August 2011

Biomass Supply for a Bioenergy and Bioproducts Industry

U.S. BILLI N-TON UPDATE

U.S. Billion-Ton Update:

Biomass Supply for a Bioenergy and Bioproducts Industry

 

A Study Sponsored by

U.S. Department of Energy

Energy Efficiency and Renewable Energy

Office of the Biomass Program

August 2011

Prepared by

OAK RIDGE NATIONAL LABORATORY

Oak Ridge, Tennessee 37831–6335 managed by

UT-Battelle, LLC

for the

U.S. DEPARTMENT OF ENERGY

under contract DE-AC05-00OR22725

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 any of their employees, makes any warranty, expressed 

or implied, or assumes any legal liability 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. The views and opinions of authors expressed 

herein do not necessarily state or reflect those of the United States Government or any agency thereof.

This report is being disseminated by the Department of Energy. As such, the document was prepared in 

compliance with Section 515 of the Treasury and General Government Appropriations Act for Fiscal Year 2001 

(Public Law 106-554) and information quality guidelines issued by the Department of Energy. Further, this report 

could be “influential scientific information” as that term is defined in the Office of Management and Budget’s 

Information Quality Bulletin for Peer Review (Bulletin). This report has been peer reviewed pursuant to section II 

of the Bulletin.
Availability

This report, as well as supporting documentation, data, and analysis tools, can be found on the 

Bioenergy Knowledge Discovery Framework at http://bioenergykdf.net.

Citation

Please cite as follows:

U.S. Department of Energy. 2011. U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and 

Bioproducts Industry. R.D. Perlack and B.J. Stokes (Leads), ORNL/TM-2011/224. Oak Ridge National 

Laboratory, Oak Ridge, TN. 227p.

Additional Information

The U.S. Department of Energy, the Office of Energy Efficiency and Renewable Energy, the Office 

of the Biomass Program, and Oak Ridge National Laboratory provide access to information and 

publications on biomass availability and other topics. The following websites are available:

http://www.energy.gov/

http://www.eere.energy.gov/

http://www.eere.energy.gov/topics/biomass.html

http://www.ornl.gov/sci/bioenergy/

The Federal Government prohibits discrimination in all its programs and activities on the basis of race, 

color, national origin, age, disability, and, where applicable, sex, marital status, familial status, parental 

status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because all or part 

of an individual’s income is derived from any public assistance program.

DISCLAIMER

Although the authors have made every attempt to use the best information 

and data available, to provide transparency in the analysis, and had 

experts provide input and review, the readers need to be reminded that 

the updated U.S. Billion-Ton Update is still a strategic assessment, albeit 

an improved assessment, of potential biomass. It alone is not sufficiently 

designed, developed, and validated to be a tactical planning and decision 

tool. Even though the analysis does provide county by county estimates 

of the feedstocks at a selected cost, these estimates are only useful for 

strategic assessments. The users are encouraged to use the website and 

the associated information to better understand the assumptions and 

ramifications of using the analysis. Furthermore, the updated 

Billion-Ton Update should only be used with other tools and specific data 

and information for tactical business decisions. When used correctly, the 

updated Billion-Ton Update is a valuable tool.
i

Oak Ridge National Laboratory

Robert D. Perlack,* Ph.D. – Senior Scientist, 

Bioenergy Resource and Engineering Systems 

Group, Environmental Sciences Division

Laurence M. Eaton – Research Scientist, Bioenergy 

Resource and Engineering Systems Group, 

Environmental Sciences Division

Anthony F. Turhollow Jr., Ph.D. – Research Scientist, 

Bioenergy Resource and Engineering Systems 

Group, Environmental Sciences Division

Matt H. Langholtz, Ph.D. – Research Scientist, 

Bioenergy Resource and Engineering Systems 

Group, Environmental Sciences Division

Craig C. Brandt – Research Scientist, Microbial 

Ecology & Physiology Group, Biosciences Division

Mark E. Downing, Ph.D. – Group Leader, Bioenergy 

Resource and Engineering Systems Group, 

Environmental Sciences Division

Robin L. Graham, Ph.D. – Bioenergy Program 

Manager, Environmental Sciences Division

Lynn L. Wright – Consultant, Bioenergy Resource 

and Engineering Systems Group, Environmental 

Sciences Division

Jacob M. Kavkewitz – Bioenergy Resource and 

Engineering Systems Group, Environmental Sciences 

Division

Anna M. Shamey – Bioenergy Resource and 

Engineering Systems Group, Environmental Sciences 

Division

Kansas State University

Richard G. Nelson, Ph.D. – Director and Department 

Head, Engineering Extension Programs, Manhattan, 

Kansas

CNJV/ DOE Golden Field Office

Bryce J. Stokes,* Ph.D. – Senior Advisor, 

Golden, Colorado

Texas A&M University

William L. Rooney, Ph.D. – Professor, Department 

of Soil and Crop Sciences, College Station, Texas

Idaho National Laboratory

David J. Muth – Engineer, Bioenergy Programs, 

Idaho Falls, Idaho

J. Richard Hess, Ph.D. – Program Leader, 

Bioenergy Programs, Idaho Falls, Idaho

Jared M. Abodeely – Research Engineer, Bioenergy

Programs, Idaho Falls, Idaho

University of Tennessee

Chad Hellwinckel, Ph.D. – Research Assistant 

Professor, Agricultural Policy Analysis Center, 

Knoxville, Tennessee

Daniel De La Torre Ugarte, Ph.D. – Professor, 

Agricultural Policy Analysis Center, Knoxville, 

Tennessee

Daniel C. Yoder, Ph.D. – Professor, Biosystems 

Engineering and Soil Science, Knoxville, 

Tennessee

James P. Lyon – Analyst, Biosystems Engineering 

and Soil Science, Knoxville, Tennessee

Timothy G. Rials, Ph.D. – Director, Center for

Renewable Carbon, Knoxville, Tennessee

State University of New York

Timothy A. Volk, Ph.D. – Senior Research 

Associate, College of Environmental Science and 

Forestry, Syracuse, New York

Thomas S. Buchholz, Ph.D. – Research Scientist, 

College of Environmental Science and Forestry, 

Syracuse, New York

Lawrence P. Abrahamson, Ph.D. – Senior Research 

Associate, College of Environmental Science and

Forestry, Syracuse, New York

CONTRIBUTORS

* Study Leads
ii

Iowa State University

Robert P. Anex, Ph.D. – Professor, Agricultural and 

Biosystems Engineering, Ames, Iowa

University of Illinois

Thomas B. Voigt, Ph.D. – Extension Specialist, 

Department of Crop Sciences, Urbana, Illinois

University of Minnesota

William Berguson – Director, Natural Resources

Research Institute, Duluth, Minnesota

Don E. Riemenschneider, Ph.D. – Geneticist, Natural 

Resources Research Institute, Duluth, Minnesota

USDA Agricultural Research Service

Douglas L. Karlen, Ph.D. – Soil Scientist, Soil,

Water, and Air Resources Research Unit, Ames, Iowa

Jane M. F. Johnson, Ph.D. – Research Soil Scientist, 

North Central Soil Conservation Research Lab, 

Morris, Minnesota

Robert B. Mitchell, Ph.D. – Research Agronomist, 

Grain, Forage & Bioenergy Research Unit, Lincoln, 

Nebraska

Kenneth P. Vogel, Ph.D. – Supervisory Research 

Geneticist, Grain, Forage & Bioenergy Research 

Unit, Lincoln, Nebraska

Edward P. Richard Jr., Ph.D. – Research Leader, 

Sugarcane Research Unit, Houma, Louisiana

John Tatarko, Ph.D. – Soil Scientist, Engineering and 

Wind Erosion Research Unit, Manhattan, Kansas

Larry E. Wagner, Ph.D. – Agricultural Engineer, 

Engineering and Wind Erosion Research Unit, 

Manhattan, Kansas

USDA Forest Service

Kenneth E. Skog, Ph.D. – Economist, Forest

Products Laboratory, Madison, Wisconsin

Patricia K. Lebow, Ph.D. – Mathematical Statistician, 

Forest Products Laboratory, Madison, Wisconsin

Dennis P. Dykstra, Ph.D. – Research Forest Products 

Technologist, Pacific Northwest Research Station, 

Portland, Oregon

Marilyn A. Buford, Ph.D. – Silviculture Research 

National Program Leader, Washington Office, 

Washington, D.C.

Patrick D. Miles – Research Forester, Northern 

Research Station, St. Paul, Minnesota

D. Andrew Scott, Ph.D. – Soil Scientist, Southern 

Research Station, Normal, Alabama

James H. Perdue – Senior Biological Scientist, 

Southern Research Station, Knoxville, Tennessee

Robert B. Rummer, Ph.D. – Research Engineer, 

Southern Research Station, Auburn, Alabama

Jamie Barbour, Ph.D. – Program Manager, Pacific 

Northwest Research Station, Portland, Oregon

John A. Stanturf, Ph.D. – Research Ecologist, 

Southern Research Station, Athens, Georgia

David B.McKeever – Research Forester, Forest 

Products Laboratory, Madison, Wisconsin

Ronald S. Zalesny Jr., Ph.D. – Geneticist, Northern 

Research Station, Rhinelander, Wisconsin

Edmund A. Gee – National Woody Biomass 

Utilization Team Lead, Washington, D.C.

USDA National Institute of Food and Agriculture

P. Daniel Cassidy, Ph.D. – National Program Leader 

for Forest-based Bioenergy, Washington, D.C.

USDA Natural Resources Conservation Service

David Lightle, Ph.D. – Agronomist, National Soil 

Survey Center, Lincoln, Nebraska
iii

Many people worked to provide the data; develop and improve the methodology; complete the analysis; and 

assemble this report, supporting documentation, and associated products. This effort is the culmination of the 

leadership, contributions, and support of many individuals and the numerous organizations that they represent. As 

overall study lead, Oak Ridge National Laboratory (ORNL) would like to formally recognize these contributions 

and acknowledge the hard work of so many who made this report possible.

The publication of the original Billion-Ton Study in April 2005 was heralded as a significant endeavor, providing 

quantification to speculation and opening up discussion—if not debate—about a contentious topic, the sustainable 

supply of biomass. More importantly, the report provided a starting point for the evaluation of underlying data 

and methods. It also established relevance and use of such an analysis in the burgeoning bioenergy community. 

The report, a national strategic assessment of biomass resources, has had both supporters and detractors, many 

of whom have provided insightful comments regarding how to improve the report’s usefulness. The study leads 

greatly appreciate those who have provided input and acknowledge the many solicited and unsolicited comments 

that provided a basis for this report update. The hope, if not the belief, is that the updated study is a better product 

because of the efforts of all who took time to analyze and comment on the original report.

The study leads would like to acknowledge the support of the Department of Energy (DOE), especially the Office 

of the Biomass Program (OBP). The continued support of John Ferrell and Zia Haq and their leadership and 

commitment to the development of a quality product is greatly appreciated. The study leads recognize Sarah Lynch 

for her efforts in the review and clearance process. The study leads also want to thank Alison Goss Eng and Laura 

McCann of OBP and Robin Graham and Mark Downing of ORNL for continued support and involvement in the 

study.

Many people worked together to bring their talents and knowledge to the forefront in the data assimilation, 

analysis, and writing processes. The result of these efforts is a more comprehensive assessment of U.S. biomass 

resources. The study leads would like to formally recognize the immeasurable contributions of those who worked 

so diligently to update the report, but more importantly, to develop quantitative methodologies and supporting 

data and tools that will hopefully provide even more value over time with further improvements. Many thanks 

and much appreciation go to all the contributing authors for data assimilation, model development, review, and/or 

writing. The long list of contributors shows the breadth and depth of the effort behind the analysis and subsequent 

report.

The core of the analysis was performed at ORNL and included staff in Environmental and Biosciences Divisions. 

Analysts at other institutions provided critical expertise to complement the ORNL analysis. The study leads want 

to especially thank Craig Brandt, Laurence Eaton, and Anthony Turhollow of ORNL; Chad Hellwinckel at the 

Agricultural Policy and Analysis Center of the University of Tennessee; and Ken Skog, Patti Lebow, and Dennis 

Dykstra of the U.S. Department of Agriculture (USDA) Forest Service for using their analytical skills in both 

developing and improving methods and completing the analysis. Considerable time also went into developing 

erosion and soil organic matter retention coefficients for agricultural residues. The study leads greatly appreciate 

the efforts of Richard Nelson of Kansas State University and David Muth and Richard Hess of the Idaho National 

Laboratory (INL). They tackled the quantitative assessment of agricultural residue sustainability by applying data 

and tools developed through the DOE Regional Biomass Feedstock Partnership Program. Within the Regional 

Partnership, a special acknowledgement goes to the leadership and efforts of Doug Karlen and Jane Johnson 

of USDA Agricultural Research Services (ARS), and Rob Anex of Iowa State University. The study leads also 

appreciate the analytic support from John Tatarko and Larry Wagner of USDA-ARS, David Lightle of the USDA 

Natural Resources Conservation Service, Jared Abodeely of INL, Anna Shamey and Jacob Kavkewitz of ORNL, 

Daniel Yoder and James Lyon of the University of Tennessee, Thomas Buchholz of the State University of New 

ACKNOWLEDGEMENTS
iv

York (SUNY), and Daniel De La Torre Ugarte of the University of Tennessee.

A special recognition goes to those that wrote sections of the report on energy crops:

Lawrence P. Abrahamson, Ph.D. – Senior Research Associate, State University of New York, College of 

Environmental Science and Forestry, Syracuse, New York: Willow

Matt H. Langholtz, Ph.D. – Economist, Oak Ridge National Laboratory, Bioenergy Resource and Engineering 

Systems Group, Environmental Sciences Division, Oak Ridge, Tennessee: Eucalyptus

Robert B. Mitchell, Ph.D. – Research Agronomist, USDA Agricultural Research Services, Grain, Forage & 

Bioenergy Research Unit, Lincoln, Nebraska: Switchgrass and Other Perennial Grasses

Edward P. Richard Jr., Ph.D. – Research Leader, USDA Agricultural Research Services, Sugarcane Research Unit, 

Houma, Louisiana: Sugarcane

Don E. Riemenschneider, Ph.D. – Geneticist, University of Minnesota, Natural Resources Research Institute, 

Duluth, Minnesota: Poplar

William L. Rooney, Ph.D. – Professor, Texas A&M University, Department of Soil and Crop Sciences, College 

Station, Texas: Sorghum

Kenneth P. Vogel, Ph.D. – Supervisory Research Geneticist, USDA Agricultural Research Services, Grain, Forage 

& Bioenergy Research Unit, Lincoln, Nebraska: Switchgrass and Other Perennial Grasses

Timothy A. Volk, Ph.D. – Senior Research Associate, State University of New York, College of Environmental 

Science and Forestry, Syracuse, New York: Willow

Thomas B. Voigt, Ph.D. – Extension Specialist, University of Illinois, Department of Crop Sciences, Urbana, 

Illinois: Giant Miscanthus

Lynn L. Wright – Consultant, Oak Ridge National Laboratory, Bioenergy Resource and Engineering Systems 

Group, Environmental Sciences Division, Oak Ridge, Tennessee: Southern Pines

Another special acknowledgement goes to USDA Forest Service contributors Andrew Scott, for the sustainability 

section for forest residues; Pat Miles, for providing data; and Ken Skog and Dennis Dykstra, for writing sections 

of the forest residue chapter. The study leads want to recognize the entire forestry group for their long-term 

involvement, support, and individual contributions. This includes Ken Skog, Patti Lebow, Dennis Dykstra, Marilyn 

Buford, Pat Miles, Andrew Scott, James Perdue, Robert Rummer, Jamie Barbour, John Stanturf, David McKeever, 

Ron Zalesny Jr., and Ed Gee of the USDA Forest Service; Tim Volk of SUNY; Daniel Cassidy of the USDA 

National Institute of Food and Agriculture; Tim Rials of the University of Tennessee; and Bill Berguson of the 

University of Minnesota.

The study leads also greatly appreciate Elizabeth Penniman and Taryn McKinnon of BCS, Inc. for their outstanding 

editing. A special thanks also goes to Jared Largen, Jaime Redick, Christina Fagerholm, Borys Mar, and Rick 

Ostopowicz of BCS, Inc. for editing the report and providing graphics, and to Christian Biddle of Flying Circle, 

LLC who provided the report cover illustration. They significantly added to the report.
v

The report was reviewed by a distinguished group of individuals to whom ORNL and DOE are most grateful for 

their valuable comments on content as well as context. They are:

Harry Baumes, Ph.D. – Acting Director, Office of Energy Policy and New Uses, U.S. Department of Agriculture, 

Washington, D.C.

Mary Bohman, Ph.D. – Division Director, Resource and Rural Economics Division, Economic Research Service, 

U.S. Department of Agriculture, Washington, D.C.

Craig Frear, Ph.D. – Bioproducts Specialist, Biological Systems Engineering, Washington State University, 

Pullman, Washington

Göran Berndes, Ph.D. – Leader, International Energy Agreement Bioenergy Task 43 and Docent, Department of 

Energy and Environment, Chalmers University of Technology, Gothenburg, Sweden

Bryan Jenkins, Ph.D. – Professor, Biological & Agricultural Engineering, University of California, Davis, 

California

Alan A. Lucier, Ph.D. – Senior Vice President, National Council on Air and Stream Improvement, Research 

Triangle Park, North Carolina

Ken Moore, Ph.D. – Professor, Department of Agronomy, Iowa State University, Ames, Iowa

Jim Richardson – Leader (Retired), International Energy Agreement Bioenergy Task 31, J. Richardson Consulting, 

Ottawa, Ontario, Canada

V. Alaric Sample, Ph.D. – President, Pinchot Institute for Conservation, Washington, D.C.

David Sjoding – Renewable Energy Specialist, Washington State University, Olympia, Washington

David N. Wear, Ph.D. – Project Leader, USDA-Forest Service, Southern Research Station, Research Triangle Park, 

North Carolina

REVIEWERS
vi

WORKSHOP PARTICIPANTS

As part of the update process, there became an apparent need to understand and project changes in technologies 

of the future to better develop scenarios. DOE hosted workshops to solicit input from the industrial, academic, 

and government sectors to help identify potential technology changes in the future and the impacts of biomass 

availability. The study leads certainly want to recognize and express our appreciation to those who contributed 

through this process.

Workshop Report Editors 

Leslie Park Ovard – R&D Support Specialist, Bioenergy Programs, Idaho National Laboratory, Idaho Falls, Idaho

Thomas H. Ulrich, Ph.D. – Advisory Scientist, Biological Science Department, Idaho National Laboratory, Idaho 

Falls, Idaho

David J. Muth, Jr. – Mechanical Engineer, Bioenergy Programs, Idaho National Laboratory, Idaho Falls, Idaho

J. Richard Hess, Ph.D. – Technical Program Leader, Bioenergy Programs, Idaho National Laboratory, Idaho Falls, 

Idaho

Steven Thomas, Ph.D. – Project Officer, Golden Field Office, Department of Energy, Golden, Colorado

Bryce Stokes, Ph.D. – Senior Advisor, CNJV, Golden Field Office, Department of Energy, Golden, Colorado

Workshop 1 – Corn/Agricultural Crop Residues

J. Richard Hess, Ph.D. – Technical Program Leader, Bioenergy Programs, Idaho National Laboratory, Idaho Falls, 

Idaho (Workshop Facilitator)

Steve C. Barr – Consulting Engineer, DuPont Engineering Technology, Wilmington, Delaware

Michael D. Edgerton, Ph.D. – Ethanol and Quality Traits Technical lead, Monsanto Company, St. Louis, Missouri

Douglas Haefele – Ph.D. – Senior Research Scientist, Pioneer Hi-Bred, Johnston, Iowa

Larry Hasheider – Farmer and Chair of the National Corn Growers Association’s Research and Business

Development Action Team, Okawville, Illinois

Douglas Karlen, Ph.D. – Soil Scientist and Research Lead, USDA Agricultural Research Service, Ames, Iowa

Kendall Lamkey, Ph.D. – Chair. Department of Agronomy, Iowa State University, Ames, Iowa

David Loos – Technology and Business Development Director, Illinois Corn Marketing Board/Illinois Corn 

Growers Association, Bloomington, Illinois

Todd Mathisen – Farmer, Cylinder, Iowa

Todd Peterson, Ph.D. – President, Council for Agricultural Science and Technology, Ames, Iowa 

Raymond Riley, Ph.D. – Head, Global Corn and Soybean Research and Product Development, Syngenta Seeds, 

Inc., Minnetonka, Minnesota

Michael Roth – Process Engineer, POET, Sioux Falls, Iowa

Lee Stromberg, Ph.D. – Manager of Seeds Activities, BASF Plant Science, Ames, Iowa
vii

Workshop 2 – Herbaceous Energy Crops

Steve Thomas, Ph.D. – Project Officer, Golden Field Office, Department of Energy, Golden, Colorado (Workshop 

Facilitator)

Bill Belden – Consultant, Prairie Lands Biomass LLC, Ottumwa, Iowa

John Blanton Jr., Ph.D. – Research Programs Manager, Samuel Roberts Noble Foundation, Ardmore, Oklahoma 

David Bransby, Ph.D. – Professor, Department of Agronomy and Soils, Auburn University, Auburn, Alabama 

Cory Christensen, Ph.D. – Director of Product Management, Ceres Inc., Thousand Oaks, California

Fred Circle – President and CEO, FDC Enterprises, Columbus, Ohio

Ken Goddard – Extension Specialist, University of Tennessee, Knoxville, Tennessee

Neal Gutterson, Ph.D. – CEO, Mendel Biotechnology, Hayward, California

Stephen P. Long, Ph.D. – Professor of Crop Sciences, University of Illinois, Urbana, Illinois 

Tom Lutgen, – President, Star Seed Company, Osborne, Kansas

Vance Owens, Ph.D. – Professor, South Dakota State University, Brookings, South Dakota

Ed Richard, Ph.D. – Supervisory Research Agronomist, USDA Agricultural Research Service, Houma, Louisiana

William Rooney, Ph.D. – Professor, Department of Soil and Crop Sciences, Texas A&M University, College 

Station, Texas

Workshop 3 – Woody Energy Crops

Bryce Stokes, Ph.D. – Senior Advisor, CNJV, Golden Field Office, Department of Energy, Golden, Colorado 

(Workshop Facilitator)

Bill Berguson – Director of Applied Forestry at the Natural Resources Research Institute (NRRI), University of 

Minnesota, Duluth, Minnesota

Starling Childs – Landowner, Consultant, EECOS, Norfolk, Connecticut

Mike Cunningham, Ph.D. – Director of Product Development, ArborGen, LLC, Tallahassee, Florida

Michele Curtis – Wood Supply Manager, Buckeye Cellulose/Proctor & Gamble, Perry, Florida

Howard Duzan, Ph.D. – Manager of Southern Timberlands R&D, Weyerhaeuser Company, Columbus, Mississippi

Thomas Fox, Ph.D. – Professor, Department of Forest Resources and Environmental Conservation, Virginia Tech, 

Blacksburg, Virginia

Robert Harrison, Ph.D. – Professor, University Estadual de Sao Paulo, Sao Paulo, Brazil

Alan A. Lucier, Ph.D. – Senior Vice President, National Council for Air and Stream Improvement Inc., Research 

Triangle Park, North Carolina

Mike Schmidt – Manager, Forestry Renewables, John Deere Construction & Forestry, Moline, Illinois

James Rakestraw, Ph.D. – Manager, Forest Research & Technology, International Paper Company, Bainbridge, 

Georgia 

Robert Rummer, Ph.D. – Project Leader, Southern Research Station, USDA Forest Service, Auburn, Alabama

Timothy Tschaplinski, Ph.D. – Distinguished Scientist, Environmental Sciences Division, Oak Ridge National 

Laboratory, Oak Ridge, Tennessee

Timothy A. Volk, Ph.D. – Senior Research Associate, Forest & Natural Resources Management, Syracuse, New 

York

Ronald S. Zalesny Jr., Ph.D. – Geneticist, Northern Research Station, USDA Forest Service, Rhinelander, 

Wisconsin
viii

Table of Contents

CONTRIBUTORS................................................................................................................................i

ACKNOWLEDGEMENTS.................................................................................................................iii

REVIEWERS......................................................................................................................................v

WORKSHOP PARTICIPANTS..........................................................................................................vi

LIST OF TABLES..............................................................................................................................xi

LIST OF FIGURES...........................................................................................................................xii

LIST OF TEXT BOXES ...................................................................................................................xvi

EXECUTIVE SUMMARY................................................................................................................xvii

1. INTRODUCTION............................................................................................................................ 1

1.1 Background...........................................................................................................................................1

1.2 Purpose ...............................................................................................................................................3

1.3 Report Organization.........................................................................................................................6

2. BIOMASS IN CURRENT AND PROJECTED ENERGY CONSUMPTION ...................................7

2.1 Current Consumption of Biomass Resources............................................................................ 7

2.1.1 Forest-Derived Resources ..................................................................................................................................8

2.1.2 Agriculture-Derived Resources .....................................................................................................................10

2.2 Projected Increase in Currently Used Biomass Resources................................................. 14

3. FOREST BIOMASS AND WOOD WASTE RESOURCES..........................................................16

3.1 Primary Forest Biomass..................................................................................................................18

3.1.1 Sustainability of Extracting Primary Forest Residue Biomass ........................................................... 21

3.1.2 Logging Residues and Thinnings................................................................................................................. 25

3.2 Other Removal Residues..............................................................................................................37

3.3 Forest Residue Thinnings on Other Forestlands...................................................................38

3.4 Fuelwood, Mill Residues, and Pulping Liquors ......................................................................39

3.4.1 Fuelwood ..............................................................................................................................................................39

3.4.2 Primary and Secondary Mill Residues......................................................................................................39

3.4.3 Pulping Liquors .................................................................................................................................................39

3.5 Urban Wood Wastes.....................................................................................................................40

3.6 Conventionally Sourced Wood – Pulpwood-Sized Roundwood......................................42

3.6.1 Use of Pulpwood Stumpage Supply and Stumpage Demand Curves .........................................42

3.6.2 Estimated Conventionally Sourced Wood..............................................................................................45

3.7 Total Supply of Forest Biomass and Wood Wastes .............................................................47

3.8 Summary............................................................................................................................................51

4. AGRICULTURAL BIOMASS AND WASTE RESOURCES.........................................................52

4.1 Cropland Resources (Corn Ethanol and Soybean Biodiesel).............................................52

4.2 Agricultural Crop Residues..........................................................................................................52

4.3 Sustainability of Crop Residue Removal .................................................................................53

4.4 Estimating Crop Residue Supply...............................................................................................55
ix

4.4.1 Input Assumptions for Baseline and High-Yield Scenarios............................................................... 55

4.5 Crop Residue Supply Results......................................................................................................67

4.5.1 Baseline Estimates of Crop Residue Potential....................................................................................... 67

4.5.2 High-Yield Estimates of Crop Residue Potential...................................................................................74

4.6 Secondary Cropland Residues and Waste Resources.........................................................77

4.6.1 Sugarcane Residues ......................................................................................................................................... 78

4.6.2 Cotton Gin Trash and Field Residues........................................................................................................ 79

4.6.3 Soybean Hulls ....................................................................................................................................................80

4.6.4 Rice Hulls and Field Residues......................................................................................................................80

4.6.5 Wheat Dust and Chaff.....................................................................................................................................81

4.6.6 Orchard and Vineyard Prunings ..................................................................................................................81

4.6.7 Animal Fats ..........................................................................................................................................................81

4.6.8 Animal Manure ..................................................................................................................................................82

4.6.9 Wastes Resources from Agriculture.........................................................................................................83

4.7 Total Supply of Agricultural Biomass and Waste Resources............................................ 84

4.8 Summary ......................................................................................................................................... 84

5. BIOMASS ENERGY CROPS .......................................................................................................87

5.1 Background on Energy Crops......................................................................................................87

5.1.1 Switchgrass and Other Perennial Grasses ............................................................................................... 87

5.1.2 Giant Miscanthus - Miscanthus x giganteus............................................................................................93

5.1.3 Sugarcane............................................................................................................................................................. 97

5.1.4 Sorghum ............................................................................................................................................................ 102

5.1.5 Poplar...................................................................................................................................................................106

5.1.6 Willow...................................................................................................................................................................109

5.1.7 Eucalyptus ............................................................................................................................................................113

5.1.8 Southern Pines ..................................................................................................................................................114

5.2 Estimating Future Crop Supply.................................................................................................118

5.2.1 Input and Yield Assumptions for Baseline and High-Yield Scenarios ...........................................118

5.3 Results .............................................................................................................................................129

5.3.1 Baseline Estimates of Energy Crop Potential....................................................................................... 130

5.3.2 High-Yield Estimates of Energy Crop Potential...................................................................................135

5.3.3 Land-Use Change and Economic Impacts ............................................................................................137

5.4 Total Potential Supply of Biomass Energy Crops ...............................................................143

5.5 Summary.........................................................................................................................................143

6. SUMMARY.................................................................................................................................145

6.1 Baseline Estimates ........................................................................................................................146

6.2 High-Yield Estimates ...................................................................................................................146

6.3 Report Implications and Further Discussion........................................................................150

6.3.1 Other Assessments........................................................................................................................................ 150

6.3.2 Significance of Underlying Assumptions ..............................................................................................152

6.3.3 Factors Affecting Potential Product Estimates ...................................................................................155

6.3.4 Innovations in Management and Technology ......................................................................................156

6.3.5 Data, Research, and Technology Needs.................................................................................................158
x

6.3.6 Opportunities for Further Analysis...........................................................................................................158

6.4 Conclusions....................................................................................................................................159

REFERENCES................................................................................................................................162

APPENDICES ................................................................................................................................183

Appendix A: Feedstocks ...................................................................................................................183

Appendix B: General Assumptions ...............................................................................................184

Appendix C: Major differences between 2005 BTS and updated BTS................................192

Appendix D: Data Sources................................................................................................................194
xi

LIST OF TABLES

Table ES.1 Summary of Currently Used and Potential Forest and Agriculture Biomass at 

$60 per Dry Ton or Less, under Baseline and High-Yield Scenario Assumptions .......xxv

Table 2.1 Projected Consumption of Currently Used Biomass Feedstocks 

 (Million Dry Tons per Year)......................................................................................... 15

Table 3.1 Summary of Sustainability Assumptions Used in Developing 

 Forest Residue Estimates.......................................................................................... 24

Table 3.2 Pulpwood Stumpage Prices by Region.......................................................................27

Table 3.3 Summary of Potential Forest Biomass and Wood Wastes (2012)............................... 49

Table 3.4 Summary of Baseline Potential Forest Biomass and Wood Wastes at 

 Selected Roadside Prices.......................................................................................... 50

Table 4.1 Parameters Assumed for Calculating Crop Residue Production..................................55

Table 4.2 Regional Nutrient Payments per Ton of Stover Removed ........................................... 61

Table 4.3 Regional Nutrient Payments per Ton of Small Grains Straw Removed ........................ 61

Table 4.4 Summary of Baseline and High-Yield Residue Removal Assumptions

 for Agricultural Crops ............................................................................................... 66

Table 4.5 Summary of Secondary Process Residues and Wastes .............................................. 80

Table 4.6 Summary of Baseline and High-Yield Scenarios — Agricultural Residues and

 Waste Resources ...................................................................................................... 86

Table 5.1 Reported Perennial Grass Yield and Acres Required for a 50-Million

 Gallon Cellulosic Ethanol Plant................................................................................... 91

Table 5.2 2009 (Second-Year) Miscanthus x giganteus Biomass Yields (Tons per Acre)............ 96

Table 5.3 Summary of Production Inputs and Costs for Perennial and Annual Grasses ............125

Table 5.4 Summary of Production Inputs and Costs for Woody Crops.....................................127

Table 5.5 Yield Growth Between the Baseline and High-Yield Scenarios ..................................128

Table 5.6 Summary Comparison of USDA Projections for Major Crops with Baseline 

Projections for Biomass Resources Derived from Cropland and Pastureland, at $50 

per Dry Ton Farmgate Price ......................................................................................141

Table 5.7 Summary Comparison of USDA Projections for Major Crops with High-Yield 

 Projections for Biomass Resources from Cropland and Pastureland, at $50 

 per Dry Ton FarmgatePrice ......................................................................................142

Table 5.8 Summary of Baseline and High-Yield Scenario Availability of Energy Crops .............144

Table 6.1 Summary of Available Forest and Agriculture Biomass at Selected Prices and 

Years under Baseline Assumptions (in Millions)........................................................148

Table 6.2 Summary of Available Forest and Agriculture Biomass at $60 per Dry Ton under

 High-Yield Assumptions...........................................................................................149

Table 6.3 Estimate of U.S. Cellulosic Biomass by 2020 (NAS, 2009).........................................151

Table 6.4 Estimate of Global Biomass Potential by 2030 (Bauen et al., 2009) .........................152

Table D.1 Data Sources ...........................................................................................................194
xii

LIST OF FIGURES

Figure ES.1 Estimated forest biomass under baseline assumptions.............................................xxii

Figure ES.2 Estimated agricultural biomass under baseline assumptions ...................................xxiii

Figure ES.3 Estimated forest and agricultural biomass availability at $60 per dry ton 

 or less under high-yield assumptions ......................................................................xxiii

Figure ES.4 Summary of currently used and potential resources at $60 per dry ton or 

less identified under baseline assumptions..............................................................xxiv

Figure ES.5 Summary of currently used and potential resources at $60 per dry ton or 

less identified under high-yield assumptions...........................................................xxiv

Figure 1.1 Biomass resources considered in the update to the 2005 BTS......................................6

Figure 2.1 Primary energy consumption by major fuel source from 1980–2008............................7

Figure 2.2 Total primary energy consumption in 2009 – 94.5 Quads ...........................................8

Figure 2.3 Wood waste ...............................................................................................................9

Figure 2.4 Harvesting corn in the Great Plains ...........................................................................10

Figure 2.5 Ethanol operating capacity by state............................................................................11

Figure 2.6 Historical and projected corn use, 2001–2020............................................................ 12

Figure 2.7 Historical and projected ethanol production, 2001–2019............................................. 12

Figure 2.8 Historical and projected U.S. biodiesel production with EISA mandate ....................... 13

Figure 3.1 Forest residue operation in northern California ......................................................... 20

Figure 3.2 Removal of forest biomass with a skidder ................................................................. 20

Figure 3.3 Sustainable harvest from managed forest systems..................................................... 21

Figure 3.4 Comminuting forest residue bundles..........................................................................25

Figure 3.5 Logging residues .......................................................................................................27

Figure 3.6 National supply curves for logging residues, thinnings, and composite 

 (50% logging residues and 50% thinnings) from timberland ......................................28

Figure 3.7 Current and year 2030 state quantities of logging residue available annually at 

 $80 per dry ton .........................................................................................................28

Figure 3.8 Spatial distribution of logging residues at $20 and $40 per dry ton

 (delivered to roadside) ............................................................................................. 29

Figure 3.9 State quantities per year of simulated forest thinnings at $20, $40, and

 $100 per dry ton (roadside).......................................................................................33

Figure 3.10 Spatial distribution of simulated forest residue thinnings at $30 and

 $60 per dry ton (roadside) ........................................................................................35

Figure 3.11 National supply curves for integrated harvesting operations ..................................... 36

Figure 3.12 Current and year 2030 state integrated operations quantities per year at $80 per 

 dry ton or less at roadside..........................................................................................37
xiii

Figure 3.13 Spatial availability of other removal residues at $40 per dry ton 

 (delivered to roadside)...............................................................................................38

Figure 3.14 Conversion facility .................................................................................................... 39

Figure 3.15 Spatial availability of urban wood waste 

(municipal solid waste and construction and demolition wood residues).................... 41

Figure 3.16 Theoretical pulpwood supply model for biomass ...................................................... 42

Figure 3.17 Estimated supply of pulpwood for bioenergy annually.............................................. 46

Figure 3.18 Current state shares of available forest biomass resources at 

 $80 per dry ton or less.............................................................................................. 48

Figure 4.1 Example of crop residues – stalks and leaves from corn stover...................................53

Figure 4.2 NRCS crop management zones .................................................................................57

Figure 4.3 Baling corn stover..................................................................................................... 58

Figure 4.4 Sustainable corn stover retention coefficients across tillage and selected years......... 59

Figure 4.5 Average total standing yield, no-till potential yield, and reduced tillage potential yield

 of corn stover in Adair County, Iowa.......................................................................... 59

Figure 4.6 Total factor of corn stover yield and sustainable retention coefficients, 2030 ............60

Figure 4.7 Collection costs to the field edge for corn stover and small grains residue ................ 63

Figure 4.8 Crop residue such as corn stover available for energy ................................................67

Figure 4.9 Total available supply of crop residues after sustainability constraints are 

 met at various prices under baseline assumptions..................................................... 68

Figure 4.10 Amounts of corn stover and wheat and other grain residue 

 at selected prices and years under baseline assumptions .......................................... 69

Figure 4.11 Supply curves of potential corn stover production for various years 

 under baseline assumptions...................................................................................... 69

Figure 4.12 Supply curves of potential residue production (wheat, sorghum, oats, 

 barley, and rye) for various years under baseline assumptions................................... 70

Figure 4.13 Corn stover residue yield for reduced tillage and no-till production, 2012 ................... 71

Figure 4.14 Corn stover residue yield for reduced tillage and no-till production, 2030 .................. 71

Figure 4.15 Wheat straw for reduced tillage and no-till production, 2012......................................72

Figure 4.16 Wheat straw for reduced tillage and no-till production, 2030.....................................72

Figure 4.17 Small grains and sorghum residues for reduced tillage and no-till production, 2012....73

Figure 4.18 Small grains and sorghum residues for reduced tillage and no-till production, 2030...73

Figure 4.19 Total available supply of crop residues under high-yield assumptions ........................75

Figure 4.20 Amounts of corn stover and wheat and other grain residues at selected years and

 prices under high-yield assumptions..........................................................................76

Figure 4.21 Current and future secondary processing residues, field residues,

 and waste resources ..................................................................................................77
xiv

Figure 4.22 Sugarcane tassel........................................................................................................78

Figure 4.23 Farmer clearing prunings...........................................................................................82

Figure 5.1 Switchgrass research plot......................................................................................... 88

Figure 5.2 Baling switchgrass .................................................................................................... 89

Figure 5.3 Miscanthus growth in August.................................................................................... 93

Figure 5.4 Harvesting miscanthus.............................................................................................. 95

Figure 5.5 Energy cane research plots....................................................................................... 98

Figure 5.6 Average yields from four successive fall harvests of several candidate sugarcane 

varieties as compared to the standard variety, L 79–1002.......................................... 99

Figure 5.7 Harvesting sugarcane ............................................................................................. 100

Figure 5.8 Sorghum hybrid tests ..............................................................................................102

Figure 5.9 Hybrid poplar plantation in Pacific Northwest..........................................................106

Figure 5.10 Harvesting poplar plantation....................................................................................107

Figure 5.11 Minnesota poplar plantation ....................................................................................108

Figure 5.12 Harvesting willow with a one-pass cut and chip forage harvester.............................109

Figure 5.13 Spring resprout after fall cutting of willow ................................................................110

Figure 5.14 Eucalyptus plantation in Florida................................................................................113

Figure 5.15 Pine plantation .........................................................................................................115

Figure 5.16 Potential production of energy crops at various years and farmgate prices in

 baseline scenario ......................................................................................................131

Figure 5.17 Supply curves for all energy crops at selected years in baseline scenario ..................131

Figure 5.18 Planted acres in perennial grasses, woody crops, and annual energy crops for 

 selected years and prices in baseline scenario..........................................................132

Figure 5.19 Estimated state shares of energy crops and agricultural residues supplies at 

farmgate prices of $40, $50, and $60 per dry ton in 2030 .......................................133

Figure 5.20 Year 2030 energy crop under baseline and 2% to 4% annual 

 growth in energy crop yield .....................................................................................135

Figure 5.21 Perennial grasses, woody crops, and annual energy crops for selected 

 years in high-yield scenario......................................................................................136

Figure 5.22 Planted acres in perennial grasses, woody crops, and annual energy crops for 

 selected years and prices in high yield scenario with 4% annual energy

 crop yield growth .................................................................................................... 137

Figure 5.23 Land-use change under baseline assumptions for 2017, 2022, and 2030 at 

farmgate prices of $40, $50, and $60 per dry ton....................................................139

Figure 5.24 Land-use change under high-yield (3%) assumptions for 2017, 2022, and 2030 at

farmgate prices of $40, $50, and $60 per dry ton....................................................140
xv

Figure 6.1 Summary of potential bioenergy supply from forest and agriculture residues 

and wastes and energy crops at selected prices and years under 

baseline assumptions...............................................................................................149

Figure 6.2 Summary of potential bioenergy supply from available forest and 

agriculture biomass at $60 per dry ton under high-yield assumptions .....................150

Figure 6.3 State-level shares of all potentially available resources at $60 per dry ton or less 

in 2030, under baseline assumptions .......................................................................160

Figure 6.4 Potential county-level resources at $60 per dry ton or less in 2030, 

under baseline assumptions.....................................................................................160

Figure 6.5 Summary of currently used and potential resources at $60 per dry ton or 

less identified under high-yield assumptions.............................................................161

Figure 6.6 Summary of currently used and potential resources at $60 per dry ton or 

less identified under high-yield assumptions.............................................................161
xvi

LIST OF TEXT BOXES

List of Text Boxes

TEXT BOX ES.1 BACKGROUND SUMMARY..............................................................................xviii

TEXT BOX 1.1 ENERGY INDEPENDENCE AND SECURITY ACT (EISA) 2007............................2

TEXT BOX 1.2 KEY DIFFERENCES BETWEEN THE 2005 BILLION-TON STUDY AND 

THE 2011 UPDATE ............................................................................................................................4

TEXT BOX 3.1 FOREST FEEDSTOCKS..........................................................................................16

TEXT BOX 3.2 FOREST RESOURCES OF THE UNITED STATES ............................................... 18

TEXT BOX 3.3 FEEDSTOCK COMPLIANCE WITH EISA.............................................................19

TEXT BOX 3.4 LOGGING RESIDUES AND THINNING ESTIMATES.......................................... 26

TEXT BOX 3.5 BIOMASS EQUATIONS IN FIA............................................................................ 30

TEXT BOX 3.6 FEDERAL LAND ESTIMATES...............................................................................32

TEXT BOX 3.7 DEAD AND DYING TREES .................................................................................. 34

TEXT BOX 3.8 ESTIMATING FUTURE SUPPLIES OF PULPWOOD FOR BIOENERGY........... 43

TEXT BOX 4.1 REVISED UNIVERSAL SOIL LOSS AND WIND EROSION EQUATION............ 54

TEXT BOX 4.2 SOIL CONDITIONING INDEX AND CROPPING ROTATIONS .......................... 56

TEXT BOX 4.3 CROP HARVEST INDEX...................................................................................... 65

TEXT BOX 5.1 IRRIGATION OF ENERGY CROPS....................................................................... 92

TEXT BOX 5.2 RELATIVE PROPORTION OF MAJOR LAND-USE TYPES BY STATE .............119

TEXT BOX 5.3 THE REGIONAL FEEDSTOCK PARTNERSHIP...................................................121

TEXT BOX 5.4 BARRIERS TO YIELD GROWTH........................................................................124

TEXT BOX 5.5 ESTIMATION OF SWITCHGRASS YIELD ..........................................................126

TEXT BOX 5.6 COMPETITIVENESS OF WOODY AND HERBACEOUS CROPS

WHEN SUPPLY CHAIN ADVANTAGES ARE CONSIDERED.....................................................134

TEXT BOX C.1 2005 BILLION-TON STUDY ................................................................................192
xvii

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

The report, Biomass as Feedstock for a Bioenergy 

and Bioproducts Industry: The Technical Feasibility 

of a Billion-Ton Annual Supply (generally referred 

to as the Billion-Ton Study or 2005 BTS), was an 

estimate of “potential” biomass within the contiguous 

United States based on numerous assumptions about 

current and future inventory and production capacity, 

availability, and technology. In the 2005 BTS, a 

strategic analysis was undertaken to determine if U.S. 

agriculture and forest resources have the capability 

to potentially produce at least one billion dry tons of 

biomass annually, in a sustainable manner—enough 

to displace approximately 30% of the country’s 

present petroleum consumption. To ensure reasonable 

confidence in the study results, an effort was made to 

use relatively conservative assumptions. However, for 

both agriculture and forestry, the resource potential was 

not restricted by price. That is, all identified biomass 

was potentially available, even though some potential 

feedstock would more than likely be too expensive to 

actually be economically available.

In addition to updating the 2005 study, this report 

attempts to address a number of its shortcomings. 

Specifically, the update provides:

• A spatial, county-by-county inventory of 

primary feedstocks1 

• Price and available quantities (e.g., supply 

curves) for the individual feedstocks

• A more rigorous treatment and modeling of 

resource sustainability.

Furthermore, there have been some significant changes 

in some of the underlying assumptions and analytical 

approaches used to estimate both the availability and 

prices of the various biomass feedstocks. This updated 

analysis stresses the 2012 through 2030 time period 

and how it corresponds with the implementation of the 

Renewable Fuels Standard (RFS) and other initiatives.

EXECUTIVE SUMMARY

1

 A separate database containing the disaggregated biomass supplies by county and state is available through a Web-based Bioenergy 

Knowledge Discovery Framework (http://bioenergykdf.net) for users to capture, visualize, and analyze information on the complete 

bioenergy supply chain and the infrastructure needed to support that chain (ORNL, 2010).

The report is organized similarly to the 2005 BTS, with 

separate chapters for forest and agricultural biomass 

resources. It still excludes Alaska, Hawaii, and U.S. 

territories. Although energy crops are still part of 

agricultural resources, they are discussed in a separate 

chapter because of their potential importance. The 

2005 BTS combined resources that are currently used 

for energy production with unused and prospective 

resources because they all counted toward the billionton goal. Whereas, in this update, a clearer distinction 

is made between currently used resources (e.g., corn 

grain, soybeans, pulping liquors, mill residues, and 

fuelwood) and unused and prospective resources 

available for additional energy (e.g., feedstock needed 

to meet the 16 billion gallons per year (BGY) of 

cellulosic biofuels and 4 BGY of advanced biofuels). A 

general background summary is provided in Text Box 

ES.1.

The report is similar to the 2005 BTS in that it only 

provides estimates of biomass to roadside or the 

farmgate. The potential biomass inventory at a given 

spatial scale is biomass in the form and quality of the 

production system, which is identified in the report for 

a specific feedstock. It is important to understand that 

the estimates in the report do not represent the total 

cost or the actual available tonnage to the biorefinery. 

There are additional costs to preprocess, handle, and 

transport the biomass. There may be storage costs for 

specific feedstocks. Although the estimates do include 

losses to roadside, the estimates do not include losses 

due to continued handling, additional processing, 

storage, material degradation, and quality separation. 

In effect, for example, more than one billion tons 

from estimates in the report would be required to 

have one billion tons ready to process at a biorefinery. 

The amount would be dependent on many variables 

in the continued supply chain and final conversion 

technology. In addition, the biomass is in varied forms 

and may not be directly comparable at a biorefinery in 

either cost or conversion efficiency. Determining such 

values is outside the scope of the report.
xviii

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

This update evaluates two scenarios—baseline and 

high-yield. The baseline scenario essentially assumes 

a continuation of the U.S. Department of Agriculture 

(USDA) 10-year forecast for the major food and forage 

crops, and it extends an additional 10 years to 2030. 

The average annual corn yield increase is assumed to 

be slightly more than 1% over the 20-year simulation 

period. The baseline also assumes a continuation in 

trends toward no-till and reduced cultivation. Energy 

crop yields assume an annual increase of 1%. The 

1% change in annual yield in the baseline reflects 

learning or experience in planting energy crops and 

limited gains that can be had through breeding and 

selection of better varieties. The high-yield scenario 

is more closely aligned to the assumptions in the 2005 

BTS. In this scenario, higher corn yields and a much 

larger fraction of crop acres in reduced and no-till 

cultivation are assumed. Under the high-yield scenario, 

the projected increase in corn yield averages almost 

2% annually over the 20-year simulation period. The 

energy crop productivity increases are modeled at three 

levels—2%, 3%, and 4% annually. These gains are due 

not only to experience in planting energy crops, but 

also to more aggressive implementation of breeding 

and selection programs.

The U.S. Department of Energy’s (DOE) Office of the 

Biomass Program sponsored a series of workshops 

to obtain expert input on barriers and solutions for 

securing large quantities of biomass feedstocks in the 

future (U.S. Department of Energy, 2010a). The overall 

goal of the workshops was to obtain information 

concerning the development of industry-based, highyield alternatives to the baseline assumptions used to 

develop the update. Experts were invited from industry, 

academia, and government to help identify and 

quantify high-yield alternative scenarios.

Overall, results of this update are consistent with the 

2005 BTS in terms of the magnitude of the resource 

potential. The forest residue biomass potential was 

determined to be less than the 2005 numbers due 

to the removal of unused resources and the decline 

in pulpwood and sawlog markets. The crop residue 

potential was determined to be somewhat less than 

what was in the 2005 BTS due to the consideration 

of managing for soil carbon during crop residue 

•	 Excludes Alaska, Hawaii, and territories

•	 Includes all major primary and secondary forest 

and agriculture residue feedstocks, major waste 

feedstocks, and energy crops grown specifically 

for bioenergy

•	 Based on extension of USDA projections

•	 Baseline uses 1% per annum average yield 

increases for corn and 1% for all energy crops

•	 Yield improvements derived from workshop

•	 High-yield scenario uses 2% yearly increases for 

corn yield and 2%, 3%, and 4% annual increases 

for energy crops

•	 Baseline assumes a continuation in tillage trends; 

high-yield scenario assumes a much larger 

fraction of no-till cultivation

•	 Collection of crop residues prohibited from 

conventionally tilled acres

•	 Covers 2012–2030 with summaries for 2017, 

2022, 2030

•	 Crop residue collection and energy crops modeled 

using POLYSYS

•	 Energy crops must offer growers higher net 

returns than commodity crops and pasture they 

displace

•	 Forestry analysis exogenous to POLYSYS

•	 Energy crop potential estimates limit impact on 

food, feed, exports, and fiber production

TEXT BOX ES.1 | BACKGROUND SUMMARY

removal and not allowing the removal of residue from 

conventionally tilled acres. The energy crop potential 

was estimated to be much greater because of higher 

planted acreage —a result of the spatially explicit landuse change modeling that was used.

Supply/cost curves were derived for each major 

feedstock. The cost range that was simulated varied 

significantly across the curves depending on the type 

of feedstock. For example, the processing wastes were 

relatively low and had a narrow range that was roughly 

between $20 and $40 per dry ton. 
xix

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

On the other hand, the conventionally sourced wood 

went as high as $100 per dry ton. In this report, it is 

difficult to present simple summaries for total potential 

biomass as a function of cost because the feedstock 

quantities vary so much under the different cost curves. 

They could easily be shown at various prices—such as, 

$30, $40, $50 per dry ton and even up to over a $100 

per dry ton. In all cases, the price presented includes 

biomass available up to that price. For convenience 

and ease in reading, a decision was made to show all 

feedstocks quantities and their composite total at the 

$60 per dry ton level in many of the figures and tables 

in the report. This price was selected because it brings 

in most of the available tons from all of the feedstocks 

and because the price represents a realistic, reasonable 

price for discussion purposes. For example, this price 

is comparable to the DOE cost targets for cellulosic 

feedstocks when adjusted to exclude transportation and 

handling costs (U.S. Department of Energy, 2011). The 

selection of this price for presentation purposes does 

not imply that the feedstocks will necessarily be this 

high or conversely this low. In fact, the market will 

decide the price based on many variables. 

There will also be great variation among the different 

feedstock prices. The supply/cost curves will be 

useful in generating total supply estimates under 

various, individual feedstock assumptions. The tools 

in the Web-based Bioenergy Knowledge Discovery 

Framework (KDF) will be useful in generating 

composite biomass estimates using the various 

cost curves, as well as presenting total biomass 

availabilities at selected prices beyond the $60 per dry 

ton used in the report.

Results under baseline assumptions are presented 

in Figures ES.1 and ES.2 for forest and agricultural 

resources, respectively. The figures show four years 

(2012, 2017, 2022, and 2030) and three prices—$20, 

$40, and $80 per dry ton for forest biomass and $40, 

$50, and $60 per dry ton for agricultural biomass. 

The forest resources are available over a wider price 

range than the agricultural resources, with increasing 

quantities at higher prices. Over the estimated price 

range, quantities vary from about 33 to 119 million 

dry tons currently to about 35 to 129 million dry tons 

in 2030. Primary forest biomass (i.e., logging and fuel 

treatment operations and land clearing) is the single 

largest source of feedstock. The resource potential 

does not increase much over time given the standing 

inventory nature of the resource and how it is managed. 

Results also show that very little conventional 

pulpwood is available for bioenergy at prices below 

(about) $60 per dry ton. The agricultural resources 

show considerably more supply, with the quantity 

increasing significantly over time. This increase is 

due to yield growth, which makes more crop residue 

available. The increase is also attributed to the 

deployment of energy crops. Under current conditions, 

prospective biomass supplies range from about 59 

million dry tons at a farmgate price of $40 per dry ton 

or less to 162 million dry tons at $60 per dry ton. The 

composition of this biomass is about two-thirds crop 

residue and one-third various agricultural processing 

residues and wastes. By 2030, quantities increase to 

160 million dry tons at the lowest simulated price 

($40 per dry ton) to 664 million dry tons at the highest 

simulated price ($60 per dry ton). At prices above 

$50 per dry ton, energy crops become the dominant 

resource after 2022.

The high-yield scenario assumes a greater proportion 

of corn in reduced and no-till cultivation and increased 

corn yields to about double the current rate of annual 

increase. For energy crops, the high-yield scenario 

increased the annual rate of crop productivity 

growth from the 1% baseline to 2%, 3%, and 4% 

annually. No high-yield scenario was evaluated for 

forest resources except for the woody crops. Forest 

residues come from existing timberlands, and there 

is no obvious way to increase volumes other than 

reducing the amounts of residues retained onsite 

for environmental sustainability or decreasing the 

merchantable utilization requirements—neither option 

was considered. Figure ES.3 summarizes the estimated 

quantities of biomass from forest, agricultural, and 

energy crop resources under high-yield assumptions at 

the highest simulated price of $60 per dry ton. Results 

are presented for different assumptions about the 

annual increase in the rate of growth of energy crop 

yields (2%, 3%, and 4%). Agricultural residues and 

wastes are based on higher proportions of reduced and 

no-till cultivation, as well as higher corn grain yields. 

Forest residues and wastes are the same as shown in 

Figure ES.1 and total 100 million dry tons by 2022. 
xx

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Agricultural residues and wastes are about 244 million 

dry tons currently and increase to 404 million dry 

tons by 2030 at a farmgate price of $60 per dry ton. 

In 2022, the total agricultural resources (crop residues 

and energy crops) reach 910 million dry tons at the $60 

price. Energy crops are the largest potential source of 

biomass feedstock, with potential energy crop supplies 

varying considerably depending on what is assumed 

about productivity. At a 2% annual growth rate, energy 

crop potential is 540 million dry tons by 2030 and 658 

million dry tons if an annual increase in productivity of 

3% is assumed. Increasing yield growth to 4% pushes 

the energy crop potential to nearly 800 million dry 

tons. Note that at the lowest simulated price of $40 

per dry ton, however, the energy crop potential is only 

69 million, 162 million, and 261 million dry tons in 

2030 at 2%, 3%, and 4% annual yield, respectively. In 

general, the farmgate or roadside price for feedstock 

appears to be a larger driver of biomass availability 

than yield rate increases, although both are important.

It is important to point out the significant role of energy 

crops. In the baseline, energy crops provide about 37% 

of the total biomass available at $60 per dry ton and 

half of the total potential resource. Energy crops are a 

much smaller fraction of total available biomass at $40 

per dry ton. Overall, energy crops become even more 

significant in the high-yield scenario—providing over 

half of the potential biomass at $60 per dry ton. 

Under baseline assumptions, up to 22 million acres of 

cropland and 41 million acres of pastureland shift into 

energy crops by 2030 at a simulated farmgate price 

of $60 per dry ton.2

 This land-use change is similar 

in magnitude to the 40 to 60 million acres in energy 

crops reported in the 2005 BTS. At lower simulated 

prices, total crop and pasture land-use change is much 

less—about 5.6 million acres at $40 per dry ton and 

27 million acres at $50 per dry ton. At the lowest 

simulated price, land-use change is limited to cropland. 

Higher simulated farmgate prices move energy crops 

onto pasture. At this level of land-use change, total 

feedstock production in the baseline scenario ranges 

from 34 to 400 million dry tons at simulated prices 

of $40 to $60 per dry ton, respectively. Under the 

high-yield scenario with a 4% annual increase in 

energy crop yields, greater amounts of cropland and 

pastureland shift into energy crop production. Up 

to 30 million acres of cropland and 49 million acres 

of pastureland shift into energy crops by 2030 at a 

simulated farmgate price of $60 per dry ton. At the 

lower simulated farmgate prices of $40 and $50 per 

dry ton, total land-use change is 33 and 44 million 

acres, respectively. Over the $20 per dry ton simulated 

feedstock price range, total energy crop production is 

261 million dry tons to nearly 800 million dry tons in 

2030. 

In sum, potential supplies at a forest roadside or 

farmgate price3

 of $60 per dry ton range from 602 to

1009 million dry tons by 2022 and from about 767 to 

1305 million dry tons by 2030, depending on what is 

assumed about energy crop productivity (1% to 4% 

annual increase over current yields). This estimate 

does not include resources that are currently being 

used, such as corn grain and forest products industry 

residues. By including the currently used resources, 

the total biomass estimate jumps to over one billion 

dry tons and is even higher with more aggressive 

assumptions about energy crop productivity. The last 

two figures (Figures ES.4 and ES.5) in this summary 

bring in these currently used resources for the baseline 

scenario and the high-yield scenario shown for the 

$60 per dry ton price and a 3% annual growth in 

energy crop productivity. For the baseline, projected 

consumption of currently used resources, the forest 

residues and wastes, the agricultural residues and 

wastes, and energy crops show a total of 1094 million 

dry tons by 2030. This quantity increases by 400 

million dry tons if most of the conventionally tilled 

acres shift into no-till cultivation, corn yields increase 

to a national average of about 265 bushel per acre, and 

energy crop productivity increases 3% annually instead 

of 1% annually. The quantity decreases significantly as 

the roadside or farmgate price is decreased to $50 and 

$40 per dry ton. 

2 This feedstock is assumed to be planted on cropland and pastureland. The POLYSYS model, an agricultural policy modeling framework, was 

used to estimate potential land-use change and potential economic impacts.

3

 The forest landing or farmgate price mentioned throughout this report is a basic feedstock price that includes cultivation (or acquisition), 

harvest, and delivery of biomass to the field edge or roadside. It excludes on-road transport, storage, and delivery to an end user. For 

grasses and residues, this price includes baling. For forest residues and woody crops, this includes minimal communition (e.g., chipping). 
xxi

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

The results just discussed, along with estimates 

of currently used resources are summarized in 

Table ES.1. One important year highlighted in this 

assessment is 2022—the year in which the revised 

RFS mandates the use of 36 BGY of renewable fuels. 

The feedstock shown in the baseline scenario accounts 

for conventional biofuels (corn grain, ethanol, and 

biodiesel) and shows 602 million dry tons of potential 

resource at $60 per dry ton (100 million dry tons of 

forest biomass, 221 million dry tons of crop residues 

and other cropland biomass, and 282 million dry tons 

of energy crops). This potential resource is more than 

sufficient to provide feedstock to produce the required 

20 billion gallons of cellulosic biofuels. The high-yield 

scenario demonstrates potential at the $60 price that far 

exceeds the RFS mandate.

As noted at the outset, the results of this updated 

assessment are consistent with the 2005 BTS in terms 

of overall magnitude. In fact, the scenario assumptions 

required to show a “billion-ton” resource (i.e., 

sufficient feedstock to potentially displace 30% or 

more of the country’s present petroleum consumption) 

are much more plausible. The forest resources take 

into account sawlog and pulpwood demands, and 

they factor in a more explicit accounting of resource 

sustainability. The agricultural resources now take into 

account soil organic matter in the assessment of crop 

residue potential and require less significant shifts of 

land into no-till cultivation. The energy crop potential 

is formally modeled and accounts for competition 

among various competing uses of the land. Although 

the focus is more on the biomass supply and prices, 

the assumptions used to derive these estimates are 

tempered from the sustainability perspective. The 

update is not a quantitative environmental assessment 

or a comprehensive sustainability analysis, which 

means that the study does not evaluate a whole suite 

of sustainability criteria nor assess changes in the 

indicators as a function of production scenarios.

It should also be stressed that bioenergy markets 

currently do not exist for the resource potential 

identified. The analysis and results are based on very 

limited data and, as such, require making numerous 

assumptions, and the results can be sensitive to these 

assumptions, especially with respect to production 

response to price. While the methods selected 

to estimate resources were rigorously applied, 

the estimates rely heavily on the precision of the 

underlying data and assumptions. However, an effort 

was made to be as transparent as possible with the 

data and methodology, as well as assumptions. The 

underlying assumptions are based on the best available 

information and grounded in the expertise of the 

authors. The major assumptions are outlined in the 

Appendices and the significance of these assumptions 

is summarized in Chapter 6. This discussion includes 

the scenarios; tillage and yield for residue producing 

crops; management practices and inputs for energy 

crops; modeling of land-use change; markets for 

roundwood products; and environmental sustainability. 

Chapter 6 also discusses a number of factors alluded 

to earlier about tonnage and final product estimates. 

Finally, Chapter 6 provides a brief summary of data 

and research and development needs and opportunities 

for further analysis.

The Bioenergy KDF provides complementary and 

reference materials, as well as additional data and 

explanations (ORNL, 2010). The website also provides 

tools to help present the results in custom tabular, 

graphic, and spatial formats, as it is impossible to 

provide this in a reasonable length report. Hopefully 

in the future, new data and modeling results, as well as 

analysis tools, will be made available on the website. 

Users will also be able to post comments, suggestions 

for additional analysis, and add links to additional 

information.

Finally, it should be noted that the intent of this report 

is to update the 2005 BTS and change its focus from 

a strategic assessment to a comprehensive resource 

assessment. This report is not an economic assessment 

of the potential impact large-scale collection, growing, 

and harvesting of bioenergy feedstocks might have on 

forestry and agricultural (both commodity crops and 

livestock) sectors of the economy. For the baseline 

scenario, results do show a loss of commodity crop 

acres to energy crops and higher commodity crop 

prices. For crop producers, the higher crop prices 

could more than compensate for the loss in crop 

acres, reflecting greater net crop returns relative to the 

baseline. Higher energy crop price is one of the factors 
xxii

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

<$20 <$40 <$80 <$20 <$40 <$80 <$20 <$40 <$80 <$20 <$40 <$80

2012 2017 2022 2030

Million dry tons

Conventional pulpwood Urban wood wastes Mill residues Forest residues (primary)

150

120

90

60

0

30

Figure ES.1 Estimated forest biomass under baseline assumptions

that affects food and feed prices for end consumers. 

The large-scale deployment of energy crops could 

require the displacement of tens of millions of acres 

of cropland and pasture, especially under the highyield scenario. These potential changes to commodity 

crop acres and prices are within historical swings. 

However, the large projected changes in cropland 

pasture and permanent pasture acres to energy crops 

would require additional forage through one or more 

approaches to pasture intensification. As with the 2005 

BTS, the feedstock potential identified in this report 

could be realized, assuming an increased investment 

in research undertaken by the state or private interests, 

not only in crop yields, but in new, innovative 

management and production systems, harvesting and 

collection technology, and the science for sustainable 

management.
xxiii

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

0

300

600

900

1200

1500

2017 2022 2030 2017 2022 2030 2017 2022 2030

2% energy crop 3% energy crop 4% energy crop

Million dry tons

Energy crops Agricultural residues and wastes Forest residues and wastes

Estimated forest and agricultural biomass availability at $60 per dry ton or less under 

high-yield assumptions Figure ES.3

<$40 <$50 <$60 <$40 <$50 <$60 <$40 <$50 <$60 <$40 <$50 <$60

2012 2017 2022 2030

Million dry tons

Corn Stover

Agricultural process residues & wastes Energy crops

Wheat & other grain residues

800

700

600

500

400

300

200

100

0

Figure ES.2 Estimated agricultural biomass under baseline assumptions
xxiv

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

2030

2022

2017

2012

Million dry tons

Forestland resources 

currently used

Agricultural resources 

currently used

Agricultural land biomass 

& waste resource potential

Energy crops

Forestland biomass 

& waste resource potential

0 200 400 600 800 1200 1000 1400 1600 1800

Summary of currently used and potential resources at $60 per dry ton or less identified 

under high-yield assumptions Figure ES.5

2030

2022

2017

2012

Million dry tons

Forestland resources 

currently used

Agricultural resources 

currently used

Agricultural land biomass 

& waste resource potential

Energy crops

Forestland biomass 

& waste resource potential

0 200 400 600 800 1000 1200 1400 1600 1800

Summary of currently used and potential resources at $60 per dry ton or less identified 

under baseline assumptions Figure ES.4
xxv

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Feedstock 2012 2017 2022 2030

Million dry tons

Baseline scenario

Forest resources currently used 129 182 210 226

Forest biomass & waste 

resource potential 97 98 100 102

Agricultural resources currently used 85 103 103 103

Agricultural biomass & waste 

resource potential 162 192 221 265

Energy cropsa 0 101 282 400

Total currently used 214 284 312 328

Total potential resources 258 392 602 767

Total – baseline 473 676 914 1094

High-yield scenario (2%–4%)

Forest resources currently used 129 182 210 226

Forest biomass & waste 

resource potential 97 98 100 102

Agricultural resources currently used 85 103 103 103

Agricultural biomass & waste 

resource potentialb 244 310 346 404

Energy crops 0 139–180 410–564 540–799

Total currently used 214 284 312 328

Total potential 340 547–588 855–1009 1046–1305

Total high-yield (2-4%) 555 831–872 1168–1322 1374–1633

Summary of Currently Used and Potential Forest and Agriculture Biomass at $60 per 

Dry Ton or Less, under Baseline and High-Yield Scenario Assumptions Table ES.1

Note: Under the high-yield scenario, energy crops are shown for 2% to 4% annual increase in yield. Numbers may not add up due to 

rounding.

a Energy crops are planted starting in 2014.

b Agricultural residues are generated under a high-yield traditional crop scenario with high no-till adoption (see Table 4.6). Energy crop 

 yield growth follows a baseline growth pattern of 1% annually.
This page was intentionally left blank.
1

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

1.1 Background

The report, Biomass as Feedstock for a Bioenergy and 

Bioproducts Industry: The Technical Feasibility of a 

Billion-Ton Annual Supply (generally referred to as the 

Billion-Ton Study or 2005 BTS), was an estimate of 

“potential” biomass based on numerous assumptions 

about current and future inventory, production capacity, 

availability, and technology.1

 The analysis was made 

to determine if conterminous U.S. agriculture and 

forestry resources had the capability to produce at least 

one billion dry tons of sustainable biomass annually to 

displace 30% or more of the nation’s present petroleum 

consumption. An effort was made to use conservative 

estimates to assure confidence in having sufficient 

supply to reach the goal.

The potential biomass was projected to be reasonably 

available around mid-century when large-scale 

biorefineries are likely to exist. The study emphasized 

primary sources of forest- and agriculture-derived 

biomass, such as logging residues, fuel treatment 

thinnings, crop residues, and perennially grown grasses 

and trees. These primary sources have the greatest 

potential to supply large, reliable, and sustainable 

quantities of biomass. While the primary sources were 

emphasized, estimates of secondary residue and tertiary 

waste resources of biomass were also provided.2

The original Billion-Ton Resource Assessment, 

published in 2005, was divided into two parts—forestderived resources and agriculture-derived resources. 

The forest resources included residues produced during 

the harvesting of merchantable timber, forest residues, 

and small-diameter trees that could become available 

through initiatives to reduce fire hazards and improve 

forest health; forest residues from land conversion; 

fuelwood extracted from forests; residues generated 

at primary forest product processing mills; and urban 

wood wastes, municipal solid wastes (MSW), and 

construction and demolition (C&D) debris. For these 

forest resources, only residues, wastes, and smalldiameter trees were considered. The 2005 BTS did not 

attempt to include any wood that would normally be 

used for higher-valued products (e.g., pulpwood) that 

could potentially shift to bioenergy applications. This 

would have required a separate economic analysis, 

which was not part of the 2005 BTS.

The agriculture resources in the 2005 BTS included 

grains used for biofuels production; crop residues 

derived primarily from corn, wheat, and small grains; 

and animal manures and other residues. The cropland 

resource analysis also included estimates of perennial 

energy crops (e.g., herbaceous grasses, such as 

switchgrass, woody crops like hybrid poplar, as well as 

willow grown under short rotations and more intensive 

management than conventional plantation forests). 

Woody crops were included under cropland resources 

because it was assumed that they would be grown 

on a combination of cropland and pasture rather than 

forestland.

In the 2005 BTS, current resource availability was 

estimated at 278 million dry tons annually from 

forestlands and slightly more than 194 million dry 

tons annually from croplands. These annual quantities 

increase to about 370 million dry tons from forestlands 

and to nearly 1 billion dry tons from croplands 

1 Perlack, R.D., Wright, L.L., Graham, R.L., Turhollow, A., Oak Ridge National Laboratory; Stokes, B., USDA Forest Service; and Erbach, D., USDA 

Agricultural Research Service. Biomass as Feedstock for a Bioenergy and Bioproducts Industry: The Technical Feasibility of a Billion-Ton 

Annual Supply. ORNL/TM-2005/66, DOE/GO-102005-2135. April 2005.

2

 In this report, primary resources are biomass feedstocks that come directly from either forest or agricultural land and include logging 

residues and forest thinnings, crop residues (such as stover and straw), and energy crops. Secondary residues are biomass materials that 

are the result of a processing activity—the generation of residues from sawmills or food processing plants. Tertiary wastes are biomass 

materials that result from the final consumption of a product, such as urban wood wastes.

1 INTRODUCTION
2

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

under scenario conditions of high-yield growth and 

large-scale plantings of perennial grasses and woody 

tree crops. This high-yield scenario reflects a midcentury timescale (~2040–2050). Under conditions of 

lower-yield growth, estimated resource potential was 

projected to be about 320 and 580 million dry tons for 

forest and cropland biomass, respectively. As noted 

earlier, the 2005 BTS emphasized the primary resources 

(agricultural and forestry residues and energy crops) 

because they represent nearly 80% of the long-term 

resource potential.

The law contains a number of provisions to increase energy 

efficiency and the availability and use of renewable energy. 

One key provision of EISA is the setting of a revised Renewable 

Fuels Standard (RFS). The revised RFS mandates the use of 36 

billion gallons per year (BGY) of renewable fuels by 2022. The 

revised RFS has specific fuel allocations for 2022 that include 

use of:

•	 16 BGY of cellulosic biofuels

•	 14 BGY of advanced biofuels

•	 1 BGY of biomass-based biodiesel 

•	 15 BGY of conventional biofuels (e.g., corn starch-based 

ethanol).

EISA legislation (see, 42 U.S.C. 7545(o)(2)) also established 

new definitions and criteria for both renewable fuels (e.g., 

greenhouse gas reduction thresholds) and the renewable 

biomass used to produce the fuels. Renewable biomass 

generally includes:

TEXT BOX 1.1 | ENERGY INDEPENDENCE AND SECURITY ACT (EISA) 2007

•	 Crops from previously cleared non-forested land

•	 Trees from actively managed plantations on non-federal 

land 

•	 Residues from non-federal forestland that is deemed not 

to be critically imperiled or rare

•	 Biomass from the immediate vicinity of buildings or 

public infrastructure at risk from wildfires

•	 Algae

•	 Separated yard or food waste.

Excluded from the qualifying renewable biomass are resources 

from ecologically sensitive or protected lands, biomass from 

federal forestlands, biomass from newly cleared or cultivated 

land, and merchantable biomass from naturally regenerated 

forestlands (see, 42 U.S.C. 7545(o)(1)(I)).

40

35

30

25

20

15

10

5

0

2006 2008 2010 2012 2014 2016 2018 2020 2022

Cellulosic Biofuel Component (BGY)

Advanced Biofuel Total (BGY)

Renewable Fuel Total (BGY) 

Biomass-based Diesel Component (BGY)

Billion gallons per year

Since publication of the BTS in April 2005, there have 

been some rather dramatic changes in energy markets. 

In fact, just prior to the actual publication of the BTS,

world oil prices started to increase as a result of a 

burgeoning worldwide demand and concerns about 

long-term supplies. By the end of the summer, oil 

prices topped $70 per barrel (bbl) and catastrophic 

hurricanes in the Gulf Coast shut down a significant 

fraction of U.S. refinery capacity. The following year, 

oil approached $80 per bbl due to supply concerns, 

as well as continued political tensions in the Middle 

East. The Energy Independence and Security Act of 

2007 (EISA) was enacted in December of that year 

(see Text Box 1.1). By the end of December 2007, oil 
3

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

prices surpassed $100 per bbl for the first time, and 

by mid-summer 2008, prices approached $150 per bbl 

because of supply concerns, speculation, and weakness 

of the U.S. dollar. As fast as they skyrocketed, oil 

prices fell, and by the end of 2008, oil prices dropped 

below $50 per bbl, falling even more a month later due 

to the global economic recession. In 2009 and 2010, 

oil prices began to increase again as a result of a weak 

U.S. dollar and the rebounding of world economies.

Other legislation has had impacts since 2005, as 

well. The 2008 Farm Bill, also known as the Food, 

Conservation, and Energy Act of 2008, provides for 

11 programs (although not all have been funded) for 

renewable energy, biobased products, and bioenergy. 

Furthermore, the Farm Bill provides for “advanced 

biofuels,” which are biofuels other than corn-kernel 

based, and provides funding for using biomass for 

power or heat. The Farm Bill also makes incentives 

available for the production of biomass through the 

Biomass Crop Assistance Program. The American 

Recovery and Reinvestment Act of 2009 provided 

additional funding for biorefineries and other clean 

energy initiatives. In effect, since the BTS was 

published, America has seen an expansion in financial 

support for renewable energy and has had both 

legislative and executive actions that support all types 

of renewable energy, including biomass. The emphasis 

has shifted to cellulosic biofuels and to the use of 

biomass for an array of products, including electricity 

and thermal applications.

3

 It should be emphasized that this resource assessment is intended to provide an overall indication of resource potential. The report is 

not an “investment-grade” assessment suitable for evaluating the merits of projects. Project feasibility requires the use of local data and 

assumptions.

In addition to cellulosic biofuels and the RFS, there 

has also been interest in developing a national RPS 

(renewable portfolio standard) to generate electricity 

from renewable energy, including biomass. A study 

by the U.S. Energy Information Administration (EIA) 

(2007a) looked at a combined 25% RFS and RPS by 

2025. This analysis suggests that to comply with such 

mandates, it would require almost a 13-fold increase 

in non-hydropower renewable generation and more 

than a 12-fold increase from 2005 levels. Although 

not all would be biomass based, the likelihood of 

increased demand for biomass for all energy uses has 

become very apparent. However, the greenhouse gas 

reductions are also providing more scrutiny in the 

use of biomass, especially in emissions accounting. 

Although this analysis does not address differences in 

emissions among feedstocks, it does address the basic 

sustainability aspects of using renewable feedstocks—a 

non-diminishing supply over the period studied.

In sum, these supply and demand forces have 

contributed to volatility in oil prices in recent years, 

and by transitioning toward higher energy efficiency 

and additional domestic sources of renewable fuels, 

such as biofuels, there is high potential to reduce 

U.S. market uncertainty and increase energy security. 

Legislative and executive actions have occurred at 

the federal and state levels in support of the use of 

biomass. There have been increased legislative actions 

and investments in the use of biomass for biopower. 

Overall, since the original report, the United States has 

accelerated efforts in using biomass for energy, and 

along with that emphasis, new questions have been 

asked about supply.

1.2 Purpose 

The purpose of this report is to update the 2005 BTS

and change its focus from a strategic assessment 

to a comprehensive resource assessment, thereby 

addressing issues raised since its publication. 

One major criticism of the 2005 BTS was that the 

identified potential biomass was not restricted by 

price, and some of the potential feedstocks would 

likely be too expensive relative to other feedstocks 

under current and prospective technological change 

(i.e., not be economically available). This update 

provides estimates of prices and quantities of the 
4

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

resource potential (i.e., supply curves).3 This update 

also treats sustainability much more rigorously, 

and it focuses on currently unused resources and 

energy crops. Full analysis of the sustainability of 

large-scale biomass production is not the intention 

of this report; however, quantitative projections 

presented may be useful for further analyses of the 

environmental and social aspects of using biomass 

for energy. Many of the sustainability aspects have 

been discussed in other studies, such as the Biomass 

Research and Development Initiative (BRDI) (2008) 

report on economics and environmental implications 

of meeting the RFS. Further, this update emphasizes 

the 2012 through 2030 time period coincident with 

implementation of EISA (see Text Box 1.2) and DOE 

initiatives, rather than on updating the mid-century 

projection results in the original study. The original 

report included biomass that was currently being used 

for energy production because it counted toward the 

billion-ton goal. In this update, currently consumed 

biomass resources, such as wood residues and pulping 

liquors used in the production of forest products, are 

treated separately to avoid confusion with the unused 

potential. The update focuses on deriving estimates 

of biomass available for additional energy production 

and bioproducts at different prices and locations across 

the continental United States. A schematic of the 

biomass resources considered in this update are shown 

in Figure 1.1. The resources noted as “currently used” 

are treated in a separate chapter. Separate chapters are 

also devoted to forest residues, agricultural residues, 

and energy crops. Although recent attention has turned 

to algal feedstocks because of their high productivity, 

algal feedstocks are not included in this assessment. 

There is insufficient information and data to estimate 

and project the availability of algal feedstocks at a 

county scale with any degree of accuracy. The National 

Algal Biofuels Technology Roadmap (U.S. Department 

of Energy, 2010b) reports that many years of research 

will likely be needed to achieve affordable, scalable, 

and sustainable algal-based fuels.

A key outcome of this update is to estimate feedstock 

supply curves by county for all major primary cropland 

and forest resources at the farmgate or forest roadside. 

These supply curves include prices to acquire or access 

the resource and costs for collecting or harvesting 

the resource and moving it to the field edge or forest 

roadside to be ready for transport. In this report, only 

national results are conveyed. A separate database 

containing the disaggregated biomass supplies by 

county and state is available through a Web-based 

Bioenergy Knowledge Discovery Framework (KDF) 

(ORNL, 2010). This framework is intended for users 

to capture, visualize, and analyze information on the 

complete bioenergy supply chain and the infrastructure 

needed to support that chain.

2005 BTS

•	 National estimates—no spatial information

•	 No cost analyses

•	 Environmental sustainability addressed from 

national perspective

•	 No explicit land-use change modeling

•	 2005 USDA agricultural projections and 2000 

forestry RPA/TPO

•	 Long-term time horizon (2025–2050)

•	 Estimates of current availability

•	 Long-term projections involving changes in crop 

productivity, crop tillage, residue collection 

efficiency, and land-use change

2011 Update

•	 County-level analysis with aggregation to state, 

regional, and national levels

•	 County supply curves for major primary feedstocks

•	 Environmental sustainability modeled for residue 

removal 

•	 2009 USDA agricultural projections and 2007 

forestry RPA/TPO 2012–2030 timeline

•	 Land-use change modeled for energy crops

•	 Annual projections based on a continuation of 

baseline trends (USDA projections)

•	 Annual projections based on changes in crop 

productivity, tillage, and land use

TEXT BOX 1.2 | KEY DIFFERENCES 

BETWEEN THE 2005 BILLION-TON STUDY 

AND THE 2011 UPDATE
5

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

In the 2005 BTS, there are three scenarios: (1) current 

sustainable availability from agricultural lands; (2) 

technology change with conventional crops only 

(no land-use change); and (3) technology change 

with perennial crops and land-use change. Scenario 

one in the original report is the baseline that used 

current crops yields, tillage practices (20% to 40% 

no-till), and agriculture residue collection technology 

(40% recovery). Scenario two in the 2005 BTS has 

corn yields (Zea mays) increasing by 25% to 50% 

by 2040–2050, with yields of other crops increased 

at lower rates; these increases are the same as the 

U.S. Department of Agriculture (USDA) projections 

(USDA-OCE, 2003). Other assumptions in the scenario 

are that no-till is practiced on all high-yield acres and 

that residue recovery is 60% for moderate-yield acres 

and 70% for high-yield acres. Finally, the 2005 BTS

scenario three assumes the addition of perennial crops 

to the landscape, land- use changes, high residue-tograin ratio for soybean (Glyine max), and the same 

technology changes as in scenario two.

In this update, two scenarios are evaluated. First, 

there is the baseline scenario that essentially assumes 

a continuation of the USDA 10-year forecast for the 

major crops and extends an additional 10 years to 

2030. Second, the update provides an opportunity 

to further evaluate and refine changes in projected 

improvements, crop yields, and technologies. These 

projected improvements use underlying assumptions to 

give the opportunity to estimate availability projections 

into the future using baseline assumptions (i.e., a 

continuation of current trends) and to determine the 

largest feedstock volume potentials over time (“highyield” scenarios). Impacts of various assumptions are 

assessed using the POLYSYS model, an agricultural 

policy modeling framework, to include land-use 

change and to better understand potential economic 

impacts on a county-by-county basis for certain 

feedstocks.

A review of the literature shows a wide range of 

both qualitative and quantitative projections on crop 

yield and the management of agricultural feedstocks 

for enhanced production, but not specifically to 

energy. The literature is not consistent and does 

not specifically address energy feedstocks from the 

industrial perspective—the optimization of current 

production systems for biomass or the development 

of new, innovative energy feedstock systems. It was 

decided that a different approach is needed to quantify 

feedstock changes in the future. The U.S. Department 

of Energy’s Office of the Biomass Program sponsored 

a series of workshops to obtain expert input on barriers 

and solutions for securing large quantities of biomass 

feedstocks in the future (U.S. Department of Energy, 

2010a). The overall goal of the workshops was to 

develop industry-based, high-yield alternatives to the 

baseline assumptions that were used to develop the 

update. Experts were invited from industry, academia, 

and government to help identify and quantify highyield alternative scenarios.

The workshops were conducted in December 2009 

and were organized by feedstock: corn and agricultural 

crop residues, herbaceous energy crops, and woody 

energy crops (U.S. Department of Energy, 2010a). 

During the workshops, inputs were collected on 

advancements needed for higher yields, the ranking of 

the timeliness and likelihood of these advancements, 

and the projected future yields. Significant input was 

collected during the workshops and is summarized 

in three reports. Because of proprietary concerns, 

participants may have been limited in the amount 

of quantitative data they could provide and much 

of the information collected from the workshops 

is qualitative. Rather than factoring qualitative 

information into quantified data, which may 

misrepresent the opinions of workshop participant, 

the workshop results are analyzed in terms of trends 

identified within their responses. A synthesis of the 

yield and other information was used to develop and 

validate high-yield alternative scenarios in the update. 

In addition, a literature review was used to gauge the 

workshop results (Gordon, 2008; Vance et al., 2010).
6

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

1.3 Report Organization

The next chapter provides a summary of biomass 

resources currently used in the production of biofuels, 

heat, and power. This chapter also provides projections 

of currently used biomass to the year 2030. Chapter 

3 assesses forest biomass and waste resources. This 

includes all of the resources listed under primary forest 

resources in Figure 1.1, with the exception of fuelwood, 

plus unused mill residues and urban wood listed under 

secondary residues and wastes. Agricultural resources 

are evaluated in two chapters. Chapter 4 assesses 

primary crop residues from the major grains, as well 

as other crop residues, crop processing residues, and 

animal manures. These latter resources were listed in 

the 2005 BTS as other crop residues and other residues. 

Chapter 5 contains the assessment of the energy crops 

and includes perennial grasses, woody crops, and 

annual energy crops. Chapter 6 provides a summary of 

the resource assessment update. For convenience and 

ease in reading, a decision was made to show feedstock 

quantities and their composite total at the $60 per dry 

ton level in many of the figures and tables in the report. 

This price was selected because it brings in most of the 

available tons from all of the feedstocks and because 

the price represents a realistic, reasonable price for 

discussion purposes. This report does not present the 

county-level information—that information can be 

found in the Bioenergy KDF (ORNL, 2010).

Figure 1.1 Biomass resources considered in the update to the 2005 BTS

Biomass

Resources

Primary

Forestland

Resources

Fuelwood

(Currently Used)

Composite

Operations

Logging 

Residue

Agricultural

Crop Residues

Unused Mill

Residue

Oil Crops

(Currently Used)

Mill Residues

(Currently Used)

Grain Crops

(Currently Used)

Pulping Liquors

(Currently Used)

Primary

Agriculture

Resources

Secondary

Residues & Waste

Resources

Thinnings

(Timberlands)

Perennial

Grasses

Crop Processing

Residues

Other Removal

Residue Woody Crops Waste Oils &

Greases

Animal Manures Annual Energy

Crops

Thinnings (Other

Forestlands)

Urban Wood

Wastes

Conventional

Products
7

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

2.1 Current Consumption of 

Biomass Resources

A variety of biomass feedstocks are currently used 

to generate electricity and produce heat and liquid 

transportation fuels. According to EIA, biomass 

contributes nearly 3.9 quadrillion British thermal 

units (Btu) (Quads) and accounts for more than 4% of 

total U.S. primary energy consumption (EIA, 2010a). 

Figure 2.1 summarizes energy consumption by fuel 

source. Although biomass ranks well below petroleum, 

natural gas, and coal and is about one-half of nuclear, 

it surpasses hydroelectric and other renewable sources. 

In 2009, the share of biomass in total U.S. energy 

consumption exceeded 4% for the first time.5

 Over 

the last 30 years, the share of biomass in total primary 

energy consumption has averaged less than 3.5% 

(EIA, 2010a). However, as shown in Figure 2.1, there 

has been a gradual increase in biomass consumption 

that started in the early 2000s. This increase is due to 

ethanol production.6

4 Currently used resources were included in the 2005 BTS because they contributed to the goal of displacing 30% of current petroleum 

consumption.

5 In the 2005 BTS, 2.9 Quads were reported—slightly less than 3% of total energy consumption.

6

 The EIA estimates include the energy content of the biofuels (ethanol and biodiesel) feedstock minus the energy content of liquid fuel 

produced.

2 BIOMASS IN CURRENT AND 

PROJECTED ENERGY CONSUMPTION

This chapter reviews currently used biomass resources 

identified in the 2005 BTS as existing uses. These 

resources are included to provide context for the 

resource potential identified in subsequent chapters of 

this report and for comparisons to the 2005 BTS.

4

 These 

currently used resources include biomass residues 

and wastes used in industry for heat and power; 

wood, and some waste wood used in the residential 

and commercial sectors for space heating; sugars and 

starches used in ethanol production; and oilseeds used 

in biodiesel production. The next section summarizes 

current consumption of biomass resources followed by 

projections to 2030.

45

40

35

30

25

20

15

10

5

0

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

QUADS

Coal

Nuclear

Natural Gas

Hydroelectric

Biomass

Solar

Petroleum

Figure 2.1 Primary energy consumption by major fuel source from 1980–2008
8

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Biomass energy consumption (excluding biobased 

products) was reported at 184 million dry tons in the 

2005 BTS. More than 50% of this consumption was 

estimated to be in the forest products industry, with 

equal amounts used in other processing industries, 

electric power generation, and the residential and 

commercial sectors. A relatively small fraction (less 

than 10%) was used to make biofuels. Based on

the most recent EIA data, current biomass energy 

consumption is nearly 200 million dry tons, or 4% of 

total primary energy consumption (see Figure 2.2).7

 

About 17% of this consumption is space heating in 

the residential and commercial sectors. The source of 

this biomass is nearly all fuelwood. The electric power 

sector represents a small percentage of total biomass 

consumption (8%) and uses a variety of biomass 

feedstocks—fuelwood, MSW biomass, MSW landfill 

gas, and biosolids (or sewage sludge). In 2009, nearly 

60% of biomass-derived electric power consumption 

was from MSW sources. Transportation accounts 

for 31% of total consumption, with ethanol used in 

gasoline blending accounting for most (90%) of the 

total. Biodiesel accounts for 8%, and the remainder 

is E85 (85% ethanol fuel) and other biomass liquids. 

The industrial sector accounts for 44% of total biomass 

energy consumption. Most of this amount (nearly 

90%) is wood and waste wood. MSW, landfill gas, and 

biosolids account for the remainder. 

Hydroelectric 2.8%

Wind 0.7%

Solar 0.1%

Geothermal 0.4%

Natural gas

25%

Coal

21%

Other

4%

Nuclear

9% Biomass

4.1%

Petroleum

37%

Total primary energy consumption in 

2009 – 94.5 Quads

In the 2005 BTS, more than 75% of the biomass 

consumed was derived from forest sources. The data in 

this update still shows most of the biomass consumed 

comes from forest sources; however, the percentage is 

less (about 65%), owing to the increase in the use of 

corn grain for ethanol production, a cropland source. 

Additional aspects of specific forest and cropland 

biomass resources are discussed below. 

2.1.1 Forest-Derived Resources

Biomass originating from forests comes primarily 

from two sources—fuelwood used in the residential 

and commercial sectors and residues generated in the 

manufacture of forest products. There is a relatively 

small amount of MSW wood that is recovered for 

energy.

Fuelwood. Fuelwood is wood that is harvested from 

forests and combusted directly for useable heat in the 

residential and commercial sectors, as well as power 

in the electric utility sector. Combined, these sectors 

account for 30% of current consumption of forest 

biomass and about 20% of total U.S. biomass energy 

consumption (Table 2.1). The residential sector is about 

four times as large as the commercial sector and five 

times as large as the electric power sector. In the most 

recent year, these three sectors consumed about 38 

million dry tons (Table 2.1), which is approximately 

the same amount as reported in the 2005 BTS. Most of 

the fuelwood consumed is in the Northeast and North 

Central regions, and to a lesser extent in the Southeast 

and Pacific Coast regions and comes mostly from 

hardwoods (Smith et al., 2009). 

7 This is the total biomass quantity as shown for 2009 in Table A17 (Reference case) of the 2010 Annual Energy Outlook (EIA-AEO, 2010c) 

excluding losses. It includes the residential, commercial, industrial, electric power, and transportation sectors. Conversion of energy to dry 

tons was based on a conversion factor of 16 million Btu per dry ton. This factor is used throughout this report.

Figure 2.2

Source: Energy Information Administration, Monthly Energy Review,

June 2010, http://www.eia.doe.gov/emeu/mer/contents.html.
9

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

8 The possibility of residues currently used in making low-value products shifting into bioenergy was not explored in this study. 

Opportunities may exist to bid away some low value uses (such as mill residue classified as used in other uses, presumably low value) into 

bioenergy applications.

Forest products industry processing residues. The 

forest products industry consumes three major sources 

of residues—primary and secondary mill residues 

generated in the processing of roundwood, roundwood 

products, and pulping liquors. Primary processing 

mills (facilities that convert roundwood into products 

such as lumber, plywood, and wood pulp) produced 

about 87 million dry tons of residues in the form of 

bark, sawmill slabs and edgings, sawdust, and peeler 

log cores in 2002 (Smith et al., 2009). Very little of 

this resource is currently unused. According to USDA 

Forest Service estimates, about 75% of bark is used 

as fuel, and about 23% is used in other low-valued 

products, such as mulch, if not used internally for 

energy or in other markets where it may have a higher 

value (Figure 2.3). For coarse residues, about 77% is 

used in the manufacture of fiber products, about 13% 

is used for fuel, and 8% is used for other applications. 

About 55% of the fine residues are used as fuel, 25% 

in fiber products, and 19% in other uses. Overall, only 

1.5% of primary mill residue currently goes unused, 

leaving 1.3 million dry tons for new bioenergy uses.8

Residues are also generated at secondary processing 

facilities—mills utilizing primary mill products. 

Examples of secondary wood processing mill products 

include millwork, containers and pallets, buildings 

and mobile homes, furniture, flooring, paper, and 

paper products. Because these industries use an 

already-processed product, they generate much smaller 

quantities of residues. In total, the secondary mill 

residue resource is considerably smaller than the 

primary mill resource (Rooney, 1998; McKeever,1998). 

The types of residues generated at secondary mills 

include sawdust and sander dust, wood chips and 

shavings, board-end cut-offs, and miscellaneous scrap 

wood. In total, 32 million dry tons of mill residues are 

currently used (Table 2.1).

In the manufacture of paper products, wood is 

converted into fiber using a variety of chemical and 

mechanical pulping process technologies. Kraft (or 

sulfate) pulping is the most common processing 

technology. In kraft pulping, about half the wood is 

converted into fiber. The other half becomes black 

liquor, a byproduct containing unutilized wood fiber, 

lignin, and other chemicals. Pulp and paper facilities 

combust black liquor in recovery boilers to produce 

energy (e.g., steam) and, more importantly, to recover 

the valuable chemicals present in the liquor. The 

amount of black liquor generated in the pulp and paper 

industry is the equivalent of nearly 45 million dry 

tons of biomass (EIA, 2010c). Because the amount of 

black liquor generated is insufficient to meet all mill 

needs, recovery boilers are usually supplemented with 

fossil and wood residue-fired boilers. The pulp and 

paper industry utilizes enough black liquor, bark, and 

other wood residues to meet a majority of its energy 

requirements.

Municipal solid wastes. Currently, about 254 million 

tons of MSW are generated annually, with slightly 

more than one-third of this quantity recovered for 

recycling or composting (EPA, 2008). Another 

13%, or 32 million tons, is combusted with energy 

recovered. Most of the MSW generated originates from 

households and includes a wide variety of biomass 

and non-biomass materials. The major forest sources 

of MSW include newsprint, paper, containers and 

packaging, yard trimmings, and wood. The quantity 

of forest-derived MSW currently used is estimated at 

about 14 million dry tons (Table 2.1).

(Courtesy of Rob Mitchell, ARS)

Figure 2.3 Wood waste 
10

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

2.1.2 Agriculture-Derived Resources 

Most cropland-derived biomass is used to produce 

ethanol from corn grain and biodiesel from oilseed 

crops (Table 2.1). Some MSW (e.g., food wastes and 

textiles) is also used to produce power.

Ethanol from starch. The primary feedstock 

for ethanol in the United States is currently corn. 

Historically, the United States has been a large 

producer of corn for a number of reasons—chiefly 

because of its high carbohydrate yield relative to 

other crops and multiple uses as food, feed, ethanol, 

and exports. Corn’s high starch content and historic 

presence in the agricultural industry situate it as an 

accessible feedstock for ethanol production. The 

highest domestic production occurred in 2009 at 13.4 

billion bushels, with about 35% of the total U.S. crop 

utilized for ethanol production.

As of May 2010, U.S. corn ethanol operating capacity 

was 12.6 BGY, with production concentrated primarily 

in the Corn Belt and Northern Plains (Figure 2.5). In 

July 2011, ethanol operating capacity had increased 

to 14.2 BGY. Actual 2010 annual production was 13.2 

billion gallons, which represents about 4.7 billion 

bushels or about 112 million dry tons of corn.9

 After 

fuel is created from the starch in corn, the residual 

fiber, protein, vegetable oil, and minerals are used as 

distillers dried or wet grains in livestock feed. Distillers 

grains account for about one third of total corn grain 

weight. 

EISA 2007 mandates the incremental increase of 

the use of biofuels and is one of the primary drivers 

in the current increase in the demand for corn grain 

(Figure 2.6).10 Specifically, EISA mandates that future 

Renewable Fuel Standards (RFS) for years 2015–2022 

be met with up to 15 billion gallons of corn-based 

ethanol.

Based on USDA estimates, a more modest rate of 

increase in ethanol production from corn grain is 

expected during the next 10 years, compared to 

production growth over the last 10 years. The projected 

production in 2020 is about 14–15 billion gallons. 

The parallel actual and projected corn production 

for ethanol over the same 20-year period is shown 

in Figure 2.7. Ethanol corn production increased 

seven fold in the last 10 years, but is only expected 

to increase to a little less than 90 million dry tons 

annually (excluding the fraction recovered as distillers 

grains) to meet the mandate. About 38% of corn grain 

produced in 2010 was used in ethanol production (up 

from 23% in 2007). This corn-to-ethanol proportion is 

expected to remain stable between 33% and 34% from 

2010 to 2020 (USDA-OCE/WAOB, 2010). In spite 

of increasing demand, USDA estimates that the price 

of corn is expected to remain stable at around $3.65 

to $3.90 per bushel (in nominal terms) in the 2011 to 

2020 timeframe (USDA-OCE/WAOB, 2010).

Increased corn production may create a number of 

unintended market and environmental effects (BRDI, 

2008). First, a food and fuel use conflict may arise, 

which suggests that the rise in demand for corn ethanol 

increases the price and decreases the quantity of corn 

available for other uses. Because corn is a major cereal 

grain and primary feed for livestock, increasing corn 

ethanol requirements could lead to price inflation of 

Figure 2.4 Harvesting corn in the Great Plains 

(Courtesy of Rob Mitchell, ARS)

9 A bushel of corn weighs 56 pounds at 15% moisture. Given current technology, a bushel of corn can produce 2.8 gallons of denatured 

ethanol. Calculation converts to dry basis and includes distillers dried or wet grains.

10 Additionally, there is an ethanol blenders’ credit, which was reduced to $0.45 per gallon in 2009.
11

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

consumer final goods—primarily food. This upward 

pressure could be relieved with substitutes for cornderived foods and livestock feed—a portion of the corn 

used for ethanol (about one-third) is still available as 

a feed as distillers grains. Consumers responding to 

the relatively lower prices of corn substitutes increase 

the demand for other grains, leading to an increase in 

prices for other grains. On the supply side, if higher 

corn prices lead farmers to grow more corn, economic 

theory suggests the increased land dedicated to corn 

leads to increased prices and decreased supply for 

other grains. The outcomes of both demand (consumers 

switching to consuming other grains) and supply 

(farmers planting more corn) suggest higher prices for 

the substitute grains and lower prices for corn, ceteris 

paribus. Careful consideration of other factors is 

important in identifying inflation in commodity crops, 

such as international events, weather, and general 

economic conditions, among other factors.

Increasing the corn yields per acre and the efficiency 

of conversion technologies would help to relieve 

the economic and environmental pressures related 

to increased ethanol production. Improving the 

supply traits (i.e., energy efficiency in production, 

harvesting, and conversion) of biofuels improves the 

environmental sustainability of this biofuel.

Figure 2.5 Ethanol operating capacity by state

Current Production Capacity

(Millions gallons per year)

0

1 - 50

51 - 100

101 - 250

251 - 1000

1001 - 3000

>3000

Source: RFA (2010a)
12

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

100%

Exports

Ethanol

Food, seed, industrial (excl ethanol)

Feed & residual

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Million dry tons

120

100

80

60

40

20

0

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Actual production EISA mandate FAPRI

Historical and projected corn use, 2001–2020

Historical and projected ethanol production, 2001–2019

Figure 2.6

Figure 2.7

Source: USDA-ERS (2010e), 

USDA-OCE/WAOB (2010)

Source: Actual Production (RFA, 2010b); Projected Production based on USDA-ERS, 2010e; USDA-OCE/WAOB, 2010; EISA Mandate, 

BRDI, 2008; FAPRI, 2010; EIA-AEO, 2010.
13

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

2001 2003 2005 2007 2009 2011 2013 2015

1000

800

600

400

200

0

Millions gallons per year

Soybean biodiesel Biodiesel EISA mandate

Figure 2.8 Historical and projected U.S. biodiesel production with EISA mandate

Source: EIA (2009, 2010a,b) (Biodiesel), USDA-ERS (2007, 2010a,b) (Soybean biodiesel) 

Note: Assume 7.5 pounds per gallon for soybean biodiesel.

Biodiesel. At present, biodiesel is currently produced 

from soybean, waste fats, and various vegetable oils.11 

Like ethanol, biodiesel production increased rapidly 

from 2005 to 2008 (Figure 2.7 and 2.8). Historically, 

soybean oil has been the dominant feedstock (83% 

of total in 2007) for biodiesel production, but this is 

changing as animal fats and waste oils are increasingly 

used. Soybean contribution to biodiesel is expected to 

decrease and stabilize around 400 million gallons per 

year after decreasing in 2009 (USDA-OCE/WAOB, 

2010) (Figure 2.8). Based on conditions in 2006, by 

2015 soybean oil is projected to make up 70% of 

biodiesel feedstock, as other sources are more widely 

used (USDA-OCE/OEPNU, 2008). A more recent 

projection expects that less than half of biodiesel 

feedstocks will come from first-use vegetable oil with 

more than half from recycled vegetable oils, or animal 

fats (USDA-OCE/WAOB, 2009).

Vegetable oils other than soybean oil are projected 

to make much smaller contributions to biodiesel 

production in the foreseeable future; this is due to 

higher relative input prices. The difference between 

soybean and vegetable oil as inputs to biodiesel and 

total biodiesel is expected to come from waste fats 

and recycled oils. Waste fats are generally a less costly 

feedstock than vegetable oils, however, they contain 

high levels of saturated fatty acids that results in a 

lower flow quality than vegetable oil. Yellow grease, 

a waste product of the food industry, is the least costly 

available feedstock for biodiesel production. Its supply 

is limited geographically and lends itself toward 

smaller capacity production.

11 Glycerin, a byproduct, is used in a number of consumer and industrial products.

12 As of June 2009, biodiesel production capacity was 2.69 BGY, though many facilities had low utilization rates (NBB, 2009). The current tax 

benefits of biodiesel are provided to the blender at the rate of $1.00 per gallon for all feedstocks (previously, credits for recycled vegetable 

oils or animal fats was $0.50 per gallon) and was extended through 2009 by the Energy Improvement and Extension Act of 2008.
14

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

The U.S. biodiesel industry must meet certain use 

benchmarks, as mandated by EISA (U.S. Congress, 

2007). EISA also requires use levels to reach 500 

million gallons by 2009. This requirement increases to 

1 billion gallons by 2012.12 

Municipal solid wastes. Agricultural sources of MSW 

include food wastes, textiles, and leather. These wastes 

currently account for about 20% of the total MSW 

generated (EPA, 2008). The quantity of croplandderived MSW currently used is estimated at about 7 

million dry tons (Table 2.1).

2.2 Projected Increase in 

Currently Used Biomass 

Resources

The projected increase in consumption of currently 

used biomass feedstocks is summarized by feedstock 

type for selected years in Table 2.1. These data reflect 

the 2010 EIA reference case projections converted 

to million dry tons. Consumption of biofuels in the 

transportation sector increases significantly owing 

to the EISA 2007 and the RFS. Electric power 

consumption using biomass feedstocks (shown as 

fuelwood in Table 2.1) also increases considerably over 

the next 20 years. As noted by EIA, a large fraction 

of the biopower increases come from increased cofiring (EIA, 2010c).13 Modest growth in industrial 

consumption of biomass is projected with little or no 

change in the residential and commercial sectors. The 

key feedstocks contributing to biomass consumption 

include fuelwood harvested from forests, primary mill 

residues, pulping liquors, and woody MSW feedstocks. 

In total, forests currently contribute nearly 130 million 

dry tons. This is somewhat lower than reported in the 

2005 BTS due to the economic downturn. By 2022 

and 2030, consumption of forest biomass increases 

to about 210 and 225 million dry tons, respectively. 

Agriculture sources of biomass include corn and other 

grains used to produce ethanol; soybean and greases 

for biodiesel production; and MSW feedstocks, 

such as food wastes and textiles. These currently 

used feedstocks total nearly 85 million dry tons. 

Consumption increases to 103 million dry tons by 

2017. Most of 2017 and beyond quantities are grains 

and soybean used to produce 15 BGY of ethanol and 1 

BGY of biodiesel—the assumed maximum available 

feedstocks for starch ethanol and oils for the RFS under 

EISA 2007. These estimates do not take into account 

any liquid production expected from cellulosic sources. 

The remainder of this report addresses the cellulosic 

resources that are currently unused and available, as 

well as energy crops.

13 Co-firing is a conversion process in which small amounts of biomass are mixed with coal in existing coal-fired plants. The amount of 

 displaced coal can vary from a few percent up to 10% or more depending on the conversion technology and fuel-handling systems.
15

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Source Current 2017 2022 2030

Forest

Fuelwood 38 72 96 106

Mill residue 32 38 39 42

Pulping liquors 45 52 54 58

MSW sources 14 20 20 20

Total forest 129 182 209 226

Agriculture

Ethanol a 76 (109) 88 (127) 88 (127) 88 (127)

Biodiesel b 2 4 4 4

MSW sources 7 11 11 11

Total agricultural resources 

currently used 85 (118) 103 (142) 103 (142) 103 (142)

Total currently used resources 214 (247) 285 (342) 312 (351) 329 (368)

Projected Consumption of Currently Used Biomass Feedstocks 

(Million Dry Tons per Year) Table 2.1

Notes: Fuelwood includes the residential commercial sector as well as biomass consumed by the electric utility industry in dedicated 

biomass plants and co-firing applications. MSW sources are allocated to forest (65%) and cropland (35%) based on EIA (2007b). 

Ethanol and biodiesel are based on EISA mandates of 15 BGY of biofuels and 1 BGY of biodiesel. Ethanol assumes corn grain at 56 

pounds per bushel, 15.5% moisture content, and 2.8 gallons per bushel.

a

 The first number is the portion of corn consumed to make ethanol. The number in parentheses is the amount of corn required. For example, 

it takes 127.5 million dry tons to make 15 BGY of ethanol. However, only 88.3 million dry tons are consumed in making the ethanol. The 

remainder (39.2 million dry tons) is distiller’s grain and is excluded from the total.

b Includes all sources of biodiesel. Current consumption is 43% from soybeans and 57% from other sources, including animal fats and waste 

oils. The proportion of sources of future feedstocks will vary and are assumed to have an average conversion rate of 7.5 pounds of oil/fats 

per gallon of biodiesel.
16

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

3 FOREST BIOMASS AND 

WOOD WASTE RESOURCES

This chapter provides estimates of forest biomass and 

wood waste quantities, as well as roadside costs (i.e., 

supply curves) for each county in the contiguous 

United States (see Text Box 3.1). Roadside price is 

the price a buyer pays for wood chips at a roadside 

in the forest, at a processing mill location in the 

case of mill residue, or at a landfill for urban wood 

wastes prior to any transport and preprocessing to the 

end-use location.14 Forest biomass and wood waste 

resources considered in this assessment include:

• Forest residues (logging residues and 

thinnings) from integrated forest operations 

from timberland15

• Other removal residue16

• Thinnings from other forestland

• Unused primary and secondary mill 

processing residues

• Urban wood wastes

• Conventionally sourced wood. 

This chapter provides estimates for forest residues and wood wastes that were reported in the 2005 BTS, as well as 

an additional feedstock, conventionally sourced wood. In the original BTS, forest residues include logging residue, 

other removal residue, and fuel treatments from both timberland and other forestlands. Wood wastes include forest 

products wood residues (both used and unused), pulping liquors, and urban wood residues. The 2005 BTS also 

included fuelwood. 

For this report, fuelwood, “used” wood wastes, and pulping liquors are included in the update, but are not counted as 

“potential” biomass resources because they are already used for other purposes, primarily energy production. Future 

prices may shift these “used feedstocks” into new or other energy uses, but for the update, they are still counted as 

used.

Fuel treatment residues are now “thinnings” obtained using an integrated forest operation, i.e., the production of 

merchantable products and biomass. A “composite” estimate is determined by combining portions of logging residue 

and thinning estimates, then by using a ratio to represent the transition from harvesting operations that leave 

logging residues to harvesting operations that integrate the removal of biomass with merchantable timber. Some 

conventionally sourced wood (e.g., small-diameter pulpwood) is also considered to be a biomass feedstock. See 

Chapter 1 for more discussion on the types of feedstocks.

TEXT BOX 3.1 | FOREST FEEDSTOCKS

14 The costs estimated are marginal costs or costs to supply each successively more expensive dry ton of biomass in each county. It is 

assumed that buyers would be buying from landowners who are aware of the cost for the most expensive units of biomass supply and 

that there would be enough buyers (a competitive market) such that landowners would only sell to buyers offering the price for the most 

expensive unit. Prices paid may be less for a given amount of biomass supply, depending on the extent that landowners are not informed 

about the highest price being offered or are not interested in maximizing profit, or to the extent that there are few buyers to compete for 

the biomass.

15 Forestland is defined as land at least 120 feet wide and 1 acre in size, with at least 10% cover (or equivalent stocking) by live trees of any 

size, including land that formerly had such tree cover and that will be naturally or artificially regenerated (Smith et al., 2009). Forestland 

is further defined as timberland and other forestland. Timberland is defined as forestland that is producing, or is capable of producing, 

in excess of 20 cubic feet per acre per year of industrial wood and not withdrawn from timber utilization by statute or administrative 

regulation. Other forestland is defined as forestland other than timberland and productive reserved forestland. It includes available 

forestland, which is incapable of annually producing 20 cubic feet per acre per year. Reserved forestland is administratively removed from 

production.

16 Unutilized wood volume from cut, or otherwise killed, growing stock from cultural operations, such as precommercial thinnings or from 

timberland clearing. Does not include volume removed from inventory through reclassification of timber land to productive reserved forest 

land (Smith et al., 2009).
17

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Forest biomass is a primary resource that consists 

of a combination of estimates from two sources: (1) 

removal of a portion of what is called logging residue 

that is currently generated during the harvesting of 

timberlands for conventional forest products and 

(2) removal of excess biomass from fuel treatment 

(reducing biomass helps forests increase fire resistance) 

and thinning operations designed to reduce risks and 

losses from catastrophic fires and improve forest 

health. This latter component consists of removing 

merchantable whole trees and excess small trees to 

the roadside. The tops and branches of merchantable 

trees, cull trees, cull-tree components, and excess 

small trees can be used for bioenergy applications. 

The merchantable tree components can be used for 

conventional forest products. Both of these resources 

were considered separately in the BTS, but in this 

update, estimates are made assuming that there will 

be a transition in conventional harvesting operations 

from leaving logging residues behind to removing them 

as part of conventional harvesting. It is projected that 

access to biomass will come from integrated harvesting 

operations that provide sawlogs and pulpwood to meet 

existing market demand, as well as provide biomass 

for energy. Two other primary resources are considered 

in this update. Thinnings from other forestland (nontimberland) are conducted to improve forest health by 

removing excess biomass on low-productivity land. 

Other removal residue is unused wood that is cut 

during the conversion of timberland to non-forest uses 

and unused wood cut in silvicultural operations, such 

as precommercial thinnings. A description of the forest 

resource land base is provided in Text Box 3.2.

The processing of sawlogs, pulpwood, and veneer 

logs into conventional forest products generates 

significant quantities of bark, mill residues (coarse and 

fine wood), and pulping liquors. With the exception 

of small quantities of mill residues, these secondary 

forest products industry residues are currently used 

in the manufacture of forest products or for heat 

and power production, and valuable chemicals are 

recovered from pulping liquors. In addition to pulping 

liquors, fuelwood—defined as wood harvested directly 

from forests and used primarily in the residential and 

commercial sectors for space heating and by some 

electric utilities for power generation—is also not 

considered beyond the estimates provided in 

Chapter 1. Some quantity of these currently used 

wood wastes could shift to bioenergy applications 

at the right price. However, estimating how many of 

these resources could move into bioenergy production 

is difficult and speculative, as many of these wood 

wastes are not only used, but are also confined or 

dedicated to a specific process. Urban wood waste, on 

the other hand, is largely destined for landfills. The 

urban wood waste resource includes a wide variety of 

woody materials, ranging from discarded furniture, 

landscaping wood wastes, and wood used in the 

construction, remodeling, and demolition of buildings. 

The final resource considered is conventionally 

sourced wood, which is defined as separate, additional 

operations to provide pulpwood-sized roundwood for 

bioenergy applications. Conventional wood was not 

included in the 2005 BTS. Excluded from the forest 

potential is wood grown under short rotations and 

dedicated to bioenergy production (see Chapter 5).

The remainder of this chapter discusses the specific 

woody biomass sources introduced above. The 

bulk of the chapter focuses on primary forest 

biomass, including extended discussion of resource 

sustainability from timberland. This is followed by 

other removals and thinnings on other forestland. 

Unused mill residues and urban wood wastes are 

discussed. The sixth section of the chapter provides 

estimates of how much conventionally sourced 

wood could be provided by additional harvest and 

by a shift of current pulpwood demand to bioenergy 

applications. The final section provides a summary of 

forest biomass and wood waste sources. All sections 

include key assumptions and data used to estimate 

applicable current and future supplies, as well as prices 

to access these resources. County-level supply curves 

are estimated for many of the resources; however, 

in this report, estimates are summarized by state and 

nationwide. A complete county-level database with 

projections of quantities and prices is available in a 

stand-alone database, the Bioenergy KDF (ORNL, 

2010).
18

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

3.1 Primary Forest Biomass

Current removals from U.S. forestlands are about 21.2 

billion cubic feet annually—nearly 320 million dry 

tons.17 This level of harvest is well below net annual 

forest growth and only a very small fraction of the 

total timberland inventory. In 2006, the ratio of forestgrowing stock growth (wood volume increases) to 

growing stock removals (harvest, land clearing, etc.) 

in the United States was 1.71,18 which indicates that 

net forest growth exceeded removals by 71% (Smith 

et al., 2009). The data also suggests a national trend 

of increasing net growth relative to growing stock 

removals. However, this trend varies by geographic 

region, species, and ownership, such as public forests 

and private industrial forests. In the case of private 

ownership (excluding Alaska) the growth to removals 

ratio is 1.3 as compared to a ratio of 5.3 for public 

lands.

17 These removals include roundwood products, logging residues, and other removals from growing stock and other sources. Removals refer 

to removal from standing timber inventory. Some roundwood (logging residue) is actually left on harvest sites. Volume is converted to dry 

tons using a factor of 30 dry pounds per cubic foot.

18 The growth to removals ratio is derived by dividing net annual growth of growing stock by annual removals of growing stock on 

timberland and excludes Alaska (Smith et al., 2009; Tables 34 and 35).

In the United States, there are about 750 million acres of forestland, with slightly more than two-thirds classified 

as timberland or land capable of producing 20 cubic feet per acre annually of industrial wood products in natural 

stands (Smith et al., 2009). Another 22% of this forestland is classified as “other” and is generally not productive 

enough for commercial timber operations owing to poor soils, lack of moisture, high elevation, or rockiness. The 

remaining 10% of forestland is withdrawn from timber utilization by statute or administrative regulations and is 

dedicated to a variety of non-timber uses, such as parks and wilderness. The timberland fraction of U.S. forestlands 

totals approximately 514 million acres. As noted by Smith et al. (2009), the map below shows forested pixels 

from the USDA Forest Service map of Forest Type Groups (Ruefenacht et al., 2008). Timberland is derived and 

summarized from RPA plot data using a hexagon sampling array developed by EPA. Reserved land is derived from 

the Conservation Biology Institute, Protected Areas Database. Other forestland is non-timberland forests.

TEXT BOX 3.2 | FOREST RESOURCES OF THE UNITED STATES
19

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

19 The Forest Inventory and Analysis Program of the U.S. Forest Service conducts annual surveys and studies of industrial users to determine 

roundwood harvests for primary wood-using mills. Additional studies are also used to determine nonindustrial (i.e., residential and 

commercial) uses of roundwood. Taken together, these studies provide a comprehensive description of timber product output for a given 

year (USDA Forest Service, 2007a).

Slightly more than 70% of the volume of current U.S. 

wood removals is roundwood, with the remainder 

consisting of logging residues and other removals. 

Total logging residue and other removals in the United 

States currently amount to nearly 93 million dry tons 

annually—68 million dry tons of logging residue and 

25 million dry tons of other removal residue (Smith 

et al., 2009).19 The logging residue material largely 

consists of tops, branches and limbs, salvageable dead 

trees, rough and rotten trees, non-commercial species, 

and small trees. Most of this residue is left onsite 

because its small piece size makes it unsuitable and 

uneconomic for the manufacturing of forest products. 

However, as markets for bioenergy feedstocks 

develop, a significant fraction of this residue could 

become economically feasible to remove, most likely 

in conjunction with conventional harvest operations 

where the costs of extraction (i.e., felling and skidding) 

are borne by the conventional forest product. [Forest 

biomass compliance with EISA is described in Text 

Box 3.3.] Other removal residue is wood cut, killed, or 

burned during the conversion of timberland to nonforest land uses (e.g., cropland, pasture, roads, and 

urban areas). 

Trees killed and unutilized because of silvicultural 

operations, such as precommercial thinning of 

commercial forests, are also included in the removal 

residue category. This woody material is unutilized 

for reasons similar to the logging residue; it could 

become available for bioenergy production and 

other uses as technology, economics, and markets 

evolve. About 70% of the other removal residue is 

hardwood, attributable to the clearing of land in the 

North and Southeast where there is a preponderance of 

hardwoods. 

In addition to forest residues generated by timber 

extraction and land-conversion activities, millions 

of acres (one estimate is at least 28 million acres in 

the West; USDA Forest Service, 2005) of forests are 

overstocked with relatively large amounts of excess 

biomass, which have accumulated as a result of forest 

growth and alterations in natural cycles through 

successful suppression of fires (USDA Forest Service, 

The Energy Security Act (PL 96-295) of 1980 defines biomass 

as “any organic matter which is available on a renewable 

basis, including agricultural wastes and residues, wood 

and wood wastes and residues, animal wastes, municipal 

wastes, and aquatic plants” (U.S. Department of Energy, 

2010c). This first-ever statutory definition became the 

standard for some legislative and programmatic purposes. 

Currently, 16 biomass definitions exist within recently 

enacted statutes and the Tax Code (Riedy and Stone, 2010). 

The 2008 Farm Bill Act and EISA definitions are typically 

regarded as the most comprehensive. 

In the enactment of a new national Renewable Fuels 

Standard, as part of EISA, Title II, Sec. 201(I)(I), a more 

stringent definition was established that not only defines 

the types of feedstocks, but also defines the sources of the 

feedstock. In effect, EISA excludes all biofuels feedstocks 

from federal lands, except in narrowly defined areas at risk 

from wildfire. 

For the purpose of this report, the original “organic matter” 

definition without additional statutory or regulatory 

definitional restrictions is used. This is because:

1. Subsequent laws, such as EISA, are for specific uses 

and final products—for EISA, it is biofuels. The 

Billion-Ton Study is an evaluation of availability without 

regard to final use.

2. There are other laws and pending legislation that 

would have to be included in a comprehensive analysis 

of available biomass “constrained by definition” that 

would detract from the goal of this report.

3. Restricting the analysis to definitional biomass 

availability reduces the usefulness of the information 

and conclusions if the definition changes.

Therefore, the availability of feedstocks from federal lands 

is analyzed and included separately. The results are shown 

by landownership for the convenience of the reader. Outputs 

are categorized as either public or private ownership. Public 

ownership includes federal, state, county, and city lands. 

Private ownership includes industrial and non-industrial 

lands.

 TEXT BOX 3.3 | FEEDSTOCK 

COMPLIANCE WITH EISA
20

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Forest residue operation

in northern California 

Removal of forest biomass 

with a skidder 

Figure 3.1

Figure 3.2

2005; Graham et al., 2004).20 As part of its Healthy 

Forest Initiative, the USDA Forest Service identified 

timberland and other forestland areas that have tree 

volumes in excess of prescribed or recommended 

stocking densities. The areas identified require some 

form of treatment or thinning to reduce the risks 

of uncharacteristically severe fires and are in close 

proximity to people and infrastructure. This excess 

biomass is classified as standing and downed trees 

in overstocked stands that would leave the forests 

healthier, more productive, and less susceptible to 

catastrophic fire hazard if removed. 

An initial estimate of the potential supply of this fuel 

treatment wood was developed for five western states 

(USDA Forest Service, 2005). The study identified 

a large recoverable residue and merchantable wood 

resource ranging from a low of 576 million dry tons 

to a high of 2.1 billion dry tons that could be removed 

over a period of years. The low estimate included only 

60% of the timberlands in the highest fire risk class 

and the same high estimate included all timberlands 

requiring some fuel treatment. About 30% of the total 

amount was considered residue—tops and limbs of 

large trees and saplings or trees too small for pulpwood 

or sawlogs, cull components of merchantable trees, and 

standing dead trees. [These operations are visualized 

in Figures 3.1 and 3.2.] A Web-based tool, the Fuel 

Treatment Evaluator, was subsequently developed 

to identify, evaluate, and prioritize fuel treatment 

opportunities that would remove excess biomass 

and promote a more natural fire regime pattern, with 

recurrence of less severe fire (Miles et al., 2006; Skog 

et al., 2006). This tool was used in the BTS to estimate 

the potential availability of fuel treatment biomass 

across the entire continental United States. The 2005 

BTS provided an estimate of 60 million dry tons per 

year, with slightly more than 80% of the biomass on 

timberland and the remainder on other forestlands. 

The key assumptions behind this analysis included the 

exclusion of forest areas not accessible by road and all 

environmentally sensitive areas, equipment recovery 

(Courtesy of Han-Sup Han, Humboldt State University)

(Courtesy of Han-Sup Han, Humboldt State University)

limitations, and merchandizing thinnings into two 

utilization groups (conventional forest products and 

bioenergy products).

Although the demand for roundwood, as well as the 

extent of land-clearing operations, ultimately 

20 In August 2000, the National Fire Plan was developed to help respond to severe forest fires and their impacts on local communities, 

while ensuring sufficient firefighting capacity for future fires. The National Fire Plan specifically addresses firefighting capabilities, forest 

rehabilitation, hazardous fuels reduction, community assistance, and accountability. The Healthy Forest Restoration Act (HFRA) of 2003 

was then enacted to encourage the removal of hazardous fuels, encourage utilization of the material, and protect, restore, and enhance 

forest ecosystem components. HFRA is also intended to support research and development to overcome both technical and market barriers 

to greater utilization of this resource for bioenergy and other commercial uses from both public and private lands. Removing excess woody 

material has the potential to make relatively large volumes of forest residues and small-diameter trees available for bioenergy and biobased 

product uses.
21

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Sustainable harvest from 

managed forest systems Figure 3.3

(Courtesy of Evergreen Magazine)

3.1.1 Sustainability of Extracting 

Primary Forest Residue Biomass

While the sustainability of harvesting traditionally 

merchantable roundwood has been studied at great 

length, the additional harvest of logging residues 

and small-diameter trees for bioenergy creates 

new concerns over forest ecosystem sustainability 

(Janowiak and Webster, 2010). Biomass feedstocks 

may be harvested from a wide variety of forest 

management systems, ranging from extensively 

managed, naturally regenerated forests to short-rotation 

woody crops (SRWC). Each forest system has its 

own issues with respect to sustainability. While these 

issues must be addressed, the sustainable extraction 

of forest residues can be achieved through either the 

application of best management practices (BMPs)—

that are voluntary or statutory (regulated by states)—or 

through formal forest certification programs (BRDI, 

2008). In all cases, these practices are science based 

and have the goals of protecting ecological functions 

and minimizing negative environmental impacts. In the 

case of fuel treatment operations, biomass harvesting 

will enhance forest health and vitality as long as some 

stand structure is left to provide continuous cover, 

erosion control, and habitat (Figure 3.3) (Graham et al., 

2004).

Within the most intensive woody biomass feedstock 

systems, maintaining site productivity is imperative 

to efficient management. Nutrient deficiencies that 

may be present are mitigated as a matter of course 

through fertilization. The management of these 

systems in terms of the intensity of soil disturbance; 

technological inputs to manage water, nutrients, and 

non-crop vegetation; and harvest intensity, is more 

intensive than traditional forestry, but usually less 

intensive than typical agricultural systems. BlancoCanqui (2010) reviewed the sustainability of these 

systems in comparison to other agronomic biomass 

feedstock systems and notes that, in comparison to 

annual systems, short-rotation woody crops offer 

several environmental advantages. When sited on 

marginal agricultural land, these systems improve 

soil productivity and offer additional environmental 

benefits, such as improved water quality and wildlife 

habitat.

Within conventionally managed forest ecosystems, 

concerns over biomass harvesting involve both 

operational concerns associated with harvesting and 

thinning operations, as well as the ecological concerns 

over the removal of additional wood following 

conventional stem-only harvests (Page-Dumroese et 

al., 2010). Some dead woody biomass is left onsite, 

as it serves several important ecological functions 

in forest ecosystems (see comprehensive review by 

Harmon et al., 1986) that are affected by harvesting. 

Dead woody material serves as a habitat for a variety 

of organisms, including fungi, mosses, liverworts, 

insects, amphibians, reptiles, small mammals, birds, 

and regenerating plants. In cool climates, downed 

logs act as nurse logs for seed germination and stand 

establishment. Birds forage, nest, and hunt in and on 

dead wood. Dead woody material affects ponding, 

sediment trapping, and aeration in streams; it also 

impacts site productivity through several mechanisms.

determines the amount of forest residue generated, 

environmental and economic considerations set the 

amount that can be sustainably removed. The next 

section of this chapter discusses forest resource 

sustainability and is followed by a discussion of 

the methods and data used to estimate county-level 

quantities and prices for the major forest residue 

feedstocks.
22

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

This dead biomass alters site water balance and water 

quality through storage and release of water and by 

reducing runoff and erosion. It is commonly used 

during harvest operations to protect wet soil areas from 

compaction and rutting, and it is used post-harvest to 

help limit runoff and erosion from skid trails and forest 

roads. Finally, dead woody material supports biological 

nitrogen fixation, thereby increasing onsite levels of 

nitrogen, and it contains nutrients that are cycled back 

into the soil.

The loss of nutrient capital and organic matter due 

to biomass harvesting is of particular concern to 

sustaining site productivity and carbon sequestration 

potential. While biomass harvesting includes more 

sources than just harvest residue from conventional 

harvest systems, the majority of research in the 

United States on nutrient removals from biomass 

harvesting has focused on the impact of whole-tree 

harvesting relative to conventional harvesting and 

the removal of small-diameter trees for silvicultural 

and fire protection purposes. Whole-tree harvest is 

usually defined as all woody biomass contained in 

standing trees above ground, where complete-tree 

harvest removes the stump and large root biomass, 

as well. More intensive biomass harvesting involves 

removing existing dead wood from the site. Logging 

residues, or the remainder of the standing tree after the 

conventionally merchantable bole is removed, contain 

a disproportionately high nutrient content relative to 

the bole. For example, a whole-tree harvesting study 

of six hardwood and five conifer stands showed the 

removal of about 23% more biomass than stemonly harvesting, but 49%, 40%, 38%, and 36% more 

nitrogen, phosphorus, potassium, and calcium (Mann 

et al., 1988). Similarly, whole-tree harvesting removes 

about 16% more biomass from Douglas-fir stands, but 

65%, 83%, 52%, and 169% more nitrogen, phosphorus, 

potassium, and calcium (Mann et al., 1988). Smalldiameter trees removed in thinning operations or in 

dedicated short- rotation woody crop systems also 

have a comparatively high nutrient capital due to 

higher proportion of high nutrient-concentration 

biomass (leaves or needles, branches, and bark). 

Thus, the nutrient removal is much greater in biomass 

harvesting systems than in conventional harvesting 

systems relative to the actual amount of biomass 

harvested. Therefore, it is important to manage the 

retention of portions of the biomass to ensure longterm productivity through leaving residues or time of 

harvest.ensure long-term productivity through leaving 

residues or time of harvest.

However, few long-term studies have followed the 

growth response of the next rotation following harvest 

to determine whether site productivity was affected. 

Johnson and others (2002) found that whole-tree 

harvesting had no effect on the 16-year growth of an 

oak-hickory forest compared to stem-only harvesting. 

Whole-tree harvesting did reduce the 16-year growth 

of a loblolly pine plantation in South Carolina, which 

was attributed to the loss of nitrogen and to physical 

property differences in soil; in stem-only harvested 

plots, the woody debris significantly improved physical 

attributes of soil (Johnson et al., 2002). Powers et al. 

(2005) summarized the findings from 26 installations 

of the USDA Forest Service Long-Term Soil 

Productivity (LTSP) study and found that complete 

aboveground organic matter harvest (including the 

forest floor) reduced the 10-year growth in aspen 

stands compared to bole-only harvest, but had no 

consistent effect for mixed conifers in California and 

Idaho or southern pine in Louisiana, Mississippi, and 

North Carolina. Scott and Dean (2006) showed that 7- 

to 10-year growth of loblolly pine was reduced by an 

average of 18% on 15 of 19 research blocks across six 

separate research studies in the Gulf Coastal Plain. Soil 

carbon sequestration is also rarely reduced substantially 

by biomass harvesting (Johnson and Curtis, 2001). 

These scattered results indicate that, in general, 

intensive harvesting does not universally reduce site 

productivity, but in some cases, it can cause substantial 

growth declines if not mitigated. Further research is 

ongoing at the more than 100 installations of the LTSP 

study (Powers et al., 2005), and as this study evolves, 

more information will be available for long-term 

growth responses and soil carbon sequestration across 

a variety of forest types and sites.

As noted by the few reports of long-term growth, 

intensive biomass removals will have no discernible 

effect across many sites. Numerous sites are well 

buffered with respect to nutrients, so that even repeated 

intensive removals over long periods may not induce 

nutrient deficiencies. Sites with low slope and little 
23

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

susceptibility to compaction do not require much 

biomass to mitigate erosion and compaction concerns. 

However, there are some regional-, soil-, and forestspecific origins. Some forests in the eastern United 

States are at a relatively high risk of calcium loss from 

harvest (Huntington, 2000). The loss is due to lowcalcium geologic parent materials, decades of acid 

precipitation that have leached much of the natural 

calcium capital from the soil, and (in the southeastern 

United States) the high degree of weathering. In 

southeastern pine forests, certain geologies are 

markedly low in phosphorus and routinely fertilized to 

overcome their natural deficiency and to avoid induced 

deficiency by harvest removals. Nitrogen is a limiting 

factor throughout the United States, with the exception 

of the Northeast. However, in dry or cold forests where 

nitrogen cycling is retarded due to climate, nitrogen 

losses in harvested materials may substantially reduce 

productivity by lowering decomposition and nitrogen 

mineralization rates. Continued research is needed to 

identify specific forest and soil types where biomass 

removal may exacerbate potential deficiencies, and 

mitigation strategies will need to be developed.

Fertilization is a common treatment that is used 

primarily to increase forest growth, but can also be 

used to mitigate nutrient removals from biomass 

harvesting. Application rates for important commercial 

species (e.g., loblolly pine and Douglas-fir) commonly 

range from 22–54 pounds per acre of phosphorus 

and 180–224 pounds per acre of nitrogen. Wood ash, 

created during wood combustion for energy, can be 

safely used to replace calcium and other basic cations 

removed through biomass harvesting (Pitman, 2006). 

Concerns related to the impact of forest fertilization on 

water quality have generally been unfounded (Binkley 

et al., 1999), even in intensively managed systems 

(McBroom et al., 2008) or when biosolids are applied 

(Pratt and Fox, 2009).

Based on the ecological- and productivity-related roles 

of dead woody debris and the fact that some timberland 

owners may not want to—or be able to—fertilize, in 

order to mitigate potential productivity loss, some 

level of woody material should be retained to protect 

these functions. Some of the material may be present 

in a stand prior to harvest, while some is created as 

logging residue or by density-induced natural mortality. 

Because dead wood is important in many complex 

functions, and the amount needed to perform these 

functions varies widely across climatic, geologic, 

edaphic, and vegetation gradients, a single retention 

percentage should not be used as an actual guideline. 

Rather, retention guidelines should be developed at 

state-to-local geographic scales, by forest type, and by 

harvesting intensity. Several states and the two largest 

certification programs in the United States (Sustainable 

Forestry Initiative® and Forest Stewardship Council) 

have released guidelines that address the productivity 

and ecological functions of dead wood (Evans and 

Perschel, 2009). Most of the guidelines were developed 

for general timberland conditions, with some additional 

restrictions for special areas, such as critical plant 

or animal habitat, shallow soils, or steep slopes. For 

example, Maine requires all coarse woody material 

that exists prior to harvest to be retained after harvest, 

and at least 20% of the logging residues with less 

than 3-inch diameters should be retained. Minnesota 

recommends that 20% of the logging residues be 

retained and scattered throughout the harvest tract. 

Wisconsin’s guidelines require 5 tons per acre of 

woody material to be retained, but the material can be 

derived from either logging slash or woody material 

present prior to harvest. Pennsylvania’s guidelines 

call for 15% to 30% of the harvestable biomass to 

be retained, while Missouri calls for 33% retention. 

Sensitive sites and soils are also protected. Minnesota 

suggests avoiding biomass harvesting in areas with 

threatened, endangered, or otherwise sensitive plant 

or animal habitats from within riparian management 

zones, on certain organic soils, and on shallow soils 

with aspen or hardwood cover types. In general, the 

literature and harvest guidelines indicate that retaining 

30% of logging residues on slopes less than 30% 

and 50% retention on steeper slopes is a reasonable 

and conservative estimate of the amount of material 

needed to maintain productivity, biodiversity, carbon 

sequestration, and prevent erosion and compaction.

For the United States, Janowiak and Webster (2010) 

offer a set of guiding principles for ensuring the 

sustainability of harvesting biomass for energy 

applications. These principles include increasing the 

extent of forest cover, including the afforestation 

of agricultural, abandoned, and degraded lands, as 
24

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

well as the establishment of plantations and shortrotation woody crops; adapting forest management to 

site conditions by balancing the benefits of biomass 

collection against ecological services provided (e.g., 

old-growth forests provide ecological services and 

habitat benefits that greatly exceed bioenergy benefits); 

using BMPs; retaining a portion of organic matter 

for soil productivity and deadwood for biodiversity; 

considering forest fertilization and wood ash recycling; 

and, where appropriate, using biomass collection 

as a tool for ecosystem restoration. When these 

principles are applied through state-based BMPs 

or biomass harvesting guidelines or certification, 

biomass harvesting can be sustainably practiced with 

reduced negative impacts on the environment, and 

harvestingcan be a much-needed tool for achieving 

forest health restoration objectives.

A summary of the operational sustainability criteria 

used to estimate primary residue supply curves is 

provided in Table 3.1.

Forest biomass 

resource Environmental sustainability Economic/technical

Logging residues, 

thinnings, and 

conventionally 

sourced wood.

Administratively reserved forestlands 

excluded. These are lands excluded from 

timber production by legislative statute 

and include wilderness and National 

Parks.

Inventoried roadless areas are excluded. 

These are USDA Forest Service lands 

identified as possibly qualifying for 

wilderness or other conservation 

protections. 

Logging residues result from conventional harvests; 

therefore, assume that there is road access to the biomass 

and no road building is required.

For the thinnings and conventionally sourced wood only, 

the FIA variable “distance to road” was used to determine 

road access. If over 0.5 miles, then the high cost excluded 

biomass because of lack of current road access.

Logging residues from 

after commercial timber 

harvesting.

Thirty percent of logging residue is left 

onsite for sustainability reasons. These 

residues include non-merchantable trees 

and tree components, as well as standing 

and dying trees.

Prices to roadside are assumed to be stumpage price plus 

chipping (no stumpage cost on federal land). Estimated 

prices were used to develop supply curves.

Integrated harvesting 

to produce commercial 

products and biomass 

from timberlands and 

other forestlands. 

Estimated biomass amounts are from 

simulated uneven-age treatments on 

overstocked stands where treatments are 

assumed to occur on a 30-year cycle

Retention was determined as a function 

of slope:

Slope is less than 40%, then 30% of 

residue is left onsite

Slope is greater than 40% to less than 

80%, then 40% of the residue left onsite

Slope is greater than 80%, then no 

residue is removed (no limbs or tops 

yarded)

Restricted to sites where stand density index is greater than 

30% of maximum by forest type 

Cable yarding sites (slope greater than 40%) are assumed 

inoperable if yarding distance exceeds 1300 feet 

Uneven-age management is practiced (selected trees are 

removed from all diameter classes) 

Biomass supply is from removal of (1) trees 1 to 5 inches in 

diameter at breast height (dbh) in the East and 1 to 7 inches 

dbh in the West and (2) tops and branches of larger trees 

Whole tree harvesting is assumed (trees are taken to 

roadside for processing) 

Costs to provide biomass from tops and branches include 

only stumpage and chipping (no stumpage cost on federal 

land) 

Prices to provide biomass from whole trees include costs for 

stumpage, harvest, and chipping (no stumpage on federal 

land) 

Table 3.1 Summary of Sustainability Assumptions Used in Developing Forest Residue Estimates 
25

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

3.1.2 Logging Residues and Thinnings

There are two major sources of residues from forest 

stands: (1) the limbs, tops, cull trees and cull tree 

components, and downed trees from harvesting 

operations (logging residues), and (2) the nonmerchantable components of stands that are thinned 

as part of fuel treatments and restoration harvests 

(thinnings). These two forest biomass resources only 

come from non-reserve forestland, which is land 

that is not removed administratively or designated as 

roadless21 (Table 3.1). These non-excluded resources 

either have existing roads, as in the case of logging 

residues, or they could be accessed from existing 

roads at an acceptable price. The largest source of 

some of the lowest-cost forest feedstocks is biomass 

removed along with sawlogs and pulpwood in 

integrated harvesting operations. This removes fuel 

that can contribute to fire risks. Integrated harvesting 

operations are assumed to take the form of removing 

whole trees to roadside, where tops and branches are 

removed and chipped for bioenergy feedstock (Figure 

3.4). Integrated operations would also remove small 

trees (less than 5 inches in diameter at breast height 

(dbh) in the East and 7 inches dbh in the West) to the 

roadside where they are also comminuted (Figure 3.4). 

In integrated operations, there is a certain fraction 

of logging residues left on the site intentionally for 

retention purposes (see Table 3.1). A minimum of 30% 

biomass was assumed to be retained on the site, and 

even more was assumed for steeper slopes.

Two separate methods—recovering logging residues 

behind conventional harvesting operations and 

simulated forest thinning with integrated harvesting 

operations—are used to estimate the quantity and 

roadside price of the available biomass (see Text 

Box 3.4). After making separate estimates of countylevel supply curves using these two methods, they 

are combined into a single, composite estimate for a 

county. This can be done by taking an average of the 

two supply curves (average of the two supply amounts 

at each supply price) or a percentage of each, such as 

50% logging residue and 50% forest thinnings, which 

is used in this analysis.

For each of the two estimates, roadside costs and 

stumpage22 prices are determined for increasing 

incremental amounts of supply. Roadside costs include 

the cost to cut and extract wood to roadside and the 

cost of chipping at roadside. These estimates were 

made using the Fuel Reduction Cost Simulator (FRCS) 

model (Dykstra et al., 2009). Stumpage prices (cost 

per ton for biomass in standing trees) are estimated as 

an increasing fraction of baseline pulpwood stumpage 

prices as the amount supplied increases. Regional 

pulpwood stumpage prices for 2007 are summarized 

in Table 3.2. The first step to estimate county-level 

supply curves is based on estimates of recent amounts 

of logging residue that are generated, and the second 

step is based on simulated silvicultural treatments on 

overstocked timberland that produce biomass, as well 

as pulpwood and sawlogs. 

Logging residue estimates. Logging residue estimates 

are available from the Timber Product Output (TPO) 

database (USDA Forest Service, 2007a). The TPO 

consists of a number of data variables that provide 

timber product harvested, logging residues, other 

removal residues, and wood and bark residues 

generated by primary forest product processing

Figure 3.4 Comminuting forest residue bundles 

(Courtesy of Han-Sup Han, Humboldt State University)

21 Roadless areas are defined as lands without constructed roads and have been delineated by government review.

22 By definition, stumpage is the value of standing trees (i.e., standing on the stump) uncut in the woods.
26

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

mills for each county. The TPO Tablemaker, a U.S. 

Forest Service online tool, was used to access the 

TPO data. [The Tablemaker is no longer available 

and has been replaced with other programs (U.S. 

Forest Service, 2011a).] The TPO database combines 

forest industry and other private ownership classes 

into an undifferentiated private class to comply with 

the Privacy Law. The undifferentiated private class 

was used in this analysis and will be available on 

the KDF. The logging residues are estimated using 

harvest utilization studies and represent the total 

volume left on the site. It is not economically feasible 

to actually recover all of the biomass, and due to 

sustainability reasons, at least 30% of the biomass 

is left onsite. Therefore, the estimated biomass was 

reduced by sustainability percentages shown in Table 

3.1. Using these reduction factors and cost curves, no 

further reductions were needed because of economic 

feasibility, which means that a recoverable factor 

The logging residue-based and simulated forest thinning-based estimates before sustainability and cost restrictions 

are shown below. Slightly more than 60% of these sources can be harvested once requirements for ensuring 

sustainability are met. All of the logging residue resources can be harvested at less than $40 per dry ton roadside 

and more than 90% can be harvested at less than $30 per dry ton roadside. At less than $40 per dry ton roadside, 

about 70% of the thinnings can be harvested. The higher costs for thinnings generally reflect the presence of smalldiameter trees, which incur harvesting and skidding costs in addition to stumpage and chipping.

TEXT BOX 3.4 | LOGGING RESIDUES AND THINNING ESTIMATES

Total resource Sustainability <$80 <$40 <$30 <$20

Supply cost at forest roadside

Million tons/year

90

80

70

60

50

40

30

20

10

0

Logging residues Forest thinnings

was not needed as used in the BTS (Perlack et al., 

2005). The non-recoverable fraction left onsite, which 

includes leaves, branches, and parts of the tree crown 

mass, provides nutrients and serves to maintain soil 

productivity.

An assumption in this analysis is that most logging 

residue is moved to roadside as part of a whole-tree 

harvest of merchantable wood, and the only costs will 

be for stumpage and chipping at roadside. In cases 

where cut-to-length systems are used, which means 

that residue is left in the stand where the trees are 

processed, the assumption is that the biomass will 

not be recovered (Figure 3.5) (see more complete 

explanation in thinning section). Chipping costs 

were determined by the FRCS model (Fight et al., 

2006) as modified and expanded to cover the U.S. 

North and South, as well as the West, by Dykstra 

and others (2009). Prices average about $13 per dry 
27

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Figure 3.5 Logging residues 

(Courtesy of Barry Wynsma, USDA Forest Service)

ton nationwide and are slightly higher in the West 

and slightly lower in the South due to differences 

in labor and fuel costs. Stumpage price is assumed 

to be zero for biomass from federal land because 

biomass removal is usually part of a fuels treatment or 

restoration activity. For privately owned timberland, 

stumpage price is assumed to begin at $4 per dry ton 

and increase to 90% of the pulpwood stumpage price 

when 100% of the available logging residue is used. 

The low entry price is based on a token payment in 

the likelihood that the biomass is only removed to 

meet other landowner objectives, such as reducing site 

preparation costs or fire risks. The higher prices are 

the result of demand increasing or supply decreasing 

to the point that biomass is almost competitive with 

pulpwood.

The supply curve based on logging residue estimates 

is shown in Figure 3.6 (thinning and composite supply 

curves shown in Figure 3.6 are discussed in subsequent 

sections). The logging residue supply curve is generally 

flat and shows 47 million dry tons per year potentially 

available at a roadside price of $40 per dry ton or 

less from all defined forestlands (Table 3.3 in Section 

3.7). There is a 9% decrease in available tons per year 

generally across all prices when the federal lands are 

removed per EISA definitions. All logging residues 

are available at this price. State supplies at $80 per 

dry ton per year are graphically summarized in Figure 

3.7. The largest supplies are where pulpwood and 

sawlog harvests are the greatest, namely the Southeast, 

Northwest, and Great Lakes. A more spatially explicit 

summary of logging residues supplies at $20 and $40 

per dry ton is shown on the maps in Figure 3.8. Table 

3.4 shows that at $60 per dry ton in 2030, about 50 

million dry tons are available. These estimates are 

derived using USDA Forest Service Resource Planning 

Act (RPA) projections of timber harvests from 

forestland by region and estimates of logging residue 

as a percentage of timber product removals (Haynes et 

al., 2007).

WEST NORTH

SOUTH

Delivered 

price

($/green ton)

Stumpage 

price

($/green ton)

Stumpage 

price

($/dry ton)

Hardwoods

North $32.00 $7.70 $15.40

South $28.80 $6.70 $13.30

Softwoods

North $33.60 $10.40 $20.70

South $29.00 $7.80 $15.70

West $40.30 $13.80 $27.60

Pulpwood Stumpage Prices by 

Region Table 3.2

Source: RISI, 2008; Fight et al., 2006; Dykstra et al., 2009

(Includes all types of ownerships)
28

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY Alabama Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas

Utah

Vermont

Virginia

Washington

West Virginia

Wisconsin

Wyoming

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

Million dry tons

Current availability Year 2030

Current and year 2030 state quantities of logging residue available 

annually at $80 per dry ton Figure 3.7

0 5 10 15 20 25 30 35 40 45 50

$250

$200

$150

$100

$50

$0

Million dry tons

$/dry ton

Logging residues Thinnings Composite operations

National supply curves for logging residues, thinnings, and composite 

(50% logging residues and 50% thinnings) from timberland Figure 3.6
29

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Spatial distribution of logging residues at $20 and $40 per dry ton 

(delivered to roadside) Figure 3.8

$20 per 

dry ton

$40 per 

dry ton
30

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Simulated forest thinning-based estimates. The 

second method used to estimate biomass supply by 

county is to simulate uneven age thinning operations 

on all non-reserved timberland in the Unites States 

using USDA Forest Service forest inventory and 

analysis (FIA) plots (Smith et al., 2009).23 The data 

were accessed from the publicly available Forest 

Inventory Database on February 3, 2010 (USDA Forest 

Service, 2010a;b). Because the database is dynamic 

(i.e., is updated as states report new data during the 

year), accessing the database after that date gives 

different results. The BTS only estimated the biomass 

from fuel reduction treatments on two specific classes 

of most overstocked stands that needed mechanical 

thinnings to reduce fire risk. The new method included 

all non-reserved forestlands, and if the stands were 

overstocked above certain densities, the stands were 

thinned regardless of the fire-risk classification 

(see Text Box 3.5). Decades of fire prevention and 

suppression efforts across the United States, especially 

in western areas, have led to overstocked stands and 

an accumulation of fuels that are increasing the risk of 

catastrophic fire. In the past, fire-adapted forests had 

relatively open canopies due to frequent low-intensity 

fires and harvestings intervals. Today, many stands 

have closed canopies and a buildup of high levels 

of small stems and biomass due to fire suppression 

and less harvesting. Highly dense forests are also 

stressed, which is compounded by more frequent and 

longer drought intervals. These conditions reduce the 

resistance to insects and diseases. 

These forests contain significant levels of carbon 

sequestered in the biomass of the dense stands. 

Conducting fuels treatment (i.e., reducing the 

biomass), can release the stored carbon. If using 

biomass for energy, there is a displacement of 

fossil carbon emissions with emissions from renewable 

feedstocks. Furthermore, the treated stands respond 

to the lower density, and the trees grow quicker 

than when stagnated, thus sequestering carbon. 

The USDA Forest Service Forest Inventory and Analysis 

Database (FIADB, version 4) as downloaded on February 

3, 2010, is used to develop logging residues, thinnings, 

and conventional wood biomass estimates (USDA Forest 

Service, 2010a). Logging and other removal residue data 

is obtained from the 2007 RPA Timber Products Output 

(TPO) database (USDA Forest Service, 2007a). These 

estimates include the small tree volumes (1–7 inch 

diameter at breast height (dbh) in the East and 1–9 inch 

dbh in the West) and the non-merchantable (limbs, tops, 

and unmerchantable bole) volumes of the merchantable 

trees greater than 7 or 9 inches dbh. [Note: the updated 

BTS uses 5-inch dbh limit in the East and 7 inches in the 

West, which are conservative estimates.] 

In August 2009, the Forest Service adopted a new 

method, the component ratio method (CRM), for 

calculating the non-merchantable volumes of the 

merchantable trees. In the original Billion-Ton Study,

these volumes were calculated using diameter-based, 

regional prediction equations of tree volumes and 

biomass in components of the tree. It became apparent 

in the analysis that the estimated biomass component 

of the merchantable trees is considerably less using the 

new database with the CRM method compared to the old 

method. It is reported that the biomass estimates in tons 

per acre are consistently lower using the CRM compared 

to the regional prediction equations (Heath et al., 2009).

The change in biomass methodology to CRM produces 

total U.S. tree biomass inventory estimates that are 

6% to 8% lower compared to estimates using the 

previous method. More importantly, there are significant 

reductions—up to 30%—for certain species, stand 

types, and locations. This change in method also 

decreased estimates of county-level biomass supply 

at given prices. The new methodology was used in 

this analysis. The reason to point out the use of the 

new methodology is to indicate that change lowers 

the estimated biomass available from thinnings and 

conventional harvest for those who may want to make 

comparisons to the original report.

TEXT BOX 3.5 | BIOMASS 

EQUATIONS IN FIA

23 The FIA program has been in continuous operation since 1928. It collects, analyzes, and reports information on the status and trends 

of America’s forests: how much forest exists, where it exists, who owns it, and how it is changing. The latest technologies are used to 

acquire a consistent core set of ecological data about forests through remote sensing and field measurements. The data in this report are 

summarized from over 100,000 permanent field plots in the United States. 

Note: The most recent inventories of the 48 states state that there are 300,900 plots of which 117,875 are forested.
31

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

24 SDI (Reineke, 1933) is a long established, science-based forest stocking guide for even-aged stands that can be adapted to uneven-aged 

stands (Long and Daniel, 1990) using data available from broad-scale inventories.

25 All the biomass wood is assumed to be residues or byproducts, lacking a higher value than energy wood, except for the conventionally 

sourced wood. Wood that would normally be used in higher value products (e.g., pulpwood, posts, and piling,) could be used for biofuels 

when prices for alternate uses are lower. Also, within the lower merchantable limits, small-diameter material can easily shift between 

conventional, commercial uses and biofuel feedstocks, depending on prices and other factors.

26 The original FRCS model was designed to simulate harvests in the Interior West. It was substantially revised for this study, including the 

development of new harvesting procedures designed to simulate harvests in the North and South and in the wetter areas of the West.

when prices for alternate uses are lower. Also, within the lower merchantable limits, small-diameter material can easily shift between 

conventional, commercial uses and biofuel feedstocks, depending on prices and other factors.

Hurteau and North (2009) reported that when including 

wildfire forecasts in a carbon emissions model, there 

were more potential greater emissions from untreated 

stands than treated stands. Their conclusion was that in 

wildfire-prone forests, tree-based carbon stocks were 

best protected by fuel treatments.

Thinning is used to reduce density, open up the stands, 

and improve resiliency to fire and pests. Uneven-aged 

thinning reduces catastrophic fire risks (Huggett et 

al., 2008) and provides other values as well, so it was 

used as a model treatment across all stands. In actual 

practice, the type of stand treatment is prescribed based 

on current conditions and desired future conditions.

Uneven-aged thinning removes trees across all age 

classes. This type of harvesting provides bioenergy 

feedstocks at the lowest cost because biomass is 

removed in combination with the removal of larger 

trees for pulpwood and sawlogs. Otherwise, harvest 

costs would be considerably more if fuel treatment 

operations were focused solely on smaller-sized trees. 

In addition, an uneven-aged treatment appears more 

likely to achieve fire-risk reductions (Skog et al., 

2006). Before simulations are conducted, FIA plots 

located in reserved and roadless areas were excluded 

and assumed unavailable for treatment.

The uneven-aged thinning simulation was done on all 

FIA plots where the plot stand density index (SDI) was 

greater than 30% of a maximum SDI for that given 

forest type (Shepperd, 2007).24 This simulates harvests 

to reduce fire hazard and to improve forest health 

on overstocked stands. Uneven- aged thinnings are 

simulated, and estimates are made of the amounts of 

biomass, pulpwood, and sawtimber that are removed.25

Beginning with a 1-inch dbh trees, a treatment 

successively removes fewer trees from each diameter 

class where the removals bring the SDI down to 30% 

of the identified maximum SDI value for that stand 

type. For the North and South, biomass removals 

include all wood from trees 1 to 5 inches dbh and tops 

and branches of trees greater than 5 inches dbh, except 

for wood left for retention purposes. For the West, 

biomass removals include all wood from harvested 

trees 1 to 7 inches dbh and tops and branches of trees 

greater than 7 inches dbh. It is assumed that all of the 

small-tree biomass can be extracted to roadside, but 

only 80% of the volume in tops and branches of larger 

trees will make it to roadside because of breakage. 

Again, a percentage of this material is retained onsite.

In estimating the cost of biomass from thinnings, it is 

assumed that:

• Biomass from federal lands have no stumpage 

costs

• Biomass from private lands range from $4 per dry 

ton to 90% of regional 2007 (circa 2006–2008) 

pulpwood stumpage prices

• Limbs, tops, and cull components of merchantable 

trees have a chipping cost (harvest cost, i.e., 

felling and transport to roadside, are borne by the 

merchantable bolewood) and stumpage cost

• Small, unmerchantable trees and dead trees have 

harvest, chipping, and stumpage costs.

Harvest costs are estimated by the FRCS model plus 

costs for chipping and stumpage (Fight et al., 2006; 

Dykstra et al., 2009).26 The FRCS estimates the cost 

of providing biomass at roadside by whichever is the 

least expensive of three alternative harvesting systems: 

ground-based, whole-tree harvesting with mechanized 

felling; ground-based, whole-tree harvesting with 

manual felling; or cable-yarding of whole trees that 

have been manually felled. Cable-yarding is used in 

the model only when the average ground slope exceeds 

40%.
32

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

To simplify the analysis, it was assumed that all 

the thinnings would be uneven-aged management 

treatments with whole-tree harvesting. This 

combination was determined to be the least-cost 

means to harvest biomass from small trees, branches, 

and tops. Currently, some stands are being thinned by 

cut-to-length systems, where the limbs and tops are 

processed and left in the stand. It is expected that the 

use of such systems will continue, if not increase, in 

the future, and biomass will be recovered in a second 

pass. This approach could be costly. The assumption 

of using whole-tree logging, either ground based or 

cable, is more indicative of how biomass will probably 

be recovered as part of thinning applications over 

the next 20 years. Because there are very few cut-tolength systems compared to whole-tree systems, the 

assumption in the analysis is that all thinning is done 

by whole-tree systems.

In the 2005 BTS, the fuel treatments were assumed 

to occur where there is road access; reduction factors 

were used to exclude land without current road 

access. In this update, the FRCS uses an FIA variable, 

“distance to road,” to estimate harvest cost. Although 

the biomass that is not near an existing road is not 

excluded as in the earlier assessment, the biomass is 

prohibitively expensive—well over $200 per dry ton. 

(See Text Box 3.6 for more information on federal 

versus private land estimates.) These high costs occur 

when biomass is harvested with cable systems over 

1,300 feet from an established road and ground-based 

systems between 0.5 and 1.0 mile from a road.

Stumpage price is developed using the following 

assumptions: (1) price is zero for biomass from federal 

land because removal is usually part of commercial 

sales or treatment contracts, and (2) biomass from 

private lands begins at a low of $4 per dry ton and 

increases linearly up to a maximum that was set to 

be 90% of the derived pulpwood stumpage price for 

private land (Table 3.2).

Because the simulated thinnings also include the 

removal of timber for merchantable products, there is 

a limit as to how much can be harvested depending 

It is important to note that the “federal land” biomass 

estimates are not obtained by subtracting “without 

federal land” from “all land.” It is only an approximation 

because the two values are simulated using composite 

plots that differ due to costs and sawtimber/pulpwood or 

sustainable allowable cut cap limits. Biomass estimates 

for each county were generated based on the lowest cost 

at the given supply curve costs without either exceeding 

the mill capacity or the net growth allowance for that 

county.

Cost differences were primarily dependent on distance 

from road and stand structure (more merchantable trees 

reduced costs). In many cases, the biomass from federal 

land was more costly because of greater distance to 

road and because of the high volume of small-diameter 

trees—this resulted in generally more wood from nonfederal land. For example, 72% of the biomass less than 

one-half mile from the road was on private land.

Although there was no analysis completed, it is 

speculated that the federal lands have less cumulative 

biomass because of the sawtimber cap. Counties with 

large amounts of federal land tend to have fewer mills 

and conversion facilities. There may be high amounts of 

biomass that could not be harvested because there were 

no markets for the conventional products.

TEXT BOX 3.6 | FEDERAL LAND ESTIMATES

on mill capacities and markets for the products. This 

thinning removal limit is assumed to be met when 

the simulated removal of sawlogs plus pulpwood 

reaches the 200627 level of total sawlog and pulpwood 

harvests. This state-level restriction is to ensure that the 

estimated biomass supply from integrated operations 

can be supported by the recent (2006) level of sawlog 

and pulpwood harvest in each state. The impact of this 

assumption can be significant, reducing the amount of 

available biomass by up to 97%28 in some states. 

In preparing the overall estimate of biomass provided 

from integrated harvesting, it was assumed that the 

simulated thinnings would provide half of the harvest 

needed to meet sawlog and pulpwood needs. The other 

half of harvest would be done in a conventional way 

27 Harvest data is from 2006 and is taken from Table C5B (USDA Forest Service 2007a).

28 The U.S. average percentage of biomass from thinnings not available because of not having roundwood markets is about 35%, calculated 

at $60 per dry ton. Regional percentages are South – 5%; Pacific Coast – 18%; North – 48%; Interior West – 49%. States range from 

0–97% of the biomass from thinnings not being counted in the assessment because there was not a market in the state for the associated 

roundwood.
33

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

29 The Biomass Treatment Evaluator—an SAS® (Statistical Analysis Software) program prepared by Patricia Lebow, USDA Forest Service 

Forest Products Laboratory—was used to prepare county-level supply curves. Alabama Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana

Nebraska

Nevada

New Hampshire

New Jersey

New Mexico

New York

North Carolina

North Dakota

Ohio

Oklahoma

Oregon

Pennsylvania

Rhode Island

South Carolina

South Dakota

Tennessee

Texas

Utah

Vermont

Virginia

Washington

West Virginia

Wisconsin

Wyoming

1.8

2.0

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0

Million dry tons

<$20/dry ton <$40/dry ton <$200/dry ton

State quantities per year of simulated forest thinnings at $20, $40, and 

$100 per dry ton (roadside) Figure 3.9

and generate logging residue, a portion of which can 

be removed for bioenergy. There is also the potential 

that the markets for sawlogs and pulpwood will 

expand as the current 2005 RPA Timber Assessment 

reflects (Haynes et al., 2007). The amount of estimated 

biomass supply from integrated harvesting (half 

from conventional harvesting, half from thinning 

simulations) is increased over time by the rate of 

increase in projected sawlog plus pulpwood harvest 

from the 2005 RPA Timber Assessment. A note about 

special situations of available biomass is provided in 

Text Box 3.7.

The Biomass Treatment Evaluator was used to estimate 

county-level supply curves for biomass and industrial 

roundwood removals on FIA plots by assigning 

stumpage prices and harvest and chipping costs using 

the FRCS model.29 Finally, simulated amounts of 

biomass supply are assumed to be harvested over a 

30-year period. This is the same period assumed for 

thinnings estimates provided in the 2005 BTS report.

The national supply curve for simulated forest residue 

thinnings on timberland is shown in Figure 3.6. The 

total simulated quantity is about 37 million dry tons 

per year at a roadside price of $100 per dry ton or 

less (Table 3.3). About 24 million dry tons annually 

are available at a roadside price of $40 per dry ton or 

less; at $60 per dry ton, about 32 million dry tons are 

available. Table 3.4 shows that there are no differences 

over the next 20 years in biomass availability because 

the thinnings are averaged over 30 years. State 

quantities are shown in Figure 3.9 at three different 

roadside costs, with more spatial detail provided in 

Figure 3.10.

In the 2005 BTS, the fuel treatments were assumed 

to occur where there is road access; reduction factors 

were used to remove land without current road 

access. In this update, the FRCS uses an FIA variable, 

“distance to road,” to estimate harvest cost. Although 

the biomass that is not near an existing road is not 

excluded as in the earlier assessment, the biomass is 

prohibitively expensive—well over $200 per dry ton. 

These high costs occur when biomass is harvested with 

cable systems over 1,300 feet from an established road 

and ground-based systems between 0.5 and 1.0 mile 

from a road.

Stumpage price is developed using the following 

assumptions: (1) price is zero for biomass from federal 

land since part of commercial sales or treatment 

contracts, and (2) biomass from private lands begins 

at a low of $4 per dry ton and increases linearly up to 

a maximum which was set to be 90% of the derived 

pulpwood stumpage price for private land (Table 3.2).
34

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

A potential feedstock for energy is the dead and dying trees associated with mortality from insects, disease, fire, wind, and 

other disturbances. In any particular year or period of years, there could be considerable volumes “available” as biomass 

for energy. A significant issue associated with this feedstock is the inconsistency of the annualized volumes within a 

designated landscape over a long term and high costs associated with the recovery and utilization of such biomass. There 

is considerable variation in acres affected annually, especially from pests (Figure 1), and the severity of the damage. In 

2008, nearly 9.0 million acres of mortality was caused by insects and disease nationally, a 2.2-million-acre increase from 

2007, when 6.8 million acres of mortality were reported (USDA Forest Service, 2009). 

However, there is growing concern about the increasing insect epidemics in the western United States and their transition 

to other areas of the country. For example, in the reported 2008 insect and disease mortality, nearly 69% of the mortality 

was caused by just the mountain pine beetle. The mountain pine beetle, Dendroctonus ponderosae, is a native species 

currently experiencing large-scale outbreaks in western North American pine forests—from Baja California in Mexico 

to central British Columbia in Canada. It affects primarily lodgepole pine, but also ponderosa and other pines (Figure 2). 

The beetles have killed more than 2 million acres of lodgepole pines across Colorado and southern Wyoming alone. Cold 

winters, which Colorado has not seen for years, are needed to kill the larvae and wetter summers are needed to help the 

trees resist the pests.

Over the past several years, widespread outbreaks of native bark beetles have occurred across the western United States, 

from pinyon woodlands to spruce-fir forests. The severity and distribution of the recent outbreaks is more than what 

can be inferred from historical records. The changing climate has given pests the opportunity to invade what has been 

inaccessible forest habitat (Logan, 2007). 

With the known increase in widespread tree deaths from insect epidemics, the issue is whether there should be an 

additional analysis of the potential wood supply from these epidemics. It was decided that the use of the FIA database 

and the methodology to address thinnings and logging residues sufficiently included the dead and dying trees. The FIA 

delineates recently dead and long-standing dead trees on all plots. The current western data averages the number of 

mortality trees over a 5-year period (Thompson, 2009). A real annual number will not be available until all inventory panels 

(percentage of all state plots) are completed over 10 years for each state. The “annualized” mortality in the FIA database 

was thought to be a better estimate of the mortality than an additional analysis, which would be subject to assumptions 

in both severity and distribution and would have high variability. Because mortality is already incorporated into the 

assessments using the FIA database, no additional analysis was needed.

TEXT BOX 3.7 | DEAD AND DYING TREES

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

14.0

12.0

10.0

8.0

6.0

4.0

2.0

0.0

Millions of Acres

Survey of insect and disease 

mortality (Courtesy of USDA 

Forest Service).

Pine beetle infestation in 

Colorado, 2007 (Courtesy of 

USDA Forest Service).

Figure 1 Figure 2
35

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Spatial distribution of simulated forest residue thinnings at $30 and 

$60 per dry ton (roadside) Figure 3.10

(Courtesy of ORNL)

$30 per 

dry ton

$60 per 

dry ton
36

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Composite integrated operations supply estimates. 

As explained in an earlier section, the logging residue 

estimates are based on the continuation of current 

conventional harvesting practices (i.e., merchantable 

stand and tree components are removed and the 

residues are left onsite). When estimating both logging 

residues and simulating integrated thinnings, there will 

be “double counting of biomass” because the TPO 

projections used to estimate logging residues do not 

take into account any reductions in logging residues 

over time, as more stands are harvested using integrated 

systems. The conceptual transition from leaving the 

biomass as logging slash to removing it when the 

merchantable timber is harvested is likely to occur in 

response to the development of biomass markets. As 

it is difficult to model the transition, an assumption 

had to be made to avoid counting the biomass as both 

logging residues and integrated thinning biomass. A 

conservative estimate is 50% of the logging residue 

supply estimates and 50% of the thinning supply 

estimates, which means that over the time of the 

projection, about half will come from the recovery 

of logging residues and half from thinnings. The 

composite operations supply curve is shown in Figure 

3.6 and Figure 3.11. The curve is generally similar to 

the logging residue supply, owing to the assumed 50:50 

ratio of logging residue to simulated forest residue 

thinnings. Almost 36 million dry tons per year are 

available at a roadside price of $40 per dry ton or less 

(Table 3.3); at $60 per dry ton, the annual potential 

volume is about 40 million dry tons. When federal land 

is removed, the amount is reduced by about 5 million 

tons per year. About 41 million dry tons are available 

in 2030 at $60 per dry ton from all lands. The residues 

from integrated operations by state are shown in Figure 

3.12 at an example price of $80 per dry ton.

0 5 10 15 20 25 30 35 40 45 50

$250

$200

$150

$100

$50

$0

$/dry ton

All timberland Without federal land

Composite operations (50:50) on timberland

with and without federal land

Figure 3.11 National supply curves for integrated harvesting operations 
37

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Alabama

Arizona

Arkansas

California

Colorado

Connecticut

Delaware

Florida

Georgia

Idaho

Illinois

Indiana

Iowa

Kansas

Kentucky

Louisiana

Maine

Maryland

Massachusetts

Michigan

Minnesota

Mississippi

Missouri

Montana

Nebraska

Nevada

New Hampshire

New Jersey

New Mexico

New York

North Carolina

North Dakota

Ohio

Oklahoma

Oregon

Pennsylvania

Rhode Island

South Carolina

South Dakota

Tennessee

Texas

Utah

Vermont

Virginia

Washington

West Virginia

Wisconsin

Wyoming

3.0

3.5

2.5

2.0

1.5

1.0

0.5

0

Million dry tons

Current 2030

Current and year 2030 state integrated operations quantities per year 

at $80 per dry ton or less at roadside Figure 3.12

3.2 Other Removal Residues

The conversion of timberland to non-forest land uses 

(cropland, pasture, roads, urban settlements, etc.) 

and precommercial thinning operations generates a 

relatively significant amount of forest residue biomass. 

These “other removals,” especially from land-clearing 

operations, usually produce different forms of residues 

and are not generally as feasible or as economical to 

recover. It is expected that only half of the residues 

from other removals can be recovered.

Amounts of other forest removals, by county, are 

obtained from the TPO database for 2007 (USDA 

Forest Service, 2007a). The 2005 BTS report assumed 

that 50% of the TPO residue estimate is recoverable 

and available. The original estimate was based on 

discussion with experts concerning the level of 

difficulty of recovering this feedstock. Specific 

characteristics of this feedstock, small land areas, 

and trees pushed up and piled, trees cut into small 

pieces, etc., make it difficult to recover them fully. The 

50% recoverable assumption is used in this update 

as well. There is little price data available for these 

types of feedstocks. Assumptions are made based on 

the expertise of the contributing authors concerning 

recovery and transport costs and market prices to 

derive the stumpage values. Specifically, one-third is 

assumed to be available at $20 per dry ton (roadside) 

and the remainder at $30 per dry ton at roadside. So 

at $60 per dry ton or less, about 12.4 million dry tons 

are available (see Table 3.3). Future estimates of other 

removal residue are based on RPA projections of 

timberland area. Through 2030, total timberland area 

is projected to decline by about 6 million acres, which 

could mean that there could be more “other removals.” 

Table 3.4 does not show an increase in recovery of 

this biomass and keeps potential tons available at 12.6 

million per year. Figure 3.13 shows the availability 

across the United States for residues from other 

removals.
38

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Figure 3.13 Spatial availability of other removal residues at $40 per dry ton (delivered to roadside)

3.3 Forest Residue Thinnings 

on Other Forestlands

Other forestlands are defined as incapable of producing 

at least 20 cubic feet per acre per year of industrial 

wood under natural conditions because of a variety of 

adverse site conditions, ranging from poor soils, lack of 

rainfall, and high elevation. Many of these woodlands 

(low-stature or sparse forests) are in the western states 

and are overstocked, especially with stands of pinyon 

pine and juniper. As with the fuel reduction thinnings 

on timberland, removal of the excess biomass could 

greatly reduce catastrophic fire hazards. FIA data 

(USDA Forest Service, 2010b: accessed on February 

3, 2010) was used to identify overstocked western 

woodlands. Similar assumptions the 2005 BTS report 

were used for the update. In Table 3.3, the total residue 

biomass from thinning other forestlands was estimated 

at 3.2 million dry tons at a price of $60 per dry ton 

(none are expected to be available below this price 

because of the high cost of thinning other forestlands). 

Above $80 per dry ton, 6.4 million dry tons annually 

becomes available for all lands. When federal 

forestlands are removed, only 3.6 million dry tons are 

available, which is a 50% reduction. By definition, 

these lands do not produce commercial-sized pulpwood 

or sawlogs, so the cost of removing the thinnings is 

borne fully by the biomass harvesting operation. An 

assumption used in the analysis was that about 50% 

of the biomass could be removed at a price of $60 per 

dry ton and the remainder at a price of $70 per dry ton. 

Again, these assumptions are the best estimates by the 

contributing authors with knowledge in these types of 

systems. The estimates are considered conservative as 

they represent the high end of thinning costs because 

no higher-valued wood is removed with the biomass.

(Courtesy of ORNL)

$40 per 

dry ton
39

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

3.4 Fuelwood, Mill Residues, 

and Pulping Liquors

3.4.1 Fuelwood

All currently used fuelwood is shown in Table 2.1 

and is estimated to be 38 million dry tons per year. 

The quantity of fuelwood used for residential and 

commercial space heating applications, as well as 

feedstock for dedicated wood-fired facilities and 

co-firing applications, is projected to increase to 106 

million dry tons per year by 2030.

3.4.2 Primary and Secondary 

Mill Residues

Amounts of wood and bark residue from milling 

operations (by county) are obtained from the TPO 

database for 2007 (USDA Forest Service, 2007a). For 

the baseline case, it is assumed that only unused mill 

residues are available (see the discussion in Chapter 

2 concerning “used” primary mill residue). Neither 

the Forest Service nor any other federal agency 

systematically collects data on secondary mill residue. 

One of the few estimates of the amount of secondary 

mill residue available is provided by Rooney (1998) 

and subsequently revised by Fehrs (1999). Fehrs 

estimates that about 15.6 million dry tons is generated 

annually, with about 40% of this potentially available 

and recoverable. The remaining fraction is used to 

make higher-valued products, is used onsite to meet 

some energy needs (such as heat for drying operations), 

or is not available for other reasons. Table 2.1 provides 

projected consumption of currently used primary and 

secondary mill residue. Currently, there are about 32 

million dry tons being used, mostly for energy. It is 

estimated that by 2030, 42 million dry tons will be 

consumed.

For the unused remaining mill residue, it is assumed 

that these residues can be purchased at the mill for $20 

per dry ton or less, which is comparable to the disposal 

cost if there are no markets available. Delivered prices 

could be much higher, especially for secondary mill 

residue where facilities are small, dispersed, and 

operate seasonally (Figure 3.14). Table 3.3 shows 

that there are 1.3 million dry tons of primary mill 

residues and 6.1 million dry tons of secondary mill 

residues annually at this mill price. It is assumed that 

any residue associated with increased future demand 

for primary and secondary wood products is offset by 

greater mill efficiencies and a continued increase in the 

use of this material for byproducts. At a price above 

$60 per dry ton, the total available used and unused 

mill residue is about 40 million dry tons. There are no 

scenarios beyond the baseline. 

3.4.3 Pulping Liquors

As explained in Chapter 2, the combustible chemical 

byproducts, such as black liquor from pulping 

facilities, are currently used for energy production and 

are not counted as an additional feedstock resource. 

The available amount is 45 million dry tons, with 

projections to 58 million dry tons in 2030.

Figure 3.14 Conversion facility 

(Courtesy of ORNL)
40

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

3.5 Urban Wood Wastes

The two major sources of urban wood residues are the 

woody components of MSW and C&D waste wood. 

MSW source consists of a variety of items, ranging 

from organic food scraps to discarded furniture, 

packaging materials, textiles, batteries, appliances, 

and other materials. In 2007, 254 million tons of 

MSW were generated (EPA, 2008). About 54% of the 

total quantity generated was discarded in municipal 

landfills. The remainder was either recycled, made into 

compost, or combusted for energy recovery. Containers 

and packaging are the single largest component of 

MSW, totaling some 78 million tons, or 31%, of the 

total. Durable goods are the second largest portion, 

accounting for 25% of total MSW generated. Yard 

trimmings are the third largest portion and account for 

about 33 million tons, or 13%, of the total.

The wood component of containers and packaging 

and durable goods (e.g., lumber scraps and discarded 

furniture) is slightly more than 14 million tons (EPA, 

2008). According to Falk and McKeever (2004), 

about 10% of this material is recycled and 22% 

is combusted for energy recovery. The remaining 

material is discarded and land filled. About one-third 

of this discarded material is unacceptable for recovery 

because of contamination, commingling with other 

wastes, or other reasons, such as size and distribution 

of the material (McKeever, 2004). The remainder that 

is potentially available for bioenergy totals about 5.7 

million dry tons annually.

Yard and tree trimmings are the other woody 

component of MSW. Currently, about 32 million tons 

are generated annually, with nearly 21 million tons of 

this amount recovered (EPA, 2008). In this update,an 

additional 4.3 million dry tons of wood is assumed 

recoverable and available for bioenergy applications 

after accounting for quantities that are likely to be 

composted, combusted, recycled, or contaminated and 

unavailable. The fractions composted, combusted, 

and contaminated are based on technical coefficients 

developed by McKeever (2004).

The other principal source of urban wood residue is 

C&D debris. C&D wood waste is generated during 

the construction of new buildings and structures, 

the repair and remodeling of existing buildings and 

structures, and the demolition of existing buildings 

and structures (McKeever, 2004). These materials are 

considered separately from MSW because they come 

from many different sources. These debris materials are 

correlated with economic activity (e.g., housing starts), 

population, demolition activity, and the extent of 

recycling and reuse programs. The updated estimates 

of C&D debris wastes total about 21.7 million dry tons. 

About 9.4 million dry tons are construction debris, 

and 12.2 million dry tons are demolition debris. These 

estimates are based on technical coefficients developed 

by McKeever (2004). They are slightly higher than the 

2005 BTS estimates because of changes in population 

and economic activity.

MSW wood waste along with C&D debris together 

sum to nearly 32 million dry tons per year as 

potential energy feedstocks. As noted by McKeever 

(1998), many factors affect the availability of urban 

wood residues, such as size and condition of the 

material, extent of commingling with other materials; 

contamination; location and concentration; and costs 

associated with acquisition, transport, and processing. 

A map of urban wood wastes availability is shown in 

Figure 3.15.

In the previous chapter (Table 2.1), the currently used 

MSW wood was estimated at 14 million dry tons 

annually and projected to increase to 20 million dry 

tons per year by 2030. In this chapter, the unused MSW 

wood and yard trimming wastes total 10 million dry 

tons; and, the unused C&D debris wood could provide 

an additional 21.7 million dry tons. Future quantities 

of unused urban wood wastes (MSW and C&D 

sources) will no doubt rise as population increases; 

however, the increase will likely be less owing to 

ongoing waste recovery efforts and higher landfill 

disposal costs. For construction waste, it is likely that 

higher fractions will be recycled and reused, and there 

will be greater use of engineered lumber, which will 

reduce dimensional lumber use and also make less 

waste available. For demolition wastes, improved 

recycling and reuse efforts should lead to increases 
41

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

in the number of buildings deconstructed as opposed 

to demolished, which will tend to lower quantities of 

waste wood available for bioenergy. For these reasons, 

future quantities of urban wood wastes are assumed to 

increase at one-half of the rate of population growth.

Table 3.3 shows the supply schedule for urban wood 

wastes by MSW and C&D categories. As noted, the 

total potential resource is estimated at 10 and 21.7 

million dry tons for MSW wood wastes and C&D 

wood waste at prices greater than $60 per dry ton, 

respectively. As explained by Walsh (2006), the 

quantity of urban wood wastes available at given 

prices depends on many factors. Chief factors include 

whether the materials are collected as mixed wastes or 

are source separated and the prevailing landfill tipping 

fees (i.e., the levelized costs of operating a landfill). 

Prices to acquire these materials could be very low if 

collected as mixed wastes and where landfill tipping 

fees (avoided costs) are high. In this update, the prices 

to acquire urban wood wastes are based on the results 

of Walsh (2006). The report assumes that about 75% 

of the MSW wood waste can be acquired for $20 per 

dry ton or less, 85% at $30 per dry ton or less, and 

90% at $40 per dry ton or less. All of the identified 

MSW wood is assumed to be available at $60 per dry 

ton or less. For C&D wood wastes, it is assumed that 

20%, 50%, 65%, and 100% are available at $20, $30, 

$40, and $60 per dry ton, respectively. In total, MSW 

wood is about 24 million dry tons at $60 per dry ton 

or less. This quantity includes the 14 million dry tons 

currently used (Chapter 2) and 10 million dry tons of 

unused MSW wood wastes. In addition, there are 21.7 

million dry tons of C&D wood wastes for a total of 

45 million dry tons. Table 3.3 shows the urban wood 

waste supplies and Table 3.4 shows current and future 

supplies at selected prices and future years.

Spatial availability of urban wood waste (municipal solid waste and 

construction and demolition wood residues) Figure 3.15

(Courtesy of ORNL)
42

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

3.6 Conventionally Sourced 

Wood – Pulpwood-Sized 

Roundwood

The 2005 BTS, as well as most of this update, only 

considers non-merchantable and waste woody 

resources. A final resource added to the update is 

conventionally sourced wood, which is wood that 

has a commercial value for other uses but is used as 

an energy feedstock instead because of competitive 

market conditions. In reality, only the pulpwood-sized 

roundwood would be used for biomass and probably 

just the smaller diameter pulpwood-sized trees.

If pulpwood-sized material is used as biomass for 

bioenergy, it will most likely be obtained through 

two approaches: (1) from “additional harvests” of 

pulpwood-sized trees and biomass together in thinning 

operations that are in addition to the previously 

discussed thinnings and (2) from a shift of wood 

being cut for pulpwood from current uses into uses 

for bioenergy (i.e., “pulpwood supply”). Both are 

referred to as conventionally sourced wood because 

the pulpwood-sized trees are usually harvested for 

conventional products, such as paper and panels. 

To ensure sustainability in the additional harvests, 

pulpwood harvests were restricted to only removing the 

annual growth, which means, not reducing inventory 

(using the 2006 harvest levels from Smith et al. (2009). 

When using pulpwood to supply bioenergy, the shift 

from pulpwood to bioenergy was restricted to 20% of 

the 2006 pulpwood harvest because of the underlying 

assumptions in the analyses. The assumptions are 

explained in the following sections.

3.6.1 Use of Pulpwood Stumpage 

Supply and Stumpage Demand Curves

To estimate supply from additional harvests, it 

is assumed that there will be additional thinning 

operations that are separate from integrated harvesting 

operations that take pulpwood-sized trees and 

associated biomass (tops and branches) in a given 

region. These additional thinning operations, in 

response to increasing demand for wood for bioenergy, 

move up the existing pulpwood stumpage supply 

curve (see Figure 3.16) for each state and increase 

the marginal stumpage price (Q2 to Q3 and P1 to 

P2). As the stumpage price increases, an amount of 

pulpwood previously demanded and used is diverted 

from integrated harvesting operations to bioenergy use. 

This corresponds to an amount obtained by shifting 

stumpage price upward on the pulpwood demand curve 

(P1 to P2 and Q2 to Q1). The simplifying assumption 

for the time period covered by the supply estimate is 

that there is little shift in the pulpwood supply curve 

or in the pulpwood demand curve for pulp or panel 

production (see Text Box 3.8). In reality, supply 

curves will shift with changes in the amount and age 

composition of timber inventory and technology. Also 

the demand curve will shift with a number of drivers, 

including the level of Gross Domestic Product and 

strength of the dollar relative to other currencies, which 

will influence demand for pulp, paper, and composite 

panel exports.

S

D

P2

P1

Q1 Q2 Q3

Theoretical pulpwood supply 

model for biomass Figure 3.16 Price ($/dt)

P – Price D – Demand

S – Supply Q – Quantity

(Courtesy of ORNL)
43

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

The conventionally sourced supply curve was developed holding the supply function constant over time, which means that 

supply does not change in response to changing inventory, changes in pulpwood demand for pulp and panels, or change 

in product imports. This approach was done for simplicity and convenience, recognizing the lack of a sufficient model to 

project future supply changes. Future supply of pulpwood for bioenergy will be influenced by the outward shift of pulpwood 

supply curves (more wood becomes available at a given cost) in each region and by shifts in demand curves (outward shift 

would mean an increase in demand amount for a given price). 

The outward shift in pulpwood supply curves in each region will be influenced in part by increases in available inventory of 

pulpwood-sized trees. The 2005 RPA Timber Assessment projects increases in some regions and decreases in other regions 

for pole timber-sapling acres and young sawtimber acres on timberlands privately owned (Haynes et al., 2007, Table 39). 

The most notable increase between 2006 and 2020 is for softwood poletimber in the North (13%) and young softwood 

sawtimber in the West (20%). The U.S. average change in private pole timber acres between 2006 and 2020 is minus 5% 

and for young sawtimber acres, plus 1%. The changes in acres through 2030 are minus 18% for pole timber and minus 2% 

for young sawtimber. Volume of timber could be increasing more than the change in acres coming into the timber size class 

because of a higher density of timber. The total inventory of sawtimber and non-sawtimber for the North, South, and West 

is projected to increase from 2006 to 2020 by 10%–12% and 15%–19% by 2030. These shifts in acres and inventory of 

standing timber would tend to shift pulpwood supply curves outward by 2030 in major regions by amounts on the order of 

20%. Shifts could be larger in subregions.

The pulpwood demand curves, demand for pulp and panels, in each region will be shifted outward with increases in 

economic activity that demands paper (e.g., office use, shipping) and composite panel products (e.g., buildings). These 

outward shifts, shifts that increase demand for traditional products at a given price, will decrease biomass supply 

available for bioenergy and tend to offset supply increases due to outward shifts in the pulpwood supply curves. 

Alternately, if pulpwood demand decreases, or more pulpwood, pulp, paper, or composite panels are imported, then more 

of the pulpwood supply at a given price will be available for bioenergy. 

Projections from the 2005 RPA Timber Assessment (Haynes et al., 2007, Table 11) indicate that hardwood pulpwood 

supply curves for the South would be shifting outward more rapidly than outward shifts in demand curves as evidenced by 

decreasing pulpwood prices through 2020, but by 2030, the outward supply shift would slow relative to outward shifting 

demand, and price would increase to the 2006 level. This suggests economic availability of hardwood pulpwood in the 

South by 2030 could be similar to 2006 if these projections are approximately correct. Projections suggest softwood 

pulpwood supply curves’ outward shift would lag outward shifts in demand through 2020 as evidenced by the increasing 

pulpwood price by 2020; then, supply shifts would exceed demand shifts as indicated by the decreasing price through 2030 

when price may be lower than the 2006 level. 

With the current economic downturn of pulpwood demand levels, there may be less demand than projected in the 2005 

RPA Assessment through 2020 or 2030. In this case, pulpwood-sized material needed for pulp, paper and panels would be 

less than projected. Then, more wood would be available for bioenergy, which would result in more conventionally sourced 

wood going to bioenergy that could match or exceed a 20% increase (for a given price) in response to a 20% increase in 

timber inventory. 

It should be pointed out that the 2005 RPA Timber Assessment was developed without expectations of an economic 

downturn and notably expanding bioenergy markets. A better analysis of these dynamics will be forthcoming in the 2011 

RPA Forest Resources Assessment. The updated BTS analysis likely indicates a conservative estimate of pulpwood supply 

compared to supply in the future.

TEXT BOX 3.8 | ESTIMATING FUTURE SUPPLIES OF PULPWOOD FOR BIOENERGY
44

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Estimating pulpwood supply from additional 

harvest. The initial step (see Figure 3.16) to estimating 

county-level pulpwood supply curves from additional 

thinning operations is to specify a new higher regionallevel stumpage price; for example, 10% higher than 

the base price (P1 to P2) and note the quantity obtained 

will move up the supply curve (Q2 to Q3). Next, the 

regional-level quantity of pulpwood and biomass is 

allocated to counties based on lowest harvest and 

transport costs to roadside. Each county quantity is 

assigned a roadside price equal to harvest cost plus the 

state-level stumpage price. The process is repeated for 

successive increases in the regional-level stumpage 

price to form county-level supply curves.

The pulpwood harvest prices are estimated by first 

simulating thinnings on higher-density (higher SDI) 

FIA plots using diameter-limit aged silvicultural 

prescriptions that gradually remove diameter classes 

until the SDI target is met. The thinnings only remove 

pulpwood-sized and smaller trees, where pulpwoodsized trees are defined as trees 5–7 inches dbh in the 

North and South, and 5–9 inches dbh in the West. 

The FRCS model is used to estimate harvest costs to 

remove pulpwood-sized trees plus biomass. When 

allocating regional pulpwood supply amount (at a 

given regional stumpage price) to the county level, the 

amount is allocated to counties using quantities and 

harvest costs where harvest costs are the lowest. As 

more pulpwood is supplied at higher regional stumpage 

prices, it is allocated to counties where harvest costs 

are higher.

A cornerstone of this method is a set of estimates for 

elasticity of pulpwood supply quantity and demand 

quantity with respect to changes in pulpwood stumpage 

price (obtained from a review of literature). The 

elasticity estimates from the literature are made using 

time series data where quantity and price vary over

a certain range and use econometric equation forms, 

which limit their use and application. Typically, the 

price and quantity data are annual, and the percentage 

change in prices over the entire time series is less than 

50%. Most of the econometric equation forms do not 

distinguish between elasticity with respect to price in 

the short term (roughly a year or less) versus quantity 

response in the long term (more than one year) where 

capital investments may occur that will influence 

supply or demand response to pulpwood price change. 

Given that short-term elasticities are generally not 

estimated; the elasticities found in the literature reflect 

responses to prices that will occur over several years.

Estimated historical average pulpwood supply elasticity 

with respect to stumpage price for the U.S. South is 

suggested to be about 0.34, as indicated by results of 

six studies (Newman, 1987; Carter, 1992; Newman and 

Wear, 1993; Prestemon and Wear, 2000; Polyakov et 

al., 2005; Lao and Zhang, 2008). Elasticity estimates 

from studies that covered the entire South range from 

0.23 to 0.49. These are averages for both hardwoods 

and softwoods for all land where most supply was 

from private land. While pulpwood supply elasticity 

estimates are not available explicitly for the North and 

West, an estimate within this range is consistent with 

estimates of supply elasticity with respect to stumpage 

price for all timber from two national studies (Adams 

and Haynes, 1980; 1996). These two studies estimate 

that the private timberland area-weighted national 

average supply elasticity for all timber in the North and 

West is 0.42 to 0.47. In addition, studies for the South 

suggest supply elasticity for sawtimber alone to be 

0.42 to 0.55 (Lao and Zhang, 2008; Newman, 1987). If 

elasticity for sawtimber in the North and West is about 

0.45, then pulpwood supply elasticity in the North and 

the West is about 0.3. If the sawtimber supply elasticity 

in the North and West is 10% higher or lower, the 

North and West pulpwood supply elasticity could range 

from 0.16 to 0.44. Given the wide range associated 

with these estimates, a pulpwood supply elasticity of 

0.35 is used for all states. 

Given that these estimates are based on large areas 

and that pulpwood prices are inherently locally driven, 

it is clear that the estimates of quantity supplied for 

any given price at the county level could vary notably 

from actual supply quantities for the given price. 

The estimates are only intended as an indicator of 

approximate supply, which may aid in determining 

when more local estimates are warranted. Given the 

uncertainty in the supply elasticity estimates and 

concern about sustainability of increased harvest levels, 

the possible annual pulpwood supply at the regional 

level is limited so as not to exceed the level of annual 

timber (growing stock) growth in each state elasticity 
45

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

estimates and concern about sustainability of increased 

harvest levels, the possible annual pulpwood supply 

at the regional level is limited so as not to exceed the 

level of annual timber (growing stock) growth in each 

state.

Given the uncertainty in the supply elasticity estimates 

and concern about sustainability of increased harvest 

levels, the possible annual pulpwood supply at the 

regional level is limited so as not to exceed the level of 

annual timber (growing stock) growth in each state.

Pulpwood supply estimates diverted to bioenergy 

use. Estimates of average pulpwood and panel wood 

demand elasticity with respect to stumpage price are 

found in two studies—for the South as a whole (-0.43) 

and for Texas (-0.41), respectively (Newman, 1987; 

Carter, 1992). An elasticity of -0.42 is used for each 

state. Estimates of potential pulpwood supply are made 

by using backward shifts along the demand curve for 

successive increments in pulpwood stumpage price 

(e.g., 10%). At each price point, the biomass amount 

is allocated to counties according to lowest harvest 

costs. Resulting county-level supply curves indicate the 

quantity supplied at particular total roadside prices.

The methods used to estimate pulpwood supply, 

although simplified, parallel the methods used 

to estimate amounts of biomass from integrated 

harvesting operations. The estimates are based on 

detailed analyses of harvest quantities and costs from 

treatments on FIA plots across the United States. The 

stumpage price to obtain supply amount or a currently 

demanded amount is estimated using basic information 

about the elasticities of supply or demand quantity with 

respect to price. These estimates should be considered 

only as approximate potential supply in localized areas. 

The analysis is overly simplified in that it does not 

take into account potential inventory changes over the 

longer term because of investments in afforestation 

or significant disturbances. A model with both spatial 

detail and time dynamics is not available for this 

analysis. The estimates are only intended to be both 

short term and without significant inventory changes.

Given the uncertainty in the demand elasticity estimate 

for the nation as a whole and a higher uncertainty 

for a region or county, the possible shift in pulpwood 

away from current users to biomass is limited to 

20% of pulpwood supply, which is reported in 2007 

Forest Service TPO database (USDA Forest Service, 

2007a). An analysis was conducted to determine the 

sensitivity to this limit. When the allowable shift from 

the pulpwood supply is increased to 30% of the 2006 

pulpwood supply, the available biomass only increases 

a few percent at the $90 per dry ton price and only 

up 9% above $120 per dry ton when the allowable 

biomass is increased to 30% of the pulpwood supply.

The limitation on shifting of current pulpwood use to 

20% was imposed on the recognition that the price 

elasticity estimate was based on currently available 

data with a certain variation over time. If prices change 

substantially, it is possible that demand elasticity 

could increase, which would cause the pulpwood 

supply to remain with current users and not be used 

for biomass. Rather than assume continuing steady 

shifting in response to increasing prices, a conservative 

assumption was made to limit the shifting of pulpwood 

supply from current users to biomass users at the 20% 

level.

3.6.2 Estimated Conventionally 

Sourced Wood

Pulpwood supplied to make pulp and panel products 

was 4.4 billion cubic feet, or about 66 million dry 

tons, in 2006. As the price for wood fuel feedstock 

approaches the price for pulpwood in a locality, there 

will be additional acres harvested for pulpwood to be 

used for energy, and some of the pulpwood going to 

pulp or panel mills will be diverted to wood energy 

use.

Supply curves (Figure 3.17) for pulpwood-sized 

roundwood at the county level were developed in 

several steps using basic concepts about supply and 

demand curves for existing pulpwood markets for each 

major region—North, South, and West. In general, 

it was assumed that regional levels of pulpwood 

supply can be approximated for bioenergy by starting 

with recent stumpage prices (Table 3.2), and starting 

quantities supplied are taken to be equal to recent 

quantities harvested.
46

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

30 It is assumed that percent change in pulpwood biomass supply is equal to (0.34 + .42) x percent change in stumpage price. The quantity 

includes both additional supply from new harvesting and supply from a shift of current pulpwood harvest away from current users to 

bioenergy users.

0 5 10 15 20 25 30 35 40

120

100

80

60

40

20

0

Million dry tons

$/dry ton (forest roadside)

Figure 3.17 Estimated supply of pulpwood for bioenergy annually

Pulpwood for bioenergy starts to be supplied at current 

pulpwood stumpage prices, and harvest costs increase 

as the price that buyers are willing to pay increases. 

Pulpwood can either come from additional harvesting 

operations that specifically harvest pulpwood for 

bioenergy (possibly more expensive than current 

integrated harvesting) or from a shift in pulpwood use 

from current users to bioenergy producers. In the first 

case, additional harvesting operations are analogous to 

movement along state-level pulpwood supply curves 

to obtain bioenergy pulpwood. In the second case, it 

is backward movement along the current pulpwood 

demand curve, indicating shifts from current pulpwood 

uses to bioenergy.

At $60 per dry ton at roadside, the estimated pulpwood 

supply from additional harvest or shifts from current 

users is 1.4 million dry tons per year. At a roadside 

price of $80 per dry ton, the amount of pulpwood for 

use as biomass is 18 million dry tons per year; from 

that total, 13 million tons is the main stem of trees, or a 

20% increase over the 2006 harvest level of 66 million 

dry tons. Such an increased amount would be provided 

with a stumpage price increase of about 26%.30 The rest 

of the price increase is due to increased harvest costs 

needed to obtain additional pulpwood supply. Supply 

at $100 per dry ton or less is 38.6 million dry tons 

annually, of which 29 million tons is from the main 

stem of the trees—a 44% increase. This increase would 

be generated by a stumpage price increase of about 

58%. The estimated increases in pulpwood supply are 

fairly coarse and are particularly uncertain for higher 

levels of price increase, which are outside the range 

of prices used to estimate the supply and demand 

elasticities.
47

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

3.7 Total Supply of Forest 

Biomass and Wood Wastes

Table 3.3 provides a summary of the currently available 

biomass at a range of prices for the forest biomass 

and wood wastes feedstocks. There are estimates for 

the two major sources of forest biomass feedstocks: 

logging residues and thinnings (shown as a composite), 

which are based on an assumption of a 50:50 ratio 

as the transition from logging residues to integrated 

harvesting occurs. This avoids double counting for 

both residues and thinnings. At the highest price 

estimate shown in Table 3.3 of $100 per dry ton, the 

available biomass from logging residues and thinnings 

as integrated composite operations is about 43 million 

dry tons annually. Even at a price of $200 per dry 

ton (not shown in the table), the additional biomass 

is much less than 10 million dry tons per year. These 

levels already account for the biomass that is retained 

onsite for sustainability purposes. At a price of $60 per 

dry ton, annual availability is estimated to be about 

97 million dry tons. The thinnings portion of these 

numbers is for all land ownerships and includes federal 

lands, even though they do not currently qualify under 

the Renewable Fuels Standard. Removal of the federal 

lands has little effect on the total biomass availability, 

reducing the estimated total at the $60 price by only 7 

million dry tons. For conventional pulpwood to energy, 

the higher quantities have considerable uncertainty as 

they are based only on a 50% change in the current 

base stumpage price. Volume estimates above $80 per 

dry ton are outside the model parameters. Figure 3.18 

depicts the estimated forestland cellulosic feedstocks 

by states at an example price of $80 per dry ton.

Future estimates are shown in Table 3.4. Because the 

thinnings are already averaged across the next 30 years 

and there is limited data for many of the feedstocks, 

there is little estimated change over the next 20 years. 

Assuming a price of $60 per dry ton, the total available 

tonnage only increases from 97 million dry tons per 

year in 2010 to 102 million dry tons per year in 2030. 

Using a forest roadside price of $80 per dry ton, the 

total quantity of composite residues increases from 

1.6 to 2.0 million dry tons for each year (depending 

on whether federal land is counted). Conventional 

pulpwood is fairly constant at the prices shown in 

the table over the time period. Only after prices are 

higher than $60 per dry ton, conventionally sourced 

feedstocks start making significant contributions. All 

other residue quantities at $80 per dry ton are the same 

as shown at $60 per dry ton. There are no scenario 

changes with the forest biomass and wood wastes—

only the baseline.
48

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Figure 3.18 Current state shares of available forest biomass resources at $80 per dry ton or less
49

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Table 3.3 Summary of Potential Forest Biomass and Wood Wastes (2012)

Feedstock ($ per dry ton) <$20 <$30 <$40 <$60 <$80 <$100

 Million dry tons

Other Removal Residue 4.4 12 12 12 12 12

Composite Operations 9.5 30 36 40 42 43

 Without Federal Land 8.3 26 31 35 36 37

Treatment Thinnings, 

Other Forestland 0 0 0 3.2 6.4 6.4

 Without Federal Land 0 0 0 1.8 3.6 3.6

Mill residue, unused primary 1.3 1.3 1.3 1.3 1.3 1.3

Mill residue, unused secondary 6..1 6.1 6.1 6.1 6.1 6.1

Urban Wood Waste – C & D 4.4 11 14 22 22 22

Urban Wood Waste – MSW 7.7 8.7 9.2 10 10 10

Conventional Pulpwood to Energy* 0 0 0 1.5 19 40

Total – All Land 33 70 79 97 119 142

Total – Without Federal Land 32 66 75 90 111 133

Notes: Does not include currently used biomass from Chapter 2. Totals may not add up correctly due to rounding

* Although shown here for convenience, the estimated conventional pulpwood used as bioenergy above $80 per dry ton is outside the 

model parameters, which could result in significant errors.
50

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Summary of Baseline Potential Forest Biomass and Wood Wastes at 

Selected Roadside Prices Table 3.4

Feedstock 

($ per dry ton) 40 50 60 40 50 60 40 50 60 40 50 60

2012 2017 2022 2030

Million dry tons

Other Removal Residues 12 12 12 12 12 12 13 13 13 13 13 13

Conventional 

Pulpwood to Energy 0.0 0.1 1.5 0.0 0.1 1.6 0.0 0.1 1.8 0.0 0.1 1.9

Composite 

Operations 36 38 40 36 39 40 37 39 41 37 39 41

Without Federal Land 31 33 35 32 34 35 32 34 35 32 34 36

Treatment Thinnings, 

Other Forestland 0.0 0.0 3.2 0.0 0.0 3.2 0.0 0.0 3.2 0.0 0.0 3.2

Without Federal Land 0.0 0.0 1.8 0.0 0.0 1.8 0.0 0.0 1.8 0.0 0.0 1.8

Mill residue, 

unused secondary 6.1 6.1 6.1 6.1 6.1 6.1 6.1 6.1 6.1 6.1 6.1 6.1

Mill residue, 

unused primary 1.3 1.3 1.3 1.3 1.3 1.3 1.4 1.4 1.4 1.4 1.4 1.4

Urban Wood 

Waste – C & D 14 22 22 15 23 23 15 23 23 16 25 25

Urban Wood 

Waste – MSW 9.2 10 10 9.5 10 10 10 11 11 10 11 11

Total – All Land 79 91 97 81 92 98 82 93 100 83 95 102

Total – Without 

Federal Land 75 86 90 76 87 92 77 88 93 79 90 95
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3.8 Summary

Although a significant amount of effort went into 

the analysis, the estimates are still only as good as 

the underlying data and dependent on the underlying 

assumptions. This concern is further compounded 

when developing comprehensive cost estimates at 

county levels. The FIA database brings significant 

amounts of data to the analyses. However, there are 

limitations concerning its use for biomass since the 

primary FIA focus is on merchantable inventory. The 

use of the data and some of the issues associated with 

using FIA data at the county level are discussed.

There is very little data on stumpage prices for 

biomass, and the extrapolation of the available data has 

limitations. This is especially true when estimating the 

availability of conventionally sourced biomass, which 

has high uncertainty at higher prices. Furthermore, 

the model is developed from historical supply/

demand elasticity parameters that may or may not be 

representative of future market dynamics. There is 

very little data on biomass harvest systems tailored for 

handling logging residues, small stems, or integrated 

production. The largest gap in data is post-consumer 

wood residues. Limited data are available for C&D 

wood, and there are large voids for the amounts and 

costs for recovery of urban wood. 

The underlying assumptions are based on the best 

available information and grounded in the expertise of 

the authors. The biomass estimates can readily change 

with them. The primary example is the development 

of the supply curves. Another considerable example is 

the recovery of logging residues—whether they will 

be collected after the original harvest or as part of that 

harvest. The speculation is that integrated systems will 

be used to recover residues because of costs. As a last 

point on assumptions, the use of biomass retention 

is a primary concern for long-term site productivity 

and a surrogate for other sustainability criteria, such 

as habitat. Retention alone, not assuming the use of 

BMPs and assuming that removals do not exceed 

growth, does not truly represent the full measure of 

sustainability. Other considerations are needed. To aid 

the readers with interpreting the results, they will have 

access to the KDF for additional analyses using various 

assumptions.

Finally, the development of this chapter pointed to 

several needs, as summarized below:

• Improving the biomass portions of the FIA 

database

• Understanding and modeling the long-term 

effects of biomass removal under a range of soil, 

climate, and management schemes 

• Improving the databases (e.g., mill residues, 

urban wastes, and costs) 

• Developing and integrating biological and 

economic models for sustainability assessments. 
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4 AGRICULTURAL BIOMASS 

AND WASTE RESOURCES

This chapter provides estimates of quantities and 

farmgate prices (i.e., supply curves) for agricultural 

crop residue biomass, as well as residues and wastes 

generated mostly by food processing industries. 

Farmgate price is the price a buyer pays for crop 

residue at the farm, at a mill location in the case of 

processing residue, or at a landfill or feedlot in the 

case of waste resources. The agricultural resources 

considered in this assessment include:

• Crop residues from the major grain-producing 

crops

• Other crop residues

• Secondary agricultural processing residues

• Waste or tertiary resources (e.g., manures, waste 

fats, and greases).

For corn stover and other major grain residues, countylevel supply curves are estimated using an agricultural 

policy simulation model. The chapter provides 

background on each of these resources and explains 

how estimates are made. The largest quantities are 

from crop residues. A number of factors are taken into 

account when estimating available crop residues: soil 

erosion and soil organic matter constraints, as well as 

the physical ability of machinery to harvest residues. 

Included in the price of these residues are the collection 

costs, a payment to the grower based on the nutrient 

value of the residue, and a profit. Estimates are made 

for a baseline and a high-yield scenario.

4.1 Cropland Resources (Corn 

Ethanol and Soybean Biodiesel)

These resources are accounted for in Chapter 2. The 

current total feedstocks for corn-based ethanol is 76 

million dry tons per year (see Table 2.1). It is estimated 

that in 2017 the corn production for ethanol will meet 

the EISA mandate at 88 million dry tons and will be 

produced at that level through 2030. Soybean biodiesel 

feedstocks are estimated at 5 million dry tons per year, 

increasing to 18 million dry tons annually in 2017 and 

continuing at that level to 2030.

4.2 Agricultural Crop Residues

Crop residues are desirable feedstocks for bioenergy 

applications because of their low cost, immediate 

availability, and relatively concentrated location in 

the major grain growing regions. The most plentiful 

residues include stalks and leaves from corn (stover) 

and straw and stubble from other small grains, such 

as wheat, barley, oats, and sorghum (Figure 4.1). 

The 2005 BTS included a number of crop residue 

removal scenarios involving changes in crop yields, 

cropland tillage, and the efficiency of residue collection 

technology. In the 2005 report, the sustainable quantity 

of stover and straw residue was estimated at about 210 

to slightly more than 320 million dry tons annually, 

depending on what was assumed about crop yield, 

tillage, and the fraction collected. If all crops are 

considered, then the crop residue potential is more than 

400 million dry tons.31 Corn stover, the largest single 

source of residue, was estimated between 170 and 

256 million dry tons, depending on yield and tillage 

assumptions.

31 The higher amount for the 2005 study included nearly 50 million dry tons of residues from forage-type soybeans. This potential is not 

included in this update.
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

The production of crop residues is significant, with the 

average annual tonnage between 1998 and 2007 from 

corn, grain sorghum, winter and spring wheat, barley, 

oats, and rye exceeding 350 million dry tons; corn 

stover consisted of about 70% of this total. Residue 

production is directly related to yield. Projections by 

USDA indicate yields for corn and wheat will increase 

approximately 9.5% and 5.2% over the next ten years, 

respectively. Although significant quantities of residue 

are produced and will increase over time, how much 

of this residue can be sustainably collected has been 

subject to much debate. The next section of this chapter 

discusses sustainability and provides an overview of 

the approach used to determine how much residue 

needs to be retained on fields in order to limit erosion 

to tolerable levels and maintain soil organic matter.

32 See Andrews and Aschmann (2006) for a primer on crop residue removal and bioenergy production.

Example of crop residues – stalks 

and leaves from corn stover Figure 4.1

(Courtesy of ORNL)

4.3 Sustainability of Crop 

Residue Removal

Crop residues provide a number of important soil 

enhancing and safeguarding functions. These include 

protecting the soil and controlling erosion from 

water and wind, retaining soil moisture, increasing or 

maintaining soil organic matter, adding to the available 

pool of soil nutrients, increasing biological activity and 

improving soil structure, and improving crop yields 

(Andrews and Aschmann, 2006).32 Soil erosion is an 

extremely important national issue and most, if not all, 

agricultural cropland in the United States experiences 

some degree of soil erosion each year due to rainfall 

and/or wind. Soil erosion reduces soil productivity and 

soil organic matter, removes plant nutrients, and has an 

adverse effect on water quality through the transfer of 

suspended solids, nitrogen, and phosphorus, both on 

the surface and in groundwater. Rainfall erosion (sheet 

and rill) occurs when rain directly strikes the soil, 

dislodging particles in the top layer of soil.

Degradation of soil quality as influenced by land 

management is also an extremely important issue 

to the agricultural and environmental community. 

Soil quality is defined as a soil’s ability to sustain 

plant growth and contribute to the maintenance or 

enhancement of air and water quality. Soil organic 

matter content is particularly important because of its 

immediate and direct impact on several critical soil 

functions. Enhancing soil organic matter can improve 

soil productive capacity, nutrient cycling, filtering and 

buffering of potential pollutants, water storage, and 

resistance to compaction and erosion.

Sustainable agricultural residue removal rates must 

maintain soil quality and future productive capacity. 

Building from the work presented by Wilhelm et al. 

(2011), the amount of agricultural residue that can 

potentially be removed from agricultural cropland is 

subject to two modeled constraints in this analysis. 

First, removals cannot exceed the tolerable soil 

loss limit as recommended by the USDA’s Natural 
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33 As summarized by Andrews and Aschmann (2006) “current USDA-NRCS practice standards for residue management do not specify 

residue quantities but do suggest the use of the RUSLE2 model for guidance (USDA-NRCS, 2005). In the future, specific guidelines for 

residue harvest could be developed to prevent soil degradation resulting from over-harvest of crop residue, partially based on modeling 

results from RUSLE2 and the Soil Conditioning Index (SCI).”

Resource Conservation Service (NRCS). Second, 

removal cannot result in long-term loss of soil organic 

matter as estimated by the Revised Universal Soil Loss 

Equation (RUSLE2) and the Wind Erosion Prediction 

System (WEPS). Both of these programs incorporate 

a soil quality index referred to as the soil conditioning 

index (USDA-ARS, 2010; USDA-NRCS, 2008) 

and are employed by NRCS to help guide farmers, 

ranchers, and landowners in making their conservation 

plans.33 In general, both programs are designed to 

provide estimates of soil erosion and other pertinent 

soil tilth parameters due to types of crops, rotations, 

field management practices (e.g., tillage), and field 

topography (see Text Box 4.1).

Revised Universal Soil Loss Equation (RUSLE2)

RUSLE2 is intended to describe and estimate the main effects of agricultural cropping practices on soil erosion by rainfall 

and/or overland flow. It is mainly used as a guide for conservation planning to represent trends demonstrated in field data. 

RUSLE2 can be, and has been, applied to applications involving cropland, pastureland, rangeland, and disturbed forestland. 

The equation for RUSLE2, presented below, provides a daily calculation of certain time-varying factors that define soil 

erosion due to rainfall:

A = f (r, k, l, s, c, p)

Where: 

r – Rainfall/Runoff s – Slope steepness

k – Soil erodibility c – Cover-management

l – Slope length p – Supporting practices

Average annual soil loss is a function of both erodibility and erosivity, with erodibility related to the susceptibility of the 

soil to erosion and management. Erosivity is a measure of the force of raindrops, water falling from plant canopy, and 

surface runoff. Therefore, erodibility and erosivity jointly impact actual erosion rates. RUSLE2 was used to provide average 

annual estimates of soil erosion on individual soils types for a variety of cropping rotations both with and without residue 

removal. 

Wind Erosion Equation (WEQ)

The primary method for estimating the amount of soil loss due to wind erosion on agricultural cropland was to employ the 

Wind Erosion Equation (WEQ). WEQ is an empirical equation that has been applied to various agricultural and engineering 

situations to predict annual soil loss from a single, uniform isolated field according to cropping and land management 

practices. It has been used by the NRCS to predict wind erosion on cropland and to guide and plan wind erosion control 

practices for the agricultural community. The general functional relationship in WEQ between the independent variable, 

(E), the potential average annual soil loss, and the variables that directly affect wind erosion is as follows:

E = f (I, K, C, L, V) 

Where:

I – Soil erodibility index K – Soil ridge-roughness factor C – Climatic factor

L – Unsheltered median travel distance of wind across a field V – Vegetative cover

TEXT BOX 4.1 | REVISED UNIVERSAL SOIL LOSS AND WIND EROSION EQUATION
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

4.4 Estimating Crop 

Residue Supply

In this update, supplies of corn stover and small grain 

residues are estimated using POLYSYS, a policy 

simulation model of the U.S. agricultural sector (De La 

Torre Ugarte and Ray, 2000). The model is anchored 

to the USDA 10-year projections and includes national 

demand, county supply, livestock, and income 

modules. In POLYSYS, supplies of crop residues are 

estimated simultaneously with energy crops since 

they must compete with energy crops for land and any 

changes in land use affects estimated quantities. [More 

discussion of the POLYSYS modeling framework can 

be found in the Section 5.2 of this report.] The model 

estimates potential crop residue supplies from corn, 

wheat, grain sorghum, oats, and barley by accounting 

for how much residue is produced (a function of crop 

yield, moisture, and residue to grain ratio), residue 

production costs (a fixed per ton grower payment plus 

collection costs per ton of residue removed), and how 

much residue that must remain to keep erosion within 

tolerable soil loss levels and maintain soil carbon 

levels. For cotton and rice, two of the three other major 

crops in POLYSYS, residues are estimated separately. 

For soybeans, it is assumed there is no residue 

available.34

4.4.1 Input Assumptions for Baseline 

and High-Yield Scenarios

The amount of crop residue produced depends on 

the crop yield and the harvest index (HI) or ratio of 

residue to grain (Table 4.1). The amount that can be 

sustainably removed is governed by the retention 

coefficients, which are estimated from application of 

RUSLE2 and WEPS models incorporating the soil 

conditioning index and tillage (Muth et al., 2011). The 

amount that can be physically removed depends on 

the combined efficiency of the collection equipment 

(e.g., shredders, rakes, and balers). And the amount 

that can be economically removed depends on grower 

payments, collection costs, and prices offered for the 

feedstocks. The remainder of this section discusses 

these underlying assumptions for the baseline and highyield scenarios.

34 Most, if not all, soybean residue needs to be left on the ground to meet conservation practice requirements. Some USDA genetic 

improvement research has focused on developing varieties that have a higher ratio of straw to beans, grow taller, have improved lodging 

resistance, and have a better over-winter residue persistence. It is evident from data on the forage soybean varieties that the potential 

exists to produce 100% more crop residue and thus provide more soil conservation benefits than the conventional varieties (Wu et al., 

2004). It cannot be predicted whether farmers will adopt these new varieties, but clearly the technology will be available. Increased use of 

soybeans in double cropping could also allow for more soybean residue removal. Potentially, with such varieties and/or double cropping, 

soybean acreage could contribute to the availability of residues.

Table 4.1 Parameters Assumed for Calculating Crop Residue Production

Crop Weight 

(lbs/bu)

Moisture 

content (%)

Dry weight 

(lbs/bu)

Residue to 

grain ratio

Residue

(Dry tons/bu)

Corn 56 15.5 47.32 1.0 0.0237

Sorghum 56 14.0 48.16 1.0 0.0241

Oat 32 14.0 27.52 2.0 0.0275

Barley 48 14.5 41.04 1.5 0.0308

Winter wheat 60 13.5 51.09 1.7 0.0441

Spring wheat 60 13.5 51.09 1.3 0.0337

Notes: Technically a bushel is a unit of volume, 1.244 cubic feet or 32 quarts. Weights for bushels (bu) have been standardized at 

given moisture contents. For corn (shelled), a bushel has a weight of 56 pounds at 15.5% moisture (Rankin, 2008). The actual weight 

of a bushel of corn varies with moisture content (Table 3, Murphy 2008), but the standard is 56 pounds at 15.5% moisture.
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Soil Conditioning Index (SCI)

SCI is a tool used by the NRCS in conservation planning to help estimate the effect of certain conservation practices (e.g., 

residue removal) on maintaining and increasing levels of soil organic matter. Specifically, the SCI expresses the effects of 

the system on organic matter trends as a primary indicator of soil condition and is an indicator of how modifications of a 

management system will affect the level of soil organic matter. The index was developed from RUSLE2.

The SCI models the top 4 inches of the soil and combines the effects of three determinants of soil conservation: organic 

matter, field operations, and erosion. Although 4 inches does not account for all soil characteristics, most soil quality 

improvements result from changes in the surface layer. Organic material, or biomass factor, accounts for the effect of 

biomass returned to the soil, including material from plant or animal sources, and material either imported to the site 

or grown and retained on the site. Field operations factor is directly related to practices that stimulate organic matter 

breakdown. The erosion factor accounts for the effect of removal and sorting of surface soil by water and wind erosion 

models. 

Cropping Rotations

Previous analyses used very generic cropping rotations, such as continuous corn, corn-soybean, and continuous wheat 

(Nelson, 2002; Nelson et al., 2003). In reality, these rotations did not really reflect more ‘localized’ cropping practices. 

In order to present a more realistic picture of actual cropping practices (crops, rotations, and tillage) and gain a better 

understanding of sustainable levels of residue removal, specific cropping rotations developed by the NRCS for use in 

preparing conservation plans at the local, multi-county, state, and/or national levels were utilized in this analysis. These 

crop management zones (CMZ) tend to represent the major types of agricultural practices employed by farmers in multicounty areas (see Figure 4.2). These divisions combine counties with similar crop production (e.g., corn, winter wheat, 

etc.), cropping rotations (e.g., corn-soybean-wheat), and field management practices (e.g., conservation tillage, reduced 

tillage and no-till, etc.).

For each CMZ, NRCS provides common corn and small grain cropping rotations and managements based on experience 

from field agents and national personnel. For each management and crop, certain residue removal practices were 

selected. An important note about the selection of the residue removal is that each of the scenarios used is based on 

actual equipment that would be used in the field in current operations. While this limits the range of removals that can 

be investigated, it is critical to the soil sustainability analysis that the orientation of the material (stover and straw) be 

accurately represented. In many cases, the orientation of the material is more important than the quantity of material 

left in the field. Residue removals were selected using operations from which the orientation of the residue remaining 

is understood and can be properly represented. Figure 4.1 highlights four no-till cropping rotations for CMZ 4, which is a 

majority of the Corn Belt.

TEXT BOX 4.2 | SOIL CONDITIONING INDEX AND CROPPING ROTATIONS

4.4.1.1 Baseline Scenario

The key residue producing crops are corn and the small 

grains (wheat, barley, oats, and sorghum). The amount 

of crop residue produced and potentially available for 

removal is calculated as a function of crop yield, the 

grain weight and moisture content, and the HI or ratio 

of residue to grain. These factors are summarized in 

Table 4.1. The parameters are the same as used in the 

2005 BTS.

Crop residue retention coefficients. Removing 

most of the technically recoverable residue is not 

warranted because of the importance of such residue 

in maintaining soil nutrients and soil carbon levels 

and controlling erosion. The amount of residue that 

must be left in the field to satisfy these environmental 

constraints depends on soil properties, field slope, 

crop rotation, and tillage system (i.e., conventional till, 

mulch till, or no-till) (see Text Box 4.2).35

35 Conventional tillage involves the use of plowing that disturbs the entire soil surface, leaving a small amount of residue cover—usually 

less than 15%. No-till leaves the soil surface undisturbed prior to planting or seed drilling. Reduced tillage is defined as the minimal soil 

disturbance required for crop planting and emergence (e.g., strip or mulch tilling).
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Figure 4.2 NRCS crop management zones

In the 2005 BTS, residue removal constraints for corn 

stover were based on Graham et al. (2007); for small 

grains, the restraints were based on average national 

estimates corresponding to maintenance of 30% soil 

cover—roughly 1.6 dry tons per acre. An alternative 

to using residue retention to ensure sustainable yields 

is to recover nutrients at the processing facility and 

recycle them back to the land, along with restoring 

carbon through organic applications. These are feasible 

options that will probably be implemented under 

certain conditions for specific feedstocks. In this 

update, residue retention coefficients were estimated 

for erosion and soil carbon in the following sequence 

of steps:

1. Obtain realistic 1- to 4-year commodity crop 

rotations from NRCS within the multi-state crop 

management zones (Figure 4.2) 

2. Establish, using RUSLE2 and WEPS, “baseline” 

erosion and carbon levels for each crop rotation 

subject to tillage, soils, topography, and climate

3. Identify how much residue can be removed under 

low-, moderate-, and high-harvesting systems (e.g., 

windrow pickup, rake and windrow pickup, flail 

shred, and rake) for the corn stover and small grain 

straw portions of each rotation (e.g., corn-soybeanwinter wheat; stover in year 1, straw in year 3) 

(Figure 4.3)

4. Obtain, from RUSLE2 and WEPS, the increase 

in erosion and decrease in carbon as determined 

levels of residue are removed (consistent with the 

removal/harvest system)

5. Calculate average retention coefficients by county 

for wind, rain, and soil carbon for each rotation 

and tillage combination by crop management zone.
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The baseline erosion and carbon levels for each crop 

are yield dependent and were calculated through 

2030 to determine retention coefficients. Due to the 

concern about residue removal and long-term soil 

fertility, removing residue from conventionally tilled 

acres was not allowed. For acres under reduced-till 

cultivation, the Organic Matter subfactor of the SCI 

Figure 4.3 Baling corn stover

(Courtesy of ORNL)

36 The SCI-OM subfactor is more conservative. Conservation management planning, as implemented by the NRCS, uses the combined SCI as 

the qualitative carbon metric and allows for more residue removal because the Field Operations (FO) and Erosion (ER) subfactors pull the 

negative OM subfactor positive when combined across the yield and removal rate spectrum (Muth et. al, 2011).

(SCI-OM) was used as the carbon trigger; for acres 

under no-till, the combined SCI was used as the carbon 

trigger.36 Figure 4.4 shows national average residue 

retention coefficients for reduced-till and no-till corn. 

In this assessment, residue removal is not allowed on 

conventionally tilled acres. As discussed previously, 

the estimation was conducted at a county level, with 

results summarized nationally in this report. Countylevel output for one particular county is shown in 

Figure 4.5, with total residue produced and the amount 

removable under reduced till and no-till represented. 

The increasing stover yield over time is due to yield 

growth. To summarize the retention coefficient 

analysis, the left map in Figure 4.6 shows total corn 

stover production across the United States in 2030, 

with the darker shades indicating higher levels of 

stover (or grain) production. The map on the right in 

Figure 4.6 shows the sustainable retention coefficient 

(expressed as a fraction of stover that must remain on 

the field to meet sustainability requirements) for year 

2030. Areas in dark green indicate high levels of stover 

removal, and areas in dark brown indicate the large 

fractions of the produced stover that must be retained 

onsite. Similar results are generated for other years up 

to 2030, as well as for reduced tillage.
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Fraction of residue left on field

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

Reduced Till No Till

Figure 4.4 Sustainable corn stover retention coefficients across tillage and selected years 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

6

5

4

3

2

1

0

Per acre yield (dry tons)

Total Stover Yield No Till Potential Yield

Reduced Till Potential Yield

Average total standing yield, no-till potential yield, and reduced tillage potential yield 

of corn stover in Adair County, Iowa Figure 4.5
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Figure 4.6 Total factor of corn stover yield and sustainable retention coefficients, 2030

Grower payments. Crop residues are a source of 

nutrients for future crops if left to decompose. When 

residues are collected, additional fertilizer needs to 

be applied to compensate for the removed nutrients. 

Crop residue removal may also affect subsequent 

field operations and production both positively and 

negatively. A positive outcome would result if soil 

that remains wet in the spring has some of its residue 

removed in the fall; the soil may dry and/or warm more 

quickly, allowing for earlier spring field work, earlier 

planting, and earlier seed germination, which would 

result in higher yields. Residue removal may also make 

herbicides more effective or require less to be applied 

(i.e., if less herbicide is intercepted by residues, then 

more reaches its intended target). However, a negative 

outcome would occur if residue removal increased soil 

compaction through additional equipment traffic and 

reduced organic matter near the soil surface (Wilhelm 

et al., 2004). Grower payments are determined by 

valuing the removed nutrients and organic matter 

and adding a nominal profit. Valuing the competing 

positive and negative effects of removed nutrients on 

field operations and production would depend on many 

site-specific considerations that are difficult to quantify 

beyond the scope of this report. 

The nutrient value is determined as a product of 

the price of fertilizer and the amount of nutrients in 

the removed stover. This valuation is not entirely 

straightforward, given the regional variation in 

fertilizer prices by nutrient and fertilizer product 

and the variation in nutrient content of the residue 

itself. There are many nitrogen sources, with prices 

varying considerably among these sources. Anhydrous 

ammonia is the least expensive and generally applied 

to corn, while ammonium nitrate tends to be the 

most expensive. The sources of most phosphorus are 

diammonium phosphate (DAP) and monoammonium 

phosphate (MAP). For potassium, muriate of potash is 

used almost exclusively.

Fertilizer prices vary considerably from year to year 

and by region. However, in 2008, prices increased 

dramatically due to a combination of factors, including 

rising energy costs and the energy-intensive nature 

of production, increased costs for raw materials (e.g., 

natural gas), higher transportation costs, and a sharp 

increase in worldwide demand. To account somewhat 

for year-to-year variability, average regional prices 

from 2006–2009 were used to quantify the nutrient 

value of the removed residue. In addition, the nitrogen 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Table 4.2 Regional Nutrient Payments per Ton of Stover Removed

Nutrient Lake 

States Corn Belt Northeast Appalachia Southeast Northern Plains

Southern 

Plains

Pacific 

Northwest

Nitrogen $9.90 $9.90 $9.50 $9.40 $9.80 $9.30 $8.90 $10.10

Phosphorus $3.60 $3.60 $3.40 $3.40 $3.50 $3.40 $3.20 $3.70

Potassium $13.40 $13.40 $12.80 $12.90 $13.30 $12.70 $12.20 $13.70

Total 

Nutrient 

Payment

$26.90 $26.90 $25.80 $25.60 $26.60 $25.40 $24.30 $27.50

embodied in phosphorous fertilizer (18% nitrogen 

in DAP and 11% nitrogen in MAP) was valued at 

the 2006–2009 average regional price of anhydrous 

ammonia, plus a $0.05 per pound application cost.

Data from Nielson (1995), Lang (2002), Gallagher et 

al. (2003), Schechinger and Hettenhaus (2004), and 

Fixen (2007) was used to estimate an average nutrient 

composition of removed corn stover. Nutrient values 

used were 14.8 pounds nitrogen per dry ton, 5.1 pounds 

P2

O5 (phosphate) per dry ton, and 27.2 pounds K2O 

(potassium) per dry ton.

Most corn produced in the United States is grown in 

rotation with soybeans. As corn stover decomposes 

in the field, nutrients become available. Phosphorous 

and potassium are not generally lost and are utilized 

by all crops. For nitrogen, there is a question as to 

whether it becomes available during the soybean year 

of a corn-soybean rotation. The question that should be 

asked is whether the nitrogen that is released during the 

soybean year (and not used by the soybeans) remains to 

Table 4.3 Regional Nutrient Payments per Ton of Small Grains Straw Removed

Nutrient Lake 

States Corn Belt Northeast Appalachia Southeast Northern Plains

Southern 

Plains

Pacific 

Northwest

Nitrogen $10.30 $10.30 $10.40 $10.60 $10.80 $9.90 $10.00 $11.00

Phosphorus $1.90 $1.90 $2.00 $2.00 $2.00 $1.90 $1.90 $2.10

Potassium $12.50 $12.50 $12.60 $12.80 $13.10 $11.90 $12.10 $13.20

Total 

Nutrient 

Payment

$24.70 $24.70 $25.00 $25.30 $25.90 $23.60 $23.90 $26.20

be used by the following corn crop. The approach taken 

here was to assume that the nutrients from corn stover 

become available in a linear fashion over a 10-year 

period and discounted using a 6% rate. If the nitrogen 

released during the soybean year is fully valued (in 

the year of the soybean crop) and discounted, then the 

worth of the nitrogen is 78% of its undiscounted value.

Corn producers surveyed indicated that they desire to 

receive a value for their corn stover greater than the 

nutrient replacement value. Brechbill and Tyner (2008) 

add 15% of the value of the nutrients, cost of collecting 

corn stover, dry matter loss, and storage premium. 

In their second corn stover example, this amounts to 

$4.32 per dry ton. Edwards (2007) reports that sales of 

corn stover at hay auctions have resulted in prices that 

usually range from $20 to $25 per bale. He assumes a 

bale weighs 0.6 tons, and, if it is assumed that the bale 

is 85% dry matter, then the stover is worth $39 to $49 

per dry ton. Harvest includes baling and may or may 

not include mowing/shredding and raking 
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37 In the 2005 BTS, estimates assume collection equipment is capable of removing about 35% of the residue under current conditions and up 

to 75% under the high-yield scenario (assuming the availability of single-pass harvesting systems).

(Figure 4.3). Edwards estimates custom baling at 

$10.35 per bale ($20.29 per dry ton) and total harvest 

cost (stalk chopping plus raking plus baling) at $13.84 

per bale ($27.14 per dry ton). Subtracting the harvest 

cost and local transport cost (assume $3 per dry ton) 

from the bale sales price leaves a range of $9 to $19 

per dry ton for standing corn stalks. Edwards estimates 

a nutrient value of $7.53 per bale ($14.76 per dry ton), 

which leaves a residual (intrinsic) value of $6 to $4 

per dry ton. For corn stover left in a windrow behind a 

combine, a farmer was paid $15 per dry ton in 1997–

1998. To give some perspective, in Iowa, non-alfalfa 

hay prices were $60.50 per ton in the marketing year 

1997–1998, and $83 per ton in June 2010. Schechinger 

and Hettenhaus (2004) estimated the nutrient value of 

the stover at $6.90 per dry ton. Subtracting this from 

the $15 per dry ton payment amount leaves a residual 

(intrinsic) value of approximately $8 per dry ton. 

From these market-based examples, it would seem 

reasonable to allow corn stover an intrinsic value of 

$10 per dry ton. In addition, $1 per dry ton is added 

to account for the unknown value of the residue for 

organic matter.

Regional grower payments based on the nutrient 

content of the removed residue, organic matter, 

and grower profit (premium or intrinsic value) are 

summarized in Tables 4.2 and 4.3 for corn stover and 

small grain straw, respectively. The average grower 

payment in the United States is $26 per dry ton of 

removed corn stover and $25 per dry ton of removed 

wheat straw (using 2006–2009 average regional 

fertilizer prices). Grower payments are lowest in the 

Northern and Southern Plains and highest in the Pacific 

Northwest. For the Corn Belt, the grower payment is 

about the same as the national average. It should be 

noted that the occurrence of high fertilizer prices (as 

experienced in 2008) would increase the value of the 

grower payment.

Costs of crop residue collection. Corn stover can 

be collected in a number of ways: (1) turn off the 

combine’s spreader and bale the windrow using a 

large round baler; (2) after combining, rake and bale 

the resulting windrow using a large round baler; or (3) 

shred after combining, rake the shredded biomass, and 

bale the resulting windrow using a large rectangular 

baler.37 For wheat straw, turn off the combine’s 

spreader and bale the windrow using a large round 

or rectangular baler. To estimate costs for example 

purposes, shredding is performed using a 20-foot-wide 

shredder, and raking utilizes a 15-foot wheel rake. 

Based on data from Shinners et al. (2007), baling wet 

biomass is assumed, with the round baler producing 

0.50 dry-ton bales with mesh wrap, while the large 

rectangular baler produces 0.49 dry ton bales. Baler 

capacity is limited by field speed (assumed to be 15.4 

and 22.6 dry tons per hour for round and rectangular 

balers, respectively). Bales are transported to the field 

edge using self-loading and unloading wagons that are 

capable of carrying 14 round bales and 8 rectangular 

bales.

Costs decline with increasing yield. For yields of 1.0, 

1.5, and 2.5 dry tons per acre, collection costs up to 

the field edge are about $21, $18, and $14 per dry ton, 

respectively. The estimated collection cost function for 

stover and straw is displayed in Figure 4.7.
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0 0.5 1 1.5 2 2.5 3 3.5

$35

$30

$25

$20

$15

$10

$5

$0

$/dry ton

Yield (dry tons/acre)

Corn stover Small grains straw

Figure 4.7 Collection costs to the field edge for corn stover and small grains residue

4.4.1.2 High-Yield Scenario

A series of DOE workshops (U.S. Department of 

Energy, 2010a) was held to provide an opportunity 

to further evaluate and refine changes in projected 

improvements in crop yields and in technologies, 

such as management practices and tools that increase 

sustainable feedstock availability. For crop residues, 

the scope of the discussion focused primarily on 

corn—the most important residue-producing crop 

with the greatest potential for yield improvements and 

management of residue production. Participants ranked 

tolerance to drought, pest, disease, and other stress 

factors as the greatest barrier to increasing yields. Most 

thought that the development of genetic potential, 

biotechnology, and innovations in engineering and 

management could be leveraged to improve the yield 

and HI (i.e., maximize both grain and biomass yields) 

with minimum inputs and sustained soil productivity. 

A number of participants were optimistic that stover 

yields could be improved along with grain yields and 

recommended continued work in genetics, including 

selective breeding and the application of new 

biotechnology approaches. However, other participants 

stressed that without a market pull for higher stover 

yields relative to grain yields, the emphasis will 

continue to be on maximizing grain yields. Also, some 

participants emphasized that the stress factors that are 

barriers to increasing yields have to be overcome to 

have consistently higher yields, although there was 

clear agreement on continued growth of corn yields 

through 2050.38 The key divergence in opinions was 

centered on the extent to which breeding and genetic 

selection programs can overcome stress factors. A 

majority of participants supported this perspective, 

while the remaining participants considered stress 

factors too significant of a barrier. The consensus highyield estimate translates into an average annual growth 

rate of almost 2%. By 2022, corn yield would be 228 

bushels per acre in a high-yield scenario compared to 

the baseline of 183 bushels per acre in 2022. The highyield estimate of 228 bushels per acre is approximately 

the same as the 233 bushels per acre value used by EPA 

in their regulatory impact analysis of the RFS.39

38 The updated BTS estimates biomass availability over the 2010–2030 timeframe. The workshop used a longer timeframe as it addressed 

specific technology developments and implementation.
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Participant discussion and opinions relative to HI were 

even more complex and challenging (more information 

on the HI is provided in Text Box 4.3). Three themes 

emerged, depending on the understanding of the 

variable. A group with considerable experience 

provided a data-driven case that HI at harvest time is 

currently increasing with higher yields and genetic 

selection. Another broad group thought that HI can 

be improved through breeding and biotechnology. Yet 

another group held the position that, while harvest-time 

HI is demonstrably increasing with yield under current 

production, the HI at physiological maturity is a more 

important criterion. The conclusions of the participant 

discussion essentially became: (1) harvest-time HIs are 

increasing as yield increases; (2) the material balance 

calculations needed for accurate stover availability 

analysis require HI at physiological maturity, for which 

less data exists to construct HI trend analysis; and 

(3) HI is a crop characteristic that can be engineered 

to serve market drivers. A summary of the baseline 

and high-yield assumptions used in the POLYSYS 

modeling framework is shown in Table 4.4.

In addition to yield and HI, the workshop solicited 

inputs on environmental sustainability, economic 

viability, land use, and other technology/ policy 

advances, although not to the level of detail as yield 

and HI. For sustainability, the participants listed and 

ranked factors that currently limit environmentally 

sustainable increased yields. The workshop 

resulted in four of the most promising, and likely 

to be implemented, overarching actions that could 

“sufficiently be adopted by 2022” in support of 

sustainability production systems of future highyield scenarios. The proposed actions are to: (1) 

improve residue management practices; (2) use a 

holistic systems approach; (3) implement soil health 

monitoring; and (4) advance variable rate collection 

technology.

As with the sustainability-limiting factors, economics 

and land use were addressed to determine if solutions 

would be available to support high-yield alternatives 

in the future. Economic concerns included market 

access and viability, investments, and risk reduction. 

Participants suggested that market viability can be 

supported by prioritizing crop development for both 

grain and residue yield, maintaining a constant HI, and 

developing innovative landscape-scale management 

strategies that reduce inputs and increase yields. 

Economic returns could be enhanced by producing 

both grain and biomass as cash crops, using incentives 

that lower lifecycle GHGs, adopting new technologies 

that result in higher biomass and grain yields, and 

reducing equipment costs as “we move down the 

learning curve.” Risks can be better managed through 

reliable cost models, long-term contracting options, 

accounting for feedstock variation, considering land 

tenure, distributing returns between producer and user, 

and better education. Factors limiting the availability 

of land for crop expansion include competition for 

agriculture crops versus livestock production, as 

well as loss of agricultural lands to urbanization. 

Participants think there is potential for using other 

lands, such as public lands and marginal lands, for 

producing biomass feedstock. The high-yield scenario 

did not consider the use of public lands for crops or 

any changes in baseline crop acres. There is strong 

potential, however, to realize yield increases if a 

portion of marginally productive lands (including 

CRP) are brought into production. To continue this 

expansion, more field trials and data analysis are 

needed to identify which germplasm combination best 

responds to increasingly challenging environments.

39 The EPA higher corn yield scenario of 233 bushels per acre in 2022 (EPA, 2010) was developed in consultation with the USDA as well as 

industry groups (e.g., Monsanto and Pioneer).
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HI for corn for grain [ratio of grain to total biomass (grain plus stover)] has long been reported around 0.5 (Kiniry and Echarte, 

2005b). [An HI of 0.5 is equivalent to a stover:grain ratio of 1:1.] The best hybrids are in the 0.5 to 0.55 range (MAFRI, 2009). 

However, whether the corn grain is dry (i.e., 0% moisture) or 15% moisture is not always agreed upon. Pordesimo et al. (2004), 

for a trial in Tennessee, found that in the range of 18%–31% corn grain moisture (a moisture content they define as the range 

one would harvest grain at), the HI of corn grain (at its moisture content) with corn stover (on a dry basis), ranged between 

0.54 and 0.57. Putting the corn grain on a dry weight basis, the harvest index varies between 0.46 and 0.50 (Table below). 

Shinners and Binversie (2007), for Wisconsin, found that on a dry weight basis, at corn grain harvest, the harvest index was 

0.52. Similarly, Johnson et al. (2006) estimated the mean HI of dry grain and stover to be 0.53 from published literature. Data 

presented by Wilhelm et al. (2011) results in an HI of .56 for a range of yields.

TEXT BOX 4.3 | CROP HARVEST INDEX

Ratio of corn stover to corn grain based

Time after

planting (days)

Grain moisture 

content (%)

Stover yield

(dry Mg/ha)

Corn grain 

(wet) yield

(Mg/ha)

Harvest index 

(stover dry &

 grain wet)

Harvest index 

(stover dry &

grain dry)

118 30.6 15.57 19.05 0.55 0.46

122 25.1 12.02 14.43 0.55 0.47

125 23.4 11.52 14.97 0.57 0.50

132 22.5 12.11 14.64 0.55 0.48

136 18.3 11.48 13.54 0.54 0.49

Note: Based Table off Pordesimo et al. (2004).

There are some seemingly contradictory statements in the literature about the effect of plant density on HI. Tollenaar et al. 

(1994) state that harvest index decreases when plant density increases above a certain critical threshold. Dobermann et al. 

(2002) examined corn under three different plant densities (ranging from 28,000 to 47,000 plants acre-1) and two management 

intensities and found that HI decreased with increasing plant density. For their middle plant density (35,000–41,000 plants 

acre-1), HI over three years was 0.50. Duvick et al. (2004) state that over time there has been very little change when harvest 

index is averaged over plant densities, but there is a trend toward higher HI as plant densities are increased. Hashemi et al. 

(2005) present a graph (their Fig. 3) showing HI for three corn hybrids increasing up to a certain point [plant density of 6 to 9 

plants m-2 (24,000 to 36,000 acre-1) depending on the hybrid] and then decreasing. [In 2008, the major corn producing states 

of Illinois, Indiana, Iowa, Minnesota, and Wisconsin had about 40% of their acres with plant populations of 30,000+, except 

Minnesota which was 56%, (Ohio was 34%) with the rest of the acreage with plant populations less than 30,000. Other major 

corn producing states, with less favorable growing conditions, Kansas, Missouri, Nebraska, and South Dakota, had plant 

populations over 30,000 plants acre-1 at 12% or less (USDA-NASS, 2008).] Over time with the development of newer hybrids, 

planting densities have increased as yields have increased. Newer hybrids have higher yield potentials, but to reach these 

higher yields planting density must be increased. What can be said about the effect of plant density on harvest index is that 

for a given hybrid there is a plant density (or a range of densities) that maximizes harvest index. Over time with changing 

hybrids this density has been increasing. As Dobermann et al. (2002) show, the HI can be decreased while simultaneously 

increasing corn yield by increasing plant density. Whether this is economically desirable depends on input prices, corn grain 

price, and stover price.

Kiniry and Echarte (2005a) provide a brief review of some reported corn harvest indices and suggest that an HI of 0.54 is 

“reasonable” for modern hybrids at planting densities up to 10 plants m-2 (40,000 plants acre-1). [Note that this is planting 

density and not plant population. Seed mortality of 15% seems reasonable as Farnham (2001) assumes, which would 

imply that the HI is valid up to a plant density of 34,000.] Above 10 plants m-2 harvest index falls linearly to 0.375 at 30 

plants m-2 (121,000 plants acre-1). For purposes of estimating corn stover potential the suggestion is to use an HI of 0.54 as 

recommended by Kiniry and Echarte (2005b) (a stover:grain ratio of 0.85). This provides a more conservative (lower by 15%) 

estimate of corn stover than using an HI of 0.50 (a stover:grain ratio of 1.0). If grain and stover were valuable enough, one 

might actually manage corn for a lower harvest index, say 0.5, which could potentially give a greater corn stover resource.
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Parameter Baseline High-Yield

Yield

Uses the 2009 USDA baseline for 2009–2018. 

Baseline extended to 2030 by extrapolating trends 

in last 3 years of the baseline forecast. 

2009 – 157 bu/acre

2017 – 174 bu/acre

2022 – 183 bu/acre

2030 – 201 bu/acre

Used consensus workshop estimates.

2009 – 157 bu/acre

2017 – 201 bu/acre

2022 – 228 bu/acre

2030 – 265 bu/acre 

Harvest index Assumes an HI of 0.5, which is equivalent to a 

stover to grain ratio of 1:1.

Workshop HI input ranged from 0.5–0.7 with an 

assumed harvest index of 0.5 (1:1 stover: grain 

ratio). Modeling determined that results scale 

linearly with the stover to grain ratio.

Tillage

Assumes a gradual changing mix of conventional 

till (CT), reduced till (RT), and no-till (NT) in the 

following proportions from 2009 to 2030:

Corn 

2009 – 38% CT, 43% RT, 20% NT

2030 – 34% CT, 43% RT, 23% NT

Wheat

2009 – 42% CT, 43% RT, 16% NT

2030 – 35% CT, 43% RT, 23% NT

Assumes a mix of conventional till (CT), reduced till 

(RT), and no-till (NT) in the following proportions by 

2030:

Corn

2030 – 7% CT, 37% RT, 57% NT

Wheat

2030 – 13% CT, 26% RT, 61% NT

Retention coefficient

Uses RUSLE2 and WEQ for erosion, the SCI (OM 

factor) for reduced till, and the combined SCI for 

no-till as carbon triggers.

Same as baseline. Estimated retention different as 

defined by yields.

Cropping rotation Uses cropping rotations based on NRCS crop 

management zones.

Uses cropping rotations based on NRCS crop 

management zones.

Costs Same for baseline and high-yield scenarios

Summary of Baseline and High-Yield Residue Removal Assumptions 

for Agricultural Crops Table 4.4
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4.5 Crop Residue Supply Results

Estimates of primary residues are presented for two 

scenarios—a baseline and high-yield. The estimated 

baseline is an extension of the USDA 10-year 

projections to 2030. The years outside the USDA 

projections are based on trends of the last three years 

of the baseline forecast (2018–2020). The highyield scenario evaluates the impact of high corn 

yields and the effect of increased amounts of no-till 

cultivation, which has less restrictive residue retention 

requirements.

4.5.1 Baseline Estimates of Crop 

Residue Potential

Crop residue supply results from the POLYSYS 

simulation, under baseline assumptions, are 

summarized in Figure 4.9 for the combined corn stover, 

wheat straw, oat and barley straw, and grain sorghum 

stubble residues. The results are for simulated prices 

ranging from $40 to $60 per dry ton at the farmgate. At 

the lowest simulated price, about 27 million dry tons 

of crop residues are profitable to collect. This quantity 

increases to 80 million dry tons by 2030. The increase 

is attributable to yield growth, as defined by the USDA 

projections, and additional acres in no-till cultivation. 

The additional acres under no-till largely come from 

converting conventionally tilled acres. As discussed 

earlier, residue removal is allowed under both reducedtill and no-till cultivation, with the amount removable 

defined by county estimated retention coefficients 

(Figure 4.8). The no-till retention coefficients are less 

restrictive than reduced till. Residue removal is not 

allowed under conventional tillage. Higher farmgate 

prices bring in more residue, as shown in Figure 4.9. 

No residue is available below $35 per dry ton and the 

significant jump in supply between the price level 

of $40 per dry ton to $45 and $50 is attributed to the 

sensitivity of collection costs to per acre potential 

yield. At the highest price, most of the residue after 

accounting for sustainability requirements is profitable 

to collect. Currently, this price ($60 per dry ton) 

amounts to about 111 million dry tons and increases to 

180 million dry tons by 2030.

Figure 4.8 Crop residue such as corn stover available for energy

(Courtesy of Rob Mitchell, ARS)
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40 This assumes a conversion rate of 85 gallons per dry ton and about 20% of the total feedstock unavailable due to losses in hauling, storing 

and handling, and/or some of the feedstock being stranded.

The largest fraction of collectable residue is corn 

stover. At the lower prices, slightly more than 80% 

of the residue is corn stover. Higher prices bring in 

proportionately more straw residue. This is due to 

the smaller amount of collectable straw residue per 

acre and, therefore, higher collection costs. Specific 

quantities of residue are shown in Figure 4.10 for 

selected years and prices. At the median simulated 

price of $50 per dry ton, about 94 million dry tons 

of residues are profitable to collect. This quantity 

increases to 164 million dry tons in 2030 and would be 

equivalent to an annual production of about 6.2 billion 

gallons of biofuel.40 Finally, supply curves for corn 

stover and wheat and other grain straw for selected 

years are shown in Figures 4.11 and 4.12. The curves

shift outward over time owing to increasing crop yields 

that more easily offset requirements for sustainability.

The location of potential supplies of corn stover, wheat 

straw, and other grain straw are depicted in a series of 

maps in Figure 4.13 through Figure 4.18. The figures 

show the location residue in 2012 and 2030 and type 

of tillage. The maps, as expected, show large quantities 

of stover in the Corn Belt. Higher availability occurs 

under no-till conditions, as opposed to reduced till, and 

much larger quantities will be available in 2030 due to 

yield growth. Similar results should be noted for wheat 

and other grain residue, with the exception of less 

supply density and more geographic dispersal.

200

180

160

140

120

100

80

60

40

20

0

2012201320142015201620172018201920202021202220232024202520262027202820292030

Million dry tons

$40/dry ton $45/dry ton $50/dry ton

$55/dry ton $60/dry ton

Total available supply of crop residues after sustainability constraints are met at 

various prices under baseline assumptions Figure 4.9
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0

30

60

90

120

150

2012

2017

2022

2030

2012

2017

2022

2030

2012

2017

2022

2030

Million dry tons

<$40 dry ton <$50 dry ton <$60 dry ton

Corn Stover Wheat straw & other grain residue

$90

$80

$70

$60

$50

$40

$30

$20

$10

$0

0 50 100 150 200

$/ dry ton (farmgate)

Million dry tons

2012 2017 2022 2030

Amounts of corn stover and wheat and other grain residue at selected 

prices and years under baseline assumptions Figure 4.10

Supply curves of potential corn stover production for various years 

under baseline assumptions Figure 4.11
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$90

$80

$70

$60

$50

$40

$30

$20

$10

$0

0 10 20 30 40 50

$/ dry ton (farmgate)

Million dry tons

2012 2017 2022 2030

Supply curve of potential residue production (wheat, sorghum, oats, and barley) 

for various years under baseline assumptions Figure 4.12
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Corn stover residue yield for reduced tillage and no-till production, 2030

Corn stover residue yield for reduced tillage and no-till production, 2012

Figure 4.14

Figure 4.13
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Wheat straw for reduced tillage and no-till production, 2030

Wheat straw for reduced tillage and no-till production, 2012

Figure 4.16

Figure 4.15
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Small grains and sorghum residues for reduced tillage and no-till production, 2030

Small grains and sorghum residues for reduced tillage and no-till production, 2012

Figure 4.18

Figure 4.17
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4.5.2 High-Yield Estimates of 

Crop Residue Potential

The high-yield crop residue scenario considers two 

different assumptions regarding tillage and crop 

yields over the baseline scenario. The first involves 

simulating a much larger fraction of corn, wheat, and 

grains planted into no-till cultivation. These tillage 

assumptions are summarized in Table 4.4. The second 

is to simulate a more than doubling of the rate of 

increase in corn yield growth. The baseline assumes a 

national average corn yield equal to slightly more than 

200 bushels per acre—the equivalent of an average 

annual increase of about 1%. The high-yield scenario 

increases corn yields according to the high-yield 

workshop consensus estimates—about 2% annually—

and reaching a national average of about 265 bushels 

per acre in 2030 . 

Results of the POLYSYS simulation under high-yield 

scenario assumptions about tillage and corn yield 

are summarized in Figure 4.19 at five alternative 

farmgate prices. Generally, annual supply quantities 

are considerably greater than estimates under baseline 

assumptions about tillage and yield. At the lowest 

simulated price, an additional 52 million dry tons of 

stover is profitable to collect in 2012 increasing to 156 

million dry tons by 2030. The increase in the amount 

of wheat straw and other grain residue is much more 

modest only about 10 to 15 million dry tons over the 

2012 to 2030 simulation period at a $50 per dry ton 

farmgate price. Specific quantities of corn stover and 

wheat straw and other grain residue under the baseline 

and high-yield scenario are summarized in Figure 4.20 

for selected years and prices. 

Under EISA, ethanol produced from corn is limited 

to 15 BGY starting in 2015. Corn yield has been 

increasing over time, and corn grain acreage has 

remained relatively level at about 80 million acres 

per year. Demand for corn can be divided into the 

following categories: feed (and residual); exports; 

ethanol; and food, seed, and industrial uses other than 

ethanol. Exports and food, seed, and industrial uses 

other than ethanol have shown modest increases over 

time and are projected by USDA-OCE/WAOB (2010) 

to continue modest increases. Feed use has been 

relatively level but also is projected to show modest 

increases in the future. Ethanol from corn has been 

absorbing much of the increase in corn production and 

is projected to do so between now and 2017. 

High-yield-workshop participants projected corn yields 

to increase to 206, 231, 265, and 318 bushels per acre 

in 2017, 2022, 2030, and 2050 respectively. One seed 

company’s goal is to increase corn yields 40% in 

the next 10 years to more than 210 bushels per acre 

(Perkins, 2009). These are considerably higher yields 

than the USDA-OCE/WAOB projection. Rick Tolman, 

Chief Executive Officer of the National Corn Growers, 

says, “Unless we have growing ethanol production, 

you can’t use all the corn that will be produced by 

farmers in the future and you’ll have a depressing 

influence on price. We need a growing demand base to 

keep the incentives in place for farmers to grow more 

corn. Livestock producers are still our most important 

customers and exports are important, too, but the only 

growth piece we have is ethanol to keep up with that 

increased productivity (Perkins, 2009).” The simulated 

POLYSYS results under the high-yield scenario are 

very much consistent with this observation. 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

350

300

250

200

150

100

50

0

Million dry tons

High yield scenario is 2% annual yield growth

$60/dry ton $55/dry ton

$45/dry ton $40/dry ton

$50/dry ton

Figure 4.19 Total available supply of crop residues under high-yield assumptions
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

0

50

100

150

200

250

300

2012

2017

2022

2030

2012

2017

2022

2030

2012

2017

2022

2030

<$40 dry tons <$50 dry tons <$60 dry tons

Corn Stover Wheat straw & other grain residue

Million dry tons

Amounts of corn stover and wheat and other grain residues at selected years and 

prices under high-yield assumptions Figure 4.20
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4.6 Secondary Cropland 

Residues and Waste Resources

A largely unused supply of cellulosic feedstocks 

for biofuels is categorized as secondary cropland 

residues and waste resources. These supplies are 

either the result of crop harvesting and processing or 

recovered from final consumption (the supply curves 

are summarized in Figure 4.21 and in Table 4.6). The 

availability and feasibility of collecting these supplies 

for biofuels is a function of current use, regional 

supply, and storage and handling costs. The feedstocks 

themselves are varied in their quality and availability 

and may be considered economically feasible and 

environmentally beneficial with appropriate incentives, 

logistics, and processing and refining technology.

The residues and wastes considered here include 

sugarcane trash and bagasse, cotton gin trash and 

residues, soybean hulls, rice hulls and field residues, 

wheat dust and chaff, orchard and vineyard prunings, 

animal fats, animal manures, and MSW. However, this 

is not an exhaustive list of these resources—there are 

numerous other secondary processing residues and 

wastes, although the quantities are much smaller.41 

It is important to recognize that the production levels 

of the primary products for which these residues are 

generated crops may be influenced to a high degree by 

government intervention and international trade, and 

the current projections are based upon the assumption 

of continuation of current policies.

0 20 40 60 80 100

$70

$60

$50

$40

$30

$20

$10

$0

$/dry ton (farmgate)

Million dry tons

2012 Quantity 2017 Quantity 2022 Quantity 2030 Quantity

Current and future secondary processing residues, field residues, 

and waste resources Figure 4.21

41 The report by Frear et al. (2005) is an example of the wide variety of food processing residues and wastes that are generated in some

 states. 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

The price for many of these resources could be 

considered near zero, at zero, or even negative, 

indicating producers pay to dispose of these resources. 

Wheat dust, cotton gin trash, and rice hulls are 

estimated at zero prices; however, they are assumed 

to have a price of $20 per dry ton for half of available 

supply and $30 per dry ton for the other half of supply 

to cover collection and handling costs. Other resources 

with little to no costs include field residues of rice 

and cotton. In this case, collecting and baling to the 

farmgate were estimated in a similar fashion as corn 

stover and grain straws. Sugarcane bagasse is assumed 

used and unavailable. Animal manures are priced 

according to fertilizer application with an additional 

cost of $15 per dry ton added for collection and 

handling costs. Animal fats and waste oils are currently 

priced in the market, and prices were retrieved from 

USDA-ERS (2010c). Finally, MSW sources, such as 

food wastes, are assumed included in the currently 

used resources reported in Chapter 2 of this report. The 

overall supply curves for these secondary residues and 

wastes are summarized by selected year in Figure 4.21.

4.6.1 Sugarcane Residues

Sugarcane is a tall erect plant with a high-sugar-content 

stalk, leaves, and tops (Figure 4.22). After the sugar is 

extracted from the stalk, what remains of the stem is 

bagasse. The leaves and tops, and any parts of the stalk 

that remain in the field after harvest, are referred to as 

trash. There are a number of technical coefficients in 

the literature that relate the amount bagasse and trash 

produced per ton of sugarcane.42 In this update, it is 

assumed that each ton of sugarcane produces 0.14 dry 

tons of bagasse and 0.075 dry tons of field trash. It is 

further assumed that one-half of the field trash can be 

collected.

Sugarcane residues then are the product of sugarcane 

yield (as reported on a wet basis from USDA-NASS) 

and the technical coefficient—0.14 for bagasse and 

0.0375 for trash. Costs for sugarcane trash collection 

are based on the use of a rake and a large rectangular 

baler. Table 4.5 shows estimated supplies of sugarcane 

residues, which total about 1.1 million dry tons at 

farmgate prices of $40 per dry ton or less. The bagasse 

component is not included because it is already used 

for energy.

trash, which total about 1.1 million dry tons at

Figure 4.22 Sugarcane tassel 

(Courtesy of Ed Richard, USDA-ARS)

42 Assumptions vary in the range of reported moisture, ash, and energy content of bagasse and sugar cane trash. For this report, results from 

Braunbeck et al. (2005) are adopted. For additional reference, see Deepchand (2005) and Ho (2006).
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4.6.2 Cotton Gin Trash and 

Field Residues

Cotton gin trash is generated from the picking and 

cleaning processes of cotton harvesting and includes 

seeds, leaves, and other foreign material, which could 

include sand and soil. It can have high moisture and 

nutrient content, and disposal can be costly. Cotton 

residue refers to the stalks left on the field after the 

cotton lint has been harvested.

There are two main types of cotton harvesters—spindle 

pickers and strippers (National Cotton Council of 

America, 2009). The stripper is a single-pass system 

that harvests significantly more of the cotton plant 

and foreign material (sand, soil, etc.) than spindle 

pickers (0.15 to 0.50 tons per bale versus 0.04 to 0.08 

tons per bale for spindlers) and is thus suitable for 

determinate cotton (e.g., reaches an expected size 

during the growing season) (Holt et al., 2003; Kim 

et al., 2004; Mayfield, 2003; Weaver-Missick et al., 

2000). Spindle pickers can be used more than once in a 

growing season to harvest cotton and thus are suitable 

for indeterminate varieties (e.g., grows continually 

throughout the season). About 25% to 33% of the 

U.S. cotton harvest is estimated to be stripper picked, 

leaving the remaining 67% to 75% to be harvested 

with spindle pickers (Glade and Johnson, 1983–1985; 

Mayfield, 2003). 

Cotton gin trash is generated in the cotton mill from 

cleaning the lint and has been estimated at various 

levels.43 On average, cotton gin trash is produced 

at a rate of 0.16 tons of cotton gin trash per bale of 

cotton (480 pounds) after foreign material is counted.44 

Future production of cotton gin trash is estimated using 

state level harvesting type percentages and applying 

cotton production forecasts of upland and pima cotton 

production (USDA-OACE/WAOB, 2010). These 

results are shown in Table 4.5 at prices up to $40 per 

dry ton.

The USDA-OACE/WAOB (2010) projections for 2017 

of 17.8 million bales are used for upland cotton; for 

each year thereafter, upland cotton production increases 

by 0.2 million bales per year. In addition to upland 

cotton production, 0.5 million bales of Pima cotton 

are assumed to be produced each year. Total cotton 

gin trash production ranges from 1.4 to 1.8 million 

dry tons on an annual average currently and in 2030, 

respectively. This residue would be available at central 

sites and cotton gins and not dispersed in agricultural 

fields.

Conversely, cotton stalks remain in the field after 

cotton harvest. The amount in a field will differ 

according to whether a stripper or spindle harvester 

is used. The assumptions for calculating cotton gin 

trash are that spindle and stripper harvesters take 

around 0.05 and 0.18 tons of residues per bale of 

cotton with them. These amounts must be subtracted 

from the amount of residue available in the field. To 

estimate prices of cotton harvest residue, the following 

operations are assumed: shredding, raking, and bailing 

with a large rectangular baler. For cotton, shredding 

is a typical operation performed even if the residue is 

not harvested. Therefore, the shredding operation costs 

are not included in the cost of harvesting residue. The 

amount of cotton residue available is estimated at 1.2 

million dry tons currently, and up to 6.7 million dry 

tons in 2030 at a price of $40 per dry ton (Table 4.5).

43 The range of cotton gin trash estimates includes 1.3 million tons (Buser, 2001), 2.5 million tons (Comis, 2002), and 3.2 million tons (Holt et 

al., 2003). Parnell et al (1994) state that in a typical year gins that handle spindle picked cotton generate 0.5 to 1 million tons and those 

that handle stripped cotton generate 1 to 1.5 million tons of cotton ginning trash. Their total range of cotton ginning trash produced in a 

year is 1.5 to 2.5 million tons. Holt et al. (2003) state that in 2001 in the United States 19.8 million bales of cotton [lint] and 3.2 million tons 

of cotton gin trash were produced, and in Texas 4.2 million bales of cotton and 680,400 tons of cotton gin trash were produced.

44 Holt et al. (2003) state that about 80% of the cotton gin trash could be used for fuel pellets. Schacht and LePori (1978) report six cotton 

gins in Texas where 11.1% of the cotton gin waste was cotton lint. According to Holt et al. (2009) previous research shows that the quantity 

of recoverable fibers in cotton gin trash is between 10% and 25%. Based on the Texas average of cotton gin trash produced as reported 

by Holt et al. (2003), 0.1806 tons of trash per bale of cotton lint, applying the 11.1% figure of Schacht and LePori (1978), and assuming that 

cotton gin trash is 90% dry matter, 40 pounds of lint are contained in the trash produced from one bale of cotton lint.
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45 In a 60-pound bushel of soybeans, the hulls have averaged 3.48 pounds over 2007 to 2009 (USDA-ERS, 2010d).

46 A facility in Stuttgart, Arkansas, has plans to convert rice hulls into ethanol at a rate of 50 gallons of ethanol per ton and to produce silica 

sodium oxide at a rate of 440 pounds per ton (Bennett, 2008).

4.6.3 Soybean Hulls

When soybeans are processed (crushed), they are 

separated into three components: meal, oil, and hulls. 

However, not all soybeans produced in the United 

States are crushed. Some soybeans are exported 

as whole beans and processed in other countries. 

Recently, soybean production has averaged about 3 

billion bushels annually. Almost 60% of this total 

was crushed, which produced 2.74 million dry tons of 

hulls.45 Soybean production is expected to increase to 

4.4 billion bushels by 2030, and the amount of crushed 

soybeans is expected to increase to nearly 2.5 billion 

bushels (USDA-OCE/WAOB, 2010). 

The corresponding hull residue will increase to nearly 

4 million dry tons. Hulls are currently used in livestock 

feed. Nelson (2010) reports that soybean hull prices 

ranged between $49 and $175 per ton at five locations 

(Alabama/Georgia; Central Illinois; Iowa; Minneapolis, 

Minnesota; and Kansas City, Missouri) between 

2004 and 2007. Because hulls are currently utilized, 

their availability as a cellulosic feedstock would be 

at or above prices at which they are currently sold. 

No soybean hull residue is assumed available in this 

update.

4.6.4 Rice Hulls and Field Residues

When rice is milled, its hull is removed. The hull 

represents 20% of the mass of rice and generally 

presents a disposal problem, although rice hulls 

currently can be utilized as a filter product or as 

chicken house bedding (Hirschey, 2003). Rice hulls 

<$20 per dry ton <$30 per dry ton <$40 per dry ton

Feedstock 2012 2017 2022 2030 2012 2017 2022 2030 2012 2017 2022 2030

Million dry tons

Rice field residue 0.0 0.0 0.0 0.0 6.3 6.9 7.4 8.0 6.5 6.9 7.4 8.0

Rice hulls 0.8 0.8 0.8 0.9 1.5 1.6 1.7 1.7 1.5 1.6 1.7 1.7

Cotton field residue 1.2 2.1 2.3 3.3 4.1 5.3 5.9 6.7 4.2 5.3 5.9 6.7

Cotton gin trash 0.7 0.8 0.8 0.9 1.4 1.6 1.7 1.8 1.4 1.6 1.7 1.8

Wheat dust 0.3 0.3 0.3 0.3 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6

Sugarcane residues 0.1 0.1 0.1 0.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

Orchard and vineyard 

prunings 2.9 2.8 2.8 2.8 5.7 5.6 5.5 5.5 5.7 5.6 5.5 5.5

Animal manures 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 13 16 20

Animal fats 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Total secondary 

residues & wastes 5.8 6.9 7.1 8.2 21 23 24 25 33 36 40 46

Table 4.5 Summary of Secondary Process Residues and Wastes

have the potential to be used for energy.46 Rice is 

produced in six states: Arkansas, California, Louisiana, 

Mississippi, Missouri, and Texas. In recent years, total 

rice production averaged 207 million hundred weight 

(100 pounds)—nearly 9 million tons, assuming 13.5% 

moisture content. Some rice is exported as rough rice 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

47 The fruits included in this analysis are apples, apricots, avocados, cherries, dates, figs, grapes, kiwi, nectarines, olives, peaches, pears, 

persimmons, pomegranates, and other non-citrus fruits. The citrus fruits are grapefruit, lemons, limes, oranges, tangerines, and other citrus 

fruit. The nuts are almonds, pecans, pistachios, walnuts, and other nuts (Figure 4.24).

(i.e., it has not been dehulled)—approximately 35% 

of total rice production on average. Adjusting for rice 

that is exported as rough rice and assuming that rice 

hulls represent 20% of rice, about 1.5 million dry tons 

of rice hulls per year are currently produced. Rice hull 

production is projected to increase by 10% to 15%, 

depending on production and the level of exports. 

Table 4.5 shows supplies of rice hulls.

Rice field residues (or straw) usually need to be 

disposed of off the field. In the past burning was 

common, but it is not allowed now. Because it has 

such high silica content, it is undesirable as a forage 

supplement. Sometimes it is incorporated into the 

soil, or it may be removed and utilized for energy, 

for example. The HI for rice straw has been reported 

in ranges of 0.5 to 0.3 (or straw to grain ratios of 

1:1 and 2.3:1). Duke (1983) states that rice straw is 

usually estimated to be two times the grain yield, but 

goes on to state that for the dwarf varieties, a straw 

to grain ratio of 1:1 prevails (HI of 0.5). Here, a more 

conservative harvest index of 0.5 is used to estimate 

rice straw residues (i.e., the higher HI gives a lower 

estimate for rice straw). It is assumed that moisture 

content for grain is 13.5%. Total straw production is 

estimated at about 6.5 million dry tons increasing to 

8 million dry tons by 2030. Rice straw is assumed to 

be harvested like corn stover and cotton residues with 

a shredding operation, followed by raking and baling 

(assumed to be a large rectangular baler for costing 

purposes). Seventy percent of the rice straw is assumed 

harvested. All of the rice field straw is assumed to be 

available at a farmgate price of $30 per dry ton or less 

(Table 4.5).

4.6.5 Wheat Dust and Chaff

Wheat dust and chaff are produced as wheat is 

processed at a grain elevator. Approximately 1% of 

wheat is assumed to become wheat dust and chaff, 

which could potentially be used as a cellulosic 

feedstock (Nelson, 2010). Wheat production is 

currently about 2.2 billion bushels and is projected to 

4.6.6 Orchard and Vineyard Prunings

Annual orchard and vineyard prunings (Figure 4.23) 

are estimated for fruits, citrus fruits, and nuts. The 

estimated biomass available, according to Nelson 

(2010), totals 5.7 million dry tons.47 More than 80% 

of the orchard and vineyard prunings are from five 

crops: oranges, grapes, almonds, pecans, and apples. 

More than half (52%) of the resource is in California, 

19% is in Florida, and the remainder is located in 

Texas, Oklahoma, Georgia, New York, and Michigan. 

The USDA projections (USDA-OCE/WAOB, 2010) 

forecast a slight decline in the production area of 

fruits and nuts. Production estimates from the USDA 

projections are used to index future orchard and 

vineyard prunings. Half of the orchard and vineyard 

prunings are assumed to be available at $20 per dry ton 

and all are expected to be available at $30 dry ton or 

less (Table 4.5).

increase slightly by 2030 (USDAOCE/WAOB, 2010). 

Wheat is assumed to be 88% dry matter, and applying 

the 1% wheat dust and chaff factor to all wheat 

production results in about 600 million dry tons. Half 

of the wheat dust resource is assumed available at $20 

per dry ton, and all is assumed to be available at $30 

per dry ton or less (Table 4.5).

4.6.7 Animal Fats

Animal fats suitable as secondary cropland feedstocks 

in biodiesel production include edible and inedible 

tallow, lard, white grease, poultry fat, and yellow 

grease. Yellow grease is included in the supply 

estimates, but a description is provided in a following 

section of the waste cropland resources. When animals 

are processed for meats, fats are a byproduct of the 

process. For beef, these fats are separated into edible 

and inedible tallow. For hogs, these fats are lard and 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Inedible tallow is most often used as a supplement for 

animal feed—a majority of its market share—followed 

by use in fatty acids, soap, methyl esters (biodiesel), 

lubricants, and other uses. Poultry fats are used in 

soaps, pet foods, and a few other consumer products. 

Prices for edible and inedible tallow and pork lard and 

choice white grease have varied from $0.11 to $0.48 

per pound in recent years (The Jacobsen Company, 

2010). Feedstock price greatly affects the end price of 

biodiesel, as feedstock price can account for up to 80% 

of the total biodiesel cost. Because of the high price of 

animal fats and existing uses, they are not considered 

further in this update.

Figure 4.23 Farmer clearing prunings

4.6.8 Animal Manure

Over the past several decades, livestock operations 

have experienced a trend toward fewer and more 

concentrated facilities. As a consequence, manure 

storage issues have arisen. Often, large confined 

livestock operations do not have enough cropland 

or pasture to adequately distribute manure, resulting 

in excess manure that poses a risk to water quality 

and human health. Additionally, the land resources 

are constrained to absorb manure nutrients within 

proximity to concentrated animal production facilities.

There are 1.3 million livestock farms in the United 

States (EPA, 2003). In 2003, slightly less than 

20%—or 238,000—of these farms were classified as 

an animal feeding operation (AFO). EPA defines an 

AFO as a facility where animals are confined and fed 

or maintained for at least 45 days during a 12-month 

period, and where crops, vegetation, forest growth, or 

post-harvest residues are not sustained in the normal 

growing season over any portion of the facility. AFOs 

produced more than 500 million tons of manure in 

2003 (EPA, 2003). The largest and most polluting 

AFOs are categorized as Concentrated Animal Feeding 

(Courtesy of ORNL)

choice white grease. Poultry produces poultry fat. 

Animal fats are a less costly feedstock than vegetable 

oils; however, animal fats contain high levels of 

saturated fatty acids, which result in a lesser flow 

quality than vegetable oil. Animal fats tend to lose 

viscosity, causing the formation of crystals that plug 

fuel filters, especially in colder temperatures. Because 

biodiesel from animal fat feedstock has the tendency 

to solidify in colder temperatures, vegetable oil will 

likely be the feedstock of choice in northern states. The 

supply of animal fats is limited and will not increase as 

demand for biodiesel increases.

Nelson (2010) provides estimates of edible and 

inedible tallow based on cattle processing at 70 

locations in 21 states, and lard and choice white 

grease based on hog processing at 70 locations in 

26 states. Edible and inedible tallow are produced at 

95 and 90 pounds per cow slaughtered, respectively. 

Lard and choice white grease are produced at 9 and 

10.5 pounds per hog slaughtered, respectively. Edible 

tallow, inedible tallow, lard, and choice white grease 

are estimated at 1.49, 1.41, 0.43, and 0.51 million tons 

according to Nelson (2010). Nelson does not provide 

an estimate for poultry fat, but Pearl (2002) estimates 

poultry fat production at 1.11 million tons.

Not all of these fats are necessarily available for energy 

use. Tallow, lard, and choice white grease are potential 

biodiesel feedstocks, but each also is used in markets 

such as edible food, soap, lubricants, and resins and 

plastics. Edible tallow is used for baking or frying fats 

and margarine, as well as certain inedible products. 
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Operations (CAFOs), which make up about 5% of 

all AFOs but contribute to more than 65% of excess 

nutrients (Ribaudo et al., 2005).

The EPA defines three different categories of CAFOs 

that are regulated: large, medium, and small operations. 

Large CAFOs are generally defined as operations with 

1,000 or more animal units (AUs).48 Medium CAFOs 

are AFOs that hold between 300 and 1,000 AUs and 

discharge pollutants through a manmade device that 

came into contact with the confined animals. AFOs that 

hold less than 300 AUs are labeled small CAFOs only 

if they discharge waste into water through a man-made 

device or directly into waters that originate outside the 

facility and come in contact with the confined animals.

One possible solution to mitigate pollution created 

by CAFOs is to use excess manure for production of 

bioenergy through anaerobic digestion. The nutrients 

remain in the digester effluent liquid and are usually 

returned to cropland. Other systems have potential 

such as capturing some of the nutrients in biochar 

from thermochemical processes, or even integrating 

phosphorus crystallization or nitrification recovery 

systems with energy production from manure.

This report estimates recoverable and available dry 

tons of manure for a baseline scenario. Recoverable 

and available manure estimates are based on 

assumptions by Kellog et al. (2000) reported in 

pounds of manure phosphorus excreted, recoverable, 

and available in excess of farm use. Gollehon et al. 

(2001) estimates the percentage of available manure 

phosphorus in excess of county potential use, which 

is used as an estimate for recoverable manure in the 

baseline scenario. It is assumed that the percent of 

manure phosphorus that is recoverable and available 

represents a lower bound estimate (19%) of the amount 

of total manure that is recoverable and available.

For the baseline scenario, it is assumed that manure 

from the largest classifications of livestock production 

is available for bioenergy. For future years, it is 

assumed that the market for manure will mature 

and recovery will increase 2% annually, a more 

conservative approach than Kellog et al. (2000). 

The baseline scenario assumes the price is equal to 

its fertilizer substitute value, plus a $15 per dry ton 

collection and handling fee. The selling price may also 

be determined by the type of application needed for the 

individual farm on which the fertilizer is land applied. 

Prices are computed using the 3-year average price for 

nitrogen, phosphorous, and potassium.

Animal manure production was identified for beef 

(cattle and calves), swine, poultry (broilers and layers), 

and turkeys. Total production of cattle, dairy, and 

swine was estimated as the product of total AUs (1,000 

pounds of livestock) and the percentage of inventory 

produced on large farms (greater than 10,000 head 

for cattle; 1,000 head for dairy; 5,000 head for swine) 

as a proxy for CAFO inventory. Litter available from 

poultry production was estimated at 70% of total 

poultry production (chicken broilers, chicken layers, 

and turkeys). Manure is assumed to have an average 

moisture content of 82.5%. Using the recoverability 

and availability percentages described above, the 

amount available under the base year is 12 million dry 

tons, increasing to 13, 16, and 20 million dry tons for 

years 2017, 2022, and 2030, respectively (Table 4.5).

4.6.9 Wastes Resources 

from Agriculture

Waste resources potentially available from the end 

consumer are considered tertiary cropland resources. 

These sources may or may not be currently utilized, 

and their availability is contingent upon the presence 

or absence of specific industries that may compete for 

the feedstock within a particular hauling distance of 

biorefineries. Common resources within this category 

are yellow grease and MSW.

Yellow Grease. Yellow grease differs from other 

animal fat feedstock in that it is the recycled cooking 

oil from restaurants. It may contain the recycled oils 

of both vegetables and animals, but the vegetable oil is 

hydrogenated so that it acts more like animal fat when 

converted to biodiesel. Yellow grease is the cheapest 

available feedstock for biodiesel production. Its 

supply, however, is limited, making it a more attractive 

feedstock to smaller capacity production facilities that 

will be located near large population areas where the 

food service industry is concentrated. Yellow grease 

48 An animal unit is defined as one thousand pounds of live animal weight. 
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4.7 Total Supply of Agricultural 

Biomass and Waste Resources

The largest quantities of agricultural residues and 

wastes are crop residues from the major commodity 

crops. They range from 27 to 80 million dry tons 

between 2012 and 2030 at a simulated farmgate price 

of $40 per dry ton (Table 4.6). Estimated crop residues 

supplies increase to 111 to 180 million dry tons at 

the simulated price of $60 per dry ton. The highyield scenario has potential to double the quantity of 

collectable crop residue. At the simulated price of $50 

per dry ton, total corn stover and total crop residue 

increase to 264 and 309 million dry tons by 2030, 

respectively. An additional $10 per dry ton (to total $60 

per dry ton) brings in only an additional 7 to 11 million 

dry tons of residues. Most of the collectable residue 

can be had for $50 per dry ton or less.

The secondary agricultural processing and other waste 

products (excluding manure) in the aggregate are 

in the range of 21 to 25 million dry tons depending 

on the year and price ($40 to $60 per dry ton), with 

orchard and vineyard prunings, cotton field residue, 

and rice straw being the largest individual components. 

Collectible animal manure production is larger, 

estimated at 12 and 59 million dry tons between the 

present and 2030 over the $40 to $60 per dry ton price 

range. In total, the agricultural processing residues and 

wastes range from about 33 to 84 million dry tons over 

the 20-year simulation period.

Combining all of the agricultural residues and wastes 

totals about 245 million dry tons at $50 per dry ton or 

less by 2030. An additional 20 million dry tons become 

available at an additional $10 per dry ton farmgate 

price. The high-yield scenario adds 146 million dry 

tons at the $50 per dry ton simulated price and 139 

million dry tons at the $60 per dry ton farmgate price.

accounted for 1.4 million pounds of U.S. animal fat 

production in 2004 (USDA-OCE/OEPNU, 2008). 

Municipal Solid Wastes (MSW). MSW originates 

from agricultural sources, such as food wastes and 

textiles. A large fraction of these resources are 

combusted into energy as mixed wastes. In 2010, the 

currently used amount of MSW agricultural wastes 

is estimated at about 7 million dry tons (see Chapter 

2). The estimated amount increases to 10.5 million 

dry tons per year in 2017 and continues at this level 

through 2030.

4.8 Summary

The analysis of primary crop residues from the major 

grains—corn, wheat, sorghum, oats, and barley—used 

a relatively sophisticated methodology to determine 

how much residue needs to remain in-place to meet 

soil erosion restrictions due to water and wind and 

maintain soil carbon levels. A number of datasets 

involving soils, land slope, climate, cropping rotations, 

tillage, management practices, and residue collection 

technology were used in the analysis. Of all of these 

factors the crop rotation and tillage data are two areas 

where the analysis would benefit from improved and 

more up-to-date data.

Once crop residue retention was determined, the 

estimation of crop residue supplies took into account 

grower payments for removed residue and collection 

costs as a function of dry tons removed per acre. There 

is only anecdotal information on grower payments 

or what farmers would expect from the sale of crop 

residues. In this update, it was assumed that farmers 

would accept the value of the removed nutrients plus a 

fixed amount per ton of removed residue. What farmers 

will accept for crop residues will depend on a host 

of factors that are impossible to know with precision 

in the absence of any significant markets for crop 

residues.
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Included in the agricultural resource analysis are 

processing residues and wastes. With the exception of 

animal manure, these supplies are significantly smaller 

than the primary crop residues, but maybe available 

at much lower costs. Technical coefficients were used 

to estimate the amount of available residue to total 

production and very broad assumptions were made 

regarding the costs to acquire these resources. Overall, 

estimated supplies provided in this assessment should 

be considered somewhat imprecise until additional data 

are available. 

The estimation of agricultural biomass resources 

identified several needs, these are summarized below:

• There is a need to understand the long-term 

effects of residue removal on soils and to validate 

the residue retention coefficients used in this 

analysis.

• Improved tillage and cropping rotation data would 

improve the residue retention coefficient analysis. 

• As discussed in this chapter, there are differing 

opinions between an increasing harvest index 

as yields increase and a harvest index that can 

be engineered to serve markets. The analysis 

reported here assumed a harvest index of 0.5 or a 

1:1 ratio of stover to grain. 

• There is a need to improve the technical 

coefficients used to estimate available secondary 

biomass resources and costs to acquire and 

process these feedstocks.
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

<$40 per dry ton <$50 per dry ton <$60 per dry ton

Feedstock 2012 2017 2022 2030 2012 2017 2022 2030 2012 2017 2022 2030

Million dry tons

Baseline

Corn 19 32 42 65 73 93 108 129 85 106 120 140

Wheat 6.7 7.8 9.1 12 18 22 26 31 23 26 31 36

Barley, Oats, Sorghum 1.0 1.3 1.6 2.9 2.4 2.5 2.4 3.6 2.8 2.7 2.6 3.7

Total primary 

residue 27 41 52 80 94 117 136 164 111 135 154 180

Secondary residues & wastes

Rice field residue 6.5 6.9 7.4 8 6.5 6.9 7.4 8 6.5 6.9 7.4 8

Rice hulls 1.5 1.6 1.7 1.7 1.5 1.6 1.7 1.7 1.5 1.6 1.7 1.7

Cotton field residue 4.2 5.3 5.9 6.7 4.2 5.3 5.9 6.7 4.2 5.3 5.9 6.7

Cotton gin trash 1.4 1.6 1.7 1.8 1.4 1.6 1.7 1.8 1.4 1.6 1.7 1.8

Sugarcane residue 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

Orchard and vineyard 

prunings 5.7 5.6 5.5 5.5 5.7 5.6 5.5 5.5 5.7 5.6 5.5 5.5

Wheat dust 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6

Animal manures 12 13 16 20 29 34 41 56 30 35 43 59

Animal fats 0 0 0 0 0 0 0 0 0 0 0 0

Total secondary 

residues & wastes 33 36 40 46 50 56 65 82 51 58 67 84

Total baseline 59 77 92 126 143 174 201 245 162 192 221 265

High-yield scenario

Corn stover 71 132 157 221 143 200 228 264 153 209 234 271

Wheat Straw 9.8 12 13 16 60 35 38 42 35 39 42 46

Barley, Oats, Sorghum 1.5 1.5 1.4 1.7 3.6 3.4 2.8 3.1 4.0 3.6 2.9 3.0

Total primary 

residue 83 146 171 238 176 239 269 309 193 252 279 320

Total high-yield 115 182 210 284 226 295 334 391 244 310 346 404

Summary of Baseline and High-Yield Scenarios — Agricultural Residues and

Waste Resources

Table 4.6

Notes: High-yield estimates for corn, wheat, barley, oats, and sorghum assume a 1% annual growth in energy crop yields. Increasing 

the assumed energy crop yield growth rate (e.g. 2 to 4% annually) will slightly change the estimated high-yield resource estimates 

above.
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5 BIOMASS 

ENERGY CROPS

49 In addition to the BTS, many other analyses conclude significant quantities of energy crops can be grown on abandoned, idle, and marginal 

cropland or other agricultural land without impacts to food and forage supply. For example, see BRDI (2008).

50 Much of the research was conducted under the Department of Energy’s Biomass Feedstock Development Program. More than 150 woody 

and 35 herbaceous crops including nearly 20 perennial grasses were screened and evaluated as potential energy crops. A historical 

perspective on herbaceous and woody crops can be found in Wright (2007) and Wright et al. (in press).

Perennial grasses, trees, and some annual crops can 

be grown specifically to supply large volumes of 

uniform, consistent-quality feedstocks for biofuel 

and biopower production. Growing these crops is a 

natural extension of current farm systems and offers 

additional profits to farmers and landowners. The 2005 

BTS included scenarios that assumed a relatively large 

shift of land into the production of energy crops. It 

was reasoned that energy crops could displace as many 

as 40 to 60 million acres of cropland and pasture and 

produce 150 to nearly 380 million dry tons of biomass 

sustainably, provided average annual yields of 5 to 8 

dry tons per acre could be attained. Demands for food, 

feed, and exports would still be met under these BTS

scenarios because of projected yield growth and other 

technological advances in U.S. agriculture.49

Implicit in the 2005 BTS was an assumption that 

energy crops are economically competitive and offer 

risk-adjusted net returns at least as high as what could 

be earned from growing conventional agricultural 

crops or from existing uses of the land. In this update, 

an agricultural policy simulation model (POLYSYS) is 

used to assess the economic competitiveness of energy 

crop production and determine how much cropland 

and pastureland could possibly shift to energy crops. 

The next section of this chapter provides background 

on energy crops. Included in this discussion are crop 

biology and adaptation, agronomics, production costs 

and yields, and requirements for sustainability. This 

energy crop background section is followed by a 

summary of key assumptions and data used to estimate 

potential supply and land-use change. The final part of 

this chapter provides results under baseline and highyield scenarios.

5.1 Background on Energy Crops

Beginning in the late 1970s, numerous woody and 

perennial grass crops were evaluated in species trials 

on a wide range of soil types across the United States.50

One key outcome of this research was the development 

of crop management prescriptions for perennial grasses 

and woody crops. Some highlights of this research 

are presented below for representative energy crops 

deemed to have high potential. These crops include 

three perennial grasses, an annual energy crop (highyield sorghum), and four woody crops, managed either 

as a single rotation (i.e., harvest before replanting) or 

managed as a multi-rotation (i.e., coppicing) crop. 

5.1.1 Switchgrass and 

Other Perennial Grasses 

Breeding and selection research on native perennial 

grasses such as switchgrass (Panicum virgatum), big 

bluestem (Andropogon gerardii), and indian grass 

(Sorghastrum nutans) started in 1936 when the USDA 

at Lincoln, Nebraska, began breeding native grasses to 

revegetate land damaged by the drought of the 1930s. 

In 1949, the first cultivar, ‘Nebraska 28’ switchgrass, 

was released jointly by the USDA and the University of 

Nebraska. Since that time, USDA and other scientists 

have evaluated native collections and selected and bred 
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(Courtesy of SD State University)

improved cultivars for most areas of the United States. 

These initial cultivars were developed for forage and 

conservation purposes. A full array of establishment 

and management practices has been steadily refined 

and improved. Millions of acres of these grasses have 

been planted. Past and continuing genetic research 

is leading to the development of bioenergy-specific 

cultivars with substantial genetic gains. Switchgrass 

is widely considered the model perennial grass for 

bioenergy production. 

Several characteristics make switchgrass, big bluestem, 

and indian grass desirable biomass energy crops. They 

are broadly adapted and native to North America, 

which reduces the concerns for becoming invasive 

species. Each has consistently high yields with minimal 

inputs and is well-suited to marginal land. Additionally, 

they are relatively easy to establish from seed, and a 

seed industry already exists. Long-term plot trials and 

farm-scale studies indicate switchgrass is productive, 

enhances and protects environmental quality, and is 

potentially profitable given the establishment of a 

viable cellulosic biofuels market. Although stands can 

be maintained indefinitely, they are expected to last 

at least 10 years, after which time the stands could 

be renovated and replaced with new, higher-yielding 

cultivars (Figure 5.1). Currently there are additional 

public and private breeding programs throughout the 

United States. 

Biology and adaptation. Switchgrass, big bluestem, 

and indian grass are perennial warm-season grasses 

that are native to most of North America, except 

for areas west of the Rocky Mountains and north of 

55°N latitude. They grow 3 to 10 feet tall with most 

of the root mass located in the top 12 inches of the 

soil profile, according to the long-standing literature. 

There is variation by species, but the root depth can 

reach 10 feet with new varieties even deeper. More 

than 70 years of experience with these grasses used 

as hay and forage crops demonstrates that they are 

productive and sustainable on rain-fed marginal land 

east of the 100th Meridian (Mitchell et al., 2010). This 

meridian matches the western boundary of Oklahoma 

(excluding the panhandle), and bisects North Dakota, 

South Dakota, Nebraska, Kansas, and Texas. They are 

adapted to a wide range of habitats and climates and 

have few major insect or disease pests. In addition 

to potential bioenergy production, these grasses are 

used for pasture and hay production, soil and water 

conservation, and wildlife habitat.

Switchgrass has distinct lowland and upland ecotypes 

and two primary ploidy levels (chromosome numbers). 

Tetraploid plants have 36 chromosomes, while 

octaploid plants have 72 chromosomes. All lowland 

ecotypes are tetraploids, whereas upland plants can be 

tetraploids or octaploids. Tetraploids and octaploids 

do not cross. Additionally, switchgrass ecotypes are 

differentiated by the latitude of their origin. Ecotypes 

from the southern United States are not well adapted 

to the northern United States because of winter kill, 

and northern ecotypes moved to the southern United 

States have low productivity. Upland ecotypes occur in 

upland areas that are not subject to flooding, whereas 

lowland ecotypes occur on flood plains and low-lying 

areas (Vogel, 2004). Generally, lowland plants have 

a later heading date and are taller with larger and 

thicker stems. Tetraploid lowland and upland ecotypes 

have been crossed to produce true F1 hybrids that 

have a 30% to 50% yield increase over the parental 

lines (Vogel and Mitchell, 2008). These hybrids 

are promising sources for high-yielding bioenergy 

cultivars. The lowland ecotypes and the lowland x 

upland hybrids have the most potential for bioenergy 

production because of their high yields.

Figure 5.1 Switchgrass research plot 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Production and agronomics. In perennial grasses, 

successful stand establishment in the seeding year 

is mandatory for economically viable bioenergy 

production systems (Perrin et al., 2008). Weed 

competition during establishment is a major reason 

for stand failure. For example, acceptable switchgrass 

production can be delayed by at least 1 year due 

to weeds and poor stand establishment (Schmer et 

al., 2006). No-till planting has significant cost and 

environmental benefits. After the establishment year, 

well-established switchgrass stands require limited 

herbicides. Nitrogen fertilizer is not recommended 

during the planting year since nitrogen encourages 

weed growth, increases establishment cost, and 

increases economic risk associated with establishment 

if stands should fail (Mitchell et al., 2008; 2010). In 

most agricultural fields, adequate levels of phosphorus 

and potassium will be in the soil profile (Mitchell et 

al., 2010). Good weed management and favorable 

precipitation will produce a crop equal to about half of 

potential production, which can be harvested after frost 

at the end of the planting year with 75% to 100% of 

full production achieved the year after planting.

Although switchgrass can survive on low-fertility 

soils, nitrogen fertilizer is required to optimize yield. 

The optimum nitrogen rate for switchgrass managed 

for biomass varies (Mitchell et al., 2008; 2010), but 

biomass declines over years if inadequate nitrogen is 

applied, and yield will be sustainable only with proper 

nitrogen application. Vogel and others (2002) found 

that for one variety, applying 100 pounds of nitrogen 

per acre per year optimized biomass, with about the 

same amount of nitrogen being applied as was being 

removed by the crop. A general nitrogen fertilizer 

recommendation for the Great Plains and Midwest 

region is to apply 20 pounds of nitrogen per acre per 

year for each ton of anticipated biomass if harvesting 

during the growing season, with nitrogen rate reduced 

to 12 to 14 pounds per acre per year for each ton of 

anticipated biomass if harvesting after a killing frost. 

The nitrogen rate can be reduced when the harvest is 

after a killing frost because less nitrogen is removed 

from the system and some nitrogen is recycled. 

Nitrogen is applied as switchgrass greens up in the 

spring to minimize cool-season weed competition. 

Spraying herbicides to control broadleaf weeds is 

typically only needed once or twice every 10 years in 

established, well-managed switchgrass stands (Mitchell 

et al., 2010).

Switchgrass can be harvested and baled with 

commercially available haying equipment (Figure 

5.2). Self- propelled harvesters with rotary heads 

are preferred for harvesting high-yielding (greater 

than 6 tons per acre) switchgrass fields. Harvesting 

switchgrass within 6 weeks before killing frost or 

leaving a stubble height shorter than 4 inches can 

reduce stand productivity and persistence, whereas 

harvesting after a killing frost will not damage stands. 

A single harvest per growing year generally maximizes 

switchgrass yields, and harvesting after a killing frost 

ensures stand productivity and persistence. Proper 

management maintains productive stands for more than 

10 years. Round bales tend to have less storage losses 

than large square bales when stored outside uncovered, 

but square bales tend to be easier to handle and load 

without road width restrictions. After harvest, poor 

switchgrass storage conditions can result in storage 

losses of 25% in a single year and can reduce biomass 

quality. Covered storage is necessary to protect the 

harvested biomass.

Potential yield and production costs. Switchgrass 

yield is strongly influenced by precipitation, soil 

fertility, location, and genetics. Most plot- and fieldscale switchgrass research has been conducted on 

forage-type cultivars, selected for other characteristics 

in addition to yield. Consequently, the forage-type (Courtesy of ORNL)

Figure 5.2 Baling switchgrass 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

cultivars in the Great Plains and Midwest are entirely 

represented by upland ecotypes which are inherently 

lower yielding than lowland ecotypes. Yield data 

comparing forage-type upland cultivars like CaveIn-Rock, ‘Shawnee,’ ‘Summer,’ and ‘Trailblazer’ do 

not capture the full yield potential of switchgrass. For 

example, high-yielding F1 hybrids of ‘Kanlow’ and 

Summer produced 9.4 tons per acre annually, which 

was 68% greater than Summer and 50% greater than 

Shawnee (Vogel and Mitchell, 2008). New biomasstype switchgrass cultivars will be available soon for 

the Great Plains and Midwest. In a 5-year study in 

Nebraska, the potential ethanol yield of switchgrass 

averaged 372 gallons per acre and was equal to or 

greater than that for no-till corn (grain + stover) on a 

rain-fed site with marginal soils (Varvel et al., 2008). 

These results were based on switchgrass cultivars 

developed for grazing. Significantly greater yields are 

expected by the next generation of biomass-specific 

cultivars.

An economic study based on the 5-year average of 10 

farms in Nebraska, South Dakota, and North Dakota 

indicated producers can grow switchgrass at a farmgate 

price of $60 per ton (Perrin et al., 2008). Producers 

with experience growing switchgrass had 5-year 

average costs of $43 per ton, with a low of $38 per 

ton. These costs include all expenses plus labor and 

land costs. This research from nearly 50 production 

environments indicates that growing switchgrass for 

cellulosic ethanol could be economically feasible in 

the central and northern Great Plains, with sufficiently 

cost-effective fuel conversion and distribution. Fuel 

and land prices have increased since this study, so the 

cost increases for those inputs need to be considered 

when determining switchgrass production costs.

Sustainability. Sustainability is crucial for biomass 

energy crops. Switchgrass protects soil, water, and air 

quality, sequesters atmospheric carbon, creates wildlife 

habitat, increases landscape diversity, returns marginal 

farmland to production, and could potentially increase 

farm revenue. In a 5-year study, Liebig et al. (2008) 

reported that switchgrass stored large quantities of 

carbon (C), with four farms in Nebraska storing an 

average of 2,590 pounds of soil organic carbon (SOC) 

acre/year when measured to a depth of 4 feet across 

sampled sites.

The energy-efficiency and sustainability of cellulosic 

ethanol from switchgrass has been modeled using net 

energy value (NEV), net energy yield (NEY), and the 

petroleum energy ratio (PER) (Schmer et al., 2008). 

Switchgrass fields in the Midwest produced 540% 

more renewable energy (NEV) than non-renewable 

energy consumed in production over a 5-year period 

(Schmer et al., 2008). The estimated on-farm NEY 

was 93% greater than human-made prairies and 652% 

greater than low-input switchgrass grown in small plots 

in Minnesota (Tilman et al., 2006). The on-farm study 

had an estimated PER of 13.1, equivalent to producing 

93% more ethanol per acre than human-made prairies 

and 471% more ethanol per acre than low-input 

switchgrass in Minnesota (Schmer et al., 2008).

Implementing switchgrass-based bioenergy production 

systems will require converting marginal land 

from conservation plantings or annual row crops 

to switchgrass. Growing switchgrass on marginal 

sites likely will enhance ecosystem services more 

rapidly and significantly than on productive sites. 

There is concern of soil carbon loss associated with 

converting conservation grasslands such as those in 

the Conservation Reserve Program to bioenergy crops 

such as switchgrass. Recent research on converting 

grasslands to no-till corn demonstrates that using notill revegetation practices results in no measurable soil 

carbon loss (Follett et al., 2009; Mitchell et al., 2005).

Switchgrass is the leading perennial grass biofuel 

feedstock option for the Great Plains and Midwest. 

Some have questioned if switchgrass is the best 

choice from an ecological perspective, and contend 

that diverse mixtures of native plants are ecologically 

more beneficial and should be considered for biomass 

production. However, feedstock selection will be 

determined by the amount of available land and 

the ability of producers to profit by its production. 

Managed switchgrass monocultures can produce 1.5 

to 5 times more biomass than native tallgrass prairies 

and seeded polycultures (Table 5.1), which translates 

into less land being required to produce the necessary 

biomass and more profit potential for the producer.

An Oklahoma study compared monoculture and 

polyculture feedstock production managed in a lowinput system (no nitrogen fertilizer) (Griffith et al., 
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Reported Perennial Grass Yield and Acres Required for a 50-Million 

Gallon Cellulosic Ethanol Plant Table 5.1

Feedstock Yield, dry tons/acre Acres need to grow 

588,000 dry tons/year

Percent of land in 

25-mile radius

LIHD prairiea 1.75 336,000 27

Managed native prairieb 2.5 235,200 19

Shawnee switchgrassc 5 117,600 9

Bioenergy switchgrassd 7.4 79,500 6

Hybrid switchgrasse 9.4 62,600 5

a. Low-input, high-diversity man-made prairies (Tilman et al., 2006).

b. Native tallgrass prairie burned in late spring (Mitchell, 1992).

c. Shawnee is an upland forage-type switchgrass cultivar released in 1995.

d. Lowland bioenergy-specific switchgrass in the cultivar release process.

e. F1 hybrid of ‘Summer’ and ‘Kanlow’ switchgrass (Vogel and Mitchell, 2008).

2011). The monocultures were switchgrass, sand 

bluestem (Andropogon hallii Hack.), Old World 

bluestem (Bothriochloa ischaemum L. Keng), and 

big bluestem. The polycultures were four grasses, 

four grasses and four forbs, eight grasses and eight 

forbs, and Old World bluestem with alfalfa (Medicago 

sativa L.). Average yield was 2.8 tons per acre for the 

monocultures and 2.4 tons per acre for the polycultures. 

For each polyculture, a dominant species emerged by 

year three, indicating that over time polycultures may 

be similar to monocultures. These low-input systems 

produce about half the biomass of managed systems. 

Adding perennial grasses into a landscape provides 

habitat improvements over corn and soybeans, even if 

the areas are mowed every year. If the grasses fill the 

landscape, special management practices can be used to 

optimize the habitat value. These include early summer 

harvest with regrowth prior to dormancy, or leaving 

some material standing during winter to provide winter 

cover and spring nesting habitat.

Conclusions. Characteristics that lead to potential 

adoption of new crops include profitability for 

the producer, ability to fit within existing farming 

operations, ease of storage and delivery to the end 

user, and availability of extension information on 

best management practices. Each of these exists 

for switchgrass. Switchgrass can be harvested after 

frost when many farmers have completed corn and 

soybean harvests. The operational aspects of perennial 

herbaceous cropping systems are fully developed and 

accepted by farmers, and the economic opportunities 

on small, difficult to farm, or marginally productive 

fields are attractive to many farmers (additional 

considerations are provided in Text Box 5.1).

Large-scale switchgrass monocultures evoke concerns 

of potential disease and insect pests and the escape 

of switchgrass as an invasive species, especially 

since little research has been conducted on these 

topics. However, the genetic diversity available to 

switchgrass breeders, the initial pathogen screening 

conducted during cultivar development, and the fact 

that switchgrass has been a native component of U.S. 

grasslands for centuries will likely limit negative pest 

issues.

Available cultivars and production practices reliably 

produce 5 tons per acre in the central Great Plains 

and Midwest, and 10 tons per acre in the Southeast. 

Improved cultivars and agronomics will increase yields 

Note: Feedstock requirements for a 50 million gallon biorefinery require 588,000 dry tons of feedstock at a conversion rate of 85 gallons 

of ethanol per dry ton. Ethanol conversion rate from Biomass Multi-Year Program Plan (U.S. Department of Energy, 2011).
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Irrigation of energy crops can be a contentious issue. Water in the western United States has to meet a number of 

competing off-stream uses, such as municipal, agriculture, and industrial, as well as providing for hydropower generation 

and minimum in-stream flows for fisheries. In the West, the majority of water comes as winter precipitation, as rain or 

snow, and usually water for summer use comes from snow melt or storage. 

In the western United States, most crops, including hay crops, are grown under irrigation. Irrigated energy crops will never 

compete economically with high-value irrigated crops, such as fruits and vegetables, but may be able to compete with 

lower valued crops such as hay and small grains. One potential energy crop species for irrigation in the western United 

States is switchgrass (Panicum virgatum) (Fransen, 2009). It is a C plant, and as such has higher water use efficiency than 

C3 plants such as wheat. It is native to many western states except for the Pacific Coast states. 

An arena where energy crops may be able to utilize water in the West, without competing with food crops is to utilize 

water that cannot be used for crops for human consumption, such as from treated sewage waste, food processing, and 

mining and other industries. Significant quantities of produced water are extracted with the oil, gas, and coalbed methane. 

Produced water can range from being nearly fresh to being hypersaline brine. There are opportunities to improve the 

quality through treatment or use the better quality water for synergistic energy co-production (U.S. Department of Energy, 

2006). Some of the produced water could be used in feedstock production, especially as new fossil-related extraction 

systems are developed that use less recycled water in enhanced recovery. In Wyoming, a coal bed methane well produces 

about 15,000 gallons (0.046 acre-feet) of wastewater per day. This may result in over a million acre-feet of wastewater 

produced per year in Wyoming. In California, 240,000 acre-feet of municipal waste water was used for agriculture in 

2003. There is a goal of utilizing an additional 1 million acre-feet by 2020 and 2 million acre-feet over 2002 levels by 2030. 

Energy crops may be able to utilize marginal lands, including saline-affected land. In addition to the issue of water use, 

there is the issue of land competition. In California, 200,000 to 300,000 acres are classified as saline.

For high-valued crops, it may not be desirable to grow these crops 2 years in a row on the same land. While energy crops 

will not displace high valued crops, there may be opportunities to rotate some annual energy crops with some high-valued 

crops. Large irrigated acreages in the West are devoted to traditional agronomic crops (e.g. small grains, oilseeds, and 

forages) that often have low profit margins for the grower. For example, in California, low-value crops are grown on 5.5 

out of 9 million acres. There may be opportunities to integrate energy crops into forage/grain/oilseed/sugar crop rotations. 

Some grasses may be able to produce biomass under limited irrigation, when other traditional crops might not produce 

a product (e.g. feed suitable for livestock feed). Grasses response to limited irrigation is species specific. Of course, the 

decision by producers as how to utilize their land and water will be market- and value-based.

Because irrigated lands can be highly productive, land rents are high (e.g. can be $200 per acre in the Columbia basin). 

This requires high yield from energy crops. For switchgrass, a yield of 11 dry tons per acre is achievable in the Columbia 

Basin. Water can cost $15 to $50 per acre plus costs for repairs, labor, and energy. Total irrigation costs can be in the 

range of $120 to 140 per acre. Presupposing the availability of water, profitable and competitive energy crop production 

requires high yields to offset irrigation costs.

TEXT BOX 5.1 | IRRIGATION OF ENERGY CROPS

similar to the yield increases achieved in corn in the 

last 30 years.51 Hybrid switchgrass makes producing 

10 tons per acre a reality in the central United States. 

The availability of adequate land area and the profit 

potential in a region will determine the success 

of growing switchgrass for bioenergy. Production 

practices and plant materials are available to achieve 

sustainable and profitable biomass production.

51 Corn grain yields have risen at an average annual increase of 1.7 bushels per acre even while fertilizer inputs have declined 

(Dobermann et al., 2002).
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Figure 5.3 Miscanthus growth in August 

5.1.2 Giant Miscanthus –

Miscanthus x giganteus

High levels of biomass production shown in U.S. 

studies are a major reason that Miscanthus x giganteus

(Greef & Deuter ex Hodkinson & Renvoize; hereafter 

referred to as Mxg or Giant Miscanthus) is an attractive 

feedstock (Figure 5.3). Mxg also exhibits many other 

characteristics that allow it to meet or exceed the 

criteria for desirable biomass crops. As a perennial, it 

typically requires fewer yearly agronomic inputs than 

annual row crops. After establishment, time spent in 

the field is usually limited to a single annual harvest. 

In some years, in some locations, additional field time 

may be spent applying fertilizer, but applications have 

neither been shown to be required every year, nor in 

every location. For example, Christian et al. (2008) 

reported no yield response to nitrogen applications 

to a 14-year-old stand in England, while Ercoli et al. 

(1999) did see a nitrogen response when nitrogen was 

applied to Mxg in Italy. Thus, Mxg crops have not 

needed annual planting, pest controls, or fertilization 

in ongoing studies. Its perennial growth also controls 

soil erosion. As it becomes established and grows, Mxg 

develops an extensive layer of rhizomes and mass of 

fibrous roots that can hold soil in place. Finally, the 

belowground growth can contribute soil organic carbon 

levels as shown in Germany (Schneckenberger and 

Kuzyakov, 2007) and Denmark (Foereid et al., 2004).

Biology and adaptation. Giant Miscanthus is a sterile 

triploid hybrid resulting from the cross of the diploid 

M. sinensis and tetraploid M. sacchariflorus (Scally 

et al., 2001). Originally discovered in Japan, Mxg

was thereafter introduced into the United States as a 

landscape plant (Scally et al., 2001).

M. sinensis and M. sacchariflorus are native to regions 

in eastern Asia with overlapping ranges in the same 

areas of Japan (Stewart et al., 2009). There are several 

forms of M. sinensis and the species can be found in 

mountainous areas, mid-level grasslands, and in lowlying waste areas (Clifton-Brown et al., 2008). It is 

usually a rhizomatous clump-former of variable size 

that spreads by seed. M. sacchariflorus is a vigorously 

rhizomatous species that can spread both by seed and 

(Courtesy of the University of Illinois)
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

by rhizomes and is often found on the margins of rivers 

or marshes (Barkworth et al., 2007). It has escaped 

cultivation and individual clumps can cover more than 

20–32 square feet in escaped roadside settings.

The Mxg clone used in University of Illinois feedstock 

research originated from rhizomes obtained from 

the Chicago Botanic Gardens in 1988 and has been 

part of a landscape demonstration planting at the 

University since that time (Pyter et al., 2009). In 

addition to this common landscape clone, there are 

now other Giant Miscanthus types being developed 

and marketed specifically for biomass production. 

For example, ‘Freedom’ Giant Miscanthus was 

developed at Mississippi State University and is 

being produced for commercial planting by SunBelt 

Biofuels of Soperton, Georgia. Cantus Bio Power Ltd. 

of North Leamington, Ontario and Vancouver, British 

Columbia, Canada lists ‘Amuri’ Giant Miscanthus and 

‘Nagara’ Giant Miscanthus as very cold-tolerant, highyielding grasses. Both Mendel Biotechnology, Inc. 

of Hayward, California, and Ceres, Inc. of Thousand 

Oaks, California, have included feedstock types of 

Miscanthus spp. as part of their research activities.

Also of importance is the fact that Mxg grows 

efficiently in a variety of settings. Established Mxg

stands have survived air temperatures of -20°F in 

Illinois (Pyter et al., 2009). The temperature for 

optimum photosynthesis is 86°F (Naidu et al., 

2003; Naidu and Long, 2004), but it has the ability 

to photosynthesize at temperatures as low as 47°F 

(Naidu et al., 2003; Farage et al., 2006). While both 

are C4 grasses, Mxg produced 61% more biomass 

than maize in an Illinois study (Dohleman and Long, 

2009) even though maize has a higher photosynthesis 

rate in midsummer. This was due to the ability of Mxg

to begin growing earlier in the growing season and 

continue later in the season.

Established plants have exhibited tolerance to summer 

drought. While substantial water is necessary for high 

yields (Beale et al., 1999), soils—given adequate 

moisture—have not shown to effect Mxg biomass 

production (Pyter et al., 2009), but have affected 

establishment rates. In fertile soils, establishment is 

usually 2 to 3 years, while it may take 3 to 5 years in 

less fertile sites (Pyter et al., 2009).

Production and agronomics. In Illinois, new shoots 

emerge from scaly underground stems (rhizomes) in 

April, and the grass grows to approximately 6.6 feet by 

the end of May (Pyter et al., 2007). Growth continues 

through summer into autumn with sterile flowers 

emerging in late September, and it goes dormant with 

the onset of killing frosts, usually in October after it 

has reached approximately 13 feet (Pyter et al., 2007). 

With the onset of freezing temperatures, leaves drop 

and minerals are returned to the belowground portions 

of the plant. The senesced stems are harvested from 

mid-December through late March; however, the 

standing biomass can be harvested before a killing frost 

if necessary.

In established Mxg plantings, there are approximately 

5 to 10 shoots per square foot (Pyter et al., 2009). 

Harvestable stems resemble bamboo and are usually 

0.5 to 0.78 inches in diameter and approximately 9.5 

feet long (Pyter et al., 2009). Given that the original 

University of Illinois demonstration plot was planted 

in the late 1980s and has continued to produce large 

amounts of biomass for the past 20+ growing seasons, 

it is anticipated that commercial plantings of Mxg will 

provide good yields for at least 10 to 15 years

A major drawback to Mxg is increasing the planting 

stock. Because it is sterile, seed propagation is not 

an option, and Mxg is typically propagated by tissue 

culture, plugs, or by rhizome division. In Europe, tissue 

culture-produced plants were more expensive and less 

winter hardy during the initial growing season than 

rhizome-produced plants (Lewandowski, 1998). Thus, 

tissue culture has not been widely used to propagate 

large numbers of Mxg in Europe, nor in the United 

States.

Rhizome propagation entails digging dormant rootrhizome (underground stem) clumps, separating the 

rhizomes into smaller pieces, and replanting the newly 

divided rhizomes. Healthy, 1- or 2-year plants work 

well for propagation. In central Illinois, a 1-year plant 

usually yields 7 to 10 rhizomes and a 2-year plant 

normally yields 25 or more usable rhizomes (Pyter 

et al., 2009). Thus, 2 years after planting, an acre of 

well-tended Mxg can produce enough rhizomes to 

plant 25 or more acres. An acceptable planting rate is 

4,250 rhizomes per acre (Pyter et al., 2009). A planting 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

depth of 4.0 inches is recommended (Pyter et al., 2010) 

for both propagation and final planting. There are 

ongoing efforts to develop seed sources because of the 

high cost of vegetative propagation. These would be 

crosses of various varieties. The costs are expected to 

be significantly lower, as much as half and even more 

over time.

Lastly, there appears to be little or no insect or disease 

pest problems associated with Mxg. There have 

been no reports of pests in commercial plantings in 

Europe. In the United States, however, several aphids 

(Bradshaw et al., 2010) have been reported recently 

and Mxg may be a site of oviposition and emergence 

for the western corn rootworm, a major pest of corn in 

the Midwest (Spencer and Raghu, 2009). Also, a leaf 

spot disease (Ahonsi et al., 2010) has been reported 

on Mxg plantings in Kentucky. It remains to be seen if 

these recently identified pests develop into commercial 

problems.

Harvesting Mxg biomass usually begins after the grass 

is fully senesced and should be completed prior to the 

onset of spring growth (Pyter et al., 2009). There is 

not enough first-year growth to warrant harvesting, 

and second-year crops usually deliver yields of about 

half of fully established plantings. In quality soils, 

established Mxg in the third and subsequent years 

usually reaches plateau yields.Several commercial 

manufacturers are evaluating and developing 

equipment specifically designed to harvest and handle 

Mxg biomass, but at present, hay mowers, conditioners, 

and balers are used. In Illinois production, hay 

equipment from several different manufacturers has 

worked well, but slowly, due to stem toughness and 

density. There is a need for specialized harvesting 

equipment that can handle Mxg more efficiently than 

commercial hay equipment.

Potential yield and production costs. European 

study (Lewandowski et al., 2000) of 3-year-old 

and older stands from 19 variously distributed sites 

reported that dry-matter yields of spring-harvested 

Mxg ranged from 1.8 to 15.2 tons per acre. The lowest 

reported yield was obtained from Central Germany 

(50–52° N)—the site is similar latitude of Saskatoon, 

Saskatchewan. The highest yield was from northwest 

Spain (43° N) at 15.2 dry tons per acre, latitude similar 

to Saginaw, Michigan. This high-yielding site was 

fertilized with nitrogen, although there was no fertilizer 

effect. Another European site with a high yield was in 

Southern Italy (37° N – 15.2 tons per acre). This site’s 

latitude is similar to that of Lexington, Kentucky.

Plot yields in 2004, 2005, and 2006 at three Illinois 

sites have varied depending on the latitude and weather 

during the growing season. In replicated studies of 

unfertilized Mxg planted in 2002 using small potted 

plants, the average hand-harvested yields over the 

2004, 2005, and 2006 growing seasons were 9.8 tons 

per acre in northern Illinois (latitude 41.85N), 15.4 

tons per acre in central Illinois (latitude 40.12N), and 

15.5 tons per acre in southern Illinois (latitude 37.45) 

(Pyter et al., 2007). In the same 3-year period, yields 

for unfertilized upland switchgrass, ‘Cave in Rock’, 

seeded in 2002 were 2.2, 5.2, and 2.7 tons per acre at 

the same northern, central, and southern Illinois sites, 

respectively. A separate demonstration plot in Urbana, 

Illinois, yielded approximately 14.1 tons per acre of dry 

Mxg biomass in 2006 at the end of the third growing 

season (Pyter et al., 2007). Based on average yields of 

13.2 tons per acre, it would require approximately 31.2 

million acres of Mxg to produce 35 billion gallons of 

ethanol in the United States, compared to 83.5 million 

acres of switchgrass producing 4.6 tons per acre 

(Heaton et al., 2008).52 

(Courtesy of the discoversolarenergy.com)

Figure 5.4 Harvesting miscanthus 

52 Assuming an ethanol yield of about 85 gallons per dry ton (U.S. Department of Energy, 2011).
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Mxg yield data were collected after 2 years at the DOE/

Sun Grant Herbaceous Partnership sites in Kentucky, 

Nebraska, and New Jersey (Table 5.2). The plots at 

these sites receive 0, 54, and 107 pounds of nitrogen 

per acre. Second-year biomass yields increased at 

the New Jersey site with fertilization, but it did not 

increase yields in Kentucky or Nebraska. In fact, 

Nebraska yields of Mxg went down with increasing 

nitrogen levels. Further analysis will likely reveal that 

the differences were the result of native soil fertility 

or climate differences. These yields are impressive 

given that yields usually increase until the grass is fully 

established, which takes 3 to 5 years (Lewandowski et 

al., 2000).

because the variety is fertile (as opposed to the sterile 

rhizomes), risk of invasion would need to be managed 

if planted on any scale.

Sustainability. Most investigations of Mxg grown 

as a biomass feedstock have been positive because 

it is a long-lived perennial, produces high biomass 

yields, and has been shown to require minimal 

inputs in some studies. In fact, Heaton et al. (2004) 

summarized that Mxg stores carbon in the soil, has 

low fertilizer requirements, high water-use efficiency, 

dries in the field, and has the ability to stand in the 

field during winter prior to harvest. While these 

positive attributes make Mxg an attractive feedstock, 

there are concerns about the invasiveness of Mxg and 

other Miscanthus species. A search of the literature 

revealed no substantiated settings where Mxg has 

been invasive. Other types of Miscanthus, including 

the parents of Mxg, have been reported to be invasive. 

Czarapata (2005) lists M. sinensis as a lesser invader 

of natural grasslands and prairies in the northeastern, 

southeastern, and eastern Midwest and Kaufman 

and Kaufman (2007) indicate that it will take over in 

roadsides and burned pastures in areas of the United 

States where soils are naturally moist. The Minnesota 

Department of Natural Resources (2009) has identified 

M. sacchariflorus to be invasive in moist areas of the 

upper Northeast and Midwest, where it can be found in 

roadsides and openings or edges of wooded areas.

Conclusions. Miscanthus x giganteus is receiving 

much attention as a potential biomass crop. In Europe, 

work using the grass as a feedstock began in the 1980s. 

While a great deal of research has been conducted 

recently, there are still barriers to commercial 

production. For example, developing low-cost, reliable, 

commercial-scale propagation methods are critical to 

developing the crop. Identifying the genotypes best 

suited to a given region is also critical. Gaining a better 

understanding of the relationships between the grass 

and mineral fertilization and the grass and pests and 

pest controls is also necessary. Finally, developing 

efficient harvesting methods and equipment will be 

necessary to remove the crop from the fields and into 

storage in a low-cost, timely fashion (Figure 5.4).

Nitrogen

 Fertilization

Lexington, 

KY Mead, NE Adelphia, NJ

0 lbs/acre 7.5 7.1 4.9

54 lbs/acre 7.8 6.9 6.6

107 lbs/acre 7.5 6.5 6.4

Developing a crop of Mxg will likely be expensive. 

Jain et al. (2010) estimates a cost of $1,197 to establish 

an acre of Mxg planting rhizomes at a rate of 4,000 

rhizomes per acre in Illinois. Following establishment, 

Lewandowski et al. (2000) estimated the annual 

breakeven cost of producing Mxg in Denmark to be 

approximately $85 per ton (based on an exchange 

rate of $1.35 per Euro). In 2008, Khanna and others 

estimated the annual breakeven farm-gate price to 

produce Mxg to be between $37 and $52 per ton in the 

United States. Finally, Jain et al. (2010) most recently 

estimates the annual breakeven cost to produce Mxg to 

be between $46 per ton in Missouri and $139 per ton in 

Minnesota.

Future plantings may involve a seeded option closer 

to the establishment costs of grasses. Miscanthus from 

seed may reach maturity in the second year and have 

a higher yield because of higher plant density. Finally, 

2009 (Second-Year) 

Miscanthus x giganteus

Biomass Yields (Tons per Acre)

Table 5.2
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Mechanical equipment is becoming available that will 

plant more than 25 acres per day. Improvements in Mxg

genetics, agronomy, and harvesting are also coming 

quickly. Long-term production from a single planting, 

modest-input requirements, and carbon capture, 

coupled with realistic commercial biomass yields of 

9 to 16 tons per acre per year, makes Miscanthus x 

giganteus a candidate feedstock for addressing the U.S. 

renewable bioenergy demand.

5.1.3 Sugarcane

Sugarcane is a large-stature, jointed grass that is 

cultivated as a perennial row crop, primarily for its 

ability to store sucrose in the stem, in approximately 80 

countries in tropical, semi-tropical, and sub- tropical 

regions of the world (Tew, 2003). It is one of the most 

efficient C4 grasses in the world, with an estimated 

energy in: energy out (I/O) ratio of 1:8 when grown 

for 12 months under tropical conditions and processed 

for ethanol instead of sugar (Bourne, 2007; Macedo et 

al., 2004; Muchow et al., 1996). Under more temperate 

environments, where temperature and sunlight are 

limited, I/O ratios of 1:3 are easily obtainable with 

current sugarcane cultivars if ethanol production from 

both sugar and cellulosic biomass is the goal (Tew 

and Cobill, 2008). In addition, sugarcane ethanol cuts 

GHGs at least 60% compared to gasoline—better 

than any other biofuel produced today. EPA has 

confirmed sugarcane ethanol’s superior environmental 

performance by designating it as an advanced 

renewable fuel. Most of the sugarcane grown in the 

United States is dedicated to the production of sugar. 

As an energy cane industry has not developed to date, 

one can assume that the production practices of the 

mature sugarcane industry can be modified to ensure 

the sustainable production of energy cane as well.

Biology and adaptation. Sugarcane (Saccharum spp.) 

is a genetically complex crop with a genomic makeup 

that results from successful interspecific hybridization 

efforts, primarily involving S. officinarum and S. 

spontaneum (Tew and Cobill, 2008). Improvement 

of sugarcane for increased energy efficiency and 

adaptability to a wide range of environments is 

considered by many geneticists as synonymous with 

“genetic base broadening” (i.e., utilization of wild 

Saccharum germplasm), particularly S. spontaneum

in sugarcane breeding programs (Ming et al., 2006). 

S. spontaneum, considered a noxious weed in the 

United States, can be found in the continents of Africa, 

Asia, and Australia in environments ranging from the 

equator to the foothills of the Himalayas. This makes 

it an excellent source of a number of valuable genes 

(Mukherjee, 1950; Panje and Babu, 1960; Panje, 

1972; and Roach, 1978). The USDA-ARS’s Sugarcane 

Research Unit (SRU) at Houma, Louisiana, in the 

1960s took on the role of introgressing desirable genes 

from sugarcane’s wild and near relatives (Miscanthus 

and Erianthus) to build new parents for utilization in its 

commercial sugarcane varietal development program 

in what is referred to as the SRU’s basic component of 

its breeding program. Because these wild accessions 

contain only small amounts of sugar, three or four 

rounds of backcrossing to elite sugarcane varieties 

must be done to obtain a commercially acceptable 

sugarcane variety, which has high-sugar yields with 

minimum amounts of fiber.

Production and agronomics. Sugarcane is grown 

as a monoculture with fields being replanted every 

4 or 5 years. This type of culture is conducive to the 

development of perennial weeds like johnsongrass 

and bermudagrass, since the row top (i.e., raised bed) 

remains relatively undisturbed for the 5-year crop 

cycle. The selective control of these weeds within the 

crop is difficult with currently registered herbicides 

once these weeds produce rhizomes. To minimize this 

risk, growers disk the old stubble fields in the winter or 

early spring and fallow the fields until they are planted 

to sugarcane again. Frequent disking and/or the use of 

multiple applications of glyphosate are used to deplete 

the soil of weed seed and rhizomes during the fallow 

period.

Sugarcane is vegetatively planted by laying 6- to 8-foot 

long stalks end-to-end in a planting furrow along 

rows spaced 5 to 6 feet apart and covering the stalks 

with 2 to 4 inches of soil. The wide row spacing is 

needed to accommodate mechanical harvesting. One 

acre of seedcane can plant 6 to 10 acres of sugarcane, 

depending on the length and number of stalks at the 

time of harvesting the “seedcane” for planting, and the 

number of stalks per foot of row being planted. When 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

harvesting seedcane for planting, stalks are cut at the 

soil surface and at the last mature node at the top of the 

stem. An alternate method of planting is to plant 12- to 

18-inch stalk pieces (billets) that can be harvested with 

the same chopper harvester used to harvest sugarcane 

for delivery to sugar mills. Once the stalks are planted, 

a broad spectrum preemergence herbicide is applied to 

control seedling weeds. New plants emerge from the 

axillary buds located in the nodal regions along the 

stalk. Growers produce most of their own seed cane for 

planting; hence, planting is generally done a few weeks 

prior to the beginning of the harvest season to ensure 

that stalks are plentiful and tall (Figure 5.5). 

Vegetative planting of sugarcane is often considered a 

drawback by growers who are accustomed to planting 

large areas of seeded crops relatively quickly with one 

tractor and one planter. It is an expensive process as it 

requires considerable labor and equipment, is relatively 

slow, and requires that the grower plant sugarcane that 

would normally be sent to the raw sugar factory for 

processing. However, with energy cane, 20% to 30% 

higher planting ratios can be expected, because stalk 

numbers and heights are higher. In addition, at least 

two additional harvests per planting can be expected, 

significantly lowering the number of acres requiring 

planting each year. It is estimated that the cost to plant 

one acre of sugarcane is about $500 when the grower 

(Courtesy of Ed Richard, USDA-ARS)

Figure 5.5 Energy cane research plots uses seedcane from the farm. Because planting costs 

are spread over four annual harvests, the annual cost 

would be approximately $125 per acre. With energy 

cane, one acre of seedcane would plant about 13 

additional acres reducing per acre planting costs to 

$346. If spread over the anticipated six annual fall 

harvests, annual planting costs would be $58 per acre.

Vegetative planting also has advantages, especially 

when planting must be done under conditions of less 

than ideal seedbed preparation. The crop emerges 14 to 

21 days later and continues to grow until the first heavy 

frost of the fall. The first production year (plant-cane 

crop) actually begins in the spring following planting 

with the emergence of the crop from winter dormancy. 

Herbicides are applied each spring to the subsequent 

ratoon crops (first- through third-ratoon crop) to 

minimize early weed competition. Nitrogen is applied 

at rates of 70 to 90 pounds per acre to the plant-cane 

crop (first growing season) and 90 to 120 pounds per 

acre to the subsequent ratoon crops. These applications 

are generally made in the spring about two months into 

the growing season.

The crop is susceptible to the rapid spread of a number 

of bacterial, fungal, and viral pathogens that can be 

spread easily by machinery and wind currents. These 

pathogens can affect the yield and ratooning ability 

(number of yearly harvests per planting) of the crop. 

Race changes of some of these pathogens are common 

and the industry is always susceptible to new diseases. 

Insects, primarily stalk borers, grubs, and aphids also 

plague the industry. The compactness of the industry 

and the fact that the crop is grown continuously as a 

monoculture makes sugarcane especially vulnerable to 

the rapid spread of diseases and insects. The planting 

of resistant varieties is the predominant means of 

managing diseases in sugarcane. For insects, mainly 

stalk borer, an effective integrated pest management 

program that involves field scouting, the use of tolerant 

varieties, and insecticides when established infestation 

thresholds are exceeded, is used. Additional research is 

exploring the use of multiple crop-production systems 

for year-round delivery of feedstocks and to respond to 

biotic stresses (McCutchen and Avant, 2008).

With each successive fall harvest during the crop 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

cycle, yields tend to decline to the point that it is not 

practical to keep the old stubble for another year. 

The milling season in the more sub-tropical climates, 

like Louisiana, lasts about 100 days beginning in late 

October and ending in late December or early January. 

Given that the crop emergence in the spring depends 

on the date of the last killing frost, it is obvious that 

sugarcane harvested in December will produce higher 

yields than sugarcane harvested in September. For this 

reason growers try to have a balance in ratoon crop 

ages from 0 (plant cane that was never harvested) 

to 3 (third ratoon that was harvested three times 

previously) and begin the harvest with the third-ratoon 

fields that would lack the vigor of the plant-cane 

crop. In addition, early harvested crops tend not to 

yield as much the following year. A similar scenario 

is anticipated for energy cane with the exception that 

fourth- and fifth- ratoon crops would also be harvested.

In Louisiana, some energy cane varieties have 

produced average yields of 56 green tons per acre, with 

8 to 14 tons per acre being fiber and 4 to 6 tons per acre 

being brix (soluble sugars) on a dry weight basis (see 

Figure 5.6). 

Potential yields and production costs. The theoretical 

maximum for aboveground sugarcane biomass (total 

solids) yield is estimated to be 62 green tons per 

acre annually (Loomis and Williams, 1963). This is 

dependent on temperature and sunlight, and would 

probably occur under tropical conditions. Sugarcane 

breeding programs have reported sugar yield gains in 

the order of 1% to 2% per year (Edme et al., 2005). The 

economic sustainability of growing energy cane in nontraditional cane growing regions will require yearly 

biomass yield gains of this magnitude or greater, with 

a goal of ensuring that the I/O ratio of 1:8 projected for 

tropical countries can be met and ultimately exceeded 

under the sub-tropical cane growing conditions of the 

southeastern United States.

The “sun-dried crop” concept of allowing the crop 

to desiccate in the field and perhaps devoid itself 

of some of its leaves and moisture, as is proposed 

for many of the perennial grasses being considered 

for biofuels, is not an option for energy cane as the 

stalks are thick with a waxy coating on them and 

the new growing season should begin as soon after 

Biomass yield (tons/acre)

L 79-1002 Ho 02-113 Ho 03-19 Ho 03-48 Ho 99-51 Ho 99-58

Brix

Fiber

25

20

15

10

5

0

4

12

14

8

13 13

11

6

5

5

6 6

Average yields from four successive fall harvests of several candidate sugarcane 

varieties as compared to the standard variety, L 79–1002 Figure 5.6

(Courtesy of Ed Richard, USDA-ARS)
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

harvest as possible. Consequently, energy cane, 

like sugarcane, will have to be harvested green and 

dewatered if the fiber is to be stored and processed 

later in the year. The value of this liquid is in question 

because it will add to transportation costs. However, 

if water is needed for the digestion of the fiber or the 

maintenance of the bagasse under anaerobic conditions 

to minimize deterioration during outside storage, it 

would be present at no additional charge. What is also 

overlooked is the fact that the water contains sugar 

that is easily and much more cheaply converted to 

ethanol. Furthermore, in some conversion processes 

the yeast used in fermentation needs a substrate to 

grow and multiply on, and sucrose is an ideal substrate. 

Conceivably the biorefinery would have two processes 

for the production of biofuel, with one having sugar 

(brix) obtained from de-watering at the biorefinery 

as the feedstock and the other the fiber (bagasse). 

Economics would have to be considered with these 

options especially because of the very short cut-crush 

interval needed for sugar recovery and to prevent 

spoilage.

Sustainability. Green cane harvesting of sugarcane 

deposits 2.7 to 3.6 tons per acre of a mixture of brown 

and green leafy material and fragments of the stalks 

(Richard, 1999; Viator et al., 2006; 2009a;b). The 

fibrous extraneous matter generated during harvest 

has an energy value; however, the greatest value 

may be as mulch to limit soil erosion, depress weed 

development, conserve moisture, and as a means to 

recycle nutrients. All are potential contributors to the 

sustainable production of energy cane (de Resende 

et al., 2006). It is estimated that the extraneous 

residue generated during the green cane harvesting of 

sugarcane contains 0.7% of N, 0.07% of phosporous, 

and 0.7% of potassium by weight. Using 2009 USDA 

economic data (USDA-ERS 2010b), 2.7 tons of 

residues would equate to a savings of approximately 

$50 per acre.

The critical amount of post-harvest residue that can 

be removed from the field has not been determined 

for energy cane. Sugarcane is harvested mechanically 

with a chopper harvester (Figure 5.7). These harvesters 

chop the sugarcane stalks into small 6- to 8-inch 

long pieces (billets) and use wind currents from an 

extractor fan to remove the leaves attached to the 

stalks. If used to harvest energy cane, the speed of the 

extractor fans could be adjusted to deposit a percentage 

of the extraneous matter on the soil surface while the 

remainder is collected with the stalks and used as an 

additional source of fiber.

Production cost estimates are complicated by the firstyear production of seedcane crop and the number of 

ratoon crops before re-establishment. For example, 

different costs are associated with fallow field and 

seedbed preparation, seedcane planting and harvest, 

plant cane, and ratoon operations and harvest over 

multiple years (Salassi and Deliberto, 2011). Using 

the most common sugarcane assumptions from the 

production costs from Salassi and Deliberto, an 

extrapolated cost to produce and harvest the energy 

cane is estimated to be about $34 per dry ton. The 

assumed yield is about 14 dry tons per acre. 

Energy cane will be grown as a perennial. Commonly 

discussed disadvantages of perennial feedstocks 

include the difficulty of establishing a perennial crop 

from seed and rhizomes, the control of weeds during 

the establishment period, and the fact that an economic 

return will not be realized the first year. Energy cane, 

like sugarcane, is vegetatively planted by placing the 

stalks in a planting furrow in mid- to late summer. 

Herbicides are labeled for at-planting preemergence 

applications in sugarcane; presumably, these herbicides 

(Courtesy of Ed Richard, USDA-ARS)

Figure 5.7 Harvesting sugarcane
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can also be applied to energy cane with similar results. 

These herbicides are reapplied each spring during the 

course of a 3- to 4-year sugarcane production cycle. 

Because of the vigor (e.g., early spring emergence 

and high stalk population) of energy cane, use of 

these herbicides beyond the first spring after planting 

is not anticipated. The vigor of energy cane is also 

advantageous due to the fact that when the crop is 

planted in the summer, it emerges and produces a 

uniform stand quickly. The crop continues to grow 

until the aboveground portion is winter-killed in the 

fall. This aboveground material, which can equate 

to 2 to 4 dry tons per acre, could be harvested and 

converted to fuel in the first year of establishment.

The final consideration in the sustainability of energy 

cane production is the utilization of biorefinery 

byproducts to supply nutrients and reduce the impact 

of crop removal on soil health. These byproducts can 

include vinasse from the fermentation process, biochar 

from pyrolysis, and filter press mud and boiler fly ash 

from the squeezing of stalks. A positive synergistic 

response was observed when the application of 

fertilizer was combined with an application of filter 

press mud in Florida (Gilbert et al., 2008). In Brazil, 

vinasse from the ethanol distillery is typically returned 

to the recently harvested fields to supplement fertilizer 

requirements.

Conclusions. Looking to the future, the greatest 

needs to make energy cane a suitable feedstock for the 

cellulosic industry and extend its range of geographic 

distribution outside of the traditional sugarcane 

growing areas are cold tolerance for expansion outside 

of tropical areas; drought and flood (saturated soil) 

tolerance, as this crop will probably be grown on 

marginal soils that may be prone to flooding or where 

irrigation is difficult; insect and disease resistance; and 

a further exploitation of some varieties of sugarcane 

that encourages symbiotic relationships with nitrogen 

fixing bacteria.

The success of the sugarcane industry has been, 

and continues to be, dependent on the development 

of new hybrids with superior yields and increased 

resistance to many of the abiotic and biotic stresses 

previously mentioned. This formula will not change 

if the crop is grown as a dedicated feedstock for the 

production of liquid biofuels or electricity. Successful 

hybridization begins with the introgression of desirable 

traits from the wild relative of sugarcane, Saccharum 

spontaneum. Early generation progeny from these 

crosses with elite sugarcane clones exhibit high levels 

of hybrid vigor, which translates into increased cold 

tolerance, greater ratooning ability, enhanced levels 

of tolerance to moisture extremes, increased insect 

and disease tolerance, and more efficient nutrient 

utilization (Legendre and Burner, 1995). Much of 

the vigor of these early generation hybrids is lost in a 

conventional breeding program for sugar, as progeny 

from these crosses must be backcrossed with elite highsugar-producing clones three to four times before a 

commercial sugarcane variety can be produced. These 

early generation hybrids would be considered ideal 

candidates as dedicated cellulosic biomass crops like 

energy canes. Most of these varieties can average over 

15 dry tons per acre annually over at least four annual 

fall harvests.

By enhancing the level of stress tolerance through the 

conventional breeding techniques being employed by 

the basic breeding program of the USDA-ARS SRU, 

the geographic area of distribution could be expanded 

to more temperate regions of the United States to 

include those states in Hardiness Zone 8 (regions 

within Texas, Louisiana, Arkansas, Mississippi, 

Alabama, Georgia, South Carolina, North Carolina) 

and perhaps the extreme southern end of Hardiness 

Zone 7 (regions within Oklahoma, Tennessee, 

Virginia), where the annual low winter temperatures 

can approach 1o F. With this in mind, it is conceivable 

that the area devoted to this crop could be tripled, 

thus making it a more attractive feedstock for biotech 

companies with proprietary genes to further enhance 

the level of stress tolerance, or introducing genes for 

the production of saleable byproducts without the 

labeling restrictions encountered in food crops. 
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5.1.4 Sorghum 

Of the crops recently identified for their potential 

bioenergy production, sorghum (Sorghum bicolor) has 

historically had the most direct influence on human 

development (Figure 5.8). Sorghum was domesticated 

in arid areas of northeastern Africa over 6,000 years 

ago (Kimber, 2000). Sorghum is traditionally known 

for grain production; it is the fifth most widely grown 

and produced cereal crop in the world (FAO, 2007). 

However, in many regions of the world, sorghum is just 

as (if not more) important as a forage crop. In addition 

to forage and grain, sorghum types high in stalk sugar 

content and extremely lignified types (for structural 

building) have been grown throughout the world.

Given the demands for renewable fuel feedstocks, 

sorghum is now being developed as a dedicated 

bioenergy crop. This designation is not new; sorghum 

was mentioned prominently as a bioenergy crop over 

20 years ago (Burton, 1986). The interest in the crop 

is justifiable based on several independent factors that 

separately indicate good potential, but when combined, 

they clearly designate sorghum as a logical choice 

for bioenergy production. These factors include yield 

potential and composition, water-use efficiency and 

drought tolerance, established production systems, and 

the potential for genetic improvement by using both 

traditional and genomic approaches.

Biology and adaptation. Whether measured in 

grain yield or total biomass yield, sorghum is a 

highly productive C4 photosynthetic species that 

is well adapted to warm and dry growing regions. 

While sorghum is technically a perennial in tropical 

environments, it is planted from seed, then grown 

and managed as an annual crop. Sorghum has a 

long-established breeding history through which the 

productivity, adaptation, and utilization of the crop has 

continually been improved. These efforts have resulted 

in numerous cultivars and hybrids of sorghum that are 

used for various purposes.

The optimum type of sorghum to be grown for biofuels 

production is dependent on the type of conversion 

process that will be used. Sorghums are divided 

into distinct types based on the amount of different 

carbohydrates they produce.

Grain sorghum hybrids produce large quantities of 

grain (approximately 50% of total biomass); the grain 

is composed primarily of starch (approximately 75%) 

and may be used as a food grain, feed grain, or ethanol 

substrate via starch hydrolysis and fermentation. If 

mechanically harvested, these hybrids are usually less 

than 2 meters tall. Residue is typically returned to the 

soil, but it is used as forage under drought conditions, 

and it could be used as a biomass feedstock as well.

Forage sorghums are usually one of two types. 

Sorghum-sudangrass hybrids are tall, leafy, thinstalked hybrids used for grazing or hay production. 

Silage-sorghum hybrids are typically taller and thickerstalked with high grain yield (25% of total biomass), 

and they are chopped and ensiled for animal feeding 

(Figure 5.8). Both of these types of sorghum have 

been highly selected for optimum forage production, 

palatability, and conversion in an animal system. There 

have been significant breeding efforts to enhance the 

forage quality of this material by incorporating the 

brown midrib trait into many forage hybrids. These 

brown-midrib hybrids have lower lignin and are more 

palatable, which increases conversion efficiency and 

consumption rate in ruminant feeding programs (Aydin 

et al., 1999; Oliver et al., 2004). These types may have 

application in certain energy sorghum applications 

where lower lignin content is desirable.

(Courtesy of W. Rooney, Texas A&M University)

Figure 5.8 Sorghum hybrid tests
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Sweet sorghum is a unique type of sorghum that 

accumulates high concentrations of soluble sugars. 

Traditionally, these sorghums were grown for the stalk, 

which was milled to extract the juice. The juice was 

then cooked down, and the resulting syrup was used 

as sweetener. While these types of sorghum continue 

to be grown for syrup on an artisan level, there has 

been significant interest in the development of sweet 

sorghum as a dedicated bioenergy crop using a 

sugarcane system model. In the mid-1970s, significant 

research was conducted to explore the development 

of sweet sorghum as a bioenergy source for biofuels 

and energy production, and breeding programs were 

initiated to develop high-yielding sorghum specifically 

for ethanol production (McBee et al., 1987).

Dedicated biomass sorghums are the most recent class 

of sorghum that has been developed in response to the 

interest in bioenergy crops. These sorghums are highly 

photoperiod sensitive, meaning that they do not initiate 

reproductive growth until well into the fall season of 

the year. Consequently, in temperate environments 

like most of the United States, these sorghums will 

not mature. This absence of reproductive growth 

reduces sensitivity to periods of drought and allows 

the crop to effectively photosynthesize throughout the 

entire growing season. This results in higher yields of 

primarily lignocellulosic biomass that is completed in 

a single annual season. While phenotypically similar 

to forage sorghums, these biomass sorghums are 

distinctly different in that they are not selected for 

animal palatability, which results in plants with larger 

culms and flexible harvest schedules, which minimizes 

nitrogen extraction at the end of the season.

Production and agronomics. Biomass yield potential 

of sorghum is strongly influenced by both genetic and 

environmental factors. For example, grain sorghum is 

commonly grown in more arid regions of the country, 

and the plant itself is genetically designed to be shorter 

to facilitate mechanical harvesting. Alternatively, 

specific dedicated biomass sorghums are very efficient 

at producing large amounts of lignocellulosic biomass. 

Finally, both sweet sorghum and forage sorghum are 

prolific when the environmental conditions allow the 

plants to reach full genetic potential. Hallam et al. 

(2001) compared perennial grasses with annual row 

crops and found that sweet sorghum had the highest 

yield potential, averaging over 15.6 tons per acre 

(dry weight basis) and also performing well when 

intercropping with legume species. Rooney et al. 

(2007) reported biomass yield of energy sorghum in 

excess of 44.6 tons per acre (fresh weight) and 13.4 

tons per acre (dry weight). They reported that potential 

improvements could extend the potential of these 

types of hybrids to a wide range of environments. 

Under irrigation in the Texas panhandle, McCollum et 

al. (2005) reported yield of commercial photoperiod 

sensitive sorghum hybrids as high as 36 tons per acre 

(65% moisture) from a single harvest. In subtropical 

and tropical conditions, single cut yields are generally 

lower, which is likely due to increased night 

temperatures, but cumulative yields are higher due to 

the ratoon potential of the crop. Total biomass yields 

as high as 13.4 tons per acre (dry weight basis) were 

reported near College Station, Texas (Blumenthal et al., 

2007).

Composition of sorghum is highly dependent on 

the type that is produced, such as grain sorghum, 

sweet sorghum, forage, and cellulosic (high biomass) 

sorghum. Sorghum grain is high in starch, with 

lower levels of protein, fat, and ash (Rooney, 2004). 

Significant variation in the composition of grain 

is controlled by both genetic and environmental 

components, making consistency in composition a 

function of the environment at the time of production; 

consequently, these factors influence ethanol yield (Wu 

et al., 2007). Juice extracted from sweet sorghum is 

predominantly sucrose with variable levels of glucose 

and fructose, and in some genotypes, small amounts 

of starch are detectable (Clark, 1981; Billa et al., 

1997). In forage and dedicated biomass sorghums, 

the predominant compounds that are produced are 

structural carbohydrates (lignin, cellulose, and hemicellulose) (McBee et al., 1987; Monk et al., 1984). 

Amaducci et al. (2004) reported that the environment 

influences sucrose, cellulose, and hemicellulose 

concentrations, while lignin content remains relatively 

constant.

Potential yield and production costs. Sorghum has a 

long history as a grain and forage crop, and production 

costs range from $200 to $320 per acre (USDA–ERS, 
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2010a). This history provides an excellent basis for 

estimating crop production costs for energy sorghums 

with a few modifications. Seed costs, planting costs, 

and production costs will be similar to grain and/or 

forage sorghum. Fertilizer rates will likely be less than 

forage sorghum on a production dry-ton basis (due 

to the reduced nitrogen content in the mature culm), 

but it is expected that yields will be higher, so total 

nitrogen requirements will be equalized. Production 

is expected under rain-fed conditions; therefore, no 

additional costs are added for irrigation. On a dry ton 

basis, given an average production of 10 dry tons per 

acre and assuming $400 per acre cost of production for 

dedicated biomass sorghums, biomass sorghum will 

cost $40 per dry ton at the farmgate.

Production practices for dedicated biomass and sweet 

sorghum are similar to traditional sorghum crops with 

some minor modifications. For both types of energy 

sorghums, it is expected that plant populations will 

be lowered relative to grain and certainly compared 

to forage sorghum. This drop will allow the plants 

to produce larger culms and reduce the potential for 

lodging and interplant competition. Pests and diseases 

of sorghum are well known and described, and there 

are some that will require management plans and 

effective deployment of host plant resistance for 

control. Of particular note is the disease anthracnose 

(caused by Colletotrichum graminicola), which is 

prevalent in the southeastern United States and is 

capable of killing susceptible sorghum genotypes. 

Fortunately, there are many sources of genetic 

resistance to the disease, and effective control relies 

on effective integration of these anthracnose resistance 

genes.

Harvesting and preprocessing of energy sorghums is 

an area of significant research and will likely require 

the greatest amount of modification compared to 

grain sorghum. Sweet sorghum will be harvested and 

moisture extracted for soluble sugars at a centralized 

location. For dedicated biomass sorghums, the forage 

harvest systems work very well, but there is a need to 

reduce moisture content to minimize transportation and 

storage costs.

The range of sorghum production varies with the type 

being produced. Both sweet sorghum and dedicated 

biomass sorghums grow well throughout the eastern 

and central United States as far north as 40° latitude, 

and the range of dedicated biomass sorghums is 

considered to be composed of most of the eastern and 

central United States. In the western United States, 

productivity will be directly related to available 

moisture from rainfall or irrigation. It is unlikely that 

the crop (or any crop) will be economically viable 

as a biomass crop in regions with less than 20 inches 

of available moisture annually. Dedicated biomass 

sorghums have shown yields of 7–13 dry tons per 

acre in the northern areas of the United States, with 

even higher yield potential possible in a southern 

environment due to the longer growing seasons. 

Therefore, dedicated biomass sorghum should find 

wide adaptation throughout most of the country that 

is suitable for herbaceous biomass production from an 

annual crop.

While sweet sorghum is productive at northern 

latitudes, the logistics of processing make the 

production of the crop unlikely in more temperate 

latitudes. Because soluble sugars are not stable for 

long periods, a processor requires a long harvest 

and processing window for effective use of capital 

equipment. The farther north the production, the 

shorter the growing season; hence, the harvest season 

is further reduced and the ability to consistently grow 

the high-yield potential sweet sorghum varieties is 

limited (Wortmann et al., 2010). Consequently, the 

areas of the United States that process sugarcane 

are also ideal locations for the production of sweet 

sorghum. Production in other regions will be dependent 

on detailed economic analysis of the cost of processing 

versus the length of the processing season.

Sustainability. Sorghum is unique among the 

dedicated bioenergy crops because it is an annual crop. 

The general opinion is that bioenergy crops should be 

perennial for sustainability purposes. While most of 

the bioenergy crops are perennial, there are several 

reasons why annual bioenergy crops are necessary. 

First, annual crops deliver large yields in the first 

year, as compared to most perennial crops, which 

typically increase annual yield in subsequent years 
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following establishment. Given the challenges of 

propagating and establishing perennial crops, annual 

crops can provide insurance and production stability to 

industrial processors in the early phases of bringing a 

new processing facility online if perennial crop stand 

failures or establishment problems are encountered. 

Second, for the most part, the U.S. farming system 

has been based on annual crop production systems. 

Farmers, bankers, and processors are much more 

familiar and accepting of these systems, and while 

this will eventually be overcome, annual energy crops 

will be needed for that transition. Finally, annual crops 

are much more tractable to genetic improvements 

through breeding due to the simple fact that breeding is 

accelerated by multiple generations per year.

There are several traits of specific importance to 

sorghum improvement, as it relates to bioenergy 

production. These include, but are not limited to, 

maturity and height, drought tolerance, pest tolerance 

and/or resistance, and composition and/or quality. 

Improvements in these areas will increase yield 

potential, protect existing yield potential, and enhance 

conversion efficiency during processing.

While the reason for producing bioenergy feedstock 

is to produce renewable fuel, one of the critical 

components in their production will be water. Thus, 

both drought tolerance and water-use efficiency are 

critical, as many of these feedstocks will be produced 

in marginal environments where rainfall is limited 

and irrigation is either too expensive or would deplete 

water reserves. Sorghum is more drought tolerant 

than many other biomass crops. Depending on the 

type of biomass production in sorghum, both pre- and 

post-flowering drought tolerance mechanisms will be 

important. In sweet sorghum, both traits are important, 

but there has been little research regarding the impact 

of drought stress on sweet sorghum productivity.

For high-biomass, photoperiod-sensitive sorghums, 

preflowering drought tolerance is critical because, in 

most environments, this germplasm does not transition 

to the reproductive phase of growth. Each type of 

tolerance is associated with several phenotypic and 

physiological traits; these relationships have been used 

to fine map QTL (quantitative trait loci) associated 

with both pre- and post-flowering drought tolerance. 

Traits that have been associated with drought resistance 

include heat tolerance, osmotic adjustment (Basnayake 

et al., 1995), transpiration efficiency (Muchow et 

al., 1996), rooting depth and patterns (Jordan and 

Miller, 1980), epicuticular wax (Maiti et al., 1984), 

and stay green (Rosenow et al., 1983). Combining 

phenotypic and marker-assisted breeding approaches 

should enhance drought tolerance breeding in energy 

sorghums.

Unlike perennial bioenergy crops, sorghum will 

require crop rotation to maintain high yields and soil 

conditioning. Continuous cropping studies of sorghum 

have confirmed that yields will drop in subsequent 

years unless additional nitrogen is provided to maintain 

yields (Peterson and Varvel, 1989). Therefore, it 

is critical to consider rotations when accounting 

for potential land area needs in energy sorghum 

production. The exact rotation sequence and timeframe 

will vary with locale, but sorghum production once 

every 2 or 3 years will be acceptable in most regions. 

Failure to rotate may result in reduced yields and 

quality, as well as increased weed, insect, and disease 

problems. Research is needed to determine the 

appropriate rotation for energy sorghum in the target 

regions for production.

The molecular genetic resources available in the 

sorghum species are the most advanced among all of 

the potential energy crops. Combining these molecular 

genetic resources with traditional breeding approaches, 

it should be possible to rapidly develop and deploy 

improved, dedicated energy sorghum that meets the 

needs of both crop and biofuel producers.

Conclusions. Sorghum benefits from a longestablished production history, existing research 

infrastructure, and a relatively simple genetic system. 

All of these factors allow for the rapid modification of 

the crop and delivery of specific sorghum types that 

are developed specifically for bioenergy and adapted 

to the target areas of production. The future of energy 

sorghum is based on development of energy-specific 

genotypes in which composition and productivity are 

optimized, while minimizing inputs like insecticide 
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and fertilizer. In this scenario, resistance/tolerance to 

both biotic and abiotic stresses is critical. Fortunately, 

adequate genetic resources and technology are 

available to make these modifications in an efficient 

and timely manner. Composition and yield are 

obviously important, and continual enhancement 

of these factors will rely on the full use of genomic 

technology.

5.1.5 Poplar

The following section provides a brief description 

of the genus Populus, with attention to the biology, 

potential yield, production costs, and sustainability 

issues related to deploying an efficient, biomassproducing woody crop. Much more extensive 

information is available in other resources [e.g., Stettler 

et al. (1996) and Dickmann et al. (2001)].

Biology and adaptation. Today, the genus Populus

includes almost 30 species and represents several 

taxonomic sections distributed throughout the northern 

hemisphere. Poplar species are an ancient and wellestablished component of the native North American 

landscape. Hybrids within and among species 

belonging to two sections, Aigeiros and Tacamahaca

(cottonwoods), are commonly referred to as “hybrid 

poplars” (Figure 5.9).

Commercial deployment of hybrid cottonwood 

plantations for the production of fiber for paper and 

other products and biofuels and for the purpose of 

environmental remediation (e.g., phytoremediation) is 

a reality in many forested and agricultural landscapes, 

including those that lie within the temperate regions of 

the United States. Genotypes of eastern cottonwood 

(P. deltoides Bartr. ex Marsh) and hybrids between 

eastern cottonwood and Asian black poplar, European 

black poplar, and western black cottonwood 

(P. suaveolens Fish. subsp. maximowiczii A. Henry, 

P. nigra L., and P. trichocarpa Torr. & Gray, 

respectively) capable of producing in excess of 7 tons 

per acre per year by age 6-years have been identified 

by field tests, even in the harsh climate of the North 

Central region of the United States (Riemenschneider 

et al., 2001a; Zalesny et al., 2009).

The susceptibility of the cottonwoods to vegetative 

propagation was, and continues to be, in large part, a 

factor to their commercial value and domestication. 

One of the most economical means of plantation 

establishment is to plant dormant hardwood cuttings 

capable of developing adventitious roots (Heilman 

et al., 1994; Zalesny et al., 2005). As needed, rooted 

cuttings can be used to enhance survival. Thus , the 

vegetative propagation of poplars can confer significant 

genetic advantage during all stages of the breeding 

and selection strategy to an aggregate phenotype with 

high commercial utility and stability (Eriksson, 1991; 

Orlovic et al., 1998; Zalesny et al., 2005).

There are many possible breeding strategies that 

can be applied to the development of a hybrid 

poplar woody biomass crop (Riemenschneider et 

al., 2001b). Yet, all breeding strategies derive from 

the need for a commercial variety to possess several 

attributes simultaneously such as an adventitious root 

system, rapid growth, and resistance to pests. Eastern 

cottonwood, when planted in the southern United 

States, is an example of such a species.

(Courtesy of ORNL)

Hybrid poplar plantation 

in Pacific Northwest Figure 5.9
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Elsewhere, interspecific hybridization may be 

necessary. For example, in the upper Midwest, eastern 

cottonwood cuttings root erratically in the field, 

and hybridization between that species and another 

more easily rooted species is necessary to achieve an 

economical silvicultural system (Zalesny and Zalesny, 

2009). This need for an aggregate genotype possessing 

all required commercial attributes gives rise to the 

several breeding programs found throughout North 

America and elsewhere in the world. Commercial 

genotypes in use today have most, if not all, of the 

important traits affecting production. However, the 

number of commercial genotypes in use today is 

relatively low, and diversification, as well as yield 

improvement, is a goal of breeding programs.

Production and agronomics. Plant propagation for 

commercial plantation establishment is generally via 

cuttings, which are produced in densely planted “stool 

beds.” In the South, eastern cottonwood roots readily 

under field conditions, which makes for economical 

commercial deployment. In the North, eastern 

cottonwood roots erratically under field conditions, 

and it is more common to utilize a hybrid between 

eastern cottonwood and European black cottonwood 

(Populus nigra) or one of the Tacamahaca poplars. Of 

these, Populus nigra is preferred as a hybrid parental 

species because of the reduced probability of stem 

canker disease. Cuttings are harvested from stool 

beds in the winter during the dormant period, stored 

under refrigeration, and then planted in the field when 

soil temperatures reach levels appropriate to specific 

regions and genotypes. 

Poplar can be managed in a number of ways, 

depending on the desired end product and target 

rotation age. Plantations grown for the production of 

larger-diameter trees used in the manufacture of paper 

and lumber are typically planted at spacings ranging 

from 8 feet by 8 feet (680 trees per acre) to 12 feet by 

12 feet (302 trees per acre). Plantations of this type 

are currently managed commercially in Minnesota for 

pulpwood production and Oregon and Washington for 

a mix of products, including sawtimber and pulpwood. 

Poplar has the ability to resprout from established 

stumps after harvest, and thus could be managed on 

repeated coppice rotations. In light of the development 

of new genotypes and increased interest in dedicated 

energy feedstock production systems, the repeated 

coppice management option is a subject of renewed 

interest, and field research is recommended to identify 

optimal plant spacing and biomass production of such 

systems.

After planting, it is necessary to eliminate weed 

competition. As poplar plantings are mostly established 

on marginal agricultural land that has been under 

prior cultivation, weeds are mostly herbaceous and 

can be managed by preemergence herbicides, contact 

herbicides, or by cultivation. Weed control is needed 

until tree canopy closure—usually by the end of the 

second or third year of tree growth.

It is important to protect poplar plantings from insects 

and diseases. Various chemicals are available to control 

common pests, such as the cottonwood leaf beetle 

(Mattson et al., 2001; Coyle et al., 2008 ) and other 

insects, and the possibility of genetic selection for 

resistance, landscape-level deployment strategies, and 

other integrated pest management strategies can be 

considered (Mattson et al., 2001). Disease incidence 

and severity often depend on region (Newcombe et 

al., 2001). For example, Septoria stem canker is a 

serious problem in the Midwest on some hybrids, while 

much less of a concern in the Northwest, which places 

serious constraints on parental poplar species selection 

in the Midwest. Genetic selection among parental 

poplar species, among specific parental genotypes, and 

(Courtesy of B. McMahon, University of Minnesota)

Figure 5.10 Harvesting poplar plantation 
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(Courtesy of ORNL)

within hybrid poplar breeding populations is practiced Figure 5.11 Minnesota poplar plantation

in nearly all breeding programs (Newcombe et al., 

2001).

Harvesting of poplar plantations can be accomplished 

by using the same timber harvesting equipment found 

in standard forest pulpwood systems or by using 

purpose-designed equipment that combines felling and 

chipping or bundling in a single machine (Figure 5.10). 

Selection of equipment and method of harvest depends 

on average tree size and age at harvest, which are, in 

turn, determined by plantation density. A wide array of 

possibilities can be envisioned.

Potential yield and production costs. Yields from 

commercial plantations are proprietary and not readily 

available; therefore, most yield data is from research 

plots (Figure 5.11). A series of plot (10 x 10 tree 

square plots) yield trials conducted in Wisconsin, 

Minnesota, North Dakota, and South Dakota from 

1987 demonstrated yields as high as 5.0 tons per acre 

per year by age 7 years (Netzer et al., 2002). Yields of 

newly selected genotypes in smaller plot experiments 

have exceeded 7.0 dry tons per acre annually on good 

agricultural soil in southern Wisconsin and Iowa 

(Riemenschneider, 1996; Zalesny et al., 2009). In 

general, sustainable average yields of 4.5, 6, and 9 tons 

per acre annually (dry weight, stem, and branches) are 

expected in the midwestern, southern, and northwestern 

United States, respectively. With appropriate research 

and development investment, over time these yields 

could be significantly increased, even doubled (Volk et 

al., in press) (Figure 5.11).

Using cash flow models of production costs and 

expected yields, costs of poplar biomass are 

comparable to other dedicated biomass production 

systems and range from $25 to $60 per dry ton 

depending on site quality and site-specific inputs. 

Using cash flow models developed by the University 

of Minnesota for the north-central United States, the 

total discounted cost of all inputs (assuming a 12-year 

rotation pulpwood-oriented system) is $450 per acre 

or roughly $36 per acre annually. Breakeven price of 

biomass in this system is approximately $16 per dry 

ton, including input costs only. The question of where 

woody energy crops will be deployed depends less on 

the breakeven price of the energy crop itself and more 

on the profitability of the crop being replaced. Based 

on data from a survey of production costs conducted 

by the University of Minnesota (2010), per-acre profits 

are estimated to range from $50 per acre in the case of 

wheat to $200 per acre in the case of corn production. 

Thus, energy crops will have to be priced at a level in 

which profits to growers are at least equal to competing 

crops.

Sustainability. Perennial woody crops provide 

multiple benefits when managed sustainably, such as 

biological diversity, conservation of soil and water, 

maintenance of site productivity, carbon sequestration, 

and socioeconomic values (Ruark et al., 2006). In a 

summary paper on the subject published by Tolbert et 

al. (2000), several trends are identified. Soil structure, 

total organic content, and infiltration rate is shown to 

the agricultural system being replaced. Inputs of leaf 

litter and lack of annual site disturbance are thought 

to be contributing factors. Nutrient content and water 

yield of short rotation poplar plantations were found to 

be similar to older, natural aspen stands in Minnesota. 

Increased soil carbon has been documented under 

short rotation systems, particularly in those regions 

of the country where inherent soil organic content is 

low, like it is in the South. Over the long term, soil 

carbon is expected to increase under perennial woody 

crops due to inputs of leaf and root biomass and lack 
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of disturbance of the soil surface. Oxidation of carbon 

from upper soil layers has been shown to be a major 

factor, accounting for differences between perennial 

energy crops and annually tilled agricultural crops. 

Studies of wildlife effects of hybrid poplar plantings 

in Minnesota have shown increased diversity in bird 

populations compared with row crops (Hanowski et 

al., 1997). Small mammal abundance was found to be 

a function of canopy closure, with younger plantations 

being more similar to grasslands. Research done to 

date indicates that perennial woody crops will not 

mimic natural forest stands, but will contribute to 

diversification of habitat in agriculturally dominated 

landscapes.

Conclusions. The widespread natural range of eastern 

cottonwood, plus the possibility of extending the 

adaptive range by interspecific hybridization, points 

to the fact that poplar is one of the most promising 

species groups for woody crops development 

nationally. High rates of biomass productivity, 

amenability to clonal propagation and agricultural 

management, as well as coppicing ability, are factors 

that make poplar a desirable crop to produce biomass 

for energy as well as other products. Past research has 

documented acceptable yields of these systems using 

genetic material that is essentially one generation 

away from native populations. Genetic improvement 

research underway in Iowa, Minnesota, and the Pacific 

Northwest has demonstrated significant gains in 

biomass yield and the benefits of a concerted breeding 

and field testing effort. Continued research in genetics 

and stand management is needed to improve yield 

and extend the range of high-yielding varieties to all 

regions where biomass crops may be planted.

5.1.6 Willow

Interest in shrub willows (Salix spp.) as a perennial 

energy crop for the production of biomass has 

developed in Europe and North America over the past 

few decades because of the multiple environmental 

and rural development benefits associated with their 

production and use (Börjesson, 1999; Volk et al., 2004; 

Rowe et al., 2008). Initial trials with shrub willows 

as a biomass crop were conducted in the mid-1970s 

in Sweden with the first trials in the United States 

starting in 1986 (Volk et al., 2006). Since the initial 

trials in upstate New York in the mid-1980s, yield trials 

have been conducted, or are underway, in 14 states 

(Delaware, Indiana, Illinois, Maryland, Michigan, 

Minnesota, Missouri, New Jersey, New York, 

Pennsylvania, South Carolina, Virginia, Vermont, and 

Wisconsin) and six provinces in Canada.

Biology and adaptation. Willow shrubs have several 

characteristics that make them an ideal feedstock 

for biofuels, bioproducts, and bioenergy: high yields 

that can be sustained in 3- to 4-year rotations, ease 

of propagation from dormant hardwood cuttings, a 

broad underutilized genetic base, ease of breeding for 

several characteristics, ability to resprout after multiple 

harvests, and chemical composition and energy 

[3-year-old willow stems averaged 8,340 Btu per dry 

pound (Miles et al., 1996)], similar to other northern 

hardwood species.

Production and agronomics. The shrub willow 

cropping system consists of planting genetically 

improved varieties in fully prepared open land where 

weeds have been controlled. The varieties of shrub 

willow that have been bred and selected over the past 

two decades in New York can be grown successfully 

(Courtesy of T. Volk, SUNY)

Harvesting willow with a one-pass 

cut and chip forage harvester Figure 5.12
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on marginal agricultural land across the Northeast, 

Midwest, and parts of the Southeast. This range could 

be expanded with the development of new varieties. 

Weed control usually involves a combination of 

chemical and mechanical techniques and should begin 

in the fall before planting if the field contains perennial 

weeds, which is often the case with marginal land. 

Willows are planted as unrooted, dormant hardwood 

cuttings in the spring as early as the site is accessible at 

about 6,070 plants per acre using mechanized planters 

that are attached to farm tractors and operate at about 

2.0 acres per hour. To facilitate the management and 

harvesting of the crop with agricultural machinery, 

willows are planted in a double-row system with 5 

feet between double rows, 2.5 feet between rows, and 

2 feet between plants within the rows. Following the 

first year of growth, the willows are cut back close 

to the soil surface during the dormant season to force 

coppice regrowth, which increases the number of stems 

per stool from 2–4 to 8–13 depending on the variety 

(Tharakan et al., 2005). After an additional 3 to 4 years 

of growth, the stems are mechanically harvested during 

the dormant season after the willows have dropped 

their leaves (Figure 5.12). Forage harvesters with a 

specially designed cutting head cut the willow stems 

2–4 inches above the ground, feed the stems into forage 

harvester, and produce uniform and consistent sized 

chips that can be collected and delivered directly to 

end users with no additional processing (Abrahamson 

et al., 2002; Volk et al., 2006). The chipped material is 

then delivered to end users for conversion to bioenergy, 

biofuels, and/or bioproducts.

The plants will sprout again the following spring when 

they are typically fertilized with about 90 pounds per 

acre (Figure 5.13) (Abrahamson et al., 2002; Adegbidi 

et al., 2003) of commercial fertilizer or organic sources 

like manure or biosolids. The willows are allowed to 

grow for another 3- to 4-year rotation before they are 

harvested again (Figure 5.12). Projections indicate that 

the crop can be maintained for seven rotations before 

the rows of willow stools begin to expand to the point 

that they are no longer accessible with harvesting 

equipment. At this point the crop can be replanted 

by killing the existing stools with herbicides after 

harvesting and the killed stools are chopped up with 

a heavy disk and/or grinding machine followed by 

planting that year or the following year.

Potential yield and production costs. A rapid growth 

rate is one of the attributes that makes shrub willows 

an appealing biomass crop. Yields of fertilized and 

irrigated, unimproved varieties of willow grown for 

3 years have exceeded 12 dry tons per acre per year 

(Adegbidi et al., 2001; Labrecque and Teodorescu, 

2003). Due to the costs associated with irrigation and 

the relatively low value for biomass, irrigation will 

probably not be used for most large-scale production 

operations, with the exception of situations where 

willow crops could be irrigated with wastewater as 

part of a nutrient management plan. First-rotation, 

non-irrigated research-scale trials, with unimproved 

varieties in central New York, have produced yields 

of 3.8 to 5.2 dry tons per acre per year (Adegbidi et 

al., 2001; 2003; Volk et al., 2006). Second rotation 

yields of the five best-producing varieties in these trials 

increased by 18% to 62% compared to first-rotations 

(Volk et al., 2001), and in subsequent rotations, yields 

are maintained and largely dependent on weather 

conditions. The most recent yield trials using improved 

varieties of willow that have been bred and selected 

for biomass production in New York are showing yield 

increases of 20% to 40%.

Spring resprout after fall 

cutting of willow Figure 5.13

(Courtesy of T. Volk, SUNY)
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The large genetic diversity across the genus Salix 

and the limited domestication efforts to date provide 

tremendous potential to improve yield and other 

characteristics, such as insect and disease resistance 

and growth form of willow biomass crops. The species 

used in woody crop systems are primarily from the 

subgenus Caprisalix (Vetrix), which has over 125 

species worldwide (Kuzovkina et al., 2008). Breeding 

and selection of willow biomass crops in the United 

States began in the mid-1990s and has continued with 

various levels of effort since that time (Smart et al., 

2008). Selection trials of new varieties from the initial 

rounds of the breeding programs in the late 1990s 

have produced yields that are up to 40% greater in the 

first rotation than the standard varieties used in early 

yield trials. Second rotation results from these same 

trials indicate that the yield of some of the new willow 

varieties is more than 70% greater than the standard 

varieties. These results indicate that there is a large 

potential to make use of the wide genetic diversity 

of shrub willows to improve yields with traditional 

breeding and selection.

The economics of willow biomass crops have been 

analyzed using a cash flow model (EcoWillow v.1.4 

(Beta) that is publically available (Buchholz and 

Volk, 2011). The model incorporates all the stages 

of willow crop production from site preparation and 

planting through harvesting over multiple rotations to 

transportation of harvested chips to an end user. For 

the base case scenario in EcoWillow, the internal rate 

of return of willow biomass crops over seven 3-year 

harvest cycles (22 years) is 5.5%, and the payback is 

reached in the 13th year at assumed sale price of $60 

per dry ton. Harvesting, establishment, and land rent 

are the main expenses associated with willow biomass 

crops over their entire lifespan, making up 32%, 23%, 

and 16% of the total undiscounted costs. The remaining 

costs, which include crop removal, administrative 

costs, and fertilizer applications, account for about 29% 

of the total costs.

The development of new harvesting technology is 

reducing costs by optimizing productivity. Another 

approach is to reduce the frequency of harvesting 

operations. Increasing the rotation length from 3 to 4 

years reduces harvesting costs by 14% (from $14.79–

$12.70 per dry ton) and increases the IRR by 11% 

(from 5.5%–6.2%).

Establishment costs are the second largest cost in the 

willow biomass crop production system and account 

for 23% of the total cost. Over 63% of these costs 

are for planting stock, so decreasing this input cost 

will affect the overall economics of the system. For 

instance, decreasing costs from a cutting from $0.12 to 

$0.10 reduces establishment costs by $106 per acre and 

increases the IRR of the system from 5.5% to 6.5%.

Several other components of the system need to be 

developed to improve the overall economics of willow 

biomass crop systems, and one of the main ones is 

yield. Increasing yields from the base case of 5.4 dry 

tons per acre annually by 50% to 8 dry tons per acre 

per year increases the IRR from 5.5% to 14.6%. With 

ongoing breeding and selection, as well as efforts 

to improve crop management, these levels of yield 

increases should be possible in the near future.

Sustainability. Willow biomass crops are being 

developed as sustainable systems that simultaneously 

produce a suite of ecological and environmental 

benefits in addition to a renewable feedstock for 

bioproducts and bioenergy (Volk et al., 2004; Rowe et 

al., 2009). The perennial nature and extensive fine-root 

system of willow crops reduce soil erosion and nonpoint source pollution relative to annual crops, promote 

stable nutrient cycling, and enhance soil carbon 

storage in roots and the soil (Ranney and Mann, 1994; 

Aronsson et al., 2000; Tolbert et al., 2000; UlzenAppiah, 2002). In addition, the crop is constantly in 

its rapid juvenile growth stage, so the demand for 

nutrients is high, which results in very low leaching 

rates of nitrogen, even when rates of applications 

exceed what is needed for plant growth (Adegbidi, 

1999; Mortensen et al., 1998; Aronsson et al., 2000). 

The period with the greatest potential for soil erosion 

and nonpoint source pollution is during the first 1.5 

years of establishment of the crop when cover is often 

limited because weeds need to be controlled and the 

willow canopy has not closed. The use of a winter 

rye cover crop has proven to be effective at providing 

cover for the soil without impeding the establishment 

of the willow crop (Volk, 2002). Since herbicides 

are only used to control weed competition during the 
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establishment phase of willow biomass crops, the 

amount of herbicides applied per hectare is about 10% 

of that used in a typical corn-alfalfa rotation in upstate 

New York.

Nutrient removal from willow biomass crops is 

limited because only the aboveground woody portion 

of the crop is harvested during the dormant season 

after the leaves have dropped and most nutrients have 

been translocated to the root system. Nutrients not 

translocated from the foliage are returned to the system 

in litter. For most soils in the region where willow 

is being deployed, the only nutrient addition that is 

recommended is nitrogen, which is typically added 

at the rate of about 100 pounds of nitrogen per acre 

once every 3 to 4 years in the spring after the crop is 

harvested. However, research is ongoing to address 

concerns about nutrient management across a range of 

sites with new varieties of willow.

The recommended planting scheme for willow 

biomass crops is designed to maintain both genetic and 

structural diversity across a field and the landscape. 

Blocks of four or more willow varieties from different 

diversity groups should be planted in each field so that 

the structural and functional diversity of the system 

across the field is improved and any potential impact 

associated with pests and diseases in the future is 

reduced (Figure 5.13). At the landscape level, willow 

biomass crops will be in different stages of growth 

each year because they are managed on a three-year 

coppice cycle, which will further increase the structural 

diversity of the system.

Birds are one indicator of the biodiversity supported 

by willow biomass crops that have been studied in 

the United States. A study of bird diversity in willow 

biomass crops over several years found that these 

systems provide good foraging and nesting habitat for 

a diverse array of bird species (Dhondt et al., 2007). 

Thirty-nine different species made regular use of the 

willow crops and 21 of these species nested in them. 

The study found that diversity increased as the age of 

the willows and the size of the plantings increased.

It also found that birds have preferences for some 

varieties of willow over others (Dhondt et al., 2004). 

The number of bird species supported in willow 

biomass crops was similar to natural ecosystems, 

such as early succession habitats and intact eastern 

deciduous forest natural ecosystems. Willow biomass 

crops will increase diversity, especially in contrast to 

the open agricultural land that it will replace, rather 

than creating monocultures with a limited diversity 

across the landscape Lifecycle analysis of willow 

biomass crops has shown that they are low carbon 

fuels because the amount of CO2

 taken up and fixed 

by the crop during photosynthesis is almost equal 

to the amount of CO2

 that is released during the 

production, harvest, transportation, and conversion of 

the biomass crop to renewable energy (Heller et al., 

2003). The cycle is balanced for all the CO2

 inputs 

into the atmosphere from the system because only the 

aboveground portion of the willow biomass crop is 

harvested and used in the conversion process. When 

willow biomass is used to offset fossil fuels, it can help 

reduce the amount of CO2

 emitted to the atmosphere. 

If the 99 million acres of available land in the United 

States were planted and harvested with short rotation 

woody crops to offset coal use for power production, 

up to 76% (11 quadrillion tons carbon per year) of 

the carbon offset targets for the United States under 

the Kyoto Protocol could be met (Tuskan and Walsh, 

2001).

The low input intensity of willow biomass crops 

relative to agricultural crops and their perennial nature 

result in a large, positive net energy ratio for the 

biomass that is produced. Accounting for all the energy 

inputs into the production system, starting with the 

nursery where the planting stock is grown through to 

the harvesting of biomass, converting it to chips and 

delivering it to the side of the field, results in a net 

energy ratio of 1:55 (Heller et al., 2003). This means 

that for every unit of nonrenewable fossil fuel energy 

used to grow and harvest willow, 55 units of energy are 

produced and stored in biomass. Replacing commercial 

nitrogen fertilizers, which are produced with large 

inputs of fossil fuels, with organic amendments, such 

as biosolids, can increase the net energy ratio to 73–80 

(Heller et al., 2003). Transporting the woody biomass 

24 miles from the edge of the field to a coal plant 

where it is co-fired with coal to generate electricity 

results in a net energy ratio of 1:11. If a gasification 

conversion system is used, the net energy ratio is 

slightly higher (Keoleian and Volk, 2005).
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Conclusions. Shrub willows have the potential to be 

grown on marginal agricultural land as a dedicated 

energy crop across a large range in the United 

States. The decades of research in Europe and North 

America provide a solid foundation for the largescale deployment of the crop. This transition has 

begun with new varieties of shrub willow being 

scaled up in commercial nurseries in the United States 

and Canada, and the engagement of agricultural 

equipment manufacturers, like Case New Holland, 

in the development of harvesting systems. The 

continued optimization of the willow crop production 

system, a strong breeding and selection program, and 

quantification of the environmental and socioeconomic 

benefits associated with the crop are important for the 

effective and successful expansion of willow biomass 

crops. The proper deployment of willow biomass crops 

has the potential to put millions of acres of marginal 

agricultural land back into production, annually 

produce millions of tons of biomass, create thousands 

of rural jobs, and produce an array of environmental 

benefits.

5.1.7 Eucalyptus

Eucalyptus spp. is the world’s most widely planted 

hardwood species. Its fast, uniform growth, selfpruning, and ability to coppice (regrow after harvest) 

make it a desirable species for timber, pulpwood, 

and bioenergy feedstocks (Figure 5.14). It has been 

domesticated for various products and has been widely 

commercialized in the tropics and subtropics.

In the United States, eucalyptus was introduced 

as early as the 1850s on the West Coast to 

produce dimension lumber and has been produced 

commercially in Florida since the 1960s. Though 

eucalyptus has naturalized in areas of the Southwest 

raising concerns of invasiveness, there is no evidence 

of spreading in the Gulf South. In anticipation of an 

increased role in biomass production, ongoing efforts 

aim to develop eucalyptus cultivars for improved yield 

and frost resistance in the southern United States.

Biology and adaptation. There are over 700 species 

of eucalyptus, adapted to various ecological conditions 

across its native range of Australia. Less than 15 

species are commercially significant worldwide. In the 

South, genetic improvement programs are selected for 

fast growth, cold tolerance, desirable growth form, and 

reduced lignin. Genetic improvement programs aim 

to improve varieties for various growing conditions 

(Gonzalez et al., 2010; Rockwood and Carter, 2006a).

Production and silviculture. Eucalyptus production 

practices in different parts of the world vary 

with site conditions, desired products, and scale 

of commercialization. Genetic selection has led 

to commercialization of genotypes with unique 

advantages in different applications. They are 

commercially propagated by both seed and cloning of 

tissue culture. For conventional pulpwood production, 

stands are typically established at a planting density 

of 600–1,000 trees per acre, and harvested every 6–10 

years. They may be replanted at harvest, which can 

benefit from improved genetic material, or regenerated 

from coppice growth, which eliminates the cost of 

replanting. Economically optimum time between 

harvests may be 3–4 years, with replanting after 2–5 

harvests, on stands with initial densities of 3,400 

trees per acre in Florida (Langholtz et al., 2007). 

(Courtesy of ArborGen and R. Gonzalez, NCSU)

Figure 5.14 Eucalyptus plantation in Florida 
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Silvicultural strategies in the United States continue 

to evolve with changing markets, genotypes, and 

applications.

Because of high growth rates and tolerance to a range 

of growing conditions, eucalyptus can be produced in 

innovative ways, providing non-market benefits. For 

example, research trials demonstrate that E. grandis

and E. amplifolia can be used for restoration of 

phosphate-mined lands (Rockwood and Carter, 2006b, 

Langholtz et al., 2007; 2009). Eucalyptus spp. has been 

shown to be effective at phytoremediation of reclaimed 

wastewater, municipal waste, storm water, and arsenicand trichloroethylene-contaminated sites (Rockwood 

et al., 2004; Langholtz et al., 2005). Eucalyptus 

plantations that provide these types of environmental 

services may be viewed more favorably by the public, 

and compensation for non-market environmental 

services would improve the profitability of these 

systems. 

Potential yield and production costs. Eucalyptus 

yields are influenced by precipitation, fertility, soil, 

location, and genetics. Eucalyptus spp. yielded 

7.6–14.3 dry tons per acre annually after 3–5 years 

of growth on a clay settling area in central Florida, 

comparable to 8.9–13.8 dry tons per acre estimated for 

eucalyptus in Florida (Rahmani et al., 1997), but higher 

than the estimated 4–7.6 dry tons per acre estimated 

by Klass (1998), who observed that yields could be 

improved with SRWC development in the subtropical 

South. E. grandis is a high-yielding species in southern 

Florida, while E. amplifolia has the advantage of being 

more frost tolerant, with current trials as far north as 

South Carolina. The subsequent analysis in this report 

assumes a conservative annual yield average of 6.0 dry 

tons per acre.

Sustainability. Intensive management of eucalyptus, 

characterized by short rotations of genetically uniform 

monocultures, has dramatically increased yields over 

recent decades. These tree plantations maintain some 

sustainability attributes associated with forested 

landscapes, while at the same time facing sustainability 

challenges common in agriculture (Binkley and 

Stape, 2004). Infrequent tilling in tree plantations 

reduces risk of soil erosion associated with annual 

crops, and carbon sequestered in eucalyptus stand 

biomass exceeds the amount of carbon sequestered in 

herbaceous crops.

Conclusions. Eucalyptus has proven to be one of the 

most productive and economically viable biomass 

crops in the world, with expansive commercialization 

on all populated continents. As with other biomass 

crops, high yields require fertilization and water. 

Intensively managed plantations offer both 

environmental benefits over conventional agricultural 

systems and potential environmental downsides if 

native ecosystems are displaced. It is expected that 

eucalyptus will continue to be produced commercially 

in the United States and will play an increasing role as 

a feedstock for bioenergy systems.

5.1.8 Southern Pines 

Pines comprised 32% of the tree species planted 

for production purposes around the world in 2005 

(FAO, 2007) and 83% of tree species planted in the 

southern United States (USDA Forest Service, 2007b). 

Softwoods in the southern United States already 

contribute 40% of the total annual industrial wood 

supply of roundwood (USDA Forest Service, 2007c) 

and 40% of southern softwoods are used for pulpwood 

and composites. Because the fiber industry has long 

used both bark and black liquor to produce energy for 

running the pulp mills, southern pines are already a 

significant contributor to U.S. biomass energy.

Biology and adaptation. Loblolly pine (Pinus taeda 

L.) is the most important and widely cultivated timber 

species in the southern United States. Because it grows 

rapidly on a wide range of sites, it is extensively 

planted for lumber and pulpwood (Figure 5.15). This 

tree is dominant on 30 million acres and comprises 

over half of the standing pine volume in the South 

(USDA Forest Service, 2007c). A medium lived 

loblolly matures in about 150 years, with select trees 

reaching 300 years in age. Other pine species are 

found in the South, including slash pine (Pinus elliottii 

Englem), longleaf pine (Pinus palustris Mill), and 

shortleaf pine (Pinus echinata Mill); hybrids of loblolly 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

and the three other species are also found (Peter, 2008). 

Of these, loblolly and slash pine are most frequently 

planted, and loblolly is the most important southern 

pine for bioenergy feedstock production. Loblolly 

shows a strong growth response to management inputs 

and is the best choice on good sites with better-drained 

soils where hardwood competition is a problem.

Production and silviculture. Improvements in pine 

silviculture have resulted in improving southern U.S. 

pine productivity by a factor of about 6 since the 

1940s and increasing the number of planted acres of 

all pines from zero in 1940 to 37,664 million acres by 

year 2006 (USDA Forest Service, 2007a). The change 

from relying on natural pine stands to establishing 

and intensively managing pine plantations for fiber 

production is one of the major success stories in 

plantation forestry (Fox et al., 2007b). Loblolly pines 

are now deemed to be one of the most productive 

species that could be used in the southern United States 

for supplying bioenergy resources (Gonzalez et al., 

2009).

Loblolly pines are normally planted as 1-year-old 

bare-root seedlings, though the more expensive 

containerized seedlings offer several advantages, 

including better survival (Taylor, 2006). Production 

of bare-root seedlings involves planting seed in 

specialized beds with controlled conditions for 8–12 

months, top pruning, lifting, and grading. Currently 0.8 

to 1.0 billion loblolly and slash pine bare-root seedlings 

are sold annually for forest planting. Essentially all of 

the seed is genetically improved for growth and disease 

resistance, with 70% of the seedlings being loblolly 

pine and 30% slash pine (Peter, 2008).

Many steps have contributed to improving the 

productivity of loblolly pine in the South (Stanturf et 

al., 2003a; Fox et al., 2007a). Naturally regenerated 

forests were the common practice from the 1920s 

through the 1950s, with very low annual productivity. 

Improved nursery and field planting practices began 

in the 1950s with continued improvement through 

the 1970s, and as a result, whole tree aboveground 

yields tripled. Seed orchards dedicated to seed 

improvement were first established in the late 1950s. 

The first generation improved seeds increased value 

of plantation wood by 20%, and second generation 

improved seeds being used now are adding another 

14%–23%. The importance of hardwood competition 

control was recognized by the early 1970s. First 

methods of control were entirely mechanical, but by 

the late 1970s herbicides were added, and by 1990, 

chemical site preparation was predominate with limited 

mechanical site preparation involved. Fertilization of 

pine plantations was initiated in the late 1960s, but 

was implemented slowly during the 1970s and 1980s 

(Albaugh et al., 2007). Average productivity increased 

rapidly from the 1970s to 1990s primarily as a result 

of implementing use of improved site preparation, 

hardwood competition control, and genetically 

improved seeds. 

Implementation of silviculture and genetic 

improvements very much accelerated in the 1990s as 

a result of the non-proprietary research conducted by 

university-industry cooperatives. In 1999, there were 

23 research cooperatives at nine southern universities 

(Stanturf et al., 2003b). During the 1980s and 1990s, 

cooperative research clearly confirmed the benefits 

to pine productivity of fertilizing with both nitrogen 

and phosphorus, especially in mid-rotation. Further 

research published since 2000 has shown the need 

for micronutrients on certain soil types (Fox et al., 

2007a;b; Kyle et al., 2005). Other recent studies have 

(Courtesy of William M. Ciesla, Forest 

Health Management International)

Figure 5.15 Pine plantation
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with selected loblolly pine genotypes, annual 

fertilization, irrigation (in some cases), excellent site 

preparation, and weed control has increased biomass 

yields to 5.4 to 8.5 dry tons per acre per year. Based 

on recently reported research results, companies are 

predicting future operational yields of 6 to 8 dry tons 

per acre per year when greater management intensity 

is used. However, it is unlikely that yearly fertilization 

will be economically viable or indeed it may not be 

necessary for high-yield achievement.

Various ideas have been proposed on how to manage 

southern pines for bioenergy production. Both 

Gonzalez et al. (2009) and Scott and Tiarks (2008) 

have recently described management plans for 

producing both timber and bioenergy products. Both 

involve a combination of rows of widely spaced trees 

and tightly spaced rows for bioenergy. The bioenergy 

rows would be harvested in 5 to 8 years and a widely 

spaced row for lumber production to be harvested at 18 

to 22 years. While this might be a reasonable transition 

strategy, an efficient harvesting strategy for removing 

the bioenergy trees has not been discussed. Planting 

and harvesting can be much more efficient when 

pine plantations are dedicated entirely to supplying 

bioenergy feedstocks. Such plantations are likely to 

be planted at higher densities and managed on shorter 

rotations similar to poplars and eucalyptus.

The age of optimal stand harvest has not yet been 

determined for higher density loblolly pine plantings. 

Recent intensive management studies planted at 

stand densities of 454 to 670 trees per acre show total 

aboveground biomass continuing to increase between 

10 and 15 years of age (Samuelson et al., 2008; 

Borders et al., 2004). However, those same studies also 

show density-dependent mortality beginning at basal 

areas of about 153 square feet per acre on fertilized 

wet sites, which correlates to an age range of about 

9 to 10 years. The highest density study with 1,210 

trees per acre showed a slowing of the current annual 

increment by age 5, but the mean annual increment was 

still increasing (Roth et al., 2007). The cost of planting 

will depend on initial planting density and the amount 

of replanting needed (Taylor et al., 2006). Advanced 

generation, bare-root seedlings were reported to cost 

$47.50 per thousand seedlings in 2006. Over the 

compared the effects of management intensity levels 

(Borders et al., 2004; Cobb et al., 2008; Martin and 

Jokela, 2004; Roth et al., 2007; Samuelson et al., 

2008; Will et al., 2006), clearly showing the potential 

for much higher yields. Since third generation seeds 

from selected parents were beginning to be deployed 

in the early 2000s (McKeand et al., 2003), several 

of the recent research trials have included a higher 

performing genotype that resulted in enhanced yields.

At present, most loblolly pines stands in the South are 

managed for a combination of pulp and timber so that 

thinning is incorporated into the management. The 

stands are planted on average at about 600 seedlings 

per acre (~1480 seedlings per hectare), planning for a 

25-year rotation with a thinning at age 15 (Gonzalez 

et al., 2009). With many studies showing the benefits 

of weed control and fertilization, mid- rotation 

fertilization has become considerably more common 

(Albaugh et al., 2007). Average operational yields 

in the southeastern United States were reported in 

2003 to be about 4 dry tons per acre annually total 

aboveground oven-dry weights (Stanturf et al., 2003b). 

Current yield potential is assumed to be higher with 

the recent deployment of third generation loblolly 

pine seedlings on sites with site preparation treatments 

that ensure adequate survival and rapid early growth. 

Future management techniques are predicted to 

include “clonal plantations, whole rotation resource 

management regimes, use of spatially explicit spectral 

reflectance data as a major information source 

for management decisions, active management to 

minimize insect and disease losses, and more attention 

to growing wood for specific products” (Allen et al., 

2005).

Potential yield and production costs. Loblolly 

pine research plots managed with site preparation 

and weed control but no fertilizers have produced 

total aboveground biomass yields (stem, branches, 

and foliage) of 3.3 to 3.8 dry tons per acre per year. 

Research plots with site preparation, weed control, 

and fertilization only at planting have produced total 

yields in the 3.6 to 5.2 dry tons per acre per year range. 

Addition of higher levels of fertilizers plus irrigation in 

some cases has bumped yields to 5.1 to 7.3 dry tons per 

acre per year of biomass. Very intensive management 
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planting ranges mentioned above, and including culls 

and extra seedlings needed for replanting, seedling 

costs could be expected to range from about $40 to $60 

per acre. Planting with current planting equipment is 

expected to cost about $65 to $100 per acre.

Harvesting of small-diameter trees has been a 

significant cost barrier to using southern pines for 

energy (Peter, 2008) but the results of intensive 

management studies are showing that excellent growth 

can be achieved at densities low enough to allow 

individual trees to achieve an economically harvestable 

size. Consequently, harvest and handling costs (to 

roadside) using currently available equipment should 

be similar to current pulp harvesting costs or about $20 

per dry ton.

Economically optimal fertilization strategies will 

vary for each planting site. Intensive culture studies 

produce higher yields with high annual fertilization 

fairly consistently, while financial returns depend on 

the magnitude of the growth response obtained, the 

product mix, stumpage prices, cost of fertilization, 

and the length of time before harvest (Fox, 2007b). As 

with hardwoods, first fertilization with nitrogen and 

phosphorous should be delayed a year or two to avoid 

stimulating weed competition, but no later than stand 

closure. Mid-rotation fertilization applications of both 

nitrogen and phosphorous (at 200-pound nitrogen per 

acre and 25-pound phosphorous per acre applied at 

time of stand closure) have shown very positive stand 

responses lasting for several years in lower density 

stands, but more frequent fertilization at lower levels 

may be needed in higher density loblolly stands. 

Sustainability. Use of intensive management to 

produce wood specifically for bioenergy is generally 

only economically viable when the total aboveground 

portions of the trees are removed. This has raised 

concern about long-term site productivity impacts. 

Research and analysis of intensive pine production 

has shown that good site preparation, chemical control 

of non-crop vegetation, and fertilizer application 

at levels and times that optimize utilization by the 

trees, increases biomass yields in an energy-efficient 

manner, while maintaining or improving long-term 

site productivity (Scott and Dean, 2006). Allen et al. 

(2005) argue for use of a fully integrated management 

approach starting with good site selection followed 

by excellent early competition control and additional 

inputs, as needed. Such management practices will not 

only create economically sustainable woody production 

systems, but will also minimize the potential for 

adverse environmental effects.

Conclusions. In the near term, pine bioenergy 

feedstocks are most likely to be obtained by thinning 

existing loblolly pine stands that are planted for 

multiple uses (fiber and energy). If loblolly pines 

are planted specifically for energy, then they will be 

grown at relatively dense spacings and short (8–10 

year) rotations. Research studies suggest that the 

lowest planting density under intensive management 

that might be expected to achieve an economically 

harvestable size within that time period is about 726 

trees per acre. Average yields of about 5.5 dry tons 

per acre annually in the Southeast, Atlantic Coast, 

and Delta regions are obtainable with appropriate 

management. This includes plowing, disking, and 

application of a total kill herbicide once or twice before 

planting. Non-crop vegetation is controlled during the 

first 2 years, primarily with herbicide applications. In 

the southern United States, phosphorus and potassium 

are usually added to high-yield stands in the planting 

year, and nitrogen additions of about 89 pounds per 

acre are added in years 2 through 6, based on foliar 

analysis studies showing nitrogen demand levels (Will 

et al., 2006). Economically viable harvest is expected 

to occur as early as the eighth year. Both traditional and 

molecular genetics need to continue to be aggressively 

pursued to improve the productivity potential of 

loblolly and other pines, and substantial yield 

improvements are expected between now and 2030.
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53 The county-level data provide a non-unique, representative reference scenario, which is consistent, in the aggregate, with the national level

projections.

54 Alternatively, a bioenergy feedstock production target can be set, such as an EISA 2007, the RFS and a price solved to

 meet the production target. BRDI (2008) is an example of the latter application of POLYSYS.

55 Total U.S. acreage in cropland pasture and permanent pasture is approximately 36 and 409 million acres, respectively (USDA-NASS, 

2009). POLYSYS explicitly excludes pasture in counties where there is extensive use of supplemental irrigation and pasture west of the 

100th Meridian.

 56 The primary goal of the CRP is to mitigate soil erosion (USDA-FSA, 2008). Compared to pre-CRP erosion rates, the CRP reduced erosion 

by 470 million tons in 2007. Other benefits of the program include creating wildlife habitat, reducing sedimentation, improving water 

quality, preventing excess crop production, and providing a stable source of income for farmers.

5.2 Estimating Future 

Crop Supply

The economic potential of energy crops is estimated 

using POLYSYS, a policy simulation model of the U.S. 

agricultural sector that includes four interdependent 

modules—crop supply disaggregated to 3,110 

counties, national crop demand and prices, national 

livestock supply and demand, and agricultural income 

(De La Torre Ugarte and Ray, 2000). The model 

is anchored to the USDA 10-year projection of the 

U.S. agricultural sector and is extended 10 years 

to 2030 by extrapolating crop yields, exports, and 

population. The USDA and extrapolated projections 

are further disaggregated to county levels.53 The 

projections include production and consumption 

for agricultural commodities, agricultural trade and 

exports, commodity prices, and aggregate indicators 

of the sector, such as farm income and food prices 

(USDA-OCE/WAOB, 2009). By varying prices 

offered for biomass feedstocks, POLYSYS estimates 

potential energy crop supplies and changes in land 

use, which can include acreage changes among 

crops and conversion of cropland and pastureland to 

energy crops. The model also estimates changes (i.e., 

deviations in the agricultural projections) in crop prices 

for the eight major crops (corn, grain sorghum, oats, 

barley, wheat, soybeans, cotton, and rice); production 

quantities for food, feed, and industrial uses; exports; 

crop and livestock income; and government payments 

throughout the 20-year simulation period (De La Torre 

Ugarte and Ray, 2000).54 The version of POLYSYS 

used in this assessment includes three energy crop 

options—a perennial grass, short-rotation woody 

crops, and an annual energy crop. The grasses and 

non-coppice woody crops (e.g. poplar and pine) were 

evaluated for 10- and 8-year rotations, respectively. 

The rotation length for the coppice woody crops (e.g., 

willow) was 20 years with a 4-year cutting cycle.

5.2.1 Input and Yield Assumptions for 

Baseline and High-Yield Scenarios

Baseline scenario. The land base in POLYSYS 

includes about 250 million acres planted to the eight 

major crops, 61 million acres of land in hay production, 

23 million acres of cropland used as pasture, and 117 

million acres of non-irrigated permanent pasture.55 

Land enrolled in the Conservation Reserve Program 

(CRP) is another potential source of land.56 The CRP 

was enacted through the Food Security Act of 1985 

and compensates farmers for acres that they retire 

from crop or pasture use that are highly erodible or 

otherwise environmentally sensitive. Farmers sign into 

multi-year contracts when enlisting in CRP. As these 

contracts expire, farmers have the option of keeping 

their land enrolled in the CRP or converting it back into 

crop or forage production. In this update, it is assumed 

that the approximately 32 million acres currently 

enrolled remain in the CRP. The USDA projections 

also assume acreage enrolled in the CRP will remain 

close to the legislated maximum of 32 million acres. 

POLYSYS allocates available land in each county to 

the competing crops, including energy crops based 

on the maximization of expected returns above 

variable costs of production. Energy crops will 

displace conventional crops in the model, provided 
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they are more profitable.57 (Text Box 5.2 provides 

more information on regional land-use) In the case of 

pastureland, however, POLYSYS allows conversion to 

energy crop production only if lost forage can be made 

up by intensifying pasture production.

The availability of pasture (permanent pasture and 

cropland pasture) for conversion to perennial grasses 

and woody crops is constrained to counties east of 

the 100th Meridian (for reference, this parallel runs 

through Dodge City, Kansas). Counties east of the 

100th Meridian are assumed to have sufficient rainfall 

to replace lost forage through intensification. That 

is, POLYSYS assumes no loss of forage production. 

Further, it is assumed that intensifying cropland 

currently used as pasture will cost $50 per acre the 

first year and an additional $10 per acre in subsequent 

years. For permanent pasture, first-year costs are 

assumed to be $100 per acre and $15 per acre in 

following years. First-year costs are for additional 

investments, such as fencing. Costs in subsequent years 

are for management. Energy crops must overcome 

these additional costs plus the pasture rental rate to 

come into production.

A set of restraints are used to limit the amount of land 

switching to new energy crops in a given year. These 

restraints are imposed to simulate the relative inelastic 

nature of agriculture in the near-term. These restraints 

include:

• 5% of permanent pasture can convert to energy 

crops each year. The total amount of permanent 

pasture in a given county that can convert to 

energy crops is limited to 50% (i.e., assumed 

doubling of forage through intensification)

• 20% of cropland pasture can convert to energy 

crops each year. The total amount of cropland 

pasture in a given county that can convert to 

energy crops is limited to 50% (same assumption 

as permanent pasture) 

The land base dictates regional emphasis on primary 

feedstock availability. For example, the Southeast has 

considerable potential to supply forestland biomass, 

but more limited capability to produce energy crops 

given cropland and pastureland availability, even though 

energy crop productivity is potentially high relative to 

other regions. The Central and Southern Plains have 

greater potential to produce energy crops despite lower 

productivity potential because of the high proportion of 

cropland and pastureland. The Corn Belt and Plains are 

dominant suppliers of crop residue biomass.

TEXT BOX 5.2 | RELATIVE PROPORTION 

OF MAJOR LAND-USE TYPES BY STATE

Map source: Lubowski et al., 2005.

• 10% of cropland can convert to energy crops 

each year. The total amount of cropland in any 

given county that can convert to switchgrass or 

woody crops energy crops is limited to 25%. 

This restraint serves to maintain crop diversity. 

Energy sorghum, the annual energy crop, is much 

more limited due to rotation and land suitability 

considerations (non-erosive land only).

57 In practice, POLYSYS first determines the amount of land in each county that can enter into production, switch to a different crop, or move 

 out of production (De La Torre Ugarte and Ray, 2000; De La Torre Ugarte et al., 2003). This determination generally depends on relative 

 crop profitability in preceding years. The model also contains allocation rules or flexibility constraints that limit the amount of land a given 

 crop can lose or gain each year. These rules or constraints serve to simulate the relatively inelastic nature of short-run agricultural supply. 

Once supply is solved, POLYSYS estimates market prices and demand quantities for each crop and use (food, feed, and industrial), exports, 

and carryover stocks. 
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58 For Miscanthus, establishment would be higher due to rhizome costs. However, productivity of Miscanthus is generally higher thanthat 

of switchgrass. So ultimately, whether switchgrass or Miscanthus is more profitable in a given area really depends more or less on the 

tradeoff between establishment costs and expected productivity. For energy cane, a tropical grass, establishment costs are also higher 

than switchgrass because of the use vegetative planting material rather than seed, but as with Miscanthus higher establishment costs are 

offset with higher yields at maturity.

59 Harvesting of thicker-stemmed grasses, such as Miscanthus and energy cane, would involve more robust and/or specialized equipment. 

60 Switchgrass yields have not been demonstrated at full scale-up plots and extrapolation of demonstration plot yields to full-production

 scale plots is risky. However, research plots have produced yields consistent with the estimates in Table 5.5. Of course, yield alone does not

 determine the competiveness of energy crop production. It depends not only on crop productivity, but on how profitable the crop is in

 relation to existing land uses.

In POLYSYS, energy crop production costs include 

seed or planting stock, fertilizer, herbicide, insecticide, 

machinery services, custom operations, fuel and 

lube, repairs, handling, paid labor, and technical 

services. Factor input costs are specific to broad farm 

production regions due to regional differences in 

labor rates, fertilizer prices, and other inputs. Energy 

crop production inputs, assumptions, and prices are 

summarized in Tables 5.3 and 5.4, for herbaceous 

and woody crops, respectively. They were developed 

based on the general crop guidelines provided earlier 

in the background section. For perennial crops, such as 

grasses and trees, establishment costs and harvesting 

are most important. (Field trials are conducted as a 

result of the Feedstock Partnership described in Text 

Box 5.3)

Perennial grasses are generally planted, managed, and 

harvested like a traditional hay crop and use existing 

agricultural equipment. Conventional establishment 

can involve disking, seeding, and application of 

nutrients and herbicides. Alternatively, perennial 

grasses can be established using no-till planting 

procedures. Costs are nearly the same, as the avoided 

tillage costs are replaced with the use of specialized 

planting equipment and application of additional 

herbicides, depending on the prior crop. Table 5.3 

summarizes establishment and maintenance costs for 

switchgrass, which is used as the model perennial grass 

in POLYSYS. For switchgrass, establishment year 

costs are higher in the Southeast because of the use of 

Alamo seed, a lowland variety, and lime requirements. 

The Southern Plains also utilize Alamo, but have no 

lime or potassium requirements. The Northern Plains 

have the lowest establishment year costs because they 

utilize Cave-in-Rock (an upland variety with lower 

seed cost in the base year of analysis than Alamo) and 

have no lime or potassium requirements. Otherwise, 

production inputs for establishing switchgrass are 

similar across all production regions.58

After establishment of perennial grasses, nutrients are 

applied, and annual harvests are made. Harvest costs 

assume conventional mowing, raking, and baling 

operations.59 Once established, a perennial grass 

stand is assumed to last 10 years before replanting 

is necessary. Full yield is not attained until roots are 

fully established, which is usually by the third growing 

season. 

Perennial grasses can be grown on a wide variety 

of sites, with productivity very much determined 

by precipitation, temperatures, soils, and local site 

factors (see Text Box 5.5). As summarized in Table 

5.3, productivity varies considerably with production 

regions. It is generally higher in the Southeast and 

Appalachia than the Northern or Southern Plains. 

Annual yields of perennial grass can range from 2 or 

less dry tons per acre in the western Great Plains to 

over 6 dry tons per acre farther east. In the Southeast 

and Appalachia yields can exceed 9 dry tons per acre in 

some locations.60

Like perennial grasses, woody crops are established 

and managed with conventional agricultural equipment. 

Woody crops can be planted at a variety of spacings 

and harvested after 6 to 12 years of growth, depending 

on species, region of the country, and desired 

characteristics. With the exception of pine, most woody 

crops will resprout vigorously, but current management 

guidelines suggest replanting with improved clones 

following harvest. However, there are some hardwood 

tree crops being bred specifically as coppiced managed 

crops; willow (Salix spp.) is the notable example.
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DOE, the Sun Grant Initiative universities, and members of USDA have established the Regional Biomass Energy 

Feedstock Partnership. The Partnership consists of five separate regions: Southeast, North Central, South Central, 

Western, and Northeast. The Partnership is addressing barriers associated with supplying a sustainable and reliable 

source of feedstock to a large-scale bioenergy industry. One key activity of the Partnership is to conduct field trials of 

energy crops to assist understanding the feedstock resource development potential. Each region is expected to have a 

unique contribution to the national feedstock production. A second activity is to assure existing resource supplies are 

assessed in a consistent manner across regions. National task teams are addressing these activities.

TEXT BOX 5.3 | THE REGIONAL FEEDSTOCK PARTNERSHIP
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Although willow is most productive in the Northeast 

and Lake States regions, it has considerable potential 

to be grown farther south and west. Coppice-managed 

hardwoods are usually planted at much higher densities 

than single-rotation hardwoods and harvested on 

shorter rotations of 3 to 4 years. As many as seven 

succeeding coppice stands can be expected from the 

initial establishment.

Unlike perennial grasses, harvesting is a technical 

barrier to widespread adoption of woody crops.61 

Farmers are unlikely to have the necessary equipment 

to harvest tree stands. As such, woody crops are likely 

to be harvested as a contracted operation, even as a 

conventional “timber sale.” The cost of harvesting 

woody crops is variable and is dependent on tree 

diameter size and planting density or spacing. A 

typical spatial arrangement would have narrower 

in-row spacing and wider between-row spacing to 

accommodate production (e.g., spraying and spreading 

equipment), as well as harvesting equipment. If 

managed as a single rotation, trees can be harvested 

with existing forestry equipment (e.g., feller-bunchers, 

skidders, and whole-tree chippers). The multiple stems 

characteristic of coppice-managed hardwoods are 

harvested with a standard forage harvester fitted with 

a specially designed cutting head for woody crops. 

Woody crops are generally chipped at the stump or at 

roadside and delivered to facilities as whole-tree chips. 

(Additional differences between woody and herbaceous 

crops are provided in Text Box 5.6)

Obtaining high productivity and survival requires 

specially selected planting material, good site 

preparation, effective weed control, and application of 

nutrients. If planted today, using quality cuttings and 

seedlings, woody crops are expected to produce 3.5 

to 6.0 dry tons per acre annually, with higher yields in 

parts of the Northwest (6 dry tons per acre per year) 

and sub-tropical Florida. For willow, annual yields 

are likely to range from 5 to 6 dry tons per acre in 

the establishment rotation and slightly higher in the 

subsequent coppice rotations.

Energy sorghum is assumed as the model annual 

energy crop. Energy sorghum is only allowed on 

cropland, as it is a potentially erosive row crop. It is 

also assumed to be part of a multicrop and/or fallow 

rotation. Sorghum can be established in a manner 

similar to conventional corn, using a chisel plow 

and offset disk for soil preparation; however, notill establishment is preferred. Fertilizer (nitrogen, 

phosphorous, and potassium) and lime (once every 

3 years in regions where it is needed) are spread. 

Fertilizer costs are higher than for perennials. A row 

crop planter is used to plant sorghum seed. Weed 

control consists of two herbicide applications and 

one mechanical cultivation. Harvesting is done with a 

self-propelled forage harvester and high dump forage 

wagons to transport the chopped sorghum to the field 

edge.

Miscanthus and energy cane are two potentially 

high-yielding perennial energy crops. They, however, 

have higher establishment costs as they use vegetative 

material. Energy cane is restricted to areas without 

frost. Miscanthus can be grown in the Midwest. While 

both are thick-stemmed species (as is sorghum), 

Miscanthus has been harvested with forage equipment. 

Miscanthus can be harvested in the spring before 

regrowth begins. It is assumed that energy cane and 

Miscanthus are established in a manner similar to 

sugarcane, but with lower nutrient requirements. 

Energy cane is harvested with a forage harvester in 

the fall and Miscanthus is harvested with a mowerconditioner, rake, and baler in the spring. Because 

Miscanthus and energy cane have relatively long 

productive stand lives and high yields, they are 

potentially cost-competitive or even less costly than 

other perennial grasses provided establishment costs 

can be kept low.

High-yield scenario. As discussed at the outset, 

workshops were conducted to collect information on 

advancements needed for higher yields, the ranking of 

the timeliness, the likelihood of these advancements, 

and the projected future yields. The crop types 

considered in the herbaceous crops workshop were 

61 Results of studies conducted during the last two decades suggest that cost-effective harvesting requires that equipment be appropriately

 sized and able to cut and handle large numbers of relatively small-diameter trees. Conventional forest harvesting equipment tends to be 

 inappropriate because it is designed for single-stemmed, stop-and-go severance of large trees. The equipment is also high-powered and

 expensive.
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switchgrass, mixed perennial grasses, energy cane, and 

Miscanthus. Energy sorghum was also included, even 

though it is not a perennial, because it can be grown 

explicitly as an energy crop across a wide range of 

sites.

The participants discussed a wide array of limiting 

factors to yield improvements, as well as approaches 

to overcoming these barriers. While there is great 

potential for genetic improvement of perennials, the 

process of new variety breeding, selection, and trialing 

is relatively slow compared to annual species like corn 

and sorghum. Varieties of perennials are typically well 

adapted to a relatively narrow range of environmental 

conditions (e.g., climate and geography), and different 

varieties typically perform better in some environments 

than in others. Development of many varieties of 

each species will be required to get the best possible 

production performance across the breadth of available 

U.S. environments. Because of this geographical 

correlation, yield improvements were discussed within 

seven large resource zones across the United States 

that are adapted from the USDA Land Resource 

Regions. For switchgrass, participants estimated 2030 

yield increases as high as 80% in the most productive 

zones and improvements as low as 5% and 10% in less 

productive zones.

Participants were asked for their opinions about landuse issues related to herbaceous energy crops as to how 

the integration of energy crops into cropping systems, 

germplasm improvements, and better management 

practices might allow for expansion of biomass 

production onto more marginal lands. There will be 

competition for land, regardless of implementation of 

commodity-scale energy crop production. It would be 

helpful to develop a better understanding of best uses 

for all kinds of lands, especially the vaguely defined 

marginal lands. While they may not be useable for 

row-crop production, they may be candidates for 

energy crop production. Land needs to be used more 

effectively (e.g., matching species to the environments 

to which adapted). Production systems need to be 

designed to optimize marginal lands, such as using 

corners in pivot-irrigated fields and rehabilitating acres 

idled due to crop failure, drought, poor economics, 

etc. Converting pasture to energy crop production 

introduces different productivity issues, such as soil 

fertility, sensitivity to rain, and slope. Since there is 

already competition for pastureland, there will also be 

a need to improve or intensify remaining pasture that is 

used for livestock. 

Following the herbaceous workshop, a woody crops 

workshop was held. Woody energy crops are defined 

as purpose-grown plantations in which the bolewood, 

probably the bark, and much of the limbs and tops 

are used as feedstocks for energy. They can also be 

referred to as SRWC. SRWC are grown primarily to 

use the bolewood for pulpwood and, in some limited 

cases, for lumber. In the general sense, energy crops 

and SRWC are intensively-managed, fast-growing 

species that produce large amounts of biomass over 

a short period of time, usually less than 10 years. 

Depending on the species and the production method, 

the rotation length can be shortened to as little as 3 

years when coppiced. 

The most likely woody energy crop species to be 

developed for bioenergy production are poplar, 

southern pine, eucalyptus, and willow, but there 

are many other possible species, such as sycamore 

(Plantanus occidentalis) and sweetgum (Liquidambar 

styraciflua). The workshop focused on poplar, willow, 

and southern pine. Eucalyptus was added during the 

workshop. 

As with the herbaceous crops workshop, participants 

discussed yield growth rates, identified barriers to 

achieving higher yields, and then some approaches 

to overcoming these barriers to ensure future yield 

increases. The barriers included the lack of improved 

planting stock, regeneration methods, and cultural 

practices; disease and pest risks; forest management 

practices not optimized for energy production and/ 

or integrated with conventional forestry; uncertain 

landowner expectations; matching species to sites 

with and without restrictions; limited markets and 

risks; need for new types of low-impact harvesting 

equipment; and political and social sensitivity to the 

use of transgenesis. Other barriers are provided in Text 

Box 5.4.
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As part of the environmental sustainability alternate 

assumption discussion, participants were asked 

to identify the environmental barriers, or “limiting 

factors,” constraining yield and possible solutions. Key 

considerations raised by participants included: 

•	 Implementing emerging concepts in management 

practices 

•	 Minimizing nitrogen use 

•	 Minimizing risk of new biomass crops becoming 

invasive or intercrossing 

•	 Developing improved carbon sequestration 

and methods for indirect monitoring of carbon 

accumulation in soil 

•	 Developing planned production programs 

•	 Implementing landscape-scale management 

strategies 

•	 Identifying ways to manage changes in land use for 

energy crop production with minimal soil carbon 

loss 

•	 Conducting studies to quantify benefits of 

environmental services 

•	 Leveraging ecosystem services provided by 

perennial crops for environmental sustainability 

•	 Developing an integrated pest management (IPM) 

program for switchgrass and mixed perennial 

grasses 

•	 Developing management practices and technology 

for harvesting perennial energy crops. 

TEXT BOX 5.4 | BARRIERS TO YIELD 

GROWTH

Participants identified possible advances and 

approaches to overcoming these barriers. Some of 

the more important ones identified in the workshop 

included: 

• New and improved varieties, lines, and 

families—molecular genetics and breeding 

methods for productivity, frost hardiness, and 

drought resistance 

• Improvement in vegetative propagation and 

nursery production and bridging the gap between 

genetic breeding and application 

• Germplasm development, genome sequencing, 

and QTL trait identification 

• APHIS (Animal and Plant Health Inspection 

Service) permitting, gene escape controls, and 

sterility 

• New silvicultural and stand improvement 

practices for weed control, nutrients, and 

harvesting 

• Developing better yield and economic models 

• Trials on coppice, multicrop, spacing, rotation 

length, nutrient efficiency, and carbon pools 

• Monitoring and control systems for pests and 

diseases 

• Integrated harvesting/site preparation operations, 

including application of precision forestry 

• Developing conversion technology to use more 

of the biomass 

• Developing business cases, how-to guidelines, 

and decision tools for landowners.

In POLYSYS, the high-yield scenario was 

implemented by using higher rates of productivity 

growth relative to the baseline. The effect of differing 

growth rates on crop productivity is summarized in 

Table 5.5. Under the baseline scenario, 6 dry tons 

per acre increase to 6.6 and 7.2 dry tons per acre 

by 2022 and 2030, respectively. Under the highyield scenario, productivity growth rates of 2% to 

4% increase yields from 6 dry tons per acre to 7.3 

and 8.9 dry tons per acre and 8.6 and 12.2 dry tons 

per acre by 2022 and 2030, respectively. These 

projections are well within the range of estimates 

provided by workshop participants.
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Item Units Northeast Appalachia Southeast Delta Corn 

Belt

Lake 

States

Southern 

and 

Northern 

Plains

Perennial grasses

Stand life Years 10 10 10 10 10 10 10

Productivity dry tons/

acre

4.0–7.5 5–9.5 3.5–9.5 3–7 4–7 3.5–5 2–6.5

Establishment

Seed $/lb $10 $22 $22 $22 $10 $10 $22

Planting lb/acre 5 5 5 5 5 5 5

Replants percent 25 25 25 25 25 25 25

No-till drill - 1-time 1-time 1-time 1-time 1-time 1-time 1-time

Total kill 

herbicide

No. 

applications 1-time 1-time 1-time 1-time 1-time 1-time 1-time

Pre-emergent 

herbicide

No. 

applications 1-time 1-time 1-time 1-time 1-time 1-time 1-time

Phosphorous lbs P2O5/

acre

40 40 40 40 40 40 40

Potassium lbs K2O/ac 80 80 80 80 80 80 0

Lime tons/acre 1 2 2 2 1 1 0

Total 

establishment 

costs

 $/acre $210 $340 $330 $330 $200 $200 $220

Maintenance years

Reseeding year applied 2 2 2 2 2 2 2

Pre-emergent 

herbicide

No. 

applications 0 0 0 0 0 0 0

Nitrogen lbs/acre 60 70 70 50 60 40 40

Phosphorous lbs P2O5/

acre

0 0 80 0 80 0 0

Potassium lbs K2O /

acre

0 0 80 0 80 0 0

Harvest costs $/dry ton $19.50–

 $21.00

$18.50–

$19.90

$18.00–

$20.20

$18.60–

$20.60

$19.20–

$20.60

$20.60–

$21.90

$19.20–

$22.10

Annual Energy Crops

Productivity dry tons/

acre

6–8.2 6–8.7 6–9 6–9 6.7–9 n/a 6.5–9

Production costs $/acre $310 $330 $300 $310 $420 n/a $230

Harvest costs $/dry ton $12.50 $12.10 $11.80 $11.80 $12.20 n/a $12.10

Table 5.3 Summary of Production Inputs and Costs for Perennial and Annual Grasses

Notes: Discounted average costs of production for perennial grasses are $52-$80 per dry ton in the Northeast; $43-$68 per dry ton 

in Appalachia; $42-$91 per dry ton in the Southeast ; $54-$89 per dry ton in the Delta; $53-$71 per dry ton in the Corn Belt; $70-$94 

per dry ton in the Lake States. $47-$70 in the Northern and Southern Plains. Costs assume a discount rate of 6.5% and include all 

variable costs exclusive of land rent. Discounted average cost of production for annual energy crops range from $38 to $59 per dry ton.
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Understanding how biomass yield varies as a function of crop management, climate, and soils is fundamental to deriving 

a sustainable supply of cellulosic feedstock for an emerging biofuels industry. For the herbaceous perennial switchgrass 

(Panicum virgatum L.), a database containing 1,190 observations of yield from 39 field trials conducted across the 

United States was compiled. Data includes site location, stand age, plot size, cultivar, crop management, biomass 

yield, temperature, precipitation, and information on land quality. Statistical analysis revealed the major sources of 

variation in yield. Frequency distributions of yield for upland and lowland ecotypes were unimodal, with mean biomass 

yields (± standard deviation) of 3.9 ± 1.9 and 5.6 ± 2.6 dry tons per acre for the two ecotypes, respectively. No bias was 

found toward higher yields associated with small plots or preferential establishment of stands on high quality lands. A 

parametric yield model was fit to the data and explained one-third of the observed variation in biomass yields, with an 

equal contribution of growing season precipitation, annual temperature, nitrogen fertilization, and ecotype. The model 

was used to predict yield across the continental United States. Mapped output was consistent with the natural range of 

switchgrass, and yields were shown to be limited by precipitation west of the Great Plains. Future studies should extend 

the geographic distribution of field trials and thus improve understanding of biomass production as a function of soil, 

climate, and crop management for promising biofuels such as switchgrass.

TEXT BOX 5.5 | ESTIMATION OF SWITCHGRASS YIELD

Sources: Wullschleger et al., 2010; Jager et al., 2010
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Table 5.4 Summary of Production Inputs and Costs for Woody Crops

Item Units Poplar Pine Eucalyptus Willow 

(coppiced)

Rotation Years 8 8 8 4a (5 harvests)

Spacing sq. ft. 60 60 60 7.5

trees/acre 726 726 726 5800

Productivity dry tons/acre-year 3.5–6.0 5.0–5.5 6.0 5.1

Growing range Region

Northeast, Lake 

States, Northwest, 

Midwest, Plains

Southeast Sub-tropics Northeast and 

Lake States

Establishment - year 1

Cuttings $/tree $0.10 $0.06 $0.10 $0.12

Planting $/tree $0.09 $0.09 $0.09 $0.02

Replants percent 5 5 5 0

Moldboard plow - 1-time 1-time 1-time 1-time

Disk - 1-time 1-time 1-time 1-time

Cultivate - 2-times 2-times 2-times 2-times

Total kill herbicide No. applications 1-time 1-time 1-time 1-time

lbs a.i./acre 1.5 1.5 1.5 1.5

Pre-emergent 

herbicide

No. applications 1-time 1-time 1-time 1-time

lbs a.i./acre 1.5 1.5 1.5 1.5

Phosphorous lbs/acre 0 40 0 0

Establishment costs $/acre $310 $280 $310 $1120

Maintenance years

Cultivate – year 2 - 2-times 2-times 2-times 1-time

Cultivate – year 3 1-time 1-time 1-time None

Pre-emergent 

herbicide – year 2

No. applications 1 1 1 1

lbs a.i./acre 1.5 1.5 1.5 1.5

Lime – year 3 tons/acre 90 90 90 100

year applied - year 3 year 3 -

Nitrogen – 

year 4 and 6

lbs/acre 90 90 90 100

year applied 4 and 6 2,4, and 6 4 and 6 4

- - - - -

Phosphorous – 

year 3

lbs/acre 20 40 15 -

year applied 3 3 3 -

Potassium – 

year 3

lbs/acre 35 40 25 -

year applied 3 3 3 -

 Maintenance costs 

– year 2 $/acre $60 $100 $100 $30

 Maintenance costs 

– year 3–8 $/acre $220 $200 $200 $100b

Harvest costs $/dry ton $20 $20 $20 $15

Notes: Productivity for coppiced managed systems is expected to be about 15% higher after first coppice. “a.i.” is active ingredient. 

Discounted average costs of production for poplar, pine, and willow are $43-$47, $43-$46, and $38-$45 per dry ton, respectively. Costs 

assume a discount rate of 6.5% and include all variable costs exclusive of land rent.
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Table 5.5 Yield Growth Between the Baseline and High-Yield Scenarios

2012 2017 2022 2030 2017 2022 2030

Crop Yield Baseline 1% annual growth High-yield 2%–4% annual growth

Low end of 

yield range

Dry tons/acre/year Dry tons/acre/year

2 2.1 2.2 2.4 2.2 – 2.4 2.4 – 3.0 2.9 – 4.1

3 3.2 3.3 3.6 3.3 – 3.6 3.7 – 4.4 4.3 – 6.1

4 4.2 4.4 4.8 4.4 – 4.9 4.9 – 5.9 5.7 – 8.1

5 5.3 5.5 6.0 5.5 – 6.1 6.1 – 7.4 7.1 – 10.1

6 6.3 6.6 7.2 6.6 – 7.3 7.3 – 8.9 8.6 – 12.2

Middle of 

yield range

7 7.4 7.7 8.4 7.7 – 8.5 8.5 – 10.4 10.0 – 14.2

8 8.4 8.8 9.6 8.8 – 9.7 9.8 – 11.8 11.4 – 16.2

High end of 

yield range

9 9.5 9.9 10.8 9.9 – 10.9 11.0 – 13.3 12.9 – 18.2

10 10.5 11.0 12.0 11.0 – 12.2 12.2 – 14.8 14.3 – 20.3

11 11.6 12.2 13.2 12.1 – 13.4 13.4 – 16.3 15.7 – 22.3

12 12.6 13.3 14.4 13.2 – 14.6 14.6 – 17.8 17.1– 24.3

Notes: The yields shown for 2017–2030 for the baseline and high-yield scenarios reflect the standing yield of the energy crop before 

losses. It is the yield for the energy crop planted in that particular year. For example, if the 2009–2012 yield for a particular crop is 5 

dry tons per acre, the yield for that crop would be 5.5 dry tons per acre if planted in 2022 under the baseline and 6.1 to 7.4 dry tons per 

acre under the high-yield scenario.
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5.3 Results

Two scenarios are considered—a baseline and high 

yield. The baseline and high-yield scenarios differ 

by assumed productivity growth over time. For the 

baseline, it is assumed that regional productivity 

increases by 1% annually, starting in year 2014, for all 

subsequent plantings (Table 5.5). Crop productivity 

growth is due to learning or experience in planting 

energy crops and limited gains that can be had through 

breeding and selection of better varieties. Under highyield scenarios, projected increase in crop productivity 

over time is modeled at 2% to 4% annually. These 

gains are due not only to experience in planting energy 

crops, but to more aggressive implementation of 

breeding and selection programs. One could expect that 

there might be some regional variation in productivity 

growth especially if research were targeted to a specific 

variety or clone. However, there was no clear basis to 

differentiate regional productivity growth in this study.

A number of simplifying assumptions are made to 

implement energy crop simulations in POLYSYS. 

These are summarized below:

• For all energy crops, the earliest year planting 

can start is 2014. This is a somewhat arbitrary 

decision that reflects the current availability 

of seeds, seedlings, and cuttings for planting. 

Delaying the introduction of energy crops to 

later years (e.g., 2015) would simply delay the 

deployment time path.

• It is assumed that once land is planted to an 

energy crop, it remains in that energy crop 

through the end of the simulation period (2030). 

That is, the model does not allow the shifting of 

land in and out of energy crops.62

• For the baseline and high-yield scenarios, crop 

productivity is determined by the year in which 

the crop is planted. For example, a crop planted 

in 2022 in a particular county would realize a 

yield of 4.4 dry tons per acre if the 2014 yield 

for that county was 4 dry tons per acre under 

the baseline (Table 5.5) and 4.9 to 5.9 dry tons 

per acre under the high-yield scenario.

• It is assumed that the geographic range where 

energy crop production can occur is limited 

to areas where production is under rain-fed 

conditions, without the use of supplemental 

irrigation. 

• Perennial grasses and woody crops can be 

planted on cropland, cropland currently used as 

pasture, and permanent pasture. Conversion of 

pastureland is restricted to counties east of the 

100th Meridian. Energy sorghum, the annual 

energy crop simulated in the model, is restricted 

to cropland and is assumed to be part of a 

4-year multicrop and/or fallow rotation.

• Energy crop productivity is assumed the same 

for cropland and pastureland.63

• Perennial grasses are assumed to have a stand 

life of 10 years before replanting is required; 

woody crops are managed on an 8-year rotation, 

and coppiced woody crops are managed on 

a 4-year rotation with a total stand life of 20 

years. These assumptions are made for the 

convenience of modeling only. Rotation ages 

for woody crops will be different depending on 

species, spacing, and management.

• The collection of corn stover, wheat straw, and 

other grain residue is included when estimating 

energy crop potential because the energy crops 

must compete for land, and these additional 

income streams affect the profitability of 

residue producing crops.

62 This assumption was made to facilitate model programming. However, the authors consider this to be a reasonable assumption because it 

is likely a grower will have a long-term commitment or contract to supply biomass.

63 An exhaustive analysis of switchgrass productivity data showed that there was no significant relationship between productivity and land 

capability class for either lowland or upland ecotypes that are analyzed together or separately (Wullschleger et al., 2010). The authors 

conclude that this could be due to the planting of switchgrass field trials, mainly during the 1990s, on “marginal lands” as a common 

expectation at the time was that less-than-prime agricultural lands would be used for woody and herbaceous energy crop production. It is 

likely that with additional plantings of energy crops on cropland, results may show relatively higher yields on cropland than pastureland. In 

this update, this would imply that the croplands used are too low and conservative.
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5.3.1 Baseline Estimates of 

Energy Crop Potential

Potential supplies of energy crops at alternative 

farmgate prices of $40 to $60 per dry ton are 

summarized in Figure 5.16. At the lowest farmgate 

price ($40 per dry ton), energy crop production 

reaches nearly 4 million dry tons by 2017, increases 

to 14 million dry tons by 2022, and by 2030, reaches 

34 million dry tons. There is very little woody crop 

production at this price and 4.2 million dry tons of 

energy sorghum by 2030. At $50 per dry ton, total 

energy crop production is 210 million dry tons by 

2030, with 129 million dry tons of perennial grasses, 

almost 14 million dry tons of energy sorghum, and 

67 million dry tons of woody crops. Woody crops 

account for about one-third of 2030 total energy crop 

production at the $50 and $60 farmgate prices. At the 

highest price, the model estimates a potential supply of 

255 million dry tons of perennial grasses, 126 million 

dry tons of woody crops, and 19 million dry tons of 

energy sorghum. 

Supply curves for selected years—2017, 2022, and 

2030—are shown in Figure 5.17. As previously 

explained, future supplies increase over time due to the 

assumed productivity growth (energy crops becoming 

more competitive) and woody crops coming into 

production. At the $60 simulated price, total energy 

crop production reaches nearly 282 million dry tons by 

2022 and 400 million dry tons by 2030. Total energy 

crop production would exceed 500 million dry tons as 

simulated prices approach $80 per dry ton.

The planted acres associated with the simulated energy 

crop production are displayed in Figure 5.18 by price 

and year for major energy crop type. At the lowest 

price, about 5.0 million acres of energy crops are 

planted mostly on cropland by 2030. Planted acreage 

increases significantly at the higher simulated prices. 

At $50 per dry ton total planted acreage approaches 20 

million acres by 2022 and 32 million acres by 2030. 

Sixty-four million acres are planted to energy crops 

by 2030 at the highest simulated price. About 35% of 

these 64 million acres are cropland and the remaining 

cropland is used as pasture and permanent pasture.

Energy crop production is summarized in the state 

maps shown in Figure 5.19 at simulated farmgate 

prices of $40, $50, and $60 per dry ton. These maps 

also show agricultural crop residues because their 

collection is assumed with the energy crops. What 

stands out is the dominance of the Great Plains in 

perennial grass and woody crops in the South and to a 

lesser extent in the North. The Corn Belt is very much 

the dominant area in the production of crop residues. 

The key reasons for the dominance of perennial grass 

production in the Plains is due to the availability of 

cropland and pastureland (see Text Box 5.2) and the 

relatively low profitability of current land uses.
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0

50

100

150

200

250

300

2017 2022 2030 2017 2022 2030 2017 2022 2030

$40/dry ton $50/dry ton $60/dry ton

Perennial grasses Woody crops Annual energy crops 

3.0 0 0.7

11.8

0 1.8

30

0.1 4.2

41

77

40

7.3

129

67

13.8

90

6 5

188

255

126

84

19

9.8 0.9 3.8

Million dry tons

$85

$75

$65

$55

$45

$35

$25

0 100 200 300 400 500 600

Million dry tons

2017 2022 2030

$/dry ton (farmgate)

Potential production of energy crops at various years and farmgate prices in 

baseline scenario Figure 5.16

Figure 5.17 Supply curves for all energy crops at selected years in baseline scenario
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0

10

20

30

40

50

2017 2022 2030 2017 2022 2030 2017 2022 2030

$40/dry ton $50/dry ton $60/dry ton

Perennial grasses Woody crops Annual energy crops 

0.8

0

0.1

2.3

0

0.25

5.0

0.03

0.25

9.8

2.9

0.6

13

5.6

1.1

21

9.5

2.0

21

33

43

7.8

13

18

0.8

1.5

2.8

Million acres

Planted acres in perennial grasses, woody crops, and annual energy crops for 

selected years and prices in baseline scenario Figure 5.18
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Estimated state shares of energy crops and agricultural residues supplies at farmgate 

prices of $40, $50, and $60 per dry ton in 2030 Figure 5.19

$40 per 

dry ton

$50 per 

dry ton

$60 per 

dry ton
134

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In addition to competing for land with conventional energy crops, energy crops also compete with each other. Farmgate 

analysis can distort the relative competiveness of energy crops when there are differences in feedstock logistics and 

supply chains. For example, woody crops have potentially less complex supply chains. In its simplest form, a woody 

crop can be harvested, chipped, and transported directly to the conversion facility. Further, woody crops can be stored 

on the stump, increasing volume, until needed at the conversion facility. Perennial grasses, annual energy crops, and 

crop residues have limited harvest and/or collection seasons and require storage between seasons. These herbaceous 

feedstocks also require more handling operations. 

The figure below summarizes the effect of a credit given to woody crops to account for their potential supply chain 

advantages. The results show the baseline scenario at a farmgate price of $50 per dry ton and the same baseline with a 

$5 and $10 per dry ton credit given to woody crops. There were modest decreases in herbaceous crops and large increases 

in woody crops as the credit increased. Under the baseline at $50 per dry ton, woody crops are about one-third of total 

energy crop production in 2030. This percentage increases to 50%, with a $5 per dry ton credit and 65%, with a $10 per 

dry ton credit. Of course, this is a very simplistic comparison and a more thorough analysis of the entire feedstock supply 

and conversion chain is required. But the results do show that differences in assumed costs and assumptions among 

energy crops can have significant results in terms of the energy crop mix. 

TEXT BOX 5.6 | COMPETITIVENESS OF WOODY AND HERBACEOUS CROPS 

WHEN SUPPLY CHAIN ADVANTAGES ARE CONSIDERED

0

50

100

150

200

2017 2022 2030 2017 2022 2030 2017 2022 2030

$50/dry ton Baseline +$5/dry ton Credit +$10/dry ton Credit

Herbaceous crops Woody crops

41

76

39

130

66

39

5

72 70

121

38

68

101 104

169

7

119

1

Baseline with supply chain credits to woody crops
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5.3.2 High-Yield Estimates of 

Energy Crop Potential

There are numerous opportunities for making technical 

improvements in crop establishment and harvesting 

methods that could serve to lower production costs. 

Cost reduction could also come from having machinery 

that is more efficient in handling biomass. Further, as 

farmers become more familiar with growing biomass, 

they should also become more efficient producers.

Learning-by-doing, as well as some improvements 

in machinery and fuel use, could lower energy crop 

production costs. However, the major opportunity 

for lowering production costs lies in increasing crop 

productivity. In the 2005 BTS, average annual crop 

yields of 5 and 8 dry tons per acre were assumed under 

the moderate and high-yield scenarios. In this update, 

high-yield is modeled as an annual increase in yield 

of 2%, 3%, and 4%. As shown in Table 5.5, an annual 

yield of 5 dry tons per acre attainable today increases 

to 7.1 dry tons per acre in 2030 under a 2% annual 

growth rate and over 10.1 dry tons per acre at 4% 

growth for a crop planted in 2030.

Results of the energy crop simulation at a 2% to 4% 

annual yield growth are shown in Figure 5.20; the 

baseline supply is also shown. The effect of assuming 

higher yield growth over time is that the supply curves 

shift outward. Under baseline assumptions, energy crop 

supply reaches nearly 400 million dry tons in 2030, at 

a $60 per dry ton simulated price. Under the 2% to 4% 

higher annual crop yield growth rates, supplies increase 

to 540 and 799 million dry tons, respectively. 

Figure 5.20 shows there is a wide production range of 

about 400 million dry tons between the baseline and 

the 4% yield growth curve for all simulated prices, 

with the exception of the lowest simulated price ($40). 

Meeting the feedstock source requirements for a fixed 

demand for biofuels under the RFS could potentially 

be met with energy crops at minimum farmgate prices 

ranging from mid- to high-40s, under higher yield 

growth to mid- to high-50s, under baseline yield 

growth.

The relative quantities of the three major types of 

energy crops at a $60 per dry ton simulated price 

for selected years in the high-yield scenario are 

Year 2030 energy crop production under baseline and 2% to 4% annual growth in 

energy crop yield Figure 5.20

$70

$60

$50

$40

$30

$20

$10

$0

0 200 400 600 800 1000

$/dry ton (farmgate)

Baseline 2% 3% 4%
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summarized in Figure 5.21. Relative to the baseline, 

total energy crop production in 2030 increases by 140 

million dry tons at the 2% yield growth, 258 million 

dry tons at 3%, and about 400 million dry tons at 4%. 

Under the high-yield scenario across all assumed 

growth rates, perennial grasses account for slightly less 

than 60% of total energy crops in 2030, woody crop 

slightly less than 40%, and annual energy crops about 

3% of the total. In 2022 and earlier, woody crops are 

proportionately less owing to assumed rotation lengths 

or cutting cycles.64 Planted acres for the high-yield 

scenario under the 4% annual yield growth scenario are 

summarized in Figure 5.22. Total planted acres in 2030 

are at the highest price 79 million with 53, 24, and 2.4 

million in perennial grasses, woody crops, and annual 

energy crops, respectively. At the lowest simulated 

price, planted acres are much less, totaling about 32 

million. 

0

100

200

300

400

500

2017 2022 2030 2017 2022 2030 2017 2022 2030 2017 2022 2030

Perennial grasses Woody crops Annual energy crops 

90

188

84

9.8

255

126

19

122

10

6.9

253

145

319

207

15

138

296

169

390

251

18

154

16

9.4

338

212

14

462

315

22

65

11

14

8

12

Baseline 2% yield growth 3% yield growth 4% yield growth

Million dry tons

Perennial grasses, woody crops, and annual energy crops production for selected 

years in baseline and high-yield scenarios Figure 5.21

 64 Additionally, because the costs to establish woody crops are relatively lower, high-yield growth scenarios may have a disproportionate 

 effect on net returns of woody crops relative to herbaceous crops.
137

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0

10

20

30

40

50

60

Perennial grasses Woody crops Annual energy crops 

2017 2022 2030 2017 2022 2030 2017 2022 2030

$40/dry ton $50/dry ton $60/dry ton

7.1

0.1 0.4

13

2.9

1

23

8.4

1.6

21

35

14

1.4

46

19

1.9

30

44

53

24

2.4

19

1.7

13

1.2

7.9

0.9

Million acres

Figure 5.22

Planted acres in perennial grasses, woody crops, and annual energy crops for 

selected years and prices in high yield scenario with 4% annual energy 

crop yield growth

5.3.3 Land-Use Change and 

Economic Impacts

In this section, the estimated changes to land use 

from the collection of crop residues and the growing 

of energy crops are summarized. Associated changes 

in planted acres, crop prices, livestock production, 

inventories, and prices, and net returns to agriculture 

are also described. Potential economic impacts are 

within a range that is typical of normal market forces at 

lower simulated prices. At higher simulated prices and/

or assumptions about yield growth, estimated changes 

in acres and crop prices are somewhat greater, but 

reasonable given the extent of land-use change.

Land-use change. The growing of energy crops and, to 

a lesser extent, the collection of crop residues changes 

the allocation of land among conventional crops. Under 

baseline conditions, energy crop production ranges 

from nearly 4 million dry tons in 2017 at a $40 per dry 

ton farmgate price to 400 million dry tons in 2030 at 

the highest simulated farmgate price ($60 per dry ton). 

The same farmgate price range makes 41 million dry 

tons (2017 at $40 per dry ton) to 180 million dry tons 

(2030 at $60 per dry ton) of corn stover, wheat straw, 

and other crops that are profitable to collect. 

The land-use changes among conventional and energy 

crops under the baseline scenario are reported in Figure 

5.23 for three farmgate prices and for years 2017, 

2022, and 2030. There are a few clear observations. 

First, the acres displaced by energy crops increase over 

time as energy crops deploy. At the lowest simulated 

price, land-use change is limited to cropland. Wheat 

declines by 3.2 million acres over the baseline forecast 

by 2030, followed by soybeans at 0.5 million acres 

and corn at 0.3 million acres. Land planted in the 

five other major crops declines by about 0.8 million 

acres in total, with cotton (almost 0.4 million acres) 

and grain sorghum (almost 0.4 million acres) being 

the largest. Higher simulated farmgate prices move 

energy crops onto pasture. This result is clearly seen 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

at the highest simulated price ($60 per dry ton) in 

Figure 5.23. By 2022, it is estimated that energy crops 

could displace 28 million acres of cropland used 

as pasture and permanent pasture. As energy crops 

deploy, this quantity of displaced pasture increases 

to more than 41 million acres by 2030 and is split 

almost equally between perennial grasses and woody 

crops. The amount of cropland displaced increases 

proportionately, with higher simulated prices and 

through time.

Figure 5.24 shows results of a simulation in which the 

rate of growth in energy crop yields were increased 

from 1% to 3% annually beginning in 2014 and in 

subsequent plantings. Results are as expected, with 

changes in crop acres much greater than under the 

baseline. In this simulation, higher yields move energy 

crops onto pasture at the lowest simulated price—

slightly more than 16 million acres by 2030. However, 

at the higher simulated prices there are significantly 

more energy crops planted on pasture than under the 

baseline. At the highest simulated price, there are 

about 49 million acres of energy crops planted on 

pastureland, in addition to 30 million acres of cropland. 

This is a significant land-use change requiring 

pasture intensification to make up for lost forage. As 

recognized by participants in the high-yield workshops, 

understanding the competition for pastureland and 

seeking ways to improve pasture productivity are 

relevant avenues for research for this degree of landuse change.

Economic impacts. Changes in crop prices, planted 

acres, and crop net returns are summarized in Table 

5.6 for the baseline scenario. The results shown for the 

baseline scenario assume a $50 per dry ton farmgate 

price for biomass feedstocks. Relative to the USDA 

projections, simulated results show a loss of crop acres 

to energy crops and higher crop prices for all major 

crops. For producers, the higher crop prices more than 

compensate for the loss in crop acres. This is reflected 

in higher net crop returns relative to the baseline as 

shown in Table 5.6. For consumers, however, these 

higher crop prices are one of the factors that affect food 

and feed prices. The same set of results is shown in 

Table 5.7 for the high-yield scenario, in which energy 

crop yields increase at an annual rate of 4%. Crop 

acres are generally somewhat lower than the baseline 

scenario results (Table 5.6), with most crop prices and 

net crop returns somewhat higher. The price of corn is 

lower due to the excess grain produced under this highyield scenario.

Comparing the simulated results to the USDA 

projections shows only minor changes in total livestock 

production, beef cattle farm prices, and inventories 

of cattle. The key assumption is that increased forage 

productivity compensates for losses because of the 

presence of energy crops on pastureland.

Total net crop returns increase significantly under the 

baseline scenario where crop residues are collected and 

energy crops produced. Total net returns from livestock 

production decline by a relatively small amount. 

Overall, total net returns to agriculture increase by 

about $9.5 billion by 2030. Under the high-yield 

scenario, total net returns to agriculture are nearly $27 

billion higher by 2030.
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-10

-5

0

5

10

15

20

25

30

-10

-5

0

5

10

15

20

25

30

Million acres Million acres

2017 2022 2030

2017 2022 2030

Land-use change – baseline, $50/dry ton

Land-use change – baseline, $60/dry ton

-10

-5

0

5

10

15

20

25

30

Million acres

2017 2022 2030

Land-use change – baseline, $40/dry ton

Grasses – cropland

Other crops

Soybean

Corn

Wheat

Annual EC – cropland

Woody – pasture

Grasses – pasture

woody – cropland

Grasses – cropland

Other crops

Soybean

Corn

Wheat

Annual EC – cropland

Woody – pasture

Grasses – pasture

woody – cropland

Grasses – cropland

Other crops

Soybean

Corn

Wheat

Annual EC – cropland

Woody – pasture

Grasses – pasture

woody – cropland

Land-use change under baseline assumptions for 2017, 2022, and 2030 at 

farmgate prices of $40, $50, and $60 per dry ton Figure 5.23
140

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

-10

-5

0

5

10

15

20

25

30

Million acres

2017 2022 2030

Land-use change – high yield, $40/dry ton

Grasses – cropland

Other crops

Soybean

Corn

Wheat

Annual EC – cropland

Woody – pasture

Grasses – pasture

woody – cropland

-10

-5

0

5

10

15

20

25

30

Million acres

2017 2022 2030

Land-use change – high yield, $50/dry ton

Grasses – cropland

Other crops

Soybean

Corn

Wheat

Annual EC – cropland

Woody – pasture

Grasses – pasture

woody – cropland

-15

-10

-5

0

5

10

15

20

25

30

35

Million acres

2017 2022 2030

Land-use change – high yield, $60/dry ton

Grasses – cropland

Other crops

Soybean

Corn

Wheat

Annual EC – cropland

Woody – pasture

Grasses – pasture

woody – cropland

Land-use change under high-yield (3%) assumptions for 2017, 2022, and 2030 at 

farmgate prices of $40, $50, and $60 per dry ton Figure 5.24
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Summary Comparison of USDA Projections for Major Crops with Baseline Projections 

for Biomass Resources Derived from Cropland and Pastureland, at $50 per Dry Ton 

Farmgate Price

Table 5.6

Crop USDA Baseline Forecast1 Baseline scenario

20121 20171 20222 20302 2017 2022 2030

Crop prices ($/bu)

Corn 3.70 3.75 3.60 3.43 3.79 3.71 3.57

Grain Sorghum 3.30 3.35 3.20 3.79 2.89 3.71 3.57

Oat 2.35 2.35 2.28 2.11 2.37 2.39 2.32

Barley 3.90 3.95 3.79 3.36 4.27 4.39 3.89

Wheat 5.35 5.45 5.43 5.32 6.15 6.20 6.35

Soybean 8.70 8.75 8.42 7.92 8.95 8.72 8.44

Cotton ($/lb) 0.61 0.63 0.63 0.63 0.67 0.68 0.68

Rice ($/cwt) 10.60 11.78 12.20 12.46 13.31 13.76 13.80

Crop acres (millions)

Corn 90 90.5 89.35 87.69 89.9 88.1 86.2

Grain Sorghum 7.5 7.4 7.24 7.05 6.8 6.2 5.7

Oat 3.4 3.4 3.4 3.34 3.3 3.3 3.1

Barley 4.0 4.0 3.97 3.85 3.8 4.0 3.9

Wheat 60.5 59.5 59.73 61.06 54.0 54.6 54.2

Soybean 71.5 71.0 70.2 68.0 69.9 68.4 64.7

Cotton 9.7 10.2 10.47 10.81 9.0 9.2 9.1

Rice 3.0 3.1 3.1 3.1 2.8 2.8 2.8

Crop net returns (millions)

Corn 25,441 28,383 27,430 26,564 29,153 29,182 29,074

Grain Sorghum 446 433 351 205 593 519 459

Oat 73 75 61 33 77 72 51

Barley 463 499 461 337 563 614 507

Wheat 6,213 6,706 6,619 6,347 7,923 8,013 8,252

Soybean 17,801 18,931 18,480 17,494 19,373 19,153 18,643

Cotton 1,370 1,475 1,400 1,222 1,498 1,451 1,365

Rice 1,092 1,523 1,628 1,702 1,794 1,927 1,979

Livestock

Total production (million lbs) 25,763 27,395 27,972 27,970 27,497 28,059 28,066

Price ($/cwt) 104 106 105 105 106 105 105

Inventory (1000 head) 93,241 96,847 102,410 110,766 96,834 102,362 110,669

Total crop net returns 

(millions) 49,593 54,424 52,203 49,603 59,183 59,081 59,569

Total Livestock net returns 

(millions) 115,413 124,240 127,914 123,479 124,069 127,645 123,004

Total agriculture net returns 

(millions) 165,006 178,664 180,117 173,082 183,252 186,726 182,573

1 Source: USDA -OLE/WAOB, 2009

2

 Extended baseline
142

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Summary Comparison of USDA Projections for Major Crops with High-Yield Projections 

for Biomass Resources from Cropland and Pastureland, at $50 per Dry Ton Farmgate 

Price

Table 5.7

Crop USDA Projections1 High-Yield scenario

20121 20171 20222 20302 2017 2022 2030

Crop prices ($/bu)

Corn 3.70 3.75 3.60 3.43 2.87 3.04 3.01

Grain Sorghum 3.30 3.35 3.20 2.89 2.87 3.04 3.01

Oat 2.35 2.35 2.28 2.11 1.88 2.09 2.50

Barley 3.90 3.95 3.79 3.36 4.05 4.39 4.26

Wheat 5.35 5.45 5.43 5.32 5.62 6.16 6.80

Soybean 8.70 8.75 8.42 7.92 8.75 9.50 9.77

Cotton ($/lb) 0.61 0.63 0.63 0.63 0.67 0.70 0.72

Rice ($/cwt) 10.60 11.78 12.20 12.46 13.69 14.03 15.69

Crop acres (millions)

Corn 90.0 90.5 89.4 87.7 82.1 84.7 89.9

Grain Sorghum 7.5 7.4 7.2 7.1 5.0 4.3 4.2

Oat 3.4 3.4 3.4 3.3 3.3 3.0 2.7

Barley 4.0 4.0 4.0 3.9 3.9 3.8 3.6

Wheat 60.5 59.5 59.7 61.1 54.9 54.4 54.2

Soybean 71.5 71.0 70.2 68.0 70.2 64.7 60.8

Cotton 9.7 10.2 10.5 10.8 9.1 8.8 8.3

Rice 3.0 3.1 3.1 3.1 2.7 2.7 2.6

Crop net returns (millions)

Corn 25,441 28,383 27,430 26,564 21,045 28,764 35,765

Grain Sorghum 446 433 351 205 184 144 71

Oat 73 75 61 33 29 35 46

Barley 463 499 461 337 518 588 491

Wheat 6,213 6,706 6,619 6,347 6,968 7,796 8,693

Soybean 17,801 18,931 18,480 17,494 19,053 20,535 20,332

Cotton 1,370 1,475 1,400 1,222 1,718 1,889 1,611

Rice 1,092 1,523 1,628 1,702 1,874 1,961 2,182

Livestock

Total production (million lbs) 25,763 27,395 27,972 27,970 27,462 28,044 28,002

Price ($/cwt) 104 106 105 105 104 104 105

Inventory (1000 head) 93,241 96,847 102,410 110,766 97,471 102,698 110,877

Total crop net returns 

(millions) 49,593 54,424 52,203 49,603 52,396 65,335 77,896

Total Livestock net returns 

(millions) 115,413 124,240 127,914 123,479 124,415 127,333 121,944

Total agriculture net returns 

(millions) 165,006 178,664 180,117 173,082 176,811 192,668 199,813

Note: High-yield scenario is 3% yield increase annually.

1 Source: USDA -OLE/WAOB, 2009

2

 Extended baseline
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5.4 Total Potential Supply of 

Biomass Energy Crops

Table 5.8 shows the estimated supplies of energy 

crops at three simulated prices. Over the $20 per dry 

ton price range, estimated supplies vary from a low of 

about 34 million dry tons in 2030 to 400 million dry 

tons at the highest simulated price. Results are also 

shown for high-yield scenarios. Estimated supplies 

increase from 69 to 540 million dry tons for $40 to 

$60 per dry ton, assuming energy crop productivity 

increases 2% annually. The high-yield scenario at 

the 3% and 4% annual change in crop productivity 

increases potential supply to 658 and nearly 799 

million dry tons by 2030, respectively.

5.5 Summary

Results were reported for three major classes of energy 

crops—perennial grasses, woody crops, and annual 

energy crops. Each of these crop classes must compete 

for land with existing uses and with each other. The 

existing uses are crops on cropland and forage on 

pastureland. Energy crops must offer higher net returns 

to displace crops on cropland. For pastureland, energy 

crop returns must be greater than the rental value of 

the pastureland plus additional intensification costs 

to make up for lost forage. In assessing the relative 

profitability of energy crops, the most important data 

relate to crop yield. In this assessment, annual energy 

crop yields assumed vary considerably across the 

United States, ranging from 2 to 9.5 dry tons per acre 

for perennial grasses, 3.5 to 6 dry tons per acre for 

woody crops, and 6 to 9 dry tons per acre for annual 

energy crops. These baseline yields for perennial 

grasses and woody crops are well within observed 

test plot yields. However, these estimates are based 

on rather limited field trial data even for species that 

have had relatively more attention, such as switchgrass. 

Additional field trial data are clearly needed to develop 

more precise spatial estimates of yield and how these 

yields might vary according to land use (cropland 

or pastureland) within a county. Further, additional 

field trial data are lacking for more regionally specific 

species, such as energy cane, as well as annual energy 

crops.

In addition to the yield data, the estimation of potential 

energy crop supplies identified several needs; these are 

summarized next:

• The modeling of energy crops assume all 

pastureland is currently used by the livestock 

sector and lost forage from displaced pastureland 

must be made up. In POLYSYS, lost forage is 

made up through pasture intensification, which is 

assumed possible where there is sufficient rainfall 

or in counties east of the 100th meridian and the 

Pacific Northwest. Additional research is needed 

on the implications of pasture displacement by 

energy crops, as well as to determine the intensity 

of current pastureland use and whether in fact all 

pastureland is currently used as forage.

• The analysis assumes energy crops become 

available for planting in 2014. This decision 

assumes two to three years are required for energy 

crop planting material to become available for 

large-scale deployment. Additional study is 

needed to understand more fully the requirements 

for commercial scale-up.

• At the highest simulated prices reported in the 

study ($60 per dry ton), about 60 million acres 

of cropland and pastureland could potentially 

convert to energy crops under the baseline 

scenario and up to 80 million acres under the 

high-yield scenario. This is a considerable 

amount of land use change requiring the annual 

establishment of many millions of acres. Although 

these levels of annual acre changes are within 

what has been seen for the major commodity 

crops, there is a need to understand more fully 

potential economic and environmental impacts 

associated with potential land use changes of this 

magnitude.
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<$40 per dry ton <$50 per dry ton <$60 per dry ton

Feedstock 2017 2022 2030 2017 2022 2030 2017 2022 2030

Baseline scenario (Million dry tons)

Perennial grasses 3.0 12 30 41 77 129 90 188 255

Woody crops 0.0 0.0 0.1 0.9 40 67 5.7 84 126

Annual energy crops 0.7 1.8 4.2 3.8 7.3 14 5.0 10 19

Total 3.7 14 34 46 124 210 101 282 400

High-Yield (2% annual growth)

Perennial grasses 11 43 57 67 152 239 122 253 319

Woody crops 0.0 0.1 4.2 1.9 78 127 10 145 207

Annual energy crops 1.6 4.1 7.4 5.5 8.7 12 6.9 11 15

Total 13 47 69 75 239 378 139 409 540

High-Yield (3% annual growth)

Perennial grasses 24 71 107 85 213 329 138 296 390

Woody crops 0.0 1.5 43 9.3 101 186 14 168 251

Annual energy crops 2.4 6.6 11 6.2 10 14 8.0 12 18

Total 26 79 162 101 324 520 160 476 658

High-Yield (4% annual growth)

Perennial grasses 35 100 202 106 270 406 154 338 462

Woody crops 0.1 5.3 45 12 118 199 16 212 315

Annual energy crops 3.4 9.0 14 6.8 11 18 9.4 14 22

Total 39 114 261 124 399 622 180 564 799

Table 5.8 Summary of Baseline and High-Yield Scenario Availability of Energy Crops

Note: Totals may not add up due to rounding.

• The analysis assumes energy crops are 

grown under rain-fed conditions without any 

supplemental irrigation. This assumption 

overlooks potential opportunities to use 

wastewater and processing water especially in 

western areas of the United States where growing 

of energy crops without supplemental water is 

limited. There is a need to evaluate this potential.

• Potential energy crop supplies were estimated at 

the farmgate. This assumption makes for a clear 

point of comparison with the existing land uses 

(i.e., comparing energy crop net returns with 

conventional commodity crops). However, the 

energy crops have different supply chains and 

stopping the analysis at the farmgate can bias 

results to those energy crops requiring additional 

processing and storage.
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

6 SUMMARY

65 A separate database containing the disaggregated biomass supplies by county and state is available through the Bioenergy Knowledge 

Discovery Framework (http://bioenergykdf.net/) with other data for users to capture, visualize, and analyze information on the complete 

bioenergy supply chain and the infrastructure needed to support that chain (ORNL, 2010).

As noted at the outset, a major limitation of the 2005 

BTS is that the identified biomass is not restricted by 

cost, and some of the potential would likely be too 

expensive relative to other renewable feedstocks under 

current and prospective technological changes. This 

update attempts to estimate biomass supplies (costs 

and quantities) of feedstocks identified in the 2005 

BTS. For the major primary resources, estimates are 

made at a county level and summarized in this report as 

national totals. For some resources, such as processing 

residues and wastes, only state-level estimates can be 

developed.65

The 2005 BTS combined resources that are currently 

used for energy production with unused and 

prospective resources because they all counted toward 

the billion-ton goal. In this update, a clearer distinction 

is made between currently used resources (e.g., corn 

grain, soybeans, pulping liquors, mill residues, and 

fuelwood) and unused and prospective resources 

available for additional energy (such as feedstock 

needed to meet the EISA RFS targets of 16 BGY 

of cellulosic biofuels and 4 BGY of other advanced 

biofuels by 2022).

This updated resource assessment treats environmental 

sustainability much more comprehensively and 

rigorously than the approach taken in the 2005 BTS. 

For primary crop residues, sustainability is explicitly 

modeled, accounting for soil erosion and carbon. 

The sustainability of forest residue harvests, which is 

defined as maintaining sufficient amounts of residue 

onsite to maintain soil productivity and prevent 

erosion, was accounted for at the individual forest 

plot level. For energy crops, costs generally assume 

application of BMPs. Land-use change associated with 

the growing of energy crops is evaluated at a county 

level using the POLYSYS modeling framework. The 

POLYSYS model allocates land to the most profitable 

activities and tracks changes in crop prices for the eight 

major crops (corn, grain sorghum, oats, barley, wheat, 

soybeans, cotton, and rice); production quantities for 

food, feed, and industrial uses; exports; and crop and 

livestock income. When energy crops displace cropland 

(i.e., any of the eight major crops), cropland used as 

pasture, or permanent pasture, it implies that they are 

more profitable. Generally, energy crops are planted on 

the more marginal cropland and pastureland.

As discussed in previous chapters, the POLYSYS 

model is anchored to the USDA projections. As such, 

POLYSYS simulation results are conditioned by the 

accuracy of the baseline forecast. POLYSYS results 

also depend on a host of county-level databases 

developed by a number of sources, such as NASS 

(National Agricultural Statistics Service) and the 

Census of Agriculture, as well as databases that 

relate to energy crop yields, hay and pasture acres, 

and productivity. Although an attempt was made to 

use the best available data, there is still a great deal 

of uncertainty that cannot be overcome without a 

concerted effort to develop new data; for example, 

research underway by the Regional Feedstock 

Partnership, or through the development of new data 

sources (e.g., use of remotely sensed data). Finally, the 

POLYSYS model is deterministic and thus does not 

allow one to provide confidence intervals around model 

output. Results are thus presented as point estimates 

and should be interpreted with all the appropriate 

caveats related to data uncertainty. The remainder of 

this chapter summarizes the resource assessment for 

the baseline and high-yield scenarios.
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

66 Inclusion of the currently used biomass resources identified in Chapter 2 would increase the year 2030 total quantity to nearly 1.1 billion 

dry tons (an amount that approaches what was found in the 2005 BTS under the high-yield scenario).

67 Table 6.1 and 6.2 quantities account for losses in collection or harvesting and handling to the farmgate or forest roadside. They do not 

account for additional losses that may occur in storage, transportation, and handling to conversion facilities. The estimates in Figure 6.1 and 

6.2 reflect additional losses of 20%. These additional losses could be lower depending on the specifics of feedstock supply chain, length of 

storage, handling, and other factors.

6.1 Baseline Estimates

Under baseline assumptions, the current combined 

resources from forests and agricultural lands range 

from about 138 to nearly 258 million dry tons at forest 

roadside or farmgate prices from $40 to $60 per dry 

ton (Table 6.1). The combined forest and agricultural 

resource supply increases to 187 to 602 million dry 

tons by 2022 over the $40 to $60 per dry ton price 

range and to 243 to 767 million dry tons by 2030 at the 

same prices. The forest resources are estimated with 

and without the inclusion of resources from federally 

owned land. This consideration affects the total by 

about 5 to 7 million dry tons.

The estimated quantities in Table 6.1 only show 

supplies available at forest roadside or farmgate prices 

of $40 to $60 per dry ton.66 Additional resources are 

available at higher prices. Further, Table 6.1 only 

shows biomass resources that are currently unused and 

do not include resources now used for energy. These 

currently unused resources are potentially available for 

conversion into biofuels and biopower. 

Figure 6.1 summarizes the estimated baseline resources 

for each major biomass source—forest residues and 

wood wastes, agricultural residues and wastes, and 

energy crops. The results of this update find that there 

are potentially sufficient biomass feedstocks to meet 

EISA and RFS mandates, provided that viable, efficient 

conversion and transport systems are available.67 For 

2022, under baseline assumptions, a combination 

of forest and agricultural residues and energy crops 

could meet 20 BGY of cellulosic and advanced 

biofuels at a forest roadside or farmgate price of $50 

per dry ton or less. This assumes a conversion rate of 

85 gallons per dry ton (U.S. Department of Energy, 

2011). At higher simulated prices, significantly more 

feedstock is available for conversion into biofuel. The 

secondary axis in Figure 6.1 shows the conversion of 

these resources into biopower. The year 2022 supply 

at a simulated price of $60 per dry ton equates to 

potentially 590 billion kWh of electricity assuming 

a heat rate of about 13,000 Btu per kWh. By 2030, 

sufficient feedstock is available to generate potentially 

750 billion kWh. As summarized next, changes in 

the baseline crop yield assumptions can dramatically 

increase the resource potential.

6.2 High-Yield Estimates

The high-yield scenario increases the proportion of 

corn in reduced and no-till cultivation and increases 

corn yields to about double the current rate of annual 

increase. For energy crops, the high-yield scenario 

increases the annual rate of crop productivity growth 

from 1% to 2%, 3%, and 4% annually. No high-yield 

scenario is evaluated for forest resources, except for 

the woody crops. Forest residues come from existing 

timberlands, and there is no obvious way to increase 

volumes other than reducing fractions left behind 

to meet environmental sustainability, which is not 

recommended.

Table 6.2 summarizes the estimated (unused) quantities 

of forestland, agricultural land, and energy crop 

resources at an assumed price of $60 per dry ton. 

Results are presented for three assumptions about 

annual energy crop yield growth: 2%, 3%, and 4%. 

The agricultural residues estimate assumes higher 

proportions of reduced and no-till cultivation, as well 

as higher corn grain yields. The forest residue and 

waste estimates total about 100 million dry tons by 

year 2022, only slightly higher than the 2012 estimates. 

The agricultural residues and wastes total 244 million 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

dry tons currently, which include about 51 million dry 

tons of secondary residues and wastes (the amount as 

in the baseline scenario). In 2022, the total agricultural 

resources approach 350 million dry tons due to higher 

corn yields and additional acres in no-till and exceed 

400 million dry tons by 2030. By 2022, the energy 

crops are the largest potential source of biomass 

feedstock. As shown in Table 6.2, potential energy 

crop supplies vary considerably depending on what is 

assumed about productivity. At a 2% annual growth 

rate, energy crops total 139, 409, and 540 million dry 

tons per year in 2017, 2022, and 2030, respectively. 

Potential energy crop supplies at the 3% productivity 

growth rate increase to 476 and 658 million dry tons 

in years 2022 and 2030, respectively, and for the 4% 

annual yield increase to 564 million dry tons in 2022 

and 799 million dry tons in 2030. In total, potential 

supplies at a forest roadside or farmgate price of $60 

per dry ton range from 856 to 1009 million dry tons 

by 2022 and from about 1047 to 1304 million dry tons 

by 2030, depending on what is assumed about energy 

crop productivity (2% to 4% annual increase over 2014 

yields). 

This feedstock potential under the high-yield 

assumptions at a $60 per dry ton price offers enough 

feedstock to produce up to nearly 70 BGY of biofuels 

by 2022 and substantially more by 2030 (Figure 6.2). 

Alternatively, biomass resources are large enough to 

potentially produce almost a trillion kWh by 2022 with 

much higher quantities by 2030.
148

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

<$40 per dry ton <$50 per dry ton <$60 per dry ton

Feedstock 2012 2017 2022 2030 2012 2017 2022 2030 2012 2017 2022 2030

Million dry tons

Forest resources

Primary forest residues 

– all land 48 49 44 50 51 51 52 52 57 58 58 59

Primary forest residues 

without federal land 44 44 45 45 46 46 47 47 50 51 51 52

Forest processing 

residues and wastes 31 32 32 34 40 41 42 43 40 41 42 44

Total forest & wood 

wastes resources 79 81 82 83 91 92 93 95 97 98 100 102

Total without 

federal land 74 76 77 79 85 87 88 90 90 92 93 95

Agricultural resources

Crop residues 

(major crops) 27 41 52 80 94 117 136 164 111 135 154 180

Agriculture processing 

residues and wastes 31 36 40 46 50 56 65 82 51 58 67 84

Total agricultural 

residues & wastes 59 77 92 126 143 174 201 245 162 192 221 265

Energy crops1

Perennial grasses - 3.0 12 30 - 41 77 129 - 90 188 252

Woody crops - 0.0 0.0 0.1 - 0.9 40 67 - 5.9 84 126

Annual energy crops - 0.7 1.8 4.2 - 3.8 7.3 14 - 5.0 10 19

Total energy crops - 3.7 14 34 - 46 124 210 - 101 282 400

Total with all land 138 161 187 243 234 311 418 551 258 392 602 767

Total without 

federal land 134 157 182 238 229 306 413 545 252 385 595 760

Summary of Available Forest and Agriculture Biomass at Selected Prices and Years 

under Baseline Assumptions (in Millions) Table 6.1

Note: The total forest supply is 239 to 251 million dry tons at the highest price to roadside.
149

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

<$40 <$50 <$60 <$40 <$50 <$60 <$40 <$50 <$60 <$40 <$50 <$60

2012 2017 2022 2030

60

50

40

30

20

10

0

800

600

700

400

500

200

300

0

Billion gallons per year

100

Forest residues and waste

RFS Total Advanced Biofuel Mandate Production Levels

Agriculture residues and waste Energy crops

Billion kWh

Summary of potential bioenergy supply from forest and agriculture residues and 

wastes and energy crops at selected prices and years under baseline assumptions Figure 6.1

 2% energy crop 3% energy crop 4% energy crop

Feedstock 2012 2017 2022 2030 2012 2017 2022 2030 2012 2017 2022 2030

Million dry tons

Total forest & wood 

wastes resources 97 98 100 102 97 98 100 102 97 98 100 102

Total agricultural 

residues & wastes 244 310 347 405 244 310 346 404 244 307 346 403

Total energy crops - 139 409 540 - 160 476 658 - 180 564 799

Total 340 548 856 1,047 340 568 922 1,164 340 586 1,009 1,304

Summary of Available Forest and Agriculture Biomass at $60 per Dry Ton under 

High-Yield Assumptions Table 6.2

Note: totals may not add up due to rounding.
150

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY Billion gallons per year

Billion kWh

Forest residues and waste Agriculture residues and waste

RFS Total Advanced Biofuel Mandate Production Levels

Energy crops

100

80

60

40

20

0

2012 2017 2022 2030 2012 2017 2022 2030 2012 2017 2022 2030

2% energy crop 3% energy crop 4% energy crop

1400

1200

1000

800

600

400

200

0

Summary of potential bioenergy supply from available forest and agriculture biomass 

at $60 per dry ton under high-yield assumptions Figure 6.2

6.3 Report Implications and 

Further Discussion

6.3.1 Other Assessments

National. Since the publication of the 2005 BTS, there 

has been a proliferation of biomass assessments at 

various spatial scales, from the state-level to the global 

level. For example, a Forest Service website lists 30 

states with some type of woody biomass assessment 

and three major regional studies (U.S. Forest Service, 

2011b). Many states also have an agricultural biomass 

resource assessment and some include forest resources. 

Some assessments go into great detail. As an example, 

in a study completed for the State of Washington 

by Oneil and Lippke (2009), field surveys of forest 

residues on federal, state, and private lands were 

conducted to develop a model for logging residues as a 

function of harvest volume.

A recent economic biomass assessment estimates 

that about 700–1000 million dry tons of agricultural 

biomass will be available in 2030 at a price up to 

about $130 per dry ton under various costs, land, and 

yield scenarios (Khanna et al., 2011). The biomass 

availability estimates are similar to the BTS update; 

however, at higher prices and a different mixture 

of feedstocks. The timeline is 2007–2030 for this 

report whereas the update timeline is 2012–2030. At 

a comparable $60 per dry ton with the transport costs 

removed, the report estimates range from about 450 to 

780 million dry tons. The study does not include wood, 

nor does it include any currently used biomass. This 

will compare to about 250–1300 million dry tons in the 

update at $60 per dry ton over the range of scenarios 

and up to 2030. 

Parker et al. (2011) use a spatially specific supply 

model to assess the potential for large-scale biofuels 

production in the United States. The report includes 

the same feedstocks as in the update, except it 

includes more than just wood from MSW and has 

less optimistic energy crop assumptions. The analysis 

includes an assessment developed by the authors for 

2018 and another assessment using the updated BTS
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baseline assessment data for 2022. For the developed 

assessment, a low-, baseline-, and high-yield scenario 

result in about 317, 533, and 797 million dry tons, 

respectively, at a maximum roadside cost of $200 per 

dry ton. However, for the baseline scenario, a majority 

of the MSW and agricultural and forestry residues 

become available at less than $70 per dry ton, and 

energy crops are available at costs between $80 and 

$120 per dry ton. The model is then used to determine 

the amount of biofuels volumes available using the 

BTS update data, where the biorefineries would be 

distributed to meet the RFS. 

An analysis of the biomass demand for meeting both 

a theoretical 25% RFS and a 25% RES (renewable 

electricity standard) by 2025 estimates a need for 

1,302 million green tons from agricultural and forestry 

residues, urban wood wastes, and energy crops (EIA, 

2007a). Sample et al. (2010) compare the estimated 

demand to a Department of Energy supply estimate 

of 491 million dry tons (approximately 715 million 

green tons). The available supply is based on several 

assumptions, including a high energy cost of $5 per 

million Btu. The limited amount of available biomass 

is projected to result in a significant shortfall that will 

be made up from using roundwood (i.e., wood from the 

bole of trees rather than limbs, tops, and other wastes). 

The updated BTS estimates approximately 980–1440 

total dry tons available in 2025 at a price of $60 per dry 

ton, which may not result in such a large shortfall if the 

market supports that price. However, these estimates 

include “currently used” biomass and no differentiation 

as to what biomass is available to meet a specific target 

or use. 

The National Academy of Sciences (NAS, 2009) 

completed an assessment of biomass for energy and 

reports that approximately 550 million dry tons per 

year of cellulosic biomass can be produced by 2020 

without any major impact on food production or the 

environment (see Table 6.3). The estimate does not 

include corn for ethanol and oil crops for biodiesel. The 

Academy estimates compare very well to the updated 

BTS for the baseline when the currently used biomass 

is removed.

International. The Biomass Energy Europe 

organization invests considerable efforts in 

standardizing biomass assessments (Rettenmaier, 

2010). A comparison of over 150 studies in the 

European Union concludes that nearly all of the 

assessments are technical and economic potential 

studies as compared to the theoretically maximum. 

The studies are resource based and include landuse competition for biomass with other uses. The 

deviations in estimated total potentials among these 

studies are substantial, with differences up to five fold. 

The authors attribute the large deviations to varying 

methods, data, and assumptions with the latter being 

the most significant source of differences, especially 

the assumptions regarding land availability for energy 

crops. The updated BTS uses the POLYSYS model 

to handle this specific issue, but as explained below, 

assumptions are very important. Scenarios are used to 

better present and compare underlying assumptions in 

the update. 

Bauen et al. (2009) estimate that biomass can 

theoretically provide between one-quarter and onethird of the global primary energy supply by 2030 

(see Table 6.4), even when factoring in land use and 

raw material competition. The current estimate is that 

biomass supplies about 50 exajoules of primary energy 

(heating value) (calculated68 to be about 3.2 billion 

dry tons). This is mostly conventional biomass that is 

used for heating and cooking in developing countries. 

An optimistic estimate of the technical potential of 

sustainable biomass by 2050 is between 200 and 500 

exajoules per year (roughly 12–31 billion dry tons). 

Feedstock Millions of dry tons

Corn stover 112

Wheat and grass straw 18

Hay 18

Dedicated energy crops 164

Woody 124

Animal manure 12

MSW 100

Total 548

Estimate of U.S. Cellulosic

Biomass by 2020 (NAS, 2009) Table 6.3

68 Assumes 63 million dry tons per exajoule, which is based on an average of 15 million Btu/dry ton.
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Region

Energy 

crops 

(M acres)

Dry tons 

(Billion)

Europe 62-222 0.4-1.5

USA 2005 BTS 74 1.1

USA 2011BTS 63 1.4

Latin America 299 1.5

China & India 212 1.7

Australia - <4M

Estimate of Global Biomass Potential 

by 2030 (Bauen et al., 2009) Table 6.4 There are many biomass assessments at different 

spatial and technical levels. The updated BTS

provides a national-to-county-level economic biomass 

availability analysis for all agricultural and forest 

lands. The results from the update roughly align with 

other assessments, but the underlying assumptions vary 

and must be addressed in any comparison.

Notes: Timeframes are 2017–2030. 2011 BTS added at $60/

ton for 2030 with 2% scenario. Conversion to dry tons based 

on 63 million tons per exajoule.

6.3.2 Significance of Underlying 

Assumptions 

Scenarios. This update report evaluates two 

scenarios—baseline and high-yield. The baseline 

scenario assumes a continuation of the USDA 10-

year baseline forecast for the major food and forage 

crops plus a 10-year extension to 2030. The USDA 

projections are based on specific assumptions about 

macroeconomic conditions, policy, weather, and 

international developments, with no domestic or 

external shocks to global agricultural markets (USDAOCE/WAOB, 2010). It is a USDA long-term scenario 

for the agricultural sector based on a continuation 

of current policies and programs. Changes in any 

of the key fundamental assumptions underlying 

the baseline, such as economic growth, population, 

trade projections, or biofuels policy, will affect the 

projections. It is intended as a reference or business-asusual case.

Over the 20-year simulation period, the average 

annual corn yield increase is slightly more than 1%. 

The baseline scenario, as implemented in this update, 

assumes a mix of tillage with a trend toward no-till 

and reduced tillage cultivation over the simulation 

period (see Table 4.4). Corn yield and tillage are two 

of the key determinants of stover availability, the 

largest single source of currently available biomass 

residue. Energy crop yields in the baseline scenario 

assume an annual increase of 1% that reflects learning 

or experience in planting energy crops and limited 

gains attained through breeding and selection of better 

varieties and clones. 

In contrast, the high-yield scenario assumes higher 

corn yields and a much larger fraction of crop acres in 

reduced and no-till cultivation. The projected increase 

in corn yield averages almost 2% annually over the 20-

year simulation period. The energy crop productivity 

increases are modeled at three levels—2%, 3%, and 4% 

annually. These gains are due not only to experience 

in planting energy crops, but also to more aggressive 

implementation of breeding and selection programs. 

Only a baseline scenario is assumed for forest biomass, 

as these residues are contingent on the demand for 

pulpwood and sawlogs with future projections based 

on RPA projections of timber harvests.

As discussed, the baseline scenario and underlying 

assumptions used in this resource assessment 

are generally conservative and essentially reflect 

a continuation of current trends with respect to 

commodity crop yields, planted acres, and current 

and projected demand for pulpwood and sawlogs. 

The high-yield scenario examines alternative 
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assumptions about yield growth and the mix of tillage. 

In combination with market price, yield is the key 

determinant of resource availability, and tillage affects 

how much crop residue can be sustainably removed. 

Yield. Annual energy crop yields assumed for the 

baseline scenario vary considerably, ranging from 2 

to 9.5 dry tons per acre for perennial grasses, 3.5 to 6 

dry tons per acre for woody crops, and 6 to 9 dry tons 

per acre for annual energy crops (Tables 5.3 and 5.4). 

These baseline yields for perennial grasses and woody 

crops are well within observed test plot yields (See 

Section 5.1) and for specific crops (e.g., switchgrass).69

The baseline results for 2030 at a $60 per dry ton 

farmgate price (1% annual yield growth for plantings 

after year 2014) show a national average perennial 

grass harvested yield of 6 dry tons per acre, slightly 

less for woody crops, and 6.8 dry tons per acre for the 

annual energy crop. Results for the high-yield scenario 

in 2030—assuming the same farmgate price and a 3% 

annual yield growth—have perennial grass harvested 

yields increasing to a national average of 7.7 dry tons 

per acre, the same for woody crops, and 8.5 dry tons 

per acre for the annual energy crop. These yields are 

a national average based on harvested acres of energy 

crops in 2030. 

Tillage. A number of key modeling assumptions 

involve tillage. The baseline assumes a combination of 

conventional, reduced, and no-till cultivation (see Table 

4.4). Over the simulation period, a small fraction of 

corn acres shift into reduced and no-till. These tillage 

changes are relatively restrained, as about one-third of 

corn acres will still be in conventional tillage by 2030 

and will be restricted from residue collection. Under 

the high-yield scenario, a much larger fraction of acres 

are assumed to shift from conventional tillage to notill. The tillage proportions assumed in the high-yield 

scenario recognize that some corn acres will never shift 

from conventional tillage owing to farmers’ resistance 

to change; the potential for disease and weed control 

problems; and soil wetness issues in some situations. 

By comparison, the high-yield scenario in the 2005 

BTS assumed 100% no-till.

Management practices and input costs. No 

attempt was made to conduct sensitivity analysis on 

management practices and input costs as the intent is 

to understand the resource potential, which is largely 

driven by yield and, in the case of crop residues, 

by tillage restrictions in addition to crop yield. For 

example, a reduction in crop residue collection costs 

owing to technology improvement will tend to shift 

supply curves down, thus making residue collection 

more profitable at lower farmgate prices. However, 

this modeled reduction in costs will not substantially 

change the reported quantities at the higher simulated 

prices. 

Time of implementation. Throughout this report, 

currently used and unused resources, such as crop and 

forest residues, are reported for 2012 and for selected 

years through 2030. For energy crops, simulation 

modeling of these prospective resources is assumed 

to begin in 2014 with initial results reported in 2017. 

The 2017 results do not include woody crops because 

of the 4- and 8-year cutting cycles or rotation lengths. 

As noted in Chapter 5, year 2014 is perhaps the earliest 

time when seeds and other planting materials will be 

readily available, assuming it will take 3 years to scaleup nursery operations. Results of model simulations 

show delays in the 2014 start date will shift estimated 

supply curves in time. 

Energy crop demand for resources. Perennial grasses 

and woody crops generally require less fertilizer, 

pesticides, and fossil fuel than the commodity crops 

they displace—with the exception of the annual energy 

crops, which require about the same level of inputs. 

However, perennial grasses and woody crops are more 

intensive than pasture, requiring more fertilizer and 

pesticides, especially during crop establishment. 

Modeling of land-use change. Land-use change 

is principally affected by the presence of simulated 

markets (and prices) for energy crops. To be sure, 

some land-use change is associated with crop residue 

collection, but this amount is much less than the 

displacement of commodity cropland and pastureland 

69 For example, average annual yields for switchgrass ranged from about 4 to 10 dry tons per acre, with most locations having an average 

between 5.5 and 8 dry tons per acre (McLaughlin and Kszos, 2005; BRDI, 2008). For woody crops, annual yields have been generally 5 dry 

tons per acre in most locations with the exception of the Pacific Northwest and subtropics (eucalyptus) where they have been higher.
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by energy crops. Land-use change is modeled by 

POLYSYS, which allocates land to competing crops 

based on net returns. If model results show a given 

commodity crop in a particular county displaced by an 

energy crop, then the energy crop is more profitable. In 

the case of pasture, energy crop returns must be greater 

than the rental value of the pastureland plus additional 

‘intensification’ costs to make up for lost forage. A key 

assumption in this analysis is that for every acre of 

pasture converted to energy crops, an additional acre 

of pasture is intensified to make up for lost forage. 

Because sufficient rainfall is needed, the analysis 

limits the conversion of pastureland to energy crops to 

counties situated east of the 100th Meridian and in the 

Pacific Northwest. 

POLYSYS modeling includes 250 million acres planted 

to the eight major crops, 61 million acres of land in hay 

production, and 140 million acres of cropland pasture 

and non-irrigated, permanent pasture. This land base 

is assumed constant throughout the modeling period. 

The analysis does not account for any competition and 

potential losses (or gains) of land to other major land 

uses, such as the conversion of pastureland to urban 

uses and the conversion of forestland to cropland. 

The analysis does not include land currently enrolled 

in the CRP70 or land that might become available as 

contracts expire. This update (as well as the USDA 

projections) assumes that there are approximately 32 

million acres currently enrolled in the CRP throughout 

the simulation period. The analysis does not consider 

any scenarios where high biomass prices provide 

strong financial incentives for growers to withdraw 

from the CRP, give up annual rental payments, and 

convert land into energy crop production. Further, the 

analysis does not consider any policy changes to the 

CRP that will allow the harvesting of energy crops. 

Finally, the CRP is designed to reduce soil erosion and 

provide other benefits (e.g., create wildlife habitat, 

reduce sedimentation, improve water quality, prevent 

excess crop production, and provide a stable source 

of income for farmers). Removing land from the CRP 

has the potential to reduce wildlife habitat and increase 

the delivery of sediment, nutrients, and pesticides to 

water bodies (BRDI, 2008). Although it is recognized 

that the conversion of some CRP land to energy 

crops can occur without any adverse environmental 

impacts, especially if sensitive areas are removed from 

consideration, the analysis of the CRP for either energy 

crop production or crop and forage production is not 

considered in this update.

Environmental sustainability. The primary crop 

residues, on both cropland and forestlands, explicitly 

consider resource sustainability with potential 

collection quantities that are only available after 

all restrictions are satisfied. This includes meeting 

soil erosion restrictions due to water and wind and 

maintaining soil carbon levels for crop residue 

removal. The forest residue analysis removes steep, 

wet, and roadless sites and restricts residue removal 

based on slope considerations. These slope restrictions 

consider erosion, soil nutrients, biodiversity, soilorganic carbon, and LTSP. For energy crops, 

sustainability is assumed practiced as implemented 

through BMPs, and crop budgets reflect these 

considerations. Displacement of commodity crops by 

perennial grasses and woody crops should improve 

environmental sustainability because they require 

smaller amounts of fertilizers and pesticides. Once 

established, perennial grasses and woody crops require 

little maintenance. These crops can provide more 

habitat diversity and depending on how planted provide 

riparian buffers and offer opportunities to capture 

runoff of nutrients. For annual energy crops, planting 

is assumed limited to non-erosive cropland, considered 

part of a multi-crop rotation, and grown using BMPs 

so as not to impose any additional impacts to local and 

regional ecosystems. 

Roundwood markets. In Section 3.1.2 there is 

a discussion of an underlying assumption that 

unmerchantable biomass components of forest stands 

are uneconomic, unless they are removed during 

the harvest of commercial roundwood. The analysis 

includes an upper biomass availability level that is 

associated with the roundwood harvest level for each 

state. The restriction is only an approximation due 

to the fact that wood is transferred among states to 

processing facilities and is based on 2006 data and the 

2005 RPA projections, which are subject to change 

70 USDA Conservation Reserve Program, Status— April 30, 2011. http://www.fsa.usda.gov/Internet/FSA_File/april2011onepager.pdf
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

under different economic conditions. The assumption 

removes a significant amount of biomass from the 

assessment—about 7 million dry tons annually for the 

United States. More importantly, almost half the states 

lose 50% or more of the potential thinning biomass 

because of this restriction. 

6.3.3 Factors Affecting Potential 

Product Estimates

Gross versus net tonnage. The estimated dry tons 

are those that are available at the farmgate or forest 

roadside. Each type of feedstock has certain physical 

properties and harvest/recovery characteristics that 

influence the standing volumes versus how much 

biomass is available at roadside, and more importantly, 

at the biorefinery. In the report, the estimates of 

potentially available biomass account for losses 

incurred during collection or harvesting and in the 

moving of the biomass to the field edge or forest 

roadside. 

The update does not consider feedstock logistics and 

supply chain issues as comprehensively as desired, 

and not at all beyond the farmgate and roadside. One 

potential loss is from handling and storage. Such losses 

are specific to the feedstock, the feedstock condition 

and form, the type of handling system, and storage 

conditions. In cases where storage induces loss, the 

actual availability for conversion into energy is less 

than the presented estimates. 

Although the potential biomass amounts are estimated 

at the county level, the assessment does not take into 

account the many factors such as feedstock density, 

markets, feedstock preference, incentives, and 

economics that can change availability. 

Feedstock characteristics. The update, as described, 

includes no preference toward a specific feedstock or 

particular conversion process for energy production 

potential. For example, corn stover includes stalks 

and cobs, and forest residues include limbs, bark, and 

solid wood of different compositions that may or may 

not work well at a particular facility. The physical and 

chemical characteristics of feedstocks vary widely 

and greatly impact conversion yield, but also, and 

more importantly, these characteristics impact how 

much of the estimated biomass potential is actually 

available at the throat of the conversion facility 

because of specifications at the biorefinery. A report 

sponsored by the Western Governors’ Association 

(Parker et al., 2011) is an example of a source for 

such an analysis. The analysis includes updated BTS

biomass resource supply potential and estimates fuel 

price through various conversion pathways. This 

particular study represents the usefulness of resource 

potential estimates for one of multiple approaches to 

estimate the feedstock supply and cost characteristics 

of additional bioenergy. 

There are ongoing efforts to develop feedstock 

logistic systems that can handle a wider variation of 

feedstock types that meet tighter quality specifications. 

In the future, biorefineries may require feedstocks of 

consistent quality, particle size, and moisture content. 

It is likely that more uniform feedstocks will have 

greater market potential (Hess et al., 2009). The effect 

on biomass potential still needs additional study. 

Some resources may become stranded as the cost to 

improve quality may outweigh value, or conversely, 

the improvement process can make more biomass of a 

specified quality available.

Markets. As previously discussed, the POLYSYS 

model allocates land to competing uses based on net 

returns with any changes in land use affecting the 

supplies of both crop residues and energy crops. The 

model assumes that farmers will grow energy crops 

if they can produce a net return or profit equal to or 

greater than the profit made by producing one of the 

eight commodity crops or forage. Energy crops also 

compete for land with each other and for land where 

crop residue collection is profitable. Simulation results 

show much higher net crop returns relative to the 

baseline forecast and only a slight decline in net returns 

to livestock production.

In the absence of significant markets for bioenergy 

feedstocks, it is difficult to project what farmers will 

require as a minimum profit to grow energy crops. A 

study by Jensen et al. (2005) attempts to address this 

question by assessing farmers’ views and interest in 

producing switchgrass, profits that would be required 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

to induce farmers to grow switchgrass, and the amount 

of land and types of crops that farmers might be willing 

to convert to switchgrass production. Survey results 

vary considerably, especially between farmers with 

a knowledge of and interest in growing switchgrass 

and those not interested. Jensen et al. (2005) reports 

an average net return per acre among those interested 

in growing switchgrass of $102 per acre and $330 per 

acre for those not interested. The results presented in 

this analysis show profits from about $100 to $140 per 

acre (inclusive of land rents) at annual yields per acre 

of 7 to 8 dry tons per acre. These yields are currently 

attained in Genera Energy’s switchgrass plantings in 

eastern Tennessee (Genera Energy, 2011).

6.3.4 Innovations in Management 

and Technology

Use of water-limited lands. A significant underlying 

assumption within the update that restricts biomass 

availability is that energy crops on land that would 

require irrigation are excluded. Water scarcity and 

the depletion of aquifers are already looming issues 

in agriculture. There is concern that adding large 

acreages of irrigated energy crops would not be 

sustainable, especially with competition for increased 

commodity crop production. Therefore, much of the 

western United States is excluded in the analysis of 

energy crop potential. However, the assumption is an 

oversimplification because there are both barriers and 

opportunities to expanding sustainable production of 

energy crops beyond rain-fed land. The assumption 

is not meant to imply that such land will not become 

accessible for biomass production under innovative 

approaches and technologies.

A National Research Council report (NRC, 2008) 

concludes that increased agricultural production for 

biofuels will probably not alter the national aggregate 

use of water in the next 10–15 years. However, 

the report indicates that growing crops for biofuel 

production is likely to have significant regional and 

local impacts, and there is a need to encourage the 

growth of new technologies, best agricultural practices, 

and the development of traditional and cellulosic crops 

that require less water and fertilizer and are optimized 

for fuel production. There is ongoing and expanding 

research to grow traditional crops with less water and 

to develop new cultivars and crop varieties for the 

more arid lands. Dryland farming and other agronomic 

approaches, such as integrated rotations, are being 

considered for energy crops (e.g., the use of winter 

grass cover crops). Also, it appears that some crops, 

such as switchgrass, could be grown under very limited 

irrigation. Finally, as discussed in Chapter 5, new 

approaches to irrigation using saline or waste water 

from municipal to industrial sources, including mine 

and oil production, are being explored. 

Use crop rotations and multiple crops. The 

discussion and analysis in Chapter 4 on the agricultural 

crop wastes retention modeling points out the important 

role of crop rotation and the use of cover crops and 

fallowing for production and sustainability. For energy 

crops in Chapter 5, the focus is on agronomical and 

silvicultural practices, such as double or multiple 

cropping to increase productivity and other practices 

to ensure sustainability. A good example is the use of 

sweet sorghum as a complementary crop to energy 

cane. The concept is to double or triple (number of 

rows) drill sweet sorghum on conventional sugarcane 

rows and harvest with the same sugarcane harvester 

earlier than the cane (McCutchen and Avant, 2008). 

However, the analyses in the update focus primarily 

on yield and tillage and not an incorporation of crop 

rotation and multiple cropping. Another example is to 

optimize the rotation length (time between harvests) 

and the number of rotations between re-establishing 

woody crops, especially for coppice crops. The update 

uses one set of assumptions, even though other forms 

of rotation might provide significant increases in 

potential biomass. One form of rotation not included 

in this report is intercropping, which is the planting 

of grasses between the rows of trees early in the 

rotation. This report serves to establish a baseline from 

which alternative land management scenarios can 

be examined that could potentially increase biomass 

production and accessibility, as well as enhance 

environmental quality.
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Input levels of production. An attractive feature of 

cellulosic and advanced biomass feedstocks is that 

they provide higher levels of biomass with relatively 

fewer inputs than traditional bioenergy feedstocks 

(e.g. corn grain). The update addresses the importance 

of corn and energy crop yield through two scenarios, 

but does not include further analysis on reduced input 

production scenarios. In Chapter 5, the sensitivity of 

yields to input adjustments is addressed according to 

trials of individual dedicated energy crops. However, 

for the update, only one set of input levels is assumed 

for the projections. This approach does not undermine 

the need to consider resource potential in light of the 

applications of varied levels of inputs. Developing 

energy crops that efficiently utilize nutrients and water 

remains a priority in providing appropriate yields that 

maintain farm profitability and affordable feedstocks 

for conversion. As stated earlier in the report, ongoing 

efforts to increase yields or maintain yields with fewer 

inputs focus on genetic techniques (e.g., breeding, 

biotechnology, and bioengineering) and agronomic and 

silvicultural practices. 

Improved systems. The availability and cost of 

the biomass production, recovery and harvest, 

and preprocessing is predicated mostly on current 

technologies, except for the yield scenarios that include 

future increases in on-farm production. The literature 

generally does not fully address biomass feedstocks 

from the industrial perspective—the optimization 

of current production systems for biomass or the 

development of new and innovative feedstock systems. 

Much of the current research and development is 

conducted at laboratories and bench-scale, or at the 

most, small pilots. This approach has significant effects 

on biomass potential, which could change greatly 

with different assumptions of system productivity 

and costs. Current systems are mostly adapted from 

current crop, forage, and forest systems. Although the 

estimates used in the report are relevant and constitute 

very likely system functions and costs in the near term, 

the long-term impact of new machine development 

and optimization of systems may positively impact 

the results. Analysis to integrate the flow of materials 

through the system to optimize the recovery efficiency 

and access life-cycle performance is beyond the scope 

of the report. In general, as such systems are further 

developed and implemented on larger scales, the 

availability of biomass at different prices will change.

For example, DOE is developing an advanced 

uniform-format design that will make an evolutionary 

progression from present-day, conventional-logistic 

systems to high-volume, bulk-handling systems 

that provide a uniform, specified raw material in a 

commodity-type market much like grain systems (Hess 

et al., 2009). DOE has also supported the design and 

demonstration of a comprehensive system to handle 

the harvesting, collection, preprocessing, transport, and 

storage of sufficient volumes of sustainably produced 

feedstocks.
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6.3.5 Data, Research, and 

Technology Needs

A major approach for the update is to overcome some 

of the shortcomings in the 2005 BTS that required the 

development of better datasets, models, and analytical 

approaches. Although significant effort did go into 

the process, and there are many improvements in the 

update, there are still some additional needs that were 

identified. The needs that support future enhancement 

of the data and assumptions presented in this report 

are: 

• Additional availability and costs data and 

databases are needed to improve the quality of the 

analysis for:

▫ Many, if not all, of the agricultural processing 

residues and wastes

▫ Secondary mill wastes from wood processing

▫ Urban wood waste data, including tipping fees

▫ Biomass portions of the FIA database

▫ Land-use databases, especially those for pasture 

and marginal lands

• Better quantification of future potential yields and 

geographic variation

• Improved information on system integration, 

such as production, harvesting, and conversion 

efficiency loss of biomass

• Better modeling of the conventionally sourced 

wood supply curve to account for additional future 

wood resource demand

• Understanding and modeling of the long-term 

effects of wood biomass removal under a range of 

soil, climate, and management schemes

• Understanding of the impacts of climate change

• Development and integration of biological and 

economic models for sustainability

• Increases attributed to the available biomass from 

residuals and wood wastes through management 

and technology

• Better understanding of stover yields and harvest 

index with changes in grain yields from breeding 

and biotechnology

• Improvement of system data on equipment 

efficiency and cost data for current and evolving 

systems

• Understanding factors that limit the availability 

of land-for-crop expansion, which include 

competition for agriculture crops versus livestock 

production

• Increased understanding of potential pasture 

intensification and its role in making additional 

forage available

• Improved understanding of the potential for 

specialized harvesting equipment to handle 

various energy crop feedstocks (e.g., thickstemmed grasses) more efficiently than 

commercial hay equipment

• Improved understanding of the potential for 

specialized logging equipment to more efficiently 

harvest forest residues and energy crops

• Understanding of landowner response to energy 

crop market opportunities.

6.3.6 Opportunities for 

Further Analysis

Modeling output that supports the production forecasts 

within this report is available online for public 

access. The Bioenergy KDF (ORNL, 2010) is an 

online, infrastructure-utilizing, Web 2.0 technology 

that contains county- and state-level forecasts and 

online mapping services. For example, a user may 

define the feedstock, year, and price of interest for 

direct data download, online map projection, and map 

export. Additionally, a number of custom datasets are 

available for immediate access that align with figures 

and tables from the Executive Summary of this report. 

These datasets summarize the spatial distribution 

of potential feedstocks across the two scenarios and 

multiple feedstock sources. An additional feature 

of the Bioenergy KDF is the ability to submit and 

access spatially referenced data and models from peer 

reviewed articles related to bioenergy. 
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6.4 Conclusions

This updated resource assessment for the conterminous 

United States identifies sufficient biomass feedstock 

to meet near-term and potentially long-term bioenergy 

goals, depending on different cost and productivity 

scenarios. The assessment finds significant biomass 

resources across the United States with the exception 

of some areas of the arid west. These resources are 

shown for 2030 in Figure 6.3 as state-level shares of 

major categories of feedstocks and in Figure 6.4 as 

total potential resources by county. Under high-yield 

assumptions, ambitious goals may be feasible. The 

assessment takes into consideration environmental 

sustainability and identifies likely costs to access 

these resources. There are, of course, limitations to 

exploiting these prospective supplies. One limitation of 

the assessment is that it was conducted using a forest 

roadside and farmgate perspective. Reported costs 

do not represent the total cost or the actual available 

tonnage to a biorefinery. There are additional costs 

to preprocess, handle, and transport the biomass, and 

there may be storage costs for specific feedstocks.

Such losses are specific to the feedstock, the feedstock 

condition and form, the type of handling system, and 

storage conditions. For example, switchgrass can 

lose 2% to 25% depending on the type of storage 

(Turhollow et al., 2009). The reported estimates do 

include losses to roadside, but do not include losses due 

to continued handling, additional processing, storage, 

material degradation, and quality separation. In effect, 

for instance, more than one billion tons from estimates 

in the report would be required to have a billion tons 

ready to process at a biorefinery. The amount would be 

dependent on many variables in the continued supply 

chain and final conversion technology. In addition, 

the biomass is in varied form and may not be directly 

comparable at a biorefinery in either cost or conversion 

efficiency. Determining such values is outside the 

scope of the report. 

Although the assessment was conducted at a county 

level for the major primary feedstocks, the assessment 

does not account for landscape-type issues, such 

as feedstock density and nearness to demand, 

or conversion facilities that may have economic 

implications or other incentives that would change 

availability, or even preference, among feedstocks. 

These considerations would likely reduce the size of 

the resource potential as well. There are other efforts 

underway to better understand feedstock density issues, 

to optimize facility location and logistic systems. This 

report provides information complementary to the 

underlying feedstock availability at a range of prices. 

There are also limitations on available data, especially 

at the county level, for many of the feedstocks that 

do not have any historical prices. When there is price 

data, it may not represent optimal production systems 

or technology. Furthermore, many assumptions are 

used in making the estimates. An effort was made to 

use the best information and to maintain a conservative 

approach as appropriate.

Overall, results of this update are consistent with the 

2005 BTS in terms of the magnitude of the resource 

potential, assuming a farmgate or forest roadside 

feedstock price of $60 per dry ton. These findings for 

2030 are summarized in Figures 6.5 and 6.6 for the 

baseline and high-yield scenarios—assuming high 

corn yield, high no-till, and energy crop productivity 

increasing by 3% annually. The forest residue potential 

is determined to be somewhat less as measured by 

the unused resources and by properly accounting 

for pulpwood and sawlog markets that provide the 

demand and the residue. The crop residue potential 

is determined to be somewhat less owing to the 

consideration of soil carbon in crop residue removal 

and not counting any residue produced on land that 

is conventionally tilled. The energy crop potential is 

estimated to be much greater because of higher planted 

acreage—a result of the spatially explicit land use 

change modeling that was used.

This report has several enhancements over the original 

report. Just as the 2005 BTS did over the last 5 years, 

hopefully this update will provide a foundation for 

further analysis by others and resolution of issues and 

concerns. It has limitations that can be better addressed 

as the data and outputs become available through 

the KDF. Through the development of the report, 

challenges and opportunities are identified that may 

suggest needed investments in research and analysis 

capabilities.
160

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Figure 6.4

Figure 6.3 State-level shares of all potentially available resources at $60 per dry ton or less in 

2030, under baseline assumptions

Potential county-level resources at $60 per dry ton or less in 2030, 

under baseline assumptions
161

U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Summary of currently used and potential resources at $60 per dry ton or less identified 

under baseline assumptions

Summary of currently used and potential resources at $60 per dry ton or less identified 

under high-yield assumptions

Figure 6.5

Figure 6.6

2030

2022

2017

2012

Million dry tons

Forestland resources 

currently used

Agricultural resources 

currently used

Agricultural land biomass 

& waste resource potential

Energy crops

Forestland biomass 

& waste resource potential

0 200 400 600 800 1000 1200 1400 1600 1800

2030

2022

2017

2012

Million dry tons

Forestland resources 

currently used

Agricultural resources 

currently used

Agricultural land biomass 

& waste resource potential

Energy crops

Forestland biomass 

& waste resource potential

0 200 400 600 800 1200 1000 1400 1600 1800
162

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APPENDICES

Appendix A: Feedstocks

1. Currently used (Discussed in Chapter 2)

a. Agriculture

i. Grain crops (primarily corn) for ethanol up to 

 15 BGY RFS)

1) Corn

2) Sorghum

3) Barley

4) Other grains

ii. Other crops (sugarcane, sugar beets, etc.)

iii. Oil crops (primarily soybeans for biodiesel up to 

 1 BGY RFS)

1) Soybeans

2) Other crops (canola, sunflower, rapeseed, etc.)

iv. Municipal solid wastes (biomass, landfill gas, biosolids)

b. Forestry

i. Fuelwood

ii. Mill residues

iii. Pulping liquors

iv. Urban wood wastes and construction & demolition wastes

2. Forest biomass and waste resources (Discussed in Chapter 3)

a. Logging residues (timberland—see definitions in Appendix B)

b. Other removal residues (timberland and other forestland)

c. Thinnings (timberland)

d. Thinnings (other forestlands)

e. Composite (portions of logging residues and thinnings)

f. Conventional pulpwood diverted to bioenergy

g. Unused mill residues (primary and secondary processing mills)

h. Urban wood wastes and construction & demolition wastes

3. Agriculture biomass and waste resources (Discussed in Chapter 4)

a. Crop residues includes corn stover (stalk, leaves, and cobs), sorghum stubble, and straw from small grains 

 (wheat, oat, and barley)

b. Crop processing residues

c. Waste oil and greases

d. Animal manures

e. MSW (e.g., food waste and textiles)

• Used EISA Mandates

• Corn and other crops ethanol 

◦ ~12 BGY 2012–2014

◦ 15 BGY 2015–2030

◦ 2.8 gallons per bushel of corn

◦ 56 pounds per bushel at 

15.5% moisture content

◦ ~69% ethanol 

◦ ~31% distillers grains

• Biodiesel

◦ 1 BGY 2012–2030

◦ Soybeans (43%)

▫ 1.52 gallon per bushel

▫ 60 pounds per bushel at 

10% moisture content

◦ Other (57%)

▫ Fats 

▫ Vegetable oils 

Corn Starch Ethanol and Biodiesel
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4. Energy crops (Discussed in Chapter 5)

a. Perennial grasses (e.g., switchgrass, miscanthus, etc.)

b. Woody crops (poplar, willow, southern pine, eucalyptus)

c. Annual energy crops (high-yield sorghum)

5. Algae not included

Appendix B: General Assumptions1

1. Land base

a. Conterminous United States; excludes Alaska, Hawaii, and U.S. territories

b. EISA compliance

i. Agriculture lands meet established criteria. Federal forestlands are shown optionally (excluded under 

EISA)

c. Forest resources

i. FIA land definitions

1) Forestland – greater than or equal to 1 acre and has 10% live tree stocking

2) Timberland – capable of producing more than 20 cubic feet per acre per year

3) Other forestland – other than timberland or reserved forestland and incapable of producing 20 cubic 

feet per acre per year

4) Reserved forest lands excluded – set aside by statute or regulation

ii. Inventoried roadless areas excluded

iii. No or little road building (over 0.5 miles from road excluded)

iv. Areas with slopes greater than 80% (slightly less than 40 degrees) excluded

v. Selected wet-area stand types excluded

vi. Federal lands (except reserve and roadless) included separately

d. Agriculture resources

i. Perennial grasses and woody crops can be established on cropland, cropland used as pasture, and 

permanent pasture.

ii. Annual energy crops (e.g., energy sorghum) restricted to cropland with low erosion potential and assumed

part of a multicrop rotation

iii. Energy crops are not planted on land requiring supplemental irrigation

iv No forestland conversion

v. USDA baseline acres apportioned to counties using a four-year average of crop acres from NASS; county 

cropland pasture and permanent pasture acres derived from the 2007 Census of Agriculture

vi. Pasture conversion to perennial grasses and woody crops limited to counties east of the 100th Meridian, 

 except for the Pacific Northwest

1 Presented in brevity – please use main document for more detail explanation.
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2. Yields and recovery

a. Baseline scenario for agriculture

i. Anchored to USDA Baseline Agricultural Projections for 

agricultural land

ii. Production of traditional crops allocated to counties based

on 4-year trailing average of NASS surveys (2006–2009)

1) Yield projections for eight major agricultural crops

 (major crops are corn, sorghum,2

 barley, oat, wheat, rice, 

cotton, and soybean) based on USDA Agricultural 

Projections to 2019 for 2010–2019 and “straight-line”

 extension of the last 3 years of the forecast through 

2030. Baseline yields apportioned to counties based on a 

4-year average of NASS data (excluding hay, which is 

from 2007 USDA Agricultural Census)

a) Corn 2012 baseline yield (average for United States) is

163 bushels per acre increasing to 201 bushels per acre

by 2030

b) Wheat 2012 baseline yield (average for United States) 

is 44 bushels per acre increasing to 50 bushels

per acre by 2030

c) Soybean 2012 baseline yield (average for 

United States) is 44 bushels per acre increasing to 

52 bushels per acre by 2030

d) Sorghum, oat, and barley baseline yield (average for United States) is 64, 64, and 67 bushels per acre,

increasing to 74, 72, and 79 bushels per acre by 2030, respectively

2) Residue to grain ratios are 1:1 for corn and sorghum, 1:2 for oat, 1:1.5 for barley, and 1:1.7 and 1:1.3 for 

winter and spring wheat, respectively (implemented as a weighted average of winter and spring wheat 

acres). No residue collection is assumed for soybean

3) Tillage includes conventional, reduced, and no-till. Residue collection is not allowed on conventionally 

tilled acres. Separate residue retention coefficients estimated for reduced tillage and no-till. No-till allows 

for removal of more residue than reduced tillage

b. Baseline for forestry

i. Residues are based on inventory and not yield data.

1) Current logging and other removal residues from USDA Forest Service TPO data updated in 2007

2) Future logging residues derived using USDA Forest Service RPA (Resource Planning Act) projections 

of timber harvests to 2030

3) Thinnings derived from USDA Forest Service database downloaded on February 3, 2010

3. High-yield scenario

a. Agriculture

i. Used USDA Agricultural Projections as basis for agricultural crops

ii. Yield projections for eight major agricultural crops (major crops are corn, grain sorghum3

, barley, oat, wheat, 

rice, upland cotton, and soybean were based on USDA Agricultural Projections to 2019 for 2010–2019 and 

“straight-line” extension of the last 3 years of the forecast through 2030. Yields apportioned to counties 

2 Sorghum grown for energy is treated as an energy crop and assumptions are included under those for annual energy crops.

3 Sorghum grown for energy is treated as an energy crop and assumptions are included under those for other energy crops.

• Conditional scenario based

• Specific assumptions

◦ Macroeconomy

◦ Agriculture and trade policies

◦ Weather

◦ International developments

• No domestic or external shocks to 

global markets

• Normal weather

• Current law through projections (e.g., 

Farm Bill, EISA, Energy Improvement 

and Extension Act)

• Biofuels tax credits—ethanol $0.45, 

biodiesel $1.00, and $0.54 ethanol 

import tariff

• Starch biofuel at 15 billion GPY by 

2015

USDA AGRICULTURAL 

PROJECTIONS
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

based on a 4-year average of NASS data (excluding hay, which is from 2007 USDA Agricultural Census)

1) Corn 2012 baseline yield (average for United States) is 163 bushels per acre increasing to 265 bushels 

per acre by 2030

2) Wheat 2012 baseline yield (average for United States ) is 44 bushels per acre increasing to 50 bushels 

per acre by 2030

3) Soybean 2012 baseline yield (average for United States) is 44 bushels per acre increasing to 52 bushels 

per acre by 2030

4) Sorghum, oat, and barley baseline yield (average for United States) is 64, 63, and 66 bushels per acre 

increasing to 74, 72, and 79 bushels per acre by 2030, respectively.

iii. Average annual increases in yield for agricultural crops for 2010-2030 are

1) Corn – 1.95%

2) Other major crops – same as baseline (see 2. a. ii. 1) b)–d) on previous page)

iv. Residue to grain ratios – same as baseline (see 2. a. ii. 2) on previous page)

v. Tillage includes conventional, reduced, and no-till. Residue collection is not allowed on conventionally 

tilled acres. Separate residue retention coefficients estimated for reduced tillage and no-till. No-till allows 

for the most residue removal. High-yield assumes conversion of 80–85% of conventionally tilled acres to 

no-till by 2030

vi. Forestry – no high-yield scenario is assumed

4. Forest residues

a Sustainability

i. Retention of biomass

1) 30% retention of biomass by tonnage on slopes less than or equal to 40%

2) 40% retention of biomass by tonnage on slopes greater than 40% less than or equal to 80%

3) No removal of biomass on slopes greater than 80%

4) Biomass specifically retained not defined—assumed any combination of small trees, limbs and tops of 

merchantable, harvested trees, dead standing trees, cull trees, portions culled from trees, etc.

ii. No or little road building—used “distance from road” FIA variable to exclude plots

1) Excluded biomass greater than 0.5 mile for ground-based system

2) Excluded biomass greater than 1300 feet for cable-based system

iii. Excluded areas with slope greater than 80%

iv. Used cable system on slope greater than 40% instead of ground-based system

v. Assume BMPs, regulation, and certification (as applicable) compliance and costs are reflected in cost

 curves

vi. Thinnings and pulpwood volumes capped based on pulpwood and sawlog markets

1) Annual harvest in county cannot exceed annual growth (i.e., 2006 harvest levels in 2007 RPA)

2) Integrated logging of pulpwood and sawtimber harvest cannot exceed pulpwood/sawtimber market 

2006 levels in 2007 RPA

b. Biomass

i. Small trees

1) 1–5 inches dbh (diameter breast height) in the East

2) 1–7 inches dbh in the West
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

ii. Tree components – limbs, tops, and cull components of merchantable, harvested trees

iii. Dead standing trees

iv. Cull trees or components – do not meet commercial specifications because of size or quality

c. Conversion factor – 30 dry pounds per cubic foot and 50% moisture content 

5. Energy crops

a Grown on either cropland, cropland used as pasture, or permanent pastureland—not on forestland

b. Energy crops grown on pasture assume lost forage made up through the intensification of other pastureland

c. POLYSYS modeling framework

i. Assesses economic competitiveness with commodity crops (3,110 counties)

ii. Estimates land-use change (county by county) for cropland, hay land, cropland used as pasture, and 

 permanent pastureland

iii. Prices vary parametrically in $5 increments to estimate supply

iv. Uses upper limits of 250, 61, 23, and 117 million acres in cropland, hay land, cropland in pasture, and

 permanent pasture, respectively

v. Allocates land based on 

1) Maximization of expected returns above variable costs for all commodity crops (e.g., corn, wheat, and 

soybeans) and energy crops (perennial grasses, woody crops, and energy sorghum)

2) Subject to meeting demands for food, feed, industrial stocks, and exports

3) Excludes forestland

4) Excludes Conservation Reserve Program (CRP) land

5) Energy crops displace conventional crops and pasture if more profitable, but conventional crop demand 

is still met on other land

6) Only 10% of cropland can convert to energy crops each year. The total amount of cropland in any 

given county that can convert to energy crops (perennial grasses, woody crops, and energy sorghum) 

is limited to 25% 

7) Conversion of pastureland to energy crops is limited to counties east of the 100th meridian for 

sustainability except for the Pacific Northwest

a) Intensifying pasture needed to replace lost forage 

b) Only 5% of permanent pasture can convert in given year. The total amount of permanent pasture in 

a given county that can convert to energy crops is limited to 50% (i.e., assumed doubling of forage 

through intensification)

c) Only 20% of cropland pasture can convert to energy crops each year. The total amount of cropland 

pasture in a given county that can convert to energy crops is limited to 50% (same assumption as 

permanent pasture) 

vi. Eight-year rotation for non-coppice woody crops, 20-year rotation and 4-year cutting cycle for coppice 

 woody crops, 10-year stand life for perennial grasses

vii. Costs include seed (or plantings), fertilizer, herbicide, insecticide, machinery services, custom 

 operations, fuel and lube, repairs, handling, labor, and technical services

1) Broad production regions

2) Perennial grasses are species-specific by region
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

3) Intensifying cropland currently used as pasture costs $50 per acre the first year and an additional $10 

per acre in subsequent years 

4) For permanent pasture, first-year costs are $100 per acre and $15 per acre in following years. 

5) Energy crops must overcome the additional costs in 3) and 4) plus the pasture rental rate to come into 

production

viii. Systems

1) Perennial grasses

a) Planted, managed, and harvested like a hay crop

b) Uses no-till establishment

c) Annual harvests with reduced yields in first and second years, maturity reached in third year

d) Conventional mowing, raking, and baling 

e) Ten years before replanting 

f) Productivity is a function of precipitation, temperature, soils, and local site factors

g) No irrigation

2) Woody crops

a) Can be either single-rotation or coppice

b) Established and managed with conventional agriculture equipment

c) Harvested using conventional forestry equipment for single-stem and specialized equipment for 

coppice (multiple stems at the stump)

d) Up to seven stands regrown by coppice before re-establishment

3) Energy sorghum

a) Annual crop

b) Only on non-erosive cropland

c) Part of multicrop and/or fallow rotation

4) Miscanthus 

a) Higher yields are offset by higher establishment costs due to the fact that they use vegetative 

planting material, which results in similar production costs to modeled perennial grasses (e.g., 

switchgrass)

ix) Yields

1) Species and regional specific

2) Perennial grasses baseline yield

a) See Table 5.3

b) Baseline perennial grass yields (dry tons per acre): 3.0–9.9 dry tons per acre in 2014; 3.6–12.0 dry 

tons per acre in 2030

3) Woody crops baseline yield

a) See table 5.4

b) Baseline woody crop yields (dry tons per acre): 3.5–6.0 dry tons per acre in 2014; 4.2–7.2 dry tons 

per acre in 2030

4) Energy sorghum (8–11 dry tons per acre)

5) High yield – used three growth rates: 2%, 3%, and 4% annually

d) Herbaceous biomass (crop residues, perennial grasses, and annual energy crops) include 10% 

biomass losses at the field edge. Woody crops assume a 5% loss at the field edge 
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

6. Costs and production

a. All costs to farmgate or roadside (excludes handling and loading, transportation, and storage)

b. Forest

i. Includes chipping (i.e., preprocessing) for forest residues, thinnings, and conventionally sourced wood

ii.Systems

1) Logging residues are integrated harvest (i.e. biomass removed with merchantable products and as trees 

to roadside) 

2) Thinnings

a) Thinned if stand density index (SDI) greater than 30% of maximum SDI for reference stand type to 

a SDI of 30% of maximum

b) Integrated harvest (i.e., multiple products)

c) Uneven-aged prescription (some trees removed from all diameter classes as needed to meet target 

SDI)

3) Pulpwood harvest

a) Harvest only pulpwood diameter classes; do not harvest other merchantable product diameter 

classes

b) All of tree is considered to be biomass (i.e., no other product recovered from tree)

4) Ground-based logging is whole tree to roadside (no processing at stump, i.e., no cut-to-length system)

5) Cable system limited to 1300 feet from road access

iii. Stumpage costs are regional 2007 averages (data were 2006–2008)

iv. Chipping costs average $13 per dry ton across states because of labor and fuel cost difference 

v. Harvest costs (felling and transport to roadside) estimated using Fuel Reduction Cost Simulator (FRCS) 

model

vi. Supply curves

1) Logging residues and thinnings

a) Includes stumpage costs

b) Includes chipping costs for logging residues and thinnings

c) Includes harvest (felling/extraction) costs for the small, dead, and cull (non-merchantable) trees 

portion of thinning)

d) Minimum supply curve stumpage is $0 to $4 per dry ton (plus other applicable costs)

i) Stumpage is $0 per dry ton for federal lands 

ii) Stumpage is $4 per dry ton for private lands 

e) Maximum of supply curve stumpage is up to 90% of 2007 pulpwood stumpage price 

2) Pulpwood

a) Includes stumpage, harvest, and chipping costs

b) Same minimum and maximum stumpage as above

c) Only 20% of 2006 county harvest can shift from pulpwood to bioenergy use because of uncertainty 

in economic parameters and potential increases in supply

3) Other removal residues

a) Only 50% of the TPO levels recoverable (based on expert opinion)

b) Assumed one-third available at $20 per dry ton and the remaining amount available at $30 per dry 

ton
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

4) Thinnings from other forestlands

a) All costs borne by the biomass as no merchantable trees recovered

b) An assumption was one-half available at $60 per dry ton and other half at $70 per dry ton

5) Unused mill residues – $20 per dry ton, an assumed price based on past and projected costs

6) Urban wood 

a) Recoverable amounts based on Forest Products Laboratory report (McKeever 1998, 2004)

b) Cost based on Walsh (2006): Of the identified, recoverable wood, 75% can be acquired at cost of 

$20 per dry ton; 85% at $30 per dry ton; 90% at $40 per dry ton; and all at $60 per dry ton

c. Agriculture

i. Residues (corn stover, wheat straw, barley and oat straw, sorghum stubble)

1) Production

a) POLYSYS used to estimated corn, wheat, sorghum, oat, barley residues

i) Depends on crop yield and harvest index

ii) Crop yield/harvest index discussed under scenario

b) Cotton and rice residues estimated separately from other data

c) No soybean residue

d) Retention of biomass

i) No removals from conventionally tilled acres, only on reduced-tillage and no-till

ii) Depends on tolerable soil loss as indicated by NRCS

iii)Retention coefficients estimated from RUSLE2 and WEPS

iv) Technical (physical) removal depends on the collection equipment complement – moderate 

removal ~35%, moderately high removal ~50%, high removal ~80%

v) Incorporates rotation into retention

vi) Calculates county averages retention to prevent erosion from wind and rain, and carbon loss for 

each rotation, tillage combination, and crop management zone

2) Grower payment 

a) Value of removed nutrients from trailing average of regional fertilizer prices (2006–2009 prices)

b) Includes additional payment of $1 per dry ton for the organic matter value of the residues and a $10 

per dry ton grower return

c) Nutrient requirements discounted according to county-level rotation 

d) Corn stover removal averages: 14.8 pounds nitrogen per dry ton; 5.1 pounds of phosphorus per dry 

ton; 27.2 pounds of potassium per dry ton

e) Average is $26 per dry ton for corn stover and $25 per dry ton for wheat straw

3) Collection, storage, and handling costs

a) Assumed raking and large rectangular baling

b) Costs vary according to residue tonnage per acre

ii.Secondary processing wastes

1) Production

a) Calculated from available data of primary crops or animal units, and residue/byproduct coefficients 

and harvest index.

b) Sugarcane coefficient is 0.14 ratio for bagasse and 0.0375 for field trash

c) Cotton gin trash is a function of the type of picker
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

d) Excluded soybean exports in hulls (58% processed in the United States)

e) Rice hulls are 20% of rice production and exclude hulls in exports

f) Rice residue harvest index is 0.5 with moisture content of 13.5%

g) 1% of wheat is dust and chaff

2) Costs are specific to feedstock

a) Wheat dust, cotton gin trash and rice hulls

i) Zero value (no grower payment)

ii) Collection and handling cost of $10 per dry ton for half and $20 per dry ton for half

b) Rice and cotton field residue

i) Zero value (no grower payment)

ii) Collection and handling costs calculated like stover and straw, i.e., rake and large rectangle 

baling

c) Sugarcane

i) Field residue calculated like stover and straw (e.g., rake and large rectangular baling)

ii) Bagasse assumed used and unavailable

d) Manure costs include fertilizer replacement and $15 per dry ton for handling

e) Animal fats and waste oils priced at market

f) Soybean hulls priced at market

g) Rice hulls priced at market

h) Rice field residues calculated like stover and straw

i) Wheat dust and chaff available at assumed prices

j) Orchard and vineyard prunings costs available at assumed prices

k) Other wastes

l) Trap and sewage greases

m) Yellow grease

n) Municipal solid waste (food waste, textiles, etc.)
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Appendix C: Major differences between 2005 BTS and updated BTS

1.Separation of “used” and “potential” feedstocks. In 

the 2005 BTS, feedstocks currently used for energy 

production or could be shifted from another market 

to energy production were counted in the biomass 

potential. In the update, the currently used biomass 

is clearly delineated from the potential 

2.EIA Annual Energy Outlook was used more 

extensively to project future quantities of currently 

used biomass 

3.The updated BTS covers the present through 2030 

period instead of the 2025–2050 focus of the 2005 BTS

4.Additional agricultural environmental sustainability 

requirements in updated BTS

a. Cost assumptions include compliance with statutes, 

regulations, and BMPs 

b. Assumed the use of acceptable management practices

c. Explicitly modeled crop residue retention, tillage, and 

crop rotation to provide erosion protection and 

maintenance of soil organic carbon

d. Modeled nutrient replacement, crop rotation, and 

reduced tillage practices to ensure long-term site 

productivity.

5. Additional forestry environmental sustainability 

requirements in updated BTS

a. Cost assumptions include compliance with statutes, 

regulations, and BMPs 

b. Assumed the use of acceptable management practices

c. Little to no road building

d. Restricted operations above 80% ground slope

• National estimates – no spatial information

• No cost analyses

• Environmental sustainability addressed from 

national perspective

• No explicit land use change modeling

• 2005 USDA agricultural baseline and 2000 

forestry RPA/TPO

• Long-term time horizon (2025–2050)

• Estimates of current availability

• Long-term projections involving changes 

in crop productivity, crop tillage, residue 

collection efficiency, and land use change.

2011 Update to the Billion-Ton

• County-level analysis with aggregation to 

state, regional, and national levels

• County supply curves for major primary 

feedstocks

• Environmental sustainability modeled for 

residue removal 

• 2009 USDA agricultural baseline and 2007 

forestry RPA/TPO

• 2012–2030 timeline

• Land use change modeled for energy crops

• Annual projections based on a continuation of 

baseline trends (USDA Agricultural Projections 

– baseline forecast)

• Annual projections based on changes in crop 

productivity, tillage, and land use.

Text Box C.1 | 2005 Billion-Ton Study
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

6. Used gradient retention of biomass based on ground slope. Additional energy crop sustainability 

requirements in updated BTS

a. Cost assumptions include compliance with statutes, regulations, and BMPs 

b. Assumed the use of acceptable management practices

c. No conversion of forest lands

7.Energy crop potential is modeled at a county-level using an agricultural policy simulation model 

(POLYSYS)

8.High-yield scenario for agricultural resources assumes changes in corn yield, changes in tillage, and 

several scenario growth rates for energy crop yields

9.Estimates of energy crop potential in the 2005 BTS and updated BTS assume that demands for food, feed, 

and exports continue to be met
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U.S. BILLION-TON UPDATE: BIOMASS SUPPLY FOR A BIOENERGY AND BIOPRODUCTS INDUSTRY

Feedstock Sources and Notes

Forests -

Fuelwood EIA, AEO, 2010

Mill residues USFS TPO, 2007

Pulp liquors EIA, REA, 2007

Logging residues USFS TPO, 2007

Thinnings (timberland) USFS Inventory, 2010—further analysis/assumptions

Thinnings (other forestland) USFS Inventory, 2010—further analysis/assumptions

Other removals USFS TPO, 2007

Urban wood EPA, 2007; McKeever, 1998 and 2004

Conventional (pulpwood) 2005 RPA; USFS Inventory, 2010—further analysis/assumptions

Agriculture -

Corn starch ethanol EISA

Biodiesel EISA

Secondary Field Residues USDA Agricultural Projections

Appendix D: Data Sources

Appendix Table D.1. Data Sources
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DOE/EE-0363