Winter 1997
U.S. Department of Energy
Bioenergy Feedstock Development Program at
Oak Ridge National Laboratory

Energy Crops Forum was published periodically by the Bioenergy Feedstock Development Program, Environmental Sciences Division, Oak Ridge National Laboratory, managed by UT-Battelle, LLC., for the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Table of Contents

• Editor's Note
• BIOCOST—Bioenergy Crop Production Costs Model
• ORECCL—Oak Ridge Energy Crop County-Level Database
• Can biofuels crops improve water quality?

Editor's Note

Since 1978, the U.S. Department of Energy's Biofuels Feedstock Development Program has coordinated and supported research to identify and improve short-rotation woody crops and herbaceous crops for use as feedstocks for transportation fuels and power. The BFDP is also developing tools to assess the environmental and economic costs, benefits, and trade-offs associated with these energy crops. This issue of Energy Crops Forum highlights several of these tools.

BIOCOST — Bioenergy Crop Production Costs Model

Marie Walsh¹ and Denny Becker²
Biofuels Feedstock Development Program¹, Science Applications International Corp.²

A key component in determining the economic feasibility of bioenergy crops is an assessment of the costs of producing them. Successful commercialization of bioenergy crop systems for power, fuel, and chemicals requires feedstock prices and conversion costs to be economically competitive with conventional energy sources. Additionally, the feedstock price offered to farmers must be sufficiently high to ensure profits comparable to those a farmer could earn using the land for other purposes. The Biofuels Feedstock Development Program (BFDP) at Oak Ridge National Laboratory (ORNL) has developed models to estimate the costs of producing bioenergy crops, including a user-friendly version called BIOCOST. Using a Visual Basic©-enhanced Microsoft Excel©spreadsheet, BIOCOST features pop-up windows that allow users to change parameters within the model.

BIOCOST estimates hybrid poplar and switchgrass production costs for seven U.S. regions: the Lake States (Michigan, Minnesota, Wisconsin); the Corn Belt (Iowa, Illinois, Indiana, Missouri, Ohio); Appalachia (Kentucky, North Carolina, Tennessee, Virginia, West Virginia); the Southeast (Alabama, Georgia, South Carolina); the North Plains (Kansas, Nebraska, North Dakota, South Dakota); the South Plains (Oklahoma, Texas); and the Pacific Northwest (Oregon, Washington–hybrid poplar only). Although energy crops can be produced in other regions, the regions selected correspond to the production regions for major U.S. agricultural crops.

Fig. 1 User Options

The default mode of BIOCOST estimates production costs based on management practices recommended by experts and approximates the average production costs in a region. Because costs may vary substantially over the large geographic regions used, BIOCOST allows users to tailor the production cost estimates to their situations, making BIOCOST very flexible. Users can choose among three tillage practices and can alter the rotation length and spacing for hybrid poplar and the rotation length for switchgrass. Land rental rate, expected yield, and the discount rate can also be varied. The costs of different management strategies can be estimated by changing the quantity and/or prices of fertilizer, chemicals, seeds, labor, and fuels and the number of fertilizer and herbicide applications per acre per year. Users can also override the estimated harvesting costs. Some users many not need a full economic cost estimate for all applications. BIOCOST allows them to select combinations of fixed and owned resource costs to be added to the variable cash costs. In this manner, costs relevant to the situation under analysis can be estimated.

BIOCOST estimates full economic production costs in 1995 dollars. Variable cash expenses (e.g., seeds, chemicals, fertilizer, fuel, repairs, and hired labor), fixed cash costs (e.g., overhead, taxes, and interest payments), and the costs of owned resources (e.g., producer's own labor, equipment depreciation, land rents, and the opportunity cost of capital investments) are included. The approach is consistent with the methods used by the U.S. Department of Agriculture (USDA) to estimate the cost of producing field crops. This facilitates comparison of bioenergy crop economics with those of conventional agricultural crops. Because of the perennial nature of bioenergy crops, production costs are estimated for each year in the life of the planting, as well as a net present value per acre and per ton cost over the lifetime of the planting. Only the costs of on-farm production are estimated; however, users can include transportation costs.

Because of the lack of historical production data, BIOCOST relies on an engineering approach to estimate production costs. The model uses machinery and equipment engineering specifications to estimate the number of hours needed by each machine to cover one acre of land. These estimates, combined with per hour machinery costs, are used to calculate per acre machine costs. Chemical and fertilizer costs are based on the quantity applied and the price in pounds of active ingredients. Labor costs assume hired field worker rates adjusted for social security taxes. Land rents are cash rental rates adjusted for real estate interest. Estimated harvesting costs and fertilizer costs (switchgrass only) are a function of expected yields.

The output page of BIOCOST (see Fig. 2) includes the crop, region, tillage system, assumed yield, and net present value per acre and per ton production cost based on the cost categories included by the user. Also included are fertilizer, chemical, fuel, and labor quantities used in production. Scrolling through the output page allows the user to see the estimated costs by input category for each year.

When combined with data readily available from the USDA, BIOCOST can be used to approximate the bioenergy crop price needed for producers to earn profits comparable to those of conventional crops. Thus, the model can be helpful to crop producers, state energy departments, and industries who are interested in evaluating the potential of crop-based systems. The inclusion of fertilizer, chemical, and fuel quantities in the output page extends the usefulness to those interested in calculating net energy balances.

BIOCOST and the complete model documentation are available by request from Marie Walsh at ORNL, phone 865-576-5607, E-mail: WalshME@ornl.gov. Please provide a complete mailing address, phone and fax numbers, and e-mail if available.

ORECCL — Oak Ridge Energy Crop County-Level Database

Robin Graham¹, Linda Allison¹, and Denny Becker²
BFDP¹, Science Applications International Corp.²

A county-level database on energy crops, ORECCL, has been developed. This database, which encompasses all U.S. counties, provides easy access to energy crop information specific to states or counties. The database can also be used to assess geographic variation in the potential for energy crop development.

ORECCL is based on a compilation of current information on land availability, rents, energy crop yields, and production costs. It also includes probable farmgate prices for energy crops as calculated from information within the database. County land use and land rent information was derived from USDA data. Production costs for switchgrass were calculated using BIOCOST algorithms.

The database currently includes data on switchgrass, hybrid poplar, and willow and is searchable by eitherstate and county name or the five- digit Federal Information Processing Standards (FIPS) numeric code for the state and county. ORECCL is available as an EXCEL© spreadsheet, as a SAS© transportable binary file for use with Proc Cimport and as a comma-delimited ASCII text file. It can be downloaded from the Bioenergy Databases page.

Can biofuels crops improve water quality?

Carolyn Hunsaker¹, Michael Sears², and Robin Graham¹
BFDP¹, Purdue University¹

The U.S. Geologic Survey estimates that 29% of all water bodies in the contiguous United States are moderately to severely polluted as a result of nonpoint- source pollution. Nonpoint-source pollution includes sediment, nitrogen, phosphorus, and pesticides that are carried in water that runs off the land surface into streams, rivers, or lakes. The USDA considers sediment the primary contaminant in polluted rivers and polluted lakes. In many cases, agricultural production is the primary source of this pollution. Very little water quality monitoring has been done on surface runoff from fields or watersheds with biofuels crops such as switchgrass or hybrid poplars. Only preliminary results are available from ongoing field studies; however, computer models offer another way to answer the question of whether biofuels can improve water quality.

We used the Agricultural Non-Point Source (AGNPS) pollution model developed by the USDA Soil Conservation Service and the Minnesota Pollution Control Agency to estimate stream flow and sediment and chemical transport resulting from single rainfall events. The AGNPS model uses land cover, soil characteristics, and land and stream channel slope data. We were primarily interested in comparing changes in sediment, nitrogen, and phosphorus loadings between watersheds planted in conventional crops and those planted with biofuels crops. AGNPS was linked to a geographic information system (GIS), which enabled us to efficiently capture, store, update, manipulate, analyze, and display all forms of geographically referenced information.

To calculate the differences between conventional row crops and biofuels, three land use scenarios were created: Test I—Current Conditions, Test II—Economic Biomass (biomass placed more frequently on productive soils where estimated production costs were lowest), and Test III—Water Quality Biomass (biomass placed as buffers around bodies of water and on erosive soils). The economic scenario (Test II) was based on previous modeling work at ORNL and The University of Tennessee. Fourteen storm events were evaluated for each of the scenarios.

Three small watersheds (800 to 28,000 acres) — the Little Pine (LP) watershed, the Indian Creek (IC) watershed, and the Animal Science (ANSI) watershed — located near West Lafayette, Indiana, within the Indian Pine Natural Resources Field Station of Purdue University, made up the study area. The area is mostly agricultural (corn, soybeans, wheat, and alfalfa or hay) with some wooded areas. Its topography is gently rolling to flat, and it is representative of much of the midwestern United States. The land capability classes indicate that the soils in these three watersheds are fairly productive. About 60% of the study area has moderate productivity limitations caused by excess water (land capability class 2W) and 25% is subject to erosion. Approximately 2% of the soils are considered very productive (class 1).

Current land uses were modeled in Test I. The Test II and Test III models assume some of the land currently in crops was converted to switchgrass production. In Test II, corn and soybeans were replaced by switchgrass on 25% of the land in land capability class 2W and on 45% of the land in other classes. Sixty percent of the land currently in wheat and alfalfa was converted to switchgrass in this scenario. For Test III, switchgrass was planted on all agricultural lands within 100 meters of streams and 200 meters of lakes with slopes of 6% or greater, and on soils classified "highly erodible" by the Natural Resource Conservation Service.

The presence of switchgrass in the three study watersheds (Tests II and III) consistently resulted in predictions of less runoff, lower sediment yields, and reduced nutrient losses (nitrogen and phosphorus) compared with current conditions (Test I). Results are summarized for the three watersheds in Figures 3–5.

For the three watersheds, the average predicted reductions in sediment yield were 40 and 60%, respectively, for comparisons between Tests I and II (current conditions compared to economic- based switchgrass scenario) and Tests I and III (current conditions compared to water-quality-based switchgrass scenario) (Fig. 3). The soluble nitrogen losses in runoff were reduced by 23 and 30%, respectively, from Test I to Test II and from Test I to Test III (Fig. 4). Similarly, predicted reductions in the soluble phosphorous lost from the watersheds averaged 24 and 31%, respectively, from Test I to Test II and from Test I to Test III (Fig. 5). Losses of nitrogen and phosphorus attached to sediment were also reduced.

These model results support what scientists had previously predicted: energy crops such as switchgrass should reduce the amount of sediment and nutrients that flow into surface water compared with annual crops and can thus improve stream water quality. Field research to verify such conclusions is being conducted by Alabama A&M University, The University of Tennessee, Mississippi State University, Tennessee Valley Authority, and Oak Ridge National Laboratory. A multiagency (BFDP, the U.S. Forest Service, and North Carolina State University) watershed-scale study on industry lands in South Carolina is also producing water quality data for short-rotation woody crops. Similar GIS-based water quality modeling work on switchgrass and hybrid poplar is underway at ORNL for the Minnesota River basin.

For additional information refer to the following: Sears, M.J. 1996. Analysis of Surface Water Quality Impacts of Biomass Plantation Establishment in the Midwest. MS Thesis, Purdue University, West Lafayette, Indiana.