switchgrass

In response to energy security concerns, alternative energy programs such as biomass energy systems are being
developed to provide energy in the 21st century. For the biomass industry to expand, a variety of feedstocks will need
to be utilized. Large scale production of bioenergy crops could have significant impacts on the United States agricultural
sector in terms of quantities, prices and production location of traditional crops as well as farm income. Though
a number of scenarios were examined to study the impact of bioenergy crop production on the agricultural sector, two
cropland scenarios are presented in this report. Under the wildlife management scenario, the analysis indicates that, at
$30/dry ton (dt) for switchgrass, $31.74/dt for willow and $32.90 for poplar, an estimated 19.4 million acres of
cropland (8.2 million from CRP) could be used to produce 96 million dry tons of bioenergy crops annually at a profit
greater than the profit created by existing uses for the land. In this scenario, traditional crop prices increase from 3
percent to 9 percent (depending on crop) and net farm income increases by $2.8 billion annually. At $40/dt of switchgrass,
$42.32/dt for willow and $43.87/dt for poplar and assuming the production management scenario, an estimated
41.9 million acres (12.9 million from CRP) could be used to produce 188 million dry tons of biomass annually. Under
this scenario, traditional crop prices increase by 8 to 14 percent and net farm income increases by $6 billion annually.

Switchgrass (Panicum virgatum L.) is a perennial grass native to the United States that has been studied as a sustainable source of biomass fuel. Although many field-scale studies have examined the potential of this grass as a bioenergy crop, these studies have not been integrated. In this study, we present an empirical model for switchgrass yield and use this model to predict yield for the conterminous United States. We added environmental covariates to assembled yield data from field trials based on geographic location. We developed empirical models based on these data. The resulting empirical models, which account for spatial autocorrelation in the field data, provide the ability to estimate yield from factors associated with climate, soils, and management for both lowland and upland varieties of switchgrass. Yields of both ecotypes showed quadratic responses to temperature, increased with precipitation and minimum winter temperature, and decreased with stand age. Only the upland ecotype showed a positive response to our index of soil wetness and only the lowland ecotype showed a positive response to fertilizer. We view this empirical modeling effort, not as an alternative to mechanistic plant-growth modeling, but rather as a first step in the process of functional validation that will compare patterns produced by the models with those found in data. For the upland variety, the correlation between measured yields and yields predicted by empirical models was 0.62 for the training subset and 0.58 for the test subset. For the lowland variety, the correlation was 0.46 for the training subset and 0.19 for the test subset. Because considerable variation in yield remains unexplained, it will be important in the future to characterize spatial and local sources of uncertainty associated with empirical yield estimates.

Adding bioenergy to the U.S. energy portfolio requires long‐term profitability for bioenergy producers and
long‐term protection of affected ecosystems. In this study, we present steps along the path toward evaluating both sides of
the sustainability equation (production and environmental) for switchgrass (Panicum virgatum) using the Soil and Water
Assessment Tool (SWAT). We modeled production of switchgrass and river flow using SWAT for current landscapes at a
regional scale. To quantify feedstock production, we compared lowland switchgrass yields simulated by SWAT with estimates
from a model based on empirical data for the eastern U.S. The two produced similar geographic patterns. Average yields
reported in field trials tended to be higher than average SWAT‐predicted yields, which may nevertheless be more
representative of production‐scale yields. As a preliminary step toward quantifying bioenergy‐related changes in water
quality, we evaluated flow predictions by the SWAT model for the Arkansas‐White‐Red river basin. We compared monthly
SWAT flow predictions to USGS measurements from 86 subbasins across the region. Although agreement was good, we
conducted an analysis of residuals (functional validation) seeking patterns to guide future model improvements. The analysis
indicated that differences between SWAT flow predictions and field data increased in downstream subbasins and in subbasins
with higher percentage of water. Together, these analyses have moved us closer to our ultimate goal of identifying areas with
high economic and environmental potential for sustainable feedstock production.

As the US begins to integrate biomass crops and residues into its mix of energy feedstocks, tools are needed to measure the long-term sustainability of these feedstocks. Two aspects of sustainability are long-term potential for profitably producing energy and protection of ecosystems influenced by energy-related activities. The Soil and Water Assessment Tool (SWAT) is an important model used in our efforts to quantify both aspects. To quantify potential feedstock production, we used SWAT to estimate switchgrass yields at a national scale. The results from this analysis produced a map of the potential switchgrass yield along its natural eastern range. To quantify ecological protection, we are using the SWAT model to forecast changes in water quality and fish richness as result of landscape alterations due to incorporating bioenergy crops. We have implemented the SWAT model in the Arkansas-Red-White region, which drains into the Mississippi River, and we present our methods here. We identified two sub-watershed for sensitivity analysis and calibration of the water quality results, and then, explored ways to apply the calibration results to the whole region and validate the model setup. We also present an overview of our research in which results from the calibrated regional SWAT model were used to analyze potential changes in fish biodiversity. Only by evaluating the energy and environmental implications of landscape changes can we make informed decisions about bioenergy at the national scale, and the SWAT model will enable us to reach that goal.

When the lignocellulosic biofuels industry reaches maturity and many types of biomass sources become economically viable, management of multiple feedstock supplies – that vary in their yields, density (tons per unit area), harvest window, storage and seasonal costs, storage losses, transport distance to the production plant – will become increasingly important for the success of individual enterprises. The manager’s feedstock procurement problem is modeled as a multi-period sequence problem to account for dynamic management over time. The case is illustrated with a hypothetical 53 million annual US gallon cellulosic ethanol plant located in south west Kansas that requires approximately 700,000 metric dry tons of biomass. The problem is framed over 40 quarters (10 years), where the production manager minimizes cumulative costs by choosing the land acreage that has to be contracted with for corn stover collection, or dedicated energy production and the amount of biomass stored for off-season. The sensitivity of feedstock costs to changes in yield patterns, harvesting and transport costs, seasonal costs and the extent of area available for feedstock procurement are studied. The outputs of the model include expected feedstock cost and optimal mix of feedstocks used by the cellulosic ethanol plant every year. The problem is coded and solved using GAMS software. The analysis demonstrates how the feedstock choice affects the resulting raw material cost for cellulosic ethanol production, and how the optimal combination varies with two types of feedstocks (annual and perennial).

The purpose of this study is to analyse the economical and environmental performance of switchgrass and miscanthus production and supply chains in the European Union (EU25), for the years 2004 and 2030. The environmental performance refers to the greenhouse gas (GHG) emissions, the primary fossil energy use and to the impact on fresh water reserves, soil erosion and biodiversity. Analyses are carried out for regions in five countries. The lowest costs of producing (including storing and transporting across 100 km) in the year 2004 are calculated for Poland, Hungary and Lithuania at 43–64 € per oven dry tonne (odt) or 2.4–3.6 € GJ−1 higher heating value. This cost level is roughly equivalent to the price of natural gas (3.1 € GJ−1) and lower than the price of crude oil (4.6 € GJ−1) in 2004, but higher than the price of coal (1.7 € GJ−1) in 2004. The costs of biomass in Italy and the United Kingdom are somewhat higher (65–105 € odt−1 or 3.6–5.8 € GJ−1). The doubling of the price of crude oil and natural gas that is projected for the period 2004–2030, combined with nearly stable biomass production costs, makes the production of perennial grasses competitive with natural gas and fossil oil. The results also show that the substitution of fossil fuels by biomass from perennial grasses is a robust strategy to reduce fossil energy use and curb GHG emissions, provided that perennial grasses are grown on agricultural land (cropland or pastures). However, in such case deep percolation and runoff of water are reduced, which can lead to overexploitation of fresh water reservoirs. This can be avoided by selecting suitable locations (away from direct accessible fresh water reservoirs) and by limiting the size of the plantations. The impacts on biodiversity are generally favourable compared to conventional crops, but the location of the plantation compared to other vegetation types and the size and harvesting regime of the plantation are important variables.

Discussions of alternative fuel and propulsion technologies for transportation often overlook the infrastructure required to make these options practical and cost-effective. We estimate ethanol production facility locations and use a linear optimization model to consider the economic costs of distributing various ethanol fuel blends to all metropolitan areas in the United States. Fuel options include corn-based E5 (5% ethanol, 95% gasoline) to E16 from corn and switchgrass, as short-term substitutes for petroleum-based fuel. Our estimates of 1−2 cents per L of ethanol blend for downstream rail or truck transportation remain a relatively small fraction of total fuel cost. However, for even the relatively small blends of ethanol modeled, the transportation infrastructure demands would be comparably larger than the current demands of petroleum. Thus if ethanol is to be competitive in the long run, then in addition to process efficiency improvements, more efficient transportation infrastructure will need to be developed, such as pipelines. In addition to these results, national and regional policy challenges on how to pay for and optimize a new fuel and distribution infrastructure in the United States are discussed.

The U.S. Departments of Agriculture and Energy jointly analyzed the economic potential for, and impacts of, large-scale bioenergy crop production in the United States. An agricultural sector model (POLYSYS) was modified to include three potential bioenergy crops (switchgrass, hybrid poplar, and willow). At farmgate prices of US $2.44/GJ, an estimated 17 million hectares of bioenergy crops, annually yielding 171 million dry Mg of biomass, could potentially be produced at a profit greater than existing agricultural uses for the land. The estimate assumes high productivity management practices are permitted on Conservation Reserve Program lands. Traditional crops prices are estimated to increase 9 to 14 percent above baseline prices and farm income increases annually by US $6.0 billion above baseline. At farmgate prices of US $1.83/GJ, an estimated 7.9 million hectares of bioenergy crops, annually yielding 55 million dry Mg of biomass, could potentially be produced at a profit greater than existing agricultural uses for the land. The estimate assumes management practices intended to achieve high environmental benefits on Conservation Reserve Program lands. Traditional crops prices are estimated to increase 4 to 9 percent above baseline prices and farm income increases annually by US $2.8 billion above baseline.

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.