PNNL-19072 

Simulating Potential Switchgrass 

Production in the United States 

Allison M. Thomson, R. César Izarrualde, Tristram O. West, David J. Parrish, 

Donald D. Tyler, Jimmy R. Williams 

December 2009 
DISCLAIMER 

This report was prepared as an account of work sponsored by an 

agency of the United States Government. Neither the United States 

Government nor any agency thereof, nor Battelle Memorial Institute, 

nor any of their employees, makes any warranty, express or implied, 

or assumes any legal liability or responsibility for the accuracy, 

completeness, or usefulness of any information, apparatus,

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commercial product, process, or service by trade name, trademark, 

manufacturer, or otherwise does not necessarily constitute or imply its 

endorsement, recommendation, or favoring by the United States 

Government or any agency thereof, or Battelle Memorial Institute. The 

views and opinions of authors expressed herein do not necessarily state 

or reflect those of the United States Government or any agency thereof. 

PACIFIC NORTHWEST NATIONAL LABORATORY 

operated by 

BATTELLE 

for the 

UNITED STATES DEPARTMENT OF ENERGY 

under Contract DE-AC05-76RL01830
Simulating Potential Switchgrass

Production in the United States

Allison M. Thomson*1, R. César Izarrualde1, Tristram O. West2, David J. Parrish3, 

Donald D.  Williams Tyler4 5  , Jimmy R. 

December 2009 

 

Joint Global Change Research Institute, Pacific Northwest National Laboratory and the University of  1

Maryland, 8400 Baltimore Ave., Suite 201, College Park, MD 20740. 

 Environmental Sciences Division, Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN,  2

37831‐6335. 

 Virginia Polytechnic Institute, Crop & Soil Environmental Sciences, 330 Smyth Hall (0404),  3

Blacksburg, VA 24061. 

 University of Tennessee Experiment Station, Biosystems Engineering and Soil Science, 605 Airways  4

Blvd., Jackson, TN 38301. 

5 Blackland Research Center,  720 E. Blackland Rd, Temple, TX 76502. 

*

For further information contact Allison Thomson (allison.thomson@pnl.gov; phone 301-314-6750) 

1
Abstract..........................................................................................................................................................3 

1. Introduction.............................................................................................................................................4 

2. Methods .....................................................................................................................................................5 

2.1 EPIC Model Description ..............................................................................................................5 

2.2 Model Calibration ..........................................................................................................................5 

2.2.1 Dataset Description..............................................................................................................5 

2.2.2 Model Parameter Calibration...........................................................................................6 

2.3 Model Validation Methods.........................................................................................................9 

3. Results & Discussion......................................................................................................................... 10 

3.1 Model Validation Results......................................................................................................... 10 

3.2 Model Application at a National Scale ............................................................................... 12 

3.3 Further Research: Field Trials and Model Development........................................... 15 

4. Conclusions ........................................................................................................................................... 16 

Acknowledgements................................................................................................................................ 16 

References.................................................................................................................................................. 17 

2
Abstract

Using results from field trials of switchgrass (Panicum virgatum L.) in the United 

States, the EPIC (Environmental Policy Integrated Climate) process‐level 

agroecosystem model was calibrated, validated, and applied to simulate potential 

productivity of switchgrass for use as a biofuel feedstock.  The model was calibrated 

with a regional study of 10‐yr switchgrass field trials and subsequently tested 

against a separate compiled dataset of field trials from across the eastern half of the 

country.  An application of the model in a national database using 8‐digit 

watersheds as the primary modeling unit produces 30‐yr average switchgrass yield 

estimates that can be aggregated to 18 major watersheds.  The model projects 

average annual switchgrass productivity of greater than 7 Mg ha‐1 in the Upper 

Mississippi, Lower Mississippi, and Ohio watersheds. The major factors limiting 

simulated production vary by region; low precipitation is the primary limiting factor 

across the western half of the country, while moderately acidic soils limit yields on 

lands east of the Mississippi River.  Average projected switchgrass production on all 

crop land in the continental US is 5.6 Mg ha‐1.  At this level of productivity, 28.6 

million hectares of crop land would be required to produce the 16 billion gallons of 

cellulosic ethanol called for by 2022 in the 2007 Energy Independence and Security 

ct. The model described here can be applied as a tool to inform the land‐use and 

nvironmental consequences of switchgrass production.  

A

e

3
1. Introduction

Bioenergy has emerged as an energy supply option with the potential to reduce 

greenhouse gas emissions and reduce dependence on fossil fuels while providing 

economic benefit to farmers [1,2]. Cellulosic feedstocks have been identified as a 

principal component in the bioenergy portfolio by the US Department of Energy [3].   

Proposed sources of cellulosic material that can be used for bioenergy include 

agricultural and forestry residues, mixed grasslands, forests, and monocultures of 

herbaceous species such as switchgrass (Panicum virgatum L.) and Miscanthus 

(Miscanthus x giganteus).  

Switchgrass meets many of the requirements for a bioenergy crop [4,5]; it is a native 

(non‐invasive), non‐food plant species with perennial growth habit that shows a 

relatively high yield potential with relatively low nutrient requirements. 

Switchgrass is a warm‐season (C4) perennial grass native to much of the eastern 

two‐thirds of North America  [6] that has been adapted as a forage crop [7‐9].  In 

principle, switchgrass stands may bring environmental benefits such as erosion 

control, reduced runoff, and nutrient retention [10]. These beneficial effects are 

evident when using switchgrass in riparian buffer zones [11]. When grown on a 

large scale as a bioenergy crop, switchgrass may provide a positive net energy 

balance [12]; however, recent concerns about the emissions of greenhouse gases 

from conversion of lands to produce crops for biofuels production [13] highlight the 

need to more fully understand the full carbon balance and environmental 

consequences of growing bioenergy crops on a large scale. In addition, recent 

increases in global food prices and food insecurity have been attributed in part to 

the diversion of food crops for biofuels [14]. Thus, there is a need to examine the 

sustainability of switchgrass as a bioenergy crop from perspectives of land‐use 

change, biomass productivity, economic return, water and nutrient balances, net 

greenhouse gas emissions, and biodiversity. 

Simulation models of agricultural systems can be applied at a variety of scales to 

examine the environmental impacts of converting land to biofuel crops and the 

potential productivity of these crops.  Many biophysical models can simulate 

production for a range of agricultural, grassland, and forestry applications; however, 

switchgrass and other native grasses have not been widely incorporated into these 

models.  Objectives of this research were to 1) calibrate parameters in the 

Environmental Policy Integrated Climate model (EPIC) [15] for simulation of 

switchgrass production, 2) validate the model with an independent dataset, and 3) 

develop national estimates of potential switchgrass yields for the United States.  A 

national map of potential yields provides a basis for evaluating future potential 

switchgrass production. We describe model calibration and validation here, 

illustrate the gradient of national switchgrass yields, discuss differences in US yields, 

and suggest research needs concerning field trials and modeling. 

4
2. Methods

2.1 EPIC Model Description

EPIC is a daily time‐step terrestrial ecosystem model capable of simulating a wide 

array of managed and unmanaged ecosystem processes including plant growth, crop 

yields, plant competition, and soil erosion as well as heat, water, and nutrient 

balances [15]. Daily plant biomass gains in EPIC are proportional to the daily 

photosynthetically active radiation intercepted by the plant canopy.  These daily 

gains in aboveground biomass are affected by vapor pressure deficits, atmospheric 

CO2 concentration [16,17], and other physiological stresses caused by 

environmental factors including water, temperature, N, P, and soil aeration.  

Similarly, daily root growth may be affected by non‐optimal values of bulk density, 

temperature, and aluminum content [18].  Currently, EPIC can simulate about 100 

plant species including many crops, native grasses, and trees. Daily weather can be 

estimated from precipitation, air temperature, solar radiation, wind, and relative 

humidity parameters or it can be input from historical records. Soil information on 

horizon depth, texture, bulk density, and C concentration also drives EPIC. EPIC 

ver.0509, used here, incorporates a soil‐C module that allows for a complete 

calculation of the C‐balance including water and wind erosion [19].  Detailed 

management allows for daily control of the tillage, irrigation, and fertilizer 

applications.  

Initially developed by USDA in the 1970s to evaluate the role of erosion on crop 

productivity, EPIC has been continually improved and expanded for many research 

purposes [20]. As a simulation model of not only crop productivity but also the 

resulting nutrient runoff, wind and water erosion, and soil C dynamics, EPIC is 

uniquely suited for assessing environmental consequences of land‐use change. The 

model has been applied at regional [21,22] and national scales [23,24] for 

assessment of climate variability and change impacts on agricultural productivity 

and for assessments of nutrient dynamics and soil C. Izaurralde et al. [25] used EPIC 

to evaluate the role of erosion and decomposition on landscape‐scale soil C balance, 

and found clear relationships between land management and the erosion, soil C, and 

full C balance of small watersheds.  Brown et al. [21] applied an earlier version of 

EPIC to simulate switchgrass response to climate change in the central United 

States.  Since that time, model improvements have been made and additional results 

from field trials have become available, improving understanding of switchgrass 

growth and providing a basis for crop modeling improvements. 

2.2 Model Calibration  

5

2.2.1 Dataset Description   

Two major ecotypes, or forms, of switchgrass are distinguished based on 

morphological characteristics [10].  Lowland ecotype varieties are coarser, taller, 
and generally better adapted to the longer, warmer growing seasons of the southern 

United States; and upland forms are distinguished by their finer textures, earlier 

maturity, and greater cold tolerance [26].  A 10‐yr study at seven locations across 

the Southeast [27,28] that included both ecotypes was selected as the calibration 

dataset. The study locations (Table 1) represent a range of soil and climate 

characteristics. At each site, two plots each of lowland and upland ecotypes were 

established in 1992. Harvests of this split‐plot study occurred in two patterns: either 

only once in late fall (one‐cut management), or in mid‐summer and again in late fall 

(two‐cut management).  Soil data collected at the time of switchgrass establishment 

were used, while daily weather records for each study location were obtained from 

the National Climate Data Center for the simulation period. See Fike et al. [27,28] for 

more details of the 10‐yr study. 

Table 1: Si on for the seven locations in the calibration

ocation 

te informati

Switchgrass  

 dataset.  

L Latitude Elevation Soil Series  A a nnual We ther 

rg, 

Lowland 

lo t

Average

Max T 

Average 

Min T 

Total 

Precipitation

Mg ha­1

11.2 

m °C °C mm

Blacksbu

VA  37.18  600  Shottower 

am varian 17.4  5.1  937 

Jackson,  

TN  9.7  35.62  120  D  silt loam 

eanburg 21.2  9.9  1335 

Knoxville, 

TN  15.9  35.88  250  Etowah 

loam  20.7  9.4  1267 

Morgantown, 

WV 12.9  39.62  378  Dormont silt loam  17.3  6.3  1068 

Orange,  

VA  11.9  38.22  156  Davidson clay  18.8  7.5  1101 

Princeton, 

KY  12.4  37.10  173  Tilsit series  21.2  8.9  1261 

Rale

NC 

i h,   g 11.6  35.72  120  Cecil sandy 

loam  21.8  9.6  1140 

2.2.2 Model Parameter Calibration 

Initial runs of switchgrass were made incorporating key parameters from the 

ALMANAC model [29‐31], a model closely related to EPIC.  Sensitivity analysis of 

crop‐growth parameters in EPIC (data not shown) led to identification of the major 

factors limiting switchgrass growth.  Accordingly, adjustments were made to 

parameters that factor in 1) the timing of leaf decline (DLAI1 = 0.75 (lowland); DLAI 

= 0.7 (upland)), 2) the fraction of the growing season where maximum leaf area 

 

1

 Acronyms in this paragraph are variable names used in the EPIC code. See Williams [15] for full 

descriptions and model equations. 

6
occurs, (LAP2 = 50.95), 3) the maximum leaf area index (DMLA = 6 (lowland); DMLA 

= 3 (upland)), 4) leaf area decline after anthesis (RLAD = 0.1), 5) the rate of decline 

in biomass energy after anthesis (RBMD = 0.1), and 6) root weight at plant 

emergence (RWP1 = 0.4) and plant maturity (RWP2 = 0.3). In addition to plant 

growth parameter modifications, adjustment was made to the parameter controlling 

the conversion of standing live biomass to standing residue (PARM66 = 0.001) and 

residue management. In addition to these and other parameters, plant growth and 

development in EPIC is influenced by the temperature during the growing season, 

expressed within the model as “heat units” [15].  The heat units accumulate 

throughout the growing season, and the value necessary for the crop to reach 

maturity varies by latitude.   In the calibration, heat units required for maturity of 

switchgrass were automatically determined within EPIC. 

Measured yields from all study plots at the seven field trial locations were averaged 

across the four main treatments (lowland, one‐cut; lowland two‐cut; upland one‐

cut; upland two‐cut) and compared to simulated yields. While the correlation across 

sites within each management treatment is not strong (Fig. 1) (R2 = 0.41), the 

results are consistent with the findings from these field trials [27,28]. The model 

successfully captures the range of yield across the locations and the differences in 

yield between upland and lowland ecotypes. The difference in yield between one‐ 

and two‐cut managements is apparent in the results, with the upland ecotypes being 

more responsive to the two‐cut management. This was observed by Fike et al. 

[27,28] and attributed to the earlier flowering (which halts vegetative growth) of 

these upland ecotypes.  

The model also adequately captures the current understanding of soil organic C 

(SOC) dynamics under switchgrass. An analysis at these field trial locations by 

Garten and Wullschleger [32] found that SOC was not significantly different under 

switchgrass, row crops, and grass plant covers at these locations five years after 

switchgrass establishment.  However, stable isotope analysis indicates that SOC 

response to a conversion to switchgrass will depend on initial SOC levels and 

management [33]. Simulations of SOC change with EPIC (Fig. 2) are in agreement 

with these findings, projecting SOC accumulation under soils with low initial C 

ontent in the top 30‐cm of soil (<40 Mg C ha‐1), higher rates of accumulation under 

owland ecotypes, and a dynamic SOC response to cutting management. 

c

l

7
0

5

10

15

20

25

EPIC Simulated Yield (Mg ha -1)

Lowland 1 Cut

Lowland 2 Cut

Upland 1 Cut

Upland 2 Cut

1:1 line

0 5 10 15 20 25

Measured Yield (Mg ha-1)

Figure 1: Simulated average switchgrass yields from the model calibration 

ompared with measured yields from 1994‐2001 at seven locations, for two 

cotypes and two harvest managements. 

c

e

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

0 10 20 30 40 50 60 7

Lowland - 1 Cut

Lowland - 2 Cut

Upland - 1 Cut

Upland - 2 Cut

ΔSOC (Mg ha-1 yr-1)

0

Initial SOC (Mg ha-1)

Figure 2: Simulated annual change in soil organic C (SOC) to 30‐cm depth as related 

o initial soil C levels at the  seven calibration dataset locations in the upper 

outheastern United States. 

t

s

8
The observed variation across sites may be a function of several contributing factors 

including differences in elevation, site‐specific management (e.g. different dates for 

first harvest), and the interaction of site‐specific environmental characteristics with 

genotype, leading to differences in phenotypic expression. Wide variation in 

switchgrass yields within a field has been reported observationally for many field 

trials and has been quantified by Di Virgilio et al. [34], who measured yields ranging 

from 3 to 20 Mg ha‐1 within a 5‐ha field  The spatial variation was statistically 

attributed to numerous soil and nutrient characteristics, most significantly nitrogen 

and phosphorous levels and soil pH.  The calibrated model captures the explainable 

variation in yields and represents the best understanding of switchgrass 

productivity at this time. 

2.3 Model Validation Methods

Following initial model calibration, the methods and parameters were applied in 

EPIC for simulation of an independent data set of field trials. Data from field trials 

were compiled at Oak Ridge National Laboratory (ORNL) from the peer‐reviewed 

literature and from direct communication with field researchers [35].  These data 

represent 31 field trial locations with 1400 data points.  Each data point includes 

information on yields, management, and production inputs.  These data provided an 

independent test of the EPIC model over a wider geographic range and set of 

growing conditions.  

From the full dataset, 67 cases representing field trial results from 10 states and a 

variety of fertilization and harvest practices were simulated. Cases were selected for 

validation if they had two or more years of recorded yield, represented one‐ or two‐

cut management, and were grown without irrigation. Sites from the Fike et al. 

[27,28] study were used in the model calibration and therefore not considered in 

the validation. Input data for the model consisted of management details from the 

field trial database, soil profile information for the database‐specified soil type from 

the state soil survey database STATSGO [36], and long‐term climatology for each 

location. By using soils and weather data that are available nationally rather than 

site‐specific, this simulation test informs the validity of extending EPIC switchgrass 

simulations beyond field sites to regional scales. 

An additional purpose of the validation was to establish heat‐unit scheduling for 

management operations. This is necessary for simulations where perfect 

information on crop management is not available, and for simulations that extend 

the model to new regions where specific field management practices have not yet 

been established.  Based on the known management and model‐generated heat‐unit 

values from the calibration dataset, each planting, harvest and fertilizer operation in 

the model validation was assigned a time to occur relative to crop maturity.  Those 

established values then allow the model to automatically schedule management 

operations in the validation simulations.  Use of heat‐unit scheduling allows EPIC to 

account for variation in growing season timing and duration by automatically 

9
scheduling the date of each planting, harvest and fertilizer operation based on 

temperature during the growing season.  

3. Results & Discussion

3.1 Model Validation Results

Similar to the calibration dataset, a confounding factor in the validation simulations 

was the wide variation in measured switchgrass yields even within locations. 

Statistical analysis of the field trial dataset by Gunderson et al. [35] found 

considerable variability in switchgrass yield.  They found that yields were more 

highly correlated with temperature and precipitation than management (e.g. N 

application, number of harvests) or environmental parameters (e.g. soil texture 

class).  Lowland ecotypes consistently had higher reported yield than upland 

ecotypes. 

EPIC results show a good fit of simulated to observed yield for lowland ecotypes, 

with some over‐prediction of upland ecotype yields (Fig. 3). EPIC simulated a 

narrower range of yields for two‐cut management than was evident in the observed 

yields, reflecting the strong influence of weather variability within the growing 

season and the importance of the exact date of the first harvest. Both the observed 

nd simulated yields of upland ecotypes did not show significantly higher total 

nnual yield with an additional harvest in the two‐cut management.   

a

a

0

5

10

15

20

25

ORNL - Measured Yield

EPIC - Simulated Yield

Average Yield (Mg ha-1)

Lowland 1 cut Lowland 2 cut Upland 1 cut Upland 2 cut

Figure 3: Annual average and range (solid line) of measured and simulated yields by 

cotype and harvest management for 67 field trials in the validation dataset. Vertical 

ines represent the full range of measured and simulated yield. 

e

l

10
The average and range of simulated and measured yield as grouped by state (Fig. 4) 

demonstrates the range of yields across the geographic extent of the field trials.  

EPIC slightly overestimates measured switchgrass yields, particularly for states 

where few field trials were available for comparison (e.g. Arkansas, South Dakota).  

One variable with significant variation in the field trial dataset was N application.  

Both simulated and measured yield response to N application rate (Fig. 5) showed a 

yield response to N at rates up to 100 kg ha‐1 yr‐1 and little yield response to N 

application rates above 200 kg ha‐1 yr‐1.  This finding is consistent with studies that 

ecommended application rates of less than 100 kg ha‐1 yr‐1 as adequate for 

witchgrass production in most cases [35,5].  

r

s

a) Measured yields 

0

5

10

15

20

25

30

35

AL* AR* GA IA LA* ND OK SD* TX WI

Measured Yield (Mg/ha)

b) EPIC simulated yields 

0

5

10

15

20

25

30

35

AL* AR* GA IA LA* ND OK SD* TX WI

Simulated Yield (Mg/ha)

*States with too few records for standard deviation  

Figure 4: Switchgrass a) measured and b) simulated yields by state for 67 field trials 

n the validation dataset. Solid box represents one standard deviation from the 

ean, and solid line represents full range of yield.  

i

m

11
a) Measured yield 

0

5

10

15

20

25

0 200 400 600 800 1000

Annual N application (kg ha-1 yr-1)

Average Yield (Mg ha-1)

b) EPIC simulated yield 

0

5

10

15

20

25

0 200 400 600 800 1000

Average Yield (Mg ha-1)

Annual N application (kg ha-1

 yr

-1)

Figure 5: Average (point) and range (line) of a) measured and b) simulated 

witchgrass yields to N application at 22 validation dataset field trial locations 

ationwide.  

s

n

3.2 Model Application at a National Scale

Based on findings and model improvements from the calibration and validation 

described above, EPIC was applied to simulate long‐term yields of switchgrass for 

the continental United States.  Lowland ecotypes were simulated for all lands south 

of 41 °N, while upland ecotypes were simulated for the northern tier of the country.  

The modeling datasets applied here [24,37] delineate modeling units based on 

12
13

hydrologic unit area classification (USGS) at the 8‐digit and 4‐digit level, 

respectively. Major agricultural lands were simulated at the 8‐digit level, with 

multiple agricultural soils representing a minimum of 70% of the variability of soils 

within each modeling unit.  Simulations for each modeling unit were for 30‐year 

periods based on long‐term average climatology for the modeling unit.  Planting and 

harvest dates of switchgrass were adjusted based on long‐term climatology and 

EPIC heat‐unit scheduling.  

The resulting model estimates are not predictions of yield at any specific location; 

rather they are a projection of the potential for switchgrass growth in the regions 

represented by the modeling unit. Yields are averaged over the 30‐year simulation, 

and range from a high of 16 Mg ha‐1 in eastern Texas and Oklahoma and parts of 

Louisiana and Florida to a low of less than 2 Mg ha‐1 across much of the land in Utah, 

Nevada, and Arizona (Fig. 6). When the results are aggregated to 18 major 

watershed regions, the highest average yields occur in the Lower Mississippi region, 

with averages above 7 Mg ha‐1 in the Ohio and Upper Mississippi Regions (Table 2). 

The high maximum yield in the South Atlantic‐Gulf region is attributed to simulation 

of switchgrass on organic soils in Florida, which is also apparent in Fig. 6. In 

comparison, Brown et al. [21] simulated switchgrass yields of similar magnitude for 

sites in Missouri, Iowa, Nebraska, and Kansas, with highest average yields of 9 to 10 

g ha‐1 in central Iowa and Missouri, declining to 4 to 6 Mg ha‐1 M  in western Kansas 

and Nebraska.  

able 2: Average and maximum switchgrass yield by major United States region 

rom over 7700 30‐yr simulations with EPIC.  

T

f

Region  Switch

(M

gra

g h

ss Yield  

a‐1) 

Average Maximum

New England  4.1  10.1 

Mid‐Atlantic

 Atlantic‐Gulf 

4.1  11.8 

South 6.3  25.3 

Great Lakes  6.2  10.2 

Ohio  7.0  15.5 

Tennessee  6.7  14.5 

Upper Mississippi  14.2 

ssissippi

7.1 

Lower Mi 7.4  14.5 

Souris‐Red‐Rainy 

hite‐Red 

4.6  7.8 

Missouri  4.9  16.7 

17.5 

16.7 

Arkansas‐W 5.9 

Texas Gulf  6.4 

Rio Grande 

ado 

2.6  8.6 

Upper Color 1.7  5.4 

Lower Colorado 

n 

thwest 

1.4  4.4 

Great Basi

Pacific Nor

California 

1.5 

2.7 

2.5 

4.4 

8.2 

6.1 
Figure 6: Simulated potential 30‐yr average switchgrass yields for lowland and upland (north of 41 °N) eco

harvest per year.  

14
Statistical analysis informs the source of variation in yields within each of the 18 

major watershed regions.  In a multiple stepwise regression (PROC REG in SAS 

statistical software) [38], variation in switchgrass yield at the regional scale was 

found to be, on average, 76% attributable to a combination of soil properties, 

temperature, precipitation, and latitude.  Precipitation was found to be the 

dominant determinant of yields in regions west of the Mississippi River with partial 

R2 ranging from 0.57 to 0.87. The exception was for the Upper Mississippi region, 

where soil texture was the primary determinant (R2 = 0.38) of upland switchgrass 

yield, while topsoil depth was the primary determinant of lowland yield (R2 = 0.62).  

Along the East and Gulf Coastal plains, simulated yields are primarily determined by 

soil pH, with partial R2 ranging from 0.43 to 0.79.  Lower soil pH has a negative 

effect on simulated yield in the Mid‐Atlantic region and the Gulf Coast states (Fig. 6). 

In these regions, low organic matter content and less buffering of aluminum and 

manganese contribute to the sensitivity to soil acidity.  While research has shown 

that switchgrass can be effectively established and grown on acidic soils [39], 

productivity is improved by liming [40,41], indicating that low pH may limit the 

potential production of switchgrass.  

While our results indicate that switchgrass can be grown in many diverse areas 

across the United States, the highest potential productivities are in regions that are 

also the highest producing agricultural lands in the country.  This indicates that 

potential conflict between food production and large scale switchgrass production 

will need to be carefully considered.  The average long‐term simulated switchgrass 

yield on land classified as crop land [24] is 5.6 Mg ha‐1.   

The 2007 Energy Independence and Security Act calls for an annual production of 

15.2 billion gallons of renewable fuels by 2012 and 36.0 billion gallons by 2022.  Of 

these totals, 0.5 billion gallons (3.2%) in 2012 and 16 billion gallons (45%) in 2022 

are mandated to be produced as cellulosic ethanol.  Assuming a conversion rate of 

0.38 liters of ethanol from 1 kg of biomass [12], the model projections indicate this 

goal would require conversion of 0.9 million ha of current crop land to switchgrass 

production in 2012. In 2022, a total of 28.6 million ha of crop land – 23% of total US 

cropland – would be required to meet the targeted production.  

3.3 Further Research: Field Trials and Model Development

EPIC and other models will continue to be improved with results from field trials 

and research on switchgrass and other crops with bioenergy feedstock potential. By 

establishing the most accurate model possible now, the bioenergy research 

community can begin exploring the potential for and environmental consequences 

of large‐scale switchgrass production.  We demonstrate model application to a 

dataset representing agricultural lands.  Additional regional scale simulations of 

on‐agricultural land could inform decisions about locations for switchgrass 

roduction.  

n

p

15
Switchgrass is native to much of North America but has only recently begun to be 

developed for large‐scale energy cropping [5]; therefore, there remains substantial 

genetic variation as well as observed differences in phenotypic expression that are 

not yet possible to capture in a crop model. The model does successfully capture the 

explainable differences between upland and lowland ecotypes, but limitations 

remain. With further research on plant growth and a reduction in the uncertainty of 

the causes of the observed yield variability, any number of cultivars of switchgrass 

can be simulated in crop models.  

This study highlights some of the variables for which further agronomic research 

and field trials will be valuable to crop modeling. Measurements of leaf area 

throughout the growing season, switchgrass yield response to liming, and soil C 

change under switchgrass following conversion from crop, grass, or forest land 

would all be valuable.  In addition, full reporting of the management used to achieve 

eported yields, including management for weed control, nutrient applications, and 

esidue management would assist in reducing uncertainty in model simulations.  

r

r

4. Conclusions

Here the EPIC model has been calibrated with and validated against field trials 

representing switchgrass production across a wide geographic scope.  While the 

model successfully captures the explainable yield variability, there are some 

apparent determinants of switchgrass yield that are unaccounted for in the known 

details of the field trials. Studies considering soil pH effects on switchgrass growth 

in the past have found that, while switchgrass is tolerant of a wide range of pH, low 

pH negatively affects productivity.  The field trials used here were not designed to 

consider varying levels of pH, yet the findings from on the process‐based model 

illustrate the importance of soil acidity as a factor in productivity, particularly in the 

East and Gulf Coastal Plain regions.  

The model described here can now be applied with a known level of confidence to 

examine potential production of switchgrass as well as the land‐use requirements 

and environmental consequences of large‐scale production.  Simulations of 

switchgrass over the continental United States indicate the relative productivity of 

switchgrass in different regions and the scope of the land‐use change that may 

potentially be required to meet national renewable fuel production objectives.  

Acknowledgements

We thank J.R. Kiniry of USDA for model guidance and C.T. Garten of ORNL for access 

to data. This work was supported by the US Department of Energy, Office of Science 

through the Center for Carbon Sequestration in Terrestrial Ecosystems and the Integrated 

Assessment Research Program. Collection and analysis of data at ORNL for the 

validation data was supported by the US Department of Energy, Office of Biomass 

Program.

16
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