternatives can offer many benefits but will demand a comprehensive understanding of how our land-use choices affect the ecological systems around us. By incorporating both socioeconomic and ecological principles into policies, decisions made regarding biofuel production can be based on a more sustainable balance of social, economic, and ecological costs and benefits. Researchers are actively studying the potential impacts of biofuels production on land use and biodiversity, and there is not yet a firm consensus on the extent of these effects or how to measure them. In this report, we summarize the range of conclusions to date by exploring the features and benefits of a landscape approach to analyzing potential land-use changes associated with biofuel production using different feedstocks. We look at how economics and farm policies may influence the location and amount of acreage that will ultimately be put into biofuel production and how those land-use changes might affect biodiversity. We also discuss the complexities of land-use assessments, estimates of carbon emissions, and the interactions of biofuel production and the US Department of Agriculture Conservation Reserve Program. We examine the links between water and biofuel crops and how biofuel expansion might avoid “food versus fuel” conflicts. Finally, we outline ways to design bioenergy systems in order to optimize their social, economic and ecological benefits.

A primary objective of current U.S. biofuel law – the “Energy Independence and Security Act of 2007” (EISA) – is to reduce dependence on imported oil, but the law also requires biofuels to meet carbon emission reduction thresholds relative to petroleum fuels. EISA created a renewable fuel standard with annual targets for U.S. biofuel use that climb gradually from 9 billion gallons per year in 2008 to 36 billion gallons (or about 136 billion liters) of biofuels per year by 2022. The most controversial aspects of U.S. biofuel policy have centered on the global social and environmental implications of land use. In particular, there is an ongoing debate about whether “indirect land use change” (ILUC) would cause biofuels to become a net source, rather than sink, of carbon emissions. Estimates of ILUC induced by biofuel production can only be inferred through modeling. This paper evaluates how model structure, underlying assumptions, and the representation of policy instruments influence the results of U.S. biofuel policy simulations. The analysis shows that differences in these factors can lead to divergent model estimates of land use and economic effects. Model estimates of the net conversion of forests and grasslands induced by U.S. biofuel policy range from 0.09 ha/1000 gallons described in this paper to 0.73 ha/1000 gallons from early studies in the ILUC change debate. We note that several important factors governing LUC change remain to be examined. Challenges that must be addressed to improve global land use change modeling are highlighted.

ORNL Report ORNL/TM-2010-120.
The purpose of this study is to summarize the various barriers to more widespread distribution of biofuels through our common carrier fuel distribution system, which includes pipelines, barges and rail, fuel tankage, and distribution terminals, and with a special focus on biofuels, which may come into increased usage in the future. Addressing these barriers is necessary to allow the more widespread utilization and distribution of biofuels, in support of a renewable fuels standard and possible future low-carbon fuel standards. By identifying these barriers early, for fuels not currently in widespread use, they can be addressed in related research and development. These barriers can be classified into several categories, including operating practice, regulatory, technical, and acceptability barriers. Possible solutions to these issues are discussed, including compatibility evaluation, changes to biofuels, regulatory changes, and changes in the distribution system or distribution practices. No actual experimental research has been conducted in the writing of this report, but results are used to develop recommendations for future research and additional study as appropriate.

The U.S. Department of Energy (DOE) and the U.S. Department of Agriculture (USDA) are both strongly committed to expanding the role of biomass as an energy source. In particular, they support biomass fuels and products as a way to reduce the need for oil and gas imports; to support the growth of agriculture, forestry, and rural economies; and to foster major new domestic industries — biorefineries — making a variety of fuels, chemicals, and other products. As part of this effort, the Biomass R&D Technical Advisory Committee, a panel established by the Congress to guide the future direction of federally funded biomass R&D, envisioned a 30 percent replacement of the current U.S. petroleum consumption with biofuels by 2030.

The use of Geographic Information Systems (GIS) for understanding the geographic context of bioenergy supplies is discussed and a regional-scale, GIS-based modeling system for estimating potential biomass supplies from energy crops is described. While GIS models can capture geographic variation that may in?uence biomass costs and supplies, GIS models are not likely to handle uncertainty well and are often limited by the lack of spatially explicit data. The presented modeling system estimates the costs and environmental implications of supplying speci?ed amounts of energy crop feedstock across a state. The system considers where energy crops could be grown, the spatial variability in their yield, and transportation costs associated with acquiring feedstock for an energy facility. The modeling system was used to estimate potential switchgrass costs and supplies in eleven US states. Transportation costs increased with increased facility demand and were lowest in Iowa, North Dakota and South Dakota and highest in South Carolina, Missouri, Georgia, and Alabama. Farmgate feedstock costs were lowest in Alabama, North Dakota and South Dakota and highest in Iowa and Nebraska. Across the eleven states, delivered feedstock costs ranged from $33 to $55/dry tonne to supply a facility requiring 100,000 tonne/yr. Delivered feedstock costs for a 630,000 tonne/yr facility ranged from $36 to $58/dry tonne.

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 dry matter 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 dry matter 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 dry mass/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 because there were few field trials growing lowland ecotypes at high latitudes it is difficult to fully assess that projection. The final model is a predictor of optimal dry matter 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 a different empirical or process-based model, 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.