June 20 th , 201 3

MEMORANDUM

TO: Air Docket EPA

- HQ

- OAR

- 201 3

- 0178

SUBJECT: Summary of Modeling Inputs and Assumptions for the

Determination Concerning Renewable Fuels Produced from Barley u nder the Renewable Fuel Standard Program

This memorandum provides information on the key inputs and assumptions used for modeling the lifecycle greenhouse gas (GHG) emissions associated with renewable fuel produced from barley . Assumptions include energy use, energy sources, fertilizer use,

co

- p roduct production, and other important factors used in the lifecycle analysis.

Outline of this Document

I. System Boundaries

II. Data and Assumptions

A. Model Modifications

B. Agriculture Model Assumptions

C. Volume Scenarios

D. Barley Ethanol Processing

III. ReferencesI. System Boundaries

The general system boundaries for lifecycle GHG analysis of biofuels under the RFS program were determined as part of the March 26, 2010 final rule (EPA 2010). Applying these system boundaries to our analysis of ethanol produced from barley was straightforwa rd because the lifecycle system for barley ethanol are similar to those of other biofuels that were analyzed in EPA (2010).

This document focuses on our assumptions related to the various stages in the barle y ethanol lifecycle, including fuel and feedstock production and distribution. To analyze the indirect agricultural and land use change impacts, we used the same agricultural

economic model s that were used for the final RFS2 rule. The Forest and Agricult ural Sector Optimization Model (FASOM), developed by Professor Bruce McCarl of Texas A&M University and others, provides detailed information on domestic agricultural and greenhouse gas impacts of renewable fuels. The Food and Agricultural P olicy Research

Institute international models

, as maintained by the Center for Agricultural and Rural Development at Iowa State University (FAPRI

- CARD), consist of a number of econometric models that are capable of providing detailed information on impacts on

internatio nal agricultural markets from the wider use of renewable fuels in the U.S

. For

more discussion on our use of the FAPRI

- CARD model refer to the barley determination preamble (EPA 201 2 ).

II. Data and Assumptions

This section includes a summary of key assu mptions and data inputs used in our lifecycle

GHG assessment of barley ethanol . Our analysis considers direct and significant indirect emissions related to the production and use of barley ethanol . As such, we also considered GHG emissions impacts relate d to the production and use of other agricultural commodities (e.g., corn, soybeans) that are indirectly impacted by an increase in barley

ethanol production and use. For information on the data inputs and assumptions used to evaluate these other aspects of our analysis refer to the Regulatory Impact Analysis for the RFS2 final rule (EPA 2010 ).

A. Model Modifications

Since the analysis for the RFS2 final rulemaking was completed , there was an update to the land categories defined in the FASOM model with the addition of a new ‘pasture’ category. All other land use category definitions remain unchanged from the final RFS2 rulemaking. Updated land use definitions are as follows:

• Cropland is actively managed cropland, used for both traditional crops (e.g., corn and soybeans) and dedicated energy crops (e.g., switchgrass).

• Cropland pasture is managed pasture land used for livestock production, but which can also be converted to cropland production without additional improvement.• Pasture is land used only for pasture or grazing that cannot be converted into

cropland.

• Forestland contains a number of sub

- categories, tracking the number of acres of private forestland existing at the starting point of the model that remain in standing forests (i.e ., have not yet been harvested), the number of acres harvested, the number of harvested acres that are reforested, and the area converted from other land uses (afforested). Public forestland area is not explicitly tracked because it is assumed to

remain c onstant over time, although exogenous estimates of forest products production from these lands are included in the model

• Rangeland is unmanaged land that can be used for livestock grazing production. While the amount of rangeland idled or used for prod uction may vary, it is assumed that rangeland may not be used for any other purpose than for animal grazing due to its low productivity. In addition, much of the rangeland in the U.S. is publicly

owned.

• Developed (urban) land is assumed to have an in herently higher value than land used for any other use. Thus, the rate of urbanization is assumed to be exogenous based on projections of population and income growth and does not change between the cases analyzed.

• Conservation Reserve Program (CRP) re fers to land that is voluntarily taken out of crop production and placed in the USDA CRP. Land in the CRP is generally marginal cropland retired from production and converted to vegetative cover, such as grass, trees, or woody vegetation to conserve soil, improve water quality, enhance wildlife habitat, or produce other environmental benefits

.

B. Agriculture Model Assumptions

Major assumptions consistently implemented in the FASOM and FAPRI

- CARD models include ethanol conversion yields, and byproduct yie lds. In the case of barley , distiller s grains (DG) are the only byproduct from the ethanol conversion.

Table 1 Assumption Value Source

Ethanol Yield 2.16 gal/bu Consultation with USDA experts

Dried Distillers

Grains Yield 13.25 lbs/bu Y ield, nutritional value, displacement ra tes, and

i nclusion rates based on expert opinion of

USDA

All other assumptions , such as fertilizer application rates and energy use, in the FASOM

and FAPRI

- CARD models are documented in the Regulatory Impact Analysis fo r the RFS2 final rule (EPA 2010 ), as well as technical reports for the FASOM (RTI 2010) and

FAPRI

- CARD (FAPRI

- CARD Staff 2010) models in the RFS2 final rule docket.1

. Disaggregation of Spring and Winter Barley

Based on information from industry stakeholders, data from the USDA National Agricultural Statistics Service, 1 as well as in consultation with USDA, the FASOM

model assume s that barley produced in Delaware, Maryland, North Carolina, Pennsylvania, and Virgi nia is “winter” barley (i.e.

, barley planted in late fall and harvested in early spring), while barley grown in all other regions is assumed to be “spring” barley (i.e.

, barley planted in the spring and harvested in the fall).

FASOM has historically not had the ability to model winter barley production

. We have added this cropping pattern into FASOM, given the fact winter barley could be a potentially significant contributor to total barley ethanol production if this rulemaking is finalized. Therefore, w e believe , it is important for the modeling framework to consider the full range of barley production methods available to the maximum extent possible

.

Conversely, because of differences in model architecture, it was not possible to

differentiate betwee n spring and winter barley in the FAPRI

- CARD model. However, we believe not modeling double cropping for barley in the Southeast and Mid

- Atlantic region

of the US in the FAPRI

- CARD model results in a conservative estimate of lifecycle GHG emissions, as it may slightly overstate the land use change and commodity market impacts of an increase in demand for barley ethanol.

2

. Double

- Cropping of Winter Barley

Historically, winter barley is “double

- cropped” with soybeans. Farmers that utilize this

double

- cr opping method plant their soybean crop in the mid or late spring and harvest it in the early fall followed soon after with a barley crop that is planted in the fall and harvested in the early spring. Importantly, soybean acres in the Southeast and Mid

Atl antic regions of the U.S. that are not double

- cropped with barley are generally left

fal low during the winter months

. 2

Based on information from industry stakeholders, as well as in consultation with USDA, 3

the FASOM model assume s that all winter barley i s double

- cropped with soybeans. This means that a grower is assumed to plant soybeans in the spring, harvesting them in the fall, then plant barley in the winter, harvesting it in early spring. It is also assumed that

these acres would be fallow in the w inter if they were not double

- cropped. Because it was not possible to differentiate spring and winter barley in the FAPRI

- CARD model, no

double

- cropping assumption was made. However, for reasons stated above, this is not expected to have an influence on the international agriculture impacts estimated in the

FAPRI

- CARD model.

C. Volume Scenarios

1 USDA National Agricultural Statistics Service (www.nass.usda.gov)

2 Personal communication with USDA

3 Personal communication with USDATo assess the impacts of an increase in renewable fuel volume from business

- as

- usual

(what is likely to have occurred without the RFS biofuel mandates) to levels required by the statute, we established a control case and other cases for a number of biofuel s analyzed for the March, 2010 RFS rule. The control case included a projection of renewable fuel volumes that might be used to comply with the RFS renewable fuel volume mandates in full. The other cases are designed such that the only difference

between a given case and the control case is the volume of an individual biofuel, all other volumes remaining the same. In the March 2010 RFS rule, for each individual biofuel, we analyzed the incremental GHG emission impacts of increasing the volume of that fue l to the total mix of biofuels needed to meet the EISA requirements. Rather than focus on the GHG emissions impacts associated with a specific gallon of fuel and tracking inputs and outputs across different lifecycle stages, we determined the overall aggr egate impacts across sectors of the economy in response to a given volume change in the amount of biofuel produced.

For this analysis we compared impacts in the control case to the impacts in a new “barley ethanol” case. Some assumptions related to bar ley production and ethanol use were adjusted based on consultation with USDA, academic experts, and industry stakeholders. However, the volume of biofuels produced in this version of the control case is the same as what was analyzed for the March 2010 RFS rule.

For the “barley ethanol” case, our modeling analyzed a shock of 140 million gallons of barley ethanol in 2022. In FASOM, this volume was divided into 80 million gallons of “spring barley” ethanol and 60 million gallons of “winter barley” ethanol . EPA chose

this modeled volume (for both “spring barley” and “winter barley” ethanol) based upon consultations with industry stakeholders regarding their anticipated production capacity,

and USDA regarding how much barley could practically be dedicated to ethanol production 2022 given historical and anticipated barley production levels and various demands (e.g., food, animal feed

, exports) for barley in the marketplace . As stated above, input from industry stakeholders has suggested that there is intere st in utilizing both spring and winter barley as ethanol feedstock. Further, stakeholders have indicated that there is likely to be somewhat more production of ethanol from spring barley than from winter barley if this pathway is approved. Therefore, we believe it is important to consider the shifting of domestic agriculture in the context of concurrent ethanol production from both sources of barley. In the FAPRI

- CARD model, as stated above, no distinction is made between winter and spring barley. For t his reason, the volume in the

FAPRI

- CARD model is simply represented as 140 million gallons of barley ethanol.

The FASOM and FAPRI

- CARD models, described above, project how much barley will be supplied to ethanol production from a combination of increased production, decreases in others uses (e.g., animal feed), and decreases in exports, in going from the control case

to the barley case.

D. Barley Ethanol ProcessingBased on information submitted by petitioners, we expect dry milling will be the most

common process for producing ethanol from barley. Therefore this section focuses on a lifecycle GHG emissions analysis of several variations of the dry mill process. In the dry milling process, the barley is ground and fermented to produce ethanol. The remaining components (distillers grains) are then either left wet if used in the near

- term or dried for longer term use as animal feed.

For this analysis the amount of barley used for ethanol production as modeled by the

FASOM and FAPRI

- CARD models was based on yield assumptions built into those two models. Specifically, the models assume barley ethanol yields of 2.16 gallons (pure ethanol) per bushel for dry mill plants (yields represent pure ethanol).

As per the analysis done in the March 2010 RFS rule, the GHG emission calculation from ethanol production needs to account for not only the renewable fuel produced, but also

any co

- products. For barley ethanol production, this analysis accounts for the DG co

product use directly in the FASOM and FAPR I

- CARD agricultural sector modeling

described above. DG s are considered a replacement animal feed and thus reduce the need to make up for the barley production that went into ethanol production. Since FASOM takes the production and use of DG into account , no further allocation was needed at the ethanol plant and all plant emissions are accounted for there.

The barley ethanol process also produces barley hulls. The barley hulls can either be processed along with the kernel and end up in the DG, or can be removed and used for

other purposes. Communications with USDA indicated that general practice is to remove barley hulls before the DG enter the feed market. Therefore, our modeling assumed the

hulls were removed and not used in the DG. We considered cases where the hulls were used beneficially as an energy source replacing some of the energy used on

- site. Results of that analysis are shown below.

Overall process energ y and electricity use for barley ethanol production was based on the energy use information for corn ethanol production from the March 2010 RFS rule analysis. For the March 2010 RFS rule, EPA modeled future plant energy use to represent plants that would be built to meet requirements of increased ethanol production, as opposed to current or historic data on energy used in ethanol production. The energy use at dry mill ethanol plants was based on ASPEN models developed by USDA and updated to reflect change s in technology out to 2022 as described in the March 2010 RFS rule RIA Chapter 1.

The work done on ethanol production for the March 2010 RFS rule was based on converting corn to ethanol. Converting barley to ethanol will result in slightly different

e nergy use based on differences in the grains and how they are processed. For example, a barley plant requires more energy than a corn plant per gallon of ethanol produced since the starch / fiber ratio in corn is different than it is in barley. The same ASPEN USDA models used for corn ethanol in the final rule were also developed for barley ethanol. Based on the numbers from USDA, a barley ethanol plant uses 1.2 times the thermalprocess energy of a corn ethanol plant and 1.3 times the electrical energy per gallon of

ethanol produced.

The GHG emissions from production of ethanol from barley were calculated in the same way as other fuels analyzed as part of the March 2010 RFS rule. The GHG emissions were calculated by multiplying the BTUs of the differe nt types of energy inputs at the barley ethanol plant by emissions factors for combustion of those fuel sources. The emission factors for the different fuel types are the same as those used in the March 2010

RFS rule and were based on assumed carbon conte nts of the different process fuels. The emissions from producing electricity in the U.S. were also the same as used in the March 2010 RFS rule, which were taken from GREET and represent average U.S. grid electricity production emissions.

III. References

Food and Agricultural Policy and Research Institute international models as maintained by the Center for Agricultural and Rural Development at Iowa State University (FAPR

CARD) Staff. 2010. Technical Report: An Analysis of EPA Renewable Fuel Scenarios

wi th the FAPRI

– CARD International Models. January, 2010. Docket No. EPA

- HQ

OAR

- 2005

- 0161

- 3177.

RTI International. 2010. U.S. Agricultural and Forestry Impacts of the Energy Independence and Security Act: FASOM Results and Model Description. January 2010.

Do cket No. EPA

- HQ

- OAR

- 2005

- 0161

- 3178.

U.S. Environmental Protection Agency (EPA). 201 2

. Notice of Data Availability Concerning Renewable Fuels Produced from Barley Under the RFS Program

.

U.S. Environmental Protection Agency (EPA). 2010. Renewable Fuel Sta ndard Program (RFS2) Regulatory Impact Analysis. EPA

- 420

- R

- 10

- 006. http://www.epa.gov/oms/renewablefuels/420r10006.pdf