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 2009DISCLAIMER

This report was prepared as an account of work sponsored by an agency of the United States Gove rnment. 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 lia bility or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights . Reference herein to any specific 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 NA TIONAL LABORATORY operated by

BATTELLE for the

UNITED STATES DEPARTMENT OF ENERGY

under Contract DE-AC0 5 -76RL01830

# Simulating Potential Switchgrass

Production in the United States

Allison M. Thomson * 1 , R. César Izarrualde 1 , Tristram O. West 2 , David J. Parrish 3

, Donald D. Williams 5 Tyler 4 , Jimmy R.

December 2009

Joint Global Change Research Institute, Pacific Northwest Natio nal Laboratory and t he University of 1

M aryland, 8400 Baltimore Ave., Suite 201, College Park, MD 2074 0.

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

3 7831‐6335.

Virginia Polytechnic Institute, Crop & Soil Environmental Scie nces, 330 Smyt h Hall (0404), 3

B lacksburg, VA 24061.

University of Tennessee Experime nt Station, Biosystems Enginee ring and Soil Science, 605 Airways 4

B lvd., Jackson, TN 38301.

5 Blackland Research Ce nter,  720 E. Blackland Rd, Temple, TX 76 502.

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

1Abstract

.............................................................................................................................. ............................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) proc ess‐level agroecosystem model was calibrat ed, validated, and applied to s imulate potential productivity of switchgrass for use as a biofuel feedstock.  The model was calibrated with a regional study of 10‐yr s witchgrass field trials and sub sequently tested against a separate compiled data set of field trials from across the eastern half of the country.  An application of the model in a national database us ing 8‐digit watersheds as the primary modeli ng unit produces 30‐yr average switchgrass yield estimates that can be aggregated to 18 major watersheds.  The m odel projects average annual switchgrass productivity of greater than 7 Mg ha ‐1 in the Upper Mississippi, Lower Mississippi, an d Ohio watersheds. The major factors limiting simulated production vary by reg ion; low precipitation is the p rimary limiting factor across the western half of the country, while moderately acidic soils limit yields on lands east of the Mississippi River.  Average projected switchg rass 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 Indepe ndence and Security

ct. The model described here can be applied as a tool to infor m the land‐use and nvironmental consequences of switchgrass production. A

e

3

## 1. Introduction

Bioenergy has emerged as an energy supply option with the poten tial to reduce greenhouse gas emissions and red uce dependence on fossil fuels while providing economic benefit to farmers [1,2]. Cellulosic feedstocks have b een identified as a principal component in the bioen ergy portfolio by the US Depart ment of Energy [3]. Proposed sources of cellulosic ma terial that can be used for bi oenergy include agricultural and forestry residu es, mixed grasslands, forests, and monocultures of

erbaceous species such as switchgrass ( Panicum virgatum L.) and Miscanthus h ( 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 re quirements. Switchgrass is a warm‐season (C 4 ) perennial grass native to much of the eastern two‐thirds of North America  [6] t hat has been adapted as a for age crop [7‐9].  In principle, switchgrass stands may bring environmental benefits such as erosion control, reduced runoff, and nutrient retention [10]. These ben eficial effects are evident when using switchgrass in riparian buffer zones [11]. W hen grown on a large scale as a bioenergy crop, switchgrass may provide a posi tive net energy balance [12]; however, recent concerns about the emissions of g reenhouse gases from conversion of lands to produ ce crops for biofuels producti on [13] highlight the need to more fully understand th e full carbon balance and envir onmental consequences of growing bioenerg y crops on a large scale. In ad dition, recent increases in global food prices and food insecurity have been a ttributed 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 perspect ives of land‐use change, biomass productivity, economic return, water and nutrie nt balances, net greenhouse gas emissions, and biodiversity.

Simulation models of agricultura l systems can be applied at a v ariety 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 agricu ltural, grassland, and forestry applications; however, switchgrass and other native grasses have not been widely incor porated into these models.  Objectives of this rese arch were to 1) calibrate param eters in the Environmental Policy Integrated Climate model (EPIC) [15] for simulation of switchgrass production, 2) validate the model with an independe nt dataset, and 3) develop national estimates of potential switchgrass yields for the United States.  A national map of potential yields provides a basis for evaluatin g 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 concer ning 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 pl ant growth, crop yields, plant competition, and soil erosion as well as heat, wa ter, and nutrient balances [15]. Daily plant biomass gains in EPIC are proportional to the daily photosynthetically active radiation intercepted by the plant ca nopy.  These daily gains in aboveground biomass are affected by vapor pressure def icits, atmospheric

CO 2 concentration [16,17], and other physiological stresses caused by environmental factors including water, temperature, N, P, and s oil aeration. Similarly, daily root growth may be affected by non‐optimal val ues of bulk density, temperature, and aluminum content [18].  Currently, EPIC can si mulate about 100 plant species including many crops, native grasses, and trees. Daily weather can be estimated from precipitation, ai r temperature, solar radiation, wind, and relative humidity parameters or it can be input from historical records. Soil information on horizon depth, texture, bulk den sity, and C concentration also drives EPIC. EPIC ver.0509, used here, incorporates a soil‐C module that allows f or a complete

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

anagement allows for daily contr ol of the tillage, irrigation, and fertilizer m applications.

Initially developed by USDA in the 1970s to evaluate the role o f erosion on crop productivity, EPIC has been conti nually improved and expanded f or many research purposes [20]. As a simulation model of not only crop productiv ity but also the resulting nutrient runoff, wind and water erosion, and soil C d ynamics, EPIC is uniquely suited for assessing env ironmental consequences of lan d‐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 agricul tural 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 be tween land management and the e rosion, soil C, and full C balance of small watersheds.  Brown et al. [21] applied an earlier version of EPIC to simulate switchgrass resp onse 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 understandin g 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 t he longer, warmer growing seas ons of the southern United States; and upland forms are distinguished by their fine r textures, earlier maturity, and greater cold toler ance [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 (Ta ble 1) represent a range of soi l and climate characteristics. At each site, t wo plots each of lowland and up land ecotypes were

established in 1992. Harvests of this split‐plot study occurred in two patterns: either only once in late fall (one‐cut m anagement), or in mid‐summer a nd again in late fall (two‐cut management).  Soil data collected at the time of switc hgrass establishment were used, while daily weather r ecords for each study location were obtained from

he National Climate Data Center for the simulation period. See Fike et al. [27,28] for t more details of the 10‐yr study.

e 1: Si on for the seven locations in the calibr ation Tabl

ocation te informati

Switchgrass dataset.

A a L Latitude Ele vation Soil Series nnual We th er

rg, Lowland

lo t A verage
Max T Average

Min T Total Precipitation

M 1

g ha

­

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

Morganto wn,
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 paramet ers from the ALMANAC model [29‐31], a model cl osely related to EPIC.  Sensit ivity analysis of crop‐growth parameters in EPIC ( data not shown) led to identifi cation of the major factors limiting switchgrass growth.  Accordingly, adjustments were made to parameters that factor in 1) the timing of leaf decline (DLAI 1 = 0.75 (lowland); DLAI = 0.7 (upland)), 2) the fraction of the growing season where ma ximum leaf area

1 Acronyms in this paragraph are variable names us ed in the EPIC code. See Williams [15] for full descriptions and model equations.

6occurs, (LAP2 = 50.95), 3) the max imum leaf area index (DMLA = 6 (lowland); DMLA = 3 (upland)), 4) leaf area decl ine after anthesis (RLAD = 0.1) , 5) the rate of decline in biomass energy after anthesi s (RBMD = 0.1), and 6) root weig ht at plant emergence (RWP1 = 0.4) and plant maturity (RWP2 = 0.3). In addi tion to plant growth parameter modifications, adjustment was made to the parameter controlling the conversion of standing live biomass to standing residue (PA RM66 = 0.001) and residue management. In addition to these and other parameters, plant growth and development in EPIC is influence d by the temperature during the growing season, expressed within the model as “heat units” [15].  The heat unit s accumulate throughout the growing season, a nd the value necessary for the crop to reach aturity varies by latitude.   In the calibration, heat units r equired for maturity of m switchgrass were automatically determined within EPIC.

Measured yields from all study plots at the seven field trial l ocations were averaged across the four main treatments (lowland, one‐cut; lowland two‐ cut; upland one‐ cut; upland two‐cut) and compare d to simulated yields. While th e correlation across sites within each management tre atment is not strong (Fig. 1) ( R 2 = 0.41), the

results are consistent with the findings from these field trial s [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 yi eld between one‐ and two‐cut managements is apparent in the results, with the upland ecotypes being more responsive to the two‐cut m anagement. This was observed by Fike et al.

27,28] and attributed to the earlier flowering (which halts ve getative 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 tr ial locations by Garten and Wullschleger [32] fou nd that SOC was not significant ly different under switchgrass, row crops, and grass plant covers at these locatio ns five years after switchgrass establishment.  However, stable isotope analysis in dicates that SOC response to a conversion to swit chgrass will depend on initial SOC levels and management [33]. Simulations of S OC change with EPIC (Fig. 2) a re in agreement with these findings, projecting SOC accumulation under soils wi th 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 managem ent. c l

70 5 10 15 20 25EPIC 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 swit chgrass yields from the model c alibration

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 CutΔSOC (Mg ha-1yr-1)

0

Initial SOC (Mg ha -1 )
Figure 2: Simulated annual chang e in soil organic C (SOC) to 30‐cm depth as related

o initial soil C levels at the seven calibrati on dataset loca tions in the upper

outheastern United States. t

s

8The observed variation across sites may be a function of severa l contributing factors including differences in elevatio n, 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 observation ally 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 sw itchgrass productivity at this time.



### 2.3 Model Validation Methods

Following initial model calibrati on, 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 Natio nal Laboratory (ORNL) from the peer‐reviewed

literature and from direct commu nication 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.  Thes e data provided an ndependent test of the EPIC mod el over a wider geographic rang e and set of i growing conditions.

From the full dataset, 67 cases representing field trial result s from 10 states and a variety of fertilization and harvest practices were simulated. Cases were selected for validation if they had two or mo re years of recorded yield, rep resented one‐ or two‐ cut management, and were grown w ithout irrigation. Sites from t he Fike et al. [27,28] study were used in the model calibration and therefore not considered in the validation. Input data for t he model consisted of managemen t details from the field trial database, soil profile information for the database ‐specified soil type from the state soil survey database S TATSGO [36], and long‐term clim atology for each location. By using soils and wea ther data that are available na tionally rather than

ite‐specific, this simulation t est informs the validity of ext ending EPIC switchgrass s simulations beyond field s ites to regional scales.

An additional purpose of the val idation was to establish heat‐u nit scheduling for management operations. This is n ecessary for simulations where perfect information on crop management is not available, and for simula tions that extend the model to new regions where s pecific field management practi ces have not yet been established.  Based on the known management and model‐gene rated 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 c rop maturity.  Those established values then allow the model to automatically schedu le management operations in the validation sim ulations.  Use of heat‐unit sch eduling allows EPIC to account for variation in growing season timing and duration by automatically

9scheduling the date of each plan ting, harvest and fertilizer op eration 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 with in locations. Statistical analysis of the field trial dataset by Gunderson et al. [35] found considerable variability in swit chgrass yield.  They found that yields were more highly correlated with temperatu re and precipitation than manag ement (e.g. N application, number of harvests) or environmental parameters (e .g. soil texture

lass).  Lowland ecotypes consistently had higher reported yiel d than upland c 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). EP IC simulated a narrower range of yields for two‐cut management than was eviden t 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 harves t. Both the observed

nd simulated yields of upland ecotypes did not show significan tly higher total nnual yield with an additional harvest in the two‐cut manageme nt. a

a

0 5 10 15 20 25

ORNL - Measured Yield

EPIC - Simulated YieldAverage Yield (Mg ha-1)

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

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

cotype and harvest management for 67 field trials in the valid ation dataset. Vertical ines represent the full range of measured and simulated yield. e l

10The average and range of simulated and measured yield as groupe d by state (Fig. 4) demonstrates the range of yields across the geographic extent o f the field trials. EPIC slightly overestimates measured switchgrass yields, partic ularly for states

where few field trials were avai lable for comparison (e.g. Arka nsas, South Dakota). One variable with significant variation in the field trial data set was N application. Both simulated and measured yiel d response to N application rat e (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 WIMeasured Yield (Mg/ha)

b) EPIC simulated yields

0 5 10 15 20 25 30 35

AL* AR* GA IA LA * ND OK SD* TX WISimulated Yield (Mg/ha)

*States with too few records for standard deviation

Figure 4: Switchgrass a) measure d and b) simulated yields by st ate for 67 field trials n the validation dataset. Solid box represents one standard de viation from the

ean, and solid line represe nts full range of yield. i

m

11a) 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 1000Average Yield (Mg ha-1)

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

Figure 5: Average (point) and ra nge (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 impr ovements from the calibration a nd 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 her e [24,37] delineate modeling units based on

1213 hydrologic unit area classificati on (USGS) at the 8‐digit and 4 ‐digit level, respectively. Major agricultural lands were simulated at the 8‐ digit level, with multiple agricultural soils repr esenting a minimum of 70% of th e variability of soils within each modeling unit.  Simul ations for each modeling unit were for 30‐year periods based on long‐term avera ge climatology for the modeling unit.  Planting and

arvest dates of switchgrass were adjusted based on long‐term c limatology and h EPIC heat‐unit scheduling.

The resulting model estimates ar e not predictions of yield at a ny specific location; rather they are a projection of the potential for switchgrass g rowth 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). Wh en the results are aggregated t o 18 major watershed regions, the highest a verage yields occur in the Lowe r Mississippi region, with averages above 7 Mg ha ‐1 in the Ohio and Upper Mississippi Regions (Table 2). The high maximum yield in the So uth Atlantic‐Gulf region is attributed to simulation of switchgrass on organic soils i n Florida, which is also appar ent in Fig. 6. In comparison, Brown et al. [21] simulated switchgrass yields of s imilar 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 in western Kansas M

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 )

A v e e r a g M a m x i m u New England 4.1 10.1

Mid‐Atlantic

Atlant ic‐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

14.2 Upper Mississippi 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

6.1Figure 6: Simulated potential30‐yr average switchgrass yields for lowland and upland (north of 41 °N) ecotype with oneharvest per year.14Statistical analysis informs the source of variation in yields within each of the 18 major watershed regions.  In a multiple stepwise regression (PR OC REG in SAS statistical software) [38], variation in switchgrass yield at the regional scale was found to be, on average, 76% att ributable to a combination of s oil properties, temperature, precipitation, and latitude.  Precipitation was fo und to be the dominant determinant of yields in regions west of the Mississip pi River with partial

R 2 ranging from 0.57 to 0.87. The e xception was for the Upper Mis sissippi region,

where soil texture was the primary determinant (R 2 = 0.38) of upland switchgrass yield, while topsoil depth was t he primary determinant of lowla nd yield (R 2 = 0.62). Along the East and Gulf Coastal plains, simulated yields are pr imarily determined by soil pH, with partial R 2 ranging from 0.43 to 0.79.  Lower soil pH has a negative effect on simulated yield in the Mid‐Atlantic region and the Gu lf Coast states (Fig. 6). In these regions, low organic ma tter content and less buffering of aluminum and manganese contribute to the sensitivity to soil acidity.  While research has shown that switchgrass can be effective ly established and grown on ac idic soils [39],

roductivity is improved by limi ng [40,41], indicating that low pH may limit the p potential production of switchgrass.

While our results indicate that switchgrass can be grown in man y diverse areas across the United States, the hig hest potential productivities are in regions that are also the highest producing agric ultural lands in the country. This indicates that potential conflict between food p roduction and large scale swit chgrass production

ill need to be carefully conside red.  The average long‐term si mulated switchgrass w yield on land classified as crop land [24] is 5.6 Mg ha ‐1

.

The 2007 Energy Independence and Security Act calls for an annu al production of 15.2 billion gallons of renewabl e fuels by 2012 and 36.0 billio n 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 c ellulosic ethanol.  Assuming a conversion rate of 0.38 liters of ethanol from 1 kg of biomass [12], the model pro jections 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 contin ue 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 bioenerg y research community can begin exploring th e potential for and environmental consequences of large‐scale switchgrass produc tion.  We demonstrate model ap plication to a dataset representing agricultura l lands.  Additional regional s cale simulations of

on‐agricultural land could info rm decisions about locations fo r switchgrass

roduction. n

p

15Switchgrass is native to much of North America but has only rec ently begun to be developed for large‐scale energy cropping [5]; therefore, there remains substantial genetic variation as well as obs erved differences in phenotypic expression that are not yet possible to capture in a crop model. The model does suc cessfully capture the explainable differences between upland and lowland ecotypes, bu t limitations

remain. With further research on plant growth and a reduction i n the uncertainty of he causes of the observed yield variability, any number of cul tivars of switchgrass t can be simulated in crop models.

This study highlights some of the variables for which further a gronomic research and field trials will be valuable to crop modeling. Measurement s of leaf area throughout the growing season, s witchgrass yield response to li ming, and soil C change under switchgrass followi ng conversion from crop, grass, or forest land

would all be valuable.  In additi on, full reporting of the mana gement used to achieve

eported yields, including management for weed control, nutrien t applications, and esidue management would assist in reducing uncertainty in mode l simulations. r

r



## 4. Conclusions

Here the EPIC model has been cal ibrated with and validated agai nst field trials representing switchgrass producti on across a wide geographic sc ope.  While the model successfully captures the explainable yield variability, there are some apparent determinants of switchg rass yield that are unaccounted for in the known

details of the field trials. Stu dies considering soil pH effect s on switchgrass growth in the past have found that, whi le switchgrass is tolerant of a wide range of pH, low pH negatively affects productivi ty.  The field trials used here were not designed to consider varying levels of pH, yet the findings from on the pro cess‐based model llustrate the importance of soi l acidity as a factor in produc tivity, particularly in the i 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.  Simu lations of switchgrass over the continental United States indicate the rel ative productivity of switchgrass in different regions and the scope of the land‐use change that may potentially be required to meet n ational 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 Se questration in Terrestrial Ecos ystems and the Integrated Assessment Research Program. Collection a nd analysis of data at ORNL for the validation data was supported by the US De partment of Energy, Office of Biomass Program.

16

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